Download version 0.1 of EM 1110-2-2907 Remote Sensing.pdf

Download version 0.1 of EM 1110-2-2907 Remote Sensing.pdf
EM 1110-2-2907
1 October 2003
US Army Corps
of Engineers
ENGINEERING AND DESIGN
Remote Sensing
ENGINEER MANUAL
CECW-EE
DEPARTMENT OF THE ARMY
U. S. Army Corps of Engineers
Washington, D.C. 20314-1000
Engineer Manual
No. 1110-2-2907
EM 1110-2-2907
1 October 2003
Engineering and Design
REMOTE SENSING
Table of Contents
Subject
Paragraph
Page
CHAPTER 1
Introduction to Remote Sensing
Purpose of this Manual............................................................................ 1-1
Contents of this Manual .......................................................................... 1-2
1-1
1-1
CHAPTER 2
Principles Of Remote Sensing Systems
Introduction ............................................................................................... 2-1
Definition of Remote Sensing ................................................................... 2-2
Basic Components of Remote Sensing...................................................... 2-3
Component 1: Electromagnetic Energy Is Emitted
From A Source .......................................................................................... 2-4
Component 2: Interaction of Electromagnetic Energy with Particles
in the Atmosphere ..................................................................................... 2-5
Component 3: Electromagnetic Energy Interacts with Surface and
Near Surface Objects................................................................................. 2-6
Component 4: Energy is Detected and Recorded by the Sensor ............... 2-7
Aerial Photography.................................................................................... 2-8
Brief History of Remote Sensing .............................................................. 2-9
2-1
2-1
2-1
2-2
2-14
2-20
2-29
2-42
2-44
CHAPTER 3
Sensors and Systems
Introduction ............................................................................................... 3-1
Corps 9—Civil Works Business Practice Areas ....................................... 3-2
Sensor Data Considerations....................................................................... 3-3
Value Added Products............................................................................... 3-4
Aerial Photography.................................................................................... 3-5
Airborne Digital Sensors ........................................................................... 3-6
Airborne Geometries ................................................................................. 3-7
Planning Airborne Acquisitions ................................................................ 3-8
Bathymetric and Hydrographic Sensors.................................................... 3-9
Laser Induced Fluorescence ...................................................................... 3-10
Airborne Gamma....................................................................................... 3-11
Satellite Platforms and Sensors ................................................................. 3-12
Satellite Orbits........................................................................................... 3-13
3-1
3-2
3-3
3-7
3-8
3-8
3-9
3-9
310
3-10
3-11
3-11
3-12
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Subject
Paragraph
Page
Planning Satellite Acquisitions ................................................................. 3-14
Ground Penetrating Radar Sensors............................................................ 3-15
Match to the Corps 9—Civil Works Business Practice Areas .................. 3-16
3-13
3-14
3-15
CHAPTER 4
Data Acquisition and Archives
Introduction ............................................................................................. 4-1
Specifications for Image Acquisition ..................................................4-2
Satellite Image Licensing ........................................................................ 4-3
Image Archive Search and Cost .............................................................. 4-4
Specifications for Airborne Acquisition.................................................. 4-5
Airborne Image Licensing....................................................................... 4-6
St. Louis District Air-Photo Contracting................................................. 4-7
4-1
4-2
4-3
4-3
4-6
4-7
4-7
CHAPTER 5
Processing Digital Imagery
Introduction ............................................................................................. 5-1
Image Processing Software ..................................................................... 5-2
Metadata .................................................................................................. 5-3
Viewing the Image .................................................................................. 5-4
Band/Color Composite ............................................................................ 5-5
Information About the Image .................................................................. 5-6
Datum ...................................................................................................... 5-7
Image Projections .................................................................................... 5-8
Latitude.................................................................................................... 5-9
Longitude ................................................................................................ 5-10
Latitude/Longitude Computer Entry ....................................................... 5-11
Transferring Latitude/Longitude to a Map .............................................. 5-12
Map Projections....................................................................................... 5-13
Rectification ............................................................................................ 5-14
Image to Map Rectification ..................................................................... 5-15
Ground Control Points (GCPs)................................................................ 5-16
Positional Error........................................................................................ 5-17
Project Image and Save ........................................................................... 5-18
Image to Image Rectification .................................................................. 5-19
Image Enhancement ................................................................................ 5-20
5-1
5-1
5-1
5-2
5-2
5-2
5-2
5-3
5-3
5-4
5-4
5-4
5-5
5-6
5-7
5-7
5-7
5-11
5-12
5-12
CHAPTER 6
Remote Sensing Applications in USACE
Introduction ............................................................................................. 6-1
Case Studies ............................................................................................ 6-2
Case Study 1............................................................................................ 6-3
Case Study 2............................................................................................ 6-4
Case Study 3............................................................................................ 6-5
Case Study 4............................................................................................ 6-6
Case Study 5............................................................................................ 6-7
Case Study 6............................................................................................ 6-8
ii
6-1
6-1
6-1
6-5
6-8
6-10
6-12
6-14
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Subject
Paragraph
Page
Case Study 7............................................................................................ 6-9
Case Study 8............................................................................................ 6-10
Case Study 9............................................................................................ 6-11
Case Study 10.......................................................................................... 6-12
6-15
6-17
6-19
6-22
APPENDIX A
References
APPENDIX B
Regions of the Electromagnetic Spectrum and Useful TM Band
Combinations
APPENDIX C
Paper Model of the Color Cube/Space
APPENDIX D
Satellite Sensors
APPENDIX E
Select Satellite Platforms and Sensors
APPENDIX F
Airborne Sensors
APPENDIX G
TEC’s Imagery Office (TIO) SOP
APPENDIX H
Example Contract - Statement of Work (SOW)
APPENDIX I
Example Acquisition – Memorandum of Understand (MOU)
GLOSSARY
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LIST OF TABLES
Table
Page
2-1
Different scales used to measure object temperature. ................................................ 2-4
2-2
Wavelengths of the primary colors of the visible spectrum ....................................... 2-9
2-3
Wavelengths of various bands in the microwave range ........................................... 2-10
2-4
Properties of radiation scatter and absorption in the atmosphere ............................. 2-18
2-5
Digital number value ranges for various bit data ..................................................... 2-30
2-6
Landsat Satellites and sensors .................................................................................. 2-35
2-7
Minimum image resolution required for various sized objects. ............................... 2-41
5-1
Effects of shadowing ................................................................................................ 5-21
5-2
Variety in 9-matix kernel filters used in a convolution enhancement ...................... 5-25
5-3
Omission and commission accuracy assessment matrix .......................................... 5-34
6-1
Detection Matrix for objects at various GSDS ........................................................... 6-7
6-2
Factors Important in Levee Stability ........................................................................ 6-19
LIST OF FIGURES
Figure
Page
2-1
The satellite remote sensing process .......................................................................... 2-2
2-2
Photons are emitted and absorbed by atoms............................................................... 2-3
2-3
Propagation of the electromagnetic and magnetic field ............................................. 2-4
2-4
Wave morphology ...................................................................................................... 2-5
2-5
High and low frequency wavelengths. ....................................................................... 2-5
2-6
Wave frequency.......................................................................................................... 2-6
2-7
Electromagnetic spectrum .......................................................................................... 2-6
2-8
Visible spectrum......................................................................................................... 2-7
2-9
Electromagnetic spectrum on a vertical scale............................................................. 2-8
2-10
Spectral intensity for different temperatures ............................................................ 2-13
2-11
Sun and Earth spectral emission diagram................................................................. 2-14
2-12
Various radiation obstacles and scatter paths ........................................................... 2-15
2-13
Moon rising in the Earth’s horizon. From the moon showing the Earth rising. ....... 2-16
2-14
Non-selective scattering ........................................................................................... 2-17
2-15
Atmospheric windows diagram ................................................................................ 2-17
2-16
Atmospheric windows related to the emitted energy supplied by the sun
and the Earth ............................................................................................................ 2-19
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Figure
....................................................................................................... Page
2-17
Absorbed, reflected, and transmitted radiation......................................................... 2-21
2-18
Specular reflection and diffuse reflection................................................................. 2-23
2-19
Diffuse reflection of radiation .................................................................................. 2-23
2-20
Spectral reflectance diagram of snow....................................................................... 2-25
2-21
Spectral reflectance diagram of healthy vegetation.................................................. 2-25
2-22
Spectral reflectance diagram of soil ......................................................................... 2-26
2-23
Spectral reflectance diagram of water ...................................................................... 2-26
2-24
Spectral reflectance of grass, soil, water, and snow ................................................. 2-27
2-25
Reflectance spectra of five soil types ....................................................................... 2-29
2-26
Data conversion: Analog to digital........................................................................... 2-30
2-27
Raster image ............................................................................................................. 2-32
2-28
Brightness levels relative to radiometric resolutions................................................ 2-33
2-29
Raster array and accompanying digital number values ............................................ 2-33
2-30
Landsat MSS band 5 data ......................................................................................... 2-34
2-31
Digital numbers identified in each spectral band ..................................................... 2-37
2-32
Landsat imagery band combinations: 3/2/1, 4/3/2, and 5/4/3.................................. 2-39
2-33
In this Landsat TM band 4 image, and false color composite .................................. 2-40
2-34
Aerial photograph of an agricultural area................................................................. 2-43
3-1
Image mosaic with “holidays”.................................................................................... 3-6
3-2
Satellite in Geostationary Orbit ................................................................................ 3-12
3-3
Satellite Near Polar Orbit ......................................................................................... 3-13
5-1
True color versus false color composite ..................................................................... 5-2
5-2
Geographic projection ................................................................................................ 5-4
5-3
A rectified image ........................................................................................................ 5-6
5-4
GCP selection display modules ................................................................................ 5-10
5-5
Illustration of a llinear stretch................................................................................... 5-12
5-6
Example image of a linear contrast stretch............................................................... 5-13
5-7
Pixel DN histograms illustrating enhancement stretches ......................................... 5-15
5-8
Landsat TM with accompanying image scatter plots ............................................... 5-16
5-9
Band 4 image with low-contrast data ....................................................................... 5-17
5-10
Landsat image of Denver area .................................................................................. 5-19
5-11
Landsat composite of bands 3, 2, 1 .......................................................................... 5-20
5-12
Change detection with the use of NDVI................................................................... 5-23
5-13
Landsat image and accompanying spectral plot ....................................................... 5-27
5-14
Spectral variance between two bands....................................................................... 5-28
5-15
Five images of Morro Bay, California...................................................................... 5-30
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Figure
....................................................................................................... Page
5-16
Landsat image and its corresponding thematic map with 17 thematic classes......... 5-29
5-17
Training data are selected with a selection tool........................................................ 5-31
5-18
Classification training data of 35 landscape classification features ......................... 5-32
5-19
Minimum mean distance, parallelepiped, and maximum likelihood........................ 5-33
5-20
Unsupervised and supervised classification ............................................................. 5-36
5-21
Image mosaic............................................................................................................ 5-38
5-22
Image mosaic of Western US ................................................................................... 5-39
5-23
Image subset ............................................................................................................. 5-40
5-24
Digital elevation model (DEM)................................................................................ 5-42
5-25
Hyperspectral classification image of the Kissimmee River in Florida ................... 5-43
5-26
Atlantic Gulf Stream................................................................................................. 5-44
5-27
Radarsat image ......................................................................................................... 5-45
5-28
False color composite of forest fire burn.................................................................. 5-48
5-29
Landsat image with bands 5, 4, 2 (RGB) ................................................................. 5-49
5-30
Mining activities in Nevada...................................................................................... 5-49
5-31
AVIRIS cryptogamic soil mapping .......................................................................... 5-51
5-32
MODIS image of a plankton bloom in the Gulf of Maine ....................................... 5-52
5-33
Karst topography in Orlando, Florida....................................................................... 5-53
5-34
Landsat image of Mt. Etna eruption ......................................................................... 5-54
5-35
Forest Fires in Arizona ............................................................................................. 5-54
5-36
Grounded barges in the Mississippi River delta ....................................................... 5-55
5-37
Saharan dust storm over the Mediterranean ............................................................. 5-55
5-38
Oil Trench Fires in Baghdad .................................................................................... 5-59
5-39
Mosaic of three Landsat images ............................................................................... 5-57
5-40
GIS/remote sensing map........................................................................................... 5-59
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Chapter 1
Introduction to Remote Sensing
1-1 Purpose of this Manual.
a. This manual reviews the theory and practice of remote sensing and image
processing. As a Geographical Information System (GIS) tool, remote sensing provides a
cost effective means of surveying, monitoring, and mapping objects at or near the surface
of the Earth. Remote sensing has rapidly been integrated among a variety of U.S. Army
Corps Engineers (USACE) applications, and has proven to be valuable in meeting Civil
Works business program requirements.
b. A goal of the Remote Sensing Center at the USACE Cold Regions Research Engineering Laboratory (CRREL) is to enable effective use of remotely sensed data by all
USACE divisions and districts.
c. The practice of remote sensing has become greatly simplified by useful and affordable commercial software, which has made numerous advances in recent years. Satellite
and airborne platforms provide local and regional perspective views of the Earth’s surface. These views come in a variety of resolutions and are highly accurate depictions of
surface objects. Satellite images and image processing allow researchers to better understand and evaluate a variety of Earth processes occurring on the surface and in the hydrosphere, biosphere, and atmosphere.
1-2 Contents of this Manual.
a. The objective of this manual is to provide both theoretical and practical information
to aid acquiring, processing, and interpreting remotely sensed data. Additionally, this
manual provides reference materials and sources for further study and information.
b. Included in this work is a background of the principles of remote sensing, with a
focus on the physics of electromagnetic waves and the interaction of electromagnetic
waves with objects. Aerial photography and history of remote sensing are briefly discussed.
c. A compendium of sensor types is presented together with practical information on
obtaining image data. Corps data acquisition is discussed, including the protocol for securing archived data through the USACE Topographic Engineering Center (TEC) Image
Office (TIO).
d. The fundamentals of image processing are presented along with a summary of map
projection and information extraction. Helpful examples and tips are presented to clarify
concepts and to enable the efficient use of image processing. Examples focus on the use
of images from the Landsat series of satellite sensors, as this series has the longest and
most continuous record of Earth surface multispectral data.
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e. Examples of remote sensing applications used in the Corps of Engineers mission
areas are presented. These missions include land use, forestry, geology, hydrology, geography, meteorology, oceanography, and archeology.
f. A glossary of remote sensing terms is presented at the end of this manual, also see
http://rst.gsfc.nasa.gov/AppD/glossary.html.
g. The Remote Sensing GIS Center at CRREL supports new and promising remote
sensing and GIS (Geographical Information Systems) technologies. Introductory and advanced remote sensing and GIS PROSPECT courses are offered through the Center. For
more information regarding the Remote Sensing GIS Center, please contact Andrew J.
Bruzewicz, Director, or Timothy Pangburn, Branch Chief of Remote Sensing GIS and
Water Resources, at 603-646-4372 and 603-646-4296.
h. This manual represents the combined efforts of individuals from Science and
Technology Corporation (STC), Dartmouth College, and USACE-ERDC-CRREL.
Principal contributors include Lorin J. Amidon (STC), Emily S. Bryant (Dartmouth
College), Dr. Robert L. Bolus (ERDC-CRREL), and Brian T. Tracy (ERDC-CRREL).
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Chapter 2
Principles Of Remote Sensing Systems
2-1 Introduction. The principles of remote sensing are based primarily on the properties of the electromagnetic spectrum and the geometry of airborne or satellite platforms
relative to their targets. This chapter provides a background on the physics of remote
sensing, including discussions of energy sources, electromagnetic spectra, atmospheric
effects, interactions with the target or ground surface, spectral reflectance curves, and the
geometry of image acquisition.
2-2 Definition of Remote Sensing.
a. Remote sensing describes the collection of data about an object, area, or phenomenon from a distance with a device that is not in contact with the object. More commonly,
the term remote sensing refers to imagery and image information derived by both airborne and satellite platforms that house sensor equipment. The data collected by the sensors are in the form of electromagnetic energy (EM). Electromagnetic energy is the energy emitted, absorbed, or reflected by objects. Electromagnetic energy is synonymous to
many terms, including electromagnetic radiation, radiant energy, energy, and radiation.
b. Sensors carried by platforms are engineered to detect variations of emitted and reflected electromagnetic radiation. A simple and familiar example of a platform carrying a
sensor is a camera mounted on the underside of an airplane. The airplane may be a high
or low altitude platform while the camera functions as a sensor collecting data from the
ground. The data in this example are reflected electromagnetic energy commonly known
as visible light. Likewise, spaceborne platforms known as satellites, such as Landsat
Thematic Mapper (Landsat TM) or SPOT (Satellite Pour l’Observation de la Terra), carry
a variety of sensors. Similar to the camera, these sensors collect emitted and reflected
electromagnetic energy, and are capable of recording radiation from the visible and other
portions of the spectrum. The type of platform and sensor employed will control the image area and the detail viewed in the image, and additionally they record characteristics
of objects not seen by the human eye.
c. For this manual, remote sensing is defined as the acquisition, processing, and
analysis of surface and near surface data collected by airborne and satellite systems.
2-3 Basic Components of Remote Sensing.
a. The overall process of remote sensing can be broken down into five components.
These components are: 1) an energy source; 2) the interaction of this energy with particles in the atmosphere; 3) subsequent interaction with the ground target; 4) energy recorded by a sensor as data; and 5) data displayed digitally for visual and numerical interpretation. This chapter examines components 1–4 in detail. Component 5 will be
discussed in Chapter 5. Figure 2-1 illustrates the basic elements of airborne and satellite
remote sensing systems.
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b. Primary components of remote sensing are as follows:
•
•
•
•
•
Electromagnetic energy is emitted from a source.
This energy interacts with particles in the atmosphere.
Energy interacts with surface objects.
Energy is detected and recorded by the sensor.
Data are displayed digitally for visual and numerical interpretation on a computer.
Figure 2-1. The satellite remote sensing process. A—Energy source or illumination
(electromagnetic energy); B—radiation and the atmosphere; C—interaction with the target;
D—recording of energy by the sensor; E—transmission, reception, and processing; F—
interpretation and analysis; G—application. Modified from
http://www.ccrs.nrcan.gc.ca/ccrs/learn/tutorials/fundam/chapter1/chapter1_1_e.html, courtesy of the Natural Resources Canada.
2-4 Component 1: Electromagnetic Energy Is Emitted From A Source.
a. Electromagnetic Energy: Source, Measurement, and Illumination. Remote sensing
data become extremely useful when there is a clear understanding of the physical principles that govern what we are observing in the imagery. Many of these physical principles
have been known and understood for decades, if not hundreds of years. For this manual,
the discussion will be limited to the critical elements that contribute to our understanding
of remote sensing principles. If you should need further explanation, there are numerous
works that expand upon the topics presented below (see Appendix A).
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b. Summary of Electromagnetic Energy. Electromagnetic energy or radiation is derived from the subatomic vibrations of matter and is measured in a quantity known as
wavelength. The units of wavelength are traditionally given as micrometers (µm) or nanometers (nm). Electromagnetic energy travels through space at the speed of light and
can be absorbed and reflected by objects. To understand electromagnetic energy, it is
necessary to discuss the origin of radiation, which is related to the temperature of the
matter from which it is emitted.
c. Temperature. The origin of all energy (electromagnetic energy or radiant energy)
begins with the vibration of subatomic particles called photons (Figure 2-2). All objects
at a temperature above absolute zero vibrate and therefore emit some form of electromagnetic energy. Temperature is a measurement of this vibrational energy emitted from
an object. Humans are sensitive to the thermal aspects of temperature; the higher the
temperature is the greater is the sensation of heat. A “hot” object emits relatively large
amounts of energy. Conversely, a “cold” object emits relatively little energy.
Figure 2-2. As an electron jumps from a higher to
lower energy level, shown in top figure, a photon of
energy is released. The absorption of photon energy
by an atom allows electrons to jump from a lower to
a higher energy state.
d. Absolute Temperature Scale. The lowest possible temperature has been shown to
be –273.2oC and is the basis for the absolute temperature scale. The absolute temperature
scale, known as Kelvin, is adjusted by assigning –273.2oC to 0 K (“zero Kelvin”; no de-
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gree sign). The Kelvin scale has the same temperature intervals as the Celsius scale, so
conversion between the two scales is simply a matter of adding or subtracting 273 (Table
2-1). Because all objects with temperatures above, or higher than, zero Kelvin emit electromagnetic radiation, it is possible to collect, measure, and distinguish energy emitted
from adjacent objects.
Table 2-1
Different scales used to measure object temperature. Conversion formulas are listed
below.
Object
Fahrenheit (oF)
Absolute Zero
–459.7
Frozen Water
32.0
Boiling Water
212.0
Sun
9981.0
Earth
46.4
Human body
98.6
Conversion Formulas:
Celsius to Fahrenheit: F° = (1.8 x C°) + 32
Fahrenheit to Celsius: C° = (F°- 32)/1.8
Celsius to Kelvin: K = C° + 273
Fahrenheit to Kelvin: K = [(F°- 32)/1.8] + 273
Celsius (oC)
–273.2
0.0
100.0
5527.0
8.0
37.0
Kelvin (K)
0.0
273.16
373.16
5800.0
281.0
310.0
Figure 2-3. Propagation of the electromagnetic and magnetic field. Waves vibrate
perpendicular to the direction of motion; electric and magnetic fields are at right
angle to each other. These fields travel at the speed of light.
e. Nature of Electromagnetic Waves. Electromagnetic energy travels along the path
of a sinusoidal wave (Figure 2-3). This wave of energy moves at the speed of light (3.00
8
× 10 m/s). All emitted and reflected energy travels at this rate, including light. Electromagnetic energy has two components, the electric and magnetic fields. This energy is
defined by its wavelength (λ) and frequency (ν); see below for units. These fields are inphase, perpendicular to one another, and oscillate normal to their direction of propagation
(Figure 2-3). Familiar forms of radiant energy include X-rays, ultraviolet rays, visible
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light, microwaves, and radio waves. All of these waves move and behave similarly; they
differ only in radiation intensity.
f. Measurement of Electromagnetic Wave Radiation.
(1) Wavelength. Electromagnetic waves are measured from wave crest to wave
crest or conversely from trough to trough. This distance is known as wavelength (λ or
"lambda”), and is expressed in units of micrometers (µm) or nanometers (nm) (Figures 24 and 2-5).
Crest
λ
λ
Trough
Figure 2-4. Wave morphology—wavelength (λ) is measured from crest-to-crest
or trough-to-trough.
Figure 2-5. Long wavelengths maintain a low frequency and lower energy state relative to
the short wavelengths.
(2) Frequency. The rate at which a wave passes a fixed point is known as the wave
frequency and is denoted as ν (“nu”). The units of measurement for frequency are given
as Hertz (Hz), the number of wave cycles per second (Figures 2-5 and 2-6).
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P
Figure 2-6. Frequency (ν) refers to the number
of crests of waves of the same wavelength that
pass by a point (P) in each second.
(3) Speed of electromagnetic radiation (or speed of light). Wavelength and frequency are inversely related to one another, in other words as one increases the other decreases. Their relationship is expressed as:
c = λν
where
(2-1)
c = 3.00×108 m/s, the speed of light
λ = the wavelength (m)
ν = frequency (cycles/second, Hz).
This mathematical expression also indicates that wavelength (λ) and frequency (ν) are
both proportional to the speed of light (c). Because the speed of light (c) is constant, radiation with a small wavelength will have a high frequency; conversely, radiation with a
large wavelength will have a low frequency.
Figure 2-7. Electromagnetic spectrum displayed in meter and Hz units. Short
wavelengths are shown on the left, long wavelength on the right. The visible spectrum shown in red.
g. Electromagnetic Spectrum. Electromagnetic radiation wavelengths are plotted on a
logarithmic scale known as the electromagnetic spectrum. The plot typically increases in
increments of powers of 10 (Figure 2-7). For convenience, regions of the electromagnetic
spectrum are categorized based for the most part on methods of sensing their wavelengths. For example, the visible light range is a category spanning 0.4–0.7 µm. The
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minimum and maximum of this category is based on the ability of the human eye to sense
radiation energy within the 0.4- to 0.7-µm wavelength range.
(1) Though the spectrum is divided up for convenience, it is truly a continuum of
increasing wavelengths with no inherent differences among the radiations of varying
wavelengths. For instance, the scale in Figure 2-8 shows the color blue to be approximately in the range of 435 to 520 nm (on other scales it is divided out at 446 to 520 nm).
As the wavelengths proceed in the direction of green they become increasingly less blue
and more green; the boundary is somewhat arbitrarily fixed at 520 nm to indicate this
gradual change from blue to green.
Figure 2-8. Visible spectrum illustrated here in color.
(2) Be aware of differences in the manner in which spectrum scales are drawn.
Some authors place the long wavelengths to the right (such as those shown in this manual), while others place the longer wavelengths to the left. The scale can also be drawn on
a vertical axis (Figure 2-9). Units can be depicted in meters, nanometers, micrometers, or
a combination of these units. For clarity some authors add color in the visible spectrum to
correspond to the appropriate wavelength.
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Gamma Rays
X-rays
Ultraviolet
Visible Light
Wavelength
10-19 m
10-13 m
10-9 m
0.4 µm
0.7 µm
Infrared
100 µm
Microwaves
Television Waves
(VHF and UHF)
1.0 m
1.0 m
Radio Waves
100 m
Figure 2-9. Electromagnetic spectrum on a
vertical scale.
h. Regions of the Electromagnetic Spectrum. Different regions of the electromagnetic
spectrum can provide discrete information about an object. The categories of the electromagnetic spectrum represent groups of measured electromagnetic radiation with similar
wavelength and frequency. Remote sensors are engineered to detect specific spectrum
wavelength and frequency ranges. Most sensors operate in the visible, infrared, and microwave regions of the spectrum. The following paragraphs discuss the electromagnetic
spectrum regions and their general characteristics and potential use (also see Appendix
B). The spectrum regions are discussed in order of increasing wavelength and decreasing
frequency.
(1) Ultraviolet. The ultraviolet (UV) portion of the spectrum contains radiation
just beyond the violet portion of the visible wavelengths. Radiation in this range has
short wavelengths (0.300 to 0.446 µm) and high frequency. UV wavelengths are used in
geologic and atmospheric science applications. Materials, such as rocks and minerals,
fluoresce or emit visible light in the presence of UV radiation. The florescence associated
with natural hydrocarbon seeps is useful in monitoring oil fields at sea. In the upper at-
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mosphere, ultraviolet light is greatly absorbed by ozone (O3) and becomes an important
tool in tracking changes in the ozone layer.
(2) Visible Light. The radiation detected by human eyes is in the spectrum range
aptly named the visible spectrum. Visible radiation or light is the only portion of the
spectrum that can be perceived as colors. These wavelengths span a very short portion of
the spectrum, ranging from approximately 0.4 to 0.7 µm. Because of this short range, the
visible portion of the spectrum is plotted on a linear scale (Figure 2-8). This linear scale
allows the individual colors in the visible spectrum to be discretely depicted. The shortest
visible wavelength is violet and the longest is red.
(a) The visible colors and their corresponding wavelengths are listed below
(Table 2-2) in micrometers and shown in nanometers in Figure 2.8.
Table 2-2
Wavelengths of the primary colors of the visible spectrum
Color
Violet
Blue
Green
Yellow
Orange
Red
Wavelength
0.4–0.446 µm
0.446–0.500 µm
0.500–0.578 µm
0.578–0.592 µm
0.592–0.620 µm
0.620–0.7 µm
(b) Visible light detected by sensors depends greatly on the surface reflection
characteristics of objects. Urban feature identification, soil/vegetation discrimination,
ocean productivity, cloud cover, precipitation, snow, and ice cover are only a few examples of current applications that use the visible range of the electromagnetic spectrum.
(3) Infrared. The portion of the spectrum adjacent to the visible range is the infrared (IR) region. The infrared region, plotted logarithmically, ranges from approximately
0.7 to 100 µm, which is more than 100 times as wide as the visible portion. The infrared
region is divided into two categories, the reflected IR and the emitted or thermal IR; this
division is based on their radiation properties.
(a) Reflected Infrared. The reflected IR spans the 0.7- to 3.0-µm wavelengths.
Reflected IR shares radiation properties exhibited by the visible portion and is thus used
for similar purposes. Reflected IR is valuable for delineating healthy verses unhealthy or
fallow vegetation, and for distinguishing among vegetation, soil, and rocks.
(b) Thermal Infrared. The thermal IR region represents the radiation that is
emitted from the Earth’s surface in the form of thermal energy. Thermal IR spans the 3.0to 100-µm range. These wavelengths are useful for monitoring temperature variations in
land, water, and ice.
(4) Microwave. Beyond the infrared is the microwave region, ranging on the spectrum from 1 µm to 1 m (bands are listed in Table 2-3). Microwave radiation is the longest
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wavelength used for remote sensing. This region includes a broad range of wavelengths;
on the short wavelength end of the range, microwaves exhibit properties similar to the
thermal IR radiation, whereas the longer wavelengths maintain properties similar to those
used for radio broadcasts.
Table 2-3
Wavelengths of various bands in the microwave
range
Band
Ka
K
X
C
L
P
Frequency (MHz)
40,000–26,000
26,500–18,500
12,500–8000
8000–4000
2000–1000
1000–300
Wavelength (cm)
0.8–1.1
1.1–1.7
2.4–3.8
3.8–7.5
15.0–30.0
30.0–100.0
(a) Microwave remote sensing is used in the studies of meteorology, hydrology,
oceans, geology, agriculture, forestry, and ice, and for topographic mapping. Because microwave emission is influenced by moisture content, it is useful for mapping soil moisture, sea ice, currents, and surface winds. Other applications include snow wetness analysis, profile measurements of atmospheric ozone and water vapor, and detection of oil
slicks.
(b) For more information on spectrum regions, see Appendix B.
i. Energy as it Relates to Wavelength, Frequency, and Temperature. As stated above,
energy can be quantified by its wavelength and frequency. It is also useful to measure the
intensity exhibited by electromagnetic energy. Intensity can be described by Q and is
measured in units of Joules.
(1) Quantifying Energy. The energy released from a radiating body in the form of
a vibrating photon traveling at the speed of light can be quantified by relating the energy’s wavelength with its frequency. The following equation shows the relationship
between wavelength, frequency, and amount of energy in units of Joules:
Q=hν
(2-2)
Because c = λν, Q also equals
Q = h c/λ
where
Q
h
c
λ
ν
=
=
=
=
=
energy of a photon in Joules (J)
Planck’s constant (6.6 × 10–34 J s)
3.00 × 108 m/s, the speed of light
wavelength (m)
frequency (cycles/second, Hz).
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The equation for energy indicates that, for long wavelengths, the amount of energy will
be low, and for short wavelengths, the amount of energy will be high. For instance, blue
light is on the short wavelength end of the visible spectrum (0.446 to 0.050 µm) while red
is on the longer end of this range (0.620 to 0.700 µm). Blue light is a higher energy radiation than red light. The following example illustrates this point:
Example: Using Q = h c/λ, which has more energy blue or red light?
Solution: Solve for Qblue (energy of blue light) and Qred (energy of red light)
and compare.
Calculation: λblue=0.425 µm, λred=0.660 µm (From Table 2-2)
h = 6.6 × 10-34 J s
c = 3.00 × 108 m/s
* Don’t forget to convert length µm to meters (not shown here)
Blue
Qblue = 6.6 × 10–34 J s (3.00x108 m/s)/ 0.425 µm
Qblue = 4.66 × 10–31 J
Red
Qred = 6.6 × 10–34 J seconds (3.00x108 m/s)/ 0.660 µm
Qred = 3.00 × 10–31 J
Answer: Because 4.66 × 10–31 J is greater than 3.00 x 10-31 J blue has more
energy.
This explains why the blue portion of a fire is hotter that the red portions.
(2) Implications for Remote Sensing. The relationship between energy and wavelengths has implications for remote sensing. For example, in order for a sensor to detect
low energy microwaves (which have a large λ), it will have to remain fixed over a site for
a relatively long period of time, know as dwell time. Dwell time is critical for the collection of an adequate amount of radiation. Conversely, low energy microwaves can be detected by “viewing” a larger area to obtain a detectable microwave signal. The latter is
typically the solution for collecting lower energy microwaves.
j. Black Body Emission. Energy emitted from an object is a function of its surface
temperature (refer to Paragraph 2-4c and d). An idealized object called a black body is
used to model and approximate the electromagnetic energy emitted by an object. A black
body completely absorbs and re-emits all radiation incident (striking) to its surface. A
black body emits electromagnetic radiation at all wavelengths if its temperature is above
0 Kelvin. The Wien and Stefan-Boltzmann Laws explain the relationship between temperature, wavelength, frequency, and intensity of energy.
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(1) Wien's Displacement Law. In Equation 2-2 wavelength is shown to be an inverse function of energy. It is also true that wavelength is inversely related to the temperature of the source. This is explained by Wein’s displacement law (Equation 2-3):
Lm = A/T
(2-3)
where
Lm = maximum wavelength
A = 2898 µm Kelvin
T = temperature Kelvin emitted from the object.
Using this formula (Equation 2-3), we can determine the temperature of an object by
measuring the wavelength of its incoming radiation.
Example: Using Lm = A/T, what is the maximum wavelength emitted
by a human?
Solution: Solve for Lm given T from Table 2-1
Calculation: T = 98.6oC or 310 K (From Table 2-1)
A = 2898 µm Kelvin
Lm = 2898 µm K/310K
Lm =9.3 µm
Answer: Humans emit radiation at a maximum wavelength of 9.3 µm;
this is well beyond what the eye is capable of seeing. Humans
can see in the visible part of the electromagnetic spectrum at
wavelengths of 0.4–0.7µm.
(2) The Stefan-Boltzmann Law. The Stefan-Boltzmann Law states that the total energy radiated by a black body per volume of time is proportional to the fourth power of
temperature. This can be represented by the following equation:
M = σ T4
(2-4)
where
M =
σ =
T =
radiant surface energy in watts (w)
Stefan-Boltzmann constant (5.6697 × 10-8 w/m2K4)
temperature in Kelvin emitted from the object.
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This simply means that the total energy emitted from an object rapidly increases with
only slight increases in temperature. Therefore, a hotter black body emits more radiation
at each wavelength than a cooler one (Figure 2-10).
Spectral Intensity
Yellow = 6000 K
Green = 5000K
Brown = 4000 K
0
1000
2000
3000
Wavelength (λ) nm
4000
Figure 2-10. Spectral intensity of different emitted temperatures. The horizontal axis is wavelength in nm and the vertical axis is spectral intensity. The vertical bars denote the
peak intensity for the temperatures presented. These peaks
indicate a shift toward higher energies (lower wavelengths)
with
increasing
temperatures.
Modified
from
http://rst.gsfc.nasa.gov/Front/overview.html.
(3) Summary. Together, the Wien and Stefan-Boltzmann Laws are powerful tools.
From these equations, temperature and radiant energy can be determined from an object’s
emitted radiation. For example, ocean water temperature distribution can be mapped by
measuring the emitted radiation; discrete temperatures over a forest canopy can be detected; and surface temperatures of distant solar system objects can be estimated.
k. The Sun and Earth as Black Bodies. The Sun's surface temperature is 5800 K; at
that temperature much of the energy is radiated as visible light (Figure 2-11). We can
therefore see much of the spectra emitted from the sun. Scientists speculate the human
eye has evolved to take advantage of the portion of the electromagnetic spectrum most
readily available (i.e., sunlight). Also, note from the figure the Earth’s emitted radiation
peaks between 6 to 16 µm; to “see” these wavelengths one must use a remote sensing
detector.
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Figure 2-11. The Sun and Earth both emit electromagnetic radiation. The Sun’s
temperature is approximately 5770 Kelvin, the Earth’s temperature is centered on
300 Kelvin.
l. Passive and Active Sources. The energy referred to above is classified as passive
energy. Passive energy is emitted directly from a natural source. The Sun, rocks, ocean,
and humans are all examples of passive sources. Remote sensing instruments are capable
of collecting energy from both passive and active sources (Figure 2-1; path B). Active
energy is energy generated and transmitted from the sensor itself. A familiar example of
an active source is a camera with a flash. In this example visible light is emitted from a
flash to illuminate an object. The reflected light from the object being photographed will
return to the camera where it is recorded onto film. Similarly, active radar sensors transmit their own microwave energy to the surface terrain; the strength of energy returned to
the sensor is recorded as representing the surface interaction. The Earth and Sun are the
most common sources of energy used in remote sensing.
2-5 Component 2: Interaction of Electromagnetic Energy With Particles in
the Atmosphere.
a. Atmospheric Effects. Remote sensing requires that electromagnetic radiation travel
some distance through the Earth’s atmosphere from the source to the sensor. Radiation
from the Sun or an active sensor will initially travel through the atmosphere, strike the
ground target, and pass through the atmosphere a second time before it reaches a sensor
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(Figure 2-1; path B). The total distance the radiation travels in the atmosphere is called
the path length. For electromagnetic radiation emitted from the Earth, the path length will
be half the path length of the radiation from the sun or an active source.
(1) As radiation passes through the atmosphere, it is greatly affected by the atmospheric particles it encounters (Figure 2-12). This effect is known as atmospheric scattering and atmospheric absorption and leads to changes in intensity, direction, and wavelength size. The change the radiation experiences is a function of the atmospheric
conditions, path length, composition of the particle, and the wavelength measurement
relative to the diameter of the particle.
Figure 2-12. Various radiation obstacles and scatter paths. Modified from two sources,
http://orbit-net.nesdis.noaa.gov/arad/fpdt/tutorial/12-atmra.gif and
http://rst.gsfc.nasa.gov/Intro/Part2_4.html.
(2) Rayleigh scattering, Mie scattering, and nonselective scattering are three types
of scatter that occur as radiation passes through the atmosphere (Figure 2-12). These
types of scatter lead to the redirection and diffusion of the wavelength in addition to
making the path of the radiation longer.
b. Rayleigh Scattering. Rayleigh scattering dominates when the diameter of atmospheric particles are much smaller than the incoming radiation wavelength (φ<λ). This
leads to a greater amount of short wavelength scatter owing to the small particle size of
atmospheric gases. Scattering is inversely proportional to wavelength by the 4th power,
or…
Rayleigh Scatter = 1/ λ4
(2-5)
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where λ is the wavelength (m). This means that short wavelengths will undergo a large
amount of scatter, while large wavelengths will experience little scatter. Smaller wavelength radiation reaching the sensor will appear more diffuse.
c. Why the sky is blue? Rayleigh scattering accounts for the Earth’s blue sky. We see
predominately blue because the wavelengths in the blue region (0.446–0.500 µm) are
more scattered than other spectra in the visible range. At dusk, when the sun is low in the
horizon creating a longer path length, the sky appears more red and orange. The longer
path length leads to an increase in Rayleigh scatter and results in the depletion of the blue
wavelengths. Only the longer red and orange wavelengths will reach our eyes, hence
beautiful orange and red sunsets. In contrast, our moon has no atmosphere; subsequently,
there is no Rayleigh scatter. This explains why the moon’s sky appears black (shadows on
the moon are more black than shadows on the Earth for the same reason, see Figure 2-13).
Figure 2-13. Moon rising in the Earth’s horizon (left). The Earth’s atmosphere appears blue
due to Rayleigh Scatter. Photo taken from the moon’s surface shows the Earth rising (right).
The Moon has no atmosphere, thus no atmospheric scatter. Its sky appears black. Images
taken from: http://antwrp.gsfc.nasa.gov/apod/ap001028.html, and
http://antwrp.gsfc.nasa.gov/apod/ap001231.html.
d. Mie Scattering. Mie scattering occurs when an atmospheric particle diameter is
equal to the radiation’s wavelength (φ = λ). This leads to a greater amount of scatter in
the long wavelength region of the spectrum. Mie scattering tends to occur in the presence
of water vapor and dust and will dominate in overcast or humid conditions. This type of
scattering explains the reddish hues of the sky following a forest fire or volcanic eruption.
e. Nonselective Scattering. Nonselective scattering dominates when the diameter of atmospheric particles (5–100 µm) is much larger than the incoming radiation wavelength
(φ>>λ). This leads to the scatter of visible, near infrared, and mid-infrared. All these
wavelengths are equally scattered and will combine to create a white appearance in the sky;
this is why clouds appear white (Figure 2-14).
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Figure 2-14. Non-selective scattering by larger atmospheric particles (like water droplets)
affects all wavelengths, causing white clouds.
Figure 2-15. Atmospheric windows with wavelength on the x-axis and percent transmission
measured in hertz on the y-axis. High transmission corresponds to an “atmospheric window,” which allows radiation to penetrate the Earth’s atmosphere. The chemical formula is
given for the molecule responsible for sunlight absorption at particular wavelengths across
the spectrum. Modified from
http://earthobservatory.nasa.gov:81/Library/RemoteSensing/remote_04.html.
f. Atmospheric Absorption and Atmospheric Windows. Absorption of electromagnetic
radiation is another mechanism at work in the atmosphere. This phenomenon occurs as
molecules absorb radiant energy at various wavelengths (Figure 2-12). Ozone (O3), carbon dioxide (CO2), and water vapor (H2O) are the three main atmospheric compounds
that absorb radiation. Each gas absorbs radiation at a particular wavelength. To a lesser
extent, oxygen (O2) and nitrogen dioxide (NO2) also absorb radiation (Figure 2-15). Be-
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low is a summary of these three major atmospheric constituents and their significance in
remote sensing.
g. The role of atmospheric compounds in the atmosphere.
(1) Ozone. Ozone (O3) absorbs harmful ultraviolet radiation from the sun. Without
this protective layer in the atmosphere, our skin would burn when exposed to sunlight.
(2) Carbon Dioxide. Carbon dioxide (CO2) is called a greenhouse gas because it
greatly absorbs thermal infrared radiation. Carbon dioxide thus serves to trap heat in the
atmosphere from radiation emitted from both the Sun and the Earth.
(3) Water vapor. Water vapor (H2O) in the atmosphere absorbs incoming longwave infrared and shortwave microwave radiation (22 to 1 µm). Water vapor in the lower
atmosphere varies annually from location to location. For example, the air mass above a
desert would have very little water vapor to absorb energy, while the tropics would have
high concentrations of water vapor (i.e., high humidity).
(4) Summary. Because these molecules absorb radiation in very specific regions of
the spectrum, the engineering and design of spectral sensors are developed to collect
wavelength data not influenced by atmospheric absorption. The areas of the spectrum that
are not severely influenced by atmospheric absorption are the most useful regions, and
are called atmospheric windows.
h. Summary of Atmospheric Scattering and Absorption. Together atmospheric scatter
and absorption place limitations on the spectra range useful for remote sensing. Table 2-4
summarizes the causes and effects of atmospheric scattering and absorption due to atmospheric effects.
i. Spectrum Bands. By comparing the characteristics of the radiation in atmospheric
windows (Figure 2-15; areas where reflectance on the y-axis is high), groups or bands of
wavelengths have been shown to effectively delineate objects at or near the Earth’s surface. The visible portion of the spectrum coincides with an atmospheric window, and the
maximum emitted energy from the Sun. Thermal infrared energy emitted by the Earth
corresponds to an atmospheric window around 10 µm, while the large window at wavelengths larger than 1 mm is associated with the microwave region (Figure 2-16).
Table 2-4
Properties of Radiation Scatter and Absorption in the Atmosphere
Atmospheric
Scattering
Rayleigh scattering
Mie scattering
Nonselective
scattering
Absorption
Diameter (φ) of particle
relative to incoming
wavelength (λ)
φ<λ
φ=λ
φ>>λ
No relationship
Result
Short wavelengths are scattered
Long wavelengths are scattered
All wavelengths are equally scattered
CO2, H20, and O3 remove wavelengths
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Figure 2-16. Atmospheric windows related to the emitted energy supplied by the sun and the
Earth. Notice that the sun’s maximum output (shown in yellow) coincides with an atmospheric window in the visible range of the spectrum. This phenomenon is important in optical
remote sensing. Modified from
http://www.ccrs.nrcan.gc.ca/ccrs/learn/tutorials/fundam/chapter1/chapter1_4_e.html.
j. Geometric Effects. Random and non-random error occurs during the acquisition of
radiation data. Error can be attributed to such causes as sun angle, angle of sensor, elevation of sensor, skew distortion from the Earth’s rotation, and path length. Malfunctions
in the sensor as it collects data and the motion of the platform are additional sources of
error. As the sensor collects data, it can develop sweep irregularities that result in hundreds of meters of error. The pitch, roll, and yaw of platforms can create hundreds to
thousands of meters of error, depending on the altitude and resolution of the sensor.
Geometric corrections are typically applied by re-sampling an image, a process that shifts
and recalculates the data. The most commonly used re-sampling techniques include the
use of ground control points (see Chapter 5), applying a mathematical model, or re-sampling by nearest neighbor or cubic convolution.
k. Atmospheric and Geometric Corrections. Data correction is required for calculating reflectance values from radiance values (see Equation 2-5 below) recorded at a sensor
and for reducing positional distortion caused by known sensor error. It is extremely important to make corrections when comparing one scene with another and when performing a temporal analysis. Corrected data can then be evaluated in relation to a spectral data
library (see Paragraph 2-6b) to compare an object to its standard. Corrections are not necessary if objects are to be distinguished by relative comparisons within an individual
scene.
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l. Atmospheric Correction Techniques. Data can be corrected by re-sampling with the
use of image processing software such as ERDAS Imagine or ENVI, or by the use of
specialty software. In many of the image processing software packages, atmospheric correction models are included as a component of an import process. Also, data may have
some corrections applied by the vendor. When acquiring data, it is important to be aware
of any corrections that may have been applied to the data (see Chapter 4). Correction
models can be mathematically or empirically derived.
m. Empirical Modeling Corrections. Measured or empirical data collected on the
ground at the time the sensor passes overhead allows for a comparison between ground
spectral reflectance measurements and sensor radiation reflectance measurements. Typical data collection includes spectral measurements of selected objects within a scene as
well as a sampling of the atmospheric properties that prevailed during sensor acquisition.
The empirical data are then compared with image data to interpolate an appropriate correction. Empirical corrections have many limitations, including cost, spectral equipment
availability, site accessibility, and advanced preparation. It is critical to time the field
spectral data collection to coincide with the same day and time the satellite collects radiation data. This requires knowledge of the satellite’s path and revisit schedule. For archived data it is impossible to collect the field spectral measurements needed for developing an empirical model that will correct atmospheric error. In such a case, a
mathematical model using an estimate of the field parameters must complete the correction.
n. Mathematical Modeling Corrections. Alternatively, corrections that are mathematically derived rely on estimated atmospheric parameters from the scene. These parameters include visibility, humidity, and the percent and type of aerosols present in the
atmosphere. Data values or ratios are used to determine the atmospheric parameters.
Subsequently a mathematical model is extracted and applied to the data for re-sampling.
This type of modeling can be completed with the aid of software programs such as 6S,
MODTRAN, and ATREM (see http://atol.ucsd.edu/~pflatau/rtelib/ for a list and description of correction modeling software).
2-6 Component 3: Electromagnetic Energy Interacts with Surface and Near
Surface Objects.
a. Energy Interactions with the Earth's Surface. Electromagnetic energy that reaches
a target will be absorbed, transmitted, and reflected. The proportion of each depends on
the composition and texture of the target’s surface. Figure 2-17 illustrates these three interactions. Much of remote sensing is concerned with reflected energy.
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Reflected
Emitted
Absorbed
Transmitted
Figure 2-17. Radiation striking a target is reflected, absorbed, or transmitted through the medium. Radiation is
also emitted from ground targets.
(1) Absorption. Absorption occurs when radiation penetrates a surface and is incorporated into the molecular structure of the object. All objects absorb incoming incident radiation to some degree. Absorbed radiation can later be emitted back to the atmosphere. Emitted radiation is useful in thermal studies, but will not be discussed in detail in
this work (see Lillisand and Keifer [1994] Remote Sensing and Image Interpretation for
information on emitted energy).
(2) Transmission. Transmission occurs when radiation passes through material and
exits the other side of the object. Transmission plays a minor role in the energy’s interaction with the target. This is attributable to the tendency for radiation to be absorbed before it is entirely transmitted. Transmission is a function of the properties of the object.
(3) Reflection. Reflection occurs when radiation is neither absorbed nor transmitted. The reflection of the energy depends on the properties of the object and surface
roughness relative to the wavelength of the incident radiation. Differences in surface
properties allow the distinction of one object from another.
(a) Absorption, transmission, and reflection are related to one another by
EI = EA + ET +ER
(2-6)
where
EI
EA
ET
ER
=
=
=
=
incident energy striking an object
absorbed radiation
transmitted energy
reflected energy.
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(b) The amount of each interaction will be a function of the incoming wavelength, the composition of the material, and the smoothness of the surface.
(4) Reflectance of Radiation. Reflectance is simply a measurement of the percentage of incoming or incident energy that a surface reflects
Reflectance = Reflected energy/Incident energy
(2-7)
where incident energy is the amount of incoming radiant energy and reflected energy is
the amount of energy bouncing off the object. Or from equation 2-5:
EI = EA + ET +ER
Reflectance = ER/EI
(2-8)
Reflectance is a fixed characteristic of an object. Surface features can be distinguished
by comparing the reflectance of different objects at each wavelength. Reflectance comparisons rely on the unchanging proportion of reflected energy relative to the sum of incoming energy. This permits the distinction of objects regardless of the amount of incident energy. Unique objects reflect differently, while similar objects only reflect
differently if there has been a physical or chemical change. Note: reflectance is not the
same as reflection.
Specular and diffuse reflection
The nature of reflectance is controlled by the wavelength of the
radiation relative to the surface texture. Surface texture is defined by
the roughness or bumpiness of the surface relative to the wavelength.
Objects display a range of reflectance from diffuse to specular.
Specular reflectance is a mirror-like reflection, which occurs when an
object with a smooth surface reflects in one direction. The incoming
radiation will reflect off a surface at the same angle of incidence
(Figure 2-18). Diffuse or Lambertian reflectance reflects in all
directions owing to a rough surface. This type of reflectance gives the
most information about an object.
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Figure 2-18. Specular reflection or mirror-like reflection (left) and diffuse reflection (right).
(5) Spectral Radiance. As reflected energy radiates away from an object, it moves
in a hemi-spherical path. The sensor measures only a small portion of the reflected radiation—the portion along the path between the object and the sensor (Figure 2-19). This
measured radiance is known as the spectral radiance (Equation 2-9).
I = Reflected radiance + Emitted radiance
2-9
where I = radiant intensity in watts per steradian (W sr–1). (Steradian is the unit of cone
angle, abbreviated sr, 1 sr equals 4π. See the following for more details on steradian.)
http://whatis.techtarget.com/definition/0%2C%2Csid9_gci528813%2C00.html
Figure 2-19. Diffuse reflection of radiation from a single target point.
Radiation moves outward in a hemispherical path. Notice the sensor
only samples radiation from a single vector. Modified after
http://rst.gsfc.nasa.gov/Intro/Part2_3html.html.
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(6) Summary. Spectral radiance is the amount of energy received at the sensor per
time, per area, in the direction of the sensor (measured in steradian), and it is measured
per wavelength. The sensor therefore measures the fraction of reflectance for a given
area/time for every wavelength as well as the emitted. Reflected and emitted radiance is
calculated by the integration of energy over the reflected hemisphere resulting from diffuse reflection (see http://rsd.gsfc.nasa.gov/goes/text/reflectance.pdf for details on this
complex calculation). Reflected radiance is orders of magnitude greater than emitted radiance. The following paragraphs, therefore, focus on reflected radiance.
b. Spectral Reflectance Curves.
(1) Background.
(a) Remote sensing consists of making spectral measurements over space: how
much of what “color” of light is coming from what place on the ground. One thing that a
remote sensing applications scientist hopes for, but which is not always true, is that surface features of interest will have different colors so that they will be distinct in remote
sensing data.
(b) A surface feature’s color can be characterized by the percentage of incoming
electromagnetic energy (illumination) it reflects at each wavelength across the electromagnetic spectrum. This is its spectral reflectance curve or “spectral signature”; it is an
unchanging property of the material. For example, an object such as a leaf may reflect
3% of incoming blue light, 10% of green light and 3% of red light. The amount of light it
reflects depends on the amount and wavelength of incoming illumination, but the percents are constant. Unfortunately, remote sensing instruments do not record reflectance
directly, rather radiance, which is the amount (not the percent) of electromagnetic energy
received in selected wavelength bands. A change in illumination, more or less intense sun
for instance, will change the radiance. Spectral signatures are often represented as plots
or graphs, with wavelength on the horizontal axis, and the reflectance on the vertical axis
(Figure 2-20 provides a spectral signature for snow).
(2) Important Reflectance Curves and Critical Spectral Regions. While there are
too many surface types to memorize all their spectral signatures, it is helpful to be familiar with the basic spectral characteristics of green vegetation, soil, and water. This in turn
helps determine which regions of the spectrum are most important for distinguishing
these surface types.
(3) Spectral Reflectance of Green Vegetation. Reflectance of green vegetation
(Figure 2-21) is low in the visible portion of the spectrum owing to chlorophyll absorption, high in the near IR due to the cell structure of the plant, and lower again in the
shortwave IR due to water in the cells. Within the visible portion of the spectrum, there is
a local reflectance peak in the green (0.55 µm) between the blue (0.45 µm) and red (0.68
µm) chlorophyll absorption valleys (Samson, 2000; Lillesand and Kiefer, 1994).
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Figure 2-20. Spectral reflectance of snow. Graph developed for Prospect (2002 and
2003) using Aster Spectral Library (http://speclib.jpl.nasa.gov/) data
Figure 2-21. Spectral reflectance of healthy vegetation. Graph developed for Prospect
(2002 and 2003) using Aster Spectral Library (http://speclib.jpl.nasa.gov/) data
(4) Spectral Reflectance of Soil. Soil reflectance (Figure 2-22) typically increases
with wavelength in the visible portion of the spectrum and then stays relatively constant
in the near-IR and shortwave IR, with some local dips due to water absorption at 1.4 and
1.9 µm and due to clay absorption at 1.4 and 2.2 µm (Lillesand and Kiefer, 1994).
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Figure 2-22. Spectral reflectance of one variety of soil. Graph developed
for Prospect (2002 and 2003) using Aster Spectral Library
(http://speclib.jpl.nasa.gov/) data
(5) Spectral Reflectance of Water. Spectral reflectance of clear water (Figure 2-23)
is low in all portions of the spectrum. Reflectance increases in the visible portion when
materials are suspended in the water (Lillesand and Kiefer, 1994).
Spectral Reflectance Curve for Water
100
80
Clear water has low reflectance in Visible, Near, and Mid-IR; presence
of material suspended in the water (e.g. sediment) raises reflectance in
Visible
60
40
20
0
0
0.5
1
1.5
2
2.5
Wa v e le ngt h ( um )
Figure 2-23. Spectral reflectance of water. Graph developed for Prospect (2002 and 2003)
using Aster Spectral Library (http://speclib.jpl.nasa.gov/) data
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(6) Critical Spectral Regions. The spectral regions that will be most useful in a
remote sensing application depend on the spectral signatures of the surface features to be
distinguished. The figure below (Figure 2-24) shows that the visible blue region is not
very useful for separating vegetation, soil, and water surface types, since all three have
similar reflectance, but visible red wavelengths separate soil and vegetation. In the nearIR (refers to 0.7 to 2.5 µm), all three types are distinct, with vegetation high, soil intermediate, and water low in reflectance. In the shortwave IR, water is distinctly low, while
vegetation and soil exchange positions across the spectral region. When spectral signatures cross, the spectral regions on either side of the intersection are especially useful.
For instance, green vegetation and soil signatures cross at about 0.7 µm, so the 0.6- (visible red) and 0.8-µm and larger wavelengths (near IR) regions are of particular interest in
separating these types. In general, vegetation studies include near IR and visible red data,
water vs. land distinction include near IR or SW IR. Water quality studies might include
the visible portion of the spectrum to detect suspended materials.
Figure 2-24. Spectral reflectance of grass, soil, water, and snow. Graph developed for
Prospect (2002 and 2003) using Aster Spectral Library (http://speclib.jpl.nasa.gov/) data
(7) Spectral Libraries. As noted above, detailed spectral signatures of known materials are useful in determining whether and in what spectral regions surface features are
distinct. Spectral reflectance curves for many materials (especially minerals) are available in existing reference archives (spectral libraries). Data in spectral libraries are gathered under controlled conditions, quality checked, and documented. Since these are re-
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flectance curves, and reflectance is theoretically an unvarying property of a material, the
spectra in the spectral libraries should match those of the same materials at other times or
places.
(a) If data in spectral libraries are not appropriate, reflectance curves can be acquired using a spectrometer. The instrument is aimed at a known target and records the
radiance reflected from the target over a fixed range of the spectrum (the 0.4- to 2.5-µm
range is relatively common). The instrument must also measure the radiance coming in to
the target, so that the reflected radiance can be divided by incoming radiance at each
wavelength to determine spectral reflectance of the target. Given the time and expense of
gathering spectra data, it is best to check spectral libraries first.
(b) Two major spectral libraries available on the internet (where spectra can be
downloaded and processed locally if needed) include:
• US Geological Survey Digital Spectral Library (Clark et al. 1993)
http://speclab.cr.usgs.gov/spectral-lib.html
“Researchers at the Spectroscopy lab have measured the spectral reflectance of hundreds
of materials in the lab and have compiled a spectral library. The libraries are used as references for material identification in remote sensing images.”
• ASTER Spectral Library (Jet Propulsion Laboratory, 1999)
http://speclib.jpl.nasa.gov/
“Welcome to the ASTER spectral library, a compilation of almost 2000 spectra of natural
and man made materials.”
(c) The ASTER spectral library includes data from three other spectral libraries:
the Johns Hopkins University (JHU) Spectral Library, the Jet Propulsion Laboratory
(JPL) Spectral Library, and the United States Geological Survey (USGS—Reston) Spectral Library.”
(8) Real Life and Spectral Signatures. Knowledge of spectral reflectance curves is
useful if you are searching a remote sensing image for a particular material, or if you
want to identify what material a particular pixel represents. Before comparing image data
with spectral library reflectance curves, however, you must be aware of several things.
(a) Image data, which often measure radiance above the atmosphere, may have
to be corrected for atmospheric effects and converted to reflectance.
(b) Spectral reflectance curves, which typically have hundreds or thousands of
spectral bands, may have to be resampled to match the spectral bands of the remote
sensing image (typically a few to a couple of hundred).
(c) There is spectral variance within a surface type that a single spectral library
reflectance curve does not show. For instance, the Figure 2-25 below shows spectra for a
number of different soil types. Before depending on small spectral distinctions to separate
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surface types, a note of caution is required: make sure that differences within a type do
not drown out the differences between types.
(d) While spectral libraries have known targets that are “pure types,” a pixel in a
remote sensing image very often includes a mixture of pure types: along edges of types
(e.g., water and land along a shoreline), or interspersed within a type (e.g., shadows in a
tree canopy, or soil background behind an agricultural crop).
Figure 2-25. Reflectance spectra of five soil types: A—soils having > 2% organic matter
content (OMC) and fine texture; B— soils having < 2% OMC and low iron content; C—soils
having < 2% OMC and medium iron content; D—soils having > 2% OMC, and coarse texture; and E— soil having fine texture and high iron-oxide content (> 4%).
2-7 Component 4: Energy is Detected and Recorded by the Sensor. Earlier
paragraphs of this chapter explored the nature of emitted and reflected energy and the interactions that influence the resultant radiation as it traverses from source to target to sensor. This paragraph will examine the steps necessary to transfer radiation data from the
satellite to the ground and the subsequent conversion of the data to a useable form for
display on a computer.
a. Conversion of the Radiation to Data. Data collected at a sensor are converted from
a continuous analog to a digital number. This is a necessary conversion, as electromagnetic waves arrive at the sensor as a continuous stream of radiation. The incoming radiation is sampled at regular time intervals and assigned a value (Figure 2-26). The value
given to the data is based on the use of a 6-, 7-, 8-, 9-, or 10-bit binary computer coding
scale; powers of 2 play an important role in this system. Using this coding allows a computer to store and display the data. The computer translates the sequence of binary numbers, given as ones and zeros, into a set of instructions with only two possible outcomes
(1 or 0, meaning “on” or “off”). The binary scale that is chosen (i.e., 8 bit data) will depend on the level of brightness that the radiation exhibits. The brightness level is determined by measuring the voltage of the incoming energy. Below in Table 2-5 is a list of
select bit integer binary scales and their corresponding number of brightness levels. The
ranges are derived by exponentially raising the base of 2 by the number of bits.
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255
Digital Number (DN)
Voltage (ν); recorded as a
continuous stream of data
204
192
150
138
121
112
99
118
103
95
92
68
54
0
Time
Dashed lines denote the sampling interval.
DN value is given above the sampled point.
Figure 2-26. Diagram illustrates the digital sampling of continuous analog voltage data. The
DN values above the curve represent the digital output values for that line segment.
Table 2-5
Digital number value ranges for various bit data
Number of bits
6
8
10
16
Exponent of 2
26
28
210
216
Digital Number (DN)
64
256
1024
65536
Value Range
0–63
0–255
0–1023
0–65535
b. Diversion on Data Type. Digital number values for raw remote sensing data are
usually integers. Occasionally, data can be expressed as a decimal. The most popular
code for representing real numbers (a number that contains a fraction, i.e., 0.5, which is
one-half) is called the IEEE (Institute of Electrical and Electronics Engineers, pronounced I-triple-E) Floating-Point Standard. ASCII text (American Standard Code for
Information Interchange; pronounced ask-ee) is another alternative computing value sys2-30
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tem. This system is used for text data. You may need to be aware of the type of data used
in an image, particularly when determining the digital number in a pixel.
c. Transferring the Data from the Satellite to the Ground. The transfer of data stored
in the sensor from the satellite to the user is similar to the transmission of more familiar
signals, such as radio and television broadcasts and cellular phone conversations. Everything we see and hear, whether it is a TV program with audio or a satellite image, originates as a form of electromagnetic radiation. To transfer satellite data from the sensor to a
location on the ground, the radiation is coded (described in Paragraph 2-7a) and attached
to a signal. The signal is generally a high frequency electromagnetic wave that travels at
the speed of light. The data are instantaneously transferred and detected with the use of
an appropriate antenna and receiver.
d. Satellite Receiving Stations.
(1) Satellite receiving stations are positioned throughout the world. Each satellite
program has its own fleet of receiving stations with a limited range from which it can
pick up the satellite signal. For an example of locations and coverage of SPOT receiving
stations go to
http://www.spotimage.fr/home/system/introexp/station/welcome.htm.
(2) Satellites can only transmit data when in range of a receiving station. When
outside of a receiving range, satellites will store data until they fly within range of the
next receiving station. Some satellite receiving stations are mobile and can be placed on
airplanes for swift deployment. A mobile receiving station is extremely valuable for the
immediate acquisition of data relating to an emergency situation (flooding, forest fire,
military strikes).
e. Data is Prepared for User. Once transmitted the carrier signal is filtered from the
data, which are decoded and recorded onto a high-density digital tape (HDDT) or a CDROM, and in some cases transferred via file transfer protocol (FTP). The data can then
undergo geometric and radiometric preprocessing, generally by the vendor. The data are
subsequently recorded onto tape or CD compatible for a computer.
f. Hardware and Software Requirements. The hardware and software needed for satellite image analysis will depend on the type of data to be processed. A number of free
image processing software programs are available and can be downloaded from the internet. Some vendors provide a free trial or free tutorials. Highly sophisticated and powerful
software packages are also available for purchase. These packages require robust hardware systems to sustain extended use. Software and hardware must be capable of managing the requirements of a variety of data formats and file sizes. A single satellite image
file can be 300 MB prior to enhancement processing. Once processed and enhanced, the
resulting data files will be large and will require storage for continued analysis. Because
of the size of these files, software and hardware can be pushed to its limits. Regularly
save and back up your data files as software and hardware pushed to its limits can crash,
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losing valuable information. Be sure to properly match your software requirements with
appropriate hardware capabilities.
g. Turning Digital Data into Images.
(1) Satellite data can be displayed as an image on a computer monitor by an array
of pixels, or picture elements, containing digital numbers. The composition of the image
is simply a grid of continuous pixels, known as a raster image (Figure 2-27). The digital
number (DN) of a pixel is the result of the spatial, spectral, and radiometric averaging of
reflected/emitted radiation from a given area of ground cover (see below for information
on spatial, spectral, and radiometric resolution). The DN of a pixel is therefore the average radiance of the surface area the pixel represents.
Figure 2-27. Figure illustrates the collection of raster data. Black grid (left) shows what
area on the ground is covered by each pixel in the image (right). A sensor measures
the average spectrum from each pixel, recording the photons coming in from that area.
ASTER data of Lake Kissimmee, Florida, acquired 2001-08-18. Image developed for
Prospect (2002 and 2003).
(2) The value given to the DN is based on the brightness value of the radiation (see
explanation above and Figure 2-28). For most radiation, an 8-bit scale is used that corresponds to a value range of 0–255 (Table 2-4). This means that 256 levels of brightness
(DN values are sometimes referred to as brightness values—Bv) can be displayed, each
representing the intensity of the reflected/emitted radiation. On the image this translates
to varying shades of grays. A pixel with a brightness value of zero (Bv = 0) will appear
black; a pixel with a Bv of 255 will appear white (Figure 2-29). All brightness values in
the range of Bv = 1 to 254 will appear as increasingly brighter shades of gray. In Figure 230, the dark regions represent water-dominated pixels, which have low reflectance/Bv,
while the bright areas are developed land (agricultural and forested), which has high reflectance.
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256 brightness levels (8 bits)
16 brightness levels (4 bits)
4 brightness levels (2 bits)
2 brightness levels (1 bit)
Figure 2-28. Brightness levels at different radiometric resolutions. Image developed for USACE Prospect #196 (2002).
Figure 2-29. Raster array and accompanying digital number (DN) values for a
single band image. Dark pixels have low DN values while bright pixels have high
values. Modified from Natural Resources Canada image
http://www.ccrs.nrcan.gc.ca/ccrs/learn/tutorials/fundam/chapter1/chapter1_7_e.
html.
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Figure 2-30. Landsat MSS band 5 data of San Francisco, California. The black
pixels represent water; the various levels of gray to bright pixels represent different vegetation and ground cover types across the landscape. Image taken
from http://sfbay.wr.usgs.gov/access/change_detect/Satellite_Images2.html.
h. Converting Digital Numbers to Radiance. Conversion of a digital number to its corresponding radiance is necessary when comparing images from different satellite sensors
or from different times. Each satellite sensor has its own calibration parameter, which is
based on the use of a linear equation that relates the minimum and maximum radiation
brightness. Each spectrum band (see Paragraph 2-7i) also has its own radiation minimum
and maximum.
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(1) Information pertaining to the minimum and maximum brightness (Lmin and Lmax
respectively) is usually found in the metadata (see Chapter 5). The equation for determining radiance from the digital number is:
L = (Lmax – Lmin)/255 × DN + Lmin
where
L
Lmin
Lmax
DN
=
=
=
=
(2-10)
radiance expressed in Wm-2 sr-1
spectral radiance corresponding to the minimum digital number
spectral radiance corresponding to the maximum digital number
digital number given a value based on the bit scale used.
(2) This conversion can also be used to enhance the visual appearance of an image
by reassigning the DN values so they span the full gray scale range (see Paragraph 5-20).
i. Spectral Bands.
(1) Sensors collect wavelength data in bands. A number or a letter is typically assigned to a band. For instance, radiation that spans 0.45 to 0.52 µm is designated as band
1 for Landsat 7 data; in the microwave region radiation spanning 15 to 30 cm is termed
the L-band. Not all bands are created equally. Landsat band 1 (B1) does not represent the
same wavelengths as SPOT’s B1.
(2) Band numbers are not the same as sensor numbers. For instance Landsat 4 does
not refer to band 4. It instead refers to the fourth satellite sensor placed into orbit by the
Landsat program. This can be confusing, as each satellite program has a fleet of satellites
(in or out of commission at different times), and each satellite program will define bands
differently. Two different satellites from the same program may even be collecting radiation at a slightly difference wavelength range for the same band (Table 2-6). It is, therefore, important to know which satellite program and which sensor collected the data.
Table 2-6
Landsat Satellites and Sensors
The following table lists Landsat satellites 1-7, and provides band information and pixel size. The band
numbers for one sensor does not necessarily imply the same wavelength range. For example, notice that
band 4 in Landsat 1-2 and 3 differ from the band 4 in Landsat 4-5 and Landsat 7. Source:
http://landsat.gsfc.nasa.gov/guides/LANDSAT-7_dataset.html#8.
Satellite
Landsats 1-2
Landsat 3
Sensor
Band wavelengths
Pixel Size
MSS
1)
2)
3)
4)
5)
6)
7)
0.45 to 0.57
0.58 to 0.68
0.70 to 0.83
0.5 to 0.6
0.6 to 0.7
0.7 to 0.8
0.8 to 1.1
80
80
80
79
79
79
79
RBV
1)
0.45 to 0.52
40
RBV
Band number
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Satellite
Sensor
MSS
Landsat 4-5
MSS
Landsat 7
Band number
4)
5)
6)
7)
8)
Band wavelengths
0.5 to 0.6
0.6 to 0.7
0.7 to 0.8
0.8 to 1.1
10.4 to 12.6
Pixel Size
79
79
79
79
240
4)
5)
6)
7)
0.5 to 0.6
0.6 to 0.7
0.7 to 0.8
0.8 to 1.1
82
82
82
82
TM
1)
2)
3)
4)
5)
6)
7)
0.45 to 0.52
0.52 to 0.60
0.63 to 0.69
0.76 to 0.90
1.55 to 1.75
10.4 to 12.5
2.08 to 2.35
30
30
30
30
30
120
30
ETM
1)
2)
3)
4)
5)
6)
7)
0.45 to 0.52
0.52 to 0.60
0.63 to 0.69
0.76 to 0.90
1.55 to 1.75
10.4 to 12.5
2.08 to 2.35
30
30
30
30
30
150
30
PAN
4)
0.50 to 0.90
15
j. Color in the Image. Computers are capable of imaging three primary colors: red,
green, and blue (RGB). This is different from the color system used by printers, which
uses magenta, cyan, yellow, and black. The color systems are unique because of differences in the nature of the application of the color. In the case of color on a computer
monitor, the monitor is black and the color is projected (called additive color) onto the
screen. Print processes require the application of color to paper. This is known as a subtractive process owing to the removal of color by other pigments. For example, when
white light that contains all the visible wavelengths hits a poster with an image of a yellow flower, the yellow pigment will remove the blue and green and will reflect yellow.
Hence, the process is termed subtractive. The different color systems (additive vs. subtractive) account for the dissimilarities in color between a computer image and the corresponding printed image.
(1) Similar to the gray scale, color can also be displayed as an 8-bit image with 256
levels of brightness. Dark pixels have low values and will appear black with some color,
while bright pixels will contain high values and will contain 100% of the designated
color. In Figure 2-31, the 7 bands of a Landsat image are separated to show the varying
DNs for each band.
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Figure 2-31. Individual DNs can be identified in each spectral band of an image. In this example the seven bands of a subset from a Landsat image are displayed. Image developed
for Prospect (2002 and 2003).
(2) When displaying an image on a computer monitor, the software allows a user to
assign a band to a particular color (this is termed as “loading the band”). Because there
are merely three possible colors (red, green, and blue) only three bands of spectra can be
displayed at a time. The possible band choices coupled with the three-color combinations
creates a seemingly endless number of possible color display choices.
(3) The optimal band choice for display will depend of the spectral information
needed (see Paragraph 2-6b(7)). The color you designate for each band is somewhat arbitrary, though preferences and standards do exist. For example, a typical color/band
designation of red/green/blue in bands 3/2/1 of Landsat displays the imagery as truecolor. These three bands are all in the visible part of the spectrum, and the imagery appears as we see it with our eyes (Figure 2-32a). In Figure 2-32b, band 4 (B4) is displayed
in the red (called “red-gun” or “red-plane”) layer of the bands 4/3/2, and vegetation in the
agricultural fields appear red due to the infrared location on the spectrum. In Figure 232c, band 4 (B4) is displayed as green. Green is a logical choice for band 4 as it represents the wavelengths reflected by vegetation.
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a. The true color image appears with these bands in the visible part of the spectrum.
b. Using the near infra-red (NIR) band (4) in the red gun, healthy vegetation appears red in
the imagery.
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c. Moving the NIR band into the green gun and adding band 5 to the red gun changes the
vegetation to green.
Figure 2-32. Three band combinations of Landsat imagery of 3/2/1, 4/3/2, and 5/4/3 in the
RGB. Images developed for Prospect (2002 and 2003).
k. Interpreting the Image. When interpreting the brightness of a gray scale image
(Figure 2-33), the brightness simply represents the amount of reflectance. For bright pixels the reflectance is high, while dark pixels represent areas of low reflectance. By example, in a gray scale display of Landsat 7 band 4, the brightest pixels represent areas where
there is a high reflectance in the wavelength range of 0.76 to 0.90 µm. This can be interpreted to indicate the presence of healthy vegetation (lawns and golf courses).
(1) A color composite can be somewhat difficult to interpret owing to the mixing of
color. Similar to gray scale, the bright regions have high reflectance, and dark areas have
low reflectance. The interpretation becomes more difficult when we combine different
bands of data to produce what is known as false-color composites (Figure 2-33).
(2) White and black are the end members of the band color mixing. White pixels in
a color composite represent areas where reflectance is high in all three of the bands displayed. White is produced when 100% or each color (red, green, and blue) are mixed in
equal proportions. Black pixels are areas where there is an absence of color due to the
low DN or reflectance. The remaining color variations represent the mixing of three band
DNs. A magenta pixel is one that contains equal portions of blue and red, while lacking
green. Yellow pixels are those that are high in reflectance for the bands in the green and
red planes. (Go to Appendix C for a paper model of the color cube/space.)
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a
b
Figure 2-33. Landsat 7 image of southern California (a). Landsat TM band 4 image, the
gray to bright white pixels represent the presence of healthy vegetation and urban development. (b). Landsat TM bands 4, 3, 2 (RGB) image, a false color composite, highlights vegetation in red. Images are taken from
http://landsat.gsfc.nasa.gov/data/Browse/Comparisons/L7_BandComparison.html.
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l. Data Resolution. A major consideration when choosing a sensor type is the definition of resolution capabilities. “Resolution” in remote sensing refers to the ability of a
sensor to distinguish or resolve objects that are physically near or spectrally similar to
other adjacent objects. The term high or fine resolution suggests that there is a large degree of distinction in the resolution. High resolution will allow a user to distinguish small,
adjacent targets. Low or coarse resolution indicates a broader averaging of radiation over
a larger area (on the ground or spectrally). Objects and their boundaries will be difficult
to pinpoint in images with coarse resolution. The four types of resolution in remote
sensing include spatial, spectral, radiometric, and temporal.
(1) Spatial Resolution.
(a) An increase in spatial resolution corresponds to an increase in the ability to
resolve one feature physically from another. It is controlled by the geometry and power
of the sensor system and is a function of sensor altitude, detector size, focal size, and
system configuration.
(b) Spatial resolution is best described by the size of an image pixel. A pixel is a
two-dimensional square-shaped picture element displayed on a computer. The dimensions on the ground (measured in meters or kilometers) projected in the instantaneous
field of view (IFOV) will determine the ratio of the pixel size to ground coverage. As an
example, for a SPOT image with 20- ×20-m pixels, one pixel in the digital image is
equivalent to 20 m square on the ground. To gauge the resolution needed to discern an
object, the spatial resolution should be half the size of the feature of interest. For example, if a project requires the discernment of individual tree, the spatial resolution should
be a minimum of 15 m. If you need to know the percent of timber stands versus clearcuts,
a resolution of 30 m will be sufficient.
Table 2-7
Minimum image resolution required for various sized objects.
Resolution
(m)
0.5
1.0
1.5
2.0
2.5
5.0
10.0
15.0
20.0
25.0
Feature Object
(m)
1.0
2.0
3.0
4.0
5.0
10.0
20.0
30.0
40.0
50.0
(2) Spectral Resolution. Spectral resolution is the size and number of wavelengths,
intervals, or divisions of the spectrum that a system is able to detect. Fine spectral resolution generally means that it is possible to resolve a large number of similarly sized
wavelengths, as well as to detect radiation from a variety of regions of the spectrum. A
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coarse resolution refers to large groupings of wavelengths and tends to be limited in the
frequency range.
(3) Radiometric Resolution. Radiometric resolution is a detector’s ability to distinguish differences in the strength of emitted or reflected electromagnetic radiation. A high
radiometric resolution allows for the distinction between subtle differences in signal
strength.
(4) Temporal Resolution.
(a) Temporal resolution refers to the frequency of data collection. Data collected
on different dates allows for a comparison of surface features through time. If a project
requires an assessment of change, or change detection, it is important to know: 1) how
many data sets already exist for the site; 2) how far back in time the data set ranges; and
3) how frequently the satellite returns to acquire the same location.
(b) Most satellite platforms will pass over the same spot at regular intervals that
range from days to weeks, depending on their orbit and spatial resolution (see Chapter 3).
A few examples of projects that require change detection are the growth of crops, deforestation, sediment accumulation in estuaries, and urban development.
(5) Determine the Appropriate Resolution for the Project. Increasing resolution
tends to lead to more accurate and useful information; however, this is not true for every
project. The downside to increased resolution is the need for increased storage space and
more powerful hardware and software. High-resolution satellite imagery may not be the
best choice when all that is needed is good quality aerial photographs. It is, therefore, important to determine the minimum resolution requirements needed to accomplish a given
task from the outset. This may save both time and funds.
2-8 Aerial Photography. A traditional form of mapping and surface analysis by remote sensing is the use of aerial photographs. Low altitude aerial photographs have been
in use since the Civil War, when cameras mounted on balloons surveyed battlefields.
Today, they provide a vast amount of surface detail from a low to high altitude, vertical
perspective. Because these photographs have been collected for a longer period of time
than satellite images, they allow for greater temporal monitoring of spatial changes.
Roads, buildings, farmlands, and lakes are easily identifiable and, with experience, surface terrain, rock bodies, and structural faults can be identified and mapped. In the field,
photographs can aid in precisely locating target sites on a map.
a. Aerial photographs record objects in the visible and near infrared and come in a variety of types and scales. Photos are available in black and white, natural color, false
color infrared, and low to high resolution.
b. Resolution in aerial photographs is defined as the resolvable difference between
adjacent line segments. Large-scale aerial photographs maintain a fine resolution that
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allows users to isolate small objects such as individual trees. Photographs obtained at
high altitudes produce a small-scale, which gives a broader view of surface features.
c. In addition to the actual print or digital image, aerial photographs typically include
information pertaining to the photo acquisition. This information ideally includes the
date, flight, exposure, origin/focus, scale, altitude, fiducial marks, and commissioner
(Figure 2-34). If the scale is not documented on the photo, it can be determined by taking
the ratio of the distance of two objects measured on the photo vs. the distance of the same
two objects calculated form measurements taken from a map.
Photo scale = photo distance/ground distance = d/D
(2-11)
Figure 2-34. Aerial photograph of a predominately agricultural area near Modesto, California. Notice the ancillary data located on the upper and right side margins. These data provide information regarding the location and acquisition of the photo.
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d. The measurement is best taken from one end of the photo to the other, passing
through the center (because error in the image increases away from the focus point). For
precision, it is best to average a number of ratios from across the image.
e. Photos are interpreted by recognizing various elements in a photo by the distinction
of tone, texture, size, shape, pattern, shadow, site, and association. For instance, airport
landing strips can look like roads, but their large widths, multiple intersections at small
angles, and the positioning of airport hangers and other buildings allow the interpreter to
correctly identify these “roads” as a special use area.
f. Aerial-photos are shot in a sequence with 60% overlap; this creates a stereo view
when two photos are viewed simultaneously. Stereoscopic viewing geometrically corrects
photos by eliminating errors attributable to camera tilt and terrain relief. Images are most
easily seen in stereo by viewing them through a stereoscope. With practice it is possible
to see in stereo without the stereoscope. This view will produce a three-dimensional image, allowing you to see topographic relief and resistant vs. recessive rock types.
g. To maintain accuracy it is important to correlate objects seen in the image with the
actual object in the field. This verification is known as ground truth. Without ground truth
you may not be able to differentiate two similarly toned objects. For instance, two very
different but recessive geologic units could be mistakenly grouped together. Ground truth
will also establish the level of accuracy that can be attributed to the maps created based
solely on photo interpretations.
h. For information on aerial photograph acquisition, see Chapter 4. Chapter 5 presents
a discussion on the digital display and use of aerial photos in image processing.
2-9 Brief History of Remote Sensing. Remote sensing technologies have been
built upon by the work of researchers from a variety of disciplines. One must look further
than 100 years ago to understand the foundations of this technology. For a timeline history of the development of remote sensing see http://rst.gsfc.nasa.gov/Intro/Part2_8.html.
The chronology shows that remote sensing has matured rapidly since the 1970s. This advancement has been driven by both the military and commercial sectors in an effort to
effectively model and monitor Earth processes. For brevity, this overview focuses on
camera use in remote sensing followed by the development of two NASA programs and
France’s SPOT system. To learn more about the development of remote sensing and details of other satellite programs see http://rst.gsfc.nasa.gov/Front/tofc.html.
a. The Camera. The concept of imaging the Earth’s surface has its roots in the development of the camera, a black box housing light sensitive film. A small aperture allows
light reflected from objects to travel into the black box. The light then “exposes” film,
positioned in the interior, by activating a chemical emulsion on the film surface. After
exposure, the film negative (bright and dark are reversed) can be used to produce a positive print or a visual image of a scene.
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b. Aerial Photography. The idea of mounting a camera on platforms above the
ground for a “birds-eye” view came about in the mid-1800s. In the 1800’s there were few
objects that flew or hovered above ground. During the US Civil War, cameras where
mounted on balloons to survey battlefield sites. Later, pigeons carrying cameras were
employed (http://www2.oneonta.edu/~baumanpr/ncge/rstf.htm), a platform with obvious
disadvantages. The use of balloons and other platforms created geometric problems that
were eventually solved by the development of a gyro-stabilized camera mounted on a
rocket. This gyro-stabilizer was created by the German scientist Maul and was launched
in 1912.
c. First Satellites. The world’s first artificial satellite, Sputnik 1, was launched on 4
October 1957 by the Soviet Union. It was not until NASA’s meteorological satellite
TIROS –1 was launched that the first satellite images were produced
(http://www.earth.nasa.gov/history/tiros/tiros1.html). Working on the same principles as
the camera, satellite sensors collect reflected radiation in a range of spectra and store the
data for eventual image processing (see above, this chapter).
d. NASA’s First Weather Satellites. NASA’s first satellite missions involved study of
the Earth’s weather patterns. TIROS (Television Infrared Operational Satellite) missions
launched 10 experimental satellites in the early 1960’s in an effort to prepare for a permanent weather bureau satellite system known as TOS (TIROS Operating System).
TIROS-N (next generation) satellites currently monitor global weather and variations in
the Earth’s atmosphere. The goal of TIROS-N is to acquire high resolution, diurnal data
that includes vertical profile measurements of temperature and moisture.
e. Landsat Program. The 1970’s brought the introduction of the Landsat series with
the launching of ERTS-1 (also known as Landsat 1) by NASA. The Landsat program was
the first attempt to image whole earth resources, including terrestrial (land based) and
marine resources. Images from the Landsat series allowed for detailed mapping of landmasses on a regional and continental scale.
(1) The Landsat imagery continues to provide a wide variety of information that is
highly useful for identifying and monitoring resources, such as fresh water, timberland,
and minerals. Landsat imagery is also used to assess hazards such as floods, droughts,
forest fire, and pollution. Geographers have used Landsat images to map previously unknown mountain ranges in Antarctica and to map changes in coastlines in remote areas.
(2) A notable event in the history of the Landsat program was the addition of TM
(Thematic Mapper) first carried by Landsat 4 (for a summary of Landsat satellites see
http://geo.arc.nasa.gov/sge/landsat/lpsum.html). The Thematic Mapper provides a resolution as low as 30 m, a great improvement over the 70-m resolution of earlier sensors. The
TM devise collects reflected radiation in the visible, infrared (IR), and thermal (IR) region of the spectrum.
(3) In the late 1970’s, the regulation of Landsat was transferred from NASA to
NOAA, and was briefly commercialized in the 1980s. The Landsat program is now oper-
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ated by the USGS EROS Data Center (US Geological Survey Earth Resources Observation Systems; see http://landsat7.usgs.gov/index.html).
(4) As government sponsored programs have become increasingly commercialized
and other countries develop their own remote sensors, NASA’s focus has shifted from
sensor development to data sharing. NASA’s Data Acquisition Centers serves as a clearing-house for satellite data; these data can now be shared via the internet.
f. France’s SPOT Satellite System. As a technology, remote sensing continues to advance globally with the introduction of satellite systems in other countries such as France,
Japan, and India. France’s SPOT (Satellite Pour l’Observation de la Terra) has provided
reliable high-resolution (20 to 10 m resolution) image data since 1986.
(1) The SPOT 1, 2, and 3 offer both panchromatic data (P or PAN) and three bands
of multispectral (XS) data. The panchromatic data span the visible spectrum without the
blue (0.51-0.73 µm) and maintains a 10-m resolution. The multispectral data provide 20m resolution, broken into three bands: Band 1 (Green) spans 0.50–0.59 µm, Band 2 (Red)
spans 0.61–0.68 µm, and Band 3 (Near Infrared) spans 0.79–0.89 µm. SPOT 4 also supplies a 20-m resolution shortwave Infrared (mid IR) band (B4) covering 1.58 to 1.75 µm.
SPOT 5, launched in spring 2002, provides color imagery, elevation models, and an impressive 2.5-m resolution. It houses scanners that collect panchromatic data at 5 m resolution and four band multispectral data at 10-m resolution (see Appendix D-“SPOT” file).
(2) SPOT 3 was decommissioned in 1996. SPOT 1, 2, 4, and 5 are operational at
the time of this writing. For information on the SPOT satellites go to
http://www.spotimage.fr/home/system/introsat/seltec/welcome.htm.
g. Future of Remote Sensing. The improved availability of satellite images coupled
with the ease of image processing has lead to numerous and creative applications. Remote sensing has dramatically brought about changes in the methodology associated with
studying earth processes on both regional and global scales. Advancements in sensor
resolution, particularly spatial, spectral, and temporal resolution, broaden the possible
applications of satellite data.
(1) Government agencies around the world are pushing to meet the demand for reliable and continuous satellite coverage. Continuous operation improves the temporal
data needed to assess local and global change. Researchers are currently able to perform a
30-year temporal analysis using satellite images on critical areas around the globe. This
time frame can be extended back with the incorporation of digital aerial photographs.
(2) Remote sensing has established itself as a powerful tool in the assessment and
management of U.S. lands. The Army Corps of Engineers has already incorporated this
technology into its nine business practice areas, demonstrating the tremendous value of
remote sensing in civil works projects.
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Chapter 3
Sensors and Systems
3-1 Introduction. Remotely sensed data are collected by a myriad of satellite and airborne systems. A general understanding of the sensors and the platforms they operate on
will help in determining the most appropriate data set to choose for any project. This
chapter reviews the nine business practice areas in USACE Civil Works and examines
the leading questions to be addressed before the initiation of a remote sensing project.
Airborne and satellite sensor systems are presented along with operational details such as
flight path/orbits, swath widths, acquisition, and post processing options. Ground-based
remote sensing GPR (Ground Penetrating Radar) is also introduced. This chapter concludes with a summary of remote sensing and GIS matches for each of the nine civil
works business practice areas.
a. Industry Perspective on Image Acquisition. In the past 30 years, selection of remotely sensed imagery was confined by system constraints and only provided by a few
vendors. Imagery that was available from archive, or that would become available due to
orbital frequency, maintained numerous constraints; consequently ground coverage,
rather than image resolution, was the primary concern. Additionally, minor consideration
was given to the spectral characteristics of the target and the spectral bands available, as
there were a limited number of imaging platforms. To an extent projects had to be tailored to fit the limitations of the data. This is no longer the case however, with significant technologic improvements and numerous product choices. Creative researchers are
finding new applications in the on-going advancement of remote sensing.
b. Image Improvements. Satellite sensor system developers continue to improve image cost, resolution, spectral band choices, spectral data library sets, and value-added
products or post-processing methods. Improvements in sensor development and affordability can be attributed to the commercialization and subsequent expansion of the remote
sensing industry. NASA, other US governmental agencies, and foreign space agencies,
such as those in Canada, France, India, and Japan, progressively enhance the industry by
furthering current technologic advances in the remote sensing field. Consequently, the
resolution constraints on data that existed 20 or more years ago are no longer an obstacle
with the addition of these affordable higher resolution systems. Listed here are just a few
examples of airborne and satellite data costs:
• AVHRR scene at 1 km GSD for < $50
• Landsat TM scene at 30 m GSD for $625
• Landsat ETM scene can be acquired for $800
• ERS-1 SAR scene at 25 m GSD for $2000
• ERS-2 SAR scene at 25 m GSD for $1500.
• Vendors of high-resolution satellite imaging systems products (such as
IKONOS or QUICKBIRD or other products with <4 m GSD [ground sampling distance
is the spatial resolution measurement]) charge on a per area basis. The minimum area is
11 km2 for approximately $2000.
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c. Archive Imagery. This can be accessed and purchased at a reduced rate. Some imaging systems can acquire new imagery at reasonable rates as well. It is now possible to
tailor acquisitions to meet the specific needs of Corps projects. Costs presented in this
manual will fluctuate, but generally become more affordable over time. The downward
trend in cost applies to all aspects of remote sensing - data acquisition and the required
software and hardware.
3-2 Corps 9—Civil Works Business Practice Areas. The spatial, spectral, and
temporal requirements set by the goals in a Corps business practice area should be balanced with the economic limits of the project. To achieve this result, it is helpful to consider a few preliminary steps when planning an image data acquisition. Below is a review
of the Corps 9 Civil Works Business Practice Areas. The steps that should be taken to
determine the specific data requirements follow each business practice. A list of vendor
services is presented along with details on the various platforms (airborne, satellite, and
ground penetrating radar). The nine business practice areas in Civil Works of the Corps
of Engineers and a listing of their operations follows:
a. Navigation.
• Responsible for navigation channels.
• Dredging for specified width and depth.
• Maintenance of 12,000 miles of inland waterways.
• Maintenance of 235 locks.
• 300 commercial harbors.
• 600 smaller harbors.
b. Flood Damage Reduction.
• Build and maintain levees.
• Maintenance of 383 dams and reservoirs.
• Advice on zoning, regulations and flood warning systems.
• Shore protection—protection from hurricane and coastal storms.
• Construction of jetties, seawalls, and beach sand renourishment.
• Responsibility for dam safety—inspection of Corps and other’s dams
c. Environmental Missions.
• Ecosystem restoration—many small ecosystem restoration projects, and the
larger Florida Everglades hydrologic restoration project.
• Environmental stewardship—protect forest and wildlife habitat; monitor water
quality at reservoirs; operate several fish hatcheries; support national goal of
“no net loss of wetlands”; and projects on conservation, preservation, restoration and wetland creation.
• Radioactive site cleanup—FUSRAP (Formerly Used Sites Remedial Action
Program).
d. Wetlands and Waterways Regulation and Permitting.
• Support Clean Water Act.
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•
•
•
•
Authority over dumping, dredging and filling in Waters of the US (WoUS).
Determine areas for protection as wetlands (under guidelines of 1987 Wetland
Delineation Manual), and permitting for land use.
Water supply—Washington DC aqueduct operation, manage water supply
from Corps reservoirs and water use for agriculture in arid regions of Southwestern US
Hydroelectric power—Corps operates 75 hydroelectric power plants.
e. Real Estate.
• Full range of services to Army and Air Force.
• Manage Contingency Real Estate Support Team (CREST).
• DOD agent for Recruiting Facilities Program, Homeowners Assistance Program and Defense National Relocation Program.
f. Recreation.
• Operate and maintain 2500 recreation areas at 463 lakes.
• Rangers are Dept. of Army employees.
• Corps is active in National Water Safety Program.
g. Emergency Response.
• Response to hurricanes, tornadoes, flooding, and natural disasters.
• Support to FEMA when activated by Federal Response Plan (FRP).
• Under FRP Corps has lead for public works and engineering missions.
h. Research and Development.
ERDC is composed of seven research laboratories for military, civil works and
civilian infrastructure applications:
TEC—Topographic Engineering Center
CERL—Construction Engineering and Research Laboratory
CRREL—Cold Regions Research and Engineering Laboratory
WES-GSL—Waterways Experiment Station-Geotechnical and Structures
Laboratory
WES-EL—Waterways Experiment Station-Environmental Laboratory
WES-CHL—Waterways Experiment Station-Coastal Hydraulics
Laboratory
WES –ITL—Waterways Experiment Station-Information Technology
Laboratory
i. Support to Others. This includes engineering and water resources support to state
and Federal agencies, and to foreign countries.
3-3 Sensor Data Considerations (programmatic and technical).
a. Below is a list of preliminary steps and questions to consider when planning an image data acquisition. Answer these questions in light of the Corps Civil Works 9 Business
Practice Areas before proceeding.
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• What is the primary goal of the project?
Define the problem.
How can remote sensing be applied to assist in solving the problem?
• What spatial resolution is need?
Determine the minimum, maximum, and/ or optimal GSD (ground sampling
distance).
• What is the target or what is being mapped?
High-resolution panchromatic (black and white) aerial photography may be
sufficient.
Define what spectral bands are needed.
• Will field work be included in the project budget?
What detail is needed from the imagery?
• What spectral resolution is needed?
Set bandwidth and proximity.
• Determine timing and temporal resolution requirements.
Select season(s) and time frequencies.
• How urgent is the data needed?
To capture an emergency event or temporal phenomena an airborne system may
need to be promptly employed.
• What repeat cycle do we need?
Each sensor system operates on a different cycle.
• When will ground truth data be collection?
Image data acquisition ideally coincides with ground truth data collection.
• What are the weather and light conditions?
Select radar or optical imagery or adjust acquisition timing to accommodate for
variable atmospheric conditions.
• What level of processing will be performed by the vendor?
For example, choose basic processes such as radiometric, atmospheric, and geometric corrections should be considered.
• What accuracy do we want?
Set vertical and horizontal limits.
• Where is the project geographically located?
Specify upper left/ lower right hand corner Latitudes and Longitudes.
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• What is the funding situation?
Chose a system and methods that will allow you to cost-effectively follow
through on a project.
• Do we need new or archived data?
Avoid wasting resources by soliciting imagery data that already exists. Contact
TEC Image Office (TIO) to determine image data availability and purchasing
procedures.
b. Here are some ancillary decisions to be made based on answers to the above
questions.
• What field of view is needed?
Specify image overlap if one image is not sufficient. Be aware that aircraft and
flight line paths control image overlap. Should either be altered then the overlap
could be negatively affected (Figure 3-1).
• What acquisition look direction?
Radar imagery taken in mountainous regions can have layover distortion and
shadow regions; whereas nadir looking airborne imagery has less of that effect, so
that equal amounts of backscatter and transmission are collected on both sides of
the feature.
• Are commercial analytical services needed?
Will post-processing of the imagery be accomplished in-house, or does this require external expertise — an example is the processing of radar IFSAR into elevation data, which is a very special technique done by dedicated software on
dedicated hardware and not generally done in-house. Below are examples of
vendor post-processing services.
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Figure 3-1. In this CAMIS image a decrease in aircraft altitude (due to circumstances beyond the operators control) reduced the pixel size and subsequently decreased the image scene. After mosaicing the individual
scenes the side overlaps created “holidays” or gaps in the data. Taken
from Campbell (2003).
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3-4 Value Added Products. Examples of post-processing done on imagery are listed
here with URLs to some of the companies that do the work (sometimes called level 2 or
value added products).
• Earthsat Corporation http://www.earthsat.com/ip/prodsvc/ offers geocoding,
orthorectification, seamless mosaics, data fusion, and spectral transforms including
simulated true color, minimum noise fraction (MNF), vegetation suppression, and decorrelation stretch. They offer hyperspectral processing such as atmospheric correction,
automatic endmember selection, pixel unmixing, vegetation stress mapping, and aircraft
motion compensation.
• The SPOT Corporation http://www.spot.com/home/proser/welcome.htm offers SPOTView (image map product), land use/land cover (thematic product), elevation/terrain mapping (3-D products), and vegetation products.
• Vectorization Services http://www.vectorizationservices.com/services.htm offers rectification and orthorectification, enhancement, mosaicing, fusion and image interpretation.
• Agricast http://www.agricast.com/ offers value-added products for precision
farming, agriculture, and range management.
• Science Applications International Corporation (SAIC)
http://www.saic.com/imagery/remote.html offers many value added products for industries from agriculture to utilities. See their web site for the complete list.
• Emerge http://www.emergeweb.com/Public/info/productsPage.asp offers digital ortho products and mosaics from airborne imagery.
• The J.W. Sewall Company http://www.jws.com/pages/core_sevices.html offers photogrammetric mapping, cadastral mapping, municipal GIS development, energy
and telecommunications services, and natural resources consulting.
• Analytical Imaging and Geophysics http://www.aigllc.com/research/intro.htm
offers analysis of multispectral, hyperspectral and SAR imagery with map production and
field verification.
• Spectral International, Inc. http://www.pimausa.com/services.html offers
analytical services, consulting and hyperspectral image processing.
• Earthdata http://www.earthdata.com/index2.htm offers digital orthophotos, topographic maps, planimetric maps, and LIDAR 3-D elevation data.
• Intermap Technologies http://www.intermaptechnologies.com/products.htm
offers IFSAR DEMs, DSMs, DTMs, and orthorectified radar images.
• 3Di http://www.3dicorp.com/rem-products.html offers LIDAR DEMs,
orthorectified imagery, contour mapping, wetlands mapping, vegetation mapping, 3D
perspective image drapes, and volumetric analysis.
• Terrapoint http://www.terrapoint.com/Products2.htm offers LIDAR elevation
data sets, DTMs, DEMs, canopy DTMs, building heights, land records, and floodmaps.
• i-cubed http://www.i3.com offers information integration and imaging
• Leica Geosystems http://www.gis.leica-geosystems.com offers GIS and mapping.
• PhotoScience, Inc. http:// www.photoscience.com offers aerial photography,
photogrammetry, GPS survey, GIS services, image processing
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3-5 Aerial Photography. Aerial photography is a highly useful mapping tool and
maintains the highest spatial resolution of any of the remote sensing systems. Standard 9in. (22.9 cm) aerial photos used for mapping and site identification are collected and
made available through commercial companies. USGS generates digital elevation model
(DEM) data and stereo classification of ground cover from aerial photography. These
data are derived from the National Aerial Photography Program (NAPP), formally the
National High Altitude Program (NHAP). The NAPP products are quarter quad-centered
photographs of the entire contiguous US, acquired every 5 years over 2-year intervals
since 1990. NAPP photography is acquired at 20,000 ft (~600 m) above mean terrain
with a 6-in. (~15 cm) focal length lens. The flight lines are quarter quad-centered on the
1:24,000-scale USGS maps. NAPP photographs have an approximate scale of 1:40,000,
and collect black-and-white or color infrared, as specified by state or Federal requirements. The St. Louis District of the Corps has several airborne contracts in place as well.
a. Softcopy photogrammetry is the semi-automatic processing of aerial photos after
they have been digitally scanned into files and transferred into a computer. Once in digital form, the processes of stereo imaging, stereo compilation, aerial triangulation, topographic mapping, ortho-rectification, generation of DEMs, DTMs, and DSMs and digital
map generation can be carried out.
b. Aerial photos are geometrically corrected using the fiducial marks and a camera
model and projected into the ground coordinates. Images within a stereo overlap are adjusted using a triangulation algorithm so that they fit within the constraints of the ground
control point information. At the end of the triangulation, individual stereo models are
mathematically defined between stereo images. Topographic information is extracted
from the images using autocorrelation techniques that match image patterns within a defined radius. By using parallax created by the different angle shots, elevation is measured
from the distance of matching pixels. A terrain model is used to create an ortho-rectified
image from the original photo that is precision geocoded and an ancillary Digital Surface
Model (DSM) is available.
c. Some of the companies that contract with USACE for aerial photography include:
•
•
•
•
•
Highland Geographic Inc.
James W. Sewall Company
Alcor Technologies Limited
Aero-Metric Inc.
PhotoScience, Inc.
http://www.highlandgeographic.com
http://www.sewall.com
http://www.alcortechnologies.com
http://www.aerometric.com
http://www.photoscience.com
3-6 Airborne Digital Sensors. The advancement of airborne systems to include high
resolution digital sensors is becoming available through commercial companies. These
systems are established with onboard GPS for geographic coordinates of acquisitions, and
real time image processing. Additionally, by the time the plane lands on the ground, the
data can be copied to CDROM and be available for delivery to the customer with a basic
level of processing. The data at this level would require image calibration and additional
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processing. The data at this level would require image calibration and additional processing. See Appendix F for a list of airborne system sensors.
3-7 Airborne Geometries. There are several ways in which airborne image geometry
can be controlled. Transects should always be flown parallel to the principle plane to the
sun, such that the BRDF (bi-directional reflectance distribution function) is symmetrical
on either side of the nadir direction. The pilot should attempt to keep the plane level and
fly straight line transects. But since there are always some attitude disturbances, GPS and
IMU (inertial measuring unit) data can be used in post-processing the image data to take
out this motion. The only way of guaranteeing nadir look imagery is to have the sensor
mounted on a gyro-stabilized platform. Without this, some angular distortion of the imagery will result even if it is post-processed with the plane’s attitude data and an elevation model (i.e., sides of buildings and trees will be seen and the areas hidden by these
targets will not be imaged). Shadow on one side of the buildings or trees cannot be eliminated and the dynamic range of the imagery may not be great enough to pull anything out
of the shadow region. The only way to minimize this effect is to acquire the data at or
near solar noon.
3-8 Planning Airborne Acquisitions.
a. Planning airborne acquisitions requires both business and technical skills. For example, to contract with an airborne image acquisition company, a sole source claim must
be made that this is the only company that has these special services. If not registered as a
prospective independent contractor for a Federal governmental agency, the company may
need to file a Central Contractor Registration (CCR) Application, phone (888-227-2423)
and request a DUNS number from Dun & Bradstreet, phone (800-333-0505). After this, it
is necessary for the contractee to advertise for services in the Federal Business Opportunities Daily (FBO Daily) http://www.fbodaily.com. Another way of securing an airborne
contractor is by riding an existing Corps contract; the St. Louis District has several in
place. A third way is by paying another governmental agency, which has a contract in
place. If the contractee is going to act as the lead for a group acquisition among several
other agencies, it may be necessary to execute some Cooperative Research and Development Agreements (CRDAs) between the contractee and the other agencies. As a word of
caution, carefully spell out in the legal document what happens if the contractor, for any
reason, defaults on any of the image data collection areas. A data license should be
spelled out in the contract between the parties.
b. Technically, maps must be provided to the contractor of the image acquisition area.
They must be in the projection and datum required, for example Geographic and WGS84
(World Geodetic System is an earth fixed global reference frame developed in 1984). The
collection flight lines should be drawn on the maps, with starting and ending coordinates
for each straight-line segment. If an area is to be imaged then the overlap between flight
lines must be specified, usually 20%. If the collection technique is that of overlapping
frames then both the sidelap and endlap must be specified, between 20 and 30%. It is a
good idea to generate these maps as vector coverages because they are easily changed
when in that format and can be inserted into formal reports with any caption desired later.
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The maximum angle allowable from nadir should be specified. Other technical considerations that will affect the quality of the resulting imagery include: What sun angle is
allowable? What lens focal length is allowable? What altitude will the collection be
flown? Will the imagery be flown at several resolutions or just one? Who will do the
orthorectification and mosaicing of the imagery? Will DEMs, DTMs, or DSMs be used in
the orthorectification process? How will unseen and shadow areas be treated in the final
product? When planning airborne acquisitions, these questions should be part of the decision process.
3-9 Bathymetric and Hydrographic Sensors.
a. The Scanning Hydrographic Operational Airborne Lidar Survey (SHOALS
http://shoals.sam.usace.army.mil/default.htm) system is used in airborne lidar bathymetric mapping. The Joint Airborne Lidar Bathymetry Technical Center of Expertise
(JALBTCX) is a partnership between the South Atlantic Division, US Army Corps of
Engineers (USACE), the Naval Meteorology and Oceanography Command and Naval
Oceanographic Office and USACE's Engineer Research and Development Center.
JALBTCX owns and operates the SHOALS system. SHOALS flies on small fixed wing
aircraft, Twin Otter, or on a Bell 212 helicopter. The SHOALS system can collect data on
a 4-m grid with vertical accuracy of 15 cm. In clear water bathymetry can be collected at
2–3 times Secchi depth or 60 m. It does not work in murky or sediment-laden waters.
b. The Corps uses vessels equipped with acoustic transducers for hydrographic surveys. The USACE uses multibeam sonar technology in channel and harbor surveys. Multibeam sonar systems are used for planning the depth of dredging needed in these shallow
waters, where the accuracy requirement is critical and the need for correct and thorough
calibration is necessary. USACE districts have acquired two types of multibeam transducers from different manufacturers, the Reson Seabat and the Odom Echoscan multibeam. The navigation and acquisition software commonly in use by USACE districts is
HYPACK and HYSWEEP, by Coastal Oceanographics Inc. For further information see
the web site at https://velvet.tec.army.mil/access/milgov/fact_sheet/multibea.html (due to
security restrictions this site can only be accessed by USACE employees).
3-10 Laser Induced Fluorescence.
a. Laser fluorosensors detect a primary characteristic of oil, namely their characteristic fluorescence spectral signature and intensity. There are very few substances in the
natural environment that fluoresce, those that do, fluoresce with sufficiently different
spectral signatures and intensities that they can be readily identified. The Laser Environmental Airborne Fluorosensor (LEAF) is the only sensor that can positively detect oil in
complex environments including, beaches and shorelines, kelp beds, and in ice and snow.
In situations where oil contaminates these environments, a laser fluorosensor proves to be
invaluable as a result of its ability to positively detect oil
http://www.etcentre.org/home/water_e.html.
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b. Other uses of laser fluorosensors are to detect uranium oxide present in facilities,
abandoned mines, and spill areas that require remediation. See Special Technologies
Laboratory of Bechtel, NV, http://www.nv.doe.gov/business/capabilities/lifi/.
3-11 Airborne Gamma.
a. An AC-500S Aero Commander aircraft is used by the National Operational Hydrologic Remote Sensing Center (NOHRSC) to conduct aerial snow survey operations in
the snow-affected regions of the United States and Canada. During the snow season
(January–April), snow water equivalent measurements are gathered over a number of the
1600+ pre-surveyed flight lines using a gamma radiation detection system mounted in the
cabin of the aircraft. During survey flights, this system is flown at 500 ft (152 m) above
the ground at ground speeds ranging between 100 and 120 knots (~51 to 62 m/s).
Gamma radiation emitted from trace elements of potassium, uranium, and thorium radioisotopes in the upper 20 cm of soil is attenuated by soil moisture and water mass in the
snow cover. Through careful analysis, differences between airborne radiation measurements made over bare ground are compared to those of snow-covered ground. The radiation differences are corrected for air mass attenuation and extraneous gamma contamination from cosmic sources. Air mass is corrected using output from precision temperature,
radar altimeter, and pressure sensors mounted on and within the aircraft. Output from the
snow survey system results in a mean areal snow water equivalent value within ±1 cm.
Information collected during snow survey missions, along with other environmental data,
is used by the National Weather Service (NWS), and other agencies, to forecast river levels and potential flooding events attributable to snowmelt water runoff
(http://www.aoc.noaa.gov/_).
b. Other companies use airborne gamma to detect the presence of above normal
gamma ray count, indicative of uranium, potassium, and thorium elements in the Earth’s
crust (for example, Edcon, Inc., http://www.edcon.com, and the Remote Sensing Laboratory at Bechtel, Nevada). The USGS conducted an extensive survey over the state of
Alaska as part of the National Uranium Resource Evaluation (NURE) program that ran
from 1974 to 1983, http://edc.usgs.gov/.
3-12 Satellite Platforms and Sensors.
a. There are currently over two-dozen satellite platforms orbiting the earth collecting
data. Satellites orbit in either a circular geo-synchronous or polar sun-synchronous path.
Each satellite carries one or more electromagnetic sensor(s), for example, Landsat 7 satellite carries one sensor, the ETM+, while the satellite ENVISAT carries ten sensors and
two microwave antennas. Some sensors are named after the satellite that carries them, for
instance IKONOS the satellite houses IKONOS the sensor. See Appendices D and E for a
list of satellite platforms, systems, and sensors.
b. Sensors are designed to capture particular spectral data. Nearly 100 sensors have
been designed and employed for long-term and short-term use. Appendix D summarizes
details on sensor functionality. New sensors are periodically added to the family of ex-
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isting sensors while older or poorly designed sensors become decommissioned or defunct. Some sensors are flown on only one platform; a few, such as MODIS and MSS,
are on-board more than one satellite. The spectral data collected may span the visible
(optical), blue, green, microwave, MIR/SWIR, NIR, Red, or thermal IR Sensors can detect single wavelengths or frequencies and/or ranges of the EM spectrum.
3-13 Satellite Orbits.
a. Remote sensing satellites are placed into different orbits for special purposes. The
weather satellites are geo-stationary, so that they can image the same spot on the Earth
continuously. They have equatorial orbits where the orbital period is the same as that of
the Earth and the path is around the Earth’s equator. This is similar to the communication
satellites that continuously service the same area on the Earth (Figure 3-2).
Figure 3-2. Satellite in Geostationary Orbit. Courtesy of the
Natural Resources Canada.
b. The remaining remote sensing satellites have near polar orbits and are launched
into a sun synchronous orbit (Figure 3-3). They are typically inclined 8 degrees from the
poles due to the gravitational pull from the Earth’s bulge at the equator; this allows them
to remain in orbit. Depending on the swath width of the satellite (if it is non-pointable),
the same area on the Earth will be imaged at regular intervals (16 days for Landsat, 24
days for Radarsat).
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Figure 3-3. Satellite Near
Polar Orbit, Courtesy of
the Natural Resources
Canada.
3-14 Planning Satellite Acquisitions. Corps satellite acquisition must be arranged
through the Topographic Engineering Center (TEC) Imagery Office (TIO). It is very easy
to transfer the cost of the imagery to TEC via the Corps Financial Management System
(CFMS). They will place the order, receive and duplicate the imagery for entry into the
National Imagery and Mapping Agency (NIMA) archive called the Commercial Satellite
Imagery Library (CSIL), and send the original to the Corps requester. They buy the imagery under a governmental user license contract that licenses free distribution to other
government agencies and their contractors, but not outside of these. It is important for
Corps personnel to adhere to the conditions of the license. Additional information concerning image acquisition is discussed in Chapter 4 (Section 4-1).
a. Turn Around Time. This is another item to consider. That is the time after acquisition of the image that lapses before it is shipped to TEC-TIO and the original purchaser.
Different commercial providers handle this in different ways, but the usual is to charge an
extra fee for a 1-week turn around, and another fee for a 1 to 2 day turn around. For example, SPOT Code Red programmed acquisition costs an extra $1000 and guarantees
shipment as soon as acquired. The ERS priority acquisition costs an extra $800 and guarantees shipment within 7 days, emergency acquisition cost $1200 and guarantees shipment within 2 days, and near real time costs an extra $1500 and guarantees shipment as
soon as acquired. Also arrangement may be made for ftp image transfers in emergency
situations. Costs increase in a similar way with RADARSAT, IKONOS, and QuickBird
satellite imaging systems.
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b. Swath Planners.
• Landsat acquired daily over the CONUS, use DESCW swath planner on PC
running at least Windows 2000 for orbit locations. http://earth.esa.int/services/descw/
• ERS, JERS, ENVISAT—not routinely taken, use DESCW swath planner on
PC running at least Windows 2000 for orbit locations. http://earth.esa.int/services/descw/
• RADARSAT—not routinely acquired, contact the TEC Imagery Office
regarding acquisitions of Radarsat data.
• Other commercial imaging systems, contact the TEC Imagery Office regarding acquisitions.
3-15 Ground Penetrating Radar Sensors. Ground penetrating radar (GPR) uses
electromagnetic wave propagation and back scattering to image, locate, and quantitatively identify changes in electrical and magnetic properties in the ground. Practical platforms for the GPR include on-the-ground point measurements, profiling sleds, and nearground helicopter surveys. It has the highest resolution in subsurface imaging of any geophysical method, approaching centimeters. Depth of investigation varies from meters to
several kilometers, depending upon material properties. Detection of a subsurface feature
depends upon contrast in the dielectric electrical and magnetic properties. Interpretation
of ground penetrating radar data can lead to information about depth, orientation, size,
and shape of buried objects, and soil water content.
a. GPR is a fully operational Cold Regions Research and Engineering Laboratory
(CRREL) resource. It has been used in a variety of projects: e.g., in Antarctica profiling
for crevasses, in Alaska probing for subpermafrost water table and contaminant pathways, at Fort Richardson probing for buried chemical and fuel drums, and for the ice
bathymetry of rivers and lakes from a helicopter.
b. CRREL has researched the use of radar for surveys of permafrost, glaciers, and
river, lake and sea ice covers since 1974. Helicopter surveys have been used to measure
ice thickness in New Hampshire and Alaska since 1986. For reports on the use of GPR
within cold region environments, a literature search from the CRREL website
(http://www.crrel.usace.army.mil/) will provide additional information. Current applications of GPR can be found at http://www.crrel.usace.army.mil/sid/gpr/gpr.html.
c. A radar pulse is modulated at frequencies from 100 to 1000 MHz, with the lower
frequency penetrating deeper than the high frequency, but the high frequency having
better resolution than the low frequency. Basic pulse repetition rates are up to 128 Hz on
a radar line profiling system on a sled or airborne platform. Radar energy is reflected
from both surface and subsurface objects, allowing depth and thickness measurements to
be made from two-way travel time differences. An airborne speed of 25 m/s at a low altitude of no more than 3 m allows collection of line profile data at 75 Hz in up to 4 m of
depth with a 5-cm resolution on 1-ft (30.5 cm)-grid centers. Playback rates of 1.2
km/min. are possible for post processing of the data.
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d. There are several commercial companies that do GPR surveys, such as Blackhawk
Geometrics and Geosphere Inc., found on the web at http://www.blackhawkgeo.com, and
http://www.geosphereinc.com.
3-16 Match to the Corps 9—Civil Works Business Practice Areas. Match to
the Corps business practice areas presupposes that everything about remote sensing for a
particular ground or water parameter is known or works. However, this is not the case.
Mapping for the amount of visible detail for a particular business area can be and has
been readily listed in the National Imagery Interpretability Rating Scale (NIIRS). An approximate match between NIIRS level and GSD is given in the following.
a. Navigation Needs—lock and dam modification and construction, harbor facilities
construction, channel dredging.
(1) How can remote sensing help in the maintenance, dredging, and planning for
new construction?
(2) Remote sensing match:
•
•
•
•
•
Hydrographic surveys creating maps of underwater depth and obstructions.
Maps of original land and water area to be converted.
Elevation profiles of the areas.
Maps of the surrounding area to meet requirement of no net loss of wetlands.
See Paragraph 3-3.
b. Flood Damage Reduction Needs—levee, dam, jetty, and seawall construction and
beach sand re-nourishment projects, installation of flood warning systems.
(1) How can remote sensing help in planning for construction, for beach sand renourishment projects, and for the installation of flood warning systems?
(2) Remote sensing match:
•
•
•
•
•
Maps of construction and surrounding areas.
Elevation profiles of the areas.
Beach maps and elevation profiles and near shore bathymetry.
Levee top elevations for flood warning systems.
See Paragraph 3-3.
c. Environmental Mission Needs—ecosystem restorations, protection of forest and
wildlife habitat, water quality monitoring, wetland creation, radioactive and abandoned
mine lands (AML) cleanup.
(1) How can remote sensing help in planning for ecosystem restorations, monitoring forest and wildlife habitat and water quality, and for wetland creation and AML
cleanup?
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(2) Remote sensing match:
•
•
•
•
•
Maps of current ecosystem, wetlands, rivers, streams, aquifers, natural vegetation.
Maps of forest types and vegetation communities.
Map chlorophyll and sediments in lakes and reservoirs.
Map mine sites, polluted drainage and stream and watershed areas.
See “Sensor Data Considerations (programmatic and technical).”
d. Wetlands and Waterways Needs—authority over dumping, dredging, and filling in
Waters of the US, delineate wetlands, monitor water quality of water supplies, planning
conservation of water in the arid southwest.
(1) How can remote sensing help in delineating wetlands and issuing permits for
dumping, dredging, and filling, monitoring water quality of water supplies, and in management of water in arid and agricultural regions of the west and southwest?
(2) Remote sensing match:
•
•
•
•
Maps delineating wetlands.
Maps of water quality and sedimentation of water supplies.
Maps of snow/ water equivalency and reservoir capacity and agriculture demand.
See Paragraph 3-3.
e. Real Estate Needs—locations and types.
(1) How can remote sensing help in planning real estate location and type?
(2) Remote sensing match:
•
•
Mapping urban, suburban and city locations for entry into a GIS.
see “Sensor Data Considerations (programmatic and technical).”
f. Recreation—maintain 2500 recreation areas.
(1) How can remote sensing help in maintenance and operation of recreation areas?
(2) Remote sensing match:
•
•
Mapping and classification of forests and habitat in parks and monitoring water
quality of lakes and reservoirs.
See Paragraph 3-3.
g. Emergency Response—response to hurricanes and natural disasters.
(1) How can remote sensing help in response to natural disasters?
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(2) Remote sensing match:
•
•
•
Immediate mapping of disaster area.
High resolution mapping to determine extent of personal damage (houses) and
temporary roofing capability (FEMA regulated at 50 % roof rafters still in place).
See Paragraph 3-3.
h. Research and Development—Seven research laboratories and support to the Nation’s
civil works sector.
(1) How can remote sensing help in the work carried out by the seven research
laboratories and support the nation’s civil works sector?
(2) Remote sensing match:
•
•
•
Mapping and classification in mission areas and specific projects.
Development of new methods and techniques of remote sensing and processing.
See Paragraph 3-3.
i. Support to Others—other state and Federal agencies, foreign countries, and reimbursable work done.
(1) How can remote sensing help in work done for other state and federal agencies,
foreign countries and in reimbursable work done?
(2) Remote sensing match:
•
•
Mapping, remote sensing and GIS training, and classification in ongoing projects
See Paragraph 3-3.
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Chapter 4
Data Acquisition and Archives
4-1 Introduction.
a. USACE Image Acquisition Standard Operating Procedure. Image data
should be acquired following the established protocol developed by ERDC’s TEC
Imaging Office (TIO)*. The protocol allows for efficient monitoring of image acquisition and archival practice. There are numerous advantages in using TIO’s
image procedure. The most significant advantage in using TIO’s protocol is cost
savings. This savings is the result of on-going contracts between satellite data
vendors and the federal government. In addition to a reduced cost, TIO is able to
broaden the image-share licensing allowing USACE full access to previously purchased data. The image-share licensing agreement is funded by NIMA who in
turn allows all DoD and Title 50 Intelligence members full use of imagery data. In
other words, once a USACE researcher has acquired imagery all other USACE
districts can legally access these data at no charge. These data are also available to
contractors working under a USACE contract.
b. SOP. The standard operating procedure (SOP) for acquiring new data is defined by the EM 1110-1-2909 (Appendix I), which states that no imagery shall be
purchased from a commercial vendor without first coordinating with TIO
(Appendix G). TIO has streamlined image data purchases and provides quick and
efficient turn-around. The only exception to this SOP is in the case of acquiring
free imagery downloaded from the Internet. A handful of governmental and
commercial agencies (such as NASA and SpaceImaging) have made select
satellite images available at no cost. These sites may require a login and can
provide software for viewing data free of charge.
Ordering Commercial Satellite Imagery
“No imagery shall be purchased from a commercial vendor without first
coordinating with the TIO. Any U.S. Army organization with commercial
satellite imagery requirements must forward their commercial satellite
imagery requirements to TEC for research, acquisition, and distribution of the
data.”
Contact TEC’s Imagery Office, using one of the following methods:
Web Site: WWW.tec.army.mil/tio/index.htm
Email: [email protected]
ERDC (Engineer Research and Development Center) includes the seven Corps of Engineers research
laboratories.
TEC (Topographic Engineering Center) serves the Corps of Engineers and the Department of Defense.
TIO (TEC Imaging Office) monitors and coordinates all USACE image requirements with commercial
vendors and public data libraries.
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c. Placing Image Orders. Commercial imagery and aerial photo requests can be
placed via email, memorandum, fax, or phone. The following image requirements should
be determined prior to contacting TIO.
1. Geographic area of interest in latitude/longitude coordinates in degrees and minutes (or path/row if known).
2. Acceptable date range for data coverage; cloud cover and quality
restrictions.
3. Satellite system/sensor.
4. Desired end product (digital or hard copy and preferred media).
5. Mailing and electronic address and phone number.
Consider the timing requirements for the project. For projects not involving
emergencies or hazards satellite data may be delivered by regular mail. TIO can
also deliver data by FEDEX and FTP. The TIO performs an image data search
through the CSIL (Commercial Satellite Image Library). When data is available
in the CSIL, the TIO receives a CD of the data, and copies the data for the customer.
DO
DO NOT
Verify your geo-coordinates.
Contact the vendors on your own without first
communicating with the TIO.
Review imagery for accuracy and quality make
sure the imagery covers your area of interest.
You may call a commercial satellite vendor to
discuss technical problems encountered after
you receive the imagery from the TEC Imagery
Office (i.e.: accuracy and quality problems).
During the acquisition stage, do not consult the
vendor’s technical staff to have additional
work done that is not stated in the written
proposal.
d. Points of Contact (as of September 2003).
• Army Commercial Imagery Acquisition Program Manager—Mary Pat
Santoro, 703-428-6903
• TIO Team Leader—Mary Brenke, 703-428-6909
• TIO Team Member—Alana Hubbard, 703-428-6717
4-2 Specifications for Image Acquisition. The TEC Imagery Office (TIO)
is the first stop for obtaining imagery for the USACE, contact Mary Brenke (703428-6909) or Alana Hubbard (703-428-6717). But, before contacting them, some
basic information about what is wanted should be put together. A list follows:
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•
Geographic coordinates—upper left and lower right corner
latitude–longitude coordinates or, if known, the path/row of a Landsat scene, the
K/J of a SPOT scene; the orbit and frame number for a SAR image from ERS,
Radarsat, JERS, or Envisat.
•
Acceptable coverage dates.
•
Acceptable percentage of cloud cover, image quality, and off nadir
angle limit.
•
Satellite sensor or sensors.
•
Image format—digital tape, CDROM, projection wanted, projection parameters, tar (tape archive retrieval is a compression file format), satellite
format, compression or uncompressed.
•
Your name, phone, FAX, e-mail, mailing address.
•
Payment—TIO will determine the correct cost for the imagery,
which will be purchased for you by the USGS Eros Data Center (EDC). You will
have to do a Military Interdepartmental Purchase Request (MIPR) of the money to
EDC, EDC will send the imagery to the TIO for duplication and archiving in
CSIL at NIMA, the original image will be forwarded to you.
4-3 Satellite Image Licensing. The license for satellite imagery is extended
to a no cost duplication of the data for any DOD agencies and their contractors
when the imagery is bought through the TEC-TIO contract with NIMA and the
USGS EROS Data Center. Beyond that, the license specifically states that no
other duplication of the unprocessed data is allowed.
4-4 Image Archive Search and Cost. The following is a compilation of archive search sources, along with web site addresses and the approximate cost of
imagery. Data costs reflect new purchases and archive data rates at the time of the
release of this manual.
a. USGS EROS Data Center.
http://earthexplorer.usgs.gov
LANDSAT (MSS, TM4 & 5)
LANDSAT7 (ETM)
$ 425 - 2700.00
$ 600
DOQQ
First Image $ 45.00
Additional images:
$ 7.50 – Pan
$ 15.00 - Color
Full Orbit AVHRR
DEMs and DLGs
$ 50
no cost
ALI (Landsat mimic 37km by 42km)
Hyperion (hyperspectral 7.7km by 42km)
$ 500 – 2800 http://eo1.usgs.gov/
$ 500 - 2800
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b. Space Imaging Corp.
http://www.spaceimaging.com
IRS
IKONOS
$ 2700.00
$ 18 - 200.00 per sq km
c. SPOT Image Corp.
http://www.spot.com
SPOT Pan and Multi-spectral
RADARSAT and ERS
$ 750.00 - 2500.00
$ 1500.00 - 4500.00
d. RADARSAT INC.
http://www.rsi.ca
$ 1500.00 - 3,000.0
e. NOAA—Satellite Active Archive.
http://www.saa.noaa.gov
AVHRR full swath limited Mbyte size
f. Earth Satellite Corporation.
no cost
http://www.geocover.com
Landsat scenes & mosaics
$ 250 - see price list
g. Digital Chart of the World.
http://www.maproom.psu.edu/dcw/
Penn State University Library
GIS themes including DEMs
no cost
h. AVIRIS Home Page.
http://makalu.jpl.nasa.gov
Archived or new AVIRIS scenes
$500.00 or $ 30k to 60k new data
i. Eurimage Home Page.
http://www.eurimage.com
Europe Landsat TM 4, 5,7,
IKONOS, Quickbird, ERS, IRS
Radarsat, Envisat, Resurs-01
see price list
j. AIGLLC home page.
http://www.aigllc.com
HyMap hyperspectral (2.3km x 20km)
k. ESA Home Page.
$ 5000
http://earthnet.esrin.esa.it
Mideast Envisat, ERS, IRS, Landsat,
AVHRR SeaWiFs, MODIS
4-4
see price list
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l. ALOS Home Page.
http://www.alos.nasda.go.jp
PRISM, AVNIR-2, PALSAR
see price list
m. DigitalGlobe home page.
http://www.digitalglobe.com
Quickbird
$6,000 - see price list
n. EOS DAAC.
http://edcdaac.usgs.gov
MODIS, ASTER, Landsat 7
free - $600 - see price list
o. Geostationary Satellite Server.
http://www.goes.noaa.gov
GOES, Meteosat weather satellite data
p. Espatial Home Page.
free
http://www.espatialweb.com
Emerge electronic camera
$11k for 50 sq mi mission
q. Positive Systems Home Page.
http://www.possys.com
ADAR digital camera
quote on request
r. Flight Landata Inc.
http://www.flidata.com
DMSV, variable filter hyperspectral
s. Earth Search Sciences Inc (ESSI).
quote on request
http://www.earthsearch.com
Probe-1 hyperspectral
quote on request
t. EarthData.
http://www.earthdata.com
LIDAR elevation data
quote on request
u. University of Florida.
http://www.alsm.ufl.edu
LIDAR elevation & airphoto data
(25km by 1km elev. and air photo)
quote on request
$ 75k
v. SHOALS Home Page.
http://shoals.sam.usace.army.mil
LIDAR bathymetry
quote on request
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w. Intermap.
http://www.intermaptechnologies.com
IFSAR elevation
quote on request
Archive tile (7.5min x 7.5min x 5m posting) $2000
x. Aeromap U.S.
http://www.aeromap.com
Orthophotography, imagery, DEMs
quote on request
y. TerraSystems Inc.
http://www.terrasys.com
TS-1 DMSV & TS-3 electronic
quote on request
z. DLR (German aerospace center).
http://www.dfd.dlr.de
MOS ocean color data & others
see price list
aa. NASA Goddard DAAC.
http://daac.gsfc.nasa.gov
CZCS, MODIS, OCTS, SeaWiFs
Ocean color sensors & others
see price list
bb. ENVISAT home page.
http://envisat.esa.int
MERIS ocean color data & others
DESCW swath planner
see price list
free
cc. Alaska SAR Facility.
http://www.asf.alaska.edu
Radarsat mosaic of Antarctica
Alaska High Altitude Air Photo Program
(AHAP)
dd. ITRES Research Limited.
free
http://www.itres.com
CASI (hyperspectral)
quote on request
4-5 Specifications for Airborne Acquisition. Maps must be provided to the contractor of the image acquisition area. They must be in the projection and datum required,
for example Geographic and WGS84. The collection flight lines should be drawn on the
maps with starting and ending coordinates for each straight-line segment. If an area is to
be imaged, then the overlap between flight lines must be specified, usually 20%. If the
collection technique is that of overlapping frames, then both the sidelap and endlap must
be specified, between 20 and 30%.
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4-6 Airborne Image Licensing. Licenses for data collected by aircraft vary.
The contractor must read and agree to the terms. Some state that there are no conditions, some state that the data can be passed or resold to others after a certain
period of time, some state the contractor is the sole owner of the data and that
they can never be passed without their written permission.
4-7 St. Louis District Air-Photo Contracting. The St. Louis District has an
extensive Geodesy, Cartography, and Photogrammetry (GC&P) Section. Photogrammetrists as certified by the American Society of Photogrammetry and Remote Sensing (ASPRS) have many years of experience in aerial photography,
surveying, mapping and in the A-E Contracting of these services. The GC&P section is currently responsible for the technical management of all aerial photography and mapping projects within the St. Louis District. They provide contracting
services for all photogrammetric mapping projects for other government agencies,
as well as other Corps of Engineers Districts.
a. Their experts in photogrammetry can provide assistance in developing contracts, scopes of work, government estimates or negotiation assistance. Technical
guidance is provided in the development, acquisition, accuracy, and utilization of
base topographic and planimetric mapping. They also provide advice on remote
sensing data, environmental data sets, and engineering data to be incorporated
into Geographic Information Systems (GIS) to assist engineers and scientists in
Corps of Engineers project work.
b. The Point of Contact at the St. Louis District is Dennis Morgan (314)-3318373. Appendices H and I include example contracts of a Statement of Work
(SOW) and a Memorandum of Understanding (MOU).
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Chapter 5
Processing Digital Imagery
5-1 Introduction. Image processing in the context of remote sensing refers to the
management of digital images, usually satellite or digital aerial photographs. Image
processing includes the display, analysis, and manipulation of digital image computer
files. The derived product is typically an enhanced image or a map with accompanying
statistics and metadata. An image analyst relies on knowledge in the physical and natural
sciences for aerial view interpretation combined with the knowledge of the nature of the
digital data (see Chapter 2). This chapter will explore the basic methods employed in
image processing. Many of these processes rely on concepts included in the fields of geography, physical sciences, and analytical statistics.
5-2 Image Processing Software.
a. Imaging software facilitates the processing of digital images and allows for the
manipulation of vast amounts of data in the file. There are numerous software programs
available for image processing and image correction (atmospheric and geometric corrections). A few programs are available as share-ware and can be downloaded from the
internet. Other programs are available through commercial vendors who may provide a
free trial of the software. Some vendors also provide a tutorial package for testing the
software.
b. The various programs available have many similar processing functions. There
may be minor differences in the program interface, terminology, metadata files (see below), and types of files it can read (indicated by the file extension). There can be a broad
range in cost. Be aware of the hardware requirements and limitations needed for running
such programs. An on-line search for remote sensing software is recommended to acquire pertinent information concerning the individual programs.
5-3 Metadata.
a. Metadata is simply ancillary information about the characteristics of the data; in
other words, it is data about the data. It describes important elements concerning the acquisition of the data as well as any post-processing that may have been performed on the
data. Metadata is typically a digital file that accompanies the image file or it can be a
hardcopy of information about the image. Metadata files document the source (i.e.,
Landsat, SPOT, etc.), date and time, projection, precision, accuracy, and resolution. It is
the responsibility of the vendor and the user to document any changes that have been
applied to the data. Without this information the data could be rendered useless.
b. Depending on the information needed for a project, the metadata can be an invaluable source of information about the scene. For example, if a project centers on change
detection, it will be critical to know the dates in which the image data were collected.
Numerous agencies have worked toward standardizing the documentation of metadata in
an effort to simplify the process for both vendors and users. The Army Corps of Engineers follows the Federal Geographic Data Committee (FGDC) standards for metadata
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(go to http://geology.usgs.gov/tools/metadata/standard/metadata.html). The importance
of metadata cannot be overemphasized.
5-4 Viewing the Image. Image files are typically displayed as either a gray scale or
a color composite (see Chapter 2). When loading a gray scale image, the user must
choose one band for display. Color composites allow three bands of wavelengths to be
displayed at one time. Depending on the software, users may be able to set a default
band/color composite or designate the band/color combination during image loading.
5-5 Band/Color Composite. A useful initial composite (as seen in Figure 5-1a) for
a Landsat TM image is Bands 3, 2, 1 (RGB). This will place band 3 in the red plane,
band 2 in the green plane, and band 1 in the blue plane. The resultant image is termed a
true-color composite and it will resemble the colors one would observe in a color photograph. Another useful composite is Bands 4, 3, 2 (R, G, B), known as a false-color composite (Figure 5-1b). Similar to a false-color infrared photograph, this composite displays features with color and contrast that differ from those observed in nature. For
instance, healthy vegetation will be highlighted by band 4 and will therefore appear red.
Water and roads may appear nearly black.
a. True-color Landsat TM composite 3, 2, 1
(RGB respectively).
b. False color composite 4, 3, 2.
Figure 5-1. Figure 5-1a is scene in which water, sediment, and land surfaces appear
bright. Figure 5-1b is a composite that highlights healthy vegetation (shown in red); water
with little sediment appears black. Images developed for USACE Prospect #196 (2002).
5-6 Information About the Image. Once the image is displayed it is a good idea to
become familiar with the characteristics of the data file. This information may be found
in a separate metadata file or as a header file embedded with the image file. Be sure to
note the pixel size, the sensor type, data, the projection, and the datum.
5-7 Datum.
a. A geographic datum is a spherical or ellipsoidal model used to reference a coordinate system. Datums approximate the shape and topography of the Earth. Numerous
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datums have evolved, each developed by the measurement of different aspects of the
Earth’s surface. Models are occasionally updated with the use of new technologies. For
example, in 1984 satellites carrying GPS (global position systems) refined the World
Geodetic System 1927 (WGS-27); the updated datum is referred to as WGS–84 (World
Geodetic System–1984). Satellite data collected prior to 1984 may have coordinates
linked to the WGS-27 datum. Georeferencing coordinates to the wrong datum may result in large positional errors. When working with multiple images, it is therefore important to match the datum for each image.
b. Image processing software provide different datums and will allow users to convert from one datum to another. To learn more about geodetic datums go to
http://www.ngs.noaa.gov/PUBS_LIB/Geodesy4Layman/geo4lay.pdf.
5-8 Image Projections.
a. Many projects require precise location information from an image as well as geocoding. To achieve these, the data must be georeferenced, or projected into a standard
coordinate system such as Universal Transverse Mercator (UTM), Albers Conical Equal
Area, or a State Plane system. There are a number of possible projections to choose
from, and a majority of the projections are available through image processing software.
Most software can project data from one map projection to another, as well as unprojected data. The latter is known as rectification. Rectification is the process of fitting the
grid of pixels displayed in an image to the map coordinate system grid (see Paragraph 514).
b. The familiar latitude and longitude (Lat/Long) is a coordinate system that is applied to the globe (Figure 5-2). These lines are measured in degrees, minutes, and seconds (designated by o, ', and " respectively). The value of one degree is given as 60 minutes; one minute is equivalent to 60 seconds (1o = 60'; 1'= 60"). It is customary to
present the latitude value before the longitude value.
5-9 Latitude. Latitude lines, also known as the parallels or parallel lines, are perpendicular to the longitude lines and encircle the girth of the globe. They are parallel to one
another, and therefore never intersect. The largest circular cross-section of the globe is at
the equator. For this reason the origin of latitude is at the equator. Latitude values increase north and south away from the equator. The north or south direction must be reported when sighting a coordinate, i.e., 45oN. Latitude values range from 0 to 90o,
therefore the maximum value for latitude is 90o. The geographic North Pole is at 90oN
while the geographic South Pole is at 90oS
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Figure 5-2. Geographic projection.
5-10 Longitude. The lines of longitude pass through the poles, originating at Greenwich, England (0o longitude) and terminating in the Pacific (180o). Because the Earth’s
spherodal shape approximates a circle, its degree measurement can be given as 360o.
Therefore, to travel half way around the world one must move 180o. The degrees of longitude increase to the east and west, away from the origin. The coordinate value for longitude is given by the degree number and the direction from the origin, i.e., 80oW or
130oE. Note: 180oW and 180oE share the same line of longitude.
5-11 Latitude/Longitude Computer Entry. Software cannot interpret the
north/south or east/west terms used in any coordinate system. Negative numbers must be
used when designating latitude coordinates south of the Equator or longitude values west
of Greenwich. This means that for any location in North America the latitude coordinate
will be positive and the longitude coordinate will be given as a negative number. Coordinates north of the equator and east of Greenwich will be positive. It is usually not necessary to add the positive sign (+) as the default values in most software are positive
numbers. The coordinates for Niagara Fall, New York are 43o 6' N, 79 o 57' W; these
values would be recorded as decimal degrees in the computer as 43.1o, –79.95 o. Notice
that the negative sign replaces the “W” and minutes were converted to decimal degrees
(see example problem below). Important Note: Coordinates west of Greenwich England are entered into the computer as a negative value.
5-12 Transferring Latitude/Longitude to a Map. Satellite images and aerial
photographs have inherent distortions owing to the projection of the Earth’s three-dimensional surface onto two-dimensional plane (paper or computer monitor). When the
Latitude/Longitude coordinate system is projected onto a paper plane, there are tremendous distortions. These distortions lead to problems with area, scale, distance, and direction. To alleviate this problem cartographers have developed alternative map projections.
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Problem: The Golden Gate Bridge is located at latitude 37o 49' 11" N,
and longitude 122 o 28' 40" W. Convert degrees, minutes,
and seconds (known as sexagesimal system) to decimal
degrees and format the value for computer entry.
Solution: The whole units of degrees will remain the same (i.e., the
value will begin with 37). Minutes and seconds must be
converted to degrees and added to the whole number of
degrees.
Calculation: Latitude: 37o = 37o
49' = 49'(1o /60') = 0.82 o
11" =11" (1'/60")(1o /60') = 0.003o
37o + 0.82 o + 0.003o = 37.82o
37o 49' 11" N = 37.82o
Longitude: 122o = 122o
28' = 28'(1o /60') = 0.47 o
40" =40" (1'/60")(1o /60') = 0.01 o
122o + 0.47 o +0.01 o = 122.48 o
122 o 28' 40" W = 122.48 o
Answer: 37.82o, –122.48 o
5-13 Map Projections.
a. Map projections are attempts to render the three-dimensional surface of the earth
onto a planar surface. Projections are designed to minimize distortion while preserving
the accuracy of the image elements important to the user. Categories of projections are
constructed from cylindrical, conic, and azimuthal planes, as well as a variety of other
techniques. Each type of projection preserves and distorts different properties of a map
projection. The most commonly used projections are Geographical (Lat/Lon), Universal
Transverse Mercator (UTM), and individual State Plane systems. Geographic (Lat/Lon)
is the projection of latitude and longitude with the use of a cylindrical plane tangent to
the equator. This type of projection creates great amounts of distortion away from the
poles (this explains why Greenland will appear larger than the US on some maps).
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b. The best projection and datum to use will depend on the projection of accompanying data files, location of the origin of the data set, and limitations on acceptable projection distortion.
5-14 Rectification.
a. Image data commonly need to be rectified to a standard projection and datum.
Rectification is a procedure that distorts the grid of image pixels onto a known projection and datum. The goal in rectification is to create a faithful representation of the scene
in terms of position and radiance. Rectification is performed when the data are unprojected, needs to be reprojected, or when geometric corrections are necessary. If the
analysis does not require the data to be compared or overlain onto other data, corrections
and projections may not be necessary. See Figure 5-3 for an example of a rectified image.
Figure 5-3. A rectified image typically will appear skewed. The rectification correction has rubber-sheeted the pixels to their geographically correct position.
This geometric correction seemingly tilts the image leaving black margins were
there are no data.
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b. There are two commonly used rectification methods for projecting data. Image
data can be rectified by registering the data to another image that has been projected or
by assigning coordinates to the unprojected image from a paper or digital map. The following sections detail these methods. A third method uses newly collected GIS reference points or in-house GIS data such as road, river, or other Civil Works GIS information.
5-15 Image to Map Rectification. Unprojected images can be warped into projections by creating a mathematical relationship between select features on an image and
the same feature on a map (a USGS map for instance). The mathematical relationship is
then applied to all remaining pixels, which warps the image into a projection.
5-16 Ground Control Points (GCPs). The procedure requires the use of prominent
features that exist on both the map and the image. These features are commonly referred
to as ground control points or GCPs. GCPs are well-defined features such as sharp
bends in a river or intersections in roads or airports. Figure 5-4 illustrates the selection
of GCPs in the image-to-image rectification process; this process is similar to that used
in image to map rectification. The minimum number of GCPs necessary to calculate the
transformation depends upon the order of the transformation. The order of transformation can be set within the software as 1st, 2nd, or 3rd order polynomial transformation.
The following equation (5-1) identifies the number of GCPs required to calculate the
transformation. If the minimum number is not met, an error message should inform the
user to select additional points. Using more that the minimum number of GCPs is recommended.
(t + 1)(t + 2) = minimum number of GCPs
2
5-1
where t = order of transformation (1st, 2nd, or 3rd ).
a. To begin the procedure, locate and record the coordinate position of 10 to 12 features found on the map and in the image. Bringing a digital map into the software program will simplify coordinate determination with the use of a coordinate value tool.
When using a paper map, measure feature positions as accurately as possible, and note
the map coordinate system used. The type of coordinate system used must be entered
into the software; this will be the projection that will be applied to the image. Once projected, the image can be easily projected into a different map projection.
b. After locating a sufficient number of features (and GCPs) on the map, find the
same feature on the image and assign the coordinate value to that pixel. Zooming in to
choose the precise location (pixel) will lower the error. When selecting GCPs, it is best
to choose points from across the image, balancing the distribution as much as possible;
this will increase the positional accuracy. Once the GCP pixels have been selected and
given a coordinate value, the software will interpolate and transform the remaining pixels into position.
5-17 Positional Error. The program generates a least squares or “Root Mean
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tion. The root mean square estimates the level of error in the transformation. The estimate will not be calculated until three or four GCPs have been entered. Initial estimates
will be high, and should decrease as more GCPs are added to the image. A root mean
square below 1.0 is a reasonable level of accuracy. If the RMS is higher that 1.0, simply
reposition GCPs with high individual errors or delete them and reselect new GCPs. With
an error less than 1.0 the image is ready to be warped to the projection and saved.
a. The scene appearance of the GCP selection module may look
similar to this scene capture. Each segment of the function is
presented individually below.
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b. This scene represents the original, unprojected data file
c. This geo-registered image is used to match sites within the unprojected
data file. Projected images such as this are often available on-line.
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d. GCPs are located by matching image features between the projected and
unprojected image. Notice the balanced spatial distribution of the GCPs;
this type of distribution lowers the projection error.
e. Unprojected data are then warped to the GCP positions. This results in
a skewed image. The image is now projected onto a coordinate system
and is now ready for GIS processing.
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f. RMS error for each GCP is recorded in a matrix spreadsheet. A total RMS error of
0.7742 is provided in the upper margin of Figure 5-4a.
Figure 5-4. GCP selection display modules.
5-18 Project Image and Save. The last procedure in rectification involves re-sampling the image using a “nearest neighbor” re-sampling technique. The software easily
performs this process. Nearest neighbor re-sampling uses the value of the nearest pixel
and extracts the value to the output, or re-sampled pixel. This re-sampling method preserves the digital number value (spectral value) of the original data. Additional re-sampling methods are bilinear interpolation and cubic convolution, which recalculate the
spectral data. The image is projected subsequent to re-sampling, and the file is ready to
be saved with a new name.
Recommendation: Naming altered data files and documenting
procedures
Manipulating the data alters the original data file. It is therefore a good idea
to save data files with different names after performing major alterations to
the data. This practice creates reliable data backup files.
Because of the number of data files an analysis can create, it is best to clearly
name the altered image files with the procedure name performed on the
image (i.e., “TmSept01warped” indicates Thematic Mapper data collected
September 2001, warped by user). Be sure to document your procedures and
parameters used in a journal or a text file. Include the name of the altered
file, changes applied to the data, the date, and other useful information.
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5-19 Image to Image Rectification.
a. Images can also be rectified to a second projected digital image. The procedure is
similar to that performed in image to map rectification. Simply locate common, identifiable features in both images, match the locations, and assign GCPs. Adjust GCPs until
RMS error is less than 1.0. Enter the coordinate system that will be used and designate a
re-sampling method (Figure 5-4).
b. Rectified images can easily be converted from one coordinate system to another.
Projected images can readily be superimposed onto other projected data and used for
georeferencing image features.
5-20 Image Enhancement. The major advantage of remote sensing data lies in the
ability to visually evaluate the data for overall interpretation. An accurate visual interpretation may require modification of the output brightness of a pixel in an effort to improve image quality. Here are a number of methods used in image enhancement. This
paragraph examines the operations of 1) contrast enhancement, 2) band ratio, 3) spatial
filtering, and 4) principle components. The type of enhancement performed will depend
on the appearance of the original scene and the goal of the interpretation.
a. Image Enhancement #1: Contrast Enhancement.
(1) Raw Image Data. Raw satellite data are stored as multiple levels of brightness
known as the digital number (DN). Paragraph 2-7a explained the relationship between
the number of brightness levels and the size of the data storage. Data stored in an 8-bit
data format maintain 256 levels of brightness. This means that the range in brightness
will be 0 to 255; zero is assigned the lowest brightness level (black in gray- and colorscale images), while 255 is assigned the highest brightness value (white in gray scale or
100% of the pigment in a color scale). The list below summarizes the brightness ranges
in a gray scale image.
0
= black
50
= dark gray
150 = medium gray
200 = light gray
255 = white
(a) When a satellite image is projected, the direct one-to-one assignment of
gray scale brightness to digital number values in the data set may not provide the best
visual display (Figures 5-5 and 5-6). This will happen when a number of pixel values are
clustered together. For instance, if 80% of the pixels displayed DNs ranging from 50–
95, the image would appear dark with little contrast.
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Figure 5-5. A linear stretch involves identifying the minimum
and maximum brightness values in the image histogram and
applying a transformation to stretch this range to fill the full
range across 0 to 255.
Figure 5-6. Contrast in an image before (left) and after (right) a linear contrast stretch. Taken from http://rst.gsfc.nasa.gov/Sect1/Sect1_12a.html.
(b) The raw data can be reassigned in a number of ways to improve the contrast
needed to visually interpret the data. The technique of reassigning the pixel DN value is
known as the image enhancement process. Image enhancement adds contrast to the data
by stretching clustered DNs across the 0–255 range. If only a small part of the DN range
is of interest, image enhancement can stretch those values and compress the end values
to suppress their contrast. If a number of DNs are clustered on the 255 end of the range,
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it is possible that a number of the pixels have DNs greater than 256. An image enhancement will decompress these values, thereby increasing their contrast.
Data Analysis
Histograms
Image processing software can chart the distribution of digital number
values within a scene. The distribution of the brightness values is
displayed as a histogram chart. The horizontal axis shows the spread of
the digital numbers from 0 to the maximum DN value in the data set.
The vertical axis shows the frequency or how many pixels in the scene
each value has (Figure 5-7). The histogram allows an analyst to quickly
access the type of distribution maintained by the data. Types of
distribution may be normal, bimodal, or skewed (Figure 5-7).
Histograms are particularly useful when images are enhanced.
Lookup Tables
A lookup table (LUT) graphs the intensity of the input pixel value
relative to the output brightness observed on the screen. The curve does
not provide information about the frequency of brightness, instead it
provides information regarding the range associated with the brightness
levels. An image enhancement can be modeled on a lookup table to
better evaluate the relationship between the unaltered raw data and the
adjusted display data.
Scatter plots
The correlation between bands can be seen in scatter plots generated by
the software. The scatter plots graph the digital number value of one
band relative to another (Figure 5-8). Bands that are highly correlated
will produce plots with a linear relationship and little deviation from
the line. Bands that are not well correlated will lack a linear
relationship. Digital number values will cluster or span the chart
randomly. Scatter plots allow for a quick assessment of the usefulness
of particular band combinations.
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Figure 5-7. Pixel population and distribution across the 0 to
255 digital number range. All three plots show the pixel distribution before and after a linear stretch function (white denotes pre-stretch distribution and colored elements denote
stretched pixel distribution). The stretched histogram shows
gaps between the single values due to the discrete number
of pixel values in the data set. The top histogram (red) has a
bimodal distribution. The middle (green) maintains a skewed
distribution, while the last histogram (blue) reveals a normal
distribution. The solid black line superimposed in each image indicates the maximum and minimum DN value that is
stretched across the entire range. Notice the straight lines
that join the linear segment. Image taken from Prospect (2002
and 2003).
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Figure 5-8. Landsat TM band 345 RGB color composite with accompanying image scatter
plots. The scatter plots map band 3 relative to bands 4 and 5 onto a feature space graph.
The data points in the plot are color coded to display pixel population. The table provides
the pixel count for five image features in band 3, 4, and 5. A is agricultural land, B is deep
(partially clear) water, C is sediment laden water, D is undeveloped land, E fallow fields.
Image developed for Prospect (2002 and 2003).
(2) Enhancing Pixel Digital Number Values. Images can enhance or stretch the
visual display of an image by setting up a different relationship between the DN and the
brightness level. The enhancement relationship created will depend on the distribution of
pixel DN values and which features need enhancement. The enhancement can be applied
to both gray- and color-scale images.
(3) Contrast Enhancement Techniques. The histogram chart and lookup table are
useful tools in image enhancement. Enhancement stretching involves a variety of techniques, including contrast stretching, histogram equalization, logarithmic enhancement,
and manual enhancement. These methods assume the image has a full range of intensity
(from 0–255 in 8-bit data) to display the maximum contrast.
(4) Linear Contrast Stretching. Contrast stretching takes an image with clustered
intensity values and stretches its values linearly over the 0–255 range. Pixels in a very
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bright scene will have a histogram with high intensity values, while a dark scene will
have low intensity values (Figure 5-9). The low contrast that results from this type of
DN distribution can be adjusted with contrast stretching, a linear enhancement function
performed by image processing software. The method can be monitored with the use of
a histogram display generated by the program.
Figure 5-9. Unenhanced satellite data on left. After a default stretch, image contrast
is increased as the digital number values are distributed over the 0–255 color range.
The resulting scene (shown on the right) has a higher contrast.
(a) Contrast stretching allocates the minimum and maximum input values to 0
and 255, respectively. The process assigns a gray level 0 to a selected low DN value,
chosen by the user. All DNs smaller than this value are assigned 0 as well, grouping the
low input values together. Gray level 255 is similarly assigned to a selected high DN
value and all higher DN values. Intermediate gray levels are assigned to intermediate
DN values proportionally. The resulting graph looks like a straight line (shown in Figure
5-7 as the black solid-line plot superimposed onto the three DN histograms), while the
corresponding histogram will distribute values across the range, leaving an increase to
the image contrast (Figure 5-9). The stretched histogram shows gaps between the single
values due to the discrete number of pixel values in the data set (Figure 5-7). The proportional brightness gives a more accurate appearance to the image data, and will better
accommodate visual interpretation.
(b) The linear enhancement can be greatly affected by a random error that is
particularly high or low in brightness values. For this reason, a non-linear stretch is
sometimes preferred. In non-linear stretches, such as histogram equalization and logarithmic enhancement, brightness values are reassigned using an algorithm that exaggerates contrast in the range of brightness values most common in that image.
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(5) Histogram Equalization. Low contrast can also occur when values are spread
across the entire range. The low contrast is a result of tight clustering of pixels in one
area (Figure 5-10a). Because some pixel values span the intensity range it is not possible
to apply the contrast linear stretch. In Figure 5-10a, the high peak on the low intensity
end of the histogram indicates that a narrow range of DNs is used by a large number of
pixels. This explains why the image appears dark despite the span of values across the
full 0–255 range.
(a) Histogram equalization evenly distributes the pixel values over the entire
intensity range (see steps below). The pixels in a scene are numerically arranged according to their DN values and divided into 255 equal-sized groups. The lowest level is
assigned a gray level of zero, the next group is assigned DN 1, …, the highest group is
assigned gray level 255. If a single DN value has more pixels than a group, gray levels
will be skipped. This produces gaps in the histogram distribution. The resultant shape of
the graph will depend on the frequency of the scene.
(b) This method generally reduces the peaks in the histogram, resulting in a
flatter or lower curve (Figure 5-10b). The histogram equalization method tends to enhance distinctions within the darkest and brightest pixels, sacrificing distinctions in middle-gray. This process will result in an overall increase in image contrast (Figure 5-10b).
(6) Logarithmic Enhancement. Another type of enhancement stretch uses a logarithmic algorithm. This type of enhancement distinguishes lower DN values. The high
intensity values are grouped together, which sacrifices the distinction of pixels with
higher DN.
(7) Manual Enhancement. Some software packages will allow users to define an
arbitrary enhancement. This can be done graphically or numerically. Manually adjusting
the enhancement allows the user to reduce the signal noise in addition to reducing the
contrast in unimportant pixels. Note: The processes described above do not alter the
spectral radiance of the pixel raw data. Instead, the output display of the radiance is
modified by a computed algorithm to improve image quality.
b. Image Enhancement #2: Band Arithmetic
(1) Band Arithmetic. Spectral band data values can be combined using arithmetic
to create a new “band.” The digital number values can be summed, subtracted, multiplied, and divided (see equations 5-1 and 5-2). Image software easily performs these operations. This section will review only those arithmetic processes that involve the division or ratio of digital band data.
(2) Band Ratio. Band ratio is a commonly used band arithmetic method in which
one spectral band is proportioned with another spectral band. This simple method reduces the effects of shadowing caused by topography, highlights particular image elements, and accentuates temporal differences (Figure 5-11).
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a. Image and its corresponding DN
histogram show that the majority of
pixels are clustered together (centering approximately on DN value
of 100).
b. After histogram equalization stretch the
pixels are reassigned new values and
spread out across the entire value range.
The data maximum is subdued while the
histogram leading and trailing edges are
amplified, the resulting image has an
overall increase in contrast.
Figure 5-10. Landsat image of Denver area.
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Landsat bands 3, 2, 1
Band ratio 3/1 highlights hematite
Band ratio 1/7 highlights aluminum ore
Band ratio 7/5 highlights clays
Band ratio 4/2 highlights biomass
Figure 5-11. NASA Landsat images from top to bottom: Color composite bands
3, 2, 1, band ratio 3/1 highlights iron oxide minerals, band ratios 7/5 and 1/7 reveals the presence of water in minerals—appropriate for mapping clay minerals or aluminum ore, and band ratio 4/2 allows for biomass determination.
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(3) Shadow Removal from Data. The effect of shadowing is typically caused by a
combination of sun angle and large topographic features (i.e., shadows of mountains).
Table 5-1 lists the pixel digital number values for radiance measured from two different
objects for two bands (arbitrarily chosen) under differing lighting conditions. Pixel data
representing the radiance reflecting off deciduous trees (trees that lose their leaves annually) is consistently higher for non-shadowed objects. This holds true as shadowing effectively lowers the pixel radiance. When the ratio of the two bands is taken (or divided
by one another) the resultant ratio value is not influenced by the effects of shadowing
(see Table 5-1). The band ratio therefore creates a more reliable data set.
Table 5-1
Effects of shadowing
Tree type
Deciduous Trees
Coniferous Trees
Light conditions
In sunlight
In shadow
In sunlight
In shadow
Band A
(DN)
48
18
31
11
Band B
(DN)
50
19
45
16
Band A/B (ratio)
(DN)
0.96
0.95
0.69
0.69
(4) Emphasize Image Elements. A number of ratios have been empirically developed and can highlight many aspects of a scene. Listed below are only a few common
band ratios and their uses. When choosing bands for this method, it is best to consider
bands that are poorly correlated. A greater amount of information can be extracted from
ratios with bands that are covariant.
B3/B1 – iron oxide
B3/B4 – vegetation
B4/B2 – vegetation biomass
B4/B3 – known as the RVI (Ratio Vegetation Index)
B5/B2 – separates land from water
B7/B5 – hydrous minerals
B1/B7 – aluminum hydroxides
B5/B3 – clay minerals
(5) Temporal Differences. Band ratio can also be used to detect temporal changes
in a scene. For instance, if a project requires the monitoring of vegetation change in a
scene, a ratio of band 3 from image data collected at different times can be used. The
newly created band file may have a name such as “Band3’Oct.98/Ban3’Oct.02.” When
the new band is loaded, the resulting ratio will highlight areas of change; these pixels
will appear brighter. For areas with no change, the resulting pixel values will be low and
the resulting pixel will appear gray.
(a) One advantage of the ratio function lies in its ability to not only filter out
the effects of shadowing but also the effects attributable to differences in sun angle. The
sun angle may change from image to image for a particular scene. The sun angle is controlled by the time of day the data were collected as well as the time of year (seasonal
effects). Processing images collected under different sun angle conditions may be un5-21
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avoidable. Again, a ratio of the bands of interest will limit shadowing and sun angle effects. It is therefore possible to perform a temporal analysis on data collected at different
times of the day or even at different seasons.
(b) A disadvantage of using band ratio is the emphasis that is placed on noise in
the image. This can be reduced, however, by applying a spatial filter before employing
the ratio function; this will reduce the signal noise. See Paragraph 5-20c.
(6) Create a New Band with the Ratio Data. Most software permits the user to
perform a band ratio function. The band ratio function converts the ratio value to a
meaningful digital number (using the 256 levels of brightness for 8-bit data). The ratio
can then be saved as a new band and loaded onto a gray scale image or as a single band
in a color composite.
(7) Other Types of Ratios and Band Arithmetic. There are a handful of ratios that
highlight vegetation in a scene. The NDVI (Normalized Difference Vegetation Index;
equations 5-1and 5-2) is known as the “vegetation index”; its values range from –1 to 1.
NDVI = NIR-red/NIR + red
(5-1)
where NDVI is the normalized difference vegetation index, NIR is the near infrared, and
red is the band of wavelengths coinciding with the red region of the visible portion of
the spectrum. For Landsat TM data this equation is equivalent to:
NDVI = Band 4- Band 3/ Band 4+ Band 3
(5-2)
In addition to the NDVI, there is also IPVI (Infrared Percentage Vegetation Index), DVI
(Difference Vegetation Index), and PVI (Perpendicular Vegetation Index) just to name a
few. Variation in vegetation indices stem from the need for faster computations and the
isolation of particular features. Figure 5-12 illustrates the NDVI.
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Figure 5-12. Top: True color CAMIS image. Bottom:
NDVI mask isolating vegetated pixels. This mask will
be useful during the classification process, which will
subsequently classify only the vegetation in the scene
while disregarding water and urban features. Taken
from Campbell (2003).
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c. Image Enhancement #3: Spatial Filters. It is occasionally advantageous to reduce
the detail or exaggerate particular features in an image. This can be done by a convolution method creating an altered or “filtered” output image data file. Numerous spatial
filters have been developed and can be automated within software programs. A user can
also develop his or her own spatial filter to control the output data set. Presented below
is a short introduction to the method of convolution and a few commonly used spatial
filters.
(1) Spatial Frequency. Spatial frequency describes the pattern of digital values
observed across an image. Images with little contrast (very bright or very dark) have
zero spatial frequency. Images with a gradational change from bright to dark pixel values have low spatial frequency; while those with large contrast (black and white) are
said to have high spatial frequency. Images can be altered from a high to low spatial frequency with the use of convolution methods.
(2) Convolution.
(a) Convolution is a mathematical operation used to change the spatial frequency of digital data in the image. It is used to suppress noise in the data or to exaggerate features of interest. The operation is performed with the use of a spatial kernel. A
kernel is an array of digital number values that form a matrix with odd numbered rows
and columns (Table 5-2). The kernel values, or coefficients, are used to average each
pixel relative to its neighbor across the image. The output data set will represent the averaging effect of the kernel coefficients. As a spatial filter, convolution can smooth or
blur images, thereby reducing image noise. In feature detection, such as an edge enhancement, convolution works to exaggerate the spatial frequency in the image. Kernels
can be reapplied to an image to further smooth or exaggerate spatial frequency.
(b) Low pass filters apply a small gain to the input data (Table 5-2a). The resulting output data will decrease the spatial frequency by de-emphasizing relatively
bright pixels. Two types of low pass filters are the simple mean and center-weighted
mean methods (Table 5-2a and b). The resultant image will appear blurred. Alternatively, high pass frequency filters (Table 5-2c) increase image spatial frequency. These
types of filters exaggerate edges without reducing image details (an advantage over the
Laplacian filter discussed below).
(2) Laplacian or Edge Detection Filter.
(a) The Laplacian filter detects discrete changes in spectral frequency and is
used for highlighting edge features in images. This type of filter works well for delineating linear features, such as geologic strata or urban structures. The Laplacian is calculated by an edge enhancement kernel (Table 5-2d and e); the middle number in the matrix is much higher or lower than the adjacent coefficients. This type of kernel is
sensitive to noise and the resulting output data will exaggerate the pixel noise. A
smoothing convolution filter can be applied to the image in advance to reduce the edge
filter's sensitivity to data noise.
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The Convolution Method
Convolution is carried out by overlaying a kernel onto the pixel image and
centering its middle value over the pixel of interest. The kernel is first placed
above the pixel located at the top left corner of the image and moved from top
to bottom, left to right. Each kernel position will create an output pixel value,
which is calculated by multiplying each input pixel value with the kernel
coefficient above it. The product of the input data and kernel is then averaged
over the array (sum of the product divided by the number of pixels evaluated);
the output value is assigned this average. The kernel then moves to the next
pixel, always using the original input data set for calculating averages. Go to
http://www.cla.sc.edu/geog/rslab/Rscc/rscc-frames.html for an in-depth
description and examples of the convolution method.
The pixels at the edges create a problem owing to the absence of neighboring
pixels. This problem can be solved by inventing input data values. A simpler
solution for this problem is to clip the bottom row and right column of pixels
at the margin.
(b) The Laplacian filter measures the changes in spectral frequency or pixel intensity. In areas of the image where the pixel intensity is constant, the filter assigns a
digital number value of 0. Where there are changes in intensity, the filter assigns a positive or negative value to designate an increase or decrease in the intensity change. The
resulting image will appear black and white, with white pixels defining the areas of
changes in intensity.
Table 5-2
Variety in 9-Matix Kernel Filters Used in a Convolution Enhancement.
Each graphic shows a
kernel, an example of raw DN data array, and the resultant enhanced data array.
See
http://www.cee.hw.ac.uk/hipr/html/filtops.html for further information on kernels and the filtering methods.
a. Low Pass: simple mean kernel.
1
1
1
1
1
1
1
1
1
1 1 1
1 1 1
1 1 1
1 1 1
1 1 1
1 1 1
1 1 1
Raw data
1
1
1
10
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1 1 1 1
1 1 1 1
1 1 2 2
1 1 2 2
1 1 2 2
1 1 1 1
1 1 1 1
Output data
1
1
2
2
2
1
1
5-25
1
1
1
1
1
1
1
1
1
1
1
1
1
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b. Low Pass: center weighted mean kernel.
1
1
1
1
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
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1
1
1
1
1
1
1
1
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Raw data
1
1
1
10
1
1
1
1
1
1
1
1
1
1
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1
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1
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1
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1
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1
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Output data
1
1
2
2
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1
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3
2
1
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1
1
2
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2
1
1
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1
1
1
1
1
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
c. High Pass kernel.
-1
-1
-1
-1
8
-1
-1
-1
-1
10 10 10
10 10 10
10 10 10
10 10 10
10 10 10
10 10 10
10 10 10
Raw data
10
10
10
15
10
10
10
10
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10
10
10
10
10
10
10
10
10
10
10
10
10
0
0
10
0
0
10
0
0
10
0
0
10
0
0
10
0
0
10
0
0
Output data
0
0
-5
-5
-5
0
0
0
0
-5
40
-5
0
0
0
0
-5
-5
-5
0
0
d. Direction Filter: north-south component kernel.
-1
-2
-1
2
1
2
-1
-2
-1
1
1
1
1
1
1
1
1
1
1
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1
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1
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1
1
Raw data
2
2
2
2
2
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2
1
1
1
1
1
1
1
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1
1
1
1
1
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Output data
-4
-4
-4
-4
-4
-4
-4
8
8
8
8
8
8
8
-4
-4
-4
-4
-4
-4
-4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
e. Direction Filter: East-west component kernel.
-1
2
-1
-2
4
-2
-1
2
-1
1
1
1
1
1
1
1
1
1
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Raw data
2
2
2
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1
1
1
1
1
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1
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1
1
1
1
1
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1
0
0
0
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0
0
0
Output data
0
0
0
0
0
0
0
5-26
0
0
0
0
0
0
0
0
0
0
0
0
0
0
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0
0
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d. Image Enhancement #4: Principle Components. The principle component analysis (PCA) is a technique that transforms the pixel brightness values. This transformation
compresses the data by drawing out maximum covariance and removes correlated elements. The resulting data will contain new, uncorrelated data that can be later used in
classification techniques.
(1) Band Correlation. Spectral bands display a range of correlation from one
band to another. This correlation is easily viewed by bringing up a scatter plot of the
digital data and plotting, for instance, band 1 vs. band 2. Many bands share elements of
information, particularly bands that are spectrally close to one another, such as band 1
and 2. For bands that are highly correlated, it is possible to predict the brightness outcome of one band with the data of the other (Figure 5-13). Therefore, bands that are well
correlated may not be of use when attempting to isolate spectrally similar objects.
Figure 5-13. Indian IRS-1D image and accompanying spectral plot. Representative pixel
points for four image elements (fluvial sediment in a braided channel, water, agriculture,
and forest) are plotted for each band. Plot illustrates the ease by which each element can
be spectrally separarted. For example, water is easily distinguishable from the other
elements in band 2.
(2) Principle Component Transformation. The principle component method extracts the small amount of variance that may exist between two highly correlated bands
and effectively removes redundancy in the data. This is done by “transforming” the major vertical and horizontal axes. The transformation is accomplished by rotating the
horizontal axis so that it is parallel to a least squares regression line that estimates the
data. This transformed axis is known as PC1, or Principle Component 1. A second axis,
PC2, is drawn perpendicular to PC1, and its origin is placed at the center of the PC1 range
(Figure 5-14). The digital number values are then re-plotted on the newly transformed
axes. This transformation will result in data with a broader range of values. The data can
be saved as a separate file and loaded as an image for analysis.
5-27
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255
PC1
Band B Brightness Value
PC2
0
0
255
Band A Brightness Value
Figure 5-14. Plot illustrates the spectral variance between two bands, A and B. PC1 is the
line that captures the mean of the data set. PC2 is orthogonal to PC1. PC1 and PC2 become the new horizontal and vertical axis; brightness values are redrawn onto the PC1
and PC2 scale.
c. Transformation Series (PC1, PC2, PC3, PC4, PC5, etc.). The process of transforming the axis to fit the maximum variance in the data can be performed in succession on
the same data set. Each successive axis rotation creates a new principal component axis;
a series of transformations can then be saved as individual files. Band correlation is
greatly reduced in the first PC transformation, 90% of the variance between the bands
will be isolated by PC1. Each principle component transformation extracts less and less
variance, PC2, for instance, isolates 5% of the variance, and PC3 will extract 3% of the
variance, and so on (Figure 5-15). Once PC1 and PC2 have been processed, approximately 95% of the variance within the bands will be extracted. In many cases, it is not
useful to exact the variance beyond the third principle component. Because the principle
component function reduces the size of the original data file, it functions as a pre-processing tool and better prepares the data for image classification. The de-correlation of
band data in the principle component analysis is mathematically complex. It linearly
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transforms the data using a form of factor analysis (eigen value and eigen vector matrix).
For a complete discussion of the technique see Jensen (1996).
Figure 5-15. PC-1 contains most of the variance in the data. Each successive PC-transformation isolates less and less variation in the data. Taken
from http://rst.gsfc.nasa.gov/start.html.
d. Image Classification. Raw digital data can be sorted and categorized into thematic
maps. Thematic maps allow the analyst to simplify the image view by assigning pixels
into classes with similar spectral values (Figure 5-16). The process of categorizing pixels into broader groups is known as image classification. The advantage of classification
is it allows for cost-effective mapping of the spatial distribution of similar objects (i.e.,
tree types in forest scenes); a subsequent statistical analysis can then follow. Thematic
maps are developed by two types of classifications, supervised and unsupervised. Both
types of classification rely on two primary methods, training and classifying. Training is
the designation of representative pixels that define the spectral signature of the object
class. Training site or training class is the term given to a group of training pixels. Classifying procedures use the training class to classify the remaining pixels in the image.
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Figure 5-16. Landsat image (left) and its corresponding thematic map (right) with 17 thematic classes. The black zigzag at bottom of image is the result of shortened flight line
over-lap. (Campbell, 2003).
(1) Supervised Classification. Supervised classification requires some knowledge
about the scene, such as specific vegetative species. Ground truth (field data), or data
from aerial photographs or maps can all be used to identify objects in the scene.
(2) Steps Required for Supervised Classification.
(a) Firstly, acquire satellite data and accompanying metadata. Look for information regarding platform, projection, resolution, coverage, and, importantly, meteorological conditions before and during data acquisition.
(b) Secondly, chose the surface types to be mapped. Collect ground truth data
with positional accuracy (GPS). These data are used to develop the training classes for
the discriminant analysis. Ideally, it is best to time the ground truth data collection to
coincide with the satellite passing overhead.
(c) Thirdly, begin the classification by performing image post-processing techniques (corrections, image mosaics, and enhancements). Select pixels in the image that
are representative (and homogeneous) of the object. If GPS field data were collected,
geo-register the GPS field plots onto the imagery and define the image training sites by
outlining the GPS polygons. A training class contains the sum of points (pixels) or polygons (clusters of pixels) (see Figures 5-17 and 5-18). View the spectral histogram to inspect the homogeneity of the training classes for each spectral band. Assign a color to
represent each class and save the training site as a separate file. Lastly, extract the re5-30
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1 October 2003
maining image pixels into the designated classes by using a discriminate analysis routine
(discussed below).
Figure 5-17. Landsat 7 ETM image of central Australia (4, 3, 2 RGB). Linear features in the upper portion of the scene are sand dunes. Training
data are selected with a selection tool (note the red enclosure). A similar
process was performed on data from Figure 5-16 (the DN values for figure
5-16 are presented in Figure 5-18).
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SORT #
CLASS NAME
COLOR TRAINING CLASSIFIED % TOTAL % DATA
1
Unclassified
25,207,732
68.86%
2
ROAD
Red1
77
0
0.00%
0.00
3
AG
Green1
1642
0
0.00%
0.00
4
LP
Red1
4148
2,164,089
5.91%
19.53
5
LPO
Blue1
5627
1,562,180
4.27%
14.10
6
LPH
Maroon1
4495
2,170,395
5.93%
19.58
0.90%
2.97
7
MHW-low
Aquamarine
888
329,360
8
CUT
Chartreuse
1219
1,055,063
2.88%
9.52
9
MHW-high
Sienna1
3952
1,566,698
4.28%
14.14
10 MORT
Green3
1703
4,651
0.01%
0.04
12 juncus-low-density
Red1
52
37,808
0.10%
0.34
13 juncus-high-density
Blue1
65
102,174
0.28%
0.92
13 juncus-panicum-mix
Cyan1
53
0
0.00%
0.00
14 juncus-mixed-clumps-field
Magenta1
29
3
0.00%
0.00
16 g1=hd-scol+background+w
Green1
32
610,283
1.67%
5.51
17 g2=md-scol+background
Yellow1
29
952
0.00%
0.01
18 g4=md-scol+spartina+mud
Maroon1
36
0
0.00%
0.00
19 g3=md-scol+spartina+background Purple1
50
0
0.00%
0.00
20 g5=ld-scol+mud
Aquamarine
56
617
0.00%
0.01
21 g1=md-spal+w
Red1
66
4,789
0.01%
0.04
0.39%
1.27
22 g2=hd-spal+w
Green1
52
141,060
23 g3=hd-spal+w+sppa
Cyan1
29
803,145
2.19%
7.25
24 g4=md-spal+w+sppa
Magenta1
44
0
0.00%
0.00
25 g5=hd-spal+mud
Red1
25
25
0.00%
0.00
26 g6=mixed-spal
Chartreuse
28
6,555
0.02%
0.06
26 g7=md-spal+lit+mud
Thistle1
36
6
0.00%
0.00
28 g8=md-mixed-spal
Blue4
85
0
0.00%
0.00
29 g1=hd-sppa+mix
Red1
37
74
0.00%
0.00
30 g2=hd-sppa+mud
Blue1
40
0
0.00%
0.00
31 g3=mhd-sppa+spal+background Cyan1
32
939
0.00%
0.01
32 g4=lmd-sppa+mix+background Magenta1
160
0
0.00%
0.00
1.42%
4.69
33 g9-ld-sppa+spal+mud
Blue4
28
520,290
34 g10=ld-sppa+mix+background
Cyan3
45
0
0.00%
0.00
35 g11=ld-sppa+w+mix
Green2
32
1,255
0.00%
0.01
37
11082411.00
Figure 5-18. Classification training data of 35 landscape classification features. “Training” provides the pixel count after training selection; classification provides the image pixel count after a
classification algorithm is performed. This data set accompanies Figure 5-16, the classified image.
(Campbell, 2003).
(3) Classification Algorithms. Image pixels are extracted into the designated
classes by a computed discriminant analysis. The three types of discriminant analysis
algorithms are: minimum mean distance, maximum likelihood, and parallelepiped. All
use brightness plots to establish the relationship between individual pixels and the
training class (or training site).
(a) Minimum Mean Distance. Minimum distance to the mean is a simple computation that classifies pixels based on their distance from the mean of the training class.
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It is determined by plotting the pixel brightness and calculating its Euclidean distance
(using the Pythagorean theorem) to the unassigned pixel. Pixels are assigned to the
training class for which it has a minimum distance. The user designates a minimum distance threshold for an acceptable distance; pixels with distance values above the designated threshold will be classified as unknown.
(b) Parallelepiped. In a parallelepiped computation, unassigned pixels are
grouped into a class when their brightness values fall within a range of the training
mean. An acceptable digital number range is established by setting the maximum and
minimum class range to plus and minus the standard deviation from the training mean.
The pixel brightness value simply needs to fall within the class range, and is not based
on its Euclidean distance. It is possible for a pixel to have a brightness value close to a
class and not fall within its acceptable range. Likewise, a pixel may be far from a class
mean, but fall within the range and therefore be grouped with that class. This type of
classification can create training site overlap, causing some pixels to be misclassified.
(c) Maximum Likelihood. Maximum Likelihood is computationally complex. It
establishes the variance and covariance about the mean of the training classes. This algorithm then statistically calculates the probability of an unassigned pixel belonging to
each class. The pixel is then assigned to the class for which it has the highest probability.
Figure 5-19 visually illustrates the differences between these supervised classification
methods.
Figure 5-19. From left to right, minimum mean distance, parallelepiped, and maximum
likelihood. Courtesy of the Department of Geosciences at Murray State University.
(4) Assessing Error. Accuracy can be qualitatively determined by an error matrix
(Table 5-3). The matrix establishes the level of errors due to omission (exclusion error),
commission (inclusion error), and can tabulate an overall total accuracy. The error matrix lists the number of pixels found within a given class. The rows in Table 5-2 list the
pixels classified by the image software. The columns list the number of pixels in the
reference data (or reported fro m field data). Omission error calculates the probability of
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1 October 2003
a pixel being accurately classified; it is a comparison to a reference. Commission determines the probability that a pixel represents the class for which it has been assigned. The
total accuracy is measured by calculating the proportion correctly classified pixel relative to the total tested number of pixels (Total = total correct/total tested).
Table 5-3
Omission and Commission Accuracy Assessment Matrix. Taken from Jensen (1996).
Reference Data
Classification
Residential
Commercial
Wetland
Forest
Water
Column Total
Overall Accuracy =
382/407=93.86%
Residential
70
3
0
0
0
73
Commercial
5
55
0
0
0
60
Producer’s Accuracy (measure of omission error)
Residential= 70/73 = 96–4% omission error
Commercial= 55/60 = 92–8% omission error
Wetland= 99/103 = 96–4% omission error
Forest= 97/50 = 74–26% omission error
Water= 121/121 = 100–0% omission error
Wetland
0
0
99
4
0
103
Forest
13
0
0
37
0
50
Water
0
0
0
0
121
121
Raw Total
88
58
99
41
121
407
User’s Accuracy (measure of commission error)
Residential= 70/88 = 80–20% omission error
Commercial= 55/58 = 95–5% omission error
Wetland= 99/99 = 100–0% omission error
Forest= 37/41 = 90–10% omission error
Water= 121/121 = 100–0% omission error
Example error matrix taken from Jensen (1986). Data are the result of an accuracy assessment of Landsat
TM data.
Classification method summary
Image classification uses the brightness values in one or more spectral bands,
and classifies each pixel based on its spectral information
The goal in classification is to assign remaining pixels in the image to a
designated class such as water, forest, agriculture, urban, etc.
The resulting classified image is composed of a collection of pixels, colorcoded to represent a particular theme. The overall process then leads to the
creation of a thematic map to be used to visually and statistically assess the
scene.
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(5) Unsupervised Classification. Unsupervised classification does not require prior knowledge. This type of classification relies on a computed algorithm
that clusters pixels based on their inherent spectral similarities.
(a) Steps Required for Unsupervised Classification. The user designates
1) the number of classes, 2) the maximum number of iterations, 3) the maximum
number of times a pixel can be moved from one cluster to another with each iteration, 4) the minimum distance from the mean, and 5) the maximum standard
deviation allowable. The program will iterate and recalculate the cluster data until
it reaches the iteration threshold designated by the user. Each cluster is chosen by
the algorithm and will be evenly distributed across the spectral range maintained
by the pixels in the scene. The resulting classification image (Figure 5-20) will
approximate that which would be produced with the use of a minimum mean distance classifier (see above, “classification algorithm”). When the iteration threshold has been reached the program may require you to rename and save the data
clusters as a new file. The display will automatically assign a color to each class;
it is possible to alter the color assignments to match an existing color scheme (i.e.,
blue = water, green = vegetation, red = urban) after the file has been saved. In the
unsupervised classification process, one class of pixels may be mixed and assigned the color black. These pixels represent values that did not meet the requirements set by the user. This may be attributable to spectral “mixing” represented by the pixel.
(b) Advantages of Using Unsupervised Classification. Unsupervised
classification is useful for evaluating areas where you have little or no knowledge
of the site. It can be used as an initial tool to assess the scene prior to a supervised
classification. Unlike supervised classification, which requires the user to hand
select the training sites, the unsupervised classification is unbiased in its geographical assessment of pixels.
(c) Disadvantages of Using Unsupervised Classification. The lack of information about a scene can make the necessary algorithm decisions difficult. For
instance, without knowledge of a scene, a user may have to experiment with the
number of spectral clusters to assign. Each iteration is time consuming and the
final image may be difficult to interpret (particularly if there is a large number of
unidentified pixels such as those in Figure 5-19). The unsupervised classification
is not sensitive to covariation and variations in the spectral signature to objects.
The algorithm may mistakenly separate pixels with slightly different spectral values and assign them to a unique cluster when they, in fact, represent a spectral
continuum of a group of similar objects.
(6) Evaluating Pixel Classes. The advantages of both the supervised and
unsupervised classification lie in the ease with which programs can perform statistical analysis. Once pixel classes have been assigned, it is possible to list the
exact number of pixels in each representative class (Figure 5-17, classified column). As the size of each pixel is known from the metadata, the metric area of
each class can be quickly calculated. For example, you can very quickly deter5-35
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mine the percentage of fallow field area versus productive field area in an agricultural scene.
Figure 5-20. Unsupervised and supervised classification of a clay-mine (upper center,
bright green pixels) imaged with HyMap hyperspectral data. Images courtesy of Dr.
Brigette Martini at the Earth Sciences Department, University of California, Santa Cruz, CA.
Go to http://www.es.ucsc.edu/~hyperwww/chevron/whatisrs5.html for details on the image.
e. Image Mosaics, Image subsets, and Multiple Image Analysis.
(1) Image Mosaics. It is not uncommon for a study area to include areas
beyond the range of an individual scene. In such a case, it will be necessary to
collect adjacent scenes and mosaic or piece them together (Figures 5-21–5-23). It
is preferable to choose scenes with data collected during the same season or general time frame and under similar weather conditions. Images can only be properly pieced together if their data are registered in the same projection and datum.
It will be important to assess the registration of all images before attaching the
scenes together. If any of the images are misregistered, this will lead to gaps in
the image or it will create pixel overlay.
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(2) Image Mosaic and Image Subset. The mosaic process is a common
feature in image processing programs. It is best to perform image enhancements
prior to piecing separate scenes together. Once the images are pieced together, the
resulting image may be large and include areas outside the study region. It is good
practice to take a subset of this larger scene to reduce the size of the image file.
This will make subsequent image processing faster. To do this, use the clip or
subset function in a software program. The clip function will need to know the
corner coordinates of the subset (usually the upper left and lower right). Some
software may require this procedure to be repeated for each individual band of
data. The subset should be named and saved as a separate file or files. Note: An
image subset may also be required if the margins of a newly registered scene are
skewed, or if the study only requires a small portion of one scene. Reduction of
the spatial dimensions of a scene reduces the image file size, simplifies image
classification, and prepares the image for map production.
Example: Calculate the percentage of land cover types for a classification
performed on a Landsat TM image with a spatial resolution of 30 m using a
supervised maximum likelihood classification with a 3.0 standard deviation.
Solution: Calculate the percentage based on the total
Percent Calculation
Class
Water
Forest:
Wetlands
Agriculture
Urban
Unknown
Total
Number
of class
pixels
16,903
368,641
6,736
13,853
6,255
1081
413,469
Percentage
(16,903/413,469) × 100 = 4.1%
(368,641/413,469) × 100 = 89.1%
(6,736/413,469) × 100 = 1.6%
(13,853/413,469) × 100 = 3.4%
(6,255/413,469) × 100 = 1.5%
(1081/413,469) × 100 = 0.3%
(413,469/413,469) × 100 = 100%
Maximum likelihood is a superior classifier and training classes are well
defined. This is evident in the low number of pixels in the unknown class.
Area can be calculated using the number of pixels in a class and multiplying it
by the ground dimensions of the pixel. For example the number of square
meters and hectares in the wetland class of this example is:
Wetlands
6,736 × (30m)2 = 6.1 × 106 m2
606.24 ha
This last step is often not necessary as many software programs automatically
calculate the hectares for each class.
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Figure 5-21. Multiple Landsat TM images, shown on the left (some sub-scenes are not
shown here) were pieced together to create the larger mosaic image on the right. The
seams within the mosaic image (right) are virtually invisible, an indication of the
accuracy of the projection. Taken from Prospect (2002 and 2003).
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Figure 5-22. Multi-image mosaic of Western United States centered on
the state of Utah. Mosaic seams are invisible in this scene, an indication
of good radiometric and geometric corrections. The skewed and curved
margins are an artifact of the rectification and mosaic process. Taken
from http://www.jpl.nasa.gov/images/earth/usa/misr_020602_2.html.
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Figure 5-23. Landsat 7 Image of the Boston, Massachusetts area. Image on the
right shows red box outlining the boundaries of the subset scene on the right.
Taken from http://landsat.gsfc.nasa.gov/education/l7downloads/index.html.
(3) Multiple-image Temporal Analysis. It is possible to combine bands
from different images or data sets. This allows a user to perform a change detection analysis. The process of “layering” multi-temporal data involves loading a
composite of bands from different images of the same scene. For example, a study
assessing urban development in a forested area would benefit from examining a
band combination that included band 3 data in the red plane, and band 3 data of a
later image in the green plane. If the spectral signature of the scene has changed
and is detectable within the resolution of the data, then changes in the scene will
be highlighted. This image can then be classified and the areas of change can be
statistically assessed. To perform this task accurately, it is important that both images are registered properly. Misregistration will lead to an offset in the image,
which leaves brightly colored lines of pixels. Be sure to choose images whose
data were collected under similar conditions, such as the same season, time of
day, and prevailing weather, i.e., minimum cloud cover.
f. Remote Sensing and Geospatial Information. Remote sensing data are easily integrated with other digital data, such as vector data used in a GIS (Geographical Information System). Vector data can be incorporated into a raster satellite image by overlaying the data onto an image scene. Conversely, a raster
image can be saved as a .jpeg or .tiff file and exported to a vector software processing program. Remote sensing data files can provide land cover and use information as well as digital elevation models (DEMs), and a number of geo-physical
and biophysical parameters. Satellite images coupled with GIS data can be used to
create original maps. The use of remote sensing in this type of application can
drastically cut costs of GIS database development. It also provides data for inaccessible areas.
(1) Digital Orthoquadrangle (DOQs). A digital orthoquadrangle (DOQ) is
a digital image of an aerial photograph that has had ground relief removed and is
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geometrically corrected. The removal of ground relief adds to the accuracy measurement of distances on the ground. DOQs are available over the internet through
the USGS or state level natural resources and environmental agencies. They
come in black and white and color infrared. These digital aerial photographs come
in a variety of scales and resolutions (often 1-m GSD). Due to the ortho-correction process, DOQs are typically in UTM, Geographic, or State Plane Projection.
The images typically have 50 to 300 m overlap. This overlap simplifies the mosaic process. DOQs work well in combination with GIS data and may aid in the
identification of objects in a satellite scene. It is possible to link a DOQ with a
satellite image and a one-to-one comparison can be made between a pixel on the
satellite image and the same geographic point on the DOQ.
(2) Digital Elevation Models (DEM). A Digital Elevation Model (DEM) is
a digital display of cartographic elements, particularly topographic features.
DEMs utilize two primary types of data, DTM (digital terrain model) or DSM
(digital surface model). The DTM represents elevation points of the ground, while
DSM is the elevation of points at the surface, which includes the top of buildings
and trees, in addition to terrain. The DEM incorporates the elevation data and
projects it relative to a coordinate reference point. (See
http://www.ipf.tuwien.ac.at/fr/buildings/diss/node27.html for more information
on DEM, DTM, and DEMs.
(3) DEM Generation. Elevation measurements are sampled at regular intervals to form an array of elevation points within the DEM. The elevation data
are then converted to brightness values and can be displayed as a gray scale image
(Figure 5-24). The model can be viewed in image processing software and superimposed onto satellite image data. The resulting image will appear as a “threedimensional” view of the image data.
(a) DEMs come in a variety of scales and resolutions. Be sure to check
the date and accuracy of the DEM file. DEMs produced before 2001 have as
much a 30 m of horizontal error. As with other files, the DEM must be well registered and in the same projection and datum as other files in the scene. Check the
metadata accompanying the data to verify the projection.
(b) The primary source of DEM data is digital USGS topographic maps
and not satellite data. Spaceborne elevation data will be more readily available
with the processing and public release of the Shuttle Radar Topography Mission
(SRTM) data. Some of this data is currently available through the Jet Propulsion
Laboratory (http://www.jpl.nasa.gov/srtm/) and USGS EROS Data Center
(http://srtm.usgs.gov/index.html).
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Figure 5-24. Digital elevation model (DEM). The brightness
values in this image represent elevation data. Dark pixels correspond to low elevations while the brightest pixels represent
higher elevations. Taken from the NASA tutorial at
http://rst.gsfc.nasa.gov/Sect11/Sect11_5.html.
(c) DEMs can be created for a study site with the use of a high resolution
raster topographic map. The method involved in creating a DEM is fairly advanced; see http://spatialnews.geocomm.com/features/childs3/ for information on
getting starting in DEM production.
(4) Advanced Methods in Image Processing. Remote sensing software facilitates a number of advanced image processing methods. These advanced methods include the processing of hyperspectral data, thermal data, radar data, spectral
library development, and inter-software programming.
(a) Hyperspectral Data. Hyperspectral image processing techniques
manage narrow, continuous bands of spectral data. Many hyperspectral systems
maintain over 200 bands of spectral data. The narrow bands, also known as channels, provide a high level of detail and resolution. This high resolution facilitates
the identification of specific objects, thereby improving classification (Figure 524). The advantage of hyperspectral imaging lies in its ability to distinguish individual objects that would be otherwise grouped in broadband multi-spectra imagery. Narrow bands are particularly useful for mapping resources such as crop
and mineral types. The narrow, nearly continuous bands create large data sets,
which require advance software and hardware to store and manipulate the data.
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Figure 5-25. Hyperspectral classification image of the Kissimmee River in Florida (Image created by Lowe Engineers LLC and SAIC, 2003). Classifications of 28 vegetation communities are based on a supervised classification.
(b) Thermal Data. Thermal image processing techniques are used to image objects by the analysis of their emitted energy (Figure 5-26). The thermal
band wavelength ranges are primarily 8 to 14 µm and 3 to 5 µm. The analysis of
thermal data is typically used in projects that evaluate surface temperatures, such
as oceans and ice sheets, volcano studies, and the emission of heat from manmade objects (e.g., pipelines).
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Figure 5-26. Close-up of the Atlantic Gulf Stream. Ocean temperature and current
mapping was performed with AVHRR thermal data. The temperatures have been
classified and color-coded. Yellow = water 23oC (73oF), green = 14Co (57oF), blue
= 5oC (41oF). Taken from http://www.osdpd.noaa.gov/PSB/EPS/EPS.html.
(c) Radar. Radar (radio detection and ranging) systems are able to
penetrate cloud cover in certain wavelengths. This technology is useful for imaging day or night surface features during periods of intense cloud cover, such as
storms, smoke from fire, or sand and dust storms (Figure 5-27).
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Figure 5-27. Radarsat image, pixel resolution equals 10 m. Image is centered over
the Illinois River (upper left), Mississippi River (large channel in center), and the
Missouri River (smaller channel in center. Chapter 6 case study 3 details the
analysis of this scene. Taken from Tracy (2003).
g. Customized Spectral Library. Many software programs allow users to build
and maintain a customized spectral library. This is done by importing spectra signatures from objects of interest and can be applied to identify unknown objects in
an image.
h. Internal Programming.
(1) Image processing software allows users to develop computing techniques and unique image displays by programming from within the software
package. Programming gives the user flexibility in image manipulation and information extraction. The users’ manual and online help menus are the best resources for information on how to program within particular software.
(2) New applications in image processing and analysis are rapidly being
developed and incorporated into the field of remote sensing. Other advanced uses
in image processing include the modification of standard methods to meet individual project needs and improving calibration methods. Go to
http://www.techexpo.com/WWW/opto-knowledge/IS_resources.html for more
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information on advanced and specialized hardware and software and their applications.
i. The Interpretation of Remotely Sensed Data. There are four basic steps in
processing a digital image: data acquisition, pre-processing, image display and
enhancement, and information extraction. The first three steps have been introduced in this and previous chapters. This section focuses on information extraction and the techniques used by researchers to implement and successfully complete a remote sensing analysis. The successful completion of an analysis first
begins with an assessment of the project needs. This initial assessment is critical
and is discussed below.
(1) Assessing Project Needs. Initiating a remote sensing project will require
a thorough understanding of the project goals and the limitations accompanying
its resources. Projects should begin with an overview of the objectives, followed
by plans for image processing and field data collection that best match the objectives.
(a) An understanding of the customer resources and needs will make all
aspects of the project more efficient. Practicing good client communication
throughout the project will be mutually beneficial. The customer may need to be
educated on the subject of remote sensing to better understand how the analysis
will meet their goals and to recognize how they can contribute to the project. This
can prevent false expectations of the remotely sensed imagery while laying down
the basis for decisions concerning contributions and responsibilities. Plan to discuss image processing, field data collection, assessment, and data delivery and
support.
(b) The customer may already have the knowledge and resources needed
for the project. Find out which organizations may be in partnership with the customer. Are there resources necessary for the project that can be provided by either? It is important to isolate the customer’s ultimate objective and learn what his
or her intermediate objectives may be. When assessing the objectives, keep in
mind the image classification needed by the customer and the level of error they
are willing to accept. Consider the following during the initial stages of a project:
•
•
•
•
•
•
•
•
•
•
•
•
What are the objectives?
Who is the customer and associated partners?
Who are the end users?
What is the final product?
What classification system is needed?
What are the resolution requirements?
What is the source of image data?
Does archive imagery exist?
Is season important?
What image processing software will be used? Is it adequate?
What type of computer hardware is available? Is it adequate?
Is there sufficient memory storage capacity for the new imagery?
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•
•
•
Are hardware and software upgrades needed? Who will finance upgrades?
Are plotters/printers available for making hardcopy maps?
Can the GIS import and process output map products?
(c) Field considerations:
• What are the ecosystem dynamics? What type of field data will be required?
• Will the field data be collected before, after, or during image acquisition?
• Who will be collecting the field data?
• What sampling methods will be employed?
• What field data analysis techniques will be required?
• Who will be responsible for GPS/survey control?
• Who will pay for the field data collection?
• Is the customer willing to help by providing new field data, existing
field data, or local expertise?
(2) Visualization Interpretation.
(a) Remotely sensed images are interpreted by visual and statistical
analyses. The goal in visualization is to identify image elements by recognizing
the relationship between pixels and groups of pixels and placing them in a meaningful context within their surroundings. Few computer programs are able to
mimic the adroit human skill of visual interpretation. The extraction of visual information by a human analyst relies on image elements such as pixel tone and
color, as well as association. These elements (discussed in Chapter 2) are best performed by the analyst; however, computer programs are being developed to accomplish these tasks.
(b) Humans are proficient at using ancillary data and personal knowledge in the interpretation of image data. A scientist is capable of examining images in a variety of views (gray scale, color composites, multiple images, and
various enhancements) and in different scales (image magnification and reduction). This evaluation can be coupled with additional information such as maps,
photos, and personal experience. The researcher can then judge the nature and
importance of an object in the context of his or her own knowledge or can look to
interdisciplinary fields to evaluate a phenomena or scene.
(3) Information Extraction. Images from one area of the United States will
appear vastly different from other regions owing to variations in geology and biomes across the continent. The correct identification of objects and groups of objects in a scene comes easily with experience. Below is a brief review of the
spectral characteristics of objects that commonly appear in images.
(a) Vegetation. Vegetation is distinguished from inorganic objects by its
absorption of the red and blue portions of the visible spectrum. It has high reflectance in the green range and strong reflectance in the near infrared. Slight variability in the reflectance is ascribable to differences in vegetation morphology,
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such are leaf shape, overall plant structure, and moisture content. The spacing or
vegetation density and the type of soil adjacent to the plant will also create variations in the radiance and will lead to “pixel mixing.” Vegetation density is well
defined by the near infrared wavelengths. Mid-infrared (1.5 to 1.75 µm) can be
used as an indication of turgidity (amount of water) in plants, while plant stress
can be determined by an analysis using thermal radiation. Field observations
(ground truth) and multi-temporal analysis will help in the interpretation of plant
characteristics and distributions for forest, grassland, and agricultural fields. See
Figures 5-28 and 5-29.
Figure 5-28. Forest fire assessment using Landsat imagery (Denver, Colorado). Image on
the left, courtesy of NASA, was collected in 1990; image on the right was collected in 2002
(taken from http://landsat7.usgs.gov/gallery/detail/178/). Healthy vegetation such as forests,
lawns, and agricultural areas are depicted in shades of green. Burn scares in the 2002 image appear scarlet. Together these images can assist forest managers in evaluating extend
and nature of the burned areas.
(b) Exposed Rock (Bedrock). Ground material such as bedrock, regolith
(unconsolidated rock material), and soil can be distinguished from one another
and distinguished from other objects in the scene. Exposed rock, particularly hydrothermally altered rock, has a strong reflectance in the mid-infrared region
spanning 2.08 to 2.35 µm. The red portion of the visible spectrum helps delineate
geological boundaries, while the near infrared defines the land–water boundaries.
Thermal infrared wavelengths are useful in hydrothermal studies. As discussed in
earlier sections, band ratios such as band 7/band 5, band 5/band 3, and band
3/band 1 will highlight hydrous minerals, clay minerals, and minerals rich in ferrous iron respectively. See Figure 5-30.
(c) Soil. Soil is composed of loose, unconsolidated rock material combined with organic debris and living organisms, such as fungi, bacteria, plants,
etc. Like exposed rock, the soil boundary is distinguished by high reflectance in
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the red range of the spectrum. Near infrared wavelengths highlight differences
between soil and crops. The thermal infrared region is helpful in determining
moisture content in soil. See Figure 5-31.
Figure 5-29. Landsat scene bands 5, 4, 2 (RGB). This
composite highlights healthy vegetation, which is
indicated in the scene with bright red pixels. Taken
from http://imagers.gsfc.nasa.gov/ems/infrared.html.
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Figure 5-30. ASTER (SWIR) image of a copper
mine site in Nevada. Red/pink = kaolinite, green =
limestones, and blue-gray = unaltered volcanics.
Courtesy of NASA/GSFC/METI/ERSDAC/JAROS,
and U.S./Japan ASTER Science Team.
(d) Water (Water, Clouds, Snow, and Ice). As previously mentioned, the
near infrared defines the land–water boundaries. The transmittance of radiation by
clear water peaks in the blue region of the spectrum. A ratio of band 5/band 2 is
useful in delineating water from land pixels. Mid-infrared wavelengths in the 1.5to 1.75-mm range distinguishes clouds, ice, and snow. See Figure 5-32.
(e) Urban Settings. Objects in an urban setting include man-made features, such as buildings, roads, and parks. The variations in the materials and size
of the structure will greatly affect the spectral data in an urban scene. These features are well depicted in the visible range of the spectrum. Near infrared is also
useful in distinguishing urban park areas. Urban development is well defined in
false-color and true color aerial photographs, and in high resolution hyperspectral
data. The thermal infrared range (10.5 to 11.5 µm) is another useful range owing
to the high emittance of energy. A principal components analysis may aid in
highlighting particular urban features. See Figure 5-33.
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Figure 5-31. AVIRIS image, centered on Arches National Park, produced for the mapping of
cryptogamic soil coverage in an arid environment. Taken from
http://speclab.cr.usgs.gov/PAPERS.arches.crypto.94/arches.crypto.dri.html.
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Figure 5-32. MODIS image of a plankton bloom in the Gulf of St. Lawrence near
Newfoundland, Canada. Ground pixel size is 1 km. In this image, water and clouds
are easily distinguishable from land (green pixels at top left of scene). Taken from
http://rapidfire.sci.gsfc.nasa.gov/gallery/?20032250813/Newfoundland.A2003225.1440.1km.jpg.
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Figure 5-33. Orlando, Florida, imaged in 2000 by Landsat 7 ETM+
bands 4, 3, 2 (RGB). The small circular water bodies in this image
denote the location of karst features. Karst topography presents a
challenge to development in the Orlando area. Taken from
http://edcwww.cr.usgs.gov/earthshots/slow/Orlando/Orlando
(f) Other Landscape Features. A variety of unique landscape features
are easily imaged with remote sensing. A few examples are illustrated below:
Volcanic eruption (Figure 5-34), forest fires (Figure 5-35), abandoned ships (Figure 5-36), dust storm (Figure 5-37), oil fires (Figure 5-38), and flooding (Figure
5-39).
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Figure 5-34. Landsat image of Mt. Etna eruption of
July 2001. Bands 7, 5, 2 (RGB) reveal the lava flow (orange) and eruptive cloud (purple). Taken from
http://www.usgs.gov/volcanoes/etna/.
Figure 5-35. Forest Fires in Arizona may assist forest managers in fire-fighting strategies and prevention. Meteorologist also use such images to
evaluate
air
quality.
Image
taken
from
http://rst.gsfc.nasa.gov/Front/overview.html.
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Figure 5-36. Grounded barges at the delta of the Mississippi River are
indicated by the yellow circle. Taken from
http://www.esa.ssc.nasa.gov/rs_images_display.asp?name=prj_image_
arcvip.5475.1999.101916538330.jpg&image_program=&image_type=&im
age_keywords=&offset=312&image_back=true.
Figure 5-37. July 2001 Saharan dust storm over the Mediterranean. Taken from
http://rapidfire.sci.gsfc.nasa.gov/gallery/.
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Figure 5-38. Oil trench fires and accompanying black smoke plumes over Baghdad,
Iraq (2003). This image was acquired by
Landsat 7 bands 3, 2, 1 (RGB). Urban areas
are gray, while the agricultural areas appear
green.
Taken
from
http://landsat7.usgs.gov/gallery/detail/220/.
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Figure 5-39. The mosaic of three Landsat images displays flooding along the Mississippi River, March
1997.
j. Statistical Analysis and Accuracy Assessment. Accuracy assessment means
the correctness or reliability in the data. Error is inherent in all remote sensing
data. It is important to establish an acceptable level of error and to work within
the resolution of the image. Working within the means of the resolution of an image is important for maintaining the desired accuracy. Attempting to extract information from an image for which objects are not clearly resolvable will likely
lead to incorrect assumptions. Error can be introduced during acquisition by the
sensor and while performing geometric and radiometric correction and image enhancement processes. Another major source of error lies in the misidentification
and misinterpretation of pixels and groups of pixels and their classification.
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(1) Resolution and RMS (Root Mean Squared). Some errors are simple to
quantify. For instance, the image pixel in a TM image represents the average radiance from a 30- × 30-m area on the ground. So, measurements within a TM scene
will only be accurate to within 30 m. Positional accuracy may be established by a
comparison with a standard datum giving an absolute uncertainty value. The RMS
(root mean squared) error is automatically calculated during image rectification.
This error can be improved while designating GCPs (Ground Control Points; see
Paragraph 5-17).
(2) Overall Accuracy. Overall accuracy can be established with “Ground
truth.” Ground truth is site-specific and measures the accuracy by sampling a
number of areas throughout a scene. Overall accuracy of an image is then calculated by modeling the difference between the observed pixel DN signature and
known object on the ground.
(3) Error Matrices. Assessing classification error is more involved. Solving for this type of error requires a numerical statistical analysis. Some software
incorporates accuracy assessment within the classification function. For instance,
classification error assessment compares an image classification matrix with a
reference matrix. See Paragraph 5-20d(4) for information on classification accuracy. In this type of assessment, the reference data are assumed correct. Pixels are
assessed in terms of their mistaken inclusion or exclusion from an object class;
this is known as commission and omission (see Congalton and Green, 1999). All
known error should be noted and included in any assessment. Review Congalton
and Green (1999) for further information on the practice of error assessment.
k. Presenting the Data. Once a visual and statistical evaluation has been performed, the analysis must be presented in a manner that best communicates the
information needed. The information may be presented as a hardcopy printout of
the image or presented as a map (Figure 5-40). The information may also be displayed as a statistical database, which includes data tables and graphs. Knowledge
of GIS, cartography, and spatial analysis is helpful in choosing and executing the
manner in which the data will be presented. For instance, a number of GIS software programs are capable of displaying the image in a map format with a linked
data set. Be sure to keep in mind the final product needed by the client.
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Figure 5-40. The final product may be displayed as a digital image or as a
high quality hard copy. Taken from Campbell (2003).
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Chapter 6.
Remote Sensing Applications in USACE
6-1 Introduction. Remote Sensing is currently used by Corps scientists and engineers at
the seven research and development laboratories as well as at the Districts and Divisions.
Remote sensing has proven to be a cost effective means of spatially analyzing the environment and is particularly usefully in regions with limited field access. A vast amount of literature covering remote sensing applications in environmental and engineering projects has
been published and much of it is available through the ERDC and USACE library system.
This chapter only touches the surface of the material that describes the variety of applications and products in use. Some of the references listed in Appendix A also have internet
web sites providing more in-depth information on the subject of remote sensing and current
research.
6-2 Case Studies.
a. Each study presented below uses remote sensing tools and data. Special emphasis
have been placed on Corps works and contracted work related to civil projects. Non-Corps
projects, such as NASA works, are also presented in an effort to provide broader examples
of the potential use of remote sensing and to aid in the implementation of remote sensing
into existing and future US Army Corps of Engineers projects. This chapter 1) reviews the
capabilities of sensors, 2) illustrates the value of remote sensing data analysis and integration into spatial data management systems, and 3) communicates recent studies to promote
cooperation between Corps Districts, local government, and the general public.
b. The following topics are presented in this chapter:
•
•
•
•
•
•
•
•
•
•
•
Water Quality.
Wetland mitigation.
Archeology.
Engineering.
Soil science—sediment transport.
Forestry.
Agriculture.
Environmental projects.
DEM generation.
Applications in snow and ice.
Emergency Management.
6-3 Case Study 1: Kissimmee River Restoration Remote Sensing Pilot Study
Project Final Report
•
•
•
Subject Area: Environmental Assessment.
Purpose: To evaluate the vegetative response to the restoration of the Kissimmee
River floodplain ecosystem using hyperspectral data.
Data Set: Hyperspectral Airborne.
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a. Introduction. Historically, the Kissimmee River meandered 103 miles (~166 km),
connecting Lake Kissimmee to Lake Okeechobee. The river and its floodplain supported
diverse wetland communities including aquatic and terrestrial plants and animals. The Kissimmee River was hydrologically unique owing to prolonged and extensive flood inundation. During the 1960s, the river and its 1- to 2-mile (1.6- to 3.2-km) wide floodplain was
channelized and drained in an effort to control flooding. Canal excavation eliminated onethird of the channel, and drainage destroyed two-thirds the floodplain. This Corps of Engineers project lead to a significant decrease in waterfowl, wading bird, and fish populations.
(1) An environmental restoration plan is underway in an attempt to restore the pre1960 ecosystem in the Kissimmee River floodplain. The USACE Jacksonville District and
the South Florida Water Management District are jointly responsible for this 3000- square
mile (7770 km2) restoration project. The primary goal of the restoration project is to re-establish a significant portion of the natural hydrologic connectivity between Lake Kissimmee
and Lake Okeechobee. With the natural hydrologic conditions in place, the objective of the
project is to rebuild the wetland plant communities and restore the local biological diversity
and functionality.
(2) The study reviewed here represents a pilot study conducted by SAIC (Science Applications International Corporation) to establish a baseline for environmental monitoring of
the Kissimmee Restoration Project. Their study explored the utility of hyperspectral image
data in aiding vegetative mapping and classification. The hyperspectral remote sensing data
demonstrated themselves to be highly useful in delineating complex plant communities.
Continued use of such a data set will easily aid in the management of the Kissimmee River
Restoration Project.
b. Description of Methods. The test area within the restoration site was chosen by
USACE. Preliminary field studies conducted in1996, established approximately 70 plant
communities, a handful of which were not present during the study of interest (conducted in
2002). It was determined that the rapid changes in hydrologic conditions had altered the
plant community structure during the interim between studies; in places, some plant species
and groups had entirely disappeared. Researchers monitoring the vegetation restoration at
the Kissimmee site were concerned with the establishment of native versus non-native invasive and exotic plant species. The colonization by non-native plant species, such as Brazilian Pepper and Old World Climbing Fern, are of interest because of their potential affect on
other revitalization efforts; those focusing on fauna restoration, for instance. The spectral
analysis of heterogeneous plant species communities is difficult owing to the commonality
of plant chemistry and morphology. The spectral difference between native and non-native
plants is therefore narrow, and difficulties in distinguishing them are compounded by their
mixing (or sharing of habitat). Additionally, the domination by one plant species in many
places added to the problem of accurately classifying the plant communities. See below for
vegetation classes established for this study.
(1) Examples of vegetation classes include:
•
Aquatic vegetation.
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•
•
•
•
•
•
•
•
•
Broadleaf marsh.
Miscellaneous wetland vegetation.
Upland forest.
Upland herbaceous.
Upland shrub.
Wetland forest.
Wetland shrub.
Wet prairie.
Vines
(2) Geological constrains did not aid in the identification of the vegetation classes.
Geologic constrains tend to be more useful in mapping plant communities in areas with a
more mature ecosystem or were there is significant variation in the substrate or soil.
Choosing a sensor capable of delineating healthy vegetation versus stressed vegetation was
another consideration that needed to be addressed by the researchers. This would allow land
use managers the opportunity to closely monitor the decline and rise of various species
throughout the duration of the wetland restoration.
c. Field Work.
(1) Airborne hyperspectral data were collected in conjunction with 146 ground-truth
data points (also known as training sites); this collection was made on-foot and by airboat.
Fieldwork was done and data collected during a flood by a botanist and a GIS specialist. In
the field, SAIC’s hand held spectrometer was used to collect the spectral data associated
with mixed plant communities from within the Kissimmee River floodplain. These groundcontrol points were then used to test the accuracy of the vegetation map developed from the
hyperspectral data.
(2) Problems arose using the plant classes defined by the 1996 field study. Classes
were subsequently altered to better suit the dechannelized ecology. A supervised classification was applied to the data and two vegetation maps were produced denoting 68 vegetation
communities and 12 plant habitat types (Figure 5-25). The hyperspectral map was then
compared to the existing vegetation map produced in 1996.
d. Hyperspectral Sensor Selection. Researchers on this project had the opportunity to
choose between AVIRIS and HyMap. HyMap was eventually chosen for its accuracy,
spectral capabilities, and reasonable expense. HyMap, a hyperspectral sensor (HSI), was
placed on board a HyVista aircraft. HyMap maintains 126 bands across the 15- to 20-nm
range. The error in HyMap data was found to be at ±3 m, equivalent to the accuracy of the
on board GPS unit. To learn more about HyMap and HyVista view
http://www.hyvista.com/main.html.
(1) For this project, the hyperspectral (HSI) data maintained clear advantages over
other sensor data. HSI’s high spectral resolution allows for the distinction of spectrally
similar vegetation and had the potential to monitor vegetation health status. The shortwave
infrared (SWIR) wavelengths where found to be most sensitive to the non-photosynethic
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properties in the vegetation. This further helped to discriminate among the vegetation
classes.
(2) HyVista pre-processed the digital data. Pre-processing included a smoothing algorithm to reduce the signal to noise ratio (SNR) across the scene, to an impressive >500:1.
The data were geographically rectified using ground control points identified on a geo-registered USGS orthophoto. The geo-positional accuracy was determined to be within ± 3 pixels across 95% of the scene. This was established by comparing the image with a highresolution orthophoto. A digital orthophoto was then over laid on top of the digital hyperspectral data to verify geo-positional accuracy.
e. Study Results. Analyst used KHAT (Congalton, 1991), a classification statistic used to
test the results of supervised versus unsupervised classification (Equation 6-1). KHAT considers both omission and commission errors. Statistically it is “a measure of the difference
between the actual agreement between reference data and the results of classification, and
the chance agreement between the reference data and a random classifier” (see
http://www.geog.buffalo.edu/~lbian/rsoct17.html to learn more on accuracy assessment).
KHAT values usually range from 0 to 1. Zero indicates the classification is not better than a
random assignment of pixels; one indicates that the classification maintains a 100% improvement from a random assignment. KHAT values equaled 0.69 in this study, well within
the 0.6 to 0.8 range that describes the class designation to be “very good” (≥ 0.8 is “excellent”). For this study, KHAT indicated good vegetative mapping results with the supervised
classification for distinguishing plant species and for mapping surface water vegetation. The
KHAT also verified the potential value of image classification to map submerged aquatic
vegetation using HIS data.
k=
observed accuracy − chance agreement
1 − chance agreement
(6-1)
r
r
i =1
i =1
N S xii − S ( xi + × x + i )
r
k = N 2 − S ( xi + × x + i )
i =1
where
r
xii
xi+
x+i
N
=
=
=
=
=
number of rows in the error matrix
number of observations in row i and column i (the diagonal)
total observations of row i
total observations of column i
total of observations in the matrix .
The estimated time savings of the mapping project as compared with the manual analysis
using color infrared was calculated to be a factor of 10 or better. Additional benefits include
a digital baseline for change detection and managing restoration. The study did not establish
under which conditions HSI did not work. HSI processing and analyses was shown to be a
generally valuable tool in a large-scale riparian restoration.
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f. Conclusions. HSI’s advantages over aerial panchromatic and color infrared include its
ability to automate data processing rapidly; this will be highly useful for change detection if
the hyperspectral data are collected over time. This data can then be easily coupled with
other useful GIS data when researchers attempt to combine hydrographic and wildlife data.
Wetland hyperspectral imaging paired with advanced data processing and analysis capabilities were shown to be a valuable tool in supporting large-scale programs, such as the Comprehensive Everglades Restoration Program (CERP). For continued successful management
of the Kissimmee Restoration Project, the Corps’ Jacksonville District and the South Florida
Water Management District will have to decide on a mapping method that provides the detail needed to monitor plant community evolution while balancing this need with budget
constraints.
Point of Contact: Wiener Cadet, Project Manager, Phone: (904) 232-1716
6-4 Case Study 2: Evaluation of New Sensors for Emergency Management
•
•
•
Subject Area: Emergency Management.
Purpose: To test the resolvability of high-resolution imaging to evaluate roof
condition.
Data Set: Visible and infrared.
a. Introduction.
(1) Emergency response and management efforts are best facilitated with timely and
accurate information. Typically, these data include an enormous amount of geo-spatial information detailing the extent and condition of damage, access to emergency areas or support services, and condition of urban infrastructure. Remotely sensed imagery has the capability of delivering this type of information, but it is best combined with geo-spatial data
when they are rectified and pre-processed in a way that allows for easy visual and algorithm
analysis. The amalgamation of geo-spatial data into one comprehensive map will aid emergency management organizations in their effort to coordinate and streamline their response.
(2) Understanding the utility and limitations of a sensor is highly valuable to emergency response workers. This study evaluated the effectiveness of Emerge, a new airborne
sensor that collects visible and infrared radiation. Emerge was tested in relation to four primary requirements, listed below.
•
•
•
•
Ground sampling distance (GSD).
Capability for storing large volumes of digital data.
Pre-processing and the vendors ability to orthorectify up to “500 single frames of
imagery in 12 hours or less” and save these data onto a CD-ROM or ftp for fast
delivery.
Indexing system for all resolutions collected, allowing for easy determination of
image location.
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b. Description of Methods. Originally, this study intended to evaluate roof damage
caused by an actual emergency. In the absence of such an emergency, alternate imagery was
collected over a housing development under construction in Lakeland, Florida, located 30
miles (48 km) northeast of Tampa, Florida. The different phases of housing construction
provided an analog to roof damage during an event such as strong winds or a hurricane. The
different structural states of both residential and commercial roofs included exposed rafters,
exposed plywood, and plywood covered by tarpaper or shingles.
c. Field Work. Initially, field reconnaissance established the appropriateness of using two
neighboring test areas in Lakeland, Florida. Roof conditions at individual buildings were
evaluated and geo-referenced. After the first flight, an assessment of the ground sampling
distance (GSD) and sensor data determined that a finer resolution would be required to adequately examine roof condition. Two additional flights were then acquired, resulting in a
collection of data gathered at resolutions of 3, 2, and 1 ft (91.4-, 61-, and 30.5- cm respectively), and 8-in (20.3 cm). Landscaping features, such as tree type and leaf on/off state,
were also documented with digital photos. This information was later used to establish the
feasibility in mapping vegetation using the Emerge system.
d. Sensor Data Acquisition. The two test sites, occupying 8 square miles (~21 km2),
were surveyed at several resolutions using Emerge imagery (see
http://www.directionsmag.com/pressreleases.php?press_id=6936 for more details on the
Emerge System). Multiple resolutions were collected over a 2-month period. As a result, a
one-to-one comparison of the effect of resolution on image analysis was difficult, as house
construction in some areas was completed during the 2-month interval. The volume of data
collected was equivalent to that required for a 60 square mile (~155 km2) area, with approximately 25% image overlap (at a single resolution). This volume of data totaled 5 gigabits.
e. Study Results. Evaluation of the imagery showed that roof rafters were best resolved
at a 1-ft and 8-in. (30.5 and 20.3 cm) resolution. At this resolution, plywood can be distinguished from other construction materials and individual rafters can be observed. Tarpaper
was not distinguishable from shingles owing to their spectral similarities.
(1) Despite the functionality of the 1-ft and 8-in (30.5 and 20.3 cm). resolutions, in
places with bright spectral response, saturation on the high end of the intensity scale lowered the resolvability of rafters relative to the flooring material. This was the result of a high
gain set for radiation detection within the sensor. Over-saturation lowers the contrast between rafters and the flooring, making it difficult to fully evaluate the condition of the roof.
Lowering radiation saturation requires collecting data during low to medium sun angle. This
may, however, delay data acquisition.
(2) Sun angle controls image contrast in two ways. First, a low sun angle may increase shadowing, leading to a loss in target radiation data. Secondly, a high sun angle may
over-saturate the sensor. Both extremes were shown to lower contrast in this study, making
roof analysis difficult.
(3) A scatter plot breakdown of band 1 relative to band 2 was performed to evaluate
the possibility of automating an analysis that would delineate intact roofs and damaged
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roofs. A preliminary analysis suggests that this is possible because of the strong covariance
displayed by roofs shingled with monochromatic materials. Any automated process developed would need to address the limitations posed by non-monochromatic shingles (which
would appear spectrally mixed and indistinguishable from damaged roofs).
(4) A vegetation analysis was also explored to test the resolution required to accurately describe tree type and condition. At the 1-ft (30.5 cm) resolution, researchers were
able to determine leaf on/off conditions (data were collected in February). However, at this
resolution it was not possible to delineate any details regarding leaf morphology. At the 8-in
(20.3 cm). resolution, palms were distinguishable, although it was not possible to differentiate broad versus narrow leaves.
f. Conclusions. Evaluation of the Emerge sensor led to the development of a detection
matrix. This matrix reviews the capabilities of the sensor at various spatial resolutions for all
objects studied (see Table 6-1). This study determined that Emerge could adequately meet
the requirements of emergency management systems. High-resolution data can be acquired
within 4 hours of the plane’s landing. This includes the time needed for pre-processing
(orthorectification and the production of geo-TIFF files for CD-ROM and ftp). Shingles and
tarpaper are not resolvable, though rafters and plywood are at the 2-ft (~61 cm) resolution.
For high-resolution images, a medium sun angle increased roof detail. Palm trees and leaf
on/off conditions can be visually identified at the 8-in (20.3 cm). resolution; however,
broad-leafed trees cannot be distinguished from narrow-leafed trees. The only limitations
placed on these data centered on over-saturation and sensor inability to distinguish tree
types. The covariance displayed by band 1 relative to band 2 indicates the potential success
for developing an automated algorithm to locate and count damaged roofs.
Table 6-1
Detection Matrix for Objects at Various GSDS
Objects/GSD
3-ft (91.4)
2-ft (61 cm)
Roof rafters
Not visible
Barely visible
Shingles/tarpaper
Can
Can often
(other) vs. plywood
sometimes
separate
separate
Rafters in 3-band
Causes rafter
Causes rafter
saturation
detail loss
detail loss
Broad-leaf vs. narrowCannot
Can determine
leaf
separate
leaf on/off
All in cloud shadow
Degrades
Some info
image
recoverable
Roofs as a function of
Best detail,
Best detail,
sun zenith angle
near zero
medium angle,
angle,
shadow casting
overhead sun
All in 1, 2, 3 RGB, 2∑
Enhances
Enhances
stretch
imagery
imagery
Point of Contact: Robert Bolus, Phone: (603) 646-4307
6-7
1-ft (30.5 cm)
Often visible
Can determine
wood vs. other
cover
Causes rafter
detail loss
Can determine
leaf on/off
Some info
recoverable
Best detail,
medium angle,
shadow casting
8-in. (20.3 cm)
Visible
Can determine
wood vs. other
cover
Causes rafter
detail loss
Palms are always
visible
Some info
recoverable
Best detail,
medium angle,
shadow casting
Enhances imagery
Enhances imagery
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6-5 Case Study 3: River Ice Delineation with RADARSAT SAR
•
•
•
Subject Area: Ice monitoring
Purpose: To evaluate the concentration and condition of river ice.
Data Set: RADARSAT SAR
a. Introduction. Remote sensors operating in the microwave region of the spectrum have
the advantage of seeing through clouds and atmospheric haze. RADARSAT SAR (synthetic
aperture radar) collects spectral data in the microwave region and is capable of imaging
ground targets during adverse weather conditions, such as storms. Additionally,
RADARSAT SAR collects 10-m pixel sized data, a high spatial resolution well suited for
studies examining ice in narrow river channels. The study reviewed here explored
RADARSAT SAR’s potential in delineating and monitoring ice and ice floes in rivers
ranging in stream widths of 160 to 1500 m. A better estimate of ice conditions along large
streams will allow for better navigation planning and will provide river dam regulators the
information needed to plan and prepare for ice breakup and floes.
b. Description of Methods. Three rivers of varying widths were evaluated for ice cover
over the course of two winters (2002 and 2003). The first winter was relatively mild with
partial river ice development at the three sites. Winter 2003 possessed a number of below
freezing days and was an ideal time for examining river ice in the northern mid-west. The
rivers chosen for this study were the Mississippi River near St. Louis, Missouri, the Missouri River at Bismarck, North Dakota, and the Red Lake River in Grand Forks, North Dakota. Each site offered unique contributions to the study. The Mississippi River represented
a stream with heavy navigation use, the Missouri River site included a hydropower dam,
while the Red Lake River had extensive ice jam and flood records. Coordinated efforts
among CRREL researchers, the local Corps Districts, and the RADARSAT International
(RSI) aided in the acquisition and timing of satellite data collection.
(1) Stream channels were subset and isolated for river ice classification. To accomplish this, a band ratio was applied to Landsat TM data. They were then classified by an unsupervised process and extracted for mask overlay onto the radar data. This sufficiently outlined the land/water boundaries and isolated the stream in images with wide river channels.
This process omitted vegetation and islands from the resultant image. The subsequent SAR
subset did not include mixed pixels (land/water/ice).
(2) Images with narrow channels required hand-digitization and a textural analysis,
followed by a supervised classification (to further eliminate land pixels). The hand-digitization proved less successful than the Landsat TM overlay and extraction method. Hand-digitization did not thoroughly omit pixels with mixed water, vegetation, and land (i.e., river
islands).
(3) In the SAR images, only the channel reaches were analyzed for ice conditions using an unsupervised classification. The classification mapped brash ice (accumulated floating ice fragments), river channel sheet ice, shore ice, and open water.
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c. Field Work. Direct field observations were not necessary as a web-camera mounted on
a bridge provided the visual documentation of ice conditions in the river. At the Missouri
River site, web-cameras have been strategically placed in a variety of locations in the US by
ERDC/CRREL. To view the Missouri River images used in this study, as well as other river
web-camera images, go to http://webcam.crrel.usace.army.mil. Study sites without a webcamera relied on District contacts for field information. At Red Lake River near Grand
Forks, North Dakota, field reconnaissance ice surveys were conducted by the Corps St.
Paul, Minnesota, District office.
d. Sensor Selection and Image Post-Processing.
(1) As stated above, RADARSAT SAR data was chosen for this study. Radar data
have already proven their utility in sea ice mapping and monitoring (Carsey, 1989). Radar
can aid in determining ice concentration, classification, ice motion monitoring, and ice feature changes. The study reviewed here adapted methods used to study large ice sheets to the
evaluation of smaller more temporal river ice.
(2) The acquired radar images were visually analyzed and classified using an unsupervised classification to delineate open water, moving ice floes, and stationary ice covers.
The delineation of river channels was undertaken by two methods, described above (handdigitization and TM extraction and overlay).
e. Study Results. The following description summarizes the ice condition results stemming from each river surveyed:
“In the Mississippi River imagery near St. Louis, Missouri, the wide channel width (500–2000
meters) contributed to identifying river ice with RADARSAT imagery. In the 2002 image it was
determined that 30% of the channel had ice in the flow, and in the 2003 image, it was determined that there was 100% ice cover. Additionally, this ice cover was separated into forms of
ice; brash ice and border ice. In the 2003 image it is believed that the brash ice formed as a result of navigation ice-breaking activities.
(1) In the Missouri River imagery near Bismarck, North Dakota, the channel width
(400–1000 m) was suitable, and river ice was determined from the RADARSAT imagery.
The 2002 image showed that 77% of the channel had ice in the flow, and in the 2003 image,
only 21% of the channel had ice. The 2003 imagery was acquired before full icing conditions, and a small amount of ice was interpreted to exist.
(2) In the Red Lake River imagery near the confluence with the Red River of the
North at Grand Forks, North Dakota, the river channel is narrow (40–75 m). The narrowness
of the channel limited the process of delineating the channel boundary on the imagery. As a
result of the narrow channel width, river ice was not determined by this process. However,
ice surveys were conducted by the US Army Corps of Engineers during the time of image
acquisitions, and an ice cover was recorded in both 2002 and 2003.
f. Conclusions. RADARSAT SAR data were able to detect ice on rivers with widths
ranging from 400 to 2000 m. Despite RADARSAT’s 10-m resolution, this data set was unable to detect the present of ice on the narrower Red Lake River, with a width of 40–75 m.
RADARSAT’s overall suitability for detecting river ice and ice conditions was shown to be
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of potential use. The method presented here details an important tool that may aid in hazardous wintertime navigation and assist dam regulators on decisions regarding stream flow
and reservoir levels.
Point of Contact: Brian Tracy, Phone: (603) 646-4739
6-6 Case Study 4: Tree Canopy Characterization for EO-1 Reflective and
Thermal Infrared Validation Studies in Rochester, New York
•
•
•
Subject Area: Forestry and climate change
Purpose: To collect forest canopy structure and temperature data.
Data Set: Multispectral and hyperspectral
a. Introduction. Tree and forest structure respond strongly to environmental conditions
and change. Subsequently, studies have successfully shown the utility of remote sensing in
monitoring environmental conditions through the analysis of vegetation. The study reviewed
here surveyed a mixed forest in northern New York State in an attempt to better understand
the interaction between solar radiation and tree/forest structure. An additional objective of
this study was to validate the Earth Observing satellite (EO-1, launched in 2000). The validation was performed by comparing the EO-1 satellite data with that of the Landsat-7
ETM+ data. The EO-1 satellite acquired data at the same orbit altitude as Landsat-7 while
flying approximately 1 minute behind. EO-1 reflective bands were combined with the Landsat-7 ETM+ thermal infrared bands to estimate canopy temperature. The 1-minute delay in
synchronization between the two sensors was evaluated to test the effects of separating the
thermal and reflective measurements in time. Relating scene exitance (the radiative flux
leaving a point on a surface, moving in all directions) and reflectance to the landscape provided insight to prevailing environmental characteristics for the region.
b. Description of Methods. Ground and tree canopy data were collected from mature
healthy forest stands at a site in Durant-Eastman Park in Rochester, New York. Characterization of the forest included a stem and trunk survey, tree structure geometry measurements,
regional meteorology, and leaf area index (LAI) measurements (see http://www.unigiessen.de/~gh1461/plapada/lai/lai.html for more information on LAI). Two smaller field
sites, Ballard Ridge and Smith Grove, were selected for detailed study from within the larger forested area. Tree heights for both sites averaged 20–30 m. Ballard Ridge consisted of a
dense mature stand of maple, cottonwood, elm, and oak trees. The Smith Grove consisted of
a dense mature stand of locust trees and cottonwood. Thermal and reflective spectral measurements were made on leaves, tree bark, leaf litter, soil, and grass.
c. Field Work. Leaf area index (LAI) was calculated in the field with the use of a nonimaging instrument, which measures vegetation radiation in the spectrum of 320–490 nm.
Leaf area index is a ratio of the foliage area in a forest canopy relative to the ground surface
area. It estimates the photosynthetic capability of a forest. The measured light intensity was
used to calculate the average LAI for each location within the field site. High-resolution
hemispherical photographs were collected at each site using a digital camera with a fisheye
lens (148° field-of-view). The digital photographs were taken during the early morning and
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late evening hours to reduce the effects of atmospheric haze. The digital hemispherical
photographs were later analyzed using a specialized forestry software, which measures both
LAI and canopy leaf structure. LAI calculations based on the computed hemispherical digital images compared favorably with the LAI measurements from the meter instrument.
d. Sensor System.
(1) Satellite data were collected with the use of Landsat MTI, Hyperion, and ALI
(Advanced Land Imager) on 25 August 2001. The ALI sensor has nine spectral bandwidths
plus a panchromatic band. Three bands where analyzed for this study 773.31 nm, 651.28
nm, and 508.91 nm. The forested areas appear bright red, urban areas are gray-blue, and the
water is depicted by the dark blue regions.
(2) The sensor radiance was converted with the use of 6S, an atmospheric corrections
model that converts sensor radiance to estimated surface reflectance. The differences and
consistencies in the two sensors were then easily compared with the spectral data collected
in the field. Then, a more detailed study of the forest site was made, using measured geometric and optical parameters as input to the SAIL multi-layer canopy reflectance model.
The ETM+ and ALI data were then compared with the SAIL (Scattering by Arbitrarily Inclined Leaf) reflectance model and the high resolution Hyperion, a hyperspectral imaging
instrument (see http://eo1.usgs.gov/instru/hyperion.asp for details).
e. Study Results. A comparison of the panchromatic ETM+ and ALI data show dramatic
differences. The ALI data provided better definition of the marina and pier area as well as
natural water features (urban and water targets). Relative to the ETM+ images the ALI data
maintained a reduced DN value for all forest pixels, increasing the contrast in the forest region. The authors suggested the higher resolution and the narrow bandwidths accounted for
the dramatic contrasts between the image data sets.
(1) Spectral plot comparisons of the multispectral bands for different ground targets
(grass, water, urban features, and forest) illustrating the relationship between reflectance and
wavelength indicated a close match between the two sensors. The spectral plots were created by the selection of training pixels for each target group. ALI spectral values were closer
in value than those seen in the ETM+ data; again, this is a result of the narrow bandwidths
and higher resolution. The only notable difference in the spectral response between the two
sensors was evident in band 5 for grass and urban features. These targets had up to 20%
variation in signal response between the sensors. Specifically, the ALI band 5 with a reflectance of 0.35 µm is ~20% higher than the ETM+ value of 0.29 µm.
(2) The combined spectral plot of data from ETM+, ALI, Hyperion, and the empirically derived SAIL show overall an excellent agreement. The three satellite data sets closely
match one another, with slightly different values recorded in the SAIL model data. SAIL
values best matched those of the sensors in the visible portion of the spectrum.
f. Conclusions. The authors of this study were able to establish a simple, multi-layer
canopy reflectance model using measured parameters from the site to compare the ETM+
and ALI spectra. Hyperspectral data were also compared against the satellite and ground
data. Additional work is needed to establish the relationship between leaf area index (LAI)
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and satellite data. The potential use of ALI and hyperspectral Hyperion for studies of forests
in remote locations and forests at risk may greatly enhance forest management and lower the
costs associated with ecological monitoring. Accurate estimates of LAI based on satellite
imagery have the potential to support forest biomass monitoring, and hence forest health and
changes in canopy structure attributable to pollution and climate change. The ability to estimate LAI with remote sensing techniques is, therefore, a valuable tool in modeling the ecological processes occurring within a forest and in predicting ecosystem responses.
Point of Contact: Jerry Ballard, Phone: (601) 634-2946
6-7 Case Study 5: Blended Spectral Classification Techniques for Mapping
Water Surface Transparency and Chlorophyll Concentration
•
•
•
Subject Area: Water quality
Purpose: To establish water clarity and algal growth in a dam reservoir
Data Set: Landsat TM - Visible and infrared
a. Introduction.
(1) An accurate portrayal of water clarity and algal growth in dynamic water bodies
can be difficult owing to the heterogeneity of water characteristics. Heterogeneity can stem
from the spatial distribution of sediments delivered to a lake by a tributary. Water turbidity
associated with tributary sediment load controls water clarity and subsequently will impact
algae growth. Additionally, algal growth will influence water clarity by reducing water
transparency during times of algal blooms. Both algal growth and sediment turbidity are
controlled by such factors as water depth, flow rate, and season.
(2) To better monitor the water quality at dam reservoirs, a spatial estimate of both
water clarity and algal chlorophyll over a broad area is required. To accurately capture these
properties a large number of water samples must be taken, a task that may not be feasible for
most studies. Remote sensing lends itself well to the assessment of water quality testing at a
variety of spectral scales due to the response of suspended sediment in the visible and thermal spectrum. Chlorophyll, produced by algae, can also be detected by its visible and infrared emission. The study reviewed here developed a classification algorithm to predict water
clarity and chlorophyll concentrations. The algorithm was based on a correlation between
spectral data and the empirical field data. Previous studies attempting to classify water clarity and chlorophyll required field sampled training sites. The goal of this study was to develop an algorithm based on empirical data that would illuminate the need for such test
training sites. Thus, researchers testing for water quality would then need only the Landsat
TM data to monitor water quality at a fresh water lake.
b. Field Work. Secchi Disk measurements and water samples were collected at a dam
reservoir in conjunction with a Landsat fly-over at West-Point Lake, Georgia. Water samples were frozen and stored in a dark room to preserve the algae populations. These samples
where later analyzed for chlorophyll (Ca) concentrations. Water clarity was measured in situ
with a Secchi Disk (Sd). This 20-cm disk estimates water clarity by measuring the depth to
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which the disk is visible. Remote sensors generally detect water clarity to 20–50% of the Sd
measurement. Sampling sites were chosen evenly across the reservoir and adjacent tributaries. A global positioning unit was used to locate 109 sample sites. Drift during sampling
occurred but was compensated for with the use of a 3×3 kernel during image classification.
Samples and data were collected during two periods—summer and fall of 1991.
c. Sensor System. Two Landsat TM data sets separated in time were used to develop a
linear-logarithmic cluster analysis. Visible, near, and middle infrared radiation band ratio
was employed with a stratified sampling technique. Using a variety of band ratio, the workers were able to accurately develop a blended classification scheme, which is detailed below.
d. Study Results. The authors adapted multivariate density estimation with the use of an
algorithm k-NN density estimator. This was used to group spectrally similar pixels. The
spectral classes and class structures (or groupings that separated the spectral classes) were
developed using an unsupervised classification. Within each of the two scenes, 16 unique
classes were determined. These classes were combined with the empirical data, leaving four
logarithmic algorithms. Applying a 3×3 kernel to the data compensated for the drift that occurred during data collection. This placed the positional accuracy to within ±30 m.
(1) The average spectral value was determined by a log estimation of the band ratio
for the given pixel within the kernel (Equation 6-2). Combinations of band ratios were
tested. A middle infrared ratio against the visible red showed the largest correlation with Sd
and Ca. Visible green versus near infrared also provided a good separation of the spectral
response for estimating Sd and Ca.
IR =
1 3
∑
9 x =1
3
(mid IR)
∑ ln (visible red)
y =1
6-2
x,y
(2) Observed versus predicted Sd and Ca were well correlated with the use of this log
estimate. Focused sampling and spectral blending led to the development of an accurate unsupervised classification with a 95% confidence interval. Sampling positions near tributaries were overestimated at only five sampling sites (relative to 109 sampling sites).
e. Conclusions. A strong correlation was made between the Landsat TM middle IR and
the empirical Secchi Disk and chlorophyll concentrations. Chlorophyll was shown to have
increased from 12.64 to 17.03 mg/m3, contributing to a decline in water clarity. The application of this log estimate now eliminates the need to collect empirical water quality data,
likely reducing the cost in a water quality survey.
Point of Contact: Robert Bolus, Phone: (603) 646-4307
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6-8 Case Study 6: A SPOT Survey of Wild Rice in Northern Minnesota
•
•
•
Subject Area: Agriculture
Purpose: To estimate the percentage of wild rice in a wetland environment
Data Set: Visible and near infrared
a. Introduction.
(1) A vegetation survey of natural wild rice surrounding three neighboring lakes 200
miles (518 km) south of St. Paul, Minnesota, was conducted to provide a base map for pollutant and water level monitoring. The study presented here utilized standard supervised
classification, based on ground-truth, of high-resolution SPOT data. Wild rice is a natural
marsh grass that is sensitive to water level changes and to changes in phosphorous concentrations; increases in phosphorous and water levels can significantly destroy wild rice communities. This is of concern as this important grass is a staple in the Chippewa Indian diet
and is consumed by migratory birds.
(2) The researchers in this study were tasked with mapping and estimating the acreage
of wild rice surrounding three lakes in Minnesota. Three spectral classes where developed
with the use of a supervised classification to delineate the grass and its varying substrate.
b. Description of Methods. Ground truth data were collected simultaneously with SPOT
over flight. The ground truth data included information regarding vegetation and substrate
type as well as the sites corresponding UTM (global position in the Universal Transverse
Mercator coordinate system).
c. Field Work. In the field, 18 ground control points (GCPs) were collected for rectification of the SPOT image and an additional 132 ground truth points were collected for the
supervised classification algorithm. This data collection coincided with the SPOT over
flight.
d. Sensor System. SPOT was chosen for its optimal detection of vegetation in the presence of inorganic ground cover (i.e., water). Vegetation absorbs both red and blue radiation,
while reflecting green and near infrared (NIR) because of chlorophyll production. This
matched well with the spectrum data provided by SPOT (which maintains green, red, and
NIR bands among others).
e. Study Results.
(1) Prior to the classification process, it had been predicted that the wild rice would
dominate one spectral class, as wild rice is spectrally distinct from other vegetation. Openings in the grass canopy, however, contributed to the spectral mixing observed in the image
scene. Three spectrally distinct populations were noted, likely because of the heterogeneity
of the background reflectance, varying crop canopy, and varying water content in the substrate.
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(2) A histogram plot of the digital number value assigned to each pixel in the scene
clearly reveals three distinct spectral populations. These three classes were determined to be
wild rice growing in the lake, wild rice in marsh, and wild rice in a saturated soil. Wild rice
growing in shallow or marsh water produced pixels that overlapped with more than one
class. The near-infrared (N-IR) band allowed for better spectral separation by eliminating
the effect of varying amounts of water in the substrate.
f. Conclusions.
(1) An estimate of the acreage percent based on a supervised classification determined
that 1% of the scene was dominated by wild rice. Habitat was shown to predominately exist
along the lakeshore, at inlets, ponds, on banks, and in marsh areas. Wild rice was determined to grow in saturated soil, marsh, and in shallow lake waters. The author recommend
200 ground truth points be collected per class (100 for spectral determinations and 100 for
classification designation). Application of the ground truth data to a SPOT scene collected 5
days after the ground truth data did not produce an accurate classification. This test reveals
the limitations on the usefulness of SPOT data for surveying vegetation—ground truth must
be collected at the time of data acquisition.
(2) A detailed map of the distribution of wild rice will allow land managers to better
predict the impact of changes in water level and phosphorous input on the natural production of wild rice.
Point of Contact: Robert Bolus, Phone: (603) 646-4307
6-9 Case Study 7: Duration and Frequency of Ponded Water on Arid
Southwestern Playas
•
•
•
Subject Area: Hydrology.
Purpose: To delineate playa inundation frequency and duration.
Data Set: Multispectral/thermal (Landsat 4, 5, and 7 and MTI – Multi-spectral Thermal
Imager).
a. Introduction.
(1) Playas are ephemeral shallow lakes found in the arid southwest United States.
Their hydrology is dominated by rainfall and runoff in the wet season and evaporation
throughout most of the year. Surface hydrology, particularly frequency and duration, is
poorly understood in the playa environment. US waters, including playa water, are Federally
regulated under article 33 CFR 328.3 [a] of the Clean Water Act. Water bodies are delineated to their outermost extent termed their “Ordinary High Water” (OHW). OHW is defined
by the presence of physical hydrological features representing the ordinary reaches of high
water in its bed or basin.
(2) Playas exhibit tremendous temporal variation, as they may not pond at all during a
particular year or may remain ponded for several years. The extent to which water remains
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on the surface is influenced by the ambient climate, surface properties, evaporation rate, salinity, and infiltration or discharge of groundwater. Spatial and temporal factors such as inundation, evaporative rate, relocation of brine pools by winds, and desiccation of surface
water hinder the ability to approximate the duration and frequency of ponding necessary to
accurately model flood events or to determine whether certain Federal environmental
regulations apply. This study attempted to model the frequency and duration of playa
inundation in an effort to better delineate playas for regulations.
b. Description of Methods.
(1) For this study, three playa lakes on the Edwards Air Force Base were examined
with the use of 20 years of historical Landsat and MTI imagery. These data were coupled
with 59 years of precipitation records collected on the base. Rogers Lake (114 km2) and
Rosamond Lake (53 km2) occupy the eastern and western region of the study area,
respectively. Smaller playa lakes separate Rogers and Rosamond Lakes, including Buckland (5 km2). The playa lakes are located on a Pleistocene glacial lakebed; the Pleistocene
features dwarf the present geologic structures. Chenopod vegetation and saltbush plant
communities dominate the terrestrial plain surrounding the playa.
(2) The playas remain dry for most of the year; however, winter rainstorms and summer thunderstorms cause water to periodically inundate playas. The duration of flooding depends on the magnitude and location of precipitation and ambient prevailing climate. Significant flooding is also associated with El Niño events in the Pacific Ocean, which leads to
above-normal precipitation in the Southwestern US. Precipitation records maintained at
Edwards Air Force Base provided precipitation data for the years 1942 to 2001. The average
annual precipitation was calculated to be 13 cm/year with an estimated 280cm/year evaporation rate.
c. Sensor System. The department of energy on collected visible and near infrared data
with the use of a Multi-spectral Thermal Imager (MTI) from February through May of 2001
Two sequential daily MTI images were acquired at 16-day intervals. This was done to ensure the capture of water that may exist at any time throughout the course of 31 days. The
acquisition of multiple scenes eliminated the lack of data due to cloud coverage. Seven
years of data were analyzed for this inundation study.
d. Study Results.
(1) The visible bands were not useful in visually delineating ponded water. Ponded
water was best defined by a band ratio technique of B5/B2, which evaluated the proportion
of reflective energy to input energy. The ratio values for each pixel were consistently greater
than 1.0 for non-water objects and less than 1.0 for water objects. This ratio method was
then followed by a classification that grouped pixels with values less than 1.0. Workers then
assigned a DN value of 0 for objects displaying a ratio value of less than 1.0, thereby coloring all water bodies black in the scene. This ratio technique aided the image analysis by
eliminating the problems caused by sun angle, sun intensity, and seasons—problems intrinsic to multi-temporal image analysis.
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(2) The average precipitation was calculated to be 8.28 cm/year. These data, coupled
with the image data, established the inundation frequency to be 51% of the time. This suggests that the playas are inundated, on average, every other year.
e. Conclusions. Results indicate that ponding that persists 16 days or longer occurred
approximately every other year. The average precipitation needed to initiate ponding is estimated at 8.29 cm. Duration of ponding was shown to range from 1 to 32 weeks, with a direct relationship between length of inundation and total seasonal rainfall. Playa inundation,
duration, and frequency can be determined from precipitation data and satellite imagery.
The authors suggest the addition of contributing factors such as soil type and geometry may
lead to a more robust hydrologic model of the playa system. A thorough understanding of
the playa hydrologic regime may one day lead to new land use regulations.
Point of Contact: Robert Lichvar, Phone: (603) 634-4657
6-10 Case Study 8: An Integrated Approach for Assessment of Levees in the
Lower Rio Grande Valley
•
•
•
Subject Area: Engineering.
Purpose: To detect weak areas within levees prior to flood events.
Data Set: LIDAR.
a. Introduction. A series of levees were constructed along the Lower Rio Grande in
Texas and Mexico in the 1930s. Local farmers, working with the county government, constructed the levee system to prevent flood damage to crops in low-lying areas near the river.
The levees were constructed of sediment and soil materials obtained locally. The Federal
government later completed the levee system in the 1940s and continued expansion and repairs through the 1940s. The US Army Engineer Research and Development (ERDC),
working recently with the International Boundary and Water Commission, developed a GIS
database to catalog levee condition. Knowledge of levee conditions prior to a flood is helpful in determining where repair and rebuilding are necessary on these man-made structures.
A visual display of the levee and detail on the location of potential structural failure could
then be used to prioritize levee repair and reconstruction.
b. Description of Methods.
(1) This study maintained four primary objectives. The first was to survey the levee
system of the Lower Rio Grande River. The information compiled during the course of this
survey was organized into a GIS database. The second was to extensively evaluate levee
condition. The third was to compare the results of the airborne survey with those obtained
from ground-based surveys. This objective tested the validity of implementing a remote
sensing survey. Fourth, ground-truth locations were selected based on LIDAR data, and at
these locations soil and subsurface strata were mapped using a cone penetrometer.
(2) In the course of developing the GIS database, ERDC developed a 10-point criterion for evaluating the condition of the levee system. Traditional geophysical tools were
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then merged with remote sensing methods to proceed with the levee assessment. Levee topology was assessed with the use of LIDAR, digital video, aerial photographs, soil maps,
and geological maps. Topographic deviations of 6 in (15.2 cm). or more along the centerline
of the levee were then targeted for detailed seismic field studies. In addition to targeting
segments with an undulating topography, several stretches of the levee (on the US side)
were seismically surveyed.
c. Field Work.
(1) Ground surveys were conducted at five sites ranging in length from 3000 to 5000
ft (0.91 to 1.5 km). Electrical resistivity, EM, and magnetic surveys were collected in conjunction with the airborne EM and magnetic survey. The ground-based geophysical sites
were geo-referenced with the use of a global position unit.
(2) Much of the data acquired for the GIS database were collected from previous
sources. Information was taken from state and Federal survey maps, and from new and old
aerial photographs. Digital photography and aerial photographs were used to map Holocene
and Pleistocene deposits and geomorphologic structures. In areas with recent urban development, older images dating to the 1930s were used to evaluate the underlying geology.
d. Sensor Data Acquisition. LIDAR was utilized to survey levee elevation to determine
deviations from the original design. Deviations in height indicate segments with potential
damage attributable to seepage or sediment voids. Floodwater overtopping, slope failure,
and seepage all potentially compromise levee stability. Seepage can create void spaces in
the sediment and soil, resulting in subsequent levee collapse.
e. Study Results.
(1) The levee was then mapped and tagged with a conditional assessment of good,
marginal, acceptable, or high-risk zones. The assessment was based on a numerical measure
of 10 features deemed important in determining levee stability. These 10 features were chosen by agreement among Corps experts specializing in levee construction and repair. Table
6-2 lists the 10 features ranked in order of importance.
(2) Low scores in any one of the 10 features could result in a poor rating for a given
levee segment. The levee was divided into segments based on conductivity measurements
(shown to be controlled by levee material make-up); each segment was then given a numerical value based on a weighted measure of the 10 features. Segment ratings were color coded
and presented as a layer within the GIS database. The color-coded maps provided an easy to
interpret assessment of levee condition.
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Table 6-2
Factors Important in Levee Stability
Performance history (under flood stage)
Construction history (original or upgraded)
Visual inspection apparent condition (on-site observation)
Material type (sand [worst] transition to clay [best]; (from EM, borings, soil maps)
Topographic irregularity (swags, erosion) (from LIDAR)
Potential slope stability (material type, relation to flow)
Man-made intrusions (utilities, bridges, pump stations, etc.)
Geology (old stream beds, river deposits)
Proximity of borrow area (size, depth, distance, side of levee)
Anomalies (unexplained radical conductivity “spots”)
List is modified from Dunbar et al. (2003)
f. Conclusions. LIDAR, accurate to within 2 to 3 in (5.1 to 7.6 cm), was beneficial in
economically mapping the surface morphology of the Lower Rio Grande Valley levee system. The merged remote sensing and geophysical data onto a GIS database facilitated easy
retrieval of information for individual segments and can continue to aid in the management
of the levee system. The authors view this study as a success and acknowledge that the application of these techniques to other geographical regions, while potentially of benefit, may
not hold true for levee projects in other regions.
Point of Contact: Joseph Dunbar, Phone: (601) 634-3315
6-11 Case Study 9 : From Wright Flyers to Aerial Thermography—The 1910
Wright Brother’s Hangar at Huffman Prairie
•
•
•
Subject Area: Archeology
Purpose: To review developing NASA products and detail their use in Corps
works
Data Set: Airborne CAMS
a. Introduction.
(1) The Huffman Prairie Flying Field, a National Historic Landmark located at
Wright-Patterson Air Force Base, was surveyed using a variety of ground and airborne sensors in an effort to locate the forgotten Wright Brother’s hanger. This hangar, in use from
1910 to 1916, was the training and testing site for the Wright Aeronautical Company activities. The hanger was demolished during the 1940’s with no record of its precise location.
(2) In the early 1990s, CERL researchers investigated the Huffman Prairie Flying
Field using traditional and common archeological methods, such as excavation, magnetic
and electromagnetic surveys, and ground penetrating radar (GPR). NASA aided CERL’s
effort with the addition of thermal data collected from an airborne platform. The airborne
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data isolated a rectangular footprint, which corresponded with the location of the Wright
hangar. Later ground truth data collection and excavation works unearthed well-preserved
wall posts constructed of wood. This project exemplifies the technological methods currently being adopted by archeologists. Geographic Information Systems (GIS) practices are
now in wide use among archeologists, who take advantage of the utility of spatially related
data.
b. Description of Methods. This study had two objectives; the first was to locate the precise position of the Wright hangar. In archeological terms, the site and the history centering
on the Wright Brother’s and their activities is well documented. The historical record contains many photographs and aerial photographs that trace the approximate location of the
Wright buildings. In 1994 an architectural firm established the dimensions and structural
details of the Wright hangar with the use of these photographs. They determined that the
hangar was approximately 70 by 49 ft (21.3 by 14.9 m). Knowing the approximate dimensions and location of the building would seemingly make the archeological work a simple
task. The second objective of the study was to determine if traditional and modern archeological work could add insightful information to the already well-documented site, and
thereby further detail the history of early American aviation.
(1) The authors describe the general area surrounding the Huffman Prairie Wright
Brothers field as being relatively undisturbed despite the growth and development of the
Wright-Patterson Air Force Base neighboring the site. The prairie had been subject to burning, but not plowing. The task in locating the hanger included fieldwork, near surface geophysical work, and aerial remote sensing. The initial excavation made it apparent that identifying remains of the hangar would require either a significant amount of additional
excavation or the use of technologically sophisticated, noninvasive methods. Further excavation was deemed too destructive for the site, leading to the decision to employ nearsurface and aerial remote sensing.
(2) The geophysical work included magnetic, electromagnetic, and ground penetrating
radar (GPR). Combining multiple geophysical techniques is a good practice, as one instrument may easily pick up features not identified by another. Geophysical survey methods
typically involve data collection in a grid pattern across the study site. Anomalies in the subsurface potentially indicate natural phenomena or anthropogenic disturbances in the strata.
Some anomalies may then be excavated for ground-truth data collection. It is generally good
practice to conduct some ground-truth to verify the geophysical interpretations.
c. Field Work.
(1) Fieldwork, prior to collecting the remote sensing, began in 1990. The researchers
hoped to find underground building remnants or surface features, such as the hanger’s footings or drip lines that paralleled the absent roofline. Long trenches were hand-excavated unearthing 60% industrial debris, 2% domestic articles; the remaining material consisted of
wood debris and other uncategorized items. Excavation did not identify any intact architectural remains of the actual building, and did not locate the precise position of the hanger.
(2) Additional fieldwork verifying the geophysical results uncovered three features.
Feature 1 was a well-preserved, intact wood post—possibly one of the hangar's major wall
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posts. Features 2 and 3 were pits filled with artifacts and debris. Feature 2 fill included nails,
a shell casing, and flat glass. The function of this pit was undetermined. Feature 3 contained
wood, glass, nails, and roofing fragments. It was assumed that this was a posthole pit excavated in 1924 during the remodel and repair of the hanger. The posthole was subsequently
back filled with reconstruction debris. These three features were not evenly spaced nor in a
parallel or perpendicular orientation relative to the predicted location of the hanger. The
authors did assert that possibly two of these features represented intact hangar posts.
d. Sensor Data Acquisition. The airborne remote sensing study conducted by NASA incorporated a calibrated airborne multispectral sensor (CAMS), which collects data in the
visible, infrared, and thermal bands. A hand held inframetrics thermal scanner was also
used.
e. Study Results.
(1) The geophysical survey results indicated that a rectangular area defined by the
conductivity, magnetic, and GPR anomalies most likely encompassed the hangar location,
which was initially indicated by the 1924 air photo. The airborne hand held inframetrics
confirmed the shape and location of the hanger. An explanation for the distinct thermal response remains unclear. The authors suggested that soil compaction and heat retention related to spilled petroleum products may account for the unique thermal signature at the hangar site. The field research led to the collection of over 6000 individual samples; the
majority of which were buried industrial artifacts. The authors stated that with “no historical
records, it might have been very difficult to infer the primary function of the hangar building” from the collected fragments.
(2) All artifacts were georeferenced and a GIS map was generated to indicate the distribution of materials relative to the hangar and other building units. The majority of artifact
categories are concentrated on the northern portion of the hanger as a result of demolition
processes. The inframetrics was useful in locating the hanger footprint and delineated gullies adjacent to the road. The CAMS detected the actual roadbed.
f. Conclusions. This study demonstrates how remote sensing technologies can further
traditional research efforts in the area of archeology and history. The amalgamation of GIS
with airborne and ground remote sensing methods proved highly successful in providing
additional information on the already well-documented site. The distribution mapping of
artifacts indicated that the building had been demolished by a bulldozer, differing from the
theory that the building had simply collapsed on its own accord. Even though the hangar
may have been demolished using a bulldozer, its archaeological evidence maintained some
integrity and was easily detected by the thermal sensor. Thermal sensors are thus likely to
join the growing array of near surface geophysical and aerial remote sensing techniques that
can enhance researchers ability to detect and study archaeological sites.
Point of Contact: Michael Hargrave, Phone: (217) 352-6511, x7325
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6-12 Case Study 10: Digital Terrain Modeling and Distributed Soil Erosion
Simulation/Measurement for Minimizing Environmental Impact of Military
Training
•
•
•
Subject Area: DEM generation and soil erosion modeling
Purpose: To adequately model soil erosion and transport for land use
management
Data Set: Digital Elevation Models (DEM)
a. Introduction.
(1) The conservation of soil on military land is a priority among land use managers,
second only to the protection of threatened and endangered species. A realistic model of soil
erosion and subsequent transport will provide managers the information required to better
plan military activities, such as training. A better model of the various factors that contribute
to soil loss will give insight into the best temporal and spatial use of military land.
(2) The optimal soil loss model incorporates information regarding the diurnal, seasonal, and temporary elements influencing soil properties, as well as incorporating terrain
details. Prior to this 1997 study, soil loss models tended to measure soil loss along a linear
slope, calculated as the average slope across the study area. Models with these simple slope
inputs do not consider the dynamic nature of slope terrain and its consequential control on
soil erosion, transport, and deposition. The study summarized here attempted to improve
upon existing soil erosion models by incorporating details associated with an undulating surface. The model extracted high resolution terrain information from a digital elevation model
(DEM) to better mimic erosional provenance and sediment sinks within a watershed.
b. Description of Methods. This study applied three sediment erosion/deposition models
to 30- and 10-m DEM data. The models included CASC2D, a two-dimensional rainfall/runoff model, USPED, an improved Universal Soil Loss Equation model, and SIMWE
(SIMulated Water Erosion), a landscape scale erosion/deposition model. All models attempted to simulate watershed response to military training scenarios.
(1) The first model, the CASC2D is a two-dimensional rainfall-runoff model that
simulates spatially variable surface runoff. This modeling process can be found in
GIS/remote sensing software packages
(http://www.engr.uconn.edu/~ogden/casc2d/casc2d_home.html). Model inputs include runoff hydrographs, and water infiltration rate and depth, surface moisture, surface runoff
depth, and channel runoff depth.
(2) The second model, the Revised Universal Soil Loss Equation (RUSLE; see equation 6-3), is the most widely used empirical erosion model, and is best applied to homogeneous, rectangular agricultural fields. The equation quantifies major factors that affect erosion by water. The LS (slope length factor) accounts for only the steepness of the terrain
over a given area. The authors of this study developed an LS analog for the RUSLE and
refined the soil loss equation creating the Unit Stream Power Erosion and Deposition
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(USPED) model. This model increases the accuracy of erosion and deposition prediction on
uneven terrain.
A = R×K×LS×C×P
6-3
where
A
R
K
LS
C
P
=
=
=
=
=
=
estimated average soil loss in tons per acre per year
rainfall-runoff erosivity factor
soil erodibility factor
slope length factor
cover-management factor
support practice factor.
(3) See http://www.iwr.msu.edu/rusle/about.htm for details on the Revised Universal
Soil-Loss Equation.
(4) The two models described above use statistical averages of hill slope segments for
the entire watershed, leading to inaccurate outputs. The SIMulated Water Erosion (SIMWE)
model, the third model used in this study, overcomes these shortcomings by adding a continuity equation. SIMWE is based on the solution of the continuity equation (solved by
Green’s function Monte Carlo Method) that describes the flow of sediment over the landscape area. The factors included in the SIMWE model include measurements relating to
steady-state water flow, detachment and transport capacities, and properties of soil and
ground cover. The primary advantage of this model is its ability to predict erosion and deposition on a complex terrain on a landscape-scale, thereby improving land use assessments.
c. Remotely Sensed DEM Data. In an effort to minimize environmental impacts at military training sites, CERL scientists evaluated the effectiveness of applying standard soil loss
equations with the use of DEM at varying resolutions. The optimal pixel size for landscape
level erosion and deposition modeling ranges from 5 to 20 m. Most readily available DEM
data is at the 30-m resolution. Higher resolution DEM data are slowly becoming more
available ; for older DEM data sets and the easily accessible Landsat data, it is possible to
interpolate the low resolution data and resample the data at a finer resolution. For this study
the authors converted 30-m resolution data to 10-m resolution data by applying a regularized spline with tension (RST) method, a spatial interpolation tool included in some GIS
software. The method is a smoothing function, which interpolates the resampled data from
scattered data (RST was developed by Lubos Mitas at North Carolina State University).
d. Study Results. The authors illustrated the issues associated with modeling soil loss
over a large area by evaluating a mountainous, 3000-km2 region in Fort Irwin, California.
Topographic inputs into the models served as both a tool in evaluating erosion potential and
in determining the quality of the DEM. Low quality DEMs hold a high proportion of noise
in the data. The noise in the data creates two related problems: 1) the signals could easily be
interpreted as landscape features, and 2) large terrain features could be obscured by the
noise. Resampling and smoothing techniques using the RST reduced the noise and produced
a 10-m resolution DEM. This process better highlighted prominent topographic features.
(1) The potential for net erosion/deposition was calculated using two different resolutions (the 30-m DEM and a 10-m DEM developed by the resampling of the 30-m data).
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These calculations provided the test required to determine the effectiveness of the smoothing and resampling techniques. The visual analysis of the image overlaid onto the 10-m
resolution DEM revealed little noise. The USPED model is described as being “very sensitive to artifacts in a DEM as it is a function of second order derivatives (curvatures) of the
elevation surface.” With the reduced noise in the data, the USPED model is predicted to accurately assess soil erosion and deposition.
(2) Sediment flow rates were calculated for a subset area from within a 36-km2 area of
Fort McCoy, Wisconsin. The rate was determined with the use of the SIMWE, which solves
for the continuity of mass equation. The results indicated high sediment flow rates in valley
centers and varying flow rates in adjacent areas. The SIMWE model compared well with the
USPED model results
(3) The USPED and SIMWE models were also compared in an analysis of soil transport in the Fort McCoy, Wisconsin, area. Topographic potential for erosion and deposition
were estimated with the USPED model using a 30-m and a 10-m DEM. The 10-m data were
again derived from the 30-m data by a smoothing and resampling technique.
(4) The GIS map is based on the 10-m data denoting areas of high potential for soil
erosion, typically shown to be hilly areas adjacent to streams. This landscape model showed
areas of temporary deposition, where soil and sediment resided before entering the main
stream. The map created with the 30-m data inadequately predicted the areas of soil loss; it
was suggested this was the result of concentrated flow in valleys. Furthermore, artificial
waves of erosion and deposition were shown in flat areas. This was due to the vertical resolution of up to 1 m in the 30-m pixel size DEM. The 10-m data maintains a lower 0.1-m
vertical resolution.
(5) When the 10-m resolution DEM was used with the USPED model, intense erosion
was predicted in the hilly regions adjacent the main streams and tributaries. Deposition continued to be evident in the concave areas. Distinct from the map derived with 30-m DEM,
the 10-m resolution DEM GIS map indicated high erosion in areas with concentrated flow
that could reach the main streams. The artificial pattern of erosion/deposition along nearly
flat contours was not depicted in the 10-m GIS data.
e. Conclusions. The CASC2D, USPED, and SIMWE soil erosion models significantly
advanced the simulation of runoff, erosion, and sediment transport and deposition. With the
application of factors relating to three dimensions, these models better predict the spatial
distribution and motion of soil and sediments in a watershed. The 10-m resolution was
shown to be most advantageous in revealing the detail required to model soil erosion and
deposition. The 10 m resolution was easily developed from 30-m pixel sized data with the
use of software resampling tools followed by a smoothing algorithm. In summary, this work
potentially improves land management and should reduce land maintenance and restoration
costs.
Point of Contact: Steven Warren; [email protected]
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Appendix A
References
a. Government Sources.
Ballard, J. R. and J. A. Smith (2002) Tree Canopy Characterization for EO-1 Reflective and
Thermal Infrared Validation Studies: Rochester, New York. ERDC/EL TR-02-33,
U.S. Army Engineer Research and Development Center, Vicksburg, MS.
Bolus, Robert L. (1994) A SPOT Survey of Wild Rice in Northern Minnesota. Journal of
Imaging Science and Technology, 38 (6): 594-597.
Bolus, Robert L. and A. Bruzewicz (2002) Evaluation of New Sensors for Emergency
Management, Cold Regions Research and Engineering Laboratory, ERDC/CRREL
TR-02-11.
Campbell, Michael V. and Robert L. Fisher (2003) Utilization of High Spatial Resolution Digital
Imagery, ERDC TEC report, pending publication.
Clark, R.N., G.A. Swayze, A.J. Gallagher, T.V.V. King, and W.M. Calvin (1993), The U.S.
Geological Survey, Digital Spectral Library: Version 1: 0.2 to 3.0 microns, U.S.
Geological Survey Open File Report 93-592: 1340, http://speclab.cr.usgs.gov.
Version 4 of the spectral library was available as of 2002.
Dunbar, Joseph, B., J. Stefanov, M. Bishop, L. Peyman-Dove, J.L. Lloopis, W.L. Murphy,
R.F. Ballard (2003) An Integrated Approach for Assessment of Levees in the Lower
Rio Grande Valley, ERDC, Vicksburg, MS, pending publication.
Hargrave, Michael, John Simon Isaacson, and James A. Zeidler (1998), Archeological
Investigations at the Huffman prairie Flying Field Site: Archeological, Geophysical,
and Remote Sensing Investigations of the 1910 Wright Brother’s Hangar, WrightPatterson Air Force Base, Ohio, Report Number 98/98.
Jet Propulsion Laboratory (1999) ASTER Spectral Library, California Institute of
Technology, Pasadena, CA, available on the Internet at: http://speclib.jpl.nasa.gov/.
LaPotin, Perry, Robert Kennedy, Timothy Pangburn, and Robert Bolus (2001), Blended
Spectral Classification Techniques for Mapping Water Surface Transparency and
Chlorophyll Concentration, Photogrammetric engineering and Remote Sensing, 67
(9):1059-1065.
Lichvar, Bob, Greg Gustina, and Robert L. Bolus (2002) Duration and Frequency of Ponded
Water on Arid Southwestern Playas, Wetlands Regulatory Assistance Program,
ERDC TN –WRAP –02-02.
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Lowe Engineers LLC, and SAIC (2003) Kissimmee River Restoration Remote Sensing Pilot
Study Project Final Report, generated in support by USACE Jacksonville District
and the South Florida Water Management District, unpublished contract report.
U.S. Army Corps of Engineers, Civilian and Commercial Imagery Office (2003) In
Geospatial Manual, Engineering Manual 1110-01-2909, Appendix I, publication
anticipated for October 2003.
U.S. Army Corps of Engineers (1979) Remote Sensing Applications Guide, Parts 1-3,
Planning and Management Guidance, Engineer Pamphlet 70-1-1.
Tracy, Brian, Dr. Robert L. Bolus, and Emily S. Bryant (2002 and 2003) U.S. Army Corps
of Engineers, Remote Sensing Fundamentals, PROSPECT No. 196.
Tracy, Brian T. and Steven F. Daly (2003) River Ice Delineation with RADARSAT SAR,
Committee on river Ice Processes and the Environment (CGU-HS) report, Abstracts of the
12th Workshop on the Hydraulics of Ice Covered Rivers, Edmonton, AB, 18 – 20 June, 10p.
Warren, Steven (1998) Digital Terrain Modeling and Distributed Soil Erosion
Simulation/Measurement for Minimizing Environmental Impact of Military
Training, USACERL Interim Report 99/12.
Websites used in the production of the manual:
http://rst.gsfc.nasa.gov/start.html
http://rst.gsfc.nasa.gov/Sect3/Sect3_1.html (NASA-vegetation interpretation)
http://speclab.cr.usgs.gov/spectral.lib04/spectral-lib04.html
http://www.saj.usace.army.mil/dp/Kissimmee/Kissimmee2.html
b. Non-government Sources.
American Society of Photogrammetry (1983) Manual of Remote Sensing Volumes 1 & 2, 2nd
Edition, Editor in Chief: Robert N. Colwell, 2440 pp.
Carsey, F. (1989) Review and Status of Remote Sensing of Sea Ice. IEEE J. Oceanic Engineering,
14 (2): 127-138.
Congalton, R. and K. Green. (1999) Assessing the Accuracy of Remotely Sensed Data:
Principles and Practices. CRC/Lewis Press, Boca Raton, FL.
Congalton, R. (1991) A review of assessing the accuracy of classifications of remotely
sensed data. Remote Sensing of Environment. 37: 35-46.
Jensen, J. R. (1996) Introductory Digital Image Processing: A remote sensing perspective,
2nd Edition. NJ: Prentice-Hall.
A-2
EM 1110-2-2907
1 October 2003
Kriebel, K.T. (1976), Remote Sensing of Environment, 4: 257-264.
Lillesand and Kiefer, 1994, Remote sensing and Image Interpretation, Third Edition, John
Wiley and Sons, Inc. New York, 750pp.
Lillesand and Kiefer, 1994, Remote sensing and Image Interpretation, Third Edition, New
York: John Wiley and Sons, Inc.
Pedelty, Jeffrey A., Jeffrey Morisette, James A. Smith (2002) Comparison of EO1 Landsat7 ETM+ and EO-1 Ali images over Rochester, New York. In Proceeding of SPIE
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral
Imagery VIII, Sylvia S. Shen; Paul E. Lewis Editors vol. 4725, p. 357-365.
Sabins, F. F. (2000) Remote Sensing: Principles and Interpretation. NY: W.H. Freeman and
Company.
Stoner, E.R., and M.F. Baumgardner (1981) Characteristic variations in reflectance of
surface soils, Soil Science American Journal, 45: 1161-1165.
S.A. Drury (1990) A Guide to Remote Sensing. Oxford, 199 pp.
Websites used in the production of the manual:
http://crssa.rutgers.edu/courses/remsens/remsensing9/- unsupervised classification
http://www.cla.sc.edu/geog/rslab/rsccnew/Figure%202
http://www.colorado.edu/geography/gcraft/notes/mapproj/mapproj_f.html (map projections)
Peter H. Dana, Department of Geography, University of Texas at Austin, 1995.
http://www.nrcan-rncan.gc.ca/inter/index.html
http://www.shef.ac.uk/~bryant/211lectures/2001/211L11_2001.rtf -supervised classification
http://www.geog.buffalo.edu/~lbian/rsoct17.html
http://www.directionsmag.com/pressreleases.php?press_id=6936
http://dipin.kent.edu/secchi.htm
Recommended Web sites:
http://emma.la.asu.edu/~stefanov/research.html soils
http://www.ghcc.msfc.nasa.gov/archeology/remote_sensing.html archeology
http://www.learnremotesensing.org/modules/image_classification/index.php?case=nrv&targ
et=appearance_training_data_poly
http://www.frw.ruu.nl/nicegeo.html#gis
http://rst.gsfc.nasa.gov/Front/overview.html
http://www.cla.sc.edu/geog/rslab/Rscc/rscc-frames.html
A-3
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Appendix B
Regions of the Electromagnetic Spectrum and Useful TM Band Combinations
Spectrum Region
Wavelength range
UV
0.300 − 0.446 µm
Visible - blue
0.446 − 0.500 µm
Visible - green
0.500 − 0.578 µm
Visible - red
0.579 − 0.7 µm
Near infrared (NIR)
0.7 − 0.80 µm
0.80 − 1.10 µm
Mid-infrared
1.60 − 1.71 µm
(SWIR)
2.01 − 2.40 µm
Thermal IR
3.0 − 100 µm
6.7 − 7.02 µm
10.4 − 12.5 µm
1 µm to 1 m
Landsat TM Band Combination
Microwave
Red
3
4
Color Plane
Green
Blue
2
1
3
2
4
7
6
7
5
4
2
3
3
2
1
1
4
7
7
5
2
5
7
1
4
Use
Florescent materials such as hydrocarbons and rocks. Monitor
ozone in stratosphere
Soil/vegetation discrimination, ocean productivity,
Urban
cloud cover, precipitation, snow, and ice cover
features
Corresponds to the green reflectance of healthy
vegetation and sediment in water.
Helpful in distinguishing healthy vegetation, plant species, and
soil/geological boundary mapping
Surface
Delineates healthy verses unhealthy or fallow
water,
vegetation, vegetation biomass, crop identification
snow,
(near infrared) soil, and rocks
and ice
Delineates vegetation, penetrating haze and
water/land boundary mapping
Soil and leaf moisture; can discriminate clouds, snow, and ice.
Used to remove the effects of thin clouds and smoke
Geologic mapping and plant and soil moisture, particularly
useful for mapping hydrothermally altered rocks
Monitoring temperature variations in land, water, ice, and forest
fires (and volcanic fire)
Upper-tropospheric water vapor
Vegetation classification, and plant stress analysis, soil
moisture and geothermal activity mapping, cloud top and sea
surface temperatures.
Useful for mapping soil moisture, sea ice, currents, and surface
winds, snow wetness, profile measurements of atmospheric
ozone and water vapor, detection of oil slicks
Applications
True Color. Water depth, smoke plumes visible
Similar to IR photography. Vegetation is red, urban areas appear
blue. Land/water boundaries are defined but water depth is visible
as well.
Land/water boundaries appear distinct. Wetter soil appears darker.
Algae appear light blue. Conifers are darker than deciduous
Highlights water temperature.
Helps to discriminate mineral groups. Saline deposits appear white,
rivers are dark blue.
Mineral differentiation.
Useful for mapping oil spills. Oil appears red on a dark background.
Identifies flowing lava as red/yellow. Hot lava appears yellow.
Outgassing appears as faint pink.
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Appendix C
Paper model of the color cube/space
To generate the color cube/space cut along perimeter and fold at horizontal and vertical
lines. Cube edges will need to be adhered with tape.
C-1
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Appendix D: Satellite Sensors
Satellites and sensors are commissioned and deployed annually. The list presented here is an
attempt to briefly review the utility of only a few sensors. This list, though not fully comprehensive, is a good starting point in referencing sensors.
For an extensive list of satellite sensors (acronyms and full names) see
http://ioc.unesco.org/oceanteacher/resourcekit/M3/Data/Measurements/Instrumentation/gcmd_sensors.htm.
Sensor
AATSR
Spatial
Resolution
(metric)
1000m
AC
250
ACE-FTS
0.02-1cm
4km vertical
resolution
AIRS
Band/Wavelength
or Frequency
Detection
0.555µm (green),
0.659µm (red),
0.865µm (NIR),
1.6µm (SWIR),
3.7µm (TIR),
10.85µm (TIR),
12.0µm (TIR)
0.89-1.58µm
2-13 µm (infrared)
Measures 2,300
spectral channels:
0.4 - 1.7 µm and
3.4 - 15.4 µm
10 bands across
0.433-2.35 µm
ALI
30 m
(10 m –
panchromatic)
AMI (SAR and
wind
Scattometer)
30 m
37.5 – 77 mm
AMSR
5 to 50km
depending
on frequency
band
8 frequency bands
from 6.9GHz to
89GHz bands
respectively
D-1
Application
URL
Atmosphere, forest,
vegetation, oceans,
coasts, weather &
climate
http://telsat.belspo.
be/satellites/satellit
eresult.asp?var=56
Used to atmospherically
correct high-spatial,
low-spectral resolution
multispectral sensors
http://eo1.gsfc.nasa
.gov/Technology/At
mosCorr.htm
Measures the
temperature, vertical
distribution of trace
gases and aerosols an
thin clouds
Weather, climate, O3,
and greenhouse gasses
http://www.space.g
c.ca/asc/eng/csa_s
ectors/space_scien
ce/atmospheric/scis
at/fts.asp
http://telsat.belspo.
be/satellites/satellit
eresult.asp?var=92
Land use studies,
mineral resource
assessment, coastal
processes research and
climate change studies
Ocean surface winds
and mean sea level
http://eo1.gsfc.nasa
.gov/miscPages/Te
chForum3.html
Water vapor content,
precipitation, sea
surface temperature,
sea surface wind, sea
ice, and clouds
(detectable night and
day)
http://www.eoc.nas
da.go.jp/guide/satell
ite/sendata/ami_e.h
tml
http://www.eoc.nas
da.go.jp/guide/satell
ite/sendata/amsr_e.
html
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AVHRR 2/3
1 6m
Pan – 8m
0.42 - 0.50mm
0.52 - 0.60mm
0.61 - 0.69mm
0.76 - 0.89mm
Pa : 0.52 - 0.69mm
Land and coastal zone
monitoring of such
phenomena as:
desertification,
deforestation, coastal
zone pollution, resource
exploration, land use,
fire detection (and
temperature), and
vegetation indices.
http://edcdaac.usgs.go
v/1KM/avhrr_sensor.ht
ml
AVNIR
16 km
Band1 : 0.42 0.50mm
For precise land coverage
observation
AVNIR-2
10 m
Land-use classification
EROS
1.8m
Band1 : 0.42 - 0.50
Band2 : 0.52 - 0.60
Band3 : 0.61 - 0.69
Band4 : 0.76 - 0.89
0.5 - 0.9µm
http://www.eoc.nasda.
go.jp/guide/satellite/se
ndata/avnir_e.html
http://www.eoc.nasda.
go.jp/guide/satellite/se
ndata/avnir2_e.html
ERS
5.8m
0.5 - 0.75 µm + NIR,
and mid IR
High resolution imagery
GEROS
V, N, & S, IR- 250m
Infrared - 1km
23 visible and nearinfrared bands
6 short-wave length
infrared bands
7 middle & thermal
infrared bands
HYPERION
30 m
250 bands with in the
0.4 - 2.5 µm range
IKONOS
1m and 4m
Visible and infrared
Land, ocean, clouds
sensitive to chlorophyll,
dissolved organic
substance, surface
temperature, vegetation
distribution, vegetation
biomass, distribution of
snow and ice, and albedo
of snow and ice
Measures ice sheet mass
balance, cloud and aerosol
heights, minute land
topography changes, and
vegetation characteristics
Very high resolution
imagery
ILAS
1km
Stratosphere
monitoring
7.14 - 11.76mm
2 - 8mm
12.80 - 12.83mm
753 - 784nm
IRS
5.8 – 70 m
0.52 - 0.59
0.62 - 0.68
0.77 - 0.86
1.55 – 1.70
Pan: 0.5 – 0.75
D-2
Very high resolution
imagery
Measures the vertical
profiles of O3, NO2,
aerosols, H2O, CFC11,
CH4, N2O, CIONO2,
temperature, & pressure
Vegetation (forest and
agriculture), water, and
urban features
http://www.ccrs.nrcan.
gc.ca/ccrs/data/satsen
s/eros/erostek_e.html
http://www.spaceimagi
ng.com/products/irs/irs
_technical_overview.ht
m
http://www.oso.noaa.g
ov/goes/goescalibration/index.htm
http://eo1.usgs.gov/
http://www.spaceimagi
ng.com/products/ikono
s/index.htm
http://www.eoc.nasda.
go.jp/guide/satellite/se
ndata/ilas2_e.html
http://www.fas.org/spp
/guide/india/earth/irs.ht
m
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Landsat1-7
TM
ETM+
30
Bands 1-7: 30
Band 6 at 60m
Band 1: blue, 0.450.52µm
Band 2: green, 0.520.60µm
Band 3: red, 0.630.69µm
Band 4: near IR,
0.76-0.90µm
Band 5: mid IR, 1.551.74µm
Band 6: thermal IR
10.40-12.50µm
Band 7: mid IR, 2.082.35µm
MSS
30
TM bands 1-7
Pan: 15
Pan: 0.52 - 0.90 µm
Band 1: green, 0.500.60µm
Band 2: red, 0.600.70µm
Band 3:near IR, 0.700.80µm
Band 4: near IR,
0.80-1.10µm
LIS
4km
0.77765µm
MISR
275 m to 1.1 km
blue, green, red, and
near-infrared
MODIS
250 m, 500 m,
1000 m
ORBVIEW-3
1 m, 4 m
POLDER
7km
0.4 to 14.4 µm;
Details at:
http://modis.gsfc.nas
a.gov/about/specs.ht
ml
450 – 520 nm
520 – 600 nm
625 – 695 nm
760 – 900 nm
443, 490, 565, 665,
763, 765, 865, and
910 nm
D-3
http://landsat7.usgs.gov/general.html
Water, forest,
soil/vegetation, and urban http://landsat7.usgs.gov
features
/about.html
Detects healthy
vegetation
Distinguishes plant
species, soil and geologic
boundaries
Vegetation biomass,
emphasizes soil/crop &
land/water boundaries
Plant water
content/vegetation health,
distinguishes clouds,
snow and ice
Crop stress detection,
heat intensity, insecticide
applications, thermal
pollution and geothermal
mapping
Geologic and soil
mapping and plant/soil
moisture content
See TM Bands 1-7
http://edc.usgs.gov/pro
ducts/satellite/mss.html
above.
Used to spatially sharpen
TM and MSS color
composites
Sediment in water and
urban features
Soil / geologic boundary
discrimination
Vegetation biomass and
health
Vegetation discrimination,
penetrating haze, and
water/land boundaries
Monitors lightening
http://www.eoc.nasda.g
o.jp/guide/satellite/send
ata/lis_e.html
Tropospheric Aerosol
http://terra.nasa.gov/Ab
Data
out/MISR/about_misr.ht
ml
Aerosol concentrations
http://wwwmisr.jpl.nasa.gov/
over land and water, fire
detection (biomass
burned), land-use,
vegetation
Urban, crop, terrain detail http://www.orbimage.co
for 3-dimensional
m/corp/orbimage_syste
mapping.
m/ov1/
Aerosols, clouds, over
ocean and land surfaces
http://www.eoc.nasda.g
o.jp/guide/satellite/send
ata/polder_e.html
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PR
250 km
13.796 GHz and
13.802 GHz
Measures and maps
rainfall (3-D)
PRISM
2.5 m
0.52 - 0.77mm
For digital elevation
mapping
QUICKBIRD
62 cm to 2.5 m
Forest fires, urban,
vegetation, surveillance
RADARSAT
10, 25, 50,
and 100 m
50 km
Blue: 450 - 520 nm
Green: 520 - 600 nm
Red: 630 - 690 nm
Near-IR: 760-890 nm
Pan: 450 - 900 nm
Microwave
c-band (5.6 cm)
Frequency of 13.4
GHz
SeaWinds
Land-use/cover, change
detection
Ocean wind speed and
direction
SPOT
http://www.rsi.ca/
http://www.eoc.nasda.g
o.jp/guide/satellite/send
ata/seawinds_e.html
http://www.spot.com/
•
PAN
10 m
Band 1: green-red,
0.51 - 0.73 µm
•
XS
20 m
Band 1: green, 0.500.59µm
Band 2: red, 0.610.68µm
Band 3: near IR,
0.79-0.89µm
Band 4: short-wave
IR, 1.5-1.75µm
TMI
http://www.eoc.nasda.g
o.jp/guide/satellite/send
ata/pr_e.html
http://www.nasda.go.jp/
projects/sat/alos/compo
nent_e.html
http://www.satimagingc
orp.com/galleryquickbird.html
6-50 km
10.7, 19.4, 21.3, 37,
and 85.5 GHz
D-4
Useful for visual
interpretation and
improving low spatial
resolution multi-spectral
data
Monitors healthy
vegetation
Distinguishes plant
species, soil, and
geologic boundaries
Vegetation biomass and
emphasizes soil/crop and
land/water boundaries
Soil and leaf moisture
Rainfall rates and
precipitation profiles:
http://trmm.gsfc.nasa.gov/
overview_dir/tmi.html
EM 1110-2-2907
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Appendix E
Satellite Platforms and sensors
Satellite
ALI
ALOS
On board sensors
URL
Hyperion
PRISM
AVNIR-2
PALSAR
AVNIR
OCTS
NSCAT
TOMS
POLDER
IMG
ILAS
RIS
http://eo1.usgs.gov/instru/ali.asp
http://www.nasda.go.jp/projects/sat/alos/index_e.html
AMSR
GLI
SeaWinds
POLDER
ILAS-2
AMSR-E
AIRS
AMSU
CERES
HSB
MODIS
http://www.eoc.nasda.go.jp/guide/satellite/satdata/adeos2_
e.html
ERS-1
AMI
SCAT
RA
ATSR-M
LRR
PRARE
http://earth.esa.int/ers/
ERS-2
Above plus:
AMI/SAR
ATSR
GOME
MWS
RA
http://earth.esa.int/ers/
JERS-1
SAR
OPS
ETM+
MSS
TM
MESSR
VTIR
MSR
Amsu
Avhrr
HIRS/3
POES
SAR
http://www.eorc.nasda.go.jp/JERS-1/
ADEOS
ADEOS-II
AQUA
LANDSAT
MOS-1/1b
NOAA-11
thru -17
RADAR
SAT
http://www.eorc.nasda.go.jp/ADEOS/
http://aqua.nasa.gov/
http://geo.arc.nasa.gov/sge/landsat/landsat.html
http://www.restec.or.jp/eng/data/satelite/mos.html
http://www.noaa.gov/satellites.html
http://www.space.gc.ca/asc/eng/csa_sectors/earth/radarsat
1/radarsat1.asp
E-1
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SPOT
TERRA
TRMM
HRVIR
HRV
ASTER
CERES
MISR
MODIS
MOPITT
PR
http://www.spot.com/
VIRS
TMI
CERES
LIS
http://trmm.gsfc.nasa.gov
http://terra.nasa.gov/About/
E-2
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Appendix F
Airborne Sensors
Presented here is a short list of common airborne sensors and their general performance
capabilities. For an larger list of airborne sensors (acronyms and full names) see
http://carstad.gsfc.nasa.gov/Topics/instrumentlist.htm and
http://ioc.unesco.org/oceanteacher/resourcekit/M3/Data/Measurements/Instrumentation/gcmd_sensors.htm.
Sensor
Spatial
Resolution
(metric)
Band/
Wavelength or
Frequency
Application
General
Information
AC
250
0.89-1.58µm
Used to atmospherically
correct high-spatial, lowspectral resolution
multispectral sensors
Measures the temperature,
vertical distribution of trace
gases and aerosols an thin
clouds
http://eo1.gsfc.nasa.
gov/Technology/At
mosCorr.htm
ACE-FTS 0.02–1cm
2-13 µm (infrared)
ATM
10- to 20-cm vertical
resolution
LIDAR-based
sensor (microwave)
AVIRIS
4 – 20 m
400 - 2500 nm
CAMIS
26 – 156 cm
CASI
0.5 – 10 m
450, 550, 650 and
800 nm
400 – 1000nm
4km vertical
resolution
EMERGE 0.3 – 0.6 m
Visible and infrared
HYDICE
400 - 2500 nm
HYMAP
2 – 10 m
VIS,NIR, SWIR,
MIR and TIR
IFSAR
JPL
Airsar
SHOALS
Can at collect <1 m
100 m
Microwave region
Microwave region
4–8m
TIMS
~1 – 50 m
Beach topography, ice
mapping, sea-surface
elevation, and wave
morphologies
Aerosols, ice, and water
quality mapping and
ecologic and geologic
applications
Terrestrial and
oceanographic applications
Environmental monitoring,
forestry, pipeline
engineering, military,
agriculture, and water quality
Land use and agricultural
surveys
Agriculture, forestry,
environmental, mapping,
disaster management, and
surveillance
http://www.space.g
c.ca/asc/eng/csa_se
ctors/space_science
/atmospheric/scisat/
fts.asp
http://aol.wff.nasa.g
ov/ATMindex.html
http://popo.jpl.nasa.
gov/html/aviris.ove
rview.html and
http://popo.jpl.nasa.
gov/html/aviris.free
data.html
http://www.flidata.c
om/prod02.htm
http://www.itres.co
m/
http://www.emerge
web.com/
http://www.oss.goo
drich.com/Hypersp
ectralDigitalImager
yCollectionExperim
ent.shtml
Agriculture, forestry,
environmental, urban,
geologic, and soil mapping
Topography
All-weather terrain imager.
Can penetrate forest canopy
http://www.intspec.
com/
Visible and infrared
Bathymetry
Thermal infrared
(8-12 µm)
Mineral mapping and
archeologic applications
http://shoals.sam.us
ace.army.mil/
http://www.dfrc.nas
a.gov/airsci/ER2/tims.html
F-1
http://airsar.jpl.nasa
.gov/index.html
EM 1110-2-2907
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Appendix G
TEC’s Imagery Office (TIO)
The following is taken from Appendix I of the Geospatial Engineer Manual (EM 1110-12902), which outlines the procedures for acquiring image data.
G-1 Development of TEC’s Imagery Office (TIO).
a. To help Army agencies/organizations avoid duplicating commercial and civil imagery
purchases, the Office of the Assistant Chief of Engineers designated TEC in 1990 to act as
the U.S. Army Commercial and Civil Imagery (C2I) Acquisition Program Manager. To
accomplish this task, the TIO was initiated with the added focus on educating the soldier on
the uses, types, and availability of commercial satellite imagery. As Army use of this
imagery increased and as the number of satellites increased, the TIO has grown to keep up
with the demand. Currently, TIO provides thousands of dollars of imagery support to its
customers, and is an active participant in National Imagery and Mapping Agency’s
Commercial Imagery Strategy.
b. TIO is the designated repository of selected commercial satellite imagery data
pertaining to terrain analysis and water resources operations worldwide. These data support
worldwide military applications and operations. TIO executes the Commercial Imagery
Program for TEC and the Army. The current revision of Army Regulation 115-11,
“Geospatial Information and Services,” strengthens the role of TIO as the point of contact
for acquisition of commercial satellite imagery in the Army.
G-2 How to Order Commercial Satellite Imagery.
a. USACE Commands are required to first coordinate with TIO before purchasing
satellite imagery from a commercial vendor. USACE organizations with requirements for
commercial satellite imagery must forward requests to TIO for research, acquisition, and
distribution of the data. The requests can be submitted as follows:
[email protected]
Telephone: 703-428-6909
Fax: 703-428-8176
Online Request Form
www.tec.army.mil/forms/csiform1.html
b. Each request should include the following information:
• Geographic area of interest. Please provide Upper Left and Lower Right
coordinates (e.g., 27 00 00N 087 00 00W) and path/row, if known.
• Acceptable date range for data coverage (e.g., 5 January 1999 to 3 March
2000).
• Cloud cover and quality restrictions (e.g., less than 10 percent cloud
cover, no haze, 10 degrees off nadir).
G-1
EM 1110-2-2907
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• Satellite system/sensor. (For basic satellite information, access
www.tec.army.mil/TIO/ satlink.htm.)
• Desired end product (digital or hard copy and preferred media type; e.g.,
CD-ROM).
• Point of contact, mailing and electronic address, and telephone number.
c. Purchased Commercial Satellite Imagery Submission to the Commercial Satellite
Imagery Library (CSIL)
d. Commercial satellite imagery that the TIO purchases for customers is disseminated
upon receipt to the requestor as well as to the CSIL. This provides data access for DoD/Title
50 users.
G-2
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Appendix H
Example Contract - Statement of Work (SOW)
Laser Fluorescence Oil Spill Surveillance
Statement of Work
2/14/96
To:
DOE/NV Remote Sensing Laboratory
From: RSGISC, U. S. Army Corps of Engineers
1.0 Purpose
The purpose of this SOW is to demonstrate proof-of-concept of an airborne
fluorescence imaging system capable of sensing oil on land and in wetlands. These are
issues of concern to the Corps.
2.0 Background
The U.S. Coast Guard Research and Development Center (R&D Center) and
Environment Canada have sponsored experiments with a Laser Environmental Airborne
Fluorimeter (LEAF) spot sensor for the detection of oil on water in land-locked pools. The
tests successfully demonstrated that oils fluoresce with distinct spectral signatures when
excited by a laser source.
In order to develop the fluorescence concept into a practical field instrument for
supporting oil spill response operations, an upgrade to an imaging sensor is necessary. The
EG&G, Santa Barbara, Special Technologies Laboratory (STL) is prototyping an airborne
Laser Induced Fluorescence Imager (LIFI) which can be applied to the detection of oil spills
on land and in wetlands.
3.0 Objectives/ Scope
The DOE/NV Remote Sensing Laboratory (RSL) and STL will design and perform
measurements to test airborne LIFI technology for the detection of oil-on-land and in
wetlands.
1. Acquire laboratory and ground fluorescence spectra of several types of spilled oil
on land and in wetlands in the presence of both organic and inorganic background materials.
This can be used to define the source intensity needed for required signal levels and resolve
major technical issues such as spatial resolution, swath width, aircraft altitude, and speed.
2. Acquire imagery from airborne LIFI over oiled targets for proof of concept.
4.0 Requirements
Task 1
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A series of laboratory measurements will be collected to measure the fundamental
fluorescence properties of the oils and background
materials. Optimal sensing specification for oils depend on the fluorescence efficiency of
the oil as well as the spectral and spatial resolution required for the application.
Task 2
Outdoor laser range measurements will be made for up to 20 target/background
combinations. These should include crude, diesel and home heating oils, on sand, gravel,
soil and vegetation organic backgrounds. Measurements will address the fluorescence
efficiency, emission spectra, duration and the effects of oil aging over a period of up to six
weeks. The effects of the wetness of the backgrounds on the emission efficiency, duration
and spectra will be addressed.
Task 3
When the STL LIFI system becomes airborne, imagery from flight tests over oil
targets will be collected. It is planned to operate at an altitude of 300 ft agl and provide a
swath of 60 ft., 512 pixels wide or about 1.4 in. per pixel in the cross track direction. The
spectral range will be 300nm with 128 channels covering the visible portion of the
spectrum. Excitation will be at 355nm.
5.0 Schedule
The STL shall coordinate its airborne data collection schedule to coincide with its
other sponsored flight test programs. All ground data will be collected preliminary to
airborne testing. All reports and deliverables will be completed within six months of data
collection.
6.0 Deliverables
Deliverable 1 - Presentation at Oil Spill Program Review
Test planning will be presented at the Oil Spill Program review and meetings
required by the Oil Spill Program Manager [FY96].
Deliverable 2 - Technical Report
A written summary report will be delivered to the Oil Spill
Program Manager NLT six months after acquisition of the data.
Deliverable 3 - Distribution of Data
All laboratory, ground and airborne data will become the property of and be transmitted to
the RS/GIS Center in digital computer compatible format.
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Appendix I
Example Acquisition – Memorandum of Understanding (MOU)
CONDITIONS FOR DATA ACQUISITION DURING
THE 1999 AIG HYMAP USA GROUPSHOOT CAMPAIGN
This Memorandum of Understanding, Conditions for Data Acquisition During the 1999
AIG HyMap USA Groupshoot Campaign (MOU) is entered into between Analytical Imaging and
Geophysics LLC (“AIG”) a limited liability company, with its principal place of business located
at 4450 Arapahoe Avenue, Suite 100, Boulder, Colorado, 80303, USA, and
(“Sponsor”), a
corporation, with its
principal place of business located at
.
WHEREAS, AIG is acting as the coordinator for various Sponsors for the
acquisition of HyMap data, the 1999 AIG/HYVISTA North American Group Shoot (“Group
Shoot”), and the Sponsor is will to acquire data using the HyMap sensor system.
WHEREAS, this MOU outlines the conditions for HyMap data acquisition as part of
the Group Shoot. It establishes the guidelines for AIG and HyVista Corporation (“HyVista”)
efforts to acquire data using the HyMap sensor system for sponsor-specified flight locations.
NOW, THEREFORE, in consideration of the premises and the agreements and
covenants of the parties set forth in this Agreement, the parties hereto agree as follows:
DEFINITIONS
As used in this Agreement, the following terms have the following meanings:
“Individual Site” means: a 2.3 kilometer wide x 20 kilometer long
area.
"Scene" means: an image cube of an Individual Site (2.3 kilometers
by 20 kilometers) at 5 meter spatial resolution and 126 spectral
bands.
“Research Mode” means: Scenes acquired which may be available to
anyone.
“Proprietary Mode” means: Scenes acquired which are only available
to the Sponsor ordering acquisition.
OBLIGATIONS OF AIG
AIG shall use its best efforts to perform the services and deliver the
group Scene and the Sponsor’s Scene(s), listed in Exhibit A,
within 8 weeks after completion of the mission. All work shall be
performed in a workmanlike and professional manner to industry
standards. All work will be performed using AIG facilities,
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excepting HyMap data acquisition and items below indicated by
(HyVista), which will be performed by HyVista Corporation.
AIG agrees to supply the following for each Scene either on CDs or
tape:
•
•
•
•
•
•
•
Image data in units of radiance (µW/cm-2/nm/sr); BIL format data files with
corresponding ENVI header files (HyVista)
GPS positions of the plane during data acquisition (HyVista)
Dark current measurements (HyVista)
Spectral calibration parameters: band centers and band shape (HyVista)
Image data corrected to apparent reflectance using an atmospheric model;
BIL format data files with corresponding ENVI header files
A single-band image of precision geocorrected data
Geocorrection information sufficient to precision geocode other bands/results
OBLIGATIONS OF SPONSOR
Sponsors shall supply the start and finish coordinates of each Individual Site
in latitude/longitude pairs (decimal degrees using the WGS84 datum).
Larger areas can be covered at an incremental cost. If such areas are to
be covered, in order to provide uninterrupted coverage of adjacent data
strips, a 20% overlap for each strip with its neighbor should be allowed.
AIG and HyVista Corporation accept no responsibility for sponsors
supplying incorrect coordinates for survey sites; sponsors will be charged
in full for the full data acquisition fee in these instances.
DATA ACQUISITION
Data will be acquired at a date nominated by AIG and HyVista with input
from the Sponsor. Data will be acquired in weather conditions to the
satisfaction of AIG and HyVista. Sponsor will be notified of proposed
acquisition of data from an elected study site not less than 24 hours prior
to acquisition. AIG and HyVista reserve the right to omit data
acquisition for a Sponsor’s chosen site if adverse weather conditions
preclude the acquisition of data to corporate quality standards.
DATA QUALITY
All data presented to Sponsor shall bear full 126-channel coverage. To keep
turbulence effects to a minimum the scanner is mounted on a stabilized
platform the Jena SM 2000. The geometric integrity of the data therefore
will be within the platform performance characteristics as supplied by the
manufacturers and the operators will endeavor to keep turbulence effects
to a minimum or abort the line if the motion is too great. Cloud cover
will be less than 10 percent total for a named study area. Spatial
accuracy of the data is limited by the on-board GPS of the aircraft.
Should a sponsor be dissatisfied with the data quality, the following
procedure will be followed: The data in question will be supplied for a
quality control assessment to 2 impartial experts, one selected by each
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party. The impartial experts will have no professional or financial interest
in their selector’s organization. The impartial experts will assess the data
quality, and judge whether the data is supplied in accordance to
HyVista’s specifications for the survey. Should the experts judge that
data meets or exceeds HyVista’s quoted data quality specifications, then
the data will be supplied and the sponsor will be liable to pay agreed data
acquisition costs, and all costs incurred in the data assessment. Should
the experts judge that the supplied data does not meet HyVista’s quoted
data quality specifications, then the data will not be supplied.
PAYMENT
The cost of a Research Mode Scene is Five Thousand US Dollars
($5,000.00). The cost of a Proprietary Mode Scene is Ten Thousand US
Dollars ($10,000.00). The Group Shoot Scene is also delivered to the
Sponsor at no additional cost. Custom acquisitions and larger areas can
be covered at an incremental cost and volume discounts may be applied.
These additional Scenes shall be listed in Appendix E.
Invoices will be issued upon signature of this MOU and payment is net 30
days. In instances where AIG and HyVista acquire only a portion of a
requested block of data due to adverse weather conditions or other
unforeseen circumstances, AIG and HyVista will refund a portion of the
fee at a rate proportional to the agreed fee for full data coverage. In the
event that no Sponsor data are acquired AIG will refund Sponsor all
payments received.
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POINTS OF CONTACT
Dr. Fred A. Kruse is designated as the AIG point of contact for this
program.
AIG
Sponsor:
Fred A. Kruse
Contact:
Senior Research Scientist
Title:
Analytical Imaging and Geophysics LLC
Address:
4450 Arapahoe Ave, Suite 100
Address:
Boulder, CO 80303
Address:
Phone: 303-499-9471
Phone:
FAX: 303-665-6090
FAX:
Email: [email protected]
Email:
TERM AND TERMINATION
The Agreement shall enter into force on the Effective Date and shall
continue until terminated as provided in this Section 8. After all
Scenes have been delivered to Sponsor and payment received this
Agreement shall terminate.
CONFIDENTIALITY
Both Sponsor and AIG acknowledge and agree that both parties own
confidential, proprietary and secret information related to various
aspects of Sponsor’s and AIG’s respective business and
technology.
As used in this Agreement, “Confidential Information” consists of (i)
any information designated by either party as confidential, and (ii)
any information relating to either party’s product plans, client
lists, product designs, product costs, product prices, product
names, finances, marketing plans, business opportunities,
personnel, research, development or know-how, except such
information which the parties agree in writing is not confidential.
Each party shall use the other party’s Confidential Information
solely for implementing its obligations under this Agreement.
Confidential Information shall not include information that (i) is
known to each at the time of disclosure to the other party, (ii) has
become publicly known through no wrongful act of the other
party, (iii) has been rightfully received from a third party
authorized to make such disclosure without restriction, or (iv) has
been approved for release by written authorization of the other
party.
Each party will not use in any way for its own account or the account
of any third party, nor disclose to any third party (excepting
HyVista), any such Confidential Information revealed to it by the
other party. Each party agrees to protect any Confidential
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Information from disclosure to others with at least the same
degree of care as that which is accorded to its own proprietary
information, but in no event with less than reasonable care. In the
event of termination of this Agreement, there shall be no use or
disclosure by either party of any Confidential Information.
Each party agrees to notify the other party promptly in the event of
any breach of confidentiality or security under conditions in
which it would appear that any Confidential Information was
disclosed in violation of this Agreement, prejudiced or exposed to
loss. Each party shall, upon request of the other party, take all
reasonable steps necessary to recover any compromised trade
secrets disclosed to or placed in the possession of the other party
by virtue of this Agreement.
Each party agrees to use Confidential Information under carefully
controlled conditions for the purposes set forth in this Agreement,
and to inform all persons who are given access to Confidential
Information by either party that such materials are confidential
trade secrets of the other party. Each party expressly agrees it
shall be fully responsible for the conduct of all its employees,
contractors, agents and representatives who may in any way
breach this Agreement.
Each party acknowledges that any breach of any of its obligations
under this Section 7 is likely to cause or threaten irreparable harm
to the other party, and, accordingly, each party agrees that in such
event, the other party shall be entitled to equitable relief to protect
its interest therein, as well as money damages.
WARRANTY AND INDEMNIFICATION
Sponsor shall defend, indemnify and hold harmless AIG from and
against all claims, liability, losses, damages and expenses
(including attorneys’ fees and court costs) arising from or in
connection with the use or application of AIG’s work by Sponsor
or any direct or indirect purchaser or licensee of Sponsor.
AIG SHALL NOT BE LIABLE TO SPONSOR FOR ANY DIRECT,
INDIRECT, SPECIAL, AND/OR CONSEQUENTIAL
DAMAGES WHATSOEVER, WHETHER CAUSED BY AIG’S
NEGLIGENCE, ERRORS, OMISSIONS, STRICT LIABILITY,
BREACH OF CONTRACT, BREACH OF WARRANTY OR
OTHER CAUSE OR CAUSES WHATSOEVER INCLUDING
BUT NOT LIMITED HYVISTA CORPORATIONS INABILITY
TO GATHER THE HYMAP DATA SCENES.
GENERAL PROVISIONS
No Assignability. The parties agree that this Agreement is not
assignable or transferable by AIG without the prior written
consent of Sponsor.
No Agency Relationship. This Agreement does not establish any
agency, joint venture, or partnership relationship between the
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parties, and neither party can bind the other by any contract or
representation.
Notices. All notices provided for in this Agreement shall be in
writing and will be deemed effective when either served by
personal delivery or sent by express, registered or certified mail,
postage prepaid, return receipt requested, to the other party at the
corresponding mailing address set forth on the first page hereof or
at such other address as such other party may hereafter designate
by written notice in the manner aforesaid.
Modification. The parties acknowledge and agree that this
Agreement may only be modified by the mutual written
agreement of the parties.
Entire Agreement. The written terms and provisions in this
Agreement constitute the entire agreement and understanding
between the parties relating to the subject matter hereof and
supersede all previous communications, proposals,
representations, and understandings, whether oral or written,
relating thereto. This Agreement may be executed in two or
more counterparts, each of which shall be deemed an original and
all of which together shall constitute one instrument.
Governing Law. This Agreement shall be governed by and construed
and enforced in accordance with the laws of the State of
Colorado, U.S.A., without regard to the conflict of laws
provisions thereof. Both AIG and Sponsor agree and consent to
personal jurisdiction and venue in the state and federal courts in
the State of Colorado, U.S.A. All actions relating to or arising out
of this Agreement may be brought only in federal or state courts
in the State of Colorado, U.S.A.
Binder. This Agreement is binding on and inures to the benefit of the
parties, their respective heirs, assigns, and legal representatives.
Severability. In case any one or more of the provisions contained in
this Agreement shall, for any reason, be held to be invalid, illegal,
or unenforceable in any respect, such invalidity, illegality, or
unenforceability shall not affect any other provisions of this
Agreement, and this Agreement shall be construed as if such
valid, illegal, or unenforceable provision had never been
contained herein.
Binding Agreement. Each party agrees and acknowledges that it has
read this Agreement, understands it, and agrees to be bound by
the terms and conditions herein.
IN WITNESS WHEREOF, this Agreement is executed by and between the parties as
of the Effective Date.
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AIG
SPONSOR:
By:
By:
Name: James M. Young
Name:
Title: Contracts Manager
Title:
Date:
Date:
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Glossary
This glossary was compiled from Internet Sites at the USGS, NASA, Goddard Space
Center, Canadian Center for Remote Sensing, and the ASPRS
DISCLAIMER: Any use of trade, product, or firm names is for descriptive purposes only
and does not imply endorsement by the U.S. Government.
A
Absolute temperature
Absorptance
Absorption band
Absorptivity
Achromatic vision
Active remote sensing
Acuity
Additive primary colors
Adiabatic cooling
Advanced very high
resolution radiometer
(AVHRR)
Aerial magnetic survey
Aeromagnetic
AGL
Air base
Airborne imaging
spectrometer (AIS)
Airborne visible and
infrared imaging
spectrometer (AVIRIS)
AID--Agency for
International Development
Albedo (A)
Albers Equal Area
Projection
Temperature measured on the Kelvin scale, whose base is absolute
zero, i.e. -273 °C (0 °C is expressed as 273 °K).
A measure of the ability of a material to absorb EM energy at a specific
wavelength.
Wavelength interval within which electromagnetic radiation is absorbed
by the atmosphere or by other substances.
Capacity of a material to absorb incident radiant energy.
The perception by the human eye of changes in brightness, often used
to describe the perception of monochrome or black and white scenes.
Remote sensing methods that provide their own source of
electromagnetic radiation to illuminate the terrain. Radar is one
example.
A measure of human ability to perceive spatial variations in a scene. It
varies with the spatial frequency, shape, and contrast of the variations,
and depends on whether the scene is colored or monochrome.
Blue, green, and red. Filters of these colors transmit the primary color
of the filter and absorb the other two colors.
Refers to decrease in temperature with increasing altitude.
Crosstrack multispectral scanner on a NOAA polar-orbiting satellite that
acquires five spectral bands of data (0.55 to 12.50 µm) with a ground
resolution cell of 1.1 by 1.1 km.
Survey that records variations in the earth's magnetic field.
Aeromagnetic is descriptive of data pertaining to the Earth's magnetic
field which has been collected from an airborne sensor.
Above ground level.
Ground distance between optical centers of successive overlapping
aerial photographs.
Along-track multispectral scanner with spectral bandwidth of 0.01 µm.
Experimental airborne along-track multispectral scanner under
development at JPL to acquire 224 images in the spectral region from
0.4 to 2.4 µm.
The United States Federal agency for international development
projects.
Ratio of the amount of electromagnetic energy reflected by a surface to
the amount of energy incident upon it.
The Albers Equal Area projection is a method of projection on which the
areas of all regions are shown in the same proportion of their true
areas. The meridians are equally spaced straight lines converging at a
common point, which is normally beyond the pole. The angles between
them are less than the true angles. The parallels are unequally spaced
concentric circular arcs centered on the point of convergence of the
meridians. The meridians are radii of the circular arcs. The poles are
normally circular arcs enclosing the same angle as that enclosed by the
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Along-track scanner
Alteration
Amplitude
Analog display
Analogue image
Angular beam width
Angular field of view
Angular resolving power
Anomaly
Antenna
Aperture
APL
Apollo
Apparent thermal inertia
(ATI)
ARC Export
ARC SECOND
ARC/INFO
ArcUSA
other parallels of latitude for a given range of longitude. Albers Equal
Area is frequently used in the ellipsoidal form for maps of the United
States in the National Atlas of the United States, for thematic maps,
and for world atlases. It is also used and recommended for equal-area
maps of regions that are predominantly east-west in extent.
Scanner with a linear array of detectors oriented normal to flight path.
The IFOV of each detector sweeps a path parallel with the flight
direction.
Changes in color and mineralogy of rocks surrounding a mineral
deposit that are caused by the solutions that formed the deposit. Suites
of alteration minerals commonly occur in zones.
For waves, the vertical distance from crest to trough.
A form of data display in which values are shown in graphic form, such
as curves. Differs from digital displays in which values are shown as
arrays of numbers.
An image where the continuous variation in the property being sensed
is represented by a continuous variation in image tone. In a
photograph, this is achieved directly by the grains of photosensitive
chemicals in the film; in an electronic scanner, the response in millivolts
is transformed to a display on a cathode-ray tube where it may be
photographed.
In radar, the angle subtended in the horizontal plane by the radar
beam.
Angle subtended by lines from a remote sensing system to the outer
margins of the strip of terrain that is viewed by the system.
Minimum separation between two resolvable targets, expressed as
angular separation.
An area on an image that differs from the surrounding, normal area. For
example, a concentration of vegetation within a desert scene
constitutes an anomaly.
Device that transmits and receives microwave and radio energy in
radar systems.
Opening in a remote sensing system that admits electromagnetic
radiation to the film in radar systems.
Applied Physics Laboratory of John Hopkins University.
U.S. lunar exploration program of satellites with crews of three
astronauts.
An approximation of thermal inertia calculated as one minus albedo
divided by the difference between daytime and nighttime radiant
temperatures.
EXPORT creates an ARC/INFO interchange file to transfer coverages,
INFO data files, text files, and other ARC/INFO files between various
computer systems. An interchange file contains all coverage
information and appropriate INFO file information in a fixed length,
ASCII format. It can be fully or partially compressed as well as
uncompressed ASCII depending upon the given EXPORT option.
1/3600th of a degree (1 second) of latitude or longitude.
ARC/INFO is a geographic information system (GIS) used to automate,
manipulate, analyze, and display geographic data in digital form.
ARC/INFO is a proprietary system developed and distributed by the
Environmental Systems Research Institute, Inc., in Redlands, California
Designed by ESRI, ArcUSA is a general-purpose database used to
generate thematic maps of the conterminous United States at the State
and county levels. The database contains cartographic information,
tabular information, and indices and is designed for a wide range of
applications.
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Areal
Artifact
ASA index
Ascending node
ATI
Atmosphere
Atmospheric correction
Atmospheric shimmer
Atmospheric window
Attributes
Attitude
AVHRR
AVIRIS
Azimuth
Azimuth direction
Azimuth resolution
Relating to or involving an area.
A feature on an image, which is produced by the optics of the system or
by digital image processing, and sometimes masquerades as a real
feature.
Index of the American Standards Association designating film speed, or
sensitivity to light. Higher values indicate higher sensitivity. The ASA
index has been replaced by the ISO index.
Direction satellite is traveling relative to the Equator. An ascending
node would imply a northbound Equatorial crossing.
Apparent Thermal Inertia.
Layer of gases that surrounds some planets.
Image-processing procedure that compensates for effects of selectivity
scattered light in multispectral images.
An effect produced by the movement of masses of air with different
refractive indices, which is most easily seen in the twinkling of stars.
Wavelength interval within which the atmosphere readily transmits
electromagnetic radiation.
Attributes, also called feature codes or classification attributes, are
used to describe map information represented by a node, line, or area.
For example, an attribute code for an area might identify it to be a lake
or swamp; an attribute code for a line might identify a road, railroad,
stream, or shoreline.
Angular orientation of remote sensing system with respect to a
geographic reference system.
Advanced Very High Resolution Radiometer, a multispectral imaging
system carried by the TIROS-NOAA series of meteorological satellites.
Airborne visible and infrared imaging spectrometer.
Geographic orientation of a line given as an angle measured in degrees
clockwise from north.
In radar images, the direction in which the aircraft is heading. Also
called flight direction.
In radar images, the spatial resolution in the azimuth direction.
B
Background
Backscatter
Backscatter coefficient
Band
Bandwidth
Base height ratio
Area on an image or the terrain that surrounds an area of interest, or
target.
In radar, the portion of the microwave energy scattered by the terrain
surface directly back toward the antenna.
A quantitative measure of the intensity of energy returned to a radar
antenna from the terrain.
A wavelength interval in the electromagnetic spectrum. For example, in
Landsat images the bands designate specific wavelength intervals at
which images are acquired.
The total range of frequency required to pass a specific modulated
signal without distortion or loss of data. The ideal bandwidth allows the
signal to pass under conditions of maximum AM or FM adjustment.
(Too narrow a bandwidth will result in loss of data during modulation
peaks. Too wide a bandwidth will pass excessive noise along with the
signal.) In FM, radio frequency signal bandwidth is determined by the
frequency deviation of the signal.
Air base divided by aircraft height. This ratio determines vertical
exaggeration on stereo models.
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Batch processing
Bathymetry
Beam
BIA--Bureau of Indian
Affairs, Department of the
Interior
BIL (Band-Interleaved-byLine)
Bilinear
Bin
Binary
Bit
BIP--Band-Interleaved-byPixel
Blackbody
Blind spot
BLM--Bureau of Land
Management, Department
of the Interior
BOR--Bureau of
Reclamation, Department
of the Interior
BPI--Bits Per Inch
Brightness
Brute Force Radar
BSQ--Band Sequential
Byte
Method of data processing in which data and programs are entered into
a computer that carries out the entire processing operation with no
further instructions.
Configuration of the seafloor.
A focused pulse of energy.
The BIA serves Indian and Alaska Native tribes living on or near
reservations. The BIA administers and manages approximately 52
million acres of land held in trust for Indians by the United States and
works with local tribal governments on issues including road
construction and maintenance, social services, police protection, and
economic development.
BIL is a CCT tape format that stores all bands of satellite data in one
image file. Scanlines are sequenced by interleaving all image bands.
The CCT header appears once in a set.
The term bilinear is referring to a bilinear interpolation. This is simply an
interpolation with two variables instead of one.
One of a series of equal intervals in a range of data, most commonly
employed to describe the divisions in a histogram.
Based upon the integer two. Binary Code is composed of a combination
of entities that can assume one of two possible conditions (0 or 1). An
example in binary notation of the digits 111 would represent (1 X 2) + (1
X 2) + (1 X 2) = 4 + 2 + 1 = 7.
Contraction of binary digit, which in digital computing represents an
exponent of the base 2.
When using the BIP image format, each line of an image is stored
sequentially, line 1 all bands, line 2 all bands, etc. For example, the first
line of a three-band image would be stored as p1b1, p1b2, p1b3, p2b1,
p2b2, p2b3, where p1b1 indicates pixel one, band one, p1b2 indicates
pixel one, band two, etc.
An ideal substance that absorbs the entire radiant energy incident on it
and emits radiant energy at the maximum possible rate per unit area at
each wavelength for any given temperature. No actual substance is a
true blackbody, although some substances, such as black lamps,
approach this property.
The point of the optic nerve to the retina where no radiation is detected
by the eye.
Under the Federal Land Policy and Management Act of 1976, the BLM
administers and manages approximately 300 million acres of public
lands primarily located in the western half of the lower 48 States and
Alaska. Public lands in the U.S. contain mineral and timber reserves,
support habitat for a host of wildlife, and provide recreational
opportunities.
The BOR was chartered in 1902 with the responsibility to reclaim arid
lands in the western U.S. for farming by providing secure, year-around
water supplies for irrigation. The BOR's responsibilities since have
expanded to include generating hydroelectric power; overseeing
municipal and industrial water supplies, river regulation, and flood
control; enhancing fish and wildlife habitats; and researching future
water and energy requirements.
The tape density to which the digital data were formatted.
Magnitude of the response produced in the eye by light.
See real-aperture radar.
BSQ is a CCT tape format that stores each band of satellite data in one
image file for all scanlines in the imagery array. The CCT headers are
recorded on each band.
A group of eight bits of digital data.
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C
Calibration
Calorie
Camouflage detection
photographs
Cardinal point effect
Cartographic
Cathode ray tube (CRT)
C band
CCD
CCT
CD-ROM--Compact DiscRead Only Memory
Centerpoint
Change-detection images
Channels
Charge-coupled detector
(CCD)
Chlorosis
Chromatic vision
CIR
Circular scanner
Classification
Coastal zone color
scanner (CZCS)
Coherent radiation
Color composite image
Color ratio composite
image
Complementary colors
Computer-compatible
tape (CCT)
Conduction
Cones
Process of comparing an instrument's measurements with a standard.
Amount of heat required to raise the temperature of 1g of water by 1
°C.
Another term for IR color-photograph.
In radar, very bright signatures caused by optimally oriented corner
reflectors, such as buildings.
Pertaining to cartography, the art or practice of making charts or maps.
A vacuum tube with a phosphorescent screen on which images are
displayed by an electron beam.
Radar wavelength region from 3.8 to 7.5 cm.
Charge-coupled detector.
Computer-compatible tape.
CD-ROM is a computer peripheral that employs compact disc
technology to store large amounts of data for later retrieval. The
capacity of a CD-ROM disk is over 600 megabytes, the equivalent of
over 250,000 typewritten pages.
The optical center of a photograph.
An image prepared by digitally comparing scenes acquired at different
times. The gray tones or colors of each pixel record the amount of
difference between the corresponding pixels of the original images.
A range of wavelength intervals selected from the electromagnetic
spectrum.
A device in which electrons are stored at the surface of a
semiconductor.
Yellowing of plant leaves resulting from an imbalance in the iron
metabolism caused by excess concentrations of copper, zinc,
manganese, or other elements in the plant.
The perception by the human eye of changes in hue.
Color infrared.
Scanner in which a faceted mirror rotates about a vertical axis to sweep
the detector IFOV in a series of circular scan lines on the terrain.
Process of assigning individual pixels of an image to categories,
generally based on spectral reflectance characteristics.
A satellite-carried multi-spectral scanner designed to measure
chlorophyll concentrations in the oceans.
Electromagnetic radiation whose waves are equal in length and are in
phase, so that waves at different points in space act in unison, as in
laser and synthetic aperture radar.
Color image prepared by projecting individual black-and-white
multispectral images, each through a different color filter. When the
projected images are superposed, a color composite image results.
Color composite image prepared by combining individual ratio images
for a scene using a different color for each ratio image.
Two primary colors of light (one additive and the other subtractive) that
produce white light when added together. Red and cyan are
complimentary colors.
The magnetic tape on which the digital data for Landsat MSS and TM
images are distributed.
Transfer of electromagnetic energy through a solid material by
molecular interaction.
Receptors in the retina, which are sensitive to color. There are cones
sensitive to the red, green, and blue components of light.
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Contact print
Context
Contrast
Contrast enhancement
Contrast ratio
Contrast stretching
Convection
Corner reflector
Corps
COSMIC
Cross polarized
Cross track scanner
CRT
Cubic convolution
Cut off
CWA
Cycle
CZCS
A reproduction from a photographic negative in direct contact with
photosensitive paper.
The known environment of a particular feature on an image.
The ratio between the energy emitted or reflected by an object and its
immediate surroundings.
Image-processing procedure that improves the contrast ratio of images.
The original narrow range of digital values is expanded to utilize the full
range of available digital values.
On an image, the reflectance ratio between the brightest and darkest
parts of an image.
Expanding a measured range of digital numbers in an image to a larger
range, to improve the contrast of the image and its component parts.
Transfer of heat through the physical movement of matter.
Cavity formed by two or three smooth planar surfaces intersecting at
right angles. Electromagnetic waves entering a corner reflector are
reflected directly back toward the source.
US Army Corps of Engineers (USACE)
Computer Software Management and Information Center, University of
Georgia. This facility distributes computer programs developed by U.S.
government-funded projects.
Describes a radar pulse in which the polarization direction of the return
is normal to the polarization direction of the transmission. Crosspolarized images may be HV (horizontal transmit, vertical return) or VH
(vertical transmit, horizontal return).
Scanner in which a faceted mirror rotates about a horizontal axis to
sweep the detector IFOV in a series of parallel scan lines oriented
normal to the flight direction.
Cathode ray tube.
A high order resampling technique in which the brightness value of a
pixel in a corrected image is interpolated from the brightness values of
the 16 nearest pixels around the location of the corrected pixel.
The digital number in the histogram of a digital image, which is set to
zero during contrast stretching. Usually this is a value below which
atmospheric scattering makes a major contribution.
Clean Water Act
One complete oscillation of a wave.
Coastal Zone color scanner.
D
Dangling ARC
Dangling node
Data collection system
(DCS)
An arc having the same polygon on both its left and right sides and
having at least one node that does not connect to any other arc. See
dangling node.
The dangling endpoint of a dangling arc. Often identifies that a polygon
does not close properly, that arcs do not connect properly, or that an
arc was digitized past its intersection with another arc. In many cases, a
dangling node may be acceptable. For example, in a street centerline
map, cul-de-sacs are often represented by dangling arcs.
On Landsats 1 and 2, the system that acquired information from
seismometers, flood gauges, and other measuring devices. These data
were relayed to ground receiving stations.
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Datum
In surveying, a reference system for computing or correlating the
results of surveys. There are two principal types of datums: vertical and
horizontal. A vertical datum is a level surface to which heights are
referred. In the United States, the generally adopted vertical datum for
leveling operations is the national geodetic vertical datums of 1929
(differing slightly from mean sea level). The horizontal datum, used as a
reference for position, is defined by: the latitude and longitude of an
initial point, the direction of a line between this point and a specified
second point, and two dimensions which define the spheroid. In the
United States, the initial point for the horizontal datum is located at
Meade’s Ranch in Kansas.
Defense Meteorological
Satellite Program (DMSP)
A U.S. Air Force meteorological satellite program with satellites circling
in sun-synchronous orbit. Imagery is collected in the visible- to nearinfrared band (0.4 to 1.1 micrometers) and in the thermal-infrared band
(about 8 to 13 micrometers) at a resolution of about three kilometers.
While some of the data is classified, most unclassified data is available
to civilian users.
The U.S. Geological Survey produces five primary types of digital
elevation model data. They are:
• 7.5-minute DEM (30- x 30-m data spacing, cast on Universal
Transverse Mercator (UTM) projection or 1- x 1-arc-second data
spacing). Provides coverage in 7.5- x 7.5-minute blocks. Each
product provides the same coverage as a standard USGS 7.5minute map series quadrangle. Coverage: Contiguous United
States, Hawaii, and Puerto Rico.
• Degree DEM (3- x 3-arc-second data spacing). Provides coverage
in 1- x 1-degree blocks. Two products (three in some regions of
Alaska) provide the same coverage as a standard USGS 1-x 2degree map series quadrangle. The basic elevation model is
produced by or for the Defense Mapping Agency (DMA), but is
distributed by USGS in the DEM data record format. Coverage:
United States
• 30-minute DEM (2- x 2-arc-second data spacing). Consists of four
15- x 15-minute DEM blocks. Two 30-minute DEMs provide the
same coverage as a standard USGS 30- x 60-minute map series
quadrangle. Saleable units will be 30- x 30-minute blocks, that is,
four 15- x 15-minute DEMs representing one half of a 1:100,000scale map. Coverage: Contiguous United States, Hawaii.
• 15-minute Alaska DEM (2- x 3-arc-second data spacing, latitude by
longitude). Provides coverage similar to a 15-minute DEM, except
that the longitudinal cell limits vary from 20 minutes at the
southernmost latitude of Alaska to 36 minutes at the northern most
latitude limits of Alaska. Coverage of one DEM will generally
correspond to a 1:63,360-scale quadrangle.
• 7.5-minute Alaska DEM (1- x 2-arc-second data spacing, latitude by
longitude). Provides coverage similar to a 7.5-minute DEM, except
that the longitudinal cell limits vary from 10 minutes at the
southernmost latitude of Alaska to 18 minutes at the northernmost
latitude limits of Alaska.
Optical device for measuring the density of photographic
transparencies.
Measure of the opacity, or darkness, of a negative or positive
transparency.
Ratio of mass to volume of a material, typically expressed as grams per
cubic centimeter.
DEM--Digital Elevation
Models
Densitometer
Density, of images
Density, of materials (r)
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Density slicing
Depolarized
Depression angle (y)
Descending Node
Detectability
Detector
Developing
Dielectric constant
Difference image
Diffuse reflector
Digital display
Digital image
Digital image processing
Digital number (DN)
Digitization
Digitizer
Directional filter
Distortion
Diurnal
DLG--Digital Line Graph
DMA--Defense Mapping
Agency
Doppler principle
Process of converting the continuous gray tones of an image into a
series of density intervals, or slices, each corresponding to a specific
digital range. The density slices are then displayed either as uniform
gray tones or as colors.
Refers to a change in polarization of a transmitted radar pulse as a
result of various interactions with the terrain surface.
In radar, the angle between the imaginary horizontal plane passing
through the antenna and the line connecting the antenna and the
target.
Direction satellite is traveling relative to the Equator. A descending
node would imply a southbound Equatorial crossing.
Measure of the smallest object that can be discerned on an image.
Component of a remote sensing system that converts electromagnetic
radiation into a recorded signal.
Chemical processing of an exposed photographic emulsion to produce
an image.
Electrical property of matter that influences radar returns. Also referred
to as complex dielectric constant.
Image prepared by subtracting the digital values of pixels in one image
from those in a second image to produce a third set of pixels. This third
set is used to form the difference image.
Surface that reflects incident radiation nearly equally in all directions.
A form of data display in which values are shown as arrays of numbers.
An image where the property being measured has been converted from
a continuous range of analogue values to a range expressed by a finite
number of integers, usually recorded as binary codes from 0 to 255, or
as one byte.
Computer manipulation of the digital-number values of an image.
Value assigned to a pixel in a digital image.
Process of converting an analog display into a digital display.
Device for scanning an image and converting it into numerical format.
Mathematical filter designed to enhance on an image those linear
features oriented in a particular direction.
On an image, changes in shape and position of objects with respect to
their true shape and position.
Daily.
A DLG is line map information in digital form. The DLG data files
include information about planimetric base categories, such as
transportation, hydrography, and boundaries.
The DMA was established in 1972, when mapping, charting, and
geodesy functions of the Defense Community were combined into this
joint Department of Defense agency. The mission of the Agency is to:
produce and distribute to the Joint Chiefs of Staff, unified and specified
commands, military departments, and other department of defense
users, timely and uniquely tailored mapping, charting, and geodetic
products, services, and training; provide nautical charts and marine
navigational data to worldwide merchant marine and private vessel
operators; and maintain liaison with civil agencies and other national
and international scientific and other organizations engaged in
mapping, charting, and geodetic activities.
The above activities were handled by the DMA Combat Support Center
until the Center was disbanded in 1995 and responsibilities were
transferred to the National Imagery Mapping Agency (NIMA)
Describes the change in observed frequency that electromagnetic or
other waves undergo as a result of the movement of the source of
waves relative to the observer.
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Doppler radar
Doppler shift
DOQQ
Drainage Basin
DTM--Digital Terrain
Model
Dwell time
The weather radar system that uses the Doppler shift of radio waves to
detect air motion that can result in tornadoes and precipitation, as
previously-developed weather radar systems do. It can also measure
the speed and direction of rain and ice, as well as detect the formation
of tornadoes sooner than older radars.
A change in the observed frequency of EM or other waves caused by
the relative motion between source and detector. Used principally in the
generation of synthetic-aperture radar images.
Digital ortho-quarter quadrangle
Geographic area or region containing one or more drainage areas that
discharge run-off to a single point.
A DTM is a land surface represented in digital form by an elevation grid
or lists of three-dimensional coordinates.
Time required for a detector IFOV to sweep across a ground resolution
cell.
E
Earth Observing System
(EOS)
EDAC--Earth Data
Analysis Center
EDC
Edge
Edge enhancement
Ektachrome
Electromagnetic radiation
Electromagnetic spectrum
A series of small- to intermediate-sized spacecraft that is the
centerpiece of NASA's Mission to Planet Earth (MTPE). Planned for
launch beginning in 1998, each of each of the EOS spacecraft will carry
a suite of instruments designed to study global climate change. MTPE
will use space-, aircraft-, and ground-based measurements to study our
environment as an integrated system. Designing and implementing the
MTPE is, of necessity, an international effort. The MTPE program
involves the cooperation of the U.S., the European Space Agency
(ESA), and the Japanese National Space Development Agency
(NASDA). The MTPE program is part of the U.S. interagency effort, the
Global Change Research Program.
EDAC, also known as the Technology Applications Center (TAC), has
served as a NASA center since 1964. EDAC operates under the
objective of transferring Earth-observing technologies to the user
community. It supports and works directly with industries developing
technologies related to space science and collaborating with them to
enhance and encourage the user community to adopt the new
technologies. EDAC also supports and works with public agencies,
private citizens, educational organizations, and volunteer groups to
ensure ready accessibility to NASA generated space imagery.
EROS Data Center.
A boundary in an image between areas with different tones.
Image-processing technique that emphasizes the appearance of edges
and lines.
A Kodak color positive film.
Energy propagated in the form of and advancing interaction between
electric and magnetic fields. All electromagnetic radiation moves at the
speed of light.
Continuous sequence of electromagnetic energy arranged according to
wavelength or frequency.
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El Niño
Emission
Emissivity (e )
Emittance
Emulsion
Energy flux
Enhancement
ENSO (El Niño-Southern
Oscillation)
EOSAT
Ephemeris
ERBSS
EREP
EROS
EROS Data Center (EDC)
ERTS
ESA
A warming of the surface waters of the eastern equatorial Pacific that
occurs at irregular intervals of 2-7 years, lasting 1-2 years. Along the
west coast of South America, southerly winds promote the upwelling of
cold, nutrient-rich water that sustains large fish populations that sustain
abundant sea birds, whose droppings support the fertilizer industry.
Near the end of each calendar year, a warm current of nutrient-pool
tropical water replaces the cold, nutrient-rich surface water. Because
this condition often occurs around Christmas, it was named El Niño
(Spanish for boy child, referring to the Christ child). In most years the
warming last only a few weeks or a month, after which the weather
patterns return to normal and fishing improves. However, when El Niño
conditions last for many months, more extensive ocean warming occurs
and economic results can be disastrous. El Niño has been linked to
wetter, colder winters in the United States; drier, hotter summers in
South America and Europe; and drought in Africa. See ENSO.
Process by which a body radiates electromagnetic energy. Emission is
determined by kinetic temperature and emissivity.
Ratio of radiant flux from a body to that from a blackbody at the same
kinetic temperature and emissivity.
A term for the radiant flux of energy per unit area emitted by a body.
(Now obsolete).
Suspension of photosensitive silver halide grains in gelatin that
constitutes the image-forming layer on photographic film.
Radiant flux.
Process of altering the appearance of an image so that the interpreter
can extract more information.
Interacting parts of a single global system of climate fluctuations. ENSO
is the most prominent known source of interannual variability in weather
and climate around the world, though not all areas are affected. The
Southern Oscillation (SO) is a global-scale seesaw in atmospheric
pressure between Indonesia/North Australia, and the southeast Pacific.
In major warm events El Niño warming extends over much of the
tropical Pacific and becomes clearly linked to the SO pattern. Many of
the countries most affected by ENSO events are developing countries
with economies that are largely dependent upon their agricultural and
fishery sectors as a major source of food supply, employment, and
foreign exchange. New capabilities to predict the onset of ENSO event
can have a global impact. While ENSO is a natural part of the Earth's
climate, whether its intensity or frequency may change as a result of
global warming is an important concern.
The commercial company that took over operations of the Landsat
system in 1985.
A table of predicted satellite orbital locations for specific time intervals.
The ephemeris data help to characterize the conditions under which
remotely sensed data are collected and are commonly used to correct
the sensor data prior to analysis.
Earth Radiation Budget Sensor System, carried by NOAA satellites.
Earth Resources Experiment Package, carried on Skylab and
consisting of cameras and multispectral scanner.
Earth Resources Observation System.
Facility of the U.S. Geological Survey at Sioux Falls, South Dakota, that
archives, processes, and distributes images.
Earth Resource Technology Satellite, now called Landsat.
European Space Agency, based in Paris. A consortium between
several European states for the development of space science,
including the launch of remote-sensing satellites.
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ETC
Evaporative cooling
Exitance
Earth-terrain camera.
Temperature drop caused by evaporation of water from a moist
surface.
The radiant flux.
F
False color image
False color photograph
Far range
Fiducial Marks
Film
Film speed
Film Types
Filter, digital
Filter, optical
A color image where parts of the non-visible EM spectrum are
expressed as one or more of the red, green, and blue components, so
that the colors produced by the Earth's surface do not correspond to
normal visual experience. Also called a false-color composite (FCC).
The most commonly seen false-color images display the very-near
infrared as red, red as green, and green as blue.
Another term for IR color photograph.
The portion of a radar image farthest from the aircraft or spacecraft
flight path.
A set of four marks located in the corners or edge-centered, or both, of
a photographic image. These marks are exposed within the camera
onto the original film and are used to define the frame of reference for
spatial measurements on aerial photographs. Opposite fiducial marks
connected, intersect at approximately the image center of the aerial
photograph.
Light-sensitive photographic emulsion and its base.
Measure of the sensitivity of photographic film to light. Larger numbers
indicate higher sensitivity.
Photographic products for use in image interpretation are commonly
generated from the following film types:
• Black-and-White Panchromatic (B&W): This film primarily
consists of a black-and-white negative material with a
sensitivity range comparable to that of the human eye. It has
good contrast and resolution with low graininess and a wide
exposure range.
• Black-and-White Infrared (BIR): With some exceptions, this film
is sensitive to the spectral region encompassing 0.4
micrometers to 0.9 micrometers. It is sometimes referred to as
near-infrared film because it utilizes only a narrow portion of the
total infrared spectrum (0.7 micrometers to 0.9 micrometers).
• Conventional Color: This film contains three emulsion layers
that are sensitive to blue, green, and red (the three primary
colors of the visible spectrum). This film replicates colors as
seen by the human eye and is commonly referred to as normal
or natural color. Color film is a valuable image interpretation
tool because the human eye can discern a greater variety of
color tones than gray tones.
• Color Infrared (CIR): This film, originally referred to as
camouflage-detection film because of its warfare applications,
differs from conventional color film because its emulsion layers
are sensitive to green, red, and near-infrared radiation (0.5
micrometers to 0.9 micrometers). Used with a yellow filter to
absorb the blue light, this film provides sharp images and
penetrates haze at high altitudes. Color-infrared film also is
referred to as false-color film.
Mathematical procedure for modifying values of numerical data.
A material that, by absorption or reflection, selectivity modifies the
radiation transmitted through an optical system.
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Flight path
Fluorescence
F-number
Focal length
Foreshortening
Format
Forward overlap
Fovea
Frequency (v )
F-stop
Line on the ground directly beneath a remote sensing aircraft or
spacecraft. Also called flight line.
Emission of light from a substance following exposure to radiation from
an external source.
Representation of the speed of a lens determined by the focal length
divided by diameter of the lens. Smaller numbers indicate faster lenses.
In cameras, the distance from the optical center of the lens to the plane
at which the image of a very distant object is brought into focus.
A distortion in radar images causing the lengths of slopes facing the
antenna to appear shorter on the image than on the ground. It is
produced when radar wave fronts are steeper than the topographic
slope.
Size of an image
The percent of duplication by successive photographs along a flight
line.
The region around that point on the retina intersected by the eye's optic
axis, where receptors are most densely packed. It is the most sensitive
part of the retina.
The number of wave oscillations per unit time or the number of
wavelengths that pass a point per unit time.
Focal length of a lens divided by the diameter of the lens’s adjustable
diaphragm. Smaller numbers indicate larger openings, which admit
more light to the film.
G
GAC--Global Area
Coverage
Gamma
GCP
Gemini
Geodetic
Geodetic accuracy
Geographic information
system (GIS)
Geometric correction
GAC data are derived from a sample averaging of the full resolution
AVHRR data. Four out of every five samples along the scan line are
used to compute one average value and the data from only every third
scan line are processed, yielding 1.1 km by 4 km resolution at the
subpoint.
This is a unit of magnetic intensity.
Ground-control point. GCPs are physical points on the ground whose
positions are known with respect to some horizontal coordinate system
and/or vertical datum. When mutually identifiable on the ground and on
a map or photographic image, ground control points can be used to
establish the exact spatial position and orientation of the image to the
ground. Ground control points may be horizontal control points, vertical
control points, or both.
U.S. program of two-man earth-orbiting spacecraft in 1965 and 1966.
Of or determined by geodesy; that part of applied mathematics which
deals with the determination of the magnitude and figure either of the
whole Earth or of a large portion of its surface. Also refers to the exact
location points on the Earth's surface.
The accuracy with which geographic position and elevation of features
on the Earth's surface are mapped. This accuracy incorporates
information in which the size and shape of the Earth has been taken
into account.
A data-handling and analysis system based on sets of data distributed
spatially in two dimensions. The data sets may be map oriented, when
they comprise qualitative attributes of an area recorded as lines, points,
and areas often in vector format, or image oriented, when the data are
quantitative attributes referring to cells in a rectangular grid usually in
raster format. It is also known as a geobased or geocoded information
system.
Image-processing procedure that corrects spatial distortions in an
image.
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Georegistered
Geostationary
Geostationary Operational
Environmental Satellite
(GOES)
Geostationary orbit
Geosynchronous (aka
GEO)
An image that has been geographically referenced or rectified to an
Earth model, usually to a map projection. Sometimes referred to as
geocoded or geometric registration.
Refers to satellites traveling at the angular velocity at which the earth
rotates; as a result, they remain above the same point on earth at all
times.
a NOAA satellite that acquires visible and thermal IR images for
meteorological purposes such as:
• Provide continuous day and night weather observations;
• Monitor severe weather events such as hurricanes,
thunderstorms, and flash floods;
• Relay environmental data from surface collection platforms to a
processing center;
• Perform facsimile transmissions of processed weather data to
low-cost receiving stations;
• Monitor the Earth's magnetic field, the energetic particle flux in
the satellite's vicinity, and x-ray emissions from the sun;
• Detect distress signals from downed aircraft and ships.
GOES observes the U.S. and adjacent ocean areas from vantage
points 35,790 km (22,240 miles) above the equator at 75 degrees west
and 135 degrees west. GOES satellites have an equatorial, Earthsynchronous orbit with a 24-hour period, a visible resolution of 1 km, an
IR resolution of 4 km, and a scan rate of 1864 statute miles in three
minutes. See geostationary. The transmission of processed weather
data (both visible and infrared) by GOES is called weather facsimile
(WEFAX). GOES WEFAX transmits at 1691+ mhz and is accessible via
a ground station with a satellite dish antenna.
GOES carries the following five major sensor systems:
1. The imager is a multispectral instrument capable of sweeping
simultaneously one visible and four infrared channels in a
north-to-south swath across an east-to-west path, providing full
disk imagery once every thirty minutes.
2. The sounder has more spectral bands than the imager for
producing high quality atmospheric profiles of temperature and
moisture. It is capable of stepping one visible and eighteen
infrared channels in a north-to-south swath across an east-towest path.
3. The Space Environment Monitor (SEM) measures the condition
of the Earth's magnetic field, the solar activity and radiation
around the spacecraft, and transmits these data to a central
processing facility.
4. The Data Collection System (DCS) receives transmitted
meteorological data from remotely located platforms and relays
the data to the end-users.
5. The Search and Rescue Transponder can relay distress
signals at all times, but cannot locate them. While only the
polar-orbiting satellite can locate distress signals, the two types
of satellites work together to create a comprehensive search
and rescue system.
An orbit at 41 000 km in the direction of the Earth's rotation, which
matches speed so that a satellite remains over a fixed point on the
Earth's surface.
Synchronous with respect to the rotation of the Earth. See
geostationary.
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Geothermal
Goddard Space Flight
Center (GSFC)
GMT
GOES
Gossan
GPS--Global Positioning
System
Granularity
GRASS--Geographic
Resources Analysis
Support System
Gray scale
Grid format
Ground-control point
Ground range
Ground-range image
Ground receiving station
Ground resolution cell
Ground swath
GSFC
Refers to heat from sources within the earth.
The NASA facility at Greenbelt, Maryland, that is also a Landsat ground
receiving station.
Greenwich mean time. This international 24-h system is used to
designate the time at which Landsat images are acquired.
Geostationary Operational Environmental Satellite.
Surface occurrence of iron oxide formed by the weathering of metallic
sulfide ore minerals.
The GPS is a worldwide satellite navigation system that is funded and
supervised by the U.S. Department of Defense. GPS satellites transmit
specially coded signals. These signals are processed by a GPS
receiver that computes extremely accurate measurements, including 3dimensional position, velocity, and time on a continuous basis
Graininess of developed photographic film that is determined by the
texture of the silver grains.
GRASS is a product of the U.S. Army Corps of Engineers Construction
Engineering Research Laboratories (USACERL) in Champaign, Illinois.
It is an integrated set of programs designed to provide digitizing, image
processing, map production, and geographic information system
capabilities to its users.
A sequence of gray tones ranging from black to white.
The result of interpolation from values of a variable measured at
irregularly distributed points, or along survey lines, to values referring to
square cells in a rectangular array. It forms a step in the process of
contouring data, but can also be used as the basis for a raster format to
be displayed and analyzed digitally after the values have been rescaled
to the 0-255 range.
A geographic feature of known location that is recognizable on images
and can be used to determine geometric corrections.
On radar images, the distance from the ground track to an object.
Radar image in which the scale in the range direction is constant.
Facility that records data transmitted by a satellite, such as Landsat.
Area on the terrain that is covered by the IFOV of a detector.
Width of the strip of terrain that is imaged by a scanner system.
Goddard Space Flight Center
H
Harmonic
HCMM
Heat capacity-(c)
Heat Capacity Mapping
Mission (HCMM)
Highlights
High-pass filter
HIRIS-High Resolution
Imaging Spectrometer
HIRS-High Resolution
Infrared Spectrometer
Refers to waves in which the component frequencies are wholenumber multiples of the fundamental frequency.
Heat Capacity Mapping Mission, the NASA satellite launched in 1978 to
observe thermal properties of rocks and soils. It remained in orbit for
only a few months.
Ratio of heat absorbed or released by a material to the corresponding
temperature rise or fall. Expressed in calories per gram per degree
centigrade. Also called thermal capacity.
NASA satellite orbited in 1978 to record daytime and nighttime visible
and thermal IR images of large areas.
Areas of bright tone on an image.
A spatial filter that selectively enhances contrast variations with high
spatial frequencies in an image. It improves the sharpness of images
and is a method of edge enhancement.
Possibly to be carried by the Space Shuttle.
Carried by NOAA satellites.
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Histogram
HORIZONTAL
POLARIZATION
HRPT--High Resolution
Picture Transmission
HRV--High Resolution
Visible Imaging
Instrument
Hue
HYDROLOGY
HYPSOGRAPHY
A means of expressing the frequency of occurrence of values in a data
set within a series of equal ranges or bins, the height of each bin
representing the frequency at which values in the data set fall within the
chosen range. A cumulative histogram expresses the frequency of all
values falling within a bin and lower in the range. A smooth curve
derived mathematically from a histogram is termed the probability
density function (PDF).
Transmission of microwaves so that the electric lines of force are
horizontal, while the magnetic lines of force are vertical.
HRPT data are full resolution image data transmitted to a ground
station as they are collected. The average instantaneous field-of-view
of 1.4 milliradians yields a HRPT ground resolution of approximately 1.1
km at the satellite nadir from the nominal orbit altitude of 833 km (517
mi).
The HRV instrument is a multispectral radiometer designed for SPOT
spacecraft. The HRV instrument provides for high-resolution imaging in
the visible and near-infrared portions of the electromagnetic spectrum.
The first three SPOT satellites carry twin HRVs that operate in a
number of viewing configurations and in different spectral modes. Some
of those viewing configurations and spectral modes include one HRV
only operating in a dual spectral mode (i.e., in both panchromatic mode
and multispectral mode); two HRVs operating in the twin-viewing
configuration (i.e., one HRV in panchromatic mode and one HRV in
multispectral mode); and two HRVs operating independently of each
other (i.e., not in twin-viewing configuration).
In the IHS system, represents the dominant wavelength of a color.
Scientific study of the waters of the Earth, especially with relation to the
effects of precipitation and evaporation upon the occurrence and
character of ground water.
The scientific study of the Earth's topologic configuration above sea
level, especially the measurement and mapping of land elevation.
I
IFOV
IHS
Image
Image dissection
Image striping
Image swath
Incidence angle
Incident energy
Instantaneous field of view.
Intensity, hue, and saturation system of colors.
Pictorial representation of a scene recorded by a remote sensing
system. Although image is a general term, it is commonly restricted to
representations acquired by non-photographic methods.
The breaking down of a continuous scene into discrete spatial
elements, either by the receptors on the retina, or in the process of
capturing the image artificially.
A defect produced in line scanner and push-broom imaging devices
produced by the non-uniform response of a single detector, or amongst
a bank of detectors. In a line-scan image the stripes are perpendicular
to flight direction, but parallel to it in a push-broom image.
See ground swath.
In radar, the angle formed between an imaginary line normal to the
surface and another connecting the antenna and the target.
Electromagnetic radiation impinging on a surface.
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Index of refraction (n)
Instantaneous field of
view (IV or IFOV)
Intensity
Interactive processing
Interpretation
Interpretation key
IR
IR color photograph
ISO index
Isotherm
Ratio of the wavelength or velocity of electromagnetic radiation in a
vacuum to that in a substance.
Solid angle through which a detector is sensitive to radiation. In a
scanning system, the solid angle subtended by the detector when the
scanning motion is stopped.
In the IHS system, brightness ranging from black to white.
Method of image processing in which the operator views preliminary
results and can alter the instructions to the computer to achieve desired
results.
The process in which a person extracts information from an image.
Characteristic or combination of characteristics that enable an
interpreter to identify an object on an image.
Infrared region of the electromagnetic spectrum that includes
wavelengths from 0.7µm to 1 mm.
Color photograph in which the red-imaging layer is sensitive to
photographic IR wavelengths, the green-imaging layer is sensitive to
red light, and the blue-imaging layer is sensitive to green light. Also
known as camouflage detection photographs and false-color
photographs.
Index of the International Standards Organization, designating film
speed in photography. Higher values indicate higher sensitivity.
Contour line connecting points of equal temperature. Isotherm maps
are used to portray surface-temperature patterns of water bodies.
J
Japanese National Space
Development Agency
(NASDA)
JNC--Jet Navigation Chart
Johnson Space Flight
Center
JPL
The agency reports to the Japanese Ministry of Science and
Technology.
The JNC series provides worldwide coverage at a scale of 1:2,000,000.
The information on these charts are suitable for aeronautical longrange, high-altitude, high-speed travel; map features include cities,
roads, railroads, lakes, principal drainage, and permanent snow/ice
areas. The polar regions are in a Transverse Mercator projection. All
other regions are presented in the Lambert Conformal Conic projection.
A NASA facility in Houston, Texas.
Jet Propulsion Laboratory, a NASA facility at Pasadena, California,
operated under contract by the California Institute of Technology.
K
Ka band
Kelvin Units
Kernel
Kinetic energy
Kinetic temperature
Kodachrome
Radar wavelength region from 0.8 to 1.1 cm.
A Kelvin Unit refers to a thermometric scale in which the degree
intervals are equal to those of the Celsius scale and in which zero (0)
degrees equals -273.15 degrees Celsius (absolute zero)
Two-dimensional array of digital numbers used in digital filtering.
The ability of a moving body to do work by virtue of its motion. The
molecular motion of matter is a form of kinetic energy.
Internal temperature of an object determined by random molecular
motion. Kinetic temperature is measured with a contact thermometer.
A Kodak color positive film.
L
LAC--Local Area
LAC are full resolution data recorded on an onboard tape recorder for
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Coverage
LACIE
Lambert Azimuthal Equal
Area Projection
Lambert Conformal Conic
Projection
LANDSAT (formerly
ERTS)
Laplacian filter
Large-format camera
(LFC)
Laser
Latitude (aka the geodetic
latitude)
subsequent transmission during a station overpass. The average
instantaneous field-of-view of 1.4 milliradians yields a LAC ground
resolution of approximately 1.1 km at the satellite nadir from the
nominal orbit altitude of 833 km.
Large Area Crop Inventory Experiment
Azimuthal projections are formed onto a plane, which is usually tangent
to the globe at either pole, the Equator, or any intermediate point. The
Lambert Azimuthal Equal Area projection is a method of projecting
maps on which the azimuth or direction from a given central point to
any other point is shown correctly and also on which the areas of all
regions are shown in the same proportion of their true areas. When a
pole is the central point, all meridians are spaced at their true angles
and are straight radii of concentric circles that represent the parallels.
This projection is frequently used in one of three aspects: The polar
aspect is used in atlases for maps of polar regions and of the Northern
and Southern Hemispheres; the equatorial aspect is commonly used for
atlas maps of the Eastern and Western Hemispheres; and the oblique
aspect is used for atlas maps of continents and oceans.
The Lambert Conformal Conic Projection is derived by the projection of
lines from the center of the globe onto a simple cone. This cone
intersects the Earth along two standard parallels of latitude, both of
which are on the same side of the equator. All meridians are
converging straight lines that meet at a common point beyond the limits
of the map. Parallels are concentric circles whose center is at the
intersection point of the meridians. Parallels and meridians cross at
right angles, an essential of conformality.
To minimize and distribute scale errors, the two standard parallels are
chosen to enclose two-thirds of the north to south map area. Between
these parallels, the scale will be too small, and beyond them, too large.
If the north to south extent of the mapping is limited, maximum scale
errors will rarely exceed one percent. Area exaggeration between and
near the standard parallels, is very slight; thus, the projection provides
good directional and shape relationships for areas having their long
axes running in an east to west belt.
The Landsat program, first known as the Earth Resources Technology
Satellite (ERTS) Program, is a development of the National Aeronautics
and Space Administration (NASA) in association with NOAA, USGS,
and the Space Imaging. The activities of these combined groups led to
the concept of dedicated Earth-orbiting satellites, the defining of
spectral and spatial requirements for their instruments, and the
fostering of research to determine the best means of extracting and
using information from the data. The first satellite, ERTS 1, was
launched on 7/23/72. The second satellite was launched on 1/22/75.
Concurrently the name of the satellites and program was changed to
emphasize its prime area of interest (land resources). The first two
satellites were designated as Landsats 1 and 2. Landsat 3 was
launched on 3/5/78. Landsat 4 was launched on 7/16/82. Landsat 5
(launched 3/1/84) is currently in service providing selected data to
worldwide researchers.
A form of nondirectional digital filter.
An experiment first carried on the Space Shuttle in October 1984.
Light artificially stimulated electromagnetic radiation: a beam of
coherent radiation with a single wavelength.
The angle between a perpendicular at a location, and the equatorial
plane of the Earth.
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Latent image
Layover
L band
Lens
LEVEL 1b
LFC
LIDAR
Light
Light meter
Lineament
Linear
Lineation
Line drop out
Line-pair
Line scanner
Look angle
Look direction
Look-up table (LUT)
Longitude
Low-sun-angle
photograph
Invisible image produced by the photochemical effect of light on silver
halide grains in the emulsion of film. The latent image is not visible until
after photographic development.
In radar images, the geometric displacement of the objects toward the
near range relative to their base.
Radar wavelength region from 15 to 30 cm.
One or more pieces of glass or other transparent material shaped to
form an image by refraction of light.
Level 1b is considered raw quality controlled data configured into
discrete data sets and to which Earth location and calibration
information have been appended, but not applied.
Large-format camera. The LFC was a high altitude aerial mapping
camera scaled up to operate from the Space Shuttle in Earth-orbital
altitudes. LFC specifications included:
• Film Format Size: 9 x 18 inches (23 x 46 cm)
• Lens Aperture: F/6.0 -Lens Focal Length: 12 inches (30.5 cm)
• Exposure Interval: 7.5 sec.
• Exposure Range: 1/250 to 1/31.25 seconds
• Ground Resolution: 20 meters at 160 nautical miles
• Ground Coverage: 120 x 240 nautical miles at 160 nm
Light intensity detection and ranging, which uses lasers to stimulate
fluorescence in various compounds and to measure distances to
reflecting surfaces.
Electromagnetic radiation ranging from 0.4 to 0.7µm in wavelength that
is detectable by the human eye.
Device for measuring the intensity of visible radiation and determining
the appropriate exposure of photographic film in a camera.
Linear topographic or tonal feature on the terrain and on images, maps,
and photographs that may represent a zone of structural weakness.
Adjective that describes the straight line-like nature of features on the
terrain or on images and photographs.
The one-dimensional alignment of internal components of a rock that
cannot be depicted as an individual feature on a map.
The loss of data from a scan line caused by malfunction of one of the
detectors in a line scanner.
Pair of light and dark bars of equal widths. The number of such linepairs aligned side by side that can be distinguished per unit distance
expresses the resolving power of an imaging system.
An imaging device, which uses a mirror to sweep the ground surface
normal to the flight path of the platform. An image is built up as a strip
comprising lines of data.
The angle between the vertical plane containing a radar antenna and
the direction of radar propagation. Complementary to the depression
angle.
Direction in which pulses of microwave energy are transmitted by a
radar system. The look direction is normal to the azimuth direction. Also
called range direction.
A mathematical formula used to convert one distribution of data to
another, most conveniently remembered as a conversion graph.
The angular distance from the Greenwich meridian (0 degree), along
the equator. This can be measured either east or west to the 180th
meridian (180 degrees) or 0 degree to 360 degrees W.
Aerial photograph acquired in the morning, evening, or winter when the
sun is at a low elevation above the horizon.
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Luminance
Quantitative measure of the intensity of light from a source.
M
Mach band
Median filter
Mercator Projection
Mercury
Microwave
Mid-infrared (MIR)
Mie scattering
Minimum ground
separation
Minus-blue photographs
Mixed pixel
Modular optoelectric
multispectral scanner
(MOMS)
Modulate
Modulation transfer
function (MTF)
MOMS
MOS-1
Mosaic
MSS
Multiband camera
Multispectral classification
Multispectral scanner
An optical illusion of dark and light fringes within adjacent areas of
contrasted tone. It is a psychophysiological phenomenon, which aids
human detection of boundaries or edges.
A spatial filter, which substitutes the median value of DN from
surrounding pixels for that recorded at an individual pixel. It is useful for
removing random noise.
Mercator is a conformal map projection, that is, it preserves angular
relationships. Mercator was designed and is recommended for
navigational use and is the standard for marine charts. Mercator is
often and inappropriately used as a world map projection in atlases and
for wall charts where it presents a misleading view of the world because
of the excessive distortion of area in the higher latitude areas.
U.S. program of one-man, earth-orbiting spacecraft in 1962 and 1963.
Region of the electromagnetic spectrum in the wavelength range of 0.1
to 30 cm.
The range of EM wavelengths from 8 to 14 µm dominated by emission
of thermally generated radiation from materials; also known as thermal
infrared.
The scattering of EM energy by particles in the atmosphere with
comparable dimensions to the wavelength involved.
Minimum distance on the ground between two targets at which they can
be resolved on an image.
Black-and-white photographs acquired using a filter that removes blue
wavelengths to produce higher spatial resolution.
A pixel whose DN represents the average energy reflected or emitted
by several types of surface present within the area that it represents on
the ground; sometimes called a mixel.
An along-track scanner carried on the Space Shuttle that recorded two
bands of data.
To vary the frequency, phase, or amplitude of electromagnetic waves.
A method of describing spatial resolution.
Modular optoelectric multispectral scanner.
Marine Observation Satellite, launched by Japan in 1987.
Composite image or photograph made by piecing together individual
images or photographs covering adjacent areas.
Multispectral scanner system of Landsat that acquires images of four
wavelength bands in the visible and reflected IR regions.
System that simultaneously acquires photographs of the same scene at
different wavelengths.
Identification of terrain categories by digital processing of data acquired
by multispectral scanners.
Scanner system that simultaneously acquires images of the same
scene at different wavelengths.
N
NAD27--North American
Datum of 1927
NAD27 is defined with an initial point at Meads Ranch, Kansas, and by
the parameters of the Clarke 1866 ellipsoid. The location of features on
USGS topographic maps, including the definition of 7.5-minute
quadrangle corners, are referenced to the NAD27.
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NAD83--North American
Datum of 1983
Nadir
NAPP--National Aerial
Photography Program
NASA
NDVI--Normalized
Difference Vegetation
Index
Nearest Neighbor
Resampling
Near infrared (NIR)
Near range
Negative photograph
NESDIS--National
Environmental Satellite,
Data and Information
Service
NHAP
NOAA
Noise
Nondirectional filter
Non-selective scattering
Non-spectral hue
NAD83 is an Earth-centered datum and uses the Geodetic Reference
System 1980 (GRS 80) ellipsoid, unlike NAD27, which is based on an
initial point (Meade’s Ranch, Kansas). Using recent measurements with
modern geodetic, gravimetric, astrodynamic, and astronomic
instruments, the GRS 80 ellipsoid has been defined as a best fit to the
worldwide geoid. Because the NAD83 surface deviates from the
NAD27 surface, the position of a point based on the two reference
datums will be different.
Point on the ground directly in line with the remote sensing system and
the center of the earth.
NAPP was established to coordinate the collection of aerial
photography covering the 48 contiguous States and Hawaii every five
years. NAPP's goals are to ensure that photography with uniform scale,
quality, and cloud-free coverage be made available to meet the
requirements of several Federal and State agencies. The program was
initiated in 1980 as the National High Altitude Photography (NHAP)
program. In 1987, the program was renamed to NAPP when the flying
height for the program changed from 40,000 feet to 20,000 feet. NAPP
photography is available in black and white, and in most cases, colorinfrared. The program is administered by the U.S. Geological Survey's
National Mapping Division. NAPP imagery is used by the USGS for
photo revision and land use land cover characterization work on the
standard series maps at 1:24,000; 1:100,000 and 1:250,000 scales.
National Aeronautical and Space Administration.
The NDVI is computed by calculating the ratio of the VI (vegetation
index, i.e., the difference between Channel 2 and 1) and the sum of
Channels 2 and 1. Thus NDVI = (channel 2 - channel 1) / (channel 2 +
channel 1).
When correcting image data points, the nearest neighbor technique
assigns for each new pixel that pixel value which is closest in relative
location to the newly computed pixel location.
The shorter wavelength range of the infrared region of the EM
spectrum, from 0.7 to 2.5 µm. It is often divided into very-near infrared
(VNIR) covering the range accessible to photographic emulsions (0.7 to
1.0m), and the short-wavelength infrared (SWIR) covering the
remainder of the NOR atmospheric window from 1.0 to 2.5m.
Refers to the portion of a radar image closest to the aircraft or satellite
flight path.
Photograph on film or paper in which the relationship between bright
and dark tones is the reverse of that of the features on the terrain.
NESDIS is the element in NOAA that is responsible for establishing a
digital archive of data collected from the current generation of NOAA
operational polar orbiting satellites
National High Altitude Photography program of the U.S. Geological
Survey.
National Oceanic and Atmospheric Administration.
Random or repetitive events that obscure or interfere with the desired
information.
Mathematical filter that treats all orientations of linear features equally.
The scattering of EM energy by particles in the atmosphere which are
much larger than the wavelengths of the energy, and which causes all
wavelengths to be scattered equally.
A hue which is not present in the spectrum of colors produced by the
analysis of white light by a prism of diffraction grating. Examples are
brown, magenta, and pastel shades.
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Nonsystematic distortion
Normal color film
NSSDC
Geometric irregularities on images that are not constant and cannot be
predicted from the characteristics of the imaging system.
Film in which the colors are essentially true representations of the
colors of the terrain.
National Space Science Data Center.
O
Oasis
Oblique photograph
OMS
ONC--Operational
Navigation Chart
Orbit
Orthophotograph
Orthophotoscope
Ortho-correction
Overlap
A spot in a desert made fertile by water, which normally originates as
groundwater.
Photograph acquired with the camera intentionally directed at some
angle between horizontal and vertical orientations.
Orbital maneuvering system.
The ONC series covers most of the world landmass areas at
1:1,000,000 scale. At this scale it takes 62 charts to cover the
conterminous United States. Information on these charts includes cities
and landmarks, drainage, and relief (shown by shading and contours).
International and State boundaries are shown, but not county
boundaries.
Path of a satellite around a body such as the earth, under the influence
of gravity.
A vertical aerial photograph from which the distortions due to varying
elevation, tilt, and surface topography have been removed, so that it
represents every object as if viewed directly from above.
An optical-electronic device, which converts a normal vertical aerial
photograph to an orthophotograph.
Correction applied to satellite imagery to account for terrain-induced
distortion.
Extent to which adjacent images or photographs cover the same
terrain, expressed as a percentage.
P
Panchromatic film
Parallax
Parallax difference
Parallel-polarized
Pass
Passive microwaves
Passive remote sensing
Path-and-row index
Pattern
Periodic line dropout
Black and white film that is sensitive to all visible wavelengths.
Displacement of the position of a target in an image caused by a shift in
the observation system.
The difference in the distance on overlapping vertical photographs
between two points, which represent two locations on the ground with
different elevations.
Describes a radar pulse in which the polarization of the return is the
same as that of the transmission. Parallel-polarized images may be HH
(horizontal transmit, horizontal return) or VV (vertical transmit, vertical
return).
In digital filters, refers to the spatial frequency of data transmitted by the
filter. High-pass filters transmit high-frequency data; low-pass filters
transmit low-frequency data.
Radiation in the 1 mm to 1 m range emitted naturally by all materials
above absolute zero.
Remote sensing of energy naturally reflected or radiated from the
terrain.
System for locating Landsat MSS and TM images.
Regular repetition of tonal variations on an image or photograph.
Defect on Landsat MSS or TM images in which no data are recorded
for every sixth or sixteenth scan line, causing a black line on the image.
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Periodic line striping
Photodetector
Photogeology
PhotographPhotographic IR
Photographic UV
Photomosaic
Photon
Photopic vision
Picture element
Pitch
Pixel
Planck's Law
Point spread function
(PSF)
Polarization
Polar orbit
Polarized radiation
Positive photograph
Precision
Previsual symptom
Primary colors
Principal component
analysis
Defect on Landsat MSS or TM images in which every sixth or sixteenth
scan line is brighter or darker than the others. Caused by the sensitivity
of one detector being higher or lower than the others.
Device for measuring energy in the visible-light band.
Mapping and interpretation of geologic features from aerial
photographs.
Representation of targets on film that results from the action of light on
silver halide grains in the film's emulsion.
Short-wavelength portion (0.7 to 0.9 µm) of the IR band that is
detectable by IR color film or IR black-and-white film.
Long-wavelength portion of the UV band (0.3 to 0.4 µm) that is
transmitted through the atmosphere and is detectable by film.
Mosaic composed of photographs.
Minimum discrete quantity of radiant energy.
Vision under conditions of bright illumination.
In a digitized image, the area on the ground represented by each digital
number. Commonly contracted to pixel.
Rotation of an aircraft about the horizontal axis normal to its longitudinal
axis that causes a nose-up or nose-down attitude.
Contraction of picture element.
An expression for the variation of emittance of a blackbody at a
particular temperature as a function of wavelength.
The image of a point source of radiation, such as a star, collected by an
imaging device. A measure of the spatial fidelity of the device.
The direction of orientation in which the electrical field vector of
electromagnetic radiation vibrates.
An orbit that passes close to the poles, thereby enabling a satellite to
pass over most of the surface, except the immediate vicinity of the
poles themselves.
Electromagnetic radiation in which the electrical field vector is
contained in a single plane, instead of having random orientation
relative to the propagation vector. Most commonly refers to radar
images.
Photographic image in which the tomes are directly proportional to the
terrain brightness.
Precision is a statistical measurement of repeatability that is usually
expressed as a variance or standard deviation, root mean square or
RMS, of repeated measurements. These are expressed as x, y
coordinates of arcs, label points, and tics in either single or double
precision in ARC/INFO.
Single-precision coordinates have up to seven significant digits of
precision. This allows for a level of accuracy of approximately 10
meters for a region whose extent is 1,000,000 meters across. Doubleprecision coordinates have up to 15 significant digits; this allows for the
precision necessary to represent any desired map accuracy at a global
scale.
A vegetation anomaly that is recognizable on IR film before it is visible
to the naked eye or on normal color photographs. It results when
stressed vegetation loses its ability to reflect photographic IR energy
and is recognizable on IR color film by a decrease in brightness of the
red hues.
A set of three colors that in various combinations will produce the full
range of colors in the visible spectrum. There are two sets of primary
colors, additive and subtractive.
The analysis of covariance in a multiple data set so that the data can be
projected as additive combinations on to new axes, which express
different kinds of correlation among the data.
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Principal-component (PC)
image
Principal point
Printout
Probability density
function (PDF)
Projection
Pulse
Pulse length
Pushbroom scanner
Pushbroom system
Digitally processed image produced by a transformation that recognizes
maximum variance in multispectral images.
Optical center of an aerial photograph.
Display of computer data in alphanumeric format.
A function indicating the relative frequency with which any
measurement may be expected to occur. In remote sensing it is
represented by the histogram of DN in one band for a scene.
Orderly system of lines on a plane representing a corresponding
system of imaginary lines on an adopted terrestrial or celestial datum
surface. Also, the mathematical concept of such a system. For maps of
the Earth, a projection consists of (1) a graticule of lines representing
parallels of latitude and meridians of longitude or (2) a grid.
Short burst of electromagnetic radiation transmitted by a radar antenna.
Duration of a burst of energy transmitted by a radar antenna, measured
in microseconds.
An alternate term for an along-track scanner
An imaging device consisting of a fixed linear array of many sensors,
which is swept across an area by the motion of the platform, thereby
building up an image. It relies on sensors whose response and reading
is nearly instantaneous, so that the image swathe can be segmented
into pixels representing small dimensions on the ground.
Q
Quantum
The elementary quantity of EM energy that is transmitted by a particular
wavelength. According to the quantum theory, EM radiation is emitted,
transmitted, and absorbed as numbers of quanta, the energy of each
quantum being a simple function of the frequency of the radiation.
R
Radar
Radar altimeter
Radar cross section
Radar scattering
coefficient
Radar scatterometer
Radar shadow
Radial relief displacement
Radian
Acronym for radio detection and ranging. Radar is an active form of
remote sensing that operates in the microwave and radio wavelength
regions.
A non-imaging device that records the time of radar returns from
vertically beneath a platform to estimate the distance to and hence the
elevation of the surface; carried by Seasat and the EAS-ERS-1
platforms.
A measure of the intensity of backscattered radar energy from a point
target. Expressed as the area of a hypothetica surface, which scatters
radar equally in all directions and which would return the same energy
to the antenna.
A measure of the back-scattered energy from a target with a large area.
Expressed as the average radar cross section per unit area in decibels
(db). It is the fundamental measure of the radar properties of a surface.
A non-imaging device that records radar energy backscattered from
terrain as a function of depression angle.
Dark signature on a radar image representing no signal return. A
shadow extends in the far-range direction form an object that intercepts
the radar beam.
The tendency of vertical objects to appear to learn radially away from
the center of a vertical aerial photograph. Caused by the conical field of
view of the camera lens.
Angle subtended by an arc of a circle equal in length to the radius of
the circle 1 rad = 57.3¡.
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Radiance
Radiant energy peak
Radiant flux
Radiant temperature
Radiation
Radiometer
Random line dropout
Range
Range direction
Range resolution
Raster
Raster format
Raster pattern
Ratio image
Rayleigh criterion
Rayleigh scattering
RBV
Real-aperture radar
Real time
Recognizability
Rectilinear
Redundancy
Reflectance
Measure of the energy radiated by an object. In general, radiance is a
function of viewing angle and spectral wavelength and is expressed as
energy per solid angle.
Wavelength at which the maximum electromagnetic energy is radiated
at a particular temperature.
Rate of flow of electromagnetic radiation measured in watts per square
centimeter.
Concentration of the radiant flux from a material. Radiant temperature
is the kinetic temperature multiplied by the emissivity to the one-fourth
power.
Propagation of energy in the form of electromagnetic waves.
Device for quantitatively measuring radiant energy, especially thermal
radiation.
In scanner images, the loss of data from individual scan lines in a
nonsystematic fashion.
In radar usage this is the distance in the direction of radar propagation,
usually to the side of the platform in an imaging radar system. The slant
range is the direct distance from the antenna to the object, whereas the
distance from the ground track of the platform to the object is termed
the ground range.
See look direction.
In radar images, the spatial resolution in the range direction, which is
determined by the pulse length of the transmitted microwave energy.
The scanned and illuminated area of a video display, produced by a
modulated beam of electrons sweeping the phosphorescent screen line
by line from to bottom at a regular rate of repetition.
A means of representing spatial data in the from of a grid of DN, each
line of which can be used to modulate the lines of a video raster.
Pattern of horizontal lines swept by an electron beam across the face of
a CRT that constitute the image display.
An image prepared by processing digital multi-spectral data as follows:
for each pixel, the value for one band that is divided the value of
another. The resulting digital values are displayed as an image.
In radar, the relationship between surface roughness, depression
angle, and wavelength that determines whether a surface will respond
in a rough or smooth fashion to the radar pulse.
Selective scattering of light in the atmosphere by particle that is small
compared with the wavelength of light.
Return-beam vidicon.
Radar system in which azimuth resolution is determined by the
transmitted beam width, which is in turn determined by the physical
length of the antenna and by the wavelength.
Refers to images or data made available for inspection simultaneously
with their acquisition.
Ability to identify an object on an image.
Refers to images with no geometric distortion in which the scales in the
horizontal and vertical directions are identical.
Information on an image, which is either not, required for interpretation
or cannot be seen. Redundancy may be spatial or spectral. The term
also refers to multispectral data where the degree of correlation
between bands is so high that one band contains virtually the same
information as all the bands.
Ratio of the radiant energy reflected by a body to the energy incident on
it. Spectral reflectance is the reflectance measured within a specific
wavelength interval.
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Reflected energy peak
Reflected IR
Reflectivity
Refraction
Registration
Relief
Relief displacement
Remote sensing
Resampling
Reseau marks
Resolution
Resolution target
Resolving power
Reststrahlen band
Return
Return-beam vidicon
(RBV)
Ringing
Rods
Roll
Roll compensation system
Rough criterion
Roughness
RMSE (Root Mean
Square Error)
Wavelength (0.5 µm) at which maximum amount of energy is reflected
from the earth's surface.
Electromagnetic energy of wavelengths from 0.7 µm to about 3 µm that
consists primarily of reflected solar radiation.
Ability of a surface to reflect incident energy.
Bending of electromagnetic rays as they pass from one medium into
another when each medium has a different index of refraction.
Process of superposing two or more images or photographs so that
equivalent geographic points coincide.
Vertical irregularities of a surface.
Geometric distortion on vertical aerial photographs. The tops of objects
appear in the photograph to be radially displaced from their bases
outward from the photograph's center point.
Collection and interpretation of information about an object without
being in physical contact with the object.
The calculation of new DN for pixels created during geometric
correction of a digital scene, based on the values in the local area
around the uncorrected pixels.
Pattern of small crosses added to photographs.
Ability to separate closely spaced objects on an image or photograph.
Resolution is commonly expressed as the most closely spaced linepairs per unit distance that can be distinguished. Also called spatial
resolution.
Series of regularly spaced alternating light and dark bars used to
evaluate the resolution of images or photographs.
A measure of the ability of individual components. And of remote
sensing systems, to separate closely spaced targets.
In the IR region, refers to absorption of energy as a function of silica
content.
In radar, a pulse of microwave energy reflected by the terrain and
received at the radar antenna. The strength of a return is referred to as
return intensity.
A system in which images are formed on the photosensitive surface o
a vacuum tube; the image is scanned with an electron beam and
transmitted or recorded. Landsat 3 used a pair of RBV's to acquire
images.
Fringe-like artifacts produced at edges by some forms of spatialfrequency filtering.
The receptors in the retina that are sensitive to brightness variations.
Rotation of an aircraft that causes a wing-up or wing-down attitude.
Component of an airborne scanner system that measures and records
the roll of the aircraft. This information is used to correct the imagery for
distortion due to roll.
In radar, the relationship between surface roughness, depression
angle, and wavelength that determines whether a surface will scatter
the incident radar pulse in a rough or intermediate fashion.
In radar, the average vertical relief of a small-scale irregularities of the
terrain surface. Also called surface roughness
The RMSE statistic is used to describe accuracy encompassing both
random and systematic errors. The square of the difference between a
true test point and an interpolated test point divided by the total number
of test points in the arithmetic mean. The square root of this value is the
root mean square error.
S
SAMII
Stratospheric Aerosol Measurement experiment, carried by Nimbus-7.
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SAMS
SAST (Scientific
Assessment and Strategy
Team)
Satellite
Saturation
SBUV
Scale
Scan line
Scanner
Scanner distortion
Scan skew
Scattering
Scattering coefficient
curves
Scatterometer
Scene
Scotopic vision
Seasat
Sensitivity
Sensor
Shaded relief
Shuttle imaging radar
(SIR)
Sidelap
Side-looking airborne
radar (SLAR)
Side-scanning sonarSide-scanning systemSignal
Signal to noise radio (S/N)
Silver halide
SIR
Stratospheric and Mesospheric Sounder, carried by Nimbus-7.
SAST is an interdisciplinary team of senior scientists and engineers
from various Federal Government agencies assigned to assess and
report on the damage caused by the flood of 1993 and to provide
assistance and advice to Federal officials responsible for making
decisions with respect to the flood recovery in the Upper Mississippi
and Missouri River basin.
An object in orbit around a celestial body.
In the IHS system, represents the purity of color. Saturation is also the
condition where energy flux exceeds the sensitivity range of a detector.
Solar Back-scatter Ultraviolet Instrument, carried by NOAA satellites.
Ratio of distance on an image to the equivalent distance on the ground.
Narrow strip on the ground that is swept by IFOV of a detector in a
scanning system.
An imaging system in which the IFOV of one or more detectors is swept
across the terrain.
Geometric distortion that is characteristic of cross-track scanner
images.
Distortion of scanner images caused by forward motion of the aircraft or
satellite during the time required for scanning completion.
Multiple reflections of electromagnetic waves by particles or surfaces.
Display of scatterometer data in which relative backscatter is shown as
a function of incidence angle.
Nonimaging radar device that quantitatively records backscatter of
terrain as a function of incidence angle.
Area on the ground that is covered by an image or photograph.
Vision under conditions of low illumination, when only the rods are
sensitive to light. Visual acuity under these conditions is highest in the
blue part of the spectrum.
NASA unmanned satellite that acquired L-band radar images in 1978.
Degree to which a detector responds to electromagnetic energy
incident on it.
Device that receives electromagnetic radiation and converts it into a
signal that can be recorded and displayed as either numerical data or
an image.
Shading added to an image that makes the image appear to have
three-dimensional aspects. This type of enhancement is commonly
done to satellite images and thematic maps utilizing digital topographic
data to provide the appearance of terrain relief within the image.
L-band radar system deployed on the Space Shuttle.
Extent of lateral overlap between images acquired on adjacent flight
lines.
An airborne side scanning system for acquiring radar images.
Active system for acquiring images of the seafloor using pulsed sound
waves.
A system that acquires images of a strip of terrain parallel with the flight
or orbit path but offset to one side.
Information recorded by a remote sensing system.
The ratio of the level of the signal carrying real information to that
carrying spurious information as a result of defects in the system.
Silver salts that are especially sensitive to visible light and convert to
metallic silver when developed.
Shuttle Imaging Radar, synthetic-aperture radar experiments carried
aboard the NASA Space Shuttle in 1981 and 1984.
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Skylab
Skylight
Slant range
Slant-range distance
Slant-range distortion
Slant-range image
SLAR
SMIRR
SMMR
Smooth criterion
Software
Sonar
Space Shuttle
Space Station
Spatial-frequency filtering
Specific heat
Spectral hue
Spectral reflectance
Spectral sensitivity
Spectral vegetation index
Spectrometer
Spectroradiometer
Spectrum
Specular
SPOT
Stefan-Boltzmann
constant
Stefan-Boltzmann law
Stereo base
U.S. Earth-orbiting workshop that housed three crews of three
astronauts in 1973 and 1974.
Component of light that is strongly scattered by the atmosphere and
consists predominantly of shorter wavelengths.
In radar, an imaginary line running between the antenna and the target.
Distance measured along the slant range.
Geometric distortion of a slant-range image.
In radar, an image in which objects are located at positions
corresponding to their slant-range distances from the aircraft path. On
slant-range images, the scale in the range direction is compressed in
the near-range region
Side-looking airborne radar.
Shuttle Multispectral Infrared Radiometer, a non-imaging
spectroradiometer carried by the NASA Space Shuttle covering ten
narrow wavebands in the 0.5-2.4 m range.
Scanning Multichannel Microwave Radiometer, carried by Nimbus-7.
In radar, the relationship between surface roughness, depression
angle, and wavelength that determines whether a surface will scatter
the incident radar pulse in a smooth or intermediate fashion.
Programs that control computer operations.
Acronym for sound navigation ranging. Sonar is an active form of
remote sensing that employs sonic energy to image the seafloor.
U.S. manned satellite program in the 1980s, officially called the Space
Transportation System (STS).
A planned series of three polar-orbiting, sun-synchronous satellites to
be launched by NASA, the European Space Agency, and the Japanese
Space Agency in the 1990s. They will carry a large range of remotesensing devices.
The analysis of the spatial variations in DN of an image and the
separation or suppression of selected frequency ranges.
The ratio of the heat capacity of unit mass of a material to the heat
capacity of unit mass of water.
A hue that is present in the spectral range of white light and is analyzed
by a prism or diffraction grating.
Reflectance of electromagnetic energy at specified wavelength
intervals.
Response, or sensitivity, of a film or detector to radiation in different
spectral regions.
An index of relative amount and vigor of vegetation. The index is
calculated from two spectral bands of AVHRR imagery.
Device for measuring intensity of radiation absorbed or reflected by a
materiel as a function of wavelength.
A device that measures the energy reflected or radiated by materials in
narrow EM wavebands.
Continuous sequence of electromagnetic energy arranged according to
wavelength or frequency.
Refers to a surface that is smooth with respect to the wavelength of
incident energy.
Systeme Probatoire d'Observation del la Terre. Unmanned French
remote sensing satellite orbiting in the late 1980s.
5.68 x 10 -12 W . Cm-2 .K-4.
States that radiant flux of a blackbody is equal to the temperature to the
fourth power times the Stefan-Boltzmann constant.
Distance between a pair of correlative points on a stereo pair that are
oriented for stereo viewing.
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Stereo model
Stereo pair
Stereopsis
Stereoscope
SSU
Subscene
Subtractive primary colors
Sunglint
Sun-synchronous
Sun-synchronous orbit
Supervised classification
Surface phenomenon
Surface roughness
Synthetic-aperture radar
(SAR)
Synthetic stereo images
System
Systematic distortion
Three-dimensional visual impression produced by viewing a pair of
overlapping images through a stereoscope.
Two overlapping images or photographs that may be viewed
stereoscopically.
The ability for objects to be perceived in three dimensions as a result of
the parallax differences produced by the eye base.
Binocular optical device for viewing overlapping images or diagrams.
The left eye sees only the left image, and the right eye sees only the
right image.
Stratosphere Sounding Unit, carried by NOAA-series satellites.
A portion of an image that is used for detailed analysis.
Yellow, magenta, and cyan. When used as filters for white light, these
colors remove blue, green and red light, respectively.
Bright reflectance of sunlight caused by ripples on water.
Earth satellite orbit in which the orbit plane is nearly polar and the
altitude is such that the satellite passes over all places on earth having
the same latitude twice daily at the same local sun time.
A polar orbit where the satellite always crosses the Equator at the same
local solar time.
Digital-information extraction technique in which the operator provides
training-site information that the computer uses to assign pixels to
categories.
Interaction between electromagnetic radiation and the surface of a
material.
See roughness.
Radar system in which high azimuth resolution is achieved by storing
and processing data on the Doppler shift of multiple return pulses in
such a way as to give the effect of a much longer antenna.
Stereo images constructed through digital processing of a single image.
Topographic data are used to calculate parallax.
Combination of components that constitute an imaging device.
Geometric irregularities on images that are caused by known and
predictable characteristics.
T
Target
TDRS
Telemeter
Terrain
Texture
Thematic Data
Thematic Mapper (TM)
Thermal capacity (c )
Thermal conductivity (K)
Thermal crossover
Thermal diffusivity (k)
Object on the terrain of specific interest in a remote sensing
investigation.
Tracking and Data Relay Satellite
To transmit data by radio or microwave links.
Surface of the earth.
Frequency of change and arrangement of tones on an image.
Thematic data layers in a data set are layers of information that deal
with a particular theme. These layers are typically related information
that logically go together. Examples of thematic data would include a
data layer whose contents are roads, railways, and river navigation
routes.
A cross-track scanner deployed on Landsat that records seven bands
of data from the visible through the thermal IR regions.
See heat capacity.
Measure of the rate at which heat will pass through a material,
expressed in calories per centimeter per second per degree
Centigrade.
On a plot of radiant temperature versus time, the point at which
temperature curves for two different materials intersect.
Governs the rate at which temperature changes within a substance,
expressed in centimeters squared per second.
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Thermal inertia (P)
Thermal IR
Thermal IR image
Thermal IR multispectral
scanner (TIMS)
Thermal model
Thermography
THIR
Tie-point
TIMS
TM
Tone
Topographic inversion
Topographic reversal
TOVS
Tracking and Data Relay
Satellite (TDRS)
Training area
Trade-off
Training site
Transmissivity
Transparency
Transpiration
Travel time
Tristimulus color theory
Measure of the response of a material to temperature changes,
expressed in calories per square centimeter per square root of second.
IR region from 3 to 14 µm that is employed in remote sensing. This
spectral region spans the radiant power peak of the earth.
Image acquired by a scanner that records radiation within the thermal
IR band.
Airborne scanner that acquires multispectral images within the 8-to14mm band of the thermal IR region.
Mathematical expression that relates thermal and other physical
properties of a material to its temperature. Models may be used to
predict temperature for given properties and conditions.
Medical applications of thermal IR images. Images of the body, called
thermograms, have been used to detect tumors and monitor blood
circulation.
Temperature-Humidity Infrared Radiometer, carried by Nimbus-7.
A point on the ground, which is common to two images. Several are
used in the co-registration of images.
Thermal IR multispectral scanner.
Thematic Mapper.
Each distinguishable shade of gray from white to black on an image.
An optical illusion that may occur on images with extensive shades.
Ridges appear to be valleys, and valleys appear to be ridges. The
illusion is corrected by orienting the image so that the shadows trend
from the margin of the image to the bottom.
A geomorphic phenomenon in which topographic lows coincide with
structural highs and vice versa. Valleys are eroded on crests of
anticlines to cause topographic lows, and synclines form ridge, or
topographic highs.
TIROS Operational Vertical Sounder.
Geostationary satellite used to communicate between ground receiving
stations and satellite such as Landsat.
A sample of the Earth's surface with known properties; the statistics of
the imaged data within the area are used to determine decision
boundaries in classification.
As a result of changing one factor in a remote sensing system, there
are compensating changes elsewhere in the system; such a
compensating change is known as a trade-off.
Area of terrain with known properties or characteristics that is used in
supervised classification.
Property of a material that determines the amount of energy that can
pass through the material.
Image on a transparent photographic material, normally a positive
image.
Expulsion of water vapor and oxygen by vegetation.
In radar, the time interval between the generation of a pulse of
microwave energy and its return from the terrain.
A theory of color relating all hues to the combined effects of three
additive primary colors corresponding to the sensitivities of the three
types of cone on the retina.
U
Unsupervised
classification
Digital information extraction technique in which the computer assigns
pixels to categories with no instructions from the operator.
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UTM--Universal
Transverse Mercator
Projection
UV
UTM is a widely used map projection that employs a series of identical
projections around the world in the mid-latitude areas, each spanning
six degrees of longitude and oriented to a meridian. This projection is
characterized by its conformality; that is, it preserves angular
relationships and scale plus it easily allows a rectangular grid to be
superimposed on it. Many worldwide topographic and planimetric maps
at scales ranging between 1:24,000 and 1:250,000 use this projection.
Ultraviolet region of the electromagnetic spectrum ranging in
wavelengths from 0.01 to 0.4m.
V
Variance
VAS
Vector
Vector Data
Vector format
Vegetation anomaly
Vertical exaggeration
Vertical Positional
Accuracy
Vidicon
Vignetting
Visible radiation
Visual dissonance
VISSR
Volume scattering
A measure of the dispersion of the actual values of a variable about its
mean. It is the mean of the squares of all the deviations from the mean
value of a range of data.
Atmospheric Sounder, carried by GEOS satellites
Any quantity, which has both magnitude and direction, as opposed to
scalar that has only magnitude.
Vector data, when used in the context of spatial or map information,
refers to a format where all map data is stored as points, lines, and
areas rather than as an image or continuous tone picture. These vector
data have location and attribute information associated with them.
The expression of points, lines, and areas on a map by digitized
Cartesian coordinates, directions, and values.
Deviation from the normal distribution or properties of vegetation.
Vegetation anomalies may be caused by faults, trace elements in soil,
or other factors.
In a stereo model, the extent to which the vertical scale appears larger
than the horizontal scale.
Vertical positional accuracy is based upon the use of USGS source
quadrangles, which are compiled to meet National Map Accuracy
Standards (NMAS). NMAS vertical accuracy requires that at least 90
percent of well defined points tested be within one half contour interval
of the correct value. Comparison to the graphic source is used as
control to assess digital positional accuracy.
An imaging device based on a sheet of transparent material whose
electrical conductivity increases with the intensity of EM radiation falling
on it. The variation in conductivity across the plate is measured by a
sweeping electron beam and converted into a video signal. Now largely
replaced by cameras employing arrays of charge-coupled devices
(ccds).
A gradual change in overall tone of an image from the center outwards,
caused by the imaging device gathering less radiation from the
periphery of its field of view than from the center. Most usually
associated with the radially increasing angel between a lens and the
Earth's surface, and the corresponding decrease in the light-gathering
capacity of the lens.
Energy at wavelengths from 0.4 to 0.7mm that is detectable by the
human eye.
The disturbing effect of seeing a familiar object in an unfamiliar setting
or in an unexpected color.
Visible Infrared Spin-Scan Radiometer carried by the GOES satellites.
In radar, interaction between electromagnetic radiation and the interior
of a material.
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W
Watt (W)
Wavelength
Wien's displacement law
WRS--Worldwide
Reference System
Unit of electrical power equal to rate of work done by one ampere under
a potential of one volt.
Distance between successive wave crests or other equivalent points in
a harmonic wave.
Describes the shift of the radiant power peak to shorter wavelengths as
temperature increases.
The WRS is a global indexing scheme designed for the Landsat
program based on nominal scene centers defined by path and row
coordinates.
X
X band
Radar wavelength region from 2.4 to 3.8 cm.
Y
Yaw
Rotation of an aircraft about its vertical axis so that the longitudinal axis
deviates left or right from the flight line.
Z
ZENITH
Zephyr
Zenith is the point on the celestial sphere vertically above a given
position or observer.
A Mediterranean term for any soft, gentle breeze.
Glossary-31
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