Landsat: A tool for development

0305-750X/82/100899-11$03.00/0 Pergamon Press Ltd.

World Development, Vol. 10, No. 10, pp, 899-909,1982. Printed in Great Britain.

Landsat: A Tool for Development BARRY

N. HAACK”

Regional Remote Sensing Facility, Nairobi, Kenya Summary. - A major problem for many decision-makers is the lack of reliable information on which to base decisions. This is particularly true for the extent and location of as well as changes in such natural resources as forests, rangeland, agricultural fields and water bodies. A very useful data source in providing such information is the Landsat orbiting satellites. These satellites have been repetitively collecting images of essentially the entire earth’s surface since 1972. These images are readily available, inexpensive, useable and can provide exceptional resource information. Examples are presented on the use of this data for a variety of resource analyses.

1. INTRODUCTION There is increasing attention to development needs in many of the areas of the world. Development in this context refers to improving each individual’s quality of life at least to satisfy minimum human needs by better resource utilization. Resources are such naturally occuring features as forests, grasslands, soils, water, minerals and materials which can be utilized to provide employment and to increase the availability of food and other commodities. Creating development policies is the function of decision-makers. A problem common to many decision-makers, particularly in less developed countries, is the inadequacy of information on the available resource base. Without accurate information decision-makers often fail to make decisions or make incorrect decisions. Sound decisions depend on accurate information, yet every low income country faces severe competing demands for the financial and human commitments necessary to staff an information system equal to its decision-making requirements (Cummings, 1977). The frequent inadequacy of resource base information may be due to difficulties in accessing some regions; lack of trained personnel, equipment, or funds to collect information properly; or rapid changes in the resource base not detectable by traditional data collection methods such as the high rates of deforestation in many areas of the world caused by increasing population pressures. The purpose of this article is to examine one tool useful in improving resource base information. That tool is the Landsat series of United States satellites for remote sensing of resource data on a global basis. This article will describe

the Landsat satellite system, the data collected by this system, and discipline-specific applications of that data for resource inventories and analyses.

2. LANDSAT The Landsat satellites are part of the rapidly growing techniques of remote sensing. Remote sensing is the collection of information without direct contact by use of such instruments as cameras, radar systems, acoustic sensors, seismographs, magnetometers and sonar. A narrower but more conventional definition of remote sensing is the practice of data collection without direct contact between the sensor and subject area in ultraviolet to radio regions of the electromagnetic spectrum thus neglecting acoustic, seismic and some other sensors. Remote sensing has an extensive history in aerial photointerpretation, a data source still extremely important in current remote sensing techniques. Remote sensing techniques have changed greatly in the past twenty years with (1) the use of new sensors such as thermal infrared and radar; (2) the use of new platforms, particularly satellites, to collect data; and (3) the use of computers to analyse the data. The Landsat satellites incorporate all of these techniques. The first Landsat was launched by the United States National Aeronautics and Space Administration (NASA) in July 1972. Its purpose was to demonstrate the value of continuous, worldwide data gathering from an orbital

* The author is on leave from the Department of Geography, Ball State University, Muncie, Indiana. 899

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platform. Landsat 1, with an expected lifespan of one year, functioned until January 1978. Landsat 2 was launched in January 1975 and Landsat 3 was put into orbit in March 1978. To maintain continuity of data all three of these satellites have carried essentially the same instrumentation. Both Landsat 2 and 3 have experienced some technical problems limiting their ability to collect data, The flow of data should be continued by the fourth satellite in this series, Landsat D, which is expected to be launched in late 1982. In addition to the same multispectral scanner as on Landsat 1-3 as described following, Landsat D will have a Thematic Mapper which will have improved resolution, look at smaller individual areas on the surface of the ground; greater radiometric sensitivity, separate smaller variations in colours reflected from the surface; and look at seven rather than five different reflected wavelengths or bands. This article is focused on Landsat because it is the only earth resources orbiting satellite system in operation for a decade other than the meteorological satellites and is making the transition from a research to an operational mode. There have been several specialized earth resources satellites of short duration in the past such as Seasat and Skylab and plans for other short and long-term systems in the future such as the French SPOT, the Canadian Radarsat and the Japanese ERS-1. Landsat orbits the earth 14 times each day at an altitude of about 920 km. Each satellite returns to the same orbit every 18 days recording the same series of images. There are two sensor systems on board, a return beam vidicon (RBV) system, which is basically a television sensor, and a multispectral scanner (MSS) which records differences in sun reflectance from earth surface features. The RBV system functioned only briefly on Landsat 1 and 2; however, it is being more thoroughly utilized in Landsat 3. The multispectral scanner records information in both the visible wavelengths and also infrared wavelengths invisible to the eye. The MSS takes four readings for each 1 .l acre or 0.4 hectare area on the ground; one for intensity of red light reflected, and two for the infrared. These of non-thermal intensity intensity levels are converted into digital form and are transmitted to ground receiving stations. From the receiving stations the data is generally relayed to the Master Data Processing Facility at Goddard Space Flight Center in where it is stored on Greenbelt, Maryland, computer-compatible tapes (CCTs). The data

can be converted from the tape format into photograph-like images. Reproducible negatives and computer tapes are then sent to the Earth Resources Observation System (EROS) Data Center in Sioux Falls, South Dakota, for storage and distribution. One current difficulty is the lengthy delays between data collection and distribution. These delays are often at least 90 days, which is too long for some dynamic uses such as crop yield forecasting. However for most resource analyses such delays are not a liability and the distribution time is expected to be reduced as the system becomes more fully operational. Satellite images and tapes can be ordered directly from EROS. The images are generally available as prints or transparencies in several sizes but most frequently obtained as 18.5 by 18.5 cm products at a scale of 1: 1 ,OOO,OOO.The images may be obtained in black and white for the RBV data or individual MSS bands. Individual MSS bands can be projected through colour filters and registered to produce false colour composites (FCCs). The FCC can be obtained in several band and colour filter combinations but most typically produce an image very similar to one obtained by use of colour infrared film where vegetation appears red (National Conference of State Legislatures, 1978). The procedures for visual examination of Landsat images are almost identical to those used for interpreting conventional aerial photographs. Landsat’s much smaller scale, making objects appear smaller than on aerial photos, is the principal difference. As with aerial photo interpretation the materials needed can range from nothing more than a pencil and transparent paper to expensive, sophisticated optical instruments. Landsat is not intended to be a substitute for aerial photography and its inability to provide the same type of information as aerial photography should not be cited as a deficiency. This system was primarily intended to provide thematic maps at scales of 1: 100,000 or 1: 250,000 (Morley, 1977). The spatial extent and frequency of coverage provide advantages of Landsat data over that obtained from aerial photography. The data from these satellites is often complimentary to that from aerial photography and the combination of these data types in multistage sampling is often very effective in resource analysis tasks. A limitation of Landsat is that when the satellite is beyond the range of a ground data receiving station, it is necessary to record any collected data on a tape recorder for transmis-

LANDSAT

sion to the surface as the satellite moves closer to a receiving station. The on-board tape recorders were not always reliable causing considerably lesser amounts of collected data in areas outside the range of receiving stations. This difficulty is being eliminated by the construction of additional receiving stations and eventually may be resolved by the use of other satellites to relay the data. These receiving stations are also sources of data products for their areas of coverage. Table 1 lists current and proposed receiving stations. The common reception radius for each station is 2780 km. There are several Landsat data characteristics which make it a valuable tool for resource inventory and analysis. Some of these important characteristics are the following. 1. Worldwide coverage. The entire surface of the earth between 8 1’ north and south latitude is within the satellite orbit. There is existing Landsat data for essentially all habitable land surfaces and in some areas of the world this is the only reasonably current or accurate data available. 2. Repetitive coverage. A single Landsat satellite can acquire complete global coverage every 18 days. Two synchronized satellites can obtain complete coverage every nine days. The actual amount of data obtained is a function of cloud cover, data needs and the availability of receiving stations for data reception. Repetitive data is Table 1. Regional Landsat ground receiving stations Country

Argentina Australia Brazil Canada Prince Albert Shoe Cove China European Space Agency Italy Sweden India Japan South Africa Thailand United States Alaska, California and Maryland

Operational status 1980 1980 1973

Expected

1972 1977 1982

1975 1978 1979 1979 1980 Expected 1982 1972

Notes: 1. Other countries contemplating Landsat Stations: Chile, Kenya, New Zealand, Romania, Upper Volta, Zaire. 2. A Landsat Station in Iran was largely completed and began receiving some test data in 1978. However, the station ceased operations in early 1979 as a result of the political situation in Iran.

901 important for the analysis of features only observable at specific times such as seasonal fluctuations in lake or reservoir water levels. Multitemporal data is also important to monitor crop growth, the rate of snow-melt other dynamic from mountains, and features. 3. Synoptic view. Landsat single scene coverage of large areas (34,000 km21 is at a scale and resolution not previously available. This synoptic view provides an examination of large features or regional patterns difficult if not impossible by other means. An example of this synoptic view utilization is the detection of individual or the connectivity of geologic linear or curvilinear features important for mineral, material or ground water prospecting. These features can often not be seen on mosaics of aerial photographs. The synoptic overviews can also be used in planning field work or selecting areas for detailed analysis with aerial photography. 4. Uniformity over time. Each system satellite passes over a given latitude on the earth’s surface at approximately the same local time. This uniformity of sun illumination eases data interpretation. Seasonal changes in sun angle at a given location as observed on these images may be useful for mapping geomorphologic features. 5. Uniformity over large areas. Mosaics of national, subcontinental or even continental areas can be created using Landsat images. These mosaics allow the inventory or examination of resources in a regional context. 6. Multispectral. Data are acquired simultaneously in four spectral bands through the same optical system. Some features can be better observed on individual bands or specific combinations of bands. Burned grasslands or forests can be best seen on MSS Band 7 while turbid water or shallow water features can be best seen on MSS Band 4. Table 2 identifies the MSS bands and primary features identifiable in each band. The thermal band is only on Landsat 3 and was not operational for long. 7. Digital. Landsat data are available in digital form as computer compatible tapes permitting large volumes of data to be processed by computers in relatively short periods of time. Computer or numerical processing of this data is more complicated and requires more expensive equipment than visual image interpretation. While numerical analysis can often provide greater detail of information and

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WORLD DEVELOPMENT Table 2. The spectral ranges of the Landsat MSS bands and highlighted features and conditions

Spectral band

Type of radiation

Wavelength (!Ju)

4

Visible green

0.5-0.6

5

Visible red

0.6-0.7

6

Invisible reflected IR Invisible reflected IR Invisible thermal IR

0.7-0.8

I 8* * Landsat-

Features or conditions Depth and turbidity of standing bodies of water Topographic and cultural features (drainage patterns, roads and towns) Sharp shadows, some land use patterns Land-water boundary delineations

0.8-1.1 10.2-12.6

Variations in surface temperatures

MSS, channel not on Landsats-I and -2.

perform manipulations of the data impossible by manual interpreters because of the volume of data (there are approximately 30,000,OOO MSS data values in one scene) numerical analysis is not necessary nor is it preferable for many projects. Numerical processing of remotely sensed data is a very new technique which requires a great amount of additional development, experimentation and improvement in order to understand better its potential and appropriateness. Currently numerical analysis is generally not preferable to manual image interpretation on an economic basis because of high equipment, maintenance and training costs. The recent emergence of relatively low cost stand alone numerical analysis systems incroporating mini or micro computers will assist in the deployment of these analysis techniques. These systems should not be considered as replacements for manual interpretation techniques but rather as a supplemental analysis tool. 8. Planimetric. Landsat’s high altitude and the narrow swath of land scanned produces near orthographic images. This means that shapes, dimensions and relative locations of individual features remain almost constant over the entire image. These images are cartographically correct to scales of at least 1: 250,000. 9. Readily available. There are no restrictions on the availability of the Landsat data. It is easily obtained by anyone for any place in the world. During 1980 approximately 130,000 Landsat images were sold from the EROS facility and 42% of those were sold to customers from outside of the United States (EROS Data Center, 1980). The fact that

the data exists for areas of the world for which there is little if any other near comparable data is an important attribute of this system. 10. Easily usable. A great amount of information can be obtained from Landsat data by persons with limited training and without extensive equipment. Many of the basic techniques of aerial photography interpretation can be applied to the imagery. More sophisticated equipment and extensive training can enhance the utility of this data source but are not always required. The minimal training necessary to effectively use this data is a major factor in its value in resource development planning. 11. Inexpensive. The cost of purchasing Landsat images is extremely minimal on a cost per unit area basis. As of October 1982, costs for a 18.5 by 18.5 cm colour print covering 34,000 km2 is US$45. Typically, land use/land cover mapping can be accomplished with this data at one-half to onetwentieth the cost of conventional techniques (National Conference of State Legislatures, 1978). However, the spatial resolution and detail in cover types may be considerably different for these techniques. The Landsat system has many useful attributes for national or regional resource assessments. There are many situations where this is a very appropriate data source and others where it is not appropriate. Landsat data, as most remotely sensed data, is most useful when combined with other information such as topographic, soils, or geologic maps. Individuals involved in development decisions should be aware of the availability of this data and its appropriate utilization.

LANDSAT 3. APPLICATIONS Chuck Paul (1978), the United States Agency for International Development (AID) Remote Sensing Manager, identified eight resource needs critical for developing countries. Those needs are: (1) national inventory mapping, (2) forest monitoring, (3) land use planning, (4) the identification of sub-surface water bodies, (5) the encroachment of urbanization on agricultural lands, (6) transporation planning, (7) land utility and (8) soil capability mapping. Paul does not contend that remote sensing or Landsat can meet all these needs but he does believe Landsat can make some contributions to these areas, more in some cases than others. Recent priorities for AID supported Landsat activities are agriculture, particularly tropical agriculture to provide crop inventories to predict whether there will be a shortfall or excessive crop harvest, and cartography, to map ground features so people can locate them for further investigation. In the future Paul expects Landsat data may be incorporated into geobased information systems. Such systems assemble a wide range of spatially registered data types into a system, frequently computerized, to provide statistical or mapped information to decision-makers in many subject areas. Landsat is a new tool for development and there is still a great amount of unknown information as to when and how it should be utilized. The following sections will examine by subject areas some specific applications of this data to resource assessments.

(a) Forestry The conversion of forests to farmland and the demand for fuelwood and other forest products are depleting the world’s forests by as much as 20 million hectares per year. Most of this loss is in the tropical regions of developing nations where some 40% of the remaining forests may disappear by the year 2000 (AID, 1981). The impact of forest destruction on food, fuel, soil and the global climate may be tremendous. Aerial photo interpretation has been an important tool for the forest manager. Landsat, while unable to replace aerial photography for some of the informational needs of the forest manager, can be very valuable to the forester because of its unique capabilities. The three aspects of remote sensing applications to forestry are detection, identification and monitoring. Landsat, because of its frequency of

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global coverage, may be most important in monitoring forest changes. Accelerated deforestation in some areas can often not be effectively monitored other than by satellite data. It is estimated that in the decade of the 1970s Thailand lost one-fourth of its forests, Costa Rica one-third and the Ivory Coast one-third. Landsat can often provide current information to the resource manager on the location and extent of deforestation activities. Morain and Klankamsorn (1978) summarized the use of visual analysis techniques of Landsat imagery for forestry in Thailand. Since Thailand’s first National Programme of Landsat training course in 1973, personnel of the Royal Forest Department have been utilizing this imagery in their survey and mapping efforts. By 1977 they had: (1) completed a survey of existing forest land for the whole country at scales of 1:500,000 and 1: 1 ,ooo,ooo; (2) designated forest lands to be conserved as catchment areas in the upper Chao Phya River Basin; (3) surveyed and assessed areas of shifting cultivation in northern Thailand; and (4) conducted a change detection study of forest conversion in the seven eastern provinces of the country. Associated studies by non-Royal Forest Department personnel have focused on the detection and measurement of rubber plantations in eastern and southern Thailand and on general land cover assessments in selected provinces comprising the Bangkok plain. The value of using Landsat imagery in Thailand’s national forest surveys lies in: (1) the reduction in time and personnel needed to complete a survey (1 year) compared to that required if using conventional aerial photography (10 years); (2) the ability to set more timely policies and plans for forest conservation and protection; and (3) the ability to allocate forest protection personnel better to prevent unauthorized forest destruction. Landsat images have shown that Thailand’s total forest resource diminished by more than 16% between 1961 (55% of the land area) and 1973 (38.6% of the Land area). In view of a national policy of conserving 50% of the country in forest cover, there is a continuing and urgent need to monitor forest extent and condition on a routine basis. It would appear to be an integral part of this monitoring effort. Danjoy and Sadowslci (1978) used both manual interpretation and computer processing techniques of Landsat data to identify and locate Aguaji Palm and other forest associations within lowland areas of the tropical forest region of eastern Peru. Management of the Aguaji palm could have considerable economic

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value by providing a domestic source of palm oil, edible food products for human and livestock consumption, and a source of lignocellulose for the wood pulp and paper industry. Information on the location and extent of the Aguaji necessary to implement management programmes was not well known because of the vastness and inaccessibility of this area and the costs of collecting traditional aerial photography. Landsat was investigated as a possible data source for Aguaji inventory and the study results indicated considerable potential for this tool to discriminate Aguaji and other forest associations within this area. Manual interpretation techniques were useful in discriminating generalized locations of large expanses of the forest features while numerical processing techniques were considerably more precise in locating boundaries between features and mapping complex areas. Muyed (1978) summarized the following prospects for using Landsat data in the conservation and management of forest resources. 1. Surveying and mapping - Landsat imagery can best be used for surveying and mapping. As is the case for all resource management, surveying and mapping are essential for making an inventory or working plan for forests. Although forest boundaries are easy to define because of their high contrast to adjacent areas, it is difficult to observe ground features. A proper combination of ground truth and Landsat data can produce very good results. 2. Soil classification - the spatial distribution of soils can often be delineated on Landsat imagery. This enables the forester to determine correctly the species which should be planted in a particular area. 3. Aerial photography site selection - Landsat imagery can be used to help decide where a very local type of aerial photography should be carried out, such as photography using any 35 mm camera from a small aircraft or helicopter. This reduces the cost of selecting an area for detailed survey or for laying out sample plots. 4. Delineating forest types - delineating broad forest types is possible with the help of Landsat imagery. This delineation is necessary for preparing management plans and setting up industries based on raw materials such as softwood or bamboo. 5. Large area volume estimates - although forests are a renewable resource, once they are exploited beyond their capacity of renewal they can never attain their previous state. Therefore before determining the allowable cut it is essential that an estimate of the standing stock is made. Multistage sampling using Landsat imagery can be successfully used for estimating timber volume in a forest. 6. Comparative studies - because Landsat provides coverage of the same area over a short span of time, it is possible to make comparative studies,

particularly for erosion, accretion and encroachment. 7. Detection of flowering disease and fire-gregarious flowering of trees, epidemic diseases, largescale insect damage and forest fires can be detected on Landsat imagery. The extent of damage by other natural causes may also sometimes be assessed by Landsat imagery. 8. Wildlife management - Landsat imagery is useful for studying wildlife habitat. 9. Composition studies - by studying Landsat imagery of different dates on which some species may be leafless or dormant, an assessment of the composition of a forest can be made. 10. Studying undergrowth - this kind of study is very difficult or almost impossible using Landsat imagery. However, by studying the top canopy and obtaining adequate ground truth a system may be developed which will determine the relation between the ground flora and the top canopy.

(b) Agriculture The continuous and frequently intense problem of food shortages is a priority issue for development planners. Any contribution of Landsat data to better management of agricultural systems or more timely information on food production may be very useful in understanding and ameliorating the world food crisis. Possible agricultural applications of remote sensing are: (1) crop identification, (2) crop acreage determination, (3) crop condition assessment and (4) yield forecast and estimation. The advantages of remote sensing over other agricultural data collection techniques include greater accuracy, more timeliness and lower costs per unit area. Much of the use of Landsat for agricultural analyses has been in mid-latitudes where cropping systems are large fields of homogenous crops, have distinct seasonal phenology, and frequently already have a reasonably sophisticated crop inventory and analysis framework. Landsat under these conditions can have accuracies of crop identification exceeding 90% correct. The greatest benefit of remote sensing in agriculture is in those countries without well established crop inventory and analysis systems. It is in these areas of greatest need for information that satellite data is difficult to use because of the often small irregularly shaped fields and the use of intercropping and other heterogenous crop patterns. Additionally, these often tropical areas have frequent cloud cover making collection of timely data difficult, and because vegetation seasonality, of no dormant frequently have the same crop at many stages of growth at any given time. Even under these

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LANDSAT conditions Landsat can be useful in collecting agricultural information by providing a stratification of cropping practices. Such a stratification can be the basis for a multistage area frame sampling scheme incorporating Landsat, selected aerial photography, and field examinations. This type of agricultural sampling procedure has been demonstrated to be highly successful in providing accurate and relatively low cost agricultural information including crop types, acreage and yields (National Academy of Sciences, 1977). Chaudhury (1978) used Landsat to locate and measure the extent of winter rice in some regions of Bangladesh. Using a double sampling approach between Landsat estimates of winter rice and field estimates, a high correlation between those two samples was determined which was very promising for winter rice acreage determinations. MacLeod (1974) used these images to locate channels of extensive ancient drainage systems containing large annually replenished reserves of near surface water that could be used by humans and livestock as well as for irrigation in drought stricken areas of the Sahel. Colwell (1977) formulates the following conclusions concerning the use of remote sensing for agricultural statistics in developing countries: 1. For most areas that are not satisfied with their present data on agricultural production, use of remote sensing data (especially aerial photography and satellite imagery) will undoubtedly enable them to get better information in a cost-effective way. 2. Remote sensing does not replace existing agricultural data collection procedures, but should be used as one element of a total agricultural information system. Field observations continue to be required to interpret and verify information derived from remote sensing technology. A serious problem in the use of remote sensing data is the lack of mechanisms for incorporating such data, into an integrated decision-making structure. 3. Presently remote sensing data is most useful for crop identification and acreage determination. Yield estimation using remote sensing is less well established but shows promise for the future. 4. Key to effective use of remote sensing data is the design of an appropriate agricultural sampling strategy. Multistage and double sampling procedures incorporating ground observations, aircraft and satellite data have proven effective in studies in the United States and may be useful in developing countries as well. 5. Landsat imagery provides a very low cost form of large area agricultural data. When used appropriately, Landsat data is extremely cost effective on a per unit area basis.

6. Satellites potentially can furnish very timely information concerning such dynamic phenomena as field preparation and crop maturation. However current delays in data handling and delivery may range from several weeks to several months. Future institutional developments should reduce this delivery time and thereby increase the value of the data for crop monitoring purposes. 7. Current data processing techniques work better for simple agricultural systems than for complex ones. Developing countries with large average field sizes and few crops will derive more accurate information from satellite data than countries with small fields and a great variety of crops. 8. Advanced remote sensing technology should not be used simply for technology’s sake. A considerable amount of useful information can be obtained from remote sensing by relatively unsophisticated methods, including image interpretation. The introduction of more advanced technology should occur when it can and will be utilized, and when it is shown to be cost-effective.

(c)

Water resources

The identification, evaluation and monitoring of water resources is important to agricultural, forestry, rangeland and regional planning activities among others. Landsat has been demonstrated to be very useful in the examination of some water resources and has the potential to be useful in others. One possible application of remote sensing to water resources critical to many regions is the identification and analysis of ground water. Moore (1978) concluded that the general principles of photograph interpretation may be applied to Landsat images to recognize features that are favourable for ground water occurrence. These features are landforms and landform patterns, drainage characteristics, snowmelt patterns, vegetation types and associations, outcrop patterns, soil tones, lake patterns, and land-use and land-cover characteristics. Some detected features directly imply the presence of shallow sands and gravels; other features indicated rock types or the presence of folds and fractures. A number of reports have shown a good correlation between lineaments detected aerial photographs or satellite images and the occurrence of ground water in dense, fractured limestones. There is good reason to believe that many lineaments are related to ground-water occurrence in other types of dense, fractured rocks. One of the more important things that has been learned since Landsat imagery became available is that time of year is critical for obtaining the maximum geologic and hydro-

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logic information from the images. Most landforms, lineaments and drainage patterns are enhanced by a low sun-elevation angle. Thus a Landsat image from 1 November to 15 February in the northern hemisphere or 1 May to 15 August in the southern hemisphere is desirable or necessary for some interpretations. MacLeod (1973) examined applications of Landsat to resource management and development in the Republic of Mali where water is probably the most important resource. He obtained imagery of the maximum flood stage on the Niger and Bani River watersheds and assessed the size, timing and area1 extent of the annual flood-data of particular interest to nomadic herdsmen who bring their cattle to the Inland Delta each year to forage; to fishermen who harvest 50,000 tons of fish from the Niger each year; and to cultivators in the Delta. MacLeod also determined that stream beds, lake beds, lineaments and drainage patterns can be observed on Landsat imagery and used for mapping potential groundwater resources. In one area of Mali he found no surface drainage ways of any substance, indicating very porous soils for this region. The absence of surface ways may suggest the presence of shallow aquifers which could be developed for livestock watering points for nomadic herds. Another application identified by MacLeod was the delineation of zones of potential recharge for groundwater supplies. Delineation of such recharge zones would aid in more efficient management of water quantity and quality. Krinsley (1976) has reported that the repetitive coverage of Landsat is ideally suited to provide seasonal images of playas in Iran from which changes in the area1 extent and morphology of the surficial materials can be recorded along with contemporaneous or previous field studies of actual surficial conditions. Data derived from the analysis of these images can provide a rational basis for planning the economic utilization (salt or water extraction and agriculture) and engineering development (roads and airfields) of these geomorphic features. Landsat can be a very important tool for the examination of water resources. One reason for its value in this area is the inclusion of reflective infrared bands in the multispectral scanner. Since water reflects little energy in these wavelengths, these bands are very useful in locating water or wet areas. Simple location of surface water can be done very effectively. Surface areas greater than five hectares can be identified with 99% accuracy. The difficulty in surface area delineation is simply that of available

cloud free imagery. Mapping of floods and water course or body changes for map updates can be easily accomplished with Landsat. Repetitive coverage of Landsat may determine if a stream is in continuous or intermittent flow. This data can also be effective in providing data on stream networks for drainage maps. Landsat has been able to provide reconnaissance level data needed for the design and operation of large irrigation projects and for the design of major impoundment structures. These developments can also be monitored with the same data. Landsat data has in some situations provided input to water-demand and groundwater-flow models for irrigation projects. This system can also be used to assess major watershed characteristics that affect runoff. It may be possible in semi-arid and arid watersheds to develop information on run-off prediction based on data from Landsat and available meteorological satellites.

(d)

Geology

Geologic applications of Landsat data can be with static features such as the distribution, character and structure of rock bodies or with dynamic phenomena such as landslides or fluvial processes. The synoptic view provided by these images may identify structural elements which are perhaps irregular or even discontinuous within smaller areas, as lineaments of regional extent. Geologists can use this data to trace prominent rock units across an entire fold belt without trying to piece together many individual photographs which may differ in scale, exposure or light angle. Satellite examination of static geologic features may be important in improving geologic mapping and providing more efficient geologic resource exploration. Landsat may be useful in evaluating the accuracy of existing geologic maps and locating additional features such as faults not previously mapped. In some areas, national or regional geological maps have been produced with Landsat more efficiently and more quickly than by traditional means. A plan to make a new geological map of Egypt at a scale of 1: 1 ,OOO,OOO in 10 years at a cost of $2.4 million using black and white aerial photographs was altered when satellite imagery proved to be more satisfactory. The latter offered roughly three times more geological detail and could accomplish the task more quickly and at less cost. In three years, maps covering about half the country were

LANDSAT completed and the task was expected to be finished in two more years. Location of major geologic elements on these images often allows the identification of specific areas for more detailed examination as possible resource areas. Additional geologic information may be obtained by use of multitemporal data to take advantage of details revealed by seasonal differences in vegetation or soil moisture. A Landsat derived rock-type classification map was used to select 30 sites near known copper deposits in Pakistan for additional examinations. Five of 19 sites examined had evidence of surface mineralization indicating the possibility of an enriched zone of copper below the surface (NAS, 1977). The repetitive coverage of Landsat allows the examination of dynamic phenomena from a frequency of nine days under optimal conditions to seasonal or annual examinations. Dynamic geologic phenomena which may be examined with this tool include river course changes during or after floods, coastline changes, sand dune encroachment, earthquake or landslide damage, and stripmine developments. Bangladesh scientists have used this data to identify and measure the accretion of coastal lands to allow the planting of trees to stabilize the new land for agricultural development. The United States National Academy of Sciences (1977) summarized the following possible geologic applications of remote sensing from space: 1. Develop geologic maps of areas not previously covered and evaluate existing geologic maps to determine their accuracy and completeness. 2. Identify large- and medium-scale structural and geologic features and correlate them over separate areas that may be widely spaced from one another. 3. Provide preliminary planning bases for siting communications, irrigation, of transportation, energy and industrial projects. 4. Select potential geological resource areas worthy of more detailed examination by aircraft and by ground observation. 5. Provide a base for specialized studies, such as of stream geochemistry. 6. Provide an opportunity to monitor, through use of repetitive coverage, altered or transient geologic features, such as changed stream courses and sand dunes.

(e)

Pedo2ogy

Aerial photography has been an important tool to the soil scientist to plan and operate field activities as well as to delineate soil boun-

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daries. Remote sensing techniques, other than aerial photography, including thermal or microwave sensors and satellite platforms have been examined by pedologists for possible applications to their discipline. The Landsat data attributes of importance to the soil scientist include (1) availability for some areas where no other data exists, (2) synoptic coverage to identify regional soil patterns, (3) repetitive coverage to provide data under wet or dry soil conditions and when vegetation cover is minimal and (4) capabilities because different multispectral spectral regions have different functions in soil analysis. Singh (1977) conducted a study to determine the feasibility of delineating salt-affected soils using Landsat data in the Ganges Plain area of India. Increasing population pressure in the area makes it necessary to attempt to reclaim soils affected by salt and sodium accumulations. However, maps indicating the location and characteristics of the salt affected soils are not available for many areas in India because of the time restraints of traditional soil surveys. A visual study of satellite imagery indicated that salt-affected lands in the Ganges Plain were easily differentiated in MSS bands 4 and 5 but not in band 7. El Shazly (1978) mapped soils for an extensive area (over 100,000 km*) in west central Egypt by the interpretation of Landsat imagery associated with field examinations of soil profiles. The maps were at a scale of 1: 500,000 and classified soils according to their potential land use as either arable or non-arable. Soils were further classified into seven grades including soils of the Nile River flood plain and adjacent desert fringes; pediplain and depression soils; playa, bajada and lacustrine clay soils; desert pavement soils; and sand dune soils. This basic soil delineation prioritizes areas for agricultural development which should be more closely examined. The usefulness of reflectance data from surface features as provided by Landsat for soil mapping is limited because conventional soil series are differentiated by both surface and subsurface properties. Landsat cannot discriminate between soils which are differentiated only by subsurface features. The ability to delineate soil characteristics from this data is a function of the correlation between the spectral properties and important physical or chemical properties of the soils. Soil colour is obviously important in soil reflectance, but variations in soil moisture, surface roughness, crusting or cultural practices also affect reflectance. This data can frequently identify variations in

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organic content, salinity and soil moisture. An ability to identify soil moisture will most likely improve by use of a thermal MSS band. The interference of surface vegetation in examining soils is often a problem. In some cases this problem can be minimized by use of temporal data when the vegetation interference is minimal. In other situations, the relationship between soil and vegetation is such that an identification of vegetation types or densities is an indicator of soil type. Satellite data is seldom sufficient to identify the same spatial and functional detail as traditional soil mapping but may be very effective in determining broad soil characteristics and soil patterns over wide areas in a short time particularly for reconnaissance surveys (Davis, 1975).

(f) Other disciplines The application of Landsat data is not limited to the previously discussed disciplines. This data has some value to probably all natural resources examined in development. As a cartographic tool data can be provided to map previously unmapped areas quickly and inexpensively and can update existing maps. Land cover information can be obtained from this data source for regional or transportation planning. Range inventory and analysis to assess the livestock carrying capacity of an area, identify areas where range improvement techniques may be most appropriate, and monitor grassland burning activities can be obtained from Landsat. Some oceanographic and coastal processes may also be examined with this tool. Perhaps the greatest potential of the system is a result of its applicability to many resource features. Developmental activities are increasingly multi-disciplinary requiring the integra-

tion of many data types such as forests, population, climate, hydrology, transportation and soils among others. Landsat can not only be useful in collecting some of these data types but because of its planimetric aspects and basic cellular data collection structure may be a useful base to compile a wide variety of spatially identifiable data. The contribution of satellite data to the construction of geographic information systems to serve the needs of development planners in many disciplines and promote the use of comprehensive multi-disciplinary analyses may be one of this tool’s greatest values.

4. SUMMARY Improved natural resource utilization without excessive environmental damage is necessary to provide an acceptable quality of life for current and future generations in most areas of the world. Good management decisions concerning resource utilization necessitate accurate and current information on the location, quantity and condition of the resources - information not available to many decision-makers. The United States Landsat series of satellites for the collection of natural resource data can often provide information needed by decisionmakers. The Landsat data is available for essentially all habitable land areas of the earth, collected very frequently, readily available, easily utilized and inexpensive. It has been demonstrated to be applicable to many resource areas. Landsat cannot be useful in all situations and has limitations but anyone involved in the acquisition of resource information should be aware of the existence and possible utilization of this data source because it has great applicability as a tool for development.

REFERENCES for International Development (AID), Agenda (Washington, D.C., April 1981). Chaudhury, M. U. ei al., ‘A Landsat inventory of the agricultural and forest resources in Bangladesh’, Agency

Proceedings, 12th International Symposium on Remote Sensing of the Environment (Ann Arbor, Michigan: Environmental Research Institute of Michigan, 1978), pp. 1391-1400. Colwell, John E., Uses of Remote Sensing for Agricultural Statistics in Developing Countries, Report 111800-18-T (Ann Arbor, Michigan: Environmental Research Institute of Michigan, 1977). Cummings, Jr., Ralph W., ‘Minimum information systems for agricultural development in low-income

countries’, Seminar Report Number 14 (New York: Agricultural Development Council, Inc., 1977). Danjoy, W. A. and F. G. Sadowski, ‘Use of Landsat in the study of forest classification in the tropical Proceedings, 12th In tema tional Symjungle’, posium on Remote Sensing of the Environment (Ann Arbor, Michigan: Environmental Research Institute of Michigan, 1978), pp. 947-956. Davis, S. M. (ed.), Mapping Soil Characteristics, FOCUS series (West Lafayette, Indiana: Purdue University Laboratory for the Application of Remote Sensing, 1975). El Shazly, E. M. et al., ‘Application of Landsat

LANDSAT imagery in the geological and soil investigation in the Central Western Desert, Egypt’, Proceedings, 12th International Symposium on Remote Sensing of the Environment (Ann Arbor, Michigan: Environmental Research Institute of Michigan, 1978) pp. 857-866. EROS Data Center, Landsat Data Users Notes, Issue 14 (Sioux Falls, South Dakota, 1980). Krinsley, Daniel B., ‘Monitoring water resources in, Qom Playa, West-Central Iran’, ERTS-I A New Window on Our Planet, Professional Paper 929 D.C.: United States Geological (Washington, Survey, 1976), pp. 139-142. Macleod, N. H., ‘Applications of remote sensing to resource management & development in Sahelian Africa’, Symposium on Significant Results Obtained from Earth Resources Technology Satellife (Washington, D.C.: National Aeronautic and Space Administration, 1973), pp. 1475-1481. Macleod, N. H., ‘Remote sensing experiments in West Africa’, Proceedings, Third Earth Resources Technology Satellite Symposium (Washington, D.C.: National Aeronautic and Space Administration, 1974), pp. 247-266. Moore, G. K., ‘The role of remote sensing in groundwater exploration’, Proceedings of Joint Indo-U.S. Workshop on Remote Sensing of Water Resources (Hyderabad, India, 1978), pp. 22-40. Morain, S. A. and B. Klankamsorn, ‘Forest mapping and inventory technologies through visual analyses

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of Landsat imagery, examples from Thailand’, Proceedings, 12th International Symposium on Remote Sensing of the Environment (Ann Arbor, Michigan: Environmental Research Institute of Michigan, 1978), pp. 417-426. Morley, W. W., ‘Remote sensing as a source of data for in J. J. Nossin (ed.), Surveys for development’, Development (Amsterdam: Elsevier Scientific, 1977), pp. 79-89. Muyed, M. A., Application of Remote Sensing to Forestry in Bangladesh, Report 78-21 (Brookings, South Dakota: Remote Sensing Institute, South Dakota State University, 1978). National Academy of Sciences (NAS), Remote Sensing from Space: Prospects for Developing Court tries (Washington, D.C., 1977). National Conference of State Legislatures, A Legislator’s Guide to Landsat (Denver, Colorado, 1978). Paul, C. K., ‘The USAID program in remote sensing’, Proceedings of the Second Conference on the Economics of Remote Sensing (San Jose, California: San Jose State University, 1978) pp. ll19. Singh, A. N., S. J. Kristof and M. F. Baumgardner, Delineating Salt Affected Soils in the Ganges Plain, India, by DigitaIAnalysis of Landsat Data, Report 111477 (West Lafayette, Indiana: Purdue University Laboratory for the Application of Remote Sensing, 1977).