Characteristics, sources, and management of remotely

Characteristics, sources, and management of remotely
Characteristics, sources, and management of
remotely-sensed data
This chapter focuses upon the importance of remotely-sensed data as an input to GIS, and
describes some of the ways in which they may be managed for operational use. The authors
assume that many of the information managers reading this work will be GIS novices,
practitioners, or experts who will have little knowledge of the field of remote sensing, and as
such our discussion is intended to augment that of Barnsley (Chapter 32) and Dowman
(Chapter 31) in a non-technical sense. Remote sensing is defined as the gathering of
information about objects and phenomena by systems not in intimate contact with those
objects and phenomena. Key characteristics and sources of remotely-sensed data
commonly employed as input to GIS are described, as are the key minimum set of GIS
facilities required to support the use and effective management of remote sensing data. A
number of important concepts and policy-level issues related to the uses of remote sensing
data within GIS for basic and applied research and resource management decision-support
are also explored. In short, this chapter is designed to inform managers of the sources and
potential of remotely-sensed data so that they can discharge their responsibilities for
effective monitoring or operations with maximum effectiveness but minimum cost.
GIS and remote sensing are linked both historically
and functionally. In an historic context, some of the
early work leading to the development of GIS
revolved around methods to provide better access to
aerial photographic coverage of specific areas. GIS
facilitate the storage of and access to many diverse
data types. GIS, correctly employed, also permit the
data held within a database to be readily updated.
The synergy that exists between remote sensing and
GIS technologies is built on the foundation that, for
many applications, remote sensing can be employed
effectively and efficiently to update GIS data layers;
and that data layers in a GIS can, when
appropriately employed, improve the interpretability
and information extraction potential of remotelysensed data (Star et al 1991).
GIS users routinely require timely input data in
order to optimise their systems for analysis and
decision-making (see, for instance, Shiffer, Chapter
52). In many instances, raster data are preferred.
Typically in the past, only a single type of
photography or imagery was acquired for a given
application (see Dowman, Chapter 31). While this is
still sometimes true, users are increasingly
addressing more sophisticated applications which
require multiple images from different regions of the
electromagnetic spectrum and/or different dates, at
scales ranging from local studies to global
investigations. These more sophisticated
investigations are employing data with a variety of
spatial, spectral, and temporal resolutions. More
importantly, applications have evolved that require
the integration of both raster and vector data.
Processing and analysis of these data are improved
when an analyst has access to collateral information,
often in vector format, for an area covered by the
imagery being analysed. In addition, raster data
from satellite sensors have inherent information
content beyond their use as an image backdrop.
Indeed, it is important to note that most operational
J E Estes and T R Loveland
base cartographic products produced by mapping
organisations around the world are compiled using
photogrammetric techniques to process remotelysensed data. It is maps such as these that supply the
base upon which GIS applications are accomplished.
While remotely-sensed data are used in making
maps, they are also being employed to measure a
variety of environmental parameters. Surface and
cloud top reflectances, albedo, soil and snow water
content, fraction of photosynthetically active
radiation, areas and potential yield of given crop
types, and the height and density of forest stands are
but a few of the measurements that can be made
with the aid of remotely-sensed data. Such data
measured and/or mapped over time constitute the
basis for monitoring. As we measure, map, and
monitor environmental features and phenomena we
can then employ the data generated as input to
models. Remote sensing data are being employed
today in a wide variety of modelling activities.
Finally, environmental planners, resources managers
and public policy decision-makers are employing
remotely-sensed data within the context of GIS to
improve the management of the resources for which
they are responsible.
Remote sensing data are currently acquired by aerial
camera systems and a variety of both active and
passive sensor systems that operate at wavelengths
throughout the electromagnetic spectrum
(see Barnsley, Chapter 32). Data currently acquired
by aerial, metric camera systems can be scanned,
converted into digital form, and input to GIS.
More typically, such data are interpreted and the
interpreted information is then digitised for input to
a GIS. The authors assume that most readers know
sources of aerial photography. These sources
typically include commercial photogrammetric
engineering firms, local planning and transportation
agencies, national mapping organisations,
environmental and research agencies, and university
collections. A full discussion of the sources of such
data is beyond the scope of this chapter, but see
Dowman (Chapter 31). Here we focus primarily on
sources of satellite remotely-sensed data that are
acquired in digital format.
Most satellite scanners are typically electromechanical scanners, linear array devices, or imaging
spectrometers that operate in either a ‘sweep’ (e.g.
LANDSAT) or ‘pushbroom’ (e.g. SPOT – see section
2.2) mode. These are passive systems which record
solar radiation reflected from the Earth’s surface.
Data derived from multispectral scanners can provide
information on (among other things) vegetation
types, its distribution and condition; geomorphology;
soils; surface waters; and river networks. Reflectance
or short wavelength infrared (SWIR) sensors record
emitted energy from surfaces and have been
particularly useful for monitoring fires and for
studying areas of volcanic and geothermal activity.
Thermal or long wavelength infrared (LWIR) sensors
have been popular for mapping ocean temperatures
and study of the dynamics of coastal waters and
currents. On land, plant water stress induces changes
in canopy temperatures which are detectable.
Thermal maps are also popular in monitoring urban
areas, industrial sites, manufacturing centres, and
agricultural scenes.
Active systems operating in the visible spectrum
use laser technologies – light detection and ranging
systems (LIDARS) – primarily for oceanographic
(e.g. ERS-1 and RADARSAT) and forestry
applications. Radar systems use active microwave
energy for oceanographic, navigation, forestry,
geology, and other similar studies. Regardless of the
wavelengths they employ, active systems do not
depend on reflected energy from the Sun for image
formation. They are able to acquire data during the
day or night. In addition, as longer wavelength
microwave radiation is not distorted by the
atmosphere to as great a degree as shorter
wavelength energy, radar systems can collect data
through cloud layers and some precipitating clouds.
Other remote sensing systems are employed to
detect the Earth’s magnetic and gravitational fields.
These tools are used extensively in oil and mineral
exploration. Analyses of such multispectral data
can, if properly designed and carried out, increase
both the quality and quantity of information for
given applications.
Thus there is a wide variety of satellite-based
sensors currently providing operational raster
remotely-sensed data for GIS developers and users.
Our emphasis here is upon information on the
sources of those satellite remote sensed data being
operationally acquired for use within GIS today. The
spatial, spectral, and temporal characteristics of
these systems vary according to specific design goals
and engineering trade-offs. Here, we provide basic
Characteristics, sources, and management
data characteristics (Table 1) and information for
gaining access to data from the primary sensors
being used. Again, here we make no attempt to be
comprehensive as there are dozens of platforms and
hundreds of sensors, either currently operational or
planned. Some others are described in Barnsley
(Chapter 32) and in Table 1, and readers interested
in more exhaustive listings should consult Earth
Observing Platforms and Sensors, a CD-ROM
available from the American Society of
Photogrammetry and Remote Sensing, or
Observations of the Earth and Its Environment:
Survey of Missions and Sensors published by
Springer. Here we also provide details about the
management of these common remote sensing data
sources, as well as the organisational context to
availability and access to data from them.
The LANDSAT programme has provided coverage
of the Earth for almost 25 years. LANDSAT is the
result of the NASA (National Aeronautics and
Space Administration) Earth Resources Survey
Program and involves the cooperation and shared
resources of several other US government agencies.
Landsat was known as the Earth Resources
Technology Satellite (ERTS) when the first satellite
was launched in 1972. The original satellite was part
of a proof-of-concept project to determine the
feasibility and utility of monitoring the Earth’s
natural and cultural resources using data from
orbiting satellites. Four additional satellites have
been placed into orbit since 1972, providing
continuous data for use in a wide range of
environmental applications. The first three
LANDSATs had a multispectral scanner (MSS) as
the primary sensor, while the following two satellites
added a higher resolution scanner called the
Thematic Mapper (TM). The MSS has 80 m spatial
resolution and images in the visible and nearinfrared region while the TM has 30 m spatial
resolution and images across a broader portion of
the spectrum (visible through thermal infrared).
The LANDSAT programme was instrumental in
establishing the operational viability of synoptic
space-based remotely-sensed data. To provide ready
access to LANDSAT data, the US Congress placed
the ground segment responsibilities with the US
Geological Survey’s EROS Data Center in Sioux
Falls and the Department of Commerce. The
programme was privatised by Congress in 1985 and
both the space and ground segments of LANDSAT
were transferred to the EOSAT Corporation. In
1992, the responsibilities were returned to NASA
(provider), Department of Commerce (operations),
and USGS (United States Geological Survey: data
archiving). The international network of
LANDSAT ground stations provides regional access
to LANDSAT data. Global coverage are available
from the USGS EROS Data Center and from the
Space Imaging EOSAT Corporation. Rhind
(Chapter 56) describes the changing data pricing
policies that have been applied to LANDSAT data.
2.2 Satellite Pour l’Observation de la Terre (SPOT)
SPOT is an operational, commercial remote
sensing programme that operates on an international
scale. An established global network of control
centres, receiving stations, product generation
centres, and data distribution outlets ensure access
to SPOT data. SPOT satellites are owned and
operated by the French space agency, the Centre
National d’Etudes Spatiales (CNES). Several private
companies, including SPOT Image in France, SPOT
Image Corporation in the USA, SPOT Imaging
Services in Australia, and SPOT Asia in Singapore,
are the core of the SPOT data distribution system
(see also Rhind, Chapter 56). The system is further
augmented by distributors in over 70 countries.
Three SPOT satellite have been placed into orbit
since 1986, and two more are planned for launch
during the next five years. This allows data
continuity for environmental applications for an
expected 15–20 year period.
The mission objectives for SPOT include
providing remotely-sensed data suited for land
cover, agriculture, forestry, geology, regional
planning, and cartography applications. Data
from the High Resolution Visible sensor (HRV)
provide both multispectral coverage with 20 m
spatial resolution and panchromatic imagery
with 10 m resolution. The high resolution of
SPOT’s panchromatic data are particularly well
suited to urban and cartographic applications.
SPOT data are available from SPOT Image SA
in France, SPOT Image Corporation in the
USA, SPOT Imaging Systems in Australia,
and SPOT Asia.
J E Estes and T R Loveland
Table 1 Basic data characteristics of ‘standard’ imagery from some satellites launched to date.
Dates of
Spectral properties
Multispectral Scanner
80 m
185 x 185 km
18 days
0.50–0.69, 0.60–0.70,
0.70–0.80, 0.80–1.10
Multispectral Scanner
80 m
185 x 185 km
16 days
0.50–0.69, 0.60–0.70,
0.70–0.80, 0.80–1.10
Thematic Mapper
29/30 m
185 x 185 km
16 days
0.45–0.52, 0.52–0.60,
0.63–0.69, 0.76–0.90,
1.55–1.75, 2.08–2.35,
High Resolution Visible
Sensor – multispectral
20 m
60 x 60 km
0.50–0.59, 0.61–0.68,
High Resolution Visible
Sensor – panchromatic
10 m
60 x 60 km
26 days or
1–3 days in
off-nadir mode
26 days or
1–3 days in
off-nadir mode
SPOT 1–3
NOAA–6, 8, 10
Advanced Very High
Resolution Radiometer
1.1 km
2600 km x orbital
1 day
0.58–0.68, 0.725–1.10
3.55–3.93, 10.5–11.5,
10.5–11.5 (repeated)
NOAA–7, 9, 11,
12, 14
Advanced Very High
Resolution Radiometer
1.1 km
2600 km x orbital
1 day
0.58–0.68, 0.725–1.10
3.55–3.93, 10.3–11.3,
MOS–1, 1b
Multispectral Self-Scanning
Radiometer (MESSR)
50 m
100 x 100 km or
185 x 185 km
17 days
0.51–0.59, 061–0.69,
0.72–0.80, 0.90–1.10
Visible and Thermal Infrared
Radiometer (VTIR)
900 m (visible),
2700 m (thermal)
1500 km
> 17 days
0.50–0.70, 6.0–7.0,
10.5–11.5, 11.5–12.5
Microwave Scanning
Radiometer (MSR)
32 km (23.8 GHz), 317 km
23 km (31.4 GHz)
17 days
23.8 GHz, 31.4 GHz
Synthetic Aperture
Radar (SAR)
18 m
75 km
44 days
1.275 GHz (L-Band)
Optical Sensor
18.3 x 24.2 m
75 km
44 days
0.52–0.60, 0.63–0.69,
0.76–0.86, 0.76–0.86
Optical Sensor
18.3 x 24.2 m
75 km
44 days
1.60–1.71, 2.01–2.12,
2.13–2.15, 2.27–2.40
Linear Imaging SelfScanning System (LISS)
23 m (LISS1)
36.25 m (LISS2)
148 km
22 days
0.62–0.68, 0.77–0.86
Linear Imaging SelfScanning System (LISS)
23 m, 70 m for
1.55–1.70 µm
channel (LISS3)
148 km
22 days
0.52–0.59, 0.62–0.68,
0.77–0.86, 1.55–1.70
Synthetic Aperture Radar
Nominal 30 m,
8–200 m range
80.4–99 km
16–35 days
5.3 GHz (C-Band)
(latitude dependent)
Synthetic Aperture Radar
6.25–500 m
45–500 km
1–24 days
5.3 GHz (C-Band)
(latitude dependent)
Characteristics, sources, and management
2.3 Advanced Very High Resolution Radiometer
The AVHRR sensor is carried onboard the
United States National Oceanic and Atmospheric
Administration’s (NOAA) Polar Orbiting
Environmental Satellites (POES). This operational
satellite and Earth observation programme was
primarily established to provide data for use in
meteorological applications. However, the daily
coverage provided by AVHRR and the spectral
bands used have resulted in AVHRR data being
used for many operational land mapping and
monitoring programs.
The first of the series of AVHRR instruments
was placed into orbit on the TIROS-N satellite in
1978. Because the POES programme is operational,
new satellites are launched approximately every
18–24 months. The most recent satellite, NOAA–14,
was placed into orbit in late 1994. AVHRR data are
multispectral and the data have a resolution of
1.1 km at nadir, and the orbital swath is
approximately 2600 km wide.
The AVHRR data stream can be tapped by any
ground receiving station without restrictions or
payment of subscription fees. As such, the potential
sources of AVHRR data are anywhere there is a
ground receiving station. Three sources for
obtaining global AVHRR data are NOAA/SAA
User Assistance, the USGS EROS Data Center, and
the European Space Agency/ESRIN.
and has a ground resolution of 50 m. The VTIR
instrument provides moderately coarse resolution
(900 m–2700 m) observations that are intended for use
in cloud and sea surface temperature investigations.
The MSR is a passive microwave sensor that records
long wave radiation emitted from the Earth’s surface.
The purpose of the MSR is to provide measurements
on atmospheric water vapour and water content over
the ocean, and information on sea ice and snow.
MOS-1 and -1b products are available from the
Remote Sensing Technology Centre of Japan.
2.5 Japanese Earth Resources Satellite (JERS)
JERS-1 was launched by NASDA in 1992. Its
mission is to provide global data for agriculture,
forestry, environmental protection, disaster
assessment, coastal monitoring, and fisheries studies.
JERS-1 includes two instruments: a synthetic
aperture radar (SAR) and an optical sensor (OPS).
The SAR is an active sensor which transmits L-band
microwave energy and collects the returned
backscattered signals. The active energy source
provides all-weather capabilities. The OPS consists of
a Visible and Near-Infrared Radiometer (VNIR) and
SWIR. Each radiometer has four spectral channels,
for a total of eight OPS bands. Band 4 of the VNIR
provides stereo capabilities. The SWIR channels were
designed to provide information for mineral
exploration and other geological applications. As for
MOS, data products are available from the Remote
Sensing Technology Centre of Japan.
2.4 Marine Observation Satellite (MOS)
The Japan National Space Development Agency
(NASDA) established their Earth observation
satellite programme for the purpose of using
domestic technologies for the collection of
environmental data that could be used for national
resource utilisation and environmental protection
priorities. The first Japanese earth observation
satellite, the Marine Observation Satellite 1
(MOS-1), was launched in 1987 and a second
satellite was launched in 1990.
The MOS satellite series carry three sensors: a
Multispectral Self-Scanning Radiometer (MESSR), a
Visible and Thermal Infrared Radiometer (VTIR),
and a Microwave Scanning Radiometer (MSR). The
MESSR operates in the visible and near-infrared
portions of the spectrum and was intended for land
applications. It has two spectral channels in the visible
region and two channels in the near-infrared region
2.6 India Remote sensing Satellite (IRS)
The IRS programme includes a series of satellite
systems dedicated to the collection and distribution
of land remotely sensed data. IRS is operated by the
National Remote Sensing Agency (NRSA) and the
Indian Research and Scientific Organisation (ISRO).
Three satellites (IRS-1a, -1b and -1c) have been
launched since 1988 carrying the Linear Imaging
Self-Scanning System (LISS). The LISS sensor
provides multispectral coverage in four spectral
regions. The spatial resolution of LISS data is
72.5 m for LISS-1, 36.25 m for LISS-2, and 23 m
and 50 m for LISS-3.
The IRS mission objectives includes the provision
of state-of-the-art satellite remotely-sensed data for
use in India’s National Natural Resources
Management System. NRSA has also joined with
J E Estes and T R Loveland
other international satellite data providers to form a
global network for access to IRS datasets. IRS data
are available from a variety of international sources,
including the Indian National Remote Sensing
Agency Data Centre and the Space Imaging
EOSAT Corporation.
2.7 European Resource Satellite (ERS)
The European Resource Satellite-1 (ERS-1) is a
project of the European Space Agency. The initial
ERS-1 was launched in 1991 for the purpose of
providing global synthetic aperture radar coverage
of the Earth’s surface. The ERS-1 mission was
designed to provide global measurements of sea
wind and waves, ocean and ice monitoring, and
coastal observations. ERS-1 objectives had limited
focus on land studies but, with the launch of ERS-2,
data acquisitions have a greater land observation
focus; for instance, some digital elevation models
have been computed by interferometric imaging. The
ERS SAR instrument provides C-band active
microwave measurements with a nominal ground
resolution of 30 metres. ERS-1 and -2 data are
available from EURIMAGE ERS Customer
Services, RADARSAT International ERS
Order Desk, and SPOT Image ERS Order Desk.
RADARSAT provides one of the first space-based
active microwave instruments designed for the
collection of global synthetic aperture radar data.
RADARSAT SAR data are intended for use in
studies of ice conditions, geology, agriculture, and
forestry. Special emphasis has been placed on the
provision of real-time data for use in Arctic Ocean
navigation and iceberg surveillance. Arctic regions
are imaged daily, while equatorial regions are
covered every 24 days. The SAR instrument provides
C-band observations with a selectable pixel
resolutions varying from 6.25 m to 500 m.
Information concerning sources for acquiring
RADARSAT data are available from the Canadian
Space Agency RADARSAT Programme or the
RADARSAT International ERS Order Desk.
2.9 High spatial resolution satellites
At the time of writing, the launch of a new class of
civilian satellites is imminent. These are based on
previously classified military technology and
financed by major corporations. Operating in the
visible and near-infrared wavebands and using
optical sensors, they have a predicted resolution up
to two orders of magnitude better than SPOT and
new delivery mechanisms are being deployed to
ensure the data are made available more quickly
and more easily. The investments are very
substantial: for instance, Space Imaging Inc.
purchased EOSAT Corporation in 1997 to obtain
its delivery capabilities and market contacts. The
characteristics of these are summarised in Barnsley
(Chapter 32 Table 2).
It is self-evident that the advent of these satellite
systems represents a fundamental change in remote
sensing: operating on a commercial and competitive
basis and at levels of detail hitherto unknown, they
are predicted to form stiff competition for the aerial
survey industry around the world and may in
themselves have a major influence on the GIS
industry over the next few years. National security
and sensitivity issues are also raised by the general
availability of high resolution images. In practice, the
US government controls the availability of imagery
through licensing of the commercial firms involved,
but this is only applicable for US-based data
collecting organisations (and other governments may
well be less than delighted that access to information
about their territories is controlled by the USA; see
Rhind, Chapter 56). But the proposed 1 to 3 m
imagery still lags behind estimates of the technology
currently being planned or operated by various
military bodies, for example 0.3 m in the US, 1 m in
Israel, and 1–2 m resolution (using Helios-1) in
France. On the other hand, some military agencies
are also proposing to procure imagery from the
commercial dealers – a development which appears
to confirm that civilian and military requirements are
increasingly overlapping as budgets are reduced
throughout the world (see Swann, Chapter 63, for a
general discussion).
The digital raster data received at ground stations
come in a continuous stream so long as the satellite
is within line-of-site of the receiver. These downlinked data are in binary form for satellite-to-ground
transmission. The amount of data to be telemetered
cannot exceed data transmission technology and the
Characteristics, sources, and management
bandwidth over which the data are carried.
Telemetry rates and carrier wave bandwidths have
increased significantly in the past few decades from
kilobytes per second (kbs) to megabytes per second
(mbs). This means that 6-bit partitioning of the
y-axis (64 levels of grey) in older systems can now be
designed for 8-bit accuracy or higher (as in the new
high-resolution satellites). Nevertheless, it is clear
that higher Nyquist sampling rates and increased bit
rates combine to create exponential growth rates in
data transmission requirements. These data streams
must also be processed and formatted (to remove
random and systematic distortions and to calibrate
radiometrically and rectify geometrically) by users
or data suppliers before they are suitable for input
into GIS.
Once acquired, these data are stored in data and
information centres in a number of nations around
the world. Both national (public sector) and
commercial remotely-sensed data can typically be
accessed and acquired through these data centres
directly or with the aid of an intermediary such as a
commercial vendor, a governmental agency, or an
academic institution working in the area. They may
be accessed in a variety of formats and in a variety
of forms, ranging from full scene to quarter scene to
specific area coverage. The data may also be
acquired as ‘raw’ system-corrected data or in a
variety of processed formats including geometrically
and atmospherically corrected, and georeferenced
data registered to a given geospatial coordinate
system. Typical choices of data are those listed as
‘levels of processing’ for the NASA Earth Observing
System (EOS). These processing levels are listed in
Table 2. The choice of processing level is up to the
data users and is both cost- and specific applicationdependent. Science users may want data more
toward the ‘raw’ end (level 0) of the processing
spectrum while many applications-oriented users
will opt for more processed forms of the data
(typically level 1A or higher).
A wide variety of photogrammetric, image processing,
and statistical analyses are utilised to extract
information from raster data. Systems to accomplish
such processing range from the relatively simple PCbased desktop mapping, ‘softcopy’ photogrammetry,
and combined image processing/GIS systems to
complex analytical stereoplotters, orthophotoscopes,
mainframe and super computer-based image
processing/GIS systems. The choice of the hardware
and software to be employed in any remote
sensing/GIS application depends upon a wide range
of factors (see Bernhardsen, Chapter 41). These
include: cost; type of application (e.g. commercial
operation or fundamental research); timeliness
required in data production; level of
understanding/training of the staff involved; and the
appropriateness, accessibility, and availability of the
input data, hardware, and software.
To accomplish the integration of remotely-sensed
data into vector-based GIS requires the addition of
relatively sophisticated image processing packages to
Table 2 Definitions of processing levels for Earth Observation System Data and Information System Data. Source: NASA 1995
Level 0
Processed, unprocessed instrument/payload data at full resolution; any and all communications artifacts, e.g.
synchronisation frames, communications headers, and duplicate data removed
Level 1A
Reconstructed unprocessed instrument data at full resolution, time-referenced, and annotated with ancillary information,
including radiometric and geometric calibration coefficients and georeferencing parameters, e.g. platform ephemeris,
computed and appended but not applied to the Level-0 data
Level 1B
Level 1A data that have been processed to sensor units (not all EOS instruments will have a level 1B product or
Level 2
Derived geophysical variables at the same resolution and location as the Level 1 source data
Level 3
Variables mapped on uniform space-time and grid scales usually with some completeness and consistency
Level 4
Model output or results from analyses of lower level data, e.g. variables derived from multiple measurements
J E Estes and T R Loveland
these systems. For instance, ESRI’s ARC/INFO,
ArcView, and ARC GRID software accomplish
much of this integration. Correspondingly, some
raster-based image processing systems have
considerable GIS functionality. ERDAS/Imagine,
ER Mapper, EASI/PACE, and Intergraph’s MGE
Base Imager (MAI) version are examples of such
systems that are commercially available today
(Graham and Gallion 1996). An important
consideration in linking any of these systems to a
vector-based GIS is the grid interface between the
two systems and the file structures of the systems.
Care should be taken by the managers involved, and
advice sought from users, prior to going forward
with an attempt to link raster and vector systems for
a particular applications area.
Important management issues with respect to these
data are their availability and accessibility and their
use within the context of databases. More is said
about these issues in Smith and Rhind (Chapter 47).
Here, however, we will focus on the multinational
management efforts to encourage the exchange of
Earth observation data through the Committee on
Earth Observation Systems (CEOS). CEOS
understands the need for global coordination in
achieving the goal of fullest potential use of
international Earth observing systems. It has already
initiated steps to coordinate the development of
observing satellites in order to provide
complementary data to users. To optimise the use of
the data collected by these satellites, it will be
necessary to achieve a corresponding degree of
international coordination in the systems that
disseminate and enable access to that data. The
interconnectivity of available global networks and
the interoperability of network services are key
factors in achieving this objective. ‘CEOS agencies
are in the process of defining a strategy for the use of
a global network infrastructure and standard
network services. This activity is placing significant
emphasis on the provision of user services and the
encouragement of wider use of Earth observation
data through expanded, easier access to metadata
(data about data; see Guptill, Chapter 49) and data
products on the ‘information superhighway’
(European Space Agency 1995).
CEOS has also recognised the need to provide
users with a broad array of access to Earth
observation data which is as simple and as
comprehensive as possible. To accomplish this,
CEOS working groups have undertaken a number
of experiments on interoperability of on-line user
services, such as catalogues and image browser
systems. These experiments have been designed to
develop methods which, when implemented
generally, would enable quick and easy access to
Earth observation satellite data resources held
anywhere in the world. A number of prototype
services are already in place (European Space
Agency 1995).
With a view towards maximising the use of
Earth observation data collected worldwide,
CEOS agencies have also developed principles
for the exchange of satellite remotely-sensed data.
These guiding principles were developed by the
participating agencies in support of ‘key areas
of global change research and operational
environment use for public benefit’ (European
Space Agency 1995). As such, they expand upon
the CEOS Terms of Reference which state that:
‘Members must have a continuing activity in
space-borne Earth observations, intended to
operate and provide non-discriminatory and
full access to data that will be made available to
the international community’ (European Space
Agency 1995). The mechanisms behind these
principles are already being tested in a variety
of programmes, including an International
Geosphere Biosphere Programme (IGBP) pilot
project to exchange high-resolution image data
between agencies.
Remotely-sensed data are being employed to provide
users with:
basic measurements of environmental
maps of the spatial distributions of
environmental features and phenomena;
mechanisms for monitoring changes in the world
around us;
a means to incorporate all these types of data for
use in modelling aspects of the Earth as a system.
Characteristics, sources, and management
These data can then hopefully be employed to
improve the management of our planetary resource
base at scales from local to global. We should not
forget that, prior to the development of Earthorbiting satellite remote sensing, we had no practical
means for the timely gathering of globally consistent
datasets whose accuracy could be verified in a
meaningful fashion. While some might argue that
the acquisition of such data is still not feasible, we
believe that we are rapidly approaching a time when
such data collection will become routine. Resource
management at scales from local to global which is
directed at economic growth within the context of
sustainable development (Htun 1997) can only be
achieved through the wise use of both remote
sensing and GIS. Thus we are confident that the
widespread use of remote sensing to update key GIS
data layers and of GIS to maintain the base data
layers, process the data, keep track of changes, and
allow decision-makers to assess the consequences of
alternative strategies prior to making decisions, and
then to track the actual results after decision
implementation, will become increasingly common
management functions worldwide.
European Space Agency 1995 Coordination for the next decade:
1995 Committee on Earth Observation Satellites (CEOS)
yearbook. UK, Smith Engineering Systems Ltd: 7–8
Graham L A, Gallion C 1996 Image processing under
Windows NT – a comparative review. GIS World 9(9): 36–44
Htun N 1997 The need for basic map information in support
of environmental assessment and sustainable development
strategies. In Rhind D (ed) Framework for the world,
Cambridge (UK), GeoInformation International: 111–19
NASA 1995 1995 Mission to Planet Earth, Earth Observing
System: Reference Handbook. Washington DC, National
Aeronautics and Space Administration: 40
Star J L, Estes J E, Davis F 1991 Improved integration of
remote sensing and geographic information systems: a
background to NCGIA Initiative 12. Photogrammetric
Engineering and Remote Sensing 57: 643–5
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