An Introduction to IMAGER 3.8 - Modis Land Surface Reflectance

An Introduction to IMAGER 3.8 - Modis Land Surface Reflectance
An Introduction to IMAGER 3.8
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An Introduction to IMAGER 3.8
Introduction
IMAGER
is
a program for
visualizing
MODIS
surface
reflectance products. It reads data in
the HDF format (SDS), and displays
the data as either grayscale or RGB
images.
When IMAGER is run, the File
search window is opened allowing
the user to select a file to display.
Upon the selection of a file, three
new windows are displayed:
¾ Image window
¾ Report window
¾ Scaler window.
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Hardware and software requirements
ƒ
ƒ
ƒ
ƒ
Pentium III 600 MHz processor or greater
At least 512 MB RAM memory
200 MB of free hard disk plus space for data
UNIX System
IMAGER is available at : ftp://kratmos.gsfc.nasa.gov/pub/jim/imager
The imagery to perform the exercises of this manual are included in the
Sample_Data folder as part of the downloading package.
For more information about the IMAGER functionality and installation, go to the
README.txt file which is also included in the downloading package.
Comments / suggestions are welcome to: imager@ltdri.org
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Exercise 1.- Opening and reading a MODIS Scientific Data
Set (SDS)
By the use of the Report window, IMAGER not only displays automatically a
standardized image, but also provides full information at pixel basis about the
surface reflectances and derived information.
ƒ Go to the Sample data in the File search window and left-click to
select
and
then
right-click
the
data
set:
MOD09GA.A2000339.h11v05.005.2006339052644.hdf. A true color
full display showing the coast between the Chesapeake Bay, North and
South Carolina is loaded by default and visualized in the Image
window.
Notice that the Report window is now displaying all the information
available and encompassed in the HDF file, at a pixel basis. The first column
displays the Row/Columns, bands, masks, Latitude/Longitude, and other
MODIS related features; the second column the quantitative and/or qualitative
values associated to these features. The feature and its value can be read as a
line.
ƒ Place the mouse over the Image window and left-click over different
land areas. See the values displayed in the Report window, notice that
reflectance values are provided for all of the available bands in the data
set.
Also qualitative derived information as those related to aerosols, cloud
amount, land/water condition, ice/snow occurrence etc, is provided.
ƒ Left-click over different color sea water pixels and observe the
information displayed on the land/water flag line.
ƒ Now left-click over the continental cloud mass located toward the left
upper corner of the Image window, observe the higher reflectance
values of these cloudy areas compared to the free cloud land areas.
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ƒ Go to the area A (Figure 1), and click across the feature, notice that the
information in the Report window at the MOD35 snow/ice, cloud
state and internal cloud algorithm flag lines, in the Report window;
states the occurrence of snow rather than clouds in some pixels.
If available in the data set, IMAGER can display a layer associated to any of
the derived information for example, in this data set the information related to
land/water, cloud and aerosol features can also be displayed in the Image
window.
ƒ Place the mouse over the Report window and left-click over the cirrus
detected line to select this feature, look the result at the Image
window.
ƒ In the Report window, Left-click in the aerosol quantity line to
display this layer.
ƒ Go to the Image window, and click across the aerosol quantity image
trying to define the three main value/features related to the aerosol
quantity and their associated grey tonalities.
ƒ Look at the Scaler window, the values found at the low scaling value
and high scaling value boxes. These values, 1 and 3 respectively,
represent the extreme values to be found in this layer. A vertical grey
tonalities scale is also displayed in this window.
ƒ Make active the Image window and then press n to advance over the
next layer: land/water flag. Notice that values at the Scaler window
are enhanced to 7 classes.
Generally black & white / grey tones imagery do not provide enough
differentiation on spatial features particularly at MODIS resolution level.
IMAGER enhances the visual distinction of those features through a colormapping option.
ƒ With the land/water flag layer displayed in the Image window
(active), press o to toggle rainbow color mapping. Notice the change
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from grey tonalities to a seven color classes scale in the vertical scale at
the Scaler window.
ƒ Click across the Image window and define the main spatial features
and their associated color class. Pressing o return the display to the grey
tonalities option.
ƒ With the Image window active press p to go back to the previous layer.
Either you can go back to the true color full scene displayed at the
beginning by pressing r.
ƒ Place the mouse over the Image window and press SHIFT+E to finish
the exercise and go back to the File search window.
Figure 1. True color display of the Chesapeake Bay – South Carolina region. Box A
shows the distinctiveness of snow pixels compared to the surrounding cloud pixels.
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Exercise 2.- Displaying a RGB composite
Since individual bands are used to identify different features on the ground, a
color composite made from the combination of these bands becomes a useful
tool to facilitate the visual interpretation of a scene. Every color composite is
specifically customized based in: The knowledge of the reflectance response,
band availability and the ground feature to emphasize. Table 1 describes the
key uses of the first seven MODIS bands and related channels.
Table 1. MODIS first seven bands main features.
Source: http://edcdaac.usgs.gov/modis/table2.asp
BAND
CHANNEL
RANGE nm
REFLECTED
Blue
Green
Red
NIR
MIR
SWI
FIR
459 – 479
545 – 564
620 – 670
841 – 876
1230 – 1250
1628 – 1652
2105 - 2155
MODIS
ACTUAL
BAND
B3
B4
B1
B2
B5
B6
B7
KEY USE
Soil / Vegetation differences
Green Vegetation
Vegetation chlorophyll
Cloud amount, Vegetation
Leaf/canopy differences
Snow/clouds differences
Cloud/land properties
Given that the human eye perceives all colors as a combination of red, green,
and blue; in satellite imagery, the visualization practice commonly uses the
RGB (Red, Green , Blue) addictive model to reproduce all other colors; in that
way a Color Composite 143 (CC143) means that bands 1, 4, and 3 are assigned
to the Red, Green and Blue channels respectively; and can be used to
reconstruct true color imagery of Earth - as a person would see it.
ƒ In the File search window left-click to select and then right-click the
data set: MODO9CMG.A2000338.005.2006332091104.hdf, a natural
color composite (CC143) of the entire world is loaded (Figure 2).
ƒ Place the mouse over the Report window.
ƒ Build the false color composite CC214 by placing the mouse over Band
2 and pressing SHIFT+R, this assigns MODIS Band 2 to the red
channel. Mouse-over Band 1 and pressing SHIFT+G assigns it to the
green channel and finally mouse-over Band 4 pressing SHIFT+B to put
this band into the blue channel.
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ƒ Place the mouse over the Image window and press u to visualize the
CC214 (Figure 3), click on a red area (highly vegetated) and look the
pixel values the Report window. Click on a brown pixel (scarcely
vegetated) and compare its pixel values with the previous point.
ƒ Still with the mouse on the Image window, press r to go back to the
CC143. Press u to display again the CC214; compare the views.
ƒ To generate another RGB display, it is necessary to un-set the previous
composite by making active the Image window and pressing
SHIFT+U
ƒ Given the key use described in Table 1, try to build another color
composite and compare it to the CC143 by pressing r on the Image
window.
ƒ Place the mouse over the Image window and press SHIFT+E to go back
to the File search window and finish the exercise.
Figure 2. World wide CC143.
Figure 3. World wide CC214
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Exercise 3.- Differentiation and detail, the stretching and
zooming
The Ahaggar Mountains, is a Sahara’s highland region located in southern
Algeria. The region is largely a volcanic rocky desert with a highest peak at
3,003 meter (9,852 ft) about sea level at Mount Tahat. The Ahaggar Mountains
are a major location for biodiversity and host relict species.
ƒ In the File search window right-click to select and then left-click at the
data set: MOD09GHK.A2006351.h18v06.004.2006353163945.hdf, a
false natural composite (CC143) is displayed illustrating the Ahaggar
Mountains region.
The scene is mostly dominated by the high reflectance coming from the
sandy dunes (light yellowish brown), only the rock outcrops in the mountains
shows a darker brown feature, which is probably given because of the different
soil texture, shadows and even soil moisture water content.
ƒ Go to the Image window and choose an area where the two main colortextures of the image meet sharply (Figure 4).
ƒ Press the middle mouse button and draw a box encompassing the
required area. A zoom in of the area is displayed.
ƒ If the zoomed area is not the required one, right-click to zoom out and
try again.
ƒ Zoom in the scene at the Image window several times to read in the
Report window the different color-texture reflectance values at a pixel
level.
ƒ Zoom out the scene to return to the original size
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Figure 4. Rocks and dunes at the Ahaggar Mountains region. Upper right image is the zoom in of
the red box area from the left image. (Note: IMAGER’s zooming capabilities do not generate a
second window, this caption is only for illustrative purposes).
Reflectances are absolute values which can be displayed at different scale
and distribution, allowing the visualization of certain spatial features, before
smoothed by a default displaying. Through the Scaler window, IMAGER
provides a helpful interface to display the data set under different type of
stretching. These stretching can be of logarithmic and/or linear nature, and
used to enhance the reflective response from polar, desert and standard
surfaces.
ƒ Keep the full display of the file
MOD09GHK.A2006351.h18v06.004.2006353163945.hdf
(Ahaggar Mountains region) in the default CC143.
ƒ Go to the Scaler window and read the low and high scaling values in
which the default image is stretched. Notice the linear character of the
default stretch.
ƒ Select the log, polar box. IMAGER stretches the visualization to
reflectance values from 8.3 to 9.3 (8300 to 9300), then only features
with the highest reflectance (clouds) are visualized (Figure 5 #1).
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ƒ Go to the Image window and click across the scene and read the pixel
values displayed at the Report window.
ƒ Go to the Scaler window again and Click on the Reset box to go back
to the standard display.
ƒ To have a look on how the remaining stretches work, go to the Scaler
window to display them (Figure 5). After comparing the results answer
the question: Which can be the best visualization for the Ahaggar
Mountains region ?.
Figure 5. Different types of stretch on the Ahaggar Mountains region CC143 scene: log, polar (1);
log, standard (2); log, desert (3); linear polar (4); linear standard (5); linear desert (6).
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Finally, IMAGER allows users to set the stretch scale. Suppose that you want
to know what features in the landscape have the highest reflectance.
ƒ Go to the Scaler window, left-click inside the low scaling value box
and type 7.6, and then press enter.
ƒ Click into the high scale value and type 8.0, and then press enter.
Since these values are referred to a logarithm scale, you have to select
the Click here for log box. Then click OK.
The CC143 is stretched to the entered values and it is displayed in a pseudo
color scale where a darker tone represents relative lower reflectances values
than those pixels with lighter tones (Figure 6).
ƒ Click in the reset box to display the standard CC143
ƒ End the exercise by clicking over the Image window and pressing
SHIFT+E to exit to the File search window.
Figure 6. Manual stretch (7.6 to 8) applied to the Ahaggar Mountains region scene. In this scene
a dry and bright sand has a higher reflectance hence a lighter tone, instead of the darker tone
representing the lower reflectance values from shadows and rock structures.
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Exercise 4.- Displaying a second image window
Even in the middle of the Sahara desert and with MODIS 500 m resolution
imagery, IMAGER can distinguish reflectance differences associated to soil
water content.
ƒ In the File search window left-click to select and then right-click the
data set: MOD09GA.A2006351.h18v06.004.2006353163945.hdf,
which belongs to the Ahaggar Mountains region.
ƒ Make the false color composition CC723
ƒ Zoom into the area illustrated in Figure 7.
ƒ With the image window active press r to display the CC143. Press u to
go back to the CC723. Compare the displays.
ƒ Right-click to zoom out.
The CC723 (Figure 7), shows a better discerning between soils in the
displayed area. Band 7 (FIR) is useful to define land properties hence its input
into the red channel. The input of the Band 2 (NIR) in the green channel and
Band 3 in the blue channel produce bluish and greenish tonalities that probably
are representing soil moisture and an incipient vegetation cover respectively.
IMAGER allows also the comparison of color composite and/or bands of the
same set through a second window display.
ƒ Make active the Report window and right-click on Band 1. A second
Image window is displayed over the first one, move it to another
position on the screen.
ƒ Back in the Report window, right-click on Band 7 so it can be
displayed in the already second Image window.
ƒ Make active the first Image window and zoom into the area illustrated
in Figure 7. Try several zoom levels (Figure 8) and compare the
displays by clicking across them and reading the Report window.
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ƒ Right-click to zoom out over any of the Image windows. Right-click
the necessary times to go back to the scene full display.
ƒ Make active the second Image window and press SHIFT+E to close it
and finish the exercise.
Note: You can change the display of the second window by right-click in the
Report window desired layer, but only when the main Image window is
NOT zoomed.
Figure 7. A MODIS CC723 as visualized in IMAGER could be representing incipient vegetation
cover (greenish tone) and soil moisture (bluish tones) even in the middle of the Sahara.
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Figure 8. An illustrative example on a MODIS CC723 (left) and Band 7 and corresponding areas
(red boxes) zoomed twice. (Note: IMAGER does not display simultaneous screens when
zooming).
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Exercise 5.- Opening and reading an AVHRR Scientific Data
Set (SDS)
The Advanced Very High Resolution Radiometer (AVHRR) data is also
available in scientific data set (SDS) format, then it is also suitable to be
displayed using IMAGER .
ƒ Go to the Sample_data in the File search window and left-click to
select
and
then
right-click
the
data
set:
AVH09C1.A1995192.N14.001.2006326174026.HDF. A full display
showing the entire planet is loaded and visualized in the Image
window.
Notice that the Report window is now displaying all the information
available in the HDF file, at pixel basis. The information available in this
AVHRR file encompasses three bands that survey surface reflectance and
another three ones with information on the land/cloud/sea brightness
temperatures expressed in Kelvin degrees (K°). The composite displayed
CC123 is based in a combination of Visible (red), Near infrared (green) and
Middle infrared (Blue); Table 2 describe the six channels featuring the
AVHRR.
Table 2. AVHRR / 3 six bands main features.
BAND
CHANNEL
Visible
NIR
MIR
AVHRR
CHANNEL
NUMBER
1
2
3A
RANGE nm
REFLECTED
580 – 680
725 – 1000
1580 – 1640
MIR
3B
3550 – 3930
TIR
4
10300 – 11300
5
1 .09Km
Daily
11500 – 12500
TIR
Pixel size
Temporal
KEY USE
Daytime cloud and surface mapping
Land-water boundaries
Snow and ice detection
Night cloud mapping, sea surface
temperature
Night cloud mapping, sea surface
temperature
Sea surface temperature
AVHRR data provide opportunities for studying and monitoring vegetation
conditions through its reflective bands as well as the retrieving of various
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geophysical parameters such as sea surface and cloud temperatures and energy
budget data by the processing of the thermal bands.
ƒ Zoom in the CC123 around the Gulf of Mexico area as depicted in
Figure 9, since the NIR band is in the green channel, vegetated surfaces
tend to appear green, while surfaces with much less or without
vegetational cover tend to appear yellowish.
Figure 9. AVHRR CC123 showing surface reflectance and area to be zoomed in red box (Note:
IMAGER does not display simultaneous screens when zooming).
ƒ Click across the zoomed area and notice the information displayed in
the Report window, compare the temperatures found at the yellowish
continental areas to those found at the also yellowish top of the clouds.
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This AVHRR imagery was acquired in 1995 during July, when the rising of
temperatures in the Caribbean Sea facilitates the formation of cloud systems
with high vertical development as the cumulonimbus, which are associated
with heavy precipitation and thunderstorms. High reflective surfaces as dense
cloud systems can be depicted in an AVHRR CC123 as rounded yellowish
shapes. For a better display of temperatures, a brightness temperature band can
be open. Brightness temperatures are provided in °K values
ƒ Right-click on the Image window to zoom out.
ƒ In the Report window, right-click in the Brightness Temperature 3.75
microns band to display a second Image window (Figure 10). Compare
both displays.
Figure 10. AVHRR Brightness Temperatures 3.75 microns in Degree K.
IMAGER shows individual bands in a grey scale, where values are arranged
from lower (dark tone) to higher (light tone), then the top of any high altitude
clouds in a AVHRR thermal band would tend to be depicted in a dark tone,
given the vertical atmospheric gradient, the top of those clouds will show also
the lowest temperature.
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The variability of the temperature recorded at the Brightness Temperature
3.75 microns band, could be depicted in a more identifiable way by the use of a
color scale (Figure 11).
ƒ Make active the second Image window and press o to toggle a color
mapping
ƒ Look at the Scaler Window and describe how the color scale is used to
define the lower and higher temperature values.
Figure 11. AVHRR Brightness Temperatures 3.75 microns in Degree K, displayed in a rainbow
color mapping scale.
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Probably a major problem in the displaying of large amount of data as those
encompassing world-wide imagery is the slow processing, then you can use
arguments in the command line to facilitate the displaying of data and even to
locate spatial features of known coordinates.
ƒ Press SHIFT+E to close the display windows and to exit the program
ƒ Back in the system Command Line write the following argument:
imager -f AVH09C1.A1995192.N14.001.2006326174026.hdf -S
The argument S allows the subsampling or rescaling of the imagery reducing
the quantity of data required to display a scene, then all the processing
particularly the zooming becomes easier.
Another use of the arguments is helpful illustrated when a special location or
geographical feature with known geographical coordinates must be located
from such small scale imagery as those from MODIS and AVHRR systems.
Suppose that you want to know where in the AVHRR scene Washington DC is
located.
ƒ .Back to the system Command Line by pressing SHIFT+E to close the
Image window.
ƒ Write in the Command Line the following argument:
imager -f AVH09C1.A1995192.N14.001.2006326174026.hdf -l 39 -77
ƒ Zooming the area around Washington DC as illustrated in Figure 12
ƒ Press SHIFT+E to close it and finish the exercise.
The argument has a form: –l <lat> <lon>, where 39 and -77 are the current
latitude and longitude for DC. The requested point is then displayed at the center
of a 3 x 3 pixels window, as illustrated in Figure 12. More information about the
use of arguments in IMAGER can be found in the README file.
Further AVHRR data sets are available at:
http://ltdr.nascom.nasa.gov/cgi-bin/browse/ltdrBrowse.cgi
Conventions naming for AVHRR products is described at:
http://modis.gsfc.nasa.gov/sci_team/meetings/c5meeting/pres/ltdr/pedelty.pdf
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Figure 12. AVHRR CC123 showing the location of Washington DC. The red box is the 3 x 3
window containing the requested coordinates at the central pixels (Note: IMAGER does not
display simultaneous screens when zooming).
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GLOSSARY
AVHRR
Advanced Very High Resolution Radiometer, is a broad-band, 4- or 5-channel
scanning radiometer, sensing in the visible, near-infrared, and thermal infrared
portions of the electromagnetic spectrum. It is carried aboard the National
Oceanic and Atmospheric Administration`s (NOAA) Polar Orbiting
Environmental Satellite series.
SDS
Scientific Data Set, it is a group of data structures used to store and describe
multidimensional arrays of scientific data.
HDF
A standard Hierarchical Data Format archive from EOS Data Information System
(EOSDIS) products.
UNIX
Computer (servers, desktop, laptops) operating system which can be installed with
a graphical user interface (GUI) similar to Microsoft Windows to provide a more
friendly environment.
MODIS
MODIS (or Moderate Resolution Imaging Spectroradiometer) is a key instrument
aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around
the Earth is timed so that it passes from north to south across the equator in the
morning, while Aqua passes south to north over the equator in the afternoon.
Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to
2 days, acquiring data in 36 spectral bands.
Internal snow algorithm
Output of the snow algorithm which uses a grouped criteria technique to detect
snow as well as the MODIS cloud mask product to mask clouds. Snow detection
relies primarily on the normalized difference snow index (NDSI) with the
NDSI = (band 4 – band 6)/(band4 + band 6).
BRDF
The Bidirectional Reflectance Distribution Function (BRDF) specifies the
behavior of surface scattering as a function of illumination and view angles at a
particular wavelength.
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Internal fire algorithm flag
Contextual algorithm that exploits the strong emission of mid-infrared radiation
from fires examining each pixel of the MODIS swath, and ultimately assigns to
each one of the following classes: missing data, cloud, water, non-fire, fire, or
unknown
Internal cloud algorithm flag
Output of the cloud mask algorithm indicating the certainty of cloud or clear sky
for each pixel. The cloud algorithm uses fourteen of the 36 MODIS bands in 18
cloud spectral tests following processing paths that vary with surface type,
geographic location and ancillary data input.
Cirrus
Clouds composed of ice crystals and are characterized by thin, wisplike strands,
often so extensive that they are virtually indistinguishable from one another,
forming a veil or sheet called "cirrostratus".
Aerosol quantity
Amount of airborne solid particles or liquid droplets suspended in the atmosphere
and classified in the MODIS SDS as a nominal scale: Climatology used for
atmospheric correction, Low, Intermediate and High.
Land / water flag
A land–water mask at 1–km resolution of ocean shorelines and large lake
coastlines, derived from the MODIS Nadir BRDF–Adjusted Reflectance (NBAR)
and Land Cover products, is an update to the EOS DEM land–water mask
currently used for MODIS processing.
Sensor zenith
The angle between the local zenith and the line of sight to the satellite.
Sensor azimuth
This is the direction of the Terra and/or Aqua satellites measured clockwise
around the observer's horizon from north.
Solar Zenith
The angle between the local zenith and the Solar inclination.
Solar Azimuth
This is the direction of the Solar radiation measured clockwise around the
observer's horizon from north.
Zenith
At a given point is the local vertical direction pointing away from direction of the
force of gravity at that location.
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