PRESENT STATE OF REMOTE SENSING APPLICATIONS TO LAND CLASSIFICATION AND LAND EVALUATION.
FUTURE PROSPECTS Theodore Astaras
Institute of Geology, Department of Geology-Physical Geography, Aristotle University of Thessaloniki, Thessaloniki, Macedonia Province, Greece
Introduction and purpose of the study
Remote sensing is the science of deriving information about an object without actually coming into contact with it.
The electromagnetic energy released by a target (object) provides a signal which is detected and measured by remote sensing systems such as optical sensors (film and CCD cameras, scanners) and radar sensors (imagers).
In this paper, the solar, near and far infrared and microwave spectrum were considered, because these spectrum regions are recorded by the above sensors and used for geological, geomorphological, and land classification studies.
The purposes of this paper are the following:
First, to describe the Earth Resources Satellites such as LANDSAT, SPOT, ERS, IRS, JERS, RADARSAT and RESURS series, which are in orbit around the Earth, carry remote sensors (mainly image sensors/electrooptical sensors), observe, measure and monitor the whole environment of the Earth (lithosphere, cryosphere, hydrosphere and biosphere).
Second, to describe briefly the up-to-day remote sensing technology, applied for land classification and evaluation (land-resources) studies.
Third, to describe the Earth-resources satellites, going to be operated in the first decade of 2000, which show better spatial, spectral and radiometric resolution than the up-to-day ones.
Earth-resources Satellites in the last 27 years
The LANDSAT series satellites (LANDSAT programme)
In 192, the National Aeronautics and Space Administration (NASA) initiated the first civilian program specializing in the acquistion of remotely sensed digital satellite data. The first system was called ERTS (Earth Resources Technology Satellites), and later renamed to Landsat.
There have been several Landsat satellites launched since 192. Landsats 1, 2, and 3 are no longer operating, but Landsats 4 and 5 are still in orbit gathering data. The
Landsat width of the swath observed is 15x15 km.
Landsats 1, 2, and 3 (192, 195 and 19) (NASA) gathered Multispectral Scanner (MSS) data (four bands, 0 x 0 m resolution). Landsats 4 (192) and 5 (195) (EOSAT) collect MSS and Thematic Mapper (TM) data (seven bands, 30 x 30 m resolution except from TM6 band 120 m).
Landsat 6 with ETM (Enhanced Thematic Mapper) sensor was launched in 1993 (EOSAT/NASA) but failed to remain in orbit. ETM sensor will incorporate the existing TM of Landsat 4 & 5 capability and to add a 15 m panchromatic (PAN) capability in the 0.5 - 0.9 micrometer range.
Landsat was succesfully launched (NASA/NOOA) last April (1999). This is the latest in the LANDSAT satellites, dating back to 192. The Landsat carries the scanner ETM+ (Enhanced Thematic Mapper plus, with solid state recorders). ETM+
scanner has seven bands in the XS mode and one band in the PAN mode. The spatial resolution in the six bands (TM1-5, TM7) of the visible and near infrared spectrum is 30 x 30 m (as in LANDSAT-5). The spatial resolution in TM6 (thermal) band is 60 x 60 m. PAN mode recorder gives 15 x 15 m resolution images. Landsat-7 system will ensure continuity of Thematic Mapper (TM) type data into the next century.
The SPOT system (programme)
The first Systeme Pour l’Observation de la Terre (SPOT) satellite, developed by the French Centre National d’Etudes Spatiales (CNES), was launched in first mon...
of 1986. The second SPOT satellite (SPOT-2) was launched in 1990, and the SPOT- 3 in 1993. SPOT-3 started tracking in May 1994 and entered safehold in November 1996 The sensors operate in two modes-multispectral (XS) (three bands, 20 x 20 m resolution) and panchromatic (P) (10 x 10 m resolution).
SPOT satellite can observe the same area on the globe every 26 days. SPOT scanner normally produces nadir views, but it does have offnadir viewing capability.
The width of the swath observed varies between 60 km for off- nadir viewing and 80 km for outmost off-nadir viewing at a nominal height of 832 km.
SPOT-4 was launched in 1998 and carries two HRVIR (High Resolution Visible Infrared) scanners, which give images with 10 and 20 m resolution in PAN and XS modes respectively; and a VEGETATION-1 instrument (VMI/Vegetation Monitoring Instrument), which gives images with 1 km resolution.
The Soviet systems
In the past, Soviet Union launched variety of satellites with remote sensing capabilities, many of which were initially used for military purposes.
The Soviet Earth resources satellites fall into two main categories: a) Manned space stations Salyut and MIR, and b) unmanned film-return satellites in the Kosmos, Soyuz, METEOR-PRIRODA and OCEAN satellite series.
Untill the end of the eighties, Soviet data were provided only to countries with major cooperative remote-sensing projects with Soviet Union, including among others the Eastern European countries, Cuba, Vietnam, Mongolia and Syria (R. Chipman, 1990). The last ten years, Soviet Union/Russia started offering satellite data all over the
world.
During mid-eighties, USSR started to launch a system of RESURS satellites, which included three sub-systems:
a. RESURS-F system. RESURS-F1 with photo-cameras KFA-1000 and KATE- 200 giving images with 5 and 20 m resolution, respectively, and RESURS-F2 with photocamera MK-4 giving images with 8 m resolution (Selivanov, 1992; Albedo, 1993).
b. RESURS-O system. The RESURS-O1 (Kosmos 1939) is the first non-military Russian satellite carrying scanners. At the beginning, the RESURS-O1 satellite carried the multispectral scanner MSU-SK with five bands, giving 170 m resolution (excluding the thermal infrared band which gives resolution 600 m) and scene size of 600 x 600 km, and two multispectral push-broom scanners MSU-E, with three bands (similar to three bands of SPOT), which give images with 45 x 33 m resolution. These satellites and the following of this series, were launched for environmental monitoring and mapping Earth natural resources.
In 1994, RESURS-O1-3 was launched, carrying the same scanners as the previous satellites of the same series.
Recently, in 1998, RESURS-O1-4 was launched, carrying a) the MSU-SK scanner which acquires the four spectral bands of the previous satellites plus the thermal band (10.4 - 12 µm) with swath width 714 km and 528 m resolution, and b) the MSU-E (3-channel steerable push-broom scanner), which gives images with 30 m resolution and scene size of about 60 x 60 km.
The RESURS MSU-SK data of the above satellites, with a medium resolution of 170 m and wide swath width of about 600 - 700 km, fill the gap between the Landsat/
SPOT (10 - 30 m resolution) and NOAA/AVHRR (1.1 km resolution) data.
c. OCEAN-O system, carrying radar sensor (SAR) and microwave radiometer for multitemporal ocean monitoring.
Additional to the OCEAN-O system, Russia lanched the satellite ALMAZ (Kosmos 1870), which carried radar sensor (SAR) with 15 m resolution (this satellite produced data untill 1992). Also in 1996, Russia launched the high resolution satellite SPIN-2 carrying KVR-1000 and TK-350 panchromatic cameras with 2 and 10 m resolution, respectively, and swath width 40 and 300 km, respectively.
IRS (Indian Remote Sensing) system
IRS is an Indian program to develop an indigenous capability to image Earth, particularly India. Its mission is ground water exploration, land use, forest & flood mapping, inventory of surface water.
The first satellite in the IRS series (IRS-1A) was launched in 1988 and carried two sensors: LISS-1 & LISS-2.
Sensor LISS-1: Generating resolution of 72.5 m and 148 km swath width, framing LISS-2 image pairs.
Sensor LISS-2A/B: Generating resolution of 36.5m & 74 km swath width.
IRS-1B: Launched on 29 th August 1991. Specifications are same as the IRS- 1A.
IRS-1C: Launched on 28 th December 1995. The IRS-1C acquires the highest spatial resolution remote sensing data commercially available until 24/9/99 when IKONOS-2 was launched. A 5 - metre panchromatic data (PAN), in 70 km scene size, is especially useful for urban planning and mapping, a 25 - metre multispectral data is suitable for vegetation monitoring and natural resource planning.
IRS-P2: Launched on 15 th October 1994. Specifications are the same as the IRS- 1C. IRS-P3: Launched on 21 st March 1996. Specifications are the same as the IRS- P2. IRS-1D: Launched in September 1997. Specifications are the same as the IRC- 1C satellite.
IRS-P4: It was launched succesfully into space on 26 th May, 1999. The IRS-P4 (which is also known as the Oceansat-1), has a Multi-frequency Scanning Microwave Radiometre (MSMR) and a nine-band Ocean Colour Monitor (OCM). Satellite sensors have a resolution of 250 m at nadir and a swath width of 1500 km with a repetivity of two days.
The main application of this satellite is for gathering information related to water vapour and carrying out ocean colour monitoring. The data collected from ocean colour monitoring (chlorophyll concentration recording) will be used for conducting fisheries survey and development of fisheries forecast model based on these data.
More over, is monitoring will be useful in coastal processes like sediment dynamics, dynamics of estuaries and tidal inlets, prediction of shoretime changes, circulation and dispersal pattern, upwelling of coastal and oceanic fronts, and surface currents.
The Japanese satellites
MOS 1A and 1B satellites, also known as Momo 1A and 1B, were Japan’s first Earth resources satellites. They were launched in 19 and 1990, respectively. MOS (Marine Observation Satellites) carries three sensor systems (MESSR, VTIR and MSR). The most useful to geosciences is the Multispectral Electronic Self-Scanning Radiometer (MESSR) (4 bands between 0.51 and 1.1 µ, giving 50 m resolution and 200 km swath width). Designed to observe ice distribution, land use, snow cover, ocean chlorophyll.
JERS-1 satellite, or Japanese Earth Resources Satellite, is a project of the National Space Development Agency of Japan (NASDA). It was launched in 1992 and carries two Earth observing instruments (one SAR and one Optical Sensor).
a. SAR sensor: JERS-1 SAR is a high-resolution (18 m), all-weather imaging radar (L-band) that can map topography and geological characteristics of the Earth surface.
b. Optical Sensor: is a high resolution sensor (in seven bands) that measures solar radiation reflected off the Earth surface in the visible, near infrared, and short wavelength infrared. This scanner gives images with 1.3 x 24.2 m resolution. Out of these data stereoscopic images can be made.
Also, NASDA, lunched the ADEOS satellite in 1996, but after a certain period it
stopped producing data.
European Space Agency’s (ESA) Remote Sensing Satellites
Up-to-day, ESA launched two Earth resources satellites (ERS-1 & 2). ERS- 1 satellite was successfully launched in 1991 and it has been operated until its hibernation in June 1996. ERS-2 satellite was launched in 1995. This satellite is a close copy of ERS-1. Both satellites now overfly the same ground track, with ERS-2 trailing ERS-1 by exactly one day (tandem mission). The data of this mission is for interferometer processing and production of inter programs which are useful for the temporal defferences between two successive acquisitions, e.g. detection of surface movements (caused by seismic activity and/or volcanoes) and forest monitoring, as well as for generation of D.E.M. (Digital Elevation Models).
In addition to the Synthetic Aperture Radar (X-band, which gives SAR images with 30 m resolution), ERS-1 and ERS-2 carry other four instruments:
The Radar Altimeter, Along-Track Scanning Radiometer and Microwave Sounder, Precise Range and Range Rate Equipment (functional only on ERS-2), Wind Scatterometer and Laser Retro-reflector.
RADARSAT (Canada)
In 1995, the Canadian government (Canada Centre for Remote Sensing, RADARSAT International), with participation of UK and USA, launched RADARSAT- 1. At the heart of RADARSAT-1 is an advanced radar sensor called Synthetic Aperture Radar (SAR). SAR is a microwave instrument which sends pulsed signals to Earth and processes the received reflected pulses.
RADARSAT-1 SAR-based technology provides its own microwave illumination and thus will operate day or night, regardless of weather conditions. RADARSAT-1 offers a variety of beam selections; satellite SAR will have the unique ability to shape and steer its beam from an incidence angle of less than 20 degrees to more than 50 degrees, in swaths from 35 to 500 kilometers, with various resolutions from 10 to 100 m. RADARSAT-1 SAR image swath can cover much of the Arctic daily and most of Canada every 2 hours, depending on the beam selected. The entire Earth could be covered every 24 days, using the standard 100-kilometer beam mode.
IKONOS-1 satellite (USA)
IKONOS-1 was launched on 27 th April, 1999, by the Space Imaging (USA), but it never reached orbit, because the rocket did not achieve sufficient velocity to place the satellite into Eath orbit.
IKONOS-2 („spare” twin of IKONOS-1) finally was launched on 24th September, 1999. It carries a panchromatic scanner (1 x 1 m resolution) and a multispectral scanner (4 x 4 m) with four bands, (same as LANDSAT-4 and 5 TM bands). The scene size is 11 x 11 km, the strips are 11 x 100 km up to 11 x 1000 km, and the image mosaics of up to 12.000 km2. The revisit frequency is 3 days at 1 m resolution. It is the first meter- class commercial Earth-observing satellite which can generate still and 3D imagery.
The imagery products collected by IKONOS will be sold and marketed by Space
Imaging Company CARTERRA after 60 - 90 days from the launch day.
Satellites of the EarthWatch Company (USA)
In December 1997, the EarthWatch Company launched the EarlyBird, an Earth -observing satellite capable of 3-meter resolution images. This satellite failed shortly after reaching orbit.
The same Company plans to launch the QuickBird satellite in 2000 (see next pages).
Principles of satellite imagery interpretation
Multi-view approach to data collection
In the Manual of Remote Sensing (first edition, 1983), edited by Colwell, he stresses the possibility of the „multi” concept (multi-view) approach to data collection in remote sensing, which he identifies as: multidate, multistage (multilevel), multispectral, multi-enhancement, multidisciplinary and multithematic.
Some of the above concepts are routinely used in remote sensing data interpretation as more information is usually obtainable from intelligent use of various remotely sensed products rather than through only one (Travaglia, 1990).
a. Multidate or multitemporal approach: by this approach the data about a site area are collected on more than one occasion (ranging from days to weeks to seasonal or annual time scales).
b. Multispectral (multiband) approach: by this approach the data are acquired simultaneously in several different spectral bands of the electromagnetic spectrum (e.g.
in the seven TM bands of Landsat-5).
c. Multistage (multilevel) approach: by this approach the data about a site are collected from multiple altitudes (from satellites that are orbited in different altitudes).
Principles of image analysis/interpretation
According to Travaglia (1990), the principles of image interpretation, simply stated, consist of four basic premises:
a. A remote sensor image is a pictorial representation of the landscape.
b. The image is composed of patterns, indicators of the objects and events which reflect the physical, biological and cultural components of the landscape.
c. Similar patterns in similar environments reflect similar conditions, that is, they have the same „spectral signature” (e.g. similar soil types or types of plants) and unlike patterns reflect unlike conditions (that is, they have unlike spectral signature).
These spectral characteristics (signatures) have been used in the design of the multispectral scanner used the Earth resources satellites (Landsat, SPOT, etc.).
d. The type and amount of information obtained from an image is proportional to the knowledge, experience, skill and motivation of the analyst-interpreter, the efficiency of the method used, and an awareness of the limitations imposed on the analysis by the remote sensor system, data format and analytic method.
When these principles are subjectively applied, in conjuction with a set of
objective principles of physical science, they facilitate the extraction of specific information from imaged data.
The methodology used is mainly based on two steps: analysis and interpretation, carried out on all available images, black and white original bands and false colour composite images (FCCs), according to their specific suitability, for the theme under consideration, such as drainage patterns, water bodies, forested and agricultural areas, urban residential areas, quarries, water-vegetation boundaries, etc.
The difference between analysis and interpretation, an important aspect of the study approach, is the following:
Analysis is the „separating or breaking up of any whole into its parts”. The use of several bands of the multispectral satellite image is termed „multispectral analysis”.
In this way it gives more information to the user than the B&W aerial photographs.
Also the use of combined images recorded at different times is called „multitemporal analysis”.
Interpretation, which obviously follows analysis, is the „explanation of the meaning or significance of any part with respect to the whole” (Trautwein and Taranik 19) and, as applied to image data, relates to both the spectral and spatial aspects of data as well as to their relevance to the surface relationships upon which they are imposed. In the land classification and land evaluation studies, the key to the image interpretation is to identify and use spectral characteristics at the different land surface types.
The most important diagnostic characteristics (photo-keys/recognition elements) used in analyzing remote sensing imagery are well-described in all the manuals of photo-interpretation, and they are : tone, colour brightness, texture, pattern shape and size, shadow, association and site (location) (Travaglia, 1990; Avery and Berlin, 1992).
Visual and digital image analysis techniques for land classification and land use studies (land resources studies)
Visual image analysis for land classification and land evaluation
The satellite image can be interpreted visually in the same way as an air- photograph. In visual image analysis of the conventional air-photographs, the mentioned image photo-keys were used which frequently interact on images, to give areas a uniform or homogeneous signature, called „photomorphic units” (Ackerson and Fish, 1985; Astaras, 1990). They are frequently correlated to an area or group of areas that have similar patterns of topography, soils and vegetation/land use and they were used as a tool in mapping (delineating) landscape units. These landscape units, which initially defined by Christian and Stewart (1968) as „land systems” and later by other scientists with various local terminologies (such as „geosystems” in Spain; Astaras 1984, and „geocomplexes” in Poland; Richling 1983 & 1990), were recognizable on the available TM, SPOT and other imageries, by changes of image characteristics of
the objects shown on these.
Digital image analysis for land classification & land evaluation
In recent years, the quality of satellite data has improved considerably in the spectral domains as well in the spatial and radiometric domain. LANDSAT/MSS has four multispectral bands with 0 x 0 m spatial resolution and 64 tones of gray, whereas TM has seven spectral bands with improved spatial resolution of 30 x 30 m and 256 tones of gray. SPOT has even higher spatial resolution (20 x 20 m in three multispectral bands and 10 x 10 m in the panchromatic band), but the same radiometric resolution as TM.
Digital classification becomes interesting not only because of the improved spatial and radiometric resolution, but also because of the increased amount of data sets (when large areas are mapped). At this case the use of the manual (visual) procedure is relatively slow and hence costly. Various data compression techniques can be applied and also a number of classification algorithms can be tested. Also the digital (automatic) image processing has the advantage of being objective and homogeneous, that is, without the bias of the person or persons performing the visual interpretation (Brandt et al., 1993; Tsakiri-Strati et al., 1994 and 1998).
The digital image processing technique involves the handling and modification of images (with the aid of the appropriate hardware and software), that are held as discrete units, e.g. the sampling, correction and enhancement of LANDSAT/TM image can be achieved by this method.
A discrete image comprises a number of individual picture elements, known as pixels, each one of which has an intensity value and an address in two-dimensional image space. The intensity value of a pixel, which is recorded by a digital number (DN), depends upon the level of electromagnetic radiation received by the sensor from the Earth surface and the number of intensity levels that have been used to describe the image density range.
There are many techniques for the processing digital images, and physical geographers use six of these: image restoration and correction (image pre-processing), image enhancement (e.g. filtering, contrast stretching, edge enhancement and various types of mathematical manipulations, such as spectral ratioing, etc.), data compression (reduction of several images into one image for ease of interpretation), colour display (combination of images with colour, again for ease of interpretation), image classification (density slicing of one image or the supervised classification of several images) and the development of Geographic Information Systems/GIS (combination and use of any spatial data that can be referenced by geographic coordinates). The above techniques manipulate the image data in order that the information content of the image may be improved and more readily available for visual analysis/interpretation (Mather 1987; Gupta 1991; Drury 1987; Tsakiri-Strati 1989 and 1992).
Procedures like multispectral, multitemporal and multilevel (multistage) analysis are usually performed using computers (digital image analysis), because it is difficult to handle such large amount of data in visual interpretation.
Applications of the Earth Resources satellites to land classification and land evaluation
The Earth resources satellite images are used extensively to the following applications for land classification and land evaluation:
1. Lithosphere: Detection of fractured zones which are related to seismicity and ground-water extraction; monitoring and mapping areas suffering from natural hazards, such as mass-movements, accelerated erosion, floods, fires, sediment run-off, open cast minings and possible pollution at the surroundings of the open rifts.
2. Hydrosphere and cryosphere: Multitemporal coastal areas monitoring and mapping which suffer from accelerated coastal erosion and depositional processes and pollution coming from urban and industrial waste disposals and oil slicks. Multi- temporal monitoring and mapping of ice-sheets, snowfields for water run-off prediction etc. 3. Biosphere: Monitoring and differentiating soil and vegetation, soil mois- ture mapping, biomass and crop production estimation. Identifying and characterising (mapping) the types and conditions of both forest canopies and agricultural crops, wetland vegetation mapping and desertification mapping. Monitoring changes at the urban rural boundaries, etc.
One result of the digital image analysis-interpretation is shown in the Figure 1.
Earth observation satellites in the next Century
As it is described by Stoney 1996 & 199, fourty six (46) land observation satellites are and will be operating in the decade starting in 1996, which will carry high resolution systems. These systems are equal to or better than those of LANDSAT 30 m. Emphasis is given to more than 30 land observation satellites which are planned to be operating in 2000.
SPOT-5 is under construction with resolution of 3 m and will carry the VEGETATION-2 instrument to maintain the continuity with VEGETATION-1.
QuickBird-1 is planned to be launched by the EarthWatch company (USA) in 2000. QuickBird imaging system is designed to produce high-resolution (1 m), commercial Earth imagery from Space.
American and Japan ese governments plan to launch the EOS AM-1 satellite at the beginning of 2000. EOS AM-1 which was renamed to TERRA (Greek mythical Mother Earth) will carry multispectral scanners ASTER & MODIS, which will give images up to 15 x 15 m resolution and swath width of 60 km.
Next May (2000), ESA plans also to launch ENVISAT-1 satellite which will provide continuity for ERS-1 and -2 data, in multitemporal Earth resources studies. It will carry 10 sensors, including ASAR (Advanced SAR) instrument which will operate in five different modes, giving images (in alternating polarisation mode) with resolution from 30 - 1000 m, and covering swath width from 5 - 406 km.
Fig.1. RESURS MSU-SK-derived map product showing fires (red colour) in Greece from July 1997 to mid-August 199. The estimated burnt area is about 9,960 hectares (SAI Annual Report, JRC, 199).
RESOURCE 21 is planned to be launched by NASA next year (2000). It will carry a multispectral scanner which will give images with up to 10 m resolution and swath width 200 km.
ALMAZ 1B planned to be launched next year (2000). It will carry 3 SARs and 4 scanners, giving images from 2.5 - 40 m resolution, and swath widths from 20 - 170 km. IRS-P5: also known as the Cartosat, the IRS-P5, has been intended for cartographic applications and is reported to have a PAN camera with a resolution of 1 - 2.5 m. It would mainly be useful for map making and terrain modelling. Date launch is not yet fixed. Reports suggest 2000/2001.
IRS-P6: Also known as Resourcesat, the IRS-P6, is reported to have a high resolution multi-spectral camera. Date launch is not yet fixed. Reports suggest 2002.
Also, many other satellites are going to be launched (for more information see Stoney, 1996 & 199).
Why So Many Satellites?
Stoney (1996 & 1997) writes in his papers (downloaded from NASA’s Web site) that 31 satellites may seem to be more than a few too many for needs of the Earth observing community. Before making that judgment, however, it may be useful to consider the following points:
As noted above, none of the planned satellites will provide all data characteristics needed by the broad range of user requirements. Thus at least four systems would be needed to provide the different data types the fleet is currently planning. The day of the battlestar galactica, single satellites with suites of many instruments, appears to be over.
The need for multiple satellites was also discussed in the section on coverage frequency, which emphasized the negative effects of the world 50% cloud cover.
Resource 21 is planning a four satellite system to meet their customers need for crop conditions weekly observation. The Global Change Science goal of global of seasonal coverage requires a minimum three to four satellites. The use of satellite data for disaster analysis and relief planning can be very effective but only if the satellite can acquire imagery almost immediately after the event, possibile only, if two or more pointable sensors are in orbit. For weather related disasters, radar is often the only system which can see the ground. Again multiple radar satellites would be required for sufficiently rapid coverage.
Finally, there is the need to assure operational stability. In the last two years, three land observation satellites failed to make orbit, Landsat 6, SPIN-2, and IKONOS- 1, and two failed on orbit prematurely, SPOT 3 and ADEOS. Obviously more than one system must be available to provide the operational assurance required, if users are to be able to make data a requirement for their activities. India, EarthWatch (USA) and Resource 21 (USA) are planning operationally robust systems of four satellites each.
CBERS (China - Brazil) and all of the high resolution satellite providers are planning
two recording systems each.
Conclusions
The high remote-sensing technology that has been developed in the last 27 years, offered to geoscientists satellite images which were used for detailed Earth resources surveys. This is due to the event that the Earth resources satellites (Landsat-like and the high-resolution satellites) give images that have some inherent characteristics that provide some advantages over the conventional air-photographs. These characteristics are the synoptic view (e.g. 15 x 15 km for LANDSAT images and 60 x 60 km for SPOT), and the repetitive coverage (e.g. every 16/ days for LANDSAT images and 26 days for SPOT). The multispectral capabilities (seven bands in the LANDSAT/TM and ETM images and 4 bands for SPOT-4) and the improved radiometric sensitivity of the images (e.g. TM and SPOT offer 256 gray levels to measure the intensity of the radiation in each discrete waveband).
These advantages, in combination with the accelerating development of relative hardware and software at the Computer Workstations permit to the image analyst to improve/enhance the images according to his (her) request, by the use of certain digital image analysis techniques. Therefore, the present computer processed and enhanced images yielded a superior accuracy to that obtained by analogue image processing for purposes and applications related to multitemporal land classification (land systems) and land use mapping. These digitally produced maps in combination with GIS techniques are of considerable interest, because they provide the framework for:
a) production of the same scale geomorphological maps, erosional studies, soils and vegetation/forestry maps, land use maps, natural hazard vulnerability maps (e.g. forest fires, floods, mass-movements, etc.) and other thematic maps for natural resources and environmental surveys, and b) updating already existing various thematic maps (e.g.
forest, soil and land use maps, etc.), produced in the past by other techniques.
Also, it must be pointed out that each of the various satellite data should be considered and used as complementary and not competing to other data (techniques).
If they are used together or even superimposed, the result of analysis will be enhanced, because some details of the surface characteristics (geology - geomorphology, soil, water, vegetation and land use, urban areas etc.) may not be recorded on one satellite image, but they may be recorded on other satellite imageries.
Concerning future prospects, we may say that the explosion in land observing multispectral and high resolution satellites (ENVISAT, SPOT-5 QuickBird, EarlyBird, EOS AM-1 (TERRA), Resource 21, IRS-2A, SPIN-2,3 etc.) foreseen for the next decade is a planning tool for all who are interested in knowing and keeping track of the details of what is going on with the surface of our planet and in particular for those who are developing the skills to measure and understand the breadth and detail of the information that analysis of the satellite data could make available to us for the first time. The amount and quality of the land information data which the land observing satellite fleet in 2000 will be capable of providing, could revolutionize both our scientific knowledge and our practical management of our earth’s resources.
Their value can only be realized through the ingenuity and efforts of the users (Stoney, 199).
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Correspondence address:
Institute of Geology
Department of Geology-Physical Geography
Aristotle University of Thessaloniki, University Campus 54006 Thessaloniki, Macedonia Province, Greece +30 31 9951 (552 Fax)
e-maqil: astaras@geo.auth.gr
