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Nicholas Kouchoukos, Ronald Smith, Art Gleason, Prasad Thenkabail, Frank Hole, Youssef Barkoudah*, Jeff Albert,Paul Gluhosky, and Jane Foster

Yale University*University of Damascus

ABSTRACTWe describe a variety of satellite-derived datasets and their application to understanding changes in the landscape ofsouthwest Asia, including regional climatology, natural vegetation, and the expansion and intensification of agriculturalproduction. We demonstrate the effectiveness of several remote sensing tools in gauging environmental change in thissemi-arid region as well as offering insights into the appropriate analysis of remotely sensed datasets.

INTRODUCTIONAgricultural and pastoral production in the semi-arid regions of

southwest Asia has expanded rapidly over recent decades to meet thesubsistence needs of one of the world’s fastest growing populationsand the demands of the international economy. This growth isconstrained by great interannual variation in the amount and timingof rainfall, which restricts dependable farming to very limited areas.Development, nonetheless, has proceeded undeterred—defyingclimate through feats of hydrological engineering or betting againstit by cultivating vast areas of agriculturally marginal land. These twoprocesses, the intensification and extension of agricultural produc-tion, have been among the dominant forces shaping the modernenvironments of southwest Asia, but their spatial expression is com-plex. The many nations of this region differ widely in their climateand natural resources as well as in their development priorities,available capital, and access to technology. Everywhere, however,pressure on limited water, soil, and plant resources has increased,often nearing the point of exhaustion or international conflict.Growing concern about the natural resource base has led govern-ments and independent groups across the region to initiate ambitiousand costly conservation and management programs. Ultimately, thesuccess of these efforts depends on the development of methods forefficient, quantitative measurement of the distribution, use, and re-generation of natural resources at a range of spatial scales.

Multispectral satellite data and techniques of image analysis offera potentially powerful tool for understanding problems in the clima-tology, environmental history, and natural resource management ofsouthwest Asia. Satellite data have been collected over southwest

Monitoring the Distribution, Use, and Regeneration of Natural Resourcesin Semi-arid Southwest Asia

Asia for the past twenty-five years and offer a virtually unexploredarchive for analyzing regional environmental changes. During thenext few years, a new generation of earth resource satellites willcome on line, providing more frequent and more detailed measure-ments of the earth’s surface than are yet possible. Southwest Asia isparticularly well suited to satellite observation. There are few treesand, during much of the year, few clouds to obscure the land sur-face; the major vegetation cycles of the region (winter cereal farm-ing, summer cash cropping, deciduous forest growth) occur out ofphase, allowing them to be studied easily and independently of oneanother. On the other hand, the bright reflectance of the land sur-face and the high dust content of the atmosphere can often causedifficult technical problems in the detection of change over time andin the estimation of surface biophysical parameters.

Satellite sensors are best described as imaging spectrometers thatmeasure the earth’s reflectance or emission of electromagnetic radia-tion over small areas, or pixels, ranging in size from several metersto several kilometers. Most satellite sensors measure pixel reflectanceor emission in multiple bands of the electromagnetic spectrum, sothat each pixel is characterized by several different values. Thesevalues can be treated in one of two ways. First, pixel values or ratiosamong them can be assigned to the three primary colors and used tomodulate their intensity so that they combine to produce for eachpixel one color from a palette of millions. The result is a richly de-tailed, false color representation of the land surface that can revealfeatures, contrasts, and textures not apparent on aerial photographs.A second approach treats pixel values as discrete physical quantitiesthat can be compared mathematically to each other and to knownspectral characteristics of plants, soils, rocks, and other surfacematerials (Figure 1). In this way it is possible to produce rapid land-use and land-cover classifications and to estimate land surface prop-erties such as vegetation density, temperature, and evapotrans-piration, among others.

The most commonly used satellite-derived estimate of vegetationdensity is the normalized difference vegetation index (NDVI), whichtakes advantage of the strong reflectance of green vegetation in thenear-infrared wavelengths and its relatively strong absorption ofvisible wavelengths (Tucker 1979). This index, calculated as thedifference between infrared and visible reflectance divided by theirsum, is a dimensionless quantity ranging in value from –1 to 1.

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Figure 1 Reflectance spectra for commoncrop, shrub, and soil types in semi-arid southwest Asia.

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Numerous studies have demonstrated strong positive correlationbetween NDVI and widely used surface measures of vegetationdensity such as biomass and leaf area index, though the precisenature of this relationship for a particular crop and region must bedetermined experimentally. Because it is a ratio of two reflectances,NDVI is particularly resistant to varying scene illumination and thusprovides fairly consistent measurement fields across broken terrain.On the other hand, in very dense or multi-layered vegetation cano-pies, some vegetation may be obscured from direct view and hencenot measured by the NDVI. In sparsely vegetated areas, bright soilstend to dominate the measured pixel reflectances, resulting in lowsignal-to-noise ratios. A number of alternative vegetation indiceshave been developed to minimize these problems, but their judi-cious use typically requires additional information about soil prop-erties and other surface characteristics, complicating their appli-cation over large areas (Huete 1988; Qi et al. 1994). In general,NDVI is well suited for exploratory analysis of changing vegetationpatterns in southwest Asia and is used extensively in the analysesthat follow.

At present, multispectral data collected by existing satellite sen-sors are of two general types (Table 1). The first is coarse to moder-ate spatial resolution, high temporal frequency reflectance datacollected within a limited number of spectral bands. The most com-mon sensor of this type is the Advanced Very High Resolution Radi-ometer (AVHRR) carried on NOAA’s meteorological satellites,which provides daily measurements of global reflectance and ther-mal emission in five spectral channels with a pixel size of 1 km.Another general type of satellite sensor provides high spatialresolution data in as many as seven spectral channels every 14–16days. Sensors of this type include the widely used Landsat, SPOT,and IRS systems, which produce images with pixel sizes as small as5-10 meters. New satellite systems are breaking down this di-chotomy of data types, but successful application of satellite data toecological problems has depended and will continue for some timeto depend on the integration of different spatial and temporal scalesof analysis as well as ground-level and satellite data. This paperpresents ongoing research by the Southwest Asia Project at the YaleCenter for Earth Observation on the application of satellite data tomesoscale climatology, environmental history, and resourcemanagement in southwest Asia.

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MESOSCALE CLIMATE, HYDROLOGY, ANDVEGETATION OF SOUTHWEST ASIA

One objective of the Southwest Asia Project is to understand therelationships among climate, water resources, and the distributionof vegetation across southwest Asia. There are two unique aspects ofour work in this area. First, we carry out geographical analysis on arather small spatial scale (i.e. the mesoscale) using a grid spacing offive kilometers. The advantage of mesoscale analysis is that it cap-tures many of the significant natural patterns and human inducedfeatures of the landscape, including meteorological phenomena andterrain variations. Examples of mesoscale landscape features aremountains, coastlines, rivers, cities, and rangeland and agriculturalpatterns. Second, we make use of satellite-derived data extensively tocomplement conventional climate station data. Satellite sensors,even in their current early state of technical development, can de-scribe fine scale spatial patterns of standing water, snow, and vegeta-tion at reasonably frequent intervals.

A classic problem in climate and vegetation description is thecombined spatial and temporal variation of the relevant fieldquantities: temperature, precipitation, snow cover, soil moisture andrunoff. Putting aside long-term trends and interannual variation,one must still capture the seasonal cycles of these quantities. Weapproach the problem of graphical presentation using the techniqueof cluster analysis, known also in the remote sensing field asunsupervised classification. This approach groups into a particularclass all pixels in the image having similar seasonal cycles,independent of where they occur geographically. The completespatial and temporal description of a particular field quantity (e.g.precipitation) can then be concisely presented in two diagrams: aclass map showing the geographical distribution of each class andsignature plots showing the seasonal cycle of the quantity. There isof course some lost information and some arbitrariness in such adescriptive method. First, one must choose how many differentclasses are to be defined. The more classes one defines, the more infor-mation is retained, but the more complex the result. Second, a quantita-tive measure of pixel difference must be defined. It is this difference thatwill be minimized among pixels assigned to the same class.

The objective of the Southwest Asia Project is to analyze most ofthe relevant climatological, hydrological, and vegetation fields andto examine the relationships among them using simple models. Inthis short paper, however, we can only give the briefest descriptionof two important fields: precipitation and vegetation cover.

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PRECIPITATIONThe starting point for our analysis of climatological precipitation

is a collection of station data available from FAO (FAOCLIM ver-sion 1.2). This dataset includes monthly values for standard climatequantities, averaged over a period from 1940 to 1970, for about 1500stations in southwest Asia. The station density is quite variable. It isparticularly high along the Mediterranean coast but very low in thedesert and mountainous interior regions. The typical distance be-tween stations varies from about 10 to 200 km, depending on theregion. A critical step in our analysis is the interpolation of stationdata to a regular array of grid points with 5 km spacing. Thisinterpolation is done using a Gaussian weighting function:

Figure 3 Class signatures from k-meansclassification of monthlyprecipitation

W = e-r2/R2

where r is the distance between station and grid point and R is theradius of influence. To account for the variable station density, thevalue of R is adjusted at each grid point so that the sum of the sta-tion weights remains constant. During the interpolation procedure,the temperature values are corrected for altitude using an assumedlapse rate of –5oC/km. The resulting monthly gridded fields are thensubjected to a k-means clustering algorithm to assign each gridpoint to one of ten classes. The members of each class have similarseasonal cycles of precipitation. The geographical distribution of theprecipitation classes is illustrated in Figure 2. The seasonal cycle ofprecipitation for each class is shown in Figure 3.

Several important aspects of Middle East precipitation are clearin this analysis. Throughout the region, precipitation is low or ab-sent in summer, reaching a maximum in mid-winter, excepting onlythe coastal regions of the Caspian Sea (class #10), where a strikingautumn rainfall maximum is evident. Several strong spatial gradi-ents are also clear. The plentiful winter rains along the Mediterra-nean Coast decline rapidly inland due to orographic lifting anddescent as well as decreasing water source proximity. From southand southwest to north and northeast in the interior, precipitationincreases due to the decreasing influence of the subtropical high and theincreasing effect of mid-latitude frontal storms and orographic lifting.

VEGETATION COVERThe most appropriate satellite sensor for studying vegetation

cover and dynamics over large areas is the AVHRR. This instrumenthas been in continuous operation since 1979, and there are severalongoing projects to produce consistently processed global images ata high temporal frequency (James and Kaluri 1994; Eidenshink

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1994). Because on a given day a significant part of the land surface isobscured from view by clouds, daily AVHRR images are routinelylumped into 10-day groups, and a single image is derived for theperiod by choosing the pixel with the highest NDVI. The result is animage that is largely (though not completely) cloud-free and issuitable for many types of multi-temporal analysis (Holben 1989,Myeni et al. 1997).

To produce a preliminary analysis of vegetation cover in south-west Asia, we acquired one year (October 1992 – September 1993) of1-1 km resolution AVHRR data, geometrically registered, radio-metrically calibrated, and composited into 10-day periods by theUSGS EROS data center (Eidenshank and Faundeen 1994). Aftercomputing NDVI for all pixels in each image, we stacked the imagesso that each pixel is characterized not by one but 36 NDVI values.As in the preceding analysis, we then used a k-means clusteringalgorithm to assign each pixel to one of thirteen classes havingsimilar seasonal cycles of vegetation cover. The geographicaldistribution of each class is shown in Figure 4 and the characteristicseasonal NDVI cycle for each class in Figure 5.

Table 2 Description and provisional interpretation of classes from k-means cluster analysis of NDVI from 10 day composite 1 km AVHRR data,Figure 4). Interpretations must be verified by fieldwork.

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The resulting distribution of vegetation classes is complex. Onbroad scales, it reflects the natural influence of rain and wintersnowfall. On finer scales, however, it shows the effects of city build-ing, deforestation, grazing, and rainfed and irrigated agriculture.Using characteristic NDVI curves for each class and partial analysisof corresponding climatological fields, tentative interpretations ofeach class are presented in Table 2.

ALTERNATIVE APPROACHES TO CLASSIFICATIONThe k-means algorithm employed above uses Euclidean distance

to describe similarity among pixels characterized by measurementsof a single variable at multiple time points. This approach empha-sizes the mean of the time series for each pixel and, to a lesser extent,each pixel’s value at a given time point but ignores the sequence inwhich these values occur. In other words, the k-means algorithmassigns greater significance to the amplitude of the seasonal curve

Figure 5 Class signatures from k-means classification of NDVI from 10 day composite 1 km AVHRR data.

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than to its shape. This is clear from the plot of class means from thevegetation classification (Figure 5), where several classes having thesame general shape differ only in their amplitude.

For some applications, however, it is useful to emphasize the shapeof the seasonal curve when classifying multitemporal datasets. Forexample, in Mediterranean-type ecosystems there is considerableregional and interannual variability in both the timing and the lengthof the growing season, and hence subtle changes in the timing ofseasonal maxima and minima may be of considerable interest toagroclimatologists (Benedetti et al. 1992). Patterns of land use andland cover may also be better differentiated by the shape of the annualcycle, especially in areas where there are multiple cropping cycles.

Figure 6 Rules-based classification of Fourier coefficients 1-6 computed for average monthly NDVI from 10-daycomposite 8 km AVHRR data, 1982-1993.

Finally, it is important to consider that in moderate to coarse resolu-tion satellite data vegetation signals are often diluted by mixturewith seasonally invariant terrain, such as barren or urban land. Thusin the case of pixels having the same general type of vegetation but atdifferent densities, a k-means classification would separate them bydensity, while a classification based on the shape of the curve wouldcorrectly recognize the basic ecological similarity among them.

One common method for describing the shape of time series data

is Fourier analysis, a technique that represents complex waveforms asthe sum of simpler components, usually sine waves. To assess the

utility of Fourier methods for analysis of vegetation patterns in south-west Asia, we computed mean monthly NDVI values over the period

1982-1993 using the 8 km resolution Pathfinder AVHRR dataset(James and Kaluri 1994). As in the precipitation analysis presented

earlier, averaging helps reduce inherent noise and the effects ofinterannual variability on the analysis of seasonal cycles. Following a

method described by Olsson and Eklundh (1994), we computed thefirst six Fourier coefficients for the 12-month time series for each pixel

and the proportion of the series variance explained by each compo-nent and by the sum of all components up to and including the one in

question. These components provide information about the generalshape of the curve (number of NDVI peaks), its phase (timing of

peaks), and amplitude (height of peaks). Using a series of rules andthreshold values, we then divided pixels into distinct classes based on

the number, timing, and magnitude of NDVI peaks. The results of thisclassification are presented in Figure 6.

Like the k-means method, the Fourier approach distinguishes wellamong the major ecological regions of semi-arid southwest Asia: 1)the desert and steppe (no peaks), 2) the dry-farmed areas (March-May

peaks), 3) the irrigated areas (double and triple peaks), and 4) thedeciduous forests (June-July peaks). To examine the differences be-

tween the two classification techniques more closely, we used the k-means algorithm to assign the image pixels in the 12-year average

dataset to 16 classes, the same number recognized in the Fourierapproach. We then chose a representative sample of individual pixels

and compared their seasonal NDVI cycles to those of the classes towhich they were assigned by each method. Figure 7 (left) shows two

pixels, a and d, which have very similar double-peaked seasonal cyclesbut differ in amplitude. In the Fourier classification, both of these are

assigned to class 3, while the k-means classification divides thembetween two classes (15 and 10) based on their different annual

means. Significantly, the Fourier technique recognizes neither pixel bnor c as double peaked, though the k-means technique lumps them

with Classes 15 and 10, respectively, because of general similarity ofoverall mean. The difference between the two techniques in the case of

single-peaked pixels is more subtle but still significant. Figure 7 (right)plots the seasonal cycle of five pixels (e-i) that the k-means algorithm

divides into two classes (e-f and g-i) based on differences in theirannual means. The Fourier approach, which is more sensitive to the

timing of the maximum, groups pixels f, g, and i together on the basis oftheir sharp May peak (Class 13) but classifies pixels e and h separately.

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In general, we found that the Fourier technique is a usefulmethod for classifying pixels according to the shape of their seasonalcycle and is therefore an invaluable complement to the more widelyused k-means method. Unlike the k-means algorithm, however, therules-based approach to classification contains an element of subjec-tivity, as the analyst must decide a priori what classes are to be dis-tinguished and what the threshold values should be. While thislimits the usefulness of the technique for exploratory data analysis, itprovides a powerful tool for isolating and extracting specific classesof interest.

CHARACTERIZING INTERANNUAL VARIABILITYAll of the analyses presented so far have used either a single year

of data or multiple-year averages to represent climate or vegetationpatterns generally or during a particular period of time. With theirfast and efficient repeat coverage, satellite sensors also provide im-portant data for the analysis of land surface change over time. Whilethese changes are easy to detect, they often have multiple causes thatare difficult to infer from satellite data alone. In the analysis of veg-etation dynamics, climate variation is clearly an important drivingforce, but not all types of vegetation respond uniformly to climatechange. Some cropping systems in southwest Asia are heavily buff-ered against climate variation either by irrigation or by careful as-sessment of risk. In other systems, enforced schedules of croprotation or speculative cultivation in marginal areas can amplifyminor climate fluctuations considerably (Nguyen 1989).

One very useful approach to isolating climate effects on vegeta-tion is the vegetation condition index (VCI), proposed by Kogan(1993) for use in drought monitoring. This index is computed for aparticular time point in a long, cyclical time series by comparing the

Figure 7 Comparison of k-means and Fourier classifications

NDVI value of each image pixel to that pixel’s maximum and mini-mum value at the same time point in all years spanned by the series.The result, typically scaled between 0 and 100%, provides a relativemeasure of vegetation cover within the ecological parameters of eachvegetation type. In other words, the VCI attempts to normalizeecological variability across the image and to emphasize changes dueto relatively high frequency meteorological fluctuations.

In Figure 8, we use the 8-km pathfinder AVHRR dataset to com-pute the VCI across southwest Asia in April for four years character-ized by markedly different precipitation patterns. In each image, theapproximate distribution of rainfall during the preceding six months

Figure 8 Vegetation condition indices (VCIs) for April of four successive years. Contours show the approximate distribution of total November-April precipitation for each year.

(November-April) is represented by 200mm contours derived fromthe Global Precipitation Climatology Project’s Combined Precipita-tion dataset (Huffman et al. 1997). This analysis highlights both thehigh interannual variability of rainfall and vegetation state acrosssouthwest Asia and the strong correlation between these two variables.

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MONITORING THE INTENSIFICATION AND EXPANSIONOF PRODUCTION

A steady expansion of cereal cultivation into regions used tradi-tionally as rangeland has been underway for decades in southwestAsia. The principal crop sown in these marginal areas is barley, anda graph of annual changes in its harvested area in Iraq and Syriashows rapid increase throughout the 1980s and early 1990s (Figure9). This increase is due in large part to the growing economic im-portance of barley and barley straw for livestock feed as sheep andgoat numbers far exceed the carrying capacity of available range-land. Much of this cultivation has been carried out on very largescales, and tractors are often used to plow and sow fields severalkilometers in extent. If adequate rain falls, crop production can bevery high, but crop failure is very common during normal or dry

years. Because deep plowing destroys the natural vegetation com-munities adapted to local climatic conditions, cultivation in thesteppe can result in serious erosion hazards and rapid deteriorationof forage production. Although cultivation of marginal zones is nowoutlawed in many countries, identifying areas where high-risk culti-vation has occurred and continues to occur can aid in formulatingconservation strategies and revising policy.

Figure 9 Trends in irrigation and barley cultivation in Syria and Iraq, 1960-1995 (from FAO/FAOstat).

To identify areas where high risk agriculture was practiced dur-ing the 1980s and early 1990s, we have summed March and AprilNDVI maxima computed from the 8-km Pathfinder AVHRR datasetfor each year between 1982 and 1994. The resulting series of 13images shows the approximate density and distribution of greenvegetation across the study area just before the maturation andharvest of winter crops. We then calculated the standard deviationof this series for each image pixel and divided the result by the series

mean (Figure 10). Low values of this ratio indicate stable springNDVI values as expected in core agricultural areas, healthy range-land, and barren desert. High values, on the other hand, indicateareas where spring NDVI fluctuates strongly about a lowered meanor where agriculture has been introduced or abandoned during theperiod of observation. The distribution of high values thereforeprovides a useful tool for recognizing areas where vegetation coverhas changed significantly during the period of observation.

The three regions highlighted in this analysis are A) the lowerJazirah in eastern Syria and Northern Iraq, B) the Hakkari and Vanprovinces of southeastern Turkey, and C) small areas along thebranches of the Euphrates and Tigris Rivers in southern Iraq. Ofthese regions, only the first corresponds to regions where extensivedry farming of cereals is practiced. A plot of the full NDVI time

Figure 10 Marginal agriculture in semi-arid southwest Asia. Standard deviation of March-AprilNDVI divided by mean of March-April NDVI.

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Figure 11 Mean NDVI values as a function of time, northeastern Syria test area.

Figure 12 Landsat MSS (top) and SIR-C(bottom) imagery depicting the ex-pansion of irrigated farming in the Jebel‘Abd-al ‘Aziz region, eastern Syria.

series from a roughly 400 km2 area within this zone confirms highinterannual variation of vegetation cover about a low mean (Figure11). In the last two years of the time series, NDVI values rise sharply,signaling either unusually high rainfall (which did not occur) or aqualitative change in land-use practices.

Higher resolution satellite data (Figure 12) allow further analysisof land use change in this area. A Landsat MSS image acquired June1975 (top) shows barley cultivation on an extremely large scale.Fallow and plowed fields, which appear pink and green respectively,are often several kilometers in length and are arranged haphazardlyacross a broad basin where deep calcic soils have developed in aregion with otherwise very limited agricultural potential. This pat-tern is a clear result of the use of mechanized plows to till large areasin hopes of abundant winter and spring rains. A second image ac-quired nearly twenty years later by an imaging radar system onboard the Space Shuttle (bottom), shows that these broad fields havebeen subdivided into hundreds of smaller parcels. This change,which began in 1993, resulted from the discovery and exploitationof ground water reserves and represents a transition from extensivedry farming to intensive irrigation.

The rapid growth of irrigation elsewhere in southwest Asia overthe past two decades (cf Figure 9) can also be investigated through acombination of moderate and high resolution satellite data. Becausemuch of the irrigation agriculture in the region is practiced in thesummertime, when dry farming is impossible, it is easy to recognizeirrigated areas by elevated August-September NDVI. In addition,because of higher ground surface temperatures and increased evapo-

transpiration during the summer, irrigated fields may also be recog-nized by their comparatively low emission in the thermal infraredwavelengths. To map areas where irrigation increased or decreasedbetween 1981 and 1994, we computed and standardized the differ-ence between mean August-September NDVI in 1993-94 and 1981-82 and between mean brightness temperatures at the two timepoints. A simple difference of these values (NDVI-Temperature)assigns high values to pixels where NDVI has increased and tem-peratures have decreased and low values to areas where NDVI hasdecreased and temperatures increased.

The spatial distribution of this index is shown in Figure 13. Highvalues correspond well to known locations of major irrigationprojects: A) the Ceyhan valley in southern Turkey, B) the Maskanahregion of central Syria, C) the Harran plain (GAP project area) ofsouth central Turkey, D) the Ceylanpinar-Ras al-‘Ain region on theTurkish-Syrian border, E) the upper Tigris valley, and F) theHamrin region of northern Iraq. The analysis also highlights areas

Figure 13 Intensification of irrigated agriculture in semi-arid southwest Asia. Standardized difference between mean August-September AVHRR Channel 5 brightness temperature in 1993-4 and 1981-2 subtracted from standardizeddifference between August-September NDVI in 1993-4 and 1981-2.

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where summer vegetation has declined significantly such as themarshes of southern Iraq (G) and the shores of the Caspian Sea (H).As in the preceding analysis, much more can be learned about thenature of vegetation changes in specific areas by examining the fulltime series of NDVI values. Figure 14 shows the NDVI profile of a1500 km2 region of the Harran plain in southern Turkey (area C,Figure 13). Between 1981 and 1987, the characteristic seasonalNDVI cycle of the region was characterized by a large late winter-early spring peak representing dry-farming in the region and by asecond, considerably smaller peak in July-August indicating limitedsummer cultivation. After 1987, however, the size of the late sum-mer peak grew rapidly as water from deep wells and later from theAttaturk dam was used to irrigate larger areas each year.

The magnitude and spatial distribution of these changes can beseen clearly in a pair of late summer Landsat images of the Harranplain in 1984 and 1992 (Figure 15). In this color combination,which approximates that of an infrared aerial photograph, vegeta-tion (mostly cotton) appears bright red due to its strong reflectancein the infrared wavelengths. Using unsupervised classification tech-niques to distinguish cultivated from fallow land, we estimate thatbetween these two time points, the extent of summer irrigation onthe Harran plain increased from 15,000 ha to more than 45,000.Similar estimates from images from 1985, 1987, and 1990 providefurther information about the timing of this growth and generallyconfirm trends apparent in the AVHRR data (Figure 14).

Figure 14 NDVI and summer cultivation profiles of the Harran Plain, Southeastern Turkey.

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Figure 15 Expansion of summer irrigation in the Harran Plain (GAP project area), southeastern Turkey.

Figure 16 Comparison of raw (DN) andreflectance spectra for test areas inthe Palmyra Basin, central Syria.

ESTIMATING BIOPHYSICAL PARAMETERSAs demonstrated in the preceding examples, two key aspects of

the SWAP methodology are a multi-sensor approach and the use ofsatellite data from archives maintained over the past 20-30 years bythe National Aeronautic and Space Administration (NASA), theU.S. Geological Survey (USGS), and other national and interna-tional agencies. Successful integration of data acquired by differentinstruments at different times and under different conditions re-quires careful attention to the different sensitivities and calibrationof satellite sensors as well as to the effects of atmospheric aerosolsand gases on the transmission of electromagnetic radiation.

To illustrate the importance of these considerations, we haveselected two Landsat TM images of the Palmyra region in Syria,taken one month apart (August 31 and October 10, 1984). Becausevegetation and land-use changes are minimal in the region duringthis time of year, the spectral signatures of corresponding areas onthe two images should be nearly identical. A plot of the average pixelvalues of four different 20 ha test areas present in both images,however, shows no clear similarity between the spectra of each testarea on the two different dates and no systematic difference amongthe spectra of the different test areas (Figure 16, top). These results,which bode poorly for the use of satellite images to analyze landsurface changes over time, have two main causes. First, differences

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in the relative positions of the satellite, the sun, and the earth’ssurface at each time point can result in significant differences in theamount of sunlight illuminating the earth’s surface and the amountthat is reflected toward the satellite sensor. Second, the intensity ofsolar radiation varies significantly across the visible and near-infra-red wavelengths and different scaling factors are used to convertsatellite-measured radiances within each wave band into digitalnumbers for computer image analysis. By applying known calibra-tion equations for each wave band to recover pixel radiances mea-sured at the satellite and normalizing these for the viewing angle ofthe sensor, digital numbers can be converted into estimates of sur-face radiance that are comparable across space and time. By furtherdividing this result by the total amount of incident solar radiation ineach waveband, radiance spectra can be converted to reflectance, adimensionless quantity that allows the integration and comparisonof spectra obtained by many different kinds of sensors in orbit, inthe field, or in the laboratory. Although these operations neatlyrecover coherent and reproducible reflectance spectra from satellitedata (Figure 16, bottom), full integration of multitemporal andmultisource spectral data must consider also the more subtle (butcumulatively significant) effects of changing atmospheric condi-tions, degradation of satellite sensors over time, and complexchanges in the reflectance of different materials under varying illu-mination and viewing conditions.

Once reproducible and physically meaningful spectral measure-ments are obtained from satellites, they can be combined with theresults of field investigation to develop techniques for estimatingground surface properties. The use of multispectral reflectance datato estimate physical parameters such as leaf area index, as well asrelated climate and soil conditions is well-established (Wiegand etal. 1992; Penuelas et al. 1992; Moran et al. 1996). Considerableimprovement in the accuracy and spatial scale of these estimates isexpected with the development and launch of new sensor systemsthat better describe surface reflectance characteristics. These ad-vances along with improved temporal coverage, will enable theroutine use of satellite data for agro-ecological characterization,precision farming, and agricultural decision support systems.

Taking full advantage of improvements in satellite sensor designdepends on accurate characterization of the reflectance properties ofmajor crops at different stages of their growth cycles and in differentsoil conditions. Very few studies of these relationships have beencarried out in southwest Asia, and considerable research remains tobe done before satellite-derived data can be fully integrated intoagricultural research and development in the region. For this reason,

0.2 0.4 0.6 0.8 1.0

Wet Biomass(kg/m2)

0

1

2

3

4

CottonSoybean

NDVI

0.2 0.4 0.6 0.8 1.0

Leaf AreaIndex

0

2

4

6

Figure 17 Relationships between physical andspectral parameters for cotton andsoybean.

we have begun a program of field research that uses portable spec-trometers to acquire high-resolution spectra from crops in differentsettings and stages of growth. Much of this fieldwork is scheduledduring times of satellite overpass to insure the comparability of insitu and satellite data.

This research, carried out in collaboration with scientists fromthe International Center for Agricultural Research in Dry Areas(ICARDA), has focused primarily on the region around Aleppo,Syria, where rainfall varies between 300 and 350 mm/year. The firstseason of field research in this region during the late summer andearly autumn of 1997 focused on major irrigated crops such ascotton (Gossypium), potato (Solanum erianthum), soybeans (Glycinemax), corn (Zea mays), sunflower (Helianthus), and various veg-etables. At each of 231 different locations, we measured the spectralcharacteristics of crop and soil, and took samples for laboratorymeasurement of wet biomass and leaf areas index. In the analysis ofthese data we have focused on characterizing the relationship be-tween conventional spectral indices such as NDVI and developingnew indices which are more closely related the physical characteris-tics of different crops. In general, we found that NDVI explainedbetween 50 and 80% of the observed variability in wet biomass andleaf area index for the five crops intensively studied. The closestrelationship between spectral and biophysical variables was observedfor cotton and soybean (Figure 17). In most cases, the relationshipsbetween the variables were non-linear, as other researchers havefound (e.g. Wiegand et al. 1992).

CONCLUSIONSIn this paper, we have illustrated some modern methods for

analyzing spatial and temporal patterns of climate and vegetation insouthwest Asia. These methods combine conventional climate ob-servations, multispectral satellite data, and computer-intensiveinterpolation and clustering algorithms. When combined with moresystematic fieldwork, these approaches may ultimately provide thelevel of completeness and accuracy needed for modeling, monitor-ing and managing the rapidly changing environment of the region.As the quality and accessibility of satellite data improve, we shouldexpect wider application and rapid improvement of the methods.

ACKNOWLEDGEMENTSThis research was carried out at the Yale Center for Earth Obser-

vation and supported by the National Aeronautics and Space Ad-ministration/Mission to Planet Earth. The authors would like to

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thank Prof. Dr. Adel el-Beltagy, Director General, Dr. John H.Dodds, Assistant Director General, and Dr. Eddy DePauw, of theInternational Center for Agricultural Research in the Dry Areas fortheir assistance in the design and execution of field work in Syria.

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NICHOLAS KOUCHOUKOS has just completed a doctoral thesis in Anthropology at Yale University. He is interestedin technologies of spatial representation, their application, and their social implications.

Nicholas Kouchoukos, Department of Anthropology, Yale University, 51Hillhouse Avenue, New Haven, CT 06520-8277. Tel: 203.432.9748. E-mail: kouchnt@pantheon.yale.edu

RONALD B. SMITH is professor of Geology and Geophysics and Mechanical Engineering at Yale University. He is alsoDirector of Yale’s Center for Earth Observation. His research interests include theoretical fluid dynamics and observa-tion of the atmosphere, oceans, and land surface using aircraft, satellite, and numerical modeling.

Ronald Smith, Department of Geology and Geophysics, Yale University, P.O. Box 208109, New Haven, CT 06520-8109. Tel: 203.432.3129. Fax: 203.432.3143. E-mail: ronald.smith@yale.edu

ART GLEASON was the manager of the Yale Center for Earth Observation for five years and is currently enrolled in aPh.D. program at the University of Maryland.

Art Gleason, c/o Center for Earth Observation, 106 Kline Geology Laboratory, 210 Whitney Avenue, Department ofGeology and Geophysics, Yale University, New Haven, CT 06520. Tel: 203.432.3440; fax: 203.432.3134. E-mail:art.gleason@yale.edu

PRASAD S. THENKABAIL is an associate research scientist at the Center for Earth Observation at Yale University. Hehas 12 years of experience working in remote sensing applications and has worked in over twenty countries in Africa,Asia, and North America.

Prasad S. Thenkabail, Center for Earth Observation, 106 Kline Geology Laboratory, 210 Whitney Avenue, Departmentof Geology and Geophysics, Yale University, New Haven, CT 06520. Tel: 203.432.3440; fax: 203.432.3134. E-mail:prasad.thenkabail@yale.edu

FRANK HOLE is MacCurdy Professor of Anthropology at Yale. His research interests are agricultural origins, arid landadaptations, climate change, and sustainability.

Frank Hole, Department of Anthropology, Yale University, New Haven, CT 06511. Tel: 203.432.3683. Fax:203.432.3669. E-mail: frank.hole.@yale.edu

YOUSSEF BARKOUDAH is Professor of Botany at Damascus University. Among his research interests are the ecologyand history of semi-arid and arid lands in southwest Asia.

Youssef Barkoudah. Department of Botany, Faculty of Sciences, Damascus University, Damascus, Syria. Tel: 963. 11.5953549.

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JEFF ALBERT is a Ph.D student at the Yale School of Forestry and Environmental Studies and one of the editors of thisvolume. His research interests include water quality problems associated with shared river basins and the application ofremote sensing to environmental problems.

Jeff Albert, Yale School of Forestry and Environmental Studies, 205 Prospect Street, New Haven, CT 06511. Tel:203.432.5375; fax: 203.432.3817. E-mail: jeffrey.albert@yale.edu

PAUL GLUHOSKY is a research associate in the Department of Geology and Geophysics and the Center for EarthObservation at Yale University. His research interests include synopic and observational meteorology, climatology, fluiddynamic modeling, cloud physics and remote sensing.

Paul Gluhosky, Department of Geology and Geophysics, Yale University, P.O. Box 208109, New Haven, CT 06520-8109. Tel: 203.432.5669. Fax: 203.432.3134. E-mail: paul.gluhosky@yale.edu

JANE FOSTER is currently a Masters candidate at the Yale School of Forestry and Environmental Studies. She hasworked as staff at the Center for Earth Observation for two years. She is interested in using remote sensing to measurebiophysical parameters of temperate and tropical forests.

Jane Foster, Yale School of Forestry and Environmental Studies, 205 Prospect St., New Haven, CT 06511. Tel:203.432.9785. Fax: 203.432.3134. Email: jane.foster@yale.edu