Remote-sensing monitoring ofdesertiregcation phenology and

droughtsArnon Karnieli and Giorgio DallrsquoOlmo

J Blaustein Institute for Desert Research Ben-Gurion University of theNegev Sede-Boker Campus Israel

Keywords Environment Geographical information systems Israel Egypt

Abstract Year-to-year macructuations of rainfall in the northern Negev desert provide anopportunity to characterize and assess the temporal dynamics of desertiregcation phenology anddrought processes Such information was retrieved and analyzed by combined use of satelliteimageries in the remacrectivity and thermal spectral bands Data covering four years of coarse spatialresolution and images from a high revisit time satellite namely the NOAA-14 were used Theimages were processed to produce the normalized difference vegetation index (NDVI) and the landsurface temperature (LST) These measures were applied to the sand regeld in the northwesternNegev (Israel) which is almost totally covered by biological soil crusts and to an adjacent region inSinai (Egypt) consisting mainly of bare dune sands Various manipulations of the data wereapplied Time series presentation of the NDVI and LST reveals that the NDVI values correspond tothe reaction of the vegetation to rainfall and that LST values represent seasonal climaticmacructuation Scatterplot analysis of LST vs NDVI demonstrates the following the two differentbiomes (Sinai and the Negev) exhibit different yearly variation of the phenological patterns (twoseasons in Sinai moving along the LST axis and three seasons in the Negev where the NDVI axisrepresents the growing season) the Sinai has an ecosystem similar to that found in the Saharawhile the Negev only a few kilometers away has an ecosystem similar to the one found in theSahel and drought indicators were derived by using several geometrical expressions based on thetwo extreme points of the LST-NDVI scatterplot The later analysis led to a discriminationfunction that aims to distinguish between the drought years and the wet years in both biomesResults from the current study show that a great deal of information on dryland ecosystems can bederived from four out of regve NOAAAVHRR spectral bands The NDVI is derived from the redand the near-infrared bands and the LST from the two thermal bands Combined use of these twoproducts provides more information than any product alone

IntroductionThe severe recurrence of droughts in the Sahel and other regions as well as anapparent accelerated southward advance of the Sahara Desert led to extensiveinternational discussions and to the establishment of the United NationsConference on Desertiregcation (UNCOD) At this meeting desertiregcation wasderegned as ordfland degradation in arid semi-arid and dry sub-humid areasresulting mainly from adverse human impactordm (UNEP 1992)

The term ordfphenologyordm is usually deregned as ordfthe study of the timing ofrecurring biological phases the cause of their timing with regard to biotic and

The Emerald Research Register for this journal is available at The current issue and full text archive of this journal is available at

httpwww emeraldinsight comresearchregister http wwwemeraldinsigh tcom1477-783 5htm

The authors would like to thank Mr Qin Zhihao for processing the thermal data

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Management of EnvironmentalQuality An International JournalVol 14 No 1 2003pp 22-38q MCB UP Limited1477-7835DOI 10110814777830310460360

abiotic forces and the interaction among phases of the same or differentspeciesordm (Lieth 1974) The current study deals with natural vegetationphenology rather than that of agriculture

Drought can be deregned as a period of abnormally dry weather whichpersists long enough to produce a serious ecological agricultural orhydrological imbalance (eg crop damage water shortage etc) The severityof the drought depends upon the degree of moisture deregciency the durationand the size of the affected area In this context meteorological drought refersto lower than average precipitation for some time period (Wilhite and Glantz1985)

These three basic dryland processes namely desertiregcation phenology anddrought are strongly linked Drought is part of the cause of desertiregcation andcertainly makes the situation worse Mainguet (1994) states that desertiregcationis ordfrevealed by droughtordm The phenology of natural plants is changed by eitherdesertiregcation or drought processes It is expressed for example by changesbetween grasses and shrubs C3 and C4 species or palatable to unpalatablespecies The objective of the current paper is to characterize and assess thetemporal dynamics of these three processes by jointly analyzing remacrective andthermal data acquired by satellite remote sensing means

The advanced very high resolution radiometer (AVHRR) operated by theNational Oceanic Atmospheric Administration (NOAA) with 1km spatialresolution and high temporal resolution of about one day plays a signiregcantrole in monitoring regional and global processes The most important AVHRR-derived products for ecological applications are the normalized differencevegetation index (NDVI) and the land surface temperature (LST)

The NDVI was formulated by Rouse et al (1974) as

NDVI = (rNIR 2 rR)=(rNIR + rR) (1)

where r is the remacrectance in the red (R) and near-infrared (NIR) bands of theNOAAAVHRR sensor This index as well as several other modiregcations of itthat are less common are based on the difference between the maximumabsorption of radiation in the red (due to the chlorophyll pigment) and themaximum remacrection of radiation in the NIR (due to the leaf cellular structureand the fact that soil spectra lacking these mechanisms typically do not showsuch a dramatic spectral difference) The NDVI has been proven to be wellcorrelated with various vegetation parameters such as green biomass (Tucker1979) chlorophyll concentration (Buschmann and Nagel 1993) leaf area index(Asrar et al 1984) foliar loss and damage (Vogelmann 1990) photosyntheticactivity (Sellers 1985) carbon macruxes (Tucker et al 1986) phenology (Justiceet al 1985) and others Also they have been found to be useful for a variety ofimage analyses like crop classiregcation (Ehrlich and Lambin 1996) greencoverage (Elvidge and Chen 1995) and change detection (Lambin and Strahler1994)

Desertiregcationphenology and

droughts

23

The retrieval of LST from NOAA-AVHRR data is achieved mainly throughthe application of so-called split window (Price 1984) Several split windowalgorithms have been developed on the basis of various considerations of theeffects of the atmosphere and the emitting surface derived from the equation ofthermal radiation and its transfer through the atmosphere However the effectof the atmosphere is so complex that any treatment is difregcult Thereforevarious simpliregcations have been assumed for the derivation that has led to theestablishment of different forms of split window algorithm (Qin and Karnieli1999) Thus if T4 and T5 are the brightness temperatures in bands 4 and 5 ofAVHRR data respectively which are given by inverting Planckrsquos equation forthe radiation received by the sensor the general form of split windowalgorithm can be expressed as

LST = T4 + A(T4 2 T5) + B (2)

where A and B are the coefregcients affected by the atmospheric transmittanceand surface emissivity in spectral bands 4 and 5 of AVHRR data Alltemperatures in the equation are in degrees of Kelvin The theoretical accuracyof the LST can reach plusmn 16 8 C

Study areaPerhaps the most spectacular phenomenon connected with desertiregcation vsrehabilitation can be observed across the Israel-Egypt political border(Figure 1) Although the sand regeld of the Negev desert (Israel) represents theeastern extension of the Sinai (Egypt) regelds from the geomorphological and

Figure 1Location map based onNOAA-AVHRR imageshowing the studypolygons on both sides ofthe border between Israeland Egypt

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lithological points of view the area is artiregcially divided by the politicalborderline The borderline is characterized by a sharp contrast higherremacrectance values (brighter) on the Egyptian side and lower remacrectancevalues (darker) on the Israeli side This contrast has long drawn theattention of many scientists The traditional and popular explanation assertsthat the contrast is mainly due to severe anthropogenic impact of the SinaiBedouin especially overgrazing by their black goat and sheep herds as wellas gathering of plants for regrewood The Israeli side of the border has beensubject to a conservation of nature policy since the 1950s and especiallyunder an advanced rehabilitation process since 1982 This interpretation waspioneered by Otterman (1974) and summarized in Otterman (1996)Classiregcation based on satellite and aerial photographs revealed that theSinai is dominated by bare sands (835 percent) while in the Negev thesands are overlaid by soil biological crusts (71 percent) (Table I)Consequently Sinai is characterized by shifting sand dunes while thesedunes have been stabilized in the Negev As a result a new theory recentlyproposed by Karnieli and Tsoar (1995) and Tsoar and Karnieli (1996)suggests that the contrast is not a direct result of severe overgrazing ofhigher vegetation but is caused by an almost complete cover of biologicalsoil crusts on the Israeli side while human and animal activities haveprevented the establishment and accumulation of such crusts as well astrampling and breaking up any existing crusts on the Egyptian side

Mean annual rainfall in the study area is 90mm The current project lastedfour years from October 1995 to September 1999 Two of these years wererelatively dry (drought) years plusmn 19951996 and 19981999 plusmn with 323 and312mm rainfall respectively The other two years were relatively wet plusmn19961997 and 19971998 plusmn with 793 and 836mm rainfall respectively

MethodologyThe NOAA-AVHRR data were acquired in high-resolution picturetransmission (HPRT) format ( 2 1 pound 1km) by the ground receiving stationlocated at the Sede Boker Campus (Negev Israel) NOAA-14 images wereobtained for four years from October 1995 to September 1999 Thegeometrical distortion introduced by the large scan angle was reduced bylimiting the use of the images to those with a satellite zenith angle of 308 andby using only cloud-free images

Negev () Sinai ()

Bare sandsplayas 11 835Biological soil crusts 71 12Perennials 18 45

Note Based on Qin (2001)

Table IPercentage ground

cover of differentground features inthe Negev (Israel)and Sinai (Egypt)

Desertiregcationphenology and

droughts

25

Three types of pre-processing were used in the study

(1) radiometric

(2) atmospheric and

(3) geometric corrections

The processing of the data in the visible and NIR bands was based on post-launch calibration coefregcients suggested by NOAANESDIS (Rao and Chen1998) Atmospheric correction of the top-of-atmosphere remacrectances was carriedout using the 6S code including correction for molecular scattering whilegeometry of the sensor and the sun was also applied (Vermote et al 1997) Thisprogram also requires estimates of the water vapor aerosol and ozone contentsin the atmosphere The total precipitable water and aerosol optical thickness ofthe atmosphere were obtained from an automatic tracking sun photometer(CIMEL) installed at Sede Boker about 50km away from the study area (Holbenet al 2001) Ozone content in the atmosphere was based on climatology derivedfrom the total ozone mapping spectrometer (TOMS) onboard the Nimbus-7spacecraft between 1987 and 1993 The images were geometrically corrected to amaster image using ground control points and applying a nonlinear secondorder transformation The accuracy of the correction lies at the subpixel levelOne subset of 150 pixels on each side of the border (about 15 pound 10km2) wasextracted assuming homogeneous areas from each image (Figure 1) The NDVIvalues were calculated from the surface remacrectance values of the red and NIRbands of the AVHRR images

Radiometric correction of the two NOAA AVHRR thermal bands 4 and 5was performed following Brown et al (1995) and Weinreb et al (1990)Geometric correction and subset extraction were applied to the red and NIRbands These operations were necessary at this early step of the processing inorder to later apply the local characteristics of the split window algorithm (Qinet al 2001) used to correct the atmospheric inmacruence on the thermal infraredbands The algorithm used has a modireged form of equation (2) that requirestotal precipitable water and surface emissivity to compute the constants Theregrst constant was obtained from the CIMEL data relative to the image date ofacquisition the second was estimated by laboratory analysis of soil samplestaken in the study area (Qin 2001) Based on this work the emissivity value ofbare sand dunes (average value of 095) was assigned to the Sinairsquos polygonwhile the emissivity value of the biological soil crusts (average value of 097) forthat of the Negev The maximum value composite (MVC) method (Holben 1986)was applied to the NDVI and LST values for a one-month composition period

Analysis and resultsTemporal dynamics of NDVI and LSTThe temporal variations of the NDVI and the LST along the four hydrologicalyears of the project are presented in Figure 2 Figure 2A shows that the Negev

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ecosystem under an advanced rehabilitation process since 1982 shows highervalues of vegetation index (mean NDVI for the whole data set= 0124 plusmn 0039)and high reactivity to rainfall (from 0116 plusmn 0033 during the dry period to0143 plusmn 0045 in the wet period) especially during wet years (19961997 and19971998) In contrast the Sinai area under desertireged conditions presentsa very low reaction to rainfall (from 0086 plusmn 0014 during the dry period to0096 plusmn 0015 in the wet period) maintaining approximately constant NDVIvalues (mean NDVI for the whole data set = 0089 plusmn 0015) A slight increasein the vegetation index is visible only during the wet years The differencesbetween the mean NDVI values of Negev and Sinai can be as high as 01following the rainy season of a wet year Since it is well known that higherNDVI values are caused by a dark soil background (Huete 1988) NDVI

Figure 2Temporal variations of

NDVI (A) and LST (B) inSinai and the Negev

along the fourhydrological years of

research

Desertiregcationphenology and

droughts

27

differences during the dry periods seem to be related to the brightnessdifference between the two sides of the border due to the presence of the darkbiological soil crusts and to a lesser extent the higher vegetation on the Israeliside (Karnieli and Tsoar 1995)

Figure 2B demonstrates the monthly MVC values of the computed LST inthe sampling polygons on both sides of the border No clear differences inphase are evident On the contrary the cyclical temperature trends are similarin the Negev and in Sinai and follow the climatic variations throughout the fouryears During the relatively wet winters evaporation determines large losses ofenergy from the wet soil surface low sun irradiance also contributes lesssigniregcantly than in summer to the soil heat balance Thus the minimum LSTvalues are always found in the rainy period during the winter and can be aslow as 208 C In these seasons the difference in the amplitude between thepolygons is almost negligible The maximum temperatures are found at theheight of summer and can reach values of 58 8 C The temperature differencesbetween the two sides of the border can be as high as 78 C

The explanations for the amplitude differences evident in the dry period areaddressed in Qin et al (2001) Since the sand dunes on the Israeli side are almostcompletely covered by dark biological soil crusts they absorb more incidentradiation and emit stronger thermal radiation than the bare sand on theEgyptian side Moreover although more higher and lower vegetation is presenton the Israeli side these desert plants due to their scarcity and dormancy in thehot dry summer contribute almost nothing to the regional evapotranspirationthat cools the surface Thus in the dry season the Israeli side presents highervalues of surface temperatures than in the Sinai

Desertiregcation assessmentThe combination of remacrective and thermal data for determining soil waterstatus or surface water availability has been reported in numerous studies(Goward and Hope 1989 Nemani et al 1993 Lambin and Ehrlich 1996 1997)This type of analysis has usually been undertaken by plotting LST againstNDVI values (Figure 3A) Such a method is used in the current research inorder to characterize the desertiregcation processes in the study area Ascatterplot of multi-year NDVI vs LST values was created (Figure 3B)Applying a K-mean analysis of two clusters for the data it was shown that thecombined values of the Negev are signiregcantly different from those of SinaiComparing the location of the clusters to the African continental scheme ofLambin and Ehrlich (1996) the current scatterplot demonstrates that the Sinaidesertireged ecosystem overlaps the area covered by the Sahara biome while theNegev recovered side of the border although located only a few kilometersaway exhibits characteristics similar to those of the Sahel biome (although itregts even better to that in the Southern African hemisphere discussed in thesame paper)

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PhenologyResults of the K-mean analysis reveal that 5 clusters characterize the studyarea (Figure 4) Statistics for each cluster are described in Table II The Sinaiarea under a desertiregcation process is characterized by just two clusters (1 and2) corresponding to the dry and wet seasons respectively and deregned by themacructuations of land surface temperatures throughout the year In this area thebiological activity is almost non-existent therefore only the physicalmeteorological factors are responsible for the movement inside the LST-NDVI space On the other hand the recovered ecosystem of the Negev showsrelatively intense biological activity and is characterized by movements over

Figure 3(A) Scatter diagram for

schematic discriminatingof different biomes in

Africa as proposed byLambin and Ehrlich

(1996) (B) Data obtainedfor this study

Desertiregcationphenology and

droughts

29

three clusters of the LST-NDVI space In the Negev ecosystem as in the Sinaiboth dry and rainy seasons are evident (clusters 3 and 4) while the third cluster(number 5) represents the growing season which is evident only on the Israeliside of the border and which includes the highest values of NDVI Only a fewpoints are presented inside cluster number 5 because it corresponds to the veryshort period of the year in which the desert reaches its maximum greenness dueto the blooming of the annuals

It is interesting to note that several points belonging to the Negev are locatedinside cluster 1 which represents the Sinai The explanation for this fact can befound in Holling (1973) Natural arid land systems show a resilient characterrather than a resistant one their stability is not deregned by a unique equilibrium

Figure 4Groups obtained fromthe cluster analysis ofNDVI and LST data

Cluster Season No of Negev pixels No of Sinai pixels Mean NDVI (SD) Mean LST (SD)

1 Dry 14 48 0082 (0010) 477 (36)2 Rainy 4 33 0095 (0010) 330 (58)3 Dry 51 7 0120 (0012) 492 (46)4 Rainy 16 5 0123 (0013) 285 (46)5 Growing 9 0 0182 (0018) 391 (81)

Note Based on DallrsquoOlmo and Karnieli (2002)

Table IIDescription of theclusters obtainedfrom the NDVI andLST data analysis

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state but according to macructuations of environmental parameters Beyondcertain limits they can be associated with multiple-equilibrium states of adomain inside which the ecosystem does not change its structure This is theresult of adaptation to the harsh extremely variable desert environment Theoverlapping points of cluster 1 are relative to the very dry years (19951996 and19981999) In periods extremely lacking in water the recovered ecosystem isable to reduce its activity to the minimum needed for surviving in the area ofthe LST-NDVI space typical of desertireged ecosystems However as soon as theprecious resource of water is available again it can immediately react andproduce relatively high values of NDVI On the other hand the disturbedecosystem (Sinai) shows the result of a man-made perturbation that haschanged its structure in wet years only an extremely limited response torainfall is detected

The multi-year average of the combined NDVI and LST values are presentedin Figure 5 in terms of month-by-month trajectories Phenology starts inOctober and ends in September of the following year It is shown that thedesertireged Sinai ecosystem exhibits the highest variability mostly along theLST axis and is almost unaffected by the NDVI except for a slight deviationfrom the straight line between April and July most likely due to somegreenness of perennials On the other hand the recovered Negev side of theborder exhibits variability on both LST and NDVI axes Here the NDVI axis isdominant especially during the wet months (January-July) due to the greeningof biological soil crusts annuals and perennials During the summer monthsthe trajectory moves only along the LST axis

Figure 5Multi-year average of thecombined NDVI and LSTvalues in terms of month-

by-month trajectories

Desertiregcationphenology and

droughts

31

Drought assessmentFigure 6 exhibits the breakdown of Figure 5 into the four hydrological yearsunder investigation to demonstrate the annual dynamics In the Sinai littledifference can be seen between the wet and the dry years with the samegeneral trajectory shape plusmn long narrow and with a vertical pattern along theLST axis In the Negev however there is a considerable difference between thewet and dry years During the wet years (19961997 and 19971998) the shapeof the graphs is stretched towards the high NDVI values and the phenologicalcycle is much more pronounced whereas during droughts (19951996 and19981999) the NDVI component is much less remarkable and the phenologicalcycle shrinks signiregcantly

Figure 7 shows for each of the hydrological years and for the Negev andSinai ecosystems separately the lines connected between two points deregned as

(1) the maximum LST 2 minimum NDVI and

(2) minimum LST 2 maximum LST

It may be seen that the Sinai lines are very similar in terms of position andlength however two groups can be distinguished between the Negev lines Thelines of the wet years are longer and with gentler slopes whereas the dry yearslines are shorter and steeper These characteristics can be quantireged by threegeometrical expressions as schematically illustrated in Figure 8

Angle = arctan(DLST=DNDVI) (3)

Area = DNDVI curren 05DLST (4)

Length = [(DLST)2 + (DNDVI)2]05 (5)

These three expressions can be used as indicators for quantifying droughtyears Their calculated values are presented in Table III Evaluation of thethree indicators was performed by applying the following discriminationfunction (DF) for each year (i) and each region (j)

DFij = (Xij 2 Xmeanj)=(Xmaxj 2 Xminj)

where Xij is the calculated value for each indicator either in Sinai or in theNegev in any particular year Xavg Xmax and Xmin are the yearly averagemaximum and minimum values for each region respectively The objective ofthis function is to discriminate the drought years from the wet years on bothsides of the border

Results of the discrimination function for each of the drought indicators arepresented in Figure 9 It can be noticed that for the Negev each of the indicatorssuccessfully separates the two drought years from the wet years since the DFij

receives either positive or negative values However for Sinai only the ordflengthordm

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32

Figure 6Trajectories of conmined

NDVI and LST valuesfor each of the fourhydrological years(October (10)

September (9))

Desertiregcationphenology and

droughts

33

Figure 7Lines connected betweenmaximumLSTminimum NDVIand minimumLSTmaximum NDVI forthe Sinai and the Negev

Figure 8Schematic illustration ofthree geometricalexpressions forindicating droughts

Year Sinai Negev

Angle 19951996 8036 748519961997 8320 706319971998 8331 720619981999 8323 7778

Area 19951996 043 08319961997 038 13119971998 041 14519981999 024 050

Length 19951996 2238 247519961997 2516 273119971998 2642 299519981999 2022 2117

Table IIICalculated valuesfor the threedrought indicators

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34

indicator is able to show this phenomenon The other two indicators plusmn slopeand angle plusmn produce mixed results Consequently the ordflengthordm indicator will beused for further analysis

Using the ordflengthordm indicator Figure 10 presents the DFj values as a functionof the yearly rainfall of each year Despite the fact that only eight points areinvolved in the regression analysis a clear trend is exhibited between thedrought-year cluster (negative values) and the wet-year cluster (positive

Figure 9Results of the

discrimination functionprocedure for eachdrought indicator

Figure 10Discrimination function

values vs the yearlyrainfall amounts of

each year

Desertiregcationphenology and

droughts

35

values) The crossing point between the regression line and the DF= 0 line canbe used to quantify the threshold between wet and drought years in terms ofrainfall amount (54mm in the current example)

Summary and conclusionsResults from the current study show that desertiregcation phenology anddroughts processes can be detected and characterized by using four out of regveNOAAAVHRR spectral bands The NDVI is derived from the red and the NIRbands and the LST from the two thermal bands Combined use of these twoproducts provides more information than any product alone

Time series presentation of NDVI and LST reveals that the NDVI valuescorrespond to the reaction of the vegetation to rainfall and that LST valuesrepresent seasonal climatic macructuation Scatterplot analysis of LST vs NDVIdemonstrates the following

The two different biomes (Sinai and the Negev) exhibit different yearlyvariations of the phenological patterns two seasons in Sinai movingalong the LST axis and three seasons in the Negev where the NDVI axisrepresents the growing season

The Sinai has an ecosystem similar to that found in the Sahara while theNegev only a few kilometers away has an ecosystem similar to that ofthe Sahel

Drought indicators were derived in terms of three geometricalexpressions based on the two extreme points of the NDVI-LSTscatterplots Evaluation of the suggested indicator shows only one thatcan successfully separate between the drought and the wet years It isconcluded that the AVHRR imagery can provide valuable information fordrought monitoring and characterization

References

Asrar G Fuchs M Kanemasu ET and Hatregeld JL (1984) ordfEstimating absorbedphotosynthetic radiation and leaf area index from spectral remacrectance of wheatordmAgronomy J Vol 76 pp 300-6

Brown OB Brown JW and Evans RH (1985) ordfCalibration of advanced very-high resolutionradiometer infrared observationsordm J Geophys Res Vol 90 C6 pp 11667-77

Buschmann C and Nagel E (1993) ordfIn vivo spectroscopy and internal optics of leaves as basisfor remote-sensing of vegetationordm Int J Remote Sens Vol 14 pp 711-22

DallrsquoOlmo G and Karnieli A (2002) ordfFollowing phenological cycles of desert ecosystems usingNDVI and LST data derived from the advanced very high resolution radiometerordm IntJ Remote Sens Vol 23

Ehrlich D and Lambin EF (1996) ordfBroad scale land-cover classiregcation and interannualclimatic variabilityordm Int J Remote Sens Vol 17 pp 845-62

Elvidge CD and Chen ZK (1995) ordfComparison of broad-band and narrow-band red and near-infrared vegetation indexesordm Remote Sens Environ Vol 54 pp 38-48

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The retrieval of LST from NOAA-AVHRR data is achieved mainly throughthe application of so-called split window (Price 1984) Several split windowalgorithms have been developed on the basis of various considerations of theeffects of the atmosphere and the emitting surface derived from the equation ofthermal radiation and its transfer through the atmosphere However the effectof the atmosphere is so complex that any treatment is difregcult Thereforevarious simpliregcations have been assumed for the derivation that has led to theestablishment of different forms of split window algorithm (Qin and Karnieli1999) Thus if T4 and T5 are the brightness temperatures in bands 4 and 5 ofAVHRR data respectively which are given by inverting Planckrsquos equation forthe radiation received by the sensor the general form of split windowalgorithm can be expressed as

LST = T4 + A(T4 2 T5) + B (2)

where A and B are the coefregcients affected by the atmospheric transmittanceand surface emissivity in spectral bands 4 and 5 of AVHRR data Alltemperatures in the equation are in degrees of Kelvin The theoretical accuracyof the LST can reach plusmn 16 8 C

Study areaPerhaps the most spectacular phenomenon connected with desertiregcation vsrehabilitation can be observed across the Israel-Egypt political border(Figure 1) Although the sand regeld of the Negev desert (Israel) represents theeastern extension of the Sinai (Egypt) regelds from the geomorphological and

Figure 1Location map based onNOAA-AVHRR imageshowing the studypolygons on both sides ofthe border between Israeland Egypt

MEQ141

24

lithological points of view the area is artiregcially divided by the politicalborderline The borderline is characterized by a sharp contrast higherremacrectance values (brighter) on the Egyptian side and lower remacrectancevalues (darker) on the Israeli side This contrast has long drawn theattention of many scientists The traditional and popular explanation assertsthat the contrast is mainly due to severe anthropogenic impact of the SinaiBedouin especially overgrazing by their black goat and sheep herds as wellas gathering of plants for regrewood The Israeli side of the border has beensubject to a conservation of nature policy since the 1950s and especiallyunder an advanced rehabilitation process since 1982 This interpretation waspioneered by Otterman (1974) and summarized in Otterman (1996)Classiregcation based on satellite and aerial photographs revealed that theSinai is dominated by bare sands (835 percent) while in the Negev thesands are overlaid by soil biological crusts (71 percent) (Table I)Consequently Sinai is characterized by shifting sand dunes while thesedunes have been stabilized in the Negev As a result a new theory recentlyproposed by Karnieli and Tsoar (1995) and Tsoar and Karnieli (1996)suggests that the contrast is not a direct result of severe overgrazing ofhigher vegetation but is caused by an almost complete cover of biologicalsoil crusts on the Israeli side while human and animal activities haveprevented the establishment and accumulation of such crusts as well astrampling and breaking up any existing crusts on the Egyptian side

Mean annual rainfall in the study area is 90mm The current project lastedfour years from October 1995 to September 1999 Two of these years wererelatively dry (drought) years plusmn 19951996 and 19981999 plusmn with 323 and312mm rainfall respectively The other two years were relatively wet plusmn19961997 and 19971998 plusmn with 793 and 836mm rainfall respectively

MethodologyThe NOAA-AVHRR data were acquired in high-resolution picturetransmission (HPRT) format ( 2 1 pound 1km) by the ground receiving stationlocated at the Sede Boker Campus (Negev Israel) NOAA-14 images wereobtained for four years from October 1995 to September 1999 Thegeometrical distortion introduced by the large scan angle was reduced bylimiting the use of the images to those with a satellite zenith angle of 308 andby using only cloud-free images

Negev () Sinai ()

Bare sandsplayas 11 835Biological soil crusts 71 12Perennials 18 45

Note Based on Qin (2001)

Table IPercentage ground

cover of differentground features inthe Negev (Israel)and Sinai (Egypt)

Desertiregcationphenology and

droughts

25

Three types of pre-processing were used in the study

(1) radiometric

(2) atmospheric and

(3) geometric corrections

The processing of the data in the visible and NIR bands was based on post-launch calibration coefregcients suggested by NOAANESDIS (Rao and Chen1998) Atmospheric correction of the top-of-atmosphere remacrectances was carriedout using the 6S code including correction for molecular scattering whilegeometry of the sensor and the sun was also applied (Vermote et al 1997) Thisprogram also requires estimates of the water vapor aerosol and ozone contentsin the atmosphere The total precipitable water and aerosol optical thickness ofthe atmosphere were obtained from an automatic tracking sun photometer(CIMEL) installed at Sede Boker about 50km away from the study area (Holbenet al 2001) Ozone content in the atmosphere was based on climatology derivedfrom the total ozone mapping spectrometer (TOMS) onboard the Nimbus-7spacecraft between 1987 and 1993 The images were geometrically corrected to amaster image using ground control points and applying a nonlinear secondorder transformation The accuracy of the correction lies at the subpixel levelOne subset of 150 pixels on each side of the border (about 15 pound 10km2) wasextracted assuming homogeneous areas from each image (Figure 1) The NDVIvalues were calculated from the surface remacrectance values of the red and NIRbands of the AVHRR images

Radiometric correction of the two NOAA AVHRR thermal bands 4 and 5was performed following Brown et al (1995) and Weinreb et al (1990)Geometric correction and subset extraction were applied to the red and NIRbands These operations were necessary at this early step of the processing inorder to later apply the local characteristics of the split window algorithm (Qinet al 2001) used to correct the atmospheric inmacruence on the thermal infraredbands The algorithm used has a modireged form of equation (2) that requirestotal precipitable water and surface emissivity to compute the constants Theregrst constant was obtained from the CIMEL data relative to the image date ofacquisition the second was estimated by laboratory analysis of soil samplestaken in the study area (Qin 2001) Based on this work the emissivity value ofbare sand dunes (average value of 095) was assigned to the Sinairsquos polygonwhile the emissivity value of the biological soil crusts (average value of 097) forthat of the Negev The maximum value composite (MVC) method (Holben 1986)was applied to the NDVI and LST values for a one-month composition period

Analysis and resultsTemporal dynamics of NDVI and LSTThe temporal variations of the NDVI and the LST along the four hydrologicalyears of the project are presented in Figure 2 Figure 2A shows that the Negev

MEQ141

26

ecosystem under an advanced rehabilitation process since 1982 shows highervalues of vegetation index (mean NDVI for the whole data set= 0124 plusmn 0039)and high reactivity to rainfall (from 0116 plusmn 0033 during the dry period to0143 plusmn 0045 in the wet period) especially during wet years (19961997 and19971998) In contrast the Sinai area under desertireged conditions presentsa very low reaction to rainfall (from 0086 plusmn 0014 during the dry period to0096 plusmn 0015 in the wet period) maintaining approximately constant NDVIvalues (mean NDVI for the whole data set = 0089 plusmn 0015) A slight increasein the vegetation index is visible only during the wet years The differencesbetween the mean NDVI values of Negev and Sinai can be as high as 01following the rainy season of a wet year Since it is well known that higherNDVI values are caused by a dark soil background (Huete 1988) NDVI

Figure 2Temporal variations of

NDVI (A) and LST (B) inSinai and the Negev

along the fourhydrological years of

research

Desertiregcationphenology and

droughts

27

differences during the dry periods seem to be related to the brightnessdifference between the two sides of the border due to the presence of the darkbiological soil crusts and to a lesser extent the higher vegetation on the Israeliside (Karnieli and Tsoar 1995)

Figure 2B demonstrates the monthly MVC values of the computed LST inthe sampling polygons on both sides of the border No clear differences inphase are evident On the contrary the cyclical temperature trends are similarin the Negev and in Sinai and follow the climatic variations throughout the fouryears During the relatively wet winters evaporation determines large losses ofenergy from the wet soil surface low sun irradiance also contributes lesssigniregcantly than in summer to the soil heat balance Thus the minimum LSTvalues are always found in the rainy period during the winter and can be aslow as 208 C In these seasons the difference in the amplitude between thepolygons is almost negligible The maximum temperatures are found at theheight of summer and can reach values of 58 8 C The temperature differencesbetween the two sides of the border can be as high as 78 C

The explanations for the amplitude differences evident in the dry period areaddressed in Qin et al (2001) Since the sand dunes on the Israeli side are almostcompletely covered by dark biological soil crusts they absorb more incidentradiation and emit stronger thermal radiation than the bare sand on theEgyptian side Moreover although more higher and lower vegetation is presenton the Israeli side these desert plants due to their scarcity and dormancy in thehot dry summer contribute almost nothing to the regional evapotranspirationthat cools the surface Thus in the dry season the Israeli side presents highervalues of surface temperatures than in the Sinai

Desertiregcation assessmentThe combination of remacrective and thermal data for determining soil waterstatus or surface water availability has been reported in numerous studies(Goward and Hope 1989 Nemani et al 1993 Lambin and Ehrlich 1996 1997)This type of analysis has usually been undertaken by plotting LST againstNDVI values (Figure 3A) Such a method is used in the current research inorder to characterize the desertiregcation processes in the study area Ascatterplot of multi-year NDVI vs LST values was created (Figure 3B)Applying a K-mean analysis of two clusters for the data it was shown that thecombined values of the Negev are signiregcantly different from those of SinaiComparing the location of the clusters to the African continental scheme ofLambin and Ehrlich (1996) the current scatterplot demonstrates that the Sinaidesertireged ecosystem overlaps the area covered by the Sahara biome while theNegev recovered side of the border although located only a few kilometersaway exhibits characteristics similar to those of the Sahel biome (although itregts even better to that in the Southern African hemisphere discussed in thesame paper)

MEQ141

28

PhenologyResults of the K-mean analysis reveal that 5 clusters characterize the studyarea (Figure 4) Statistics for each cluster are described in Table II The Sinaiarea under a desertiregcation process is characterized by just two clusters (1 and2) corresponding to the dry and wet seasons respectively and deregned by themacructuations of land surface temperatures throughout the year In this area thebiological activity is almost non-existent therefore only the physicalmeteorological factors are responsible for the movement inside the LST-NDVI space On the other hand the recovered ecosystem of the Negev showsrelatively intense biological activity and is characterized by movements over

Figure 3(A) Scatter diagram for

schematic discriminatingof different biomes in

Africa as proposed byLambin and Ehrlich

(1996) (B) Data obtainedfor this study

Desertiregcationphenology and

droughts

29

three clusters of the LST-NDVI space In the Negev ecosystem as in the Sinaiboth dry and rainy seasons are evident (clusters 3 and 4) while the third cluster(number 5) represents the growing season which is evident only on the Israeliside of the border and which includes the highest values of NDVI Only a fewpoints are presented inside cluster number 5 because it corresponds to the veryshort period of the year in which the desert reaches its maximum greenness dueto the blooming of the annuals

It is interesting to note that several points belonging to the Negev are locatedinside cluster 1 which represents the Sinai The explanation for this fact can befound in Holling (1973) Natural arid land systems show a resilient characterrather than a resistant one their stability is not deregned by a unique equilibrium

Figure 4Groups obtained fromthe cluster analysis ofNDVI and LST data

Cluster Season No of Negev pixels No of Sinai pixels Mean NDVI (SD) Mean LST (SD)

1 Dry 14 48 0082 (0010) 477 (36)2 Rainy 4 33 0095 (0010) 330 (58)3 Dry 51 7 0120 (0012) 492 (46)4 Rainy 16 5 0123 (0013) 285 (46)5 Growing 9 0 0182 (0018) 391 (81)

Note Based on DallrsquoOlmo and Karnieli (2002)

Table IIDescription of theclusters obtainedfrom the NDVI andLST data analysis

MEQ141

30

state but according to macructuations of environmental parameters Beyondcertain limits they can be associated with multiple-equilibrium states of adomain inside which the ecosystem does not change its structure This is theresult of adaptation to the harsh extremely variable desert environment Theoverlapping points of cluster 1 are relative to the very dry years (19951996 and19981999) In periods extremely lacking in water the recovered ecosystem isable to reduce its activity to the minimum needed for surviving in the area ofthe LST-NDVI space typical of desertireged ecosystems However as soon as theprecious resource of water is available again it can immediately react andproduce relatively high values of NDVI On the other hand the disturbedecosystem (Sinai) shows the result of a man-made perturbation that haschanged its structure in wet years only an extremely limited response torainfall is detected

The multi-year average of the combined NDVI and LST values are presentedin Figure 5 in terms of month-by-month trajectories Phenology starts inOctober and ends in September of the following year It is shown that thedesertireged Sinai ecosystem exhibits the highest variability mostly along theLST axis and is almost unaffected by the NDVI except for a slight deviationfrom the straight line between April and July most likely due to somegreenness of perennials On the other hand the recovered Negev side of theborder exhibits variability on both LST and NDVI axes Here the NDVI axis isdominant especially during the wet months (January-July) due to the greeningof biological soil crusts annuals and perennials During the summer monthsthe trajectory moves only along the LST axis

Figure 5Multi-year average of thecombined NDVI and LSTvalues in terms of month-

by-month trajectories

Desertiregcationphenology and

droughts

31

Drought assessmentFigure 6 exhibits the breakdown of Figure 5 into the four hydrological yearsunder investigation to demonstrate the annual dynamics In the Sinai littledifference can be seen between the wet and the dry years with the samegeneral trajectory shape plusmn long narrow and with a vertical pattern along theLST axis In the Negev however there is a considerable difference between thewet and dry years During the wet years (19961997 and 19971998) the shapeof the graphs is stretched towards the high NDVI values and the phenologicalcycle is much more pronounced whereas during droughts (19951996 and19981999) the NDVI component is much less remarkable and the phenologicalcycle shrinks signiregcantly

Figure 7 shows for each of the hydrological years and for the Negev andSinai ecosystems separately the lines connected between two points deregned as

(1) the maximum LST 2 minimum NDVI and

(2) minimum LST 2 maximum LST

It may be seen that the Sinai lines are very similar in terms of position andlength however two groups can be distinguished between the Negev lines Thelines of the wet years are longer and with gentler slopes whereas the dry yearslines are shorter and steeper These characteristics can be quantireged by threegeometrical expressions as schematically illustrated in Figure 8

Angle = arctan(DLST=DNDVI) (3)

Area = DNDVI curren 05DLST (4)

Length = [(DLST)2 + (DNDVI)2]05 (5)

These three expressions can be used as indicators for quantifying droughtyears Their calculated values are presented in Table III Evaluation of thethree indicators was performed by applying the following discriminationfunction (DF) for each year (i) and each region (j)

DFij = (Xij 2 Xmeanj)=(Xmaxj 2 Xminj)

where Xij is the calculated value for each indicator either in Sinai or in theNegev in any particular year Xavg Xmax and Xmin are the yearly averagemaximum and minimum values for each region respectively The objective ofthis function is to discriminate the drought years from the wet years on bothsides of the border

Results of the discrimination function for each of the drought indicators arepresented in Figure 9 It can be noticed that for the Negev each of the indicatorssuccessfully separates the two drought years from the wet years since the DFij

receives either positive or negative values However for Sinai only the ordflengthordm

MEQ141

32

Figure 6Trajectories of conmined

NDVI and LST valuesfor each of the fourhydrological years(October (10)

September (9))

Desertiregcationphenology and

droughts

33

Figure 7Lines connected betweenmaximumLSTminimum NDVIand minimumLSTmaximum NDVI forthe Sinai and the Negev

Figure 8Schematic illustration ofthree geometricalexpressions forindicating droughts

Year Sinai Negev

Angle 19951996 8036 748519961997 8320 706319971998 8331 720619981999 8323 7778

Area 19951996 043 08319961997 038 13119971998 041 14519981999 024 050

Length 19951996 2238 247519961997 2516 273119971998 2642 299519981999 2022 2117

Table IIICalculated valuesfor the threedrought indicators

MEQ141

34

indicator is able to show this phenomenon The other two indicators plusmn slopeand angle plusmn produce mixed results Consequently the ordflengthordm indicator will beused for further analysis

Using the ordflengthordm indicator Figure 10 presents the DFj values as a functionof the yearly rainfall of each year Despite the fact that only eight points areinvolved in the regression analysis a clear trend is exhibited between thedrought-year cluster (negative values) and the wet-year cluster (positive

Figure 9Results of the

discrimination functionprocedure for eachdrought indicator

Figure 10Discrimination function

values vs the yearlyrainfall amounts of

each year

Desertiregcationphenology and

droughts

35

values) The crossing point between the regression line and the DF= 0 line canbe used to quantify the threshold between wet and drought years in terms ofrainfall amount (54mm in the current example)

Summary and conclusionsResults from the current study show that desertiregcation phenology anddroughts processes can be detected and characterized by using four out of regveNOAAAVHRR spectral bands The NDVI is derived from the red and the NIRbands and the LST from the two thermal bands Combined use of these twoproducts provides more information than any product alone

Time series presentation of NDVI and LST reveals that the NDVI valuescorrespond to the reaction of the vegetation to rainfall and that LST valuesrepresent seasonal climatic macructuation Scatterplot analysis of LST vs NDVIdemonstrates the following

The two different biomes (Sinai and the Negev) exhibit different yearlyvariations of the phenological patterns two seasons in Sinai movingalong the LST axis and three seasons in the Negev where the NDVI axisrepresents the growing season

The Sinai has an ecosystem similar to that found in the Sahara while theNegev only a few kilometers away has an ecosystem similar to that ofthe Sahel

Drought indicators were derived in terms of three geometricalexpressions based on the two extreme points of the NDVI-LSTscatterplots Evaluation of the suggested indicator shows only one thatcan successfully separate between the drought and the wet years It isconcluded that the AVHRR imagery can provide valuable information fordrought monitoring and characterization

References

Asrar G Fuchs M Kanemasu ET and Hatregeld JL (1984) ordfEstimating absorbedphotosynthetic radiation and leaf area index from spectral remacrectance of wheatordmAgronomy J Vol 76 pp 300-6

Brown OB Brown JW and Evans RH (1985) ordfCalibration of advanced very-high resolutionradiometer infrared observationsordm J Geophys Res Vol 90 C6 pp 11667-77

Buschmann C and Nagel E (1993) ordfIn vivo spectroscopy and internal optics of leaves as basisfor remote-sensing of vegetationordm Int J Remote Sens Vol 14 pp 711-22

DallrsquoOlmo G and Karnieli A (2002) ordfFollowing phenological cycles of desert ecosystems usingNDVI and LST data derived from the advanced very high resolution radiometerordm IntJ Remote Sens Vol 23

Ehrlich D and Lambin EF (1996) ordfBroad scale land-cover classiregcation and interannualclimatic variabilityordm Int J Remote Sens Vol 17 pp 845-62

Elvidge CD and Chen ZK (1995) ordfComparison of broad-band and narrow-band red and near-infrared vegetation indexesordm Remote Sens Environ Vol 54 pp 38-48

MEQ141

36

lithological points of view the area is artiregcially divided by the politicalborderline The borderline is characterized by a sharp contrast higherremacrectance values (brighter) on the Egyptian side and lower remacrectancevalues (darker) on the Israeli side This contrast has long drawn theattention of many scientists The traditional and popular explanation assertsthat the contrast is mainly due to severe anthropogenic impact of the SinaiBedouin especially overgrazing by their black goat and sheep herds as wellas gathering of plants for regrewood The Israeli side of the border has beensubject to a conservation of nature policy since the 1950s and especiallyunder an advanced rehabilitation process since 1982 This interpretation waspioneered by Otterman (1974) and summarized in Otterman (1996)Classiregcation based on satellite and aerial photographs revealed that theSinai is dominated by bare sands (835 percent) while in the Negev thesands are overlaid by soil biological crusts (71 percent) (Table I)Consequently Sinai is characterized by shifting sand dunes while thesedunes have been stabilized in the Negev As a result a new theory recentlyproposed by Karnieli and Tsoar (1995) and Tsoar and Karnieli (1996)suggests that the contrast is not a direct result of severe overgrazing ofhigher vegetation but is caused by an almost complete cover of biologicalsoil crusts on the Israeli side while human and animal activities haveprevented the establishment and accumulation of such crusts as well astrampling and breaking up any existing crusts on the Egyptian side

Mean annual rainfall in the study area is 90mm The current project lastedfour years from October 1995 to September 1999 Two of these years wererelatively dry (drought) years plusmn 19951996 and 19981999 plusmn with 323 and312mm rainfall respectively The other two years were relatively wet plusmn19961997 and 19971998 plusmn with 793 and 836mm rainfall respectively

MethodologyThe NOAA-AVHRR data were acquired in high-resolution picturetransmission (HPRT) format ( 2 1 pound 1km) by the ground receiving stationlocated at the Sede Boker Campus (Negev Israel) NOAA-14 images wereobtained for four years from October 1995 to September 1999 Thegeometrical distortion introduced by the large scan angle was reduced bylimiting the use of the images to those with a satellite zenith angle of 308 andby using only cloud-free images

Negev () Sinai ()

Bare sandsplayas 11 835Biological soil crusts 71 12Perennials 18 45

Note Based on Qin (2001)

Table IPercentage ground

cover of differentground features inthe Negev (Israel)and Sinai (Egypt)

Desertiregcationphenology and

droughts

25

Three types of pre-processing were used in the study

(1) radiometric

(2) atmospheric and

(3) geometric corrections

The processing of the data in the visible and NIR bands was based on post-launch calibration coefregcients suggested by NOAANESDIS (Rao and Chen1998) Atmospheric correction of the top-of-atmosphere remacrectances was carriedout using the 6S code including correction for molecular scattering whilegeometry of the sensor and the sun was also applied (Vermote et al 1997) Thisprogram also requires estimates of the water vapor aerosol and ozone contentsin the atmosphere The total precipitable water and aerosol optical thickness ofthe atmosphere were obtained from an automatic tracking sun photometer(CIMEL) installed at Sede Boker about 50km away from the study area (Holbenet al 2001) Ozone content in the atmosphere was based on climatology derivedfrom the total ozone mapping spectrometer (TOMS) onboard the Nimbus-7spacecraft between 1987 and 1993 The images were geometrically corrected to amaster image using ground control points and applying a nonlinear secondorder transformation The accuracy of the correction lies at the subpixel levelOne subset of 150 pixels on each side of the border (about 15 pound 10km2) wasextracted assuming homogeneous areas from each image (Figure 1) The NDVIvalues were calculated from the surface remacrectance values of the red and NIRbands of the AVHRR images

Radiometric correction of the two NOAA AVHRR thermal bands 4 and 5was performed following Brown et al (1995) and Weinreb et al (1990)Geometric correction and subset extraction were applied to the red and NIRbands These operations were necessary at this early step of the processing inorder to later apply the local characteristics of the split window algorithm (Qinet al 2001) used to correct the atmospheric inmacruence on the thermal infraredbands The algorithm used has a modireged form of equation (2) that requirestotal precipitable water and surface emissivity to compute the constants Theregrst constant was obtained from the CIMEL data relative to the image date ofacquisition the second was estimated by laboratory analysis of soil samplestaken in the study area (Qin 2001) Based on this work the emissivity value ofbare sand dunes (average value of 095) was assigned to the Sinairsquos polygonwhile the emissivity value of the biological soil crusts (average value of 097) forthat of the Negev The maximum value composite (MVC) method (Holben 1986)was applied to the NDVI and LST values for a one-month composition period

Analysis and resultsTemporal dynamics of NDVI and LSTThe temporal variations of the NDVI and the LST along the four hydrologicalyears of the project are presented in Figure 2 Figure 2A shows that the Negev

MEQ141

26

ecosystem under an advanced rehabilitation process since 1982 shows highervalues of vegetation index (mean NDVI for the whole data set= 0124 plusmn 0039)and high reactivity to rainfall (from 0116 plusmn 0033 during the dry period to0143 plusmn 0045 in the wet period) especially during wet years (19961997 and19971998) In contrast the Sinai area under desertireged conditions presentsa very low reaction to rainfall (from 0086 plusmn 0014 during the dry period to0096 plusmn 0015 in the wet period) maintaining approximately constant NDVIvalues (mean NDVI for the whole data set = 0089 plusmn 0015) A slight increasein the vegetation index is visible only during the wet years The differencesbetween the mean NDVI values of Negev and Sinai can be as high as 01following the rainy season of a wet year Since it is well known that higherNDVI values are caused by a dark soil background (Huete 1988) NDVI

Figure 2Temporal variations of

NDVI (A) and LST (B) inSinai and the Negev

along the fourhydrological years of

research

Desertiregcationphenology and

droughts

27

differences during the dry periods seem to be related to the brightnessdifference between the two sides of the border due to the presence of the darkbiological soil crusts and to a lesser extent the higher vegetation on the Israeliside (Karnieli and Tsoar 1995)

Figure 2B demonstrates the monthly MVC values of the computed LST inthe sampling polygons on both sides of the border No clear differences inphase are evident On the contrary the cyclical temperature trends are similarin the Negev and in Sinai and follow the climatic variations throughout the fouryears During the relatively wet winters evaporation determines large losses ofenergy from the wet soil surface low sun irradiance also contributes lesssigniregcantly than in summer to the soil heat balance Thus the minimum LSTvalues are always found in the rainy period during the winter and can be aslow as 208 C In these seasons the difference in the amplitude between thepolygons is almost negligible The maximum temperatures are found at theheight of summer and can reach values of 58 8 C The temperature differencesbetween the two sides of the border can be as high as 78 C

The explanations for the amplitude differences evident in the dry period areaddressed in Qin et al (2001) Since the sand dunes on the Israeli side are almostcompletely covered by dark biological soil crusts they absorb more incidentradiation and emit stronger thermal radiation than the bare sand on theEgyptian side Moreover although more higher and lower vegetation is presenton the Israeli side these desert plants due to their scarcity and dormancy in thehot dry summer contribute almost nothing to the regional evapotranspirationthat cools the surface Thus in the dry season the Israeli side presents highervalues of surface temperatures than in the Sinai

Desertiregcation assessmentThe combination of remacrective and thermal data for determining soil waterstatus or surface water availability has been reported in numerous studies(Goward and Hope 1989 Nemani et al 1993 Lambin and Ehrlich 1996 1997)This type of analysis has usually been undertaken by plotting LST againstNDVI values (Figure 3A) Such a method is used in the current research inorder to characterize the desertiregcation processes in the study area Ascatterplot of multi-year NDVI vs LST values was created (Figure 3B)Applying a K-mean analysis of two clusters for the data it was shown that thecombined values of the Negev are signiregcantly different from those of SinaiComparing the location of the clusters to the African continental scheme ofLambin and Ehrlich (1996) the current scatterplot demonstrates that the Sinaidesertireged ecosystem overlaps the area covered by the Sahara biome while theNegev recovered side of the border although located only a few kilometersaway exhibits characteristics similar to those of the Sahel biome (although itregts even better to that in the Southern African hemisphere discussed in thesame paper)

MEQ141

28

PhenologyResults of the K-mean analysis reveal that 5 clusters characterize the studyarea (Figure 4) Statistics for each cluster are described in Table II The Sinaiarea under a desertiregcation process is characterized by just two clusters (1 and2) corresponding to the dry and wet seasons respectively and deregned by themacructuations of land surface temperatures throughout the year In this area thebiological activity is almost non-existent therefore only the physicalmeteorological factors are responsible for the movement inside the LST-NDVI space On the other hand the recovered ecosystem of the Negev showsrelatively intense biological activity and is characterized by movements over

Figure 3(A) Scatter diagram for

schematic discriminatingof different biomes in

Africa as proposed byLambin and Ehrlich

(1996) (B) Data obtainedfor this study

Desertiregcationphenology and

droughts

29

three clusters of the LST-NDVI space In the Negev ecosystem as in the Sinaiboth dry and rainy seasons are evident (clusters 3 and 4) while the third cluster(number 5) represents the growing season which is evident only on the Israeliside of the border and which includes the highest values of NDVI Only a fewpoints are presented inside cluster number 5 because it corresponds to the veryshort period of the year in which the desert reaches its maximum greenness dueto the blooming of the annuals

It is interesting to note that several points belonging to the Negev are locatedinside cluster 1 which represents the Sinai The explanation for this fact can befound in Holling (1973) Natural arid land systems show a resilient characterrather than a resistant one their stability is not deregned by a unique equilibrium

Figure 4Groups obtained fromthe cluster analysis ofNDVI and LST data

Cluster Season No of Negev pixels No of Sinai pixels Mean NDVI (SD) Mean LST (SD)

1 Dry 14 48 0082 (0010) 477 (36)2 Rainy 4 33 0095 (0010) 330 (58)3 Dry 51 7 0120 (0012) 492 (46)4 Rainy 16 5 0123 (0013) 285 (46)5 Growing 9 0 0182 (0018) 391 (81)

Note Based on DallrsquoOlmo and Karnieli (2002)

Table IIDescription of theclusters obtainedfrom the NDVI andLST data analysis

MEQ141

30

state but according to macructuations of environmental parameters Beyondcertain limits they can be associated with multiple-equilibrium states of adomain inside which the ecosystem does not change its structure This is theresult of adaptation to the harsh extremely variable desert environment Theoverlapping points of cluster 1 are relative to the very dry years (19951996 and19981999) In periods extremely lacking in water the recovered ecosystem isable to reduce its activity to the minimum needed for surviving in the area ofthe LST-NDVI space typical of desertireged ecosystems However as soon as theprecious resource of water is available again it can immediately react andproduce relatively high values of NDVI On the other hand the disturbedecosystem (Sinai) shows the result of a man-made perturbation that haschanged its structure in wet years only an extremely limited response torainfall is detected

The multi-year average of the combined NDVI and LST values are presentedin Figure 5 in terms of month-by-month trajectories Phenology starts inOctober and ends in September of the following year It is shown that thedesertireged Sinai ecosystem exhibits the highest variability mostly along theLST axis and is almost unaffected by the NDVI except for a slight deviationfrom the straight line between April and July most likely due to somegreenness of perennials On the other hand the recovered Negev side of theborder exhibits variability on both LST and NDVI axes Here the NDVI axis isdominant especially during the wet months (January-July) due to the greeningof biological soil crusts annuals and perennials During the summer monthsthe trajectory moves only along the LST axis

Figure 5Multi-year average of thecombined NDVI and LSTvalues in terms of month-

by-month trajectories

Desertiregcationphenology and

droughts

31

Drought assessmentFigure 6 exhibits the breakdown of Figure 5 into the four hydrological yearsunder investigation to demonstrate the annual dynamics In the Sinai littledifference can be seen between the wet and the dry years with the samegeneral trajectory shape plusmn long narrow and with a vertical pattern along theLST axis In the Negev however there is a considerable difference between thewet and dry years During the wet years (19961997 and 19971998) the shapeof the graphs is stretched towards the high NDVI values and the phenologicalcycle is much more pronounced whereas during droughts (19951996 and19981999) the NDVI component is much less remarkable and the phenologicalcycle shrinks signiregcantly

Figure 7 shows for each of the hydrological years and for the Negev andSinai ecosystems separately the lines connected between two points deregned as

(1) the maximum LST 2 minimum NDVI and

(2) minimum LST 2 maximum LST

It may be seen that the Sinai lines are very similar in terms of position andlength however two groups can be distinguished between the Negev lines Thelines of the wet years are longer and with gentler slopes whereas the dry yearslines are shorter and steeper These characteristics can be quantireged by threegeometrical expressions as schematically illustrated in Figure 8

Angle = arctan(DLST=DNDVI) (3)

Area = DNDVI curren 05DLST (4)

Length = [(DLST)2 + (DNDVI)2]05 (5)

These three expressions can be used as indicators for quantifying droughtyears Their calculated values are presented in Table III Evaluation of thethree indicators was performed by applying the following discriminationfunction (DF) for each year (i) and each region (j)

DFij = (Xij 2 Xmeanj)=(Xmaxj 2 Xminj)

where Xij is the calculated value for each indicator either in Sinai or in theNegev in any particular year Xavg Xmax and Xmin are the yearly averagemaximum and minimum values for each region respectively The objective ofthis function is to discriminate the drought years from the wet years on bothsides of the border

Results of the discrimination function for each of the drought indicators arepresented in Figure 9 It can be noticed that for the Negev each of the indicatorssuccessfully separates the two drought years from the wet years since the DFij

receives either positive or negative values However for Sinai only the ordflengthordm

MEQ141

32

Figure 6Trajectories of conmined

NDVI and LST valuesfor each of the fourhydrological years(October (10)

September (9))

Desertiregcationphenology and

droughts

33

Figure 7Lines connected betweenmaximumLSTminimum NDVIand minimumLSTmaximum NDVI forthe Sinai and the Negev

Figure 8Schematic illustration ofthree geometricalexpressions forindicating droughts

Year Sinai Negev

Angle 19951996 8036 748519961997 8320 706319971998 8331 720619981999 8323 7778

Area 19951996 043 08319961997 038 13119971998 041 14519981999 024 050

Length 19951996 2238 247519961997 2516 273119971998 2642 299519981999 2022 2117

Table IIICalculated valuesfor the threedrought indicators

MEQ141

34

indicator is able to show this phenomenon The other two indicators plusmn slopeand angle plusmn produce mixed results Consequently the ordflengthordm indicator will beused for further analysis

Using the ordflengthordm indicator Figure 10 presents the DFj values as a functionof the yearly rainfall of each year Despite the fact that only eight points areinvolved in the regression analysis a clear trend is exhibited between thedrought-year cluster (negative values) and the wet-year cluster (positive

Figure 9Results of the

discrimination functionprocedure for eachdrought indicator

Figure 10Discrimination function

values vs the yearlyrainfall amounts of

each year

Desertiregcationphenology and

droughts

35

values) The crossing point between the regression line and the DF= 0 line canbe used to quantify the threshold between wet and drought years in terms ofrainfall amount (54mm in the current example)

Summary and conclusionsResults from the current study show that desertiregcation phenology anddroughts processes can be detected and characterized by using four out of regveNOAAAVHRR spectral bands The NDVI is derived from the red and the NIRbands and the LST from the two thermal bands Combined use of these twoproducts provides more information than any product alone

Time series presentation of NDVI and LST reveals that the NDVI valuescorrespond to the reaction of the vegetation to rainfall and that LST valuesrepresent seasonal climatic macructuation Scatterplot analysis of LST vs NDVIdemonstrates the following

The two different biomes (Sinai and the Negev) exhibit different yearlyvariations of the phenological patterns two seasons in Sinai movingalong the LST axis and three seasons in the Negev where the NDVI axisrepresents the growing season

The Sinai has an ecosystem similar to that found in the Sahara while theNegev only a few kilometers away has an ecosystem similar to that ofthe Sahel

Drought indicators were derived in terms of three geometricalexpressions based on the two extreme points of the NDVI-LSTscatterplots Evaluation of the suggested indicator shows only one thatcan successfully separate between the drought and the wet years It isconcluded that the AVHRR imagery can provide valuable information fordrought monitoring and characterization

References

Asrar G Fuchs M Kanemasu ET and Hatregeld JL (1984) ordfEstimating absorbedphotosynthetic radiation and leaf area index from spectral remacrectance of wheatordmAgronomy J Vol 76 pp 300-6

Brown OB Brown JW and Evans RH (1985) ordfCalibration of advanced very-high resolutionradiometer infrared observationsordm J Geophys Res Vol 90 C6 pp 11667-77

Buschmann C and Nagel E (1993) ordfIn vivo spectroscopy and internal optics of leaves as basisfor remote-sensing of vegetationordm Int J Remote Sens Vol 14 pp 711-22

DallrsquoOlmo G and Karnieli A (2002) ordfFollowing phenological cycles of desert ecosystems usingNDVI and LST data derived from the advanced very high resolution radiometerordm IntJ Remote Sens Vol 23

Ehrlich D and Lambin EF (1996) ordfBroad scale land-cover classiregcation and interannualclimatic variabilityordm Int J Remote Sens Vol 17 pp 845-62

Elvidge CD and Chen ZK (1995) ordfComparison of broad-band and narrow-band red and near-infrared vegetation indexesordm Remote Sens Environ Vol 54 pp 38-48

MEQ141

36

Three types of pre-processing were used in the study

(1) radiometric

(2) atmospheric and

(3) geometric corrections

The processing of the data in the visible and NIR bands was based on post-launch calibration coefregcients suggested by NOAANESDIS (Rao and Chen1998) Atmospheric correction of the top-of-atmosphere remacrectances was carriedout using the 6S code including correction for molecular scattering whilegeometry of the sensor and the sun was also applied (Vermote et al 1997) Thisprogram also requires estimates of the water vapor aerosol and ozone contentsin the atmosphere The total precipitable water and aerosol optical thickness ofthe atmosphere were obtained from an automatic tracking sun photometer(CIMEL) installed at Sede Boker about 50km away from the study area (Holbenet al 2001) Ozone content in the atmosphere was based on climatology derivedfrom the total ozone mapping spectrometer (TOMS) onboard the Nimbus-7spacecraft between 1987 and 1993 The images were geometrically corrected to amaster image using ground control points and applying a nonlinear secondorder transformation The accuracy of the correction lies at the subpixel levelOne subset of 150 pixels on each side of the border (about 15 pound 10km2) wasextracted assuming homogeneous areas from each image (Figure 1) The NDVIvalues were calculated from the surface remacrectance values of the red and NIRbands of the AVHRR images

Radiometric correction of the two NOAA AVHRR thermal bands 4 and 5was performed following Brown et al (1995) and Weinreb et al (1990)Geometric correction and subset extraction were applied to the red and NIRbands These operations were necessary at this early step of the processing inorder to later apply the local characteristics of the split window algorithm (Qinet al 2001) used to correct the atmospheric inmacruence on the thermal infraredbands The algorithm used has a modireged form of equation (2) that requirestotal precipitable water and surface emissivity to compute the constants Theregrst constant was obtained from the CIMEL data relative to the image date ofacquisition the second was estimated by laboratory analysis of soil samplestaken in the study area (Qin 2001) Based on this work the emissivity value ofbare sand dunes (average value of 095) was assigned to the Sinairsquos polygonwhile the emissivity value of the biological soil crusts (average value of 097) forthat of the Negev The maximum value composite (MVC) method (Holben 1986)was applied to the NDVI and LST values for a one-month composition period

Analysis and resultsTemporal dynamics of NDVI and LSTThe temporal variations of the NDVI and the LST along the four hydrologicalyears of the project are presented in Figure 2 Figure 2A shows that the Negev

MEQ141

26

ecosystem under an advanced rehabilitation process since 1982 shows highervalues of vegetation index (mean NDVI for the whole data set= 0124 plusmn 0039)and high reactivity to rainfall (from 0116 plusmn 0033 during the dry period to0143 plusmn 0045 in the wet period) especially during wet years (19961997 and19971998) In contrast the Sinai area under desertireged conditions presentsa very low reaction to rainfall (from 0086 plusmn 0014 during the dry period to0096 plusmn 0015 in the wet period) maintaining approximately constant NDVIvalues (mean NDVI for the whole data set = 0089 plusmn 0015) A slight increasein the vegetation index is visible only during the wet years The differencesbetween the mean NDVI values of Negev and Sinai can be as high as 01following the rainy season of a wet year Since it is well known that higherNDVI values are caused by a dark soil background (Huete 1988) NDVI

Figure 2Temporal variations of

NDVI (A) and LST (B) inSinai and the Negev

along the fourhydrological years of

research

Desertiregcationphenology and

droughts

27

differences during the dry periods seem to be related to the brightnessdifference between the two sides of the border due to the presence of the darkbiological soil crusts and to a lesser extent the higher vegetation on the Israeliside (Karnieli and Tsoar 1995)

Figure 2B demonstrates the monthly MVC values of the computed LST inthe sampling polygons on both sides of the border No clear differences inphase are evident On the contrary the cyclical temperature trends are similarin the Negev and in Sinai and follow the climatic variations throughout the fouryears During the relatively wet winters evaporation determines large losses ofenergy from the wet soil surface low sun irradiance also contributes lesssigniregcantly than in summer to the soil heat balance Thus the minimum LSTvalues are always found in the rainy period during the winter and can be aslow as 208 C In these seasons the difference in the amplitude between thepolygons is almost negligible The maximum temperatures are found at theheight of summer and can reach values of 58 8 C The temperature differencesbetween the two sides of the border can be as high as 78 C

The explanations for the amplitude differences evident in the dry period areaddressed in Qin et al (2001) Since the sand dunes on the Israeli side are almostcompletely covered by dark biological soil crusts they absorb more incidentradiation and emit stronger thermal radiation than the bare sand on theEgyptian side Moreover although more higher and lower vegetation is presenton the Israeli side these desert plants due to their scarcity and dormancy in thehot dry summer contribute almost nothing to the regional evapotranspirationthat cools the surface Thus in the dry season the Israeli side presents highervalues of surface temperatures than in the Sinai

Desertiregcation assessmentThe combination of remacrective and thermal data for determining soil waterstatus or surface water availability has been reported in numerous studies(Goward and Hope 1989 Nemani et al 1993 Lambin and Ehrlich 1996 1997)This type of analysis has usually been undertaken by plotting LST againstNDVI values (Figure 3A) Such a method is used in the current research inorder to characterize the desertiregcation processes in the study area Ascatterplot of multi-year NDVI vs LST values was created (Figure 3B)Applying a K-mean analysis of two clusters for the data it was shown that thecombined values of the Negev are signiregcantly different from those of SinaiComparing the location of the clusters to the African continental scheme ofLambin and Ehrlich (1996) the current scatterplot demonstrates that the Sinaidesertireged ecosystem overlaps the area covered by the Sahara biome while theNegev recovered side of the border although located only a few kilometersaway exhibits characteristics similar to those of the Sahel biome (although itregts even better to that in the Southern African hemisphere discussed in thesame paper)

MEQ141

28

PhenologyResults of the K-mean analysis reveal that 5 clusters characterize the studyarea (Figure 4) Statistics for each cluster are described in Table II The Sinaiarea under a desertiregcation process is characterized by just two clusters (1 and2) corresponding to the dry and wet seasons respectively and deregned by themacructuations of land surface temperatures throughout the year In this area thebiological activity is almost non-existent therefore only the physicalmeteorological factors are responsible for the movement inside the LST-NDVI space On the other hand the recovered ecosystem of the Negev showsrelatively intense biological activity and is characterized by movements over

Figure 3(A) Scatter diagram for

schematic discriminatingof different biomes in

Africa as proposed byLambin and Ehrlich

(1996) (B) Data obtainedfor this study

Desertiregcationphenology and

droughts

29

three clusters of the LST-NDVI space In the Negev ecosystem as in the Sinaiboth dry and rainy seasons are evident (clusters 3 and 4) while the third cluster(number 5) represents the growing season which is evident only on the Israeliside of the border and which includes the highest values of NDVI Only a fewpoints are presented inside cluster number 5 because it corresponds to the veryshort period of the year in which the desert reaches its maximum greenness dueto the blooming of the annuals

It is interesting to note that several points belonging to the Negev are locatedinside cluster 1 which represents the Sinai The explanation for this fact can befound in Holling (1973) Natural arid land systems show a resilient characterrather than a resistant one their stability is not deregned by a unique equilibrium

Figure 4Groups obtained fromthe cluster analysis ofNDVI and LST data

Cluster Season No of Negev pixels No of Sinai pixels Mean NDVI (SD) Mean LST (SD)

1 Dry 14 48 0082 (0010) 477 (36)2 Rainy 4 33 0095 (0010) 330 (58)3 Dry 51 7 0120 (0012) 492 (46)4 Rainy 16 5 0123 (0013) 285 (46)5 Growing 9 0 0182 (0018) 391 (81)

Note Based on DallrsquoOlmo and Karnieli (2002)

Table IIDescription of theclusters obtainedfrom the NDVI andLST data analysis

MEQ141

30

state but according to macructuations of environmental parameters Beyondcertain limits they can be associated with multiple-equilibrium states of adomain inside which the ecosystem does not change its structure This is theresult of adaptation to the harsh extremely variable desert environment Theoverlapping points of cluster 1 are relative to the very dry years (19951996 and19981999) In periods extremely lacking in water the recovered ecosystem isable to reduce its activity to the minimum needed for surviving in the area ofthe LST-NDVI space typical of desertireged ecosystems However as soon as theprecious resource of water is available again it can immediately react andproduce relatively high values of NDVI On the other hand the disturbedecosystem (Sinai) shows the result of a man-made perturbation that haschanged its structure in wet years only an extremely limited response torainfall is detected

The multi-year average of the combined NDVI and LST values are presentedin Figure 5 in terms of month-by-month trajectories Phenology starts inOctober and ends in September of the following year It is shown that thedesertireged Sinai ecosystem exhibits the highest variability mostly along theLST axis and is almost unaffected by the NDVI except for a slight deviationfrom the straight line between April and July most likely due to somegreenness of perennials On the other hand the recovered Negev side of theborder exhibits variability on both LST and NDVI axes Here the NDVI axis isdominant especially during the wet months (January-July) due to the greeningof biological soil crusts annuals and perennials During the summer monthsthe trajectory moves only along the LST axis

Figure 5Multi-year average of thecombined NDVI and LSTvalues in terms of month-

by-month trajectories

Desertiregcationphenology and

droughts

31

Drought assessmentFigure 6 exhibits the breakdown of Figure 5 into the four hydrological yearsunder investigation to demonstrate the annual dynamics In the Sinai littledifference can be seen between the wet and the dry years with the samegeneral trajectory shape plusmn long narrow and with a vertical pattern along theLST axis In the Negev however there is a considerable difference between thewet and dry years During the wet years (19961997 and 19971998) the shapeof the graphs is stretched towards the high NDVI values and the phenologicalcycle is much more pronounced whereas during droughts (19951996 and19981999) the NDVI component is much less remarkable and the phenologicalcycle shrinks signiregcantly

Figure 7 shows for each of the hydrological years and for the Negev andSinai ecosystems separately the lines connected between two points deregned as

(1) the maximum LST 2 minimum NDVI and

(2) minimum LST 2 maximum LST

It may be seen that the Sinai lines are very similar in terms of position andlength however two groups can be distinguished between the Negev lines Thelines of the wet years are longer and with gentler slopes whereas the dry yearslines are shorter and steeper These characteristics can be quantireged by threegeometrical expressions as schematically illustrated in Figure 8

Angle = arctan(DLST=DNDVI) (3)

Area = DNDVI curren 05DLST (4)

Length = [(DLST)2 + (DNDVI)2]05 (5)

These three expressions can be used as indicators for quantifying droughtyears Their calculated values are presented in Table III Evaluation of thethree indicators was performed by applying the following discriminationfunction (DF) for each year (i) and each region (j)

DFij = (Xij 2 Xmeanj)=(Xmaxj 2 Xminj)

where Xij is the calculated value for each indicator either in Sinai or in theNegev in any particular year Xavg Xmax and Xmin are the yearly averagemaximum and minimum values for each region respectively The objective ofthis function is to discriminate the drought years from the wet years on bothsides of the border

Results of the discrimination function for each of the drought indicators arepresented in Figure 9 It can be noticed that for the Negev each of the indicatorssuccessfully separates the two drought years from the wet years since the DFij

receives either positive or negative values However for Sinai only the ordflengthordm

MEQ141

32

Figure 6Trajectories of conmined

NDVI and LST valuesfor each of the fourhydrological years(October (10)

September (9))

Desertiregcationphenology and

droughts

33

Figure 7Lines connected betweenmaximumLSTminimum NDVIand minimumLSTmaximum NDVI forthe Sinai and the Negev

Figure 8Schematic illustration ofthree geometricalexpressions forindicating droughts

Year Sinai Negev

Angle 19951996 8036 748519961997 8320 706319971998 8331 720619981999 8323 7778

Area 19951996 043 08319961997 038 13119971998 041 14519981999 024 050

Length 19951996 2238 247519961997 2516 273119971998 2642 299519981999 2022 2117

Table IIICalculated valuesfor the threedrought indicators

MEQ141

34

indicator is able to show this phenomenon The other two indicators plusmn slopeand angle plusmn produce mixed results Consequently the ordflengthordm indicator will beused for further analysis

Using the ordflengthordm indicator Figure 10 presents the DFj values as a functionof the yearly rainfall of each year Despite the fact that only eight points areinvolved in the regression analysis a clear trend is exhibited between thedrought-year cluster (negative values) and the wet-year cluster (positive

Figure 9Results of the

discrimination functionprocedure for eachdrought indicator

Figure 10Discrimination function

values vs the yearlyrainfall amounts of

each year

Desertiregcationphenology and

droughts

35

values) The crossing point between the regression line and the DF= 0 line canbe used to quantify the threshold between wet and drought years in terms ofrainfall amount (54mm in the current example)

Summary and conclusionsResults from the current study show that desertiregcation phenology anddroughts processes can be detected and characterized by using four out of regveNOAAAVHRR spectral bands The NDVI is derived from the red and the NIRbands and the LST from the two thermal bands Combined use of these twoproducts provides more information than any product alone

Time series presentation of NDVI and LST reveals that the NDVI valuescorrespond to the reaction of the vegetation to rainfall and that LST valuesrepresent seasonal climatic macructuation Scatterplot analysis of LST vs NDVIdemonstrates the following

The two different biomes (Sinai and the Negev) exhibit different yearlyvariations of the phenological patterns two seasons in Sinai movingalong the LST axis and three seasons in the Negev where the NDVI axisrepresents the growing season

The Sinai has an ecosystem similar to that found in the Sahara while theNegev only a few kilometers away has an ecosystem similar to that ofthe Sahel

Drought indicators were derived in terms of three geometricalexpressions based on the two extreme points of the NDVI-LSTscatterplots Evaluation of the suggested indicator shows only one thatcan successfully separate between the drought and the wet years It isconcluded that the AVHRR imagery can provide valuable information fordrought monitoring and characterization

References

Asrar G Fuchs M Kanemasu ET and Hatregeld JL (1984) ordfEstimating absorbedphotosynthetic radiation and leaf area index from spectral remacrectance of wheatordmAgronomy J Vol 76 pp 300-6

Brown OB Brown JW and Evans RH (1985) ordfCalibration of advanced very-high resolutionradiometer infrared observationsordm J Geophys Res Vol 90 C6 pp 11667-77

Buschmann C and Nagel E (1993) ordfIn vivo spectroscopy and internal optics of leaves as basisfor remote-sensing of vegetationordm Int J Remote Sens Vol 14 pp 711-22

DallrsquoOlmo G and Karnieli A (2002) ordfFollowing phenological cycles of desert ecosystems usingNDVI and LST data derived from the advanced very high resolution radiometerordm IntJ Remote Sens Vol 23

Ehrlich D and Lambin EF (1996) ordfBroad scale land-cover classiregcation and interannualclimatic variabilityordm Int J Remote Sens Vol 17 pp 845-62

Elvidge CD and Chen ZK (1995) ordfComparison of broad-band and narrow-band red and near-infrared vegetation indexesordm Remote Sens Environ Vol 54 pp 38-48

MEQ141

36

ecosystem under an advanced rehabilitation process since 1982 shows highervalues of vegetation index (mean NDVI for the whole data set= 0124 plusmn 0039)and high reactivity to rainfall (from 0116 plusmn 0033 during the dry period to0143 plusmn 0045 in the wet period) especially during wet years (19961997 and19971998) In contrast the Sinai area under desertireged conditions presentsa very low reaction to rainfall (from 0086 plusmn 0014 during the dry period to0096 plusmn 0015 in the wet period) maintaining approximately constant NDVIvalues (mean NDVI for the whole data set = 0089 plusmn 0015) A slight increasein the vegetation index is visible only during the wet years The differencesbetween the mean NDVI values of Negev and Sinai can be as high as 01following the rainy season of a wet year Since it is well known that higherNDVI values are caused by a dark soil background (Huete 1988) NDVI

Figure 2Temporal variations of

NDVI (A) and LST (B) inSinai and the Negev

along the fourhydrological years of

research

Desertiregcationphenology and

droughts

27

differences during the dry periods seem to be related to the brightnessdifference between the two sides of the border due to the presence of the darkbiological soil crusts and to a lesser extent the higher vegetation on the Israeliside (Karnieli and Tsoar 1995)

Figure 2B demonstrates the monthly MVC values of the computed LST inthe sampling polygons on both sides of the border No clear differences inphase are evident On the contrary the cyclical temperature trends are similarin the Negev and in Sinai and follow the climatic variations throughout the fouryears During the relatively wet winters evaporation determines large losses ofenergy from the wet soil surface low sun irradiance also contributes lesssigniregcantly than in summer to the soil heat balance Thus the minimum LSTvalues are always found in the rainy period during the winter and can be aslow as 208 C In these seasons the difference in the amplitude between thepolygons is almost negligible The maximum temperatures are found at theheight of summer and can reach values of 58 8 C The temperature differencesbetween the two sides of the border can be as high as 78 C

The explanations for the amplitude differences evident in the dry period areaddressed in Qin et al (2001) Since the sand dunes on the Israeli side are almostcompletely covered by dark biological soil crusts they absorb more incidentradiation and emit stronger thermal radiation than the bare sand on theEgyptian side Moreover although more higher and lower vegetation is presenton the Israeli side these desert plants due to their scarcity and dormancy in thehot dry summer contribute almost nothing to the regional evapotranspirationthat cools the surface Thus in the dry season the Israeli side presents highervalues of surface temperatures than in the Sinai

Desertiregcation assessmentThe combination of remacrective and thermal data for determining soil waterstatus or surface water availability has been reported in numerous studies(Goward and Hope 1989 Nemani et al 1993 Lambin and Ehrlich 1996 1997)This type of analysis has usually been undertaken by plotting LST againstNDVI values (Figure 3A) Such a method is used in the current research inorder to characterize the desertiregcation processes in the study area Ascatterplot of multi-year NDVI vs LST values was created (Figure 3B)Applying a K-mean analysis of two clusters for the data it was shown that thecombined values of the Negev are signiregcantly different from those of SinaiComparing the location of the clusters to the African continental scheme ofLambin and Ehrlich (1996) the current scatterplot demonstrates that the Sinaidesertireged ecosystem overlaps the area covered by the Sahara biome while theNegev recovered side of the border although located only a few kilometersaway exhibits characteristics similar to those of the Sahel biome (although itregts even better to that in the Southern African hemisphere discussed in thesame paper)

MEQ141

28

PhenologyResults of the K-mean analysis reveal that 5 clusters characterize the studyarea (Figure 4) Statistics for each cluster are described in Table II The Sinaiarea under a desertiregcation process is characterized by just two clusters (1 and2) corresponding to the dry and wet seasons respectively and deregned by themacructuations of land surface temperatures throughout the year In this area thebiological activity is almost non-existent therefore only the physicalmeteorological factors are responsible for the movement inside the LST-NDVI space On the other hand the recovered ecosystem of the Negev showsrelatively intense biological activity and is characterized by movements over

Figure 3(A) Scatter diagram for

schematic discriminatingof different biomes in

Africa as proposed byLambin and Ehrlich

(1996) (B) Data obtainedfor this study

Desertiregcationphenology and

droughts

29

three clusters of the LST-NDVI space In the Negev ecosystem as in the Sinaiboth dry and rainy seasons are evident (clusters 3 and 4) while the third cluster(number 5) represents the growing season which is evident only on the Israeliside of the border and which includes the highest values of NDVI Only a fewpoints are presented inside cluster number 5 because it corresponds to the veryshort period of the year in which the desert reaches its maximum greenness dueto the blooming of the annuals

It is interesting to note that several points belonging to the Negev are locatedinside cluster 1 which represents the Sinai The explanation for this fact can befound in Holling (1973) Natural arid land systems show a resilient characterrather than a resistant one their stability is not deregned by a unique equilibrium

Figure 4Groups obtained fromthe cluster analysis ofNDVI and LST data

Cluster Season No of Negev pixels No of Sinai pixels Mean NDVI (SD) Mean LST (SD)

1 Dry 14 48 0082 (0010) 477 (36)2 Rainy 4 33 0095 (0010) 330 (58)3 Dry 51 7 0120 (0012) 492 (46)4 Rainy 16 5 0123 (0013) 285 (46)5 Growing 9 0 0182 (0018) 391 (81)

Note Based on DallrsquoOlmo and Karnieli (2002)

Table IIDescription of theclusters obtainedfrom the NDVI andLST data analysis

MEQ141

30

state but according to macructuations of environmental parameters Beyondcertain limits they can be associated with multiple-equilibrium states of adomain inside which the ecosystem does not change its structure This is theresult of adaptation to the harsh extremely variable desert environment Theoverlapping points of cluster 1 are relative to the very dry years (19951996 and19981999) In periods extremely lacking in water the recovered ecosystem isable to reduce its activity to the minimum needed for surviving in the area ofthe LST-NDVI space typical of desertireged ecosystems However as soon as theprecious resource of water is available again it can immediately react andproduce relatively high values of NDVI On the other hand the disturbedecosystem (Sinai) shows the result of a man-made perturbation that haschanged its structure in wet years only an extremely limited response torainfall is detected

The multi-year average of the combined NDVI and LST values are presentedin Figure 5 in terms of month-by-month trajectories Phenology starts inOctober and ends in September of the following year It is shown that thedesertireged Sinai ecosystem exhibits the highest variability mostly along theLST axis and is almost unaffected by the NDVI except for a slight deviationfrom the straight line between April and July most likely due to somegreenness of perennials On the other hand the recovered Negev side of theborder exhibits variability on both LST and NDVI axes Here the NDVI axis isdominant especially during the wet months (January-July) due to the greeningof biological soil crusts annuals and perennials During the summer monthsthe trajectory moves only along the LST axis

Figure 5Multi-year average of thecombined NDVI and LSTvalues in terms of month-

by-month trajectories

Desertiregcationphenology and

droughts

31

Drought assessmentFigure 6 exhibits the breakdown of Figure 5 into the four hydrological yearsunder investigation to demonstrate the annual dynamics In the Sinai littledifference can be seen between the wet and the dry years with the samegeneral trajectory shape plusmn long narrow and with a vertical pattern along theLST axis In the Negev however there is a considerable difference between thewet and dry years During the wet years (19961997 and 19971998) the shapeof the graphs is stretched towards the high NDVI values and the phenologicalcycle is much more pronounced whereas during droughts (19951996 and19981999) the NDVI component is much less remarkable and the phenologicalcycle shrinks signiregcantly

Figure 7 shows for each of the hydrological years and for the Negev andSinai ecosystems separately the lines connected between two points deregned as

(1) the maximum LST 2 minimum NDVI and

(2) minimum LST 2 maximum LST

It may be seen that the Sinai lines are very similar in terms of position andlength however two groups can be distinguished between the Negev lines Thelines of the wet years are longer and with gentler slopes whereas the dry yearslines are shorter and steeper These characteristics can be quantireged by threegeometrical expressions as schematically illustrated in Figure 8

Angle = arctan(DLST=DNDVI) (3)

Area = DNDVI curren 05DLST (4)

Length = [(DLST)2 + (DNDVI)2]05 (5)

These three expressions can be used as indicators for quantifying droughtyears Their calculated values are presented in Table III Evaluation of thethree indicators was performed by applying the following discriminationfunction (DF) for each year (i) and each region (j)

DFij = (Xij 2 Xmeanj)=(Xmaxj 2 Xminj)

where Xij is the calculated value for each indicator either in Sinai or in theNegev in any particular year Xavg Xmax and Xmin are the yearly averagemaximum and minimum values for each region respectively The objective ofthis function is to discriminate the drought years from the wet years on bothsides of the border

Results of the discrimination function for each of the drought indicators arepresented in Figure 9 It can be noticed that for the Negev each of the indicatorssuccessfully separates the two drought years from the wet years since the DFij

receives either positive or negative values However for Sinai only the ordflengthordm

MEQ141

32

Figure 6Trajectories of conmined

NDVI and LST valuesfor each of the fourhydrological years(October (10)

September (9))

Desertiregcationphenology and

droughts

33

Figure 7Lines connected betweenmaximumLSTminimum NDVIand minimumLSTmaximum NDVI forthe Sinai and the Negev

Figure 8Schematic illustration ofthree geometricalexpressions forindicating droughts

Year Sinai Negev

Angle 19951996 8036 748519961997 8320 706319971998 8331 720619981999 8323 7778

Area 19951996 043 08319961997 038 13119971998 041 14519981999 024 050

Length 19951996 2238 247519961997 2516 273119971998 2642 299519981999 2022 2117

Table IIICalculated valuesfor the threedrought indicators

MEQ141

34

indicator is able to show this phenomenon The other two indicators plusmn slopeand angle plusmn produce mixed results Consequently the ordflengthordm indicator will beused for further analysis

Using the ordflengthordm indicator Figure 10 presents the DFj values as a functionof the yearly rainfall of each year Despite the fact that only eight points areinvolved in the regression analysis a clear trend is exhibited between thedrought-year cluster (negative values) and the wet-year cluster (positive

Figure 9Results of the

discrimination functionprocedure for eachdrought indicator

Figure 10Discrimination function

values vs the yearlyrainfall amounts of

each year

Desertiregcationphenology and

droughts

35

values) The crossing point between the regression line and the DF= 0 line canbe used to quantify the threshold between wet and drought years in terms ofrainfall amount (54mm in the current example)

Summary and conclusionsResults from the current study show that desertiregcation phenology anddroughts processes can be detected and characterized by using four out of regveNOAAAVHRR spectral bands The NDVI is derived from the red and the NIRbands and the LST from the two thermal bands Combined use of these twoproducts provides more information than any product alone

Time series presentation of NDVI and LST reveals that the NDVI valuescorrespond to the reaction of the vegetation to rainfall and that LST valuesrepresent seasonal climatic macructuation Scatterplot analysis of LST vs NDVIdemonstrates the following

The two different biomes (Sinai and the Negev) exhibit different yearlyvariations of the phenological patterns two seasons in Sinai movingalong the LST axis and three seasons in the Negev where the NDVI axisrepresents the growing season

The Sinai has an ecosystem similar to that found in the Sahara while theNegev only a few kilometers away has an ecosystem similar to that ofthe Sahel

Drought indicators were derived in terms of three geometricalexpressions based on the two extreme points of the NDVI-LSTscatterplots Evaluation of the suggested indicator shows only one thatcan successfully separate between the drought and the wet years It isconcluded that the AVHRR imagery can provide valuable information fordrought monitoring and characterization

References

Asrar G Fuchs M Kanemasu ET and Hatregeld JL (1984) ordfEstimating absorbedphotosynthetic radiation and leaf area index from spectral remacrectance of wheatordmAgronomy J Vol 76 pp 300-6

Brown OB Brown JW and Evans RH (1985) ordfCalibration of advanced very-high resolutionradiometer infrared observationsordm J Geophys Res Vol 90 C6 pp 11667-77

Buschmann C and Nagel E (1993) ordfIn vivo spectroscopy and internal optics of leaves as basisfor remote-sensing of vegetationordm Int J Remote Sens Vol 14 pp 711-22

DallrsquoOlmo G and Karnieli A (2002) ordfFollowing phenological cycles of desert ecosystems usingNDVI and LST data derived from the advanced very high resolution radiometerordm IntJ Remote Sens Vol 23

Ehrlich D and Lambin EF (1996) ordfBroad scale land-cover classiregcation and interannualclimatic variabilityordm Int J Remote Sens Vol 17 pp 845-62

Elvidge CD and Chen ZK (1995) ordfComparison of broad-band and narrow-band red and near-infrared vegetation indexesordm Remote Sens Environ Vol 54 pp 38-48

MEQ141

36

differences during the dry periods seem to be related to the brightnessdifference between the two sides of the border due to the presence of the darkbiological soil crusts and to a lesser extent the higher vegetation on the Israeliside (Karnieli and Tsoar 1995)

Figure 2B demonstrates the monthly MVC values of the computed LST inthe sampling polygons on both sides of the border No clear differences inphase are evident On the contrary the cyclical temperature trends are similarin the Negev and in Sinai and follow the climatic variations throughout the fouryears During the relatively wet winters evaporation determines large losses ofenergy from the wet soil surface low sun irradiance also contributes lesssigniregcantly than in summer to the soil heat balance Thus the minimum LSTvalues are always found in the rainy period during the winter and can be aslow as 208 C In these seasons the difference in the amplitude between thepolygons is almost negligible The maximum temperatures are found at theheight of summer and can reach values of 58 8 C The temperature differencesbetween the two sides of the border can be as high as 78 C

The explanations for the amplitude differences evident in the dry period areaddressed in Qin et al (2001) Since the sand dunes on the Israeli side are almostcompletely covered by dark biological soil crusts they absorb more incidentradiation and emit stronger thermal radiation than the bare sand on theEgyptian side Moreover although more higher and lower vegetation is presenton the Israeli side these desert plants due to their scarcity and dormancy in thehot dry summer contribute almost nothing to the regional evapotranspirationthat cools the surface Thus in the dry season the Israeli side presents highervalues of surface temperatures than in the Sinai

Desertiregcation assessmentThe combination of remacrective and thermal data for determining soil waterstatus or surface water availability has been reported in numerous studies(Goward and Hope 1989 Nemani et al 1993 Lambin and Ehrlich 1996 1997)This type of analysis has usually been undertaken by plotting LST againstNDVI values (Figure 3A) Such a method is used in the current research inorder to characterize the desertiregcation processes in the study area Ascatterplot of multi-year NDVI vs LST values was created (Figure 3B)Applying a K-mean analysis of two clusters for the data it was shown that thecombined values of the Negev are signiregcantly different from those of SinaiComparing the location of the clusters to the African continental scheme ofLambin and Ehrlich (1996) the current scatterplot demonstrates that the Sinaidesertireged ecosystem overlaps the area covered by the Sahara biome while theNegev recovered side of the border although located only a few kilometersaway exhibits characteristics similar to those of the Sahel biome (although itregts even better to that in the Southern African hemisphere discussed in thesame paper)

MEQ141

28

PhenologyResults of the K-mean analysis reveal that 5 clusters characterize the studyarea (Figure 4) Statistics for each cluster are described in Table II The Sinaiarea under a desertiregcation process is characterized by just two clusters (1 and2) corresponding to the dry and wet seasons respectively and deregned by themacructuations of land surface temperatures throughout the year In this area thebiological activity is almost non-existent therefore only the physicalmeteorological factors are responsible for the movement inside the LST-NDVI space On the other hand the recovered ecosystem of the Negev showsrelatively intense biological activity and is characterized by movements over

Figure 3(A) Scatter diagram for

schematic discriminatingof different biomes in

Africa as proposed byLambin and Ehrlich

(1996) (B) Data obtainedfor this study

Desertiregcationphenology and

droughts

29

three clusters of the LST-NDVI space In the Negev ecosystem as in the Sinaiboth dry and rainy seasons are evident (clusters 3 and 4) while the third cluster(number 5) represents the growing season which is evident only on the Israeliside of the border and which includes the highest values of NDVI Only a fewpoints are presented inside cluster number 5 because it corresponds to the veryshort period of the year in which the desert reaches its maximum greenness dueto the blooming of the annuals

It is interesting to note that several points belonging to the Negev are locatedinside cluster 1 which represents the Sinai The explanation for this fact can befound in Holling (1973) Natural arid land systems show a resilient characterrather than a resistant one their stability is not deregned by a unique equilibrium

Figure 4Groups obtained fromthe cluster analysis ofNDVI and LST data

Cluster Season No of Negev pixels No of Sinai pixels Mean NDVI (SD) Mean LST (SD)

1 Dry 14 48 0082 (0010) 477 (36)2 Rainy 4 33 0095 (0010) 330 (58)3 Dry 51 7 0120 (0012) 492 (46)4 Rainy 16 5 0123 (0013) 285 (46)5 Growing 9 0 0182 (0018) 391 (81)

Note Based on DallrsquoOlmo and Karnieli (2002)

Table IIDescription of theclusters obtainedfrom the NDVI andLST data analysis

MEQ141

30

state but according to macructuations of environmental parameters Beyondcertain limits they can be associated with multiple-equilibrium states of adomain inside which the ecosystem does not change its structure This is theresult of adaptation to the harsh extremely variable desert environment Theoverlapping points of cluster 1 are relative to the very dry years (19951996 and19981999) In periods extremely lacking in water the recovered ecosystem isable to reduce its activity to the minimum needed for surviving in the area ofthe LST-NDVI space typical of desertireged ecosystems However as soon as theprecious resource of water is available again it can immediately react andproduce relatively high values of NDVI On the other hand the disturbedecosystem (Sinai) shows the result of a man-made perturbation that haschanged its structure in wet years only an extremely limited response torainfall is detected

The multi-year average of the combined NDVI and LST values are presentedin Figure 5 in terms of month-by-month trajectories Phenology starts inOctober and ends in September of the following year It is shown that thedesertireged Sinai ecosystem exhibits the highest variability mostly along theLST axis and is almost unaffected by the NDVI except for a slight deviationfrom the straight line between April and July most likely due to somegreenness of perennials On the other hand the recovered Negev side of theborder exhibits variability on both LST and NDVI axes Here the NDVI axis isdominant especially during the wet months (January-July) due to the greeningof biological soil crusts annuals and perennials During the summer monthsthe trajectory moves only along the LST axis

Figure 5Multi-year average of thecombined NDVI and LSTvalues in terms of month-

by-month trajectories

Desertiregcationphenology and

droughts

31

Drought assessmentFigure 6 exhibits the breakdown of Figure 5 into the four hydrological yearsunder investigation to demonstrate the annual dynamics In the Sinai littledifference can be seen between the wet and the dry years with the samegeneral trajectory shape plusmn long narrow and with a vertical pattern along theLST axis In the Negev however there is a considerable difference between thewet and dry years During the wet years (19961997 and 19971998) the shapeof the graphs is stretched towards the high NDVI values and the phenologicalcycle is much more pronounced whereas during droughts (19951996 and19981999) the NDVI component is much less remarkable and the phenologicalcycle shrinks signiregcantly

Figure 7 shows for each of the hydrological years and for the Negev andSinai ecosystems separately the lines connected between two points deregned as

(1) the maximum LST 2 minimum NDVI and

(2) minimum LST 2 maximum LST

It may be seen that the Sinai lines are very similar in terms of position andlength however two groups can be distinguished between the Negev lines Thelines of the wet years are longer and with gentler slopes whereas the dry yearslines are shorter and steeper These characteristics can be quantireged by threegeometrical expressions as schematically illustrated in Figure 8

Angle = arctan(DLST=DNDVI) (3)

Area = DNDVI curren 05DLST (4)

Length = [(DLST)2 + (DNDVI)2]05 (5)

These three expressions can be used as indicators for quantifying droughtyears Their calculated values are presented in Table III Evaluation of thethree indicators was performed by applying the following discriminationfunction (DF) for each year (i) and each region (j)

DFij = (Xij 2 Xmeanj)=(Xmaxj 2 Xminj)

where Xij is the calculated value for each indicator either in Sinai or in theNegev in any particular year Xavg Xmax and Xmin are the yearly averagemaximum and minimum values for each region respectively The objective ofthis function is to discriminate the drought years from the wet years on bothsides of the border

Results of the discrimination function for each of the drought indicators arepresented in Figure 9 It can be noticed that for the Negev each of the indicatorssuccessfully separates the two drought years from the wet years since the DFij

receives either positive or negative values However for Sinai only the ordflengthordm

MEQ141

32

Figure 6Trajectories of conmined

NDVI and LST valuesfor each of the fourhydrological years(October (10)

September (9))

Desertiregcationphenology and

droughts

33

Figure 7Lines connected betweenmaximumLSTminimum NDVIand minimumLSTmaximum NDVI forthe Sinai and the Negev

Figure 8Schematic illustration ofthree geometricalexpressions forindicating droughts

Year Sinai Negev

Angle 19951996 8036 748519961997 8320 706319971998 8331 720619981999 8323 7778

Area 19951996 043 08319961997 038 13119971998 041 14519981999 024 050

Length 19951996 2238 247519961997 2516 273119971998 2642 299519981999 2022 2117

Table IIICalculated valuesfor the threedrought indicators

MEQ141

34

indicator is able to show this phenomenon The other two indicators plusmn slopeand angle plusmn produce mixed results Consequently the ordflengthordm indicator will beused for further analysis

Using the ordflengthordm indicator Figure 10 presents the DFj values as a functionof the yearly rainfall of each year Despite the fact that only eight points areinvolved in the regression analysis a clear trend is exhibited between thedrought-year cluster (negative values) and the wet-year cluster (positive

Figure 9Results of the

discrimination functionprocedure for eachdrought indicator

Figure 10Discrimination function

values vs the yearlyrainfall amounts of

each year

Desertiregcationphenology and

droughts

35

values) The crossing point between the regression line and the DF= 0 line canbe used to quantify the threshold between wet and drought years in terms ofrainfall amount (54mm in the current example)

Summary and conclusionsResults from the current study show that desertiregcation phenology anddroughts processes can be detected and characterized by using four out of regveNOAAAVHRR spectral bands The NDVI is derived from the red and the NIRbands and the LST from the two thermal bands Combined use of these twoproducts provides more information than any product alone

Time series presentation of NDVI and LST reveals that the NDVI valuescorrespond to the reaction of the vegetation to rainfall and that LST valuesrepresent seasonal climatic macructuation Scatterplot analysis of LST vs NDVIdemonstrates the following

The two different biomes (Sinai and the Negev) exhibit different yearlyvariations of the phenological patterns two seasons in Sinai movingalong the LST axis and three seasons in the Negev where the NDVI axisrepresents the growing season

The Sinai has an ecosystem similar to that found in the Sahara while theNegev only a few kilometers away has an ecosystem similar to that ofthe Sahel

Drought indicators were derived in terms of three geometricalexpressions based on the two extreme points of the NDVI-LSTscatterplots Evaluation of the suggested indicator shows only one thatcan successfully separate between the drought and the wet years It isconcluded that the AVHRR imagery can provide valuable information fordrought monitoring and characterization

References

Asrar G Fuchs M Kanemasu ET and Hatregeld JL (1984) ordfEstimating absorbedphotosynthetic radiation and leaf area index from spectral remacrectance of wheatordmAgronomy J Vol 76 pp 300-6

Brown OB Brown JW and Evans RH (1985) ordfCalibration of advanced very-high resolutionradiometer infrared observationsordm J Geophys Res Vol 90 C6 pp 11667-77

Buschmann C and Nagel E (1993) ordfIn vivo spectroscopy and internal optics of leaves as basisfor remote-sensing of vegetationordm Int J Remote Sens Vol 14 pp 711-22

DallrsquoOlmo G and Karnieli A (2002) ordfFollowing phenological cycles of desert ecosystems usingNDVI and LST data derived from the advanced very high resolution radiometerordm IntJ Remote Sens Vol 23

Ehrlich D and Lambin EF (1996) ordfBroad scale land-cover classiregcation and interannualclimatic variabilityordm Int J Remote Sens Vol 17 pp 845-62

Elvidge CD and Chen ZK (1995) ordfComparison of broad-band and narrow-band red and near-infrared vegetation indexesordm Remote Sens Environ Vol 54 pp 38-48

MEQ141

36

PhenologyResults of the K-mean analysis reveal that 5 clusters characterize the studyarea (Figure 4) Statistics for each cluster are described in Table II The Sinaiarea under a desertiregcation process is characterized by just two clusters (1 and2) corresponding to the dry and wet seasons respectively and deregned by themacructuations of land surface temperatures throughout the year In this area thebiological activity is almost non-existent therefore only the physicalmeteorological factors are responsible for the movement inside the LST-NDVI space On the other hand the recovered ecosystem of the Negev showsrelatively intense biological activity and is characterized by movements over

Figure 3(A) Scatter diagram for

schematic discriminatingof different biomes in

Africa as proposed byLambin and Ehrlich

(1996) (B) Data obtainedfor this study

Desertiregcationphenology and

droughts

29

three clusters of the LST-NDVI space In the Negev ecosystem as in the Sinaiboth dry and rainy seasons are evident (clusters 3 and 4) while the third cluster(number 5) represents the growing season which is evident only on the Israeliside of the border and which includes the highest values of NDVI Only a fewpoints are presented inside cluster number 5 because it corresponds to the veryshort period of the year in which the desert reaches its maximum greenness dueto the blooming of the annuals

It is interesting to note that several points belonging to the Negev are locatedinside cluster 1 which represents the Sinai The explanation for this fact can befound in Holling (1973) Natural arid land systems show a resilient characterrather than a resistant one their stability is not deregned by a unique equilibrium

Figure 4Groups obtained fromthe cluster analysis ofNDVI and LST data

Cluster Season No of Negev pixels No of Sinai pixels Mean NDVI (SD) Mean LST (SD)

1 Dry 14 48 0082 (0010) 477 (36)2 Rainy 4 33 0095 (0010) 330 (58)3 Dry 51 7 0120 (0012) 492 (46)4 Rainy 16 5 0123 (0013) 285 (46)5 Growing 9 0 0182 (0018) 391 (81)

Note Based on DallrsquoOlmo and Karnieli (2002)

Table IIDescription of theclusters obtainedfrom the NDVI andLST data analysis

MEQ141

30

state but according to macructuations of environmental parameters Beyondcertain limits they can be associated with multiple-equilibrium states of adomain inside which the ecosystem does not change its structure This is theresult of adaptation to the harsh extremely variable desert environment Theoverlapping points of cluster 1 are relative to the very dry years (19951996 and19981999) In periods extremely lacking in water the recovered ecosystem isable to reduce its activity to the minimum needed for surviving in the area ofthe LST-NDVI space typical of desertireged ecosystems However as soon as theprecious resource of water is available again it can immediately react andproduce relatively high values of NDVI On the other hand the disturbedecosystem (Sinai) shows the result of a man-made perturbation that haschanged its structure in wet years only an extremely limited response torainfall is detected

The multi-year average of the combined NDVI and LST values are presentedin Figure 5 in terms of month-by-month trajectories Phenology starts inOctober and ends in September of the following year It is shown that thedesertireged Sinai ecosystem exhibits the highest variability mostly along theLST axis and is almost unaffected by the NDVI except for a slight deviationfrom the straight line between April and July most likely due to somegreenness of perennials On the other hand the recovered Negev side of theborder exhibits variability on both LST and NDVI axes Here the NDVI axis isdominant especially during the wet months (January-July) due to the greeningof biological soil crusts annuals and perennials During the summer monthsthe trajectory moves only along the LST axis

Figure 5Multi-year average of thecombined NDVI and LSTvalues in terms of month-

by-month trajectories

Desertiregcationphenology and

droughts

31

Drought assessmentFigure 6 exhibits the breakdown of Figure 5 into the four hydrological yearsunder investigation to demonstrate the annual dynamics In the Sinai littledifference can be seen between the wet and the dry years with the samegeneral trajectory shape plusmn long narrow and with a vertical pattern along theLST axis In the Negev however there is a considerable difference between thewet and dry years During the wet years (19961997 and 19971998) the shapeof the graphs is stretched towards the high NDVI values and the phenologicalcycle is much more pronounced whereas during droughts (19951996 and19981999) the NDVI component is much less remarkable and the phenologicalcycle shrinks signiregcantly

Figure 7 shows for each of the hydrological years and for the Negev andSinai ecosystems separately the lines connected between two points deregned as

(1) the maximum LST 2 minimum NDVI and

(2) minimum LST 2 maximum LST

It may be seen that the Sinai lines are very similar in terms of position andlength however two groups can be distinguished between the Negev lines Thelines of the wet years are longer and with gentler slopes whereas the dry yearslines are shorter and steeper These characteristics can be quantireged by threegeometrical expressions as schematically illustrated in Figure 8

Angle = arctan(DLST=DNDVI) (3)

Area = DNDVI curren 05DLST (4)

Length = [(DLST)2 + (DNDVI)2]05 (5)

These three expressions can be used as indicators for quantifying droughtyears Their calculated values are presented in Table III Evaluation of thethree indicators was performed by applying the following discriminationfunction (DF) for each year (i) and each region (j)

DFij = (Xij 2 Xmeanj)=(Xmaxj 2 Xminj)

where Xij is the calculated value for each indicator either in Sinai or in theNegev in any particular year Xavg Xmax and Xmin are the yearly averagemaximum and minimum values for each region respectively The objective ofthis function is to discriminate the drought years from the wet years on bothsides of the border

Results of the discrimination function for each of the drought indicators arepresented in Figure 9 It can be noticed that for the Negev each of the indicatorssuccessfully separates the two drought years from the wet years since the DFij

receives either positive or negative values However for Sinai only the ordflengthordm

MEQ141

32

Figure 6Trajectories of conmined

NDVI and LST valuesfor each of the fourhydrological years(October (10)

September (9))

Desertiregcationphenology and

droughts

33

Figure 7Lines connected betweenmaximumLSTminimum NDVIand minimumLSTmaximum NDVI forthe Sinai and the Negev

Figure 8Schematic illustration ofthree geometricalexpressions forindicating droughts

Year Sinai Negev

Angle 19951996 8036 748519961997 8320 706319971998 8331 720619981999 8323 7778

Area 19951996 043 08319961997 038 13119971998 041 14519981999 024 050

Length 19951996 2238 247519961997 2516 273119971998 2642 299519981999 2022 2117

Table IIICalculated valuesfor the threedrought indicators

MEQ141

34

indicator is able to show this phenomenon The other two indicators plusmn slopeand angle plusmn produce mixed results Consequently the ordflengthordm indicator will beused for further analysis

Using the ordflengthordm indicator Figure 10 presents the DFj values as a functionof the yearly rainfall of each year Despite the fact that only eight points areinvolved in the regression analysis a clear trend is exhibited between thedrought-year cluster (negative values) and the wet-year cluster (positive

Figure 9Results of the

discrimination functionprocedure for eachdrought indicator

Figure 10Discrimination function

values vs the yearlyrainfall amounts of

each year

Desertiregcationphenology and

droughts

35

values) The crossing point between the regression line and the DF= 0 line canbe used to quantify the threshold between wet and drought years in terms ofrainfall amount (54mm in the current example)

Summary and conclusionsResults from the current study show that desertiregcation phenology anddroughts processes can be detected and characterized by using four out of regveNOAAAVHRR spectral bands The NDVI is derived from the red and the NIRbands and the LST from the two thermal bands Combined use of these twoproducts provides more information than any product alone

Time series presentation of NDVI and LST reveals that the NDVI valuescorrespond to the reaction of the vegetation to rainfall and that LST valuesrepresent seasonal climatic macructuation Scatterplot analysis of LST vs NDVIdemonstrates the following

The two different biomes (Sinai and the Negev) exhibit different yearlyvariations of the phenological patterns two seasons in Sinai movingalong the LST axis and three seasons in the Negev where the NDVI axisrepresents the growing season

The Sinai has an ecosystem similar to that found in the Sahara while theNegev only a few kilometers away has an ecosystem similar to that ofthe Sahel

Drought indicators were derived in terms of three geometricalexpressions based on the two extreme points of the NDVI-LSTscatterplots Evaluation of the suggested indicator shows only one thatcan successfully separate between the drought and the wet years It isconcluded that the AVHRR imagery can provide valuable information fordrought monitoring and characterization

References

Asrar G Fuchs M Kanemasu ET and Hatregeld JL (1984) ordfEstimating absorbedphotosynthetic radiation and leaf area index from spectral remacrectance of wheatordmAgronomy J Vol 76 pp 300-6

Brown OB Brown JW and Evans RH (1985) ordfCalibration of advanced very-high resolutionradiometer infrared observationsordm J Geophys Res Vol 90 C6 pp 11667-77

Buschmann C and Nagel E (1993) ordfIn vivo spectroscopy and internal optics of leaves as basisfor remote-sensing of vegetationordm Int J Remote Sens Vol 14 pp 711-22

DallrsquoOlmo G and Karnieli A (2002) ordfFollowing phenological cycles of desert ecosystems usingNDVI and LST data derived from the advanced very high resolution radiometerordm IntJ Remote Sens Vol 23

Ehrlich D and Lambin EF (1996) ordfBroad scale land-cover classiregcation and interannualclimatic variabilityordm Int J Remote Sens Vol 17 pp 845-62

Elvidge CD and Chen ZK (1995) ordfComparison of broad-band and narrow-band red and near-infrared vegetation indexesordm Remote Sens Environ Vol 54 pp 38-48

MEQ141

36

three clusters of the LST-NDVI space In the Negev ecosystem as in the Sinaiboth dry and rainy seasons are evident (clusters 3 and 4) while the third cluster(number 5) represents the growing season which is evident only on the Israeliside of the border and which includes the highest values of NDVI Only a fewpoints are presented inside cluster number 5 because it corresponds to the veryshort period of the year in which the desert reaches its maximum greenness dueto the blooming of the annuals

It is interesting to note that several points belonging to the Negev are locatedinside cluster 1 which represents the Sinai The explanation for this fact can befound in Holling (1973) Natural arid land systems show a resilient characterrather than a resistant one their stability is not deregned by a unique equilibrium

Figure 4Groups obtained fromthe cluster analysis ofNDVI and LST data

Cluster Season No of Negev pixels No of Sinai pixels Mean NDVI (SD) Mean LST (SD)

1 Dry 14 48 0082 (0010) 477 (36)2 Rainy 4 33 0095 (0010) 330 (58)3 Dry 51 7 0120 (0012) 492 (46)4 Rainy 16 5 0123 (0013) 285 (46)5 Growing 9 0 0182 (0018) 391 (81)

Note Based on DallrsquoOlmo and Karnieli (2002)

Table IIDescription of theclusters obtainedfrom the NDVI andLST data analysis

MEQ141

30

state but according to macructuations of environmental parameters Beyondcertain limits they can be associated with multiple-equilibrium states of adomain inside which the ecosystem does not change its structure This is theresult of adaptation to the harsh extremely variable desert environment Theoverlapping points of cluster 1 are relative to the very dry years (19951996 and19981999) In periods extremely lacking in water the recovered ecosystem isable to reduce its activity to the minimum needed for surviving in the area ofthe LST-NDVI space typical of desertireged ecosystems However as soon as theprecious resource of water is available again it can immediately react andproduce relatively high values of NDVI On the other hand the disturbedecosystem (Sinai) shows the result of a man-made perturbation that haschanged its structure in wet years only an extremely limited response torainfall is detected

The multi-year average of the combined NDVI and LST values are presentedin Figure 5 in terms of month-by-month trajectories Phenology starts inOctober and ends in September of the following year It is shown that thedesertireged Sinai ecosystem exhibits the highest variability mostly along theLST axis and is almost unaffected by the NDVI except for a slight deviationfrom the straight line between April and July most likely due to somegreenness of perennials On the other hand the recovered Negev side of theborder exhibits variability on both LST and NDVI axes Here the NDVI axis isdominant especially during the wet months (January-July) due to the greeningof biological soil crusts annuals and perennials During the summer monthsthe trajectory moves only along the LST axis

Figure 5Multi-year average of thecombined NDVI and LSTvalues in terms of month-

by-month trajectories

Desertiregcationphenology and

droughts

31

Drought assessmentFigure 6 exhibits the breakdown of Figure 5 into the four hydrological yearsunder investigation to demonstrate the annual dynamics In the Sinai littledifference can be seen between the wet and the dry years with the samegeneral trajectory shape plusmn long narrow and with a vertical pattern along theLST axis In the Negev however there is a considerable difference between thewet and dry years During the wet years (19961997 and 19971998) the shapeof the graphs is stretched towards the high NDVI values and the phenologicalcycle is much more pronounced whereas during droughts (19951996 and19981999) the NDVI component is much less remarkable and the phenologicalcycle shrinks signiregcantly

Figure 7 shows for each of the hydrological years and for the Negev andSinai ecosystems separately the lines connected between two points deregned as

(1) the maximum LST 2 minimum NDVI and

(2) minimum LST 2 maximum LST

It may be seen that the Sinai lines are very similar in terms of position andlength however two groups can be distinguished between the Negev lines Thelines of the wet years are longer and with gentler slopes whereas the dry yearslines are shorter and steeper These characteristics can be quantireged by threegeometrical expressions as schematically illustrated in Figure 8

Angle = arctan(DLST=DNDVI) (3)

Area = DNDVI curren 05DLST (4)

Length = [(DLST)2 + (DNDVI)2]05 (5)

These three expressions can be used as indicators for quantifying droughtyears Their calculated values are presented in Table III Evaluation of thethree indicators was performed by applying the following discriminationfunction (DF) for each year (i) and each region (j)

DFij = (Xij 2 Xmeanj)=(Xmaxj 2 Xminj)

where Xij is the calculated value for each indicator either in Sinai or in theNegev in any particular year Xavg Xmax and Xmin are the yearly averagemaximum and minimum values for each region respectively The objective ofthis function is to discriminate the drought years from the wet years on bothsides of the border

Results of the discrimination function for each of the drought indicators arepresented in Figure 9 It can be noticed that for the Negev each of the indicatorssuccessfully separates the two drought years from the wet years since the DFij

receives either positive or negative values However for Sinai only the ordflengthordm

MEQ141

32

Figure 6Trajectories of conmined

NDVI and LST valuesfor each of the fourhydrological years(October (10)

September (9))

Desertiregcationphenology and

droughts

33

Figure 7Lines connected betweenmaximumLSTminimum NDVIand minimumLSTmaximum NDVI forthe Sinai and the Negev

Figure 8Schematic illustration ofthree geometricalexpressions forindicating droughts

Year Sinai Negev

Angle 19951996 8036 748519961997 8320 706319971998 8331 720619981999 8323 7778

Area 19951996 043 08319961997 038 13119971998 041 14519981999 024 050

Length 19951996 2238 247519961997 2516 273119971998 2642 299519981999 2022 2117

Table IIICalculated valuesfor the threedrought indicators

MEQ141

34

indicator is able to show this phenomenon The other two indicators plusmn slopeand angle plusmn produce mixed results Consequently the ordflengthordm indicator will beused for further analysis

Using the ordflengthordm indicator Figure 10 presents the DFj values as a functionof the yearly rainfall of each year Despite the fact that only eight points areinvolved in the regression analysis a clear trend is exhibited between thedrought-year cluster (negative values) and the wet-year cluster (positive

Figure 9Results of the

discrimination functionprocedure for eachdrought indicator

Figure 10Discrimination function

values vs the yearlyrainfall amounts of

each year

Desertiregcationphenology and

droughts

35

values) The crossing point between the regression line and the DF= 0 line canbe used to quantify the threshold between wet and drought years in terms ofrainfall amount (54mm in the current example)

Summary and conclusionsResults from the current study show that desertiregcation phenology anddroughts processes can be detected and characterized by using four out of regveNOAAAVHRR spectral bands The NDVI is derived from the red and the NIRbands and the LST from the two thermal bands Combined use of these twoproducts provides more information than any product alone

Time series presentation of NDVI and LST reveals that the NDVI valuescorrespond to the reaction of the vegetation to rainfall and that LST valuesrepresent seasonal climatic macructuation Scatterplot analysis of LST vs NDVIdemonstrates the following

The two different biomes (Sinai and the Negev) exhibit different yearlyvariations of the phenological patterns two seasons in Sinai movingalong the LST axis and three seasons in the Negev where the NDVI axisrepresents the growing season

The Sinai has an ecosystem similar to that found in the Sahara while theNegev only a few kilometers away has an ecosystem similar to that ofthe Sahel

Drought indicators were derived in terms of three geometricalexpressions based on the two extreme points of the NDVI-LSTscatterplots Evaluation of the suggested indicator shows only one thatcan successfully separate between the drought and the wet years It isconcluded that the AVHRR imagery can provide valuable information fordrought monitoring and characterization

References

Asrar G Fuchs M Kanemasu ET and Hatregeld JL (1984) ordfEstimating absorbedphotosynthetic radiation and leaf area index from spectral remacrectance of wheatordmAgronomy J Vol 76 pp 300-6

Brown OB Brown JW and Evans RH (1985) ordfCalibration of advanced very-high resolutionradiometer infrared observationsordm J Geophys Res Vol 90 C6 pp 11667-77

Buschmann C and Nagel E (1993) ordfIn vivo spectroscopy and internal optics of leaves as basisfor remote-sensing of vegetationordm Int J Remote Sens Vol 14 pp 711-22

DallrsquoOlmo G and Karnieli A (2002) ordfFollowing phenological cycles of desert ecosystems usingNDVI and LST data derived from the advanced very high resolution radiometerordm IntJ Remote Sens Vol 23

Ehrlich D and Lambin EF (1996) ordfBroad scale land-cover classiregcation and interannualclimatic variabilityordm Int J Remote Sens Vol 17 pp 845-62

Elvidge CD and Chen ZK (1995) ordfComparison of broad-band and narrow-band red and near-infrared vegetation indexesordm Remote Sens Environ Vol 54 pp 38-48

MEQ141

36

state but according to macructuations of environmental parameters Beyondcertain limits they can be associated with multiple-equilibrium states of adomain inside which the ecosystem does not change its structure This is theresult of adaptation to the harsh extremely variable desert environment Theoverlapping points of cluster 1 are relative to the very dry years (19951996 and19981999) In periods extremely lacking in water the recovered ecosystem isable to reduce its activity to the minimum needed for surviving in the area ofthe LST-NDVI space typical of desertireged ecosystems However as soon as theprecious resource of water is available again it can immediately react andproduce relatively high values of NDVI On the other hand the disturbedecosystem (Sinai) shows the result of a man-made perturbation that haschanged its structure in wet years only an extremely limited response torainfall is detected

The multi-year average of the combined NDVI and LST values are presentedin Figure 5 in terms of month-by-month trajectories Phenology starts inOctober and ends in September of the following year It is shown that thedesertireged Sinai ecosystem exhibits the highest variability mostly along theLST axis and is almost unaffected by the NDVI except for a slight deviationfrom the straight line between April and July most likely due to somegreenness of perennials On the other hand the recovered Negev side of theborder exhibits variability on both LST and NDVI axes Here the NDVI axis isdominant especially during the wet months (January-July) due to the greeningof biological soil crusts annuals and perennials During the summer monthsthe trajectory moves only along the LST axis

Figure 5Multi-year average of thecombined NDVI and LSTvalues in terms of month-

by-month trajectories

Desertiregcationphenology and

droughts

31

Drought assessmentFigure 6 exhibits the breakdown of Figure 5 into the four hydrological yearsunder investigation to demonstrate the annual dynamics In the Sinai littledifference can be seen between the wet and the dry years with the samegeneral trajectory shape plusmn long narrow and with a vertical pattern along theLST axis In the Negev however there is a considerable difference between thewet and dry years During the wet years (19961997 and 19971998) the shapeof the graphs is stretched towards the high NDVI values and the phenologicalcycle is much more pronounced whereas during droughts (19951996 and19981999) the NDVI component is much less remarkable and the phenologicalcycle shrinks signiregcantly

Figure 7 shows for each of the hydrological years and for the Negev andSinai ecosystems separately the lines connected between two points deregned as

(1) the maximum LST 2 minimum NDVI and

(2) minimum LST 2 maximum LST

It may be seen that the Sinai lines are very similar in terms of position andlength however two groups can be distinguished between the Negev lines Thelines of the wet years are longer and with gentler slopes whereas the dry yearslines are shorter and steeper These characteristics can be quantireged by threegeometrical expressions as schematically illustrated in Figure 8

Angle = arctan(DLST=DNDVI) (3)

Area = DNDVI curren 05DLST (4)

Length = [(DLST)2 + (DNDVI)2]05 (5)

These three expressions can be used as indicators for quantifying droughtyears Their calculated values are presented in Table III Evaluation of thethree indicators was performed by applying the following discriminationfunction (DF) for each year (i) and each region (j)

DFij = (Xij 2 Xmeanj)=(Xmaxj 2 Xminj)

where Xij is the calculated value for each indicator either in Sinai or in theNegev in any particular year Xavg Xmax and Xmin are the yearly averagemaximum and minimum values for each region respectively The objective ofthis function is to discriminate the drought years from the wet years on bothsides of the border

Results of the discrimination function for each of the drought indicators arepresented in Figure 9 It can be noticed that for the Negev each of the indicatorssuccessfully separates the two drought years from the wet years since the DFij

receives either positive or negative values However for Sinai only the ordflengthordm

MEQ141

32

Figure 6Trajectories of conmined

NDVI and LST valuesfor each of the fourhydrological years(October (10)

September (9))

Desertiregcationphenology and

droughts

33

Figure 7Lines connected betweenmaximumLSTminimum NDVIand minimumLSTmaximum NDVI forthe Sinai and the Negev

Figure 8Schematic illustration ofthree geometricalexpressions forindicating droughts

Year Sinai Negev

Angle 19951996 8036 748519961997 8320 706319971998 8331 720619981999 8323 7778

Area 19951996 043 08319961997 038 13119971998 041 14519981999 024 050

Length 19951996 2238 247519961997 2516 273119971998 2642 299519981999 2022 2117

Table IIICalculated valuesfor the threedrought indicators

MEQ141

34

indicator is able to show this phenomenon The other two indicators plusmn slopeand angle plusmn produce mixed results Consequently the ordflengthordm indicator will beused for further analysis

Using the ordflengthordm indicator Figure 10 presents the DFj values as a functionof the yearly rainfall of each year Despite the fact that only eight points areinvolved in the regression analysis a clear trend is exhibited between thedrought-year cluster (negative values) and the wet-year cluster (positive

Figure 9Results of the

discrimination functionprocedure for eachdrought indicator

Figure 10Discrimination function

values vs the yearlyrainfall amounts of

each year

Desertiregcationphenology and

droughts

35

values) The crossing point between the regression line and the DF= 0 line canbe used to quantify the threshold between wet and drought years in terms ofrainfall amount (54mm in the current example)

Summary and conclusionsResults from the current study show that desertiregcation phenology anddroughts processes can be detected and characterized by using four out of regveNOAAAVHRR spectral bands The NDVI is derived from the red and the NIRbands and the LST from the two thermal bands Combined use of these twoproducts provides more information than any product alone

Time series presentation of NDVI and LST reveals that the NDVI valuescorrespond to the reaction of the vegetation to rainfall and that LST valuesrepresent seasonal climatic macructuation Scatterplot analysis of LST vs NDVIdemonstrates the following

The two different biomes (Sinai and the Negev) exhibit different yearlyvariations of the phenological patterns two seasons in Sinai movingalong the LST axis and three seasons in the Negev where the NDVI axisrepresents the growing season

The Sinai has an ecosystem similar to that found in the Sahara while theNegev only a few kilometers away has an ecosystem similar to that ofthe Sahel

Drought indicators were derived in terms of three geometricalexpressions based on the two extreme points of the NDVI-LSTscatterplots Evaluation of the suggested indicator shows only one thatcan successfully separate between the drought and the wet years It isconcluded that the AVHRR imagery can provide valuable information fordrought monitoring and characterization

References

Asrar G Fuchs M Kanemasu ET and Hatregeld JL (1984) ordfEstimating absorbedphotosynthetic radiation and leaf area index from spectral remacrectance of wheatordmAgronomy J Vol 76 pp 300-6

Brown OB Brown JW and Evans RH (1985) ordfCalibration of advanced very-high resolutionradiometer infrared observationsordm J Geophys Res Vol 90 C6 pp 11667-77

Buschmann C and Nagel E (1993) ordfIn vivo spectroscopy and internal optics of leaves as basisfor remote-sensing of vegetationordm Int J Remote Sens Vol 14 pp 711-22

DallrsquoOlmo G and Karnieli A (2002) ordfFollowing phenological cycles of desert ecosystems usingNDVI and LST data derived from the advanced very high resolution radiometerordm IntJ Remote Sens Vol 23

Ehrlich D and Lambin EF (1996) ordfBroad scale land-cover classiregcation and interannualclimatic variabilityordm Int J Remote Sens Vol 17 pp 845-62

Elvidge CD and Chen ZK (1995) ordfComparison of broad-band and narrow-band red and near-infrared vegetation indexesordm Remote Sens Environ Vol 54 pp 38-48

MEQ141

36

Drought assessmentFigure 6 exhibits the breakdown of Figure 5 into the four hydrological yearsunder investigation to demonstrate the annual dynamics In the Sinai littledifference can be seen between the wet and the dry years with the samegeneral trajectory shape plusmn long narrow and with a vertical pattern along theLST axis In the Negev however there is a considerable difference between thewet and dry years During the wet years (19961997 and 19971998) the shapeof the graphs is stretched towards the high NDVI values and the phenologicalcycle is much more pronounced whereas during droughts (19951996 and19981999) the NDVI component is much less remarkable and the phenologicalcycle shrinks signiregcantly

Figure 7 shows for each of the hydrological years and for the Negev andSinai ecosystems separately the lines connected between two points deregned as

(1) the maximum LST 2 minimum NDVI and

(2) minimum LST 2 maximum LST

It may be seen that the Sinai lines are very similar in terms of position andlength however two groups can be distinguished between the Negev lines Thelines of the wet years are longer and with gentler slopes whereas the dry yearslines are shorter and steeper These characteristics can be quantireged by threegeometrical expressions as schematically illustrated in Figure 8

Angle = arctan(DLST=DNDVI) (3)

Area = DNDVI curren 05DLST (4)

Length = [(DLST)2 + (DNDVI)2]05 (5)

These three expressions can be used as indicators for quantifying droughtyears Their calculated values are presented in Table III Evaluation of thethree indicators was performed by applying the following discriminationfunction (DF) for each year (i) and each region (j)

DFij = (Xij 2 Xmeanj)=(Xmaxj 2 Xminj)

where Xij is the calculated value for each indicator either in Sinai or in theNegev in any particular year Xavg Xmax and Xmin are the yearly averagemaximum and minimum values for each region respectively The objective ofthis function is to discriminate the drought years from the wet years on bothsides of the border

Results of the discrimination function for each of the drought indicators arepresented in Figure 9 It can be noticed that for the Negev each of the indicatorssuccessfully separates the two drought years from the wet years since the DFij

receives either positive or negative values However for Sinai only the ordflengthordm

MEQ141

32

Figure 6Trajectories of conmined

NDVI and LST valuesfor each of the fourhydrological years(October (10)

September (9))

Desertiregcationphenology and

droughts

33

Figure 7Lines connected betweenmaximumLSTminimum NDVIand minimumLSTmaximum NDVI forthe Sinai and the Negev

Figure 8Schematic illustration ofthree geometricalexpressions forindicating droughts

Year Sinai Negev

Angle 19951996 8036 748519961997 8320 706319971998 8331 720619981999 8323 7778

Area 19951996 043 08319961997 038 13119971998 041 14519981999 024 050

Length 19951996 2238 247519961997 2516 273119971998 2642 299519981999 2022 2117

Table IIICalculated valuesfor the threedrought indicators

MEQ141

34

indicator is able to show this phenomenon The other two indicators plusmn slopeand angle plusmn produce mixed results Consequently the ordflengthordm indicator will beused for further analysis

Using the ordflengthordm indicator Figure 10 presents the DFj values as a functionof the yearly rainfall of each year Despite the fact that only eight points areinvolved in the regression analysis a clear trend is exhibited between thedrought-year cluster (negative values) and the wet-year cluster (positive

Figure 9Results of the

discrimination functionprocedure for eachdrought indicator

Figure 10Discrimination function

values vs the yearlyrainfall amounts of

each year

Desertiregcationphenology and

droughts

35

values) The crossing point between the regression line and the DF= 0 line canbe used to quantify the threshold between wet and drought years in terms ofrainfall amount (54mm in the current example)

Summary and conclusionsResults from the current study show that desertiregcation phenology anddroughts processes can be detected and characterized by using four out of regveNOAAAVHRR spectral bands The NDVI is derived from the red and the NIRbands and the LST from the two thermal bands Combined use of these twoproducts provides more information than any product alone

Time series presentation of NDVI and LST reveals that the NDVI valuescorrespond to the reaction of the vegetation to rainfall and that LST valuesrepresent seasonal climatic macructuation Scatterplot analysis of LST vs NDVIdemonstrates the following

The two different biomes (Sinai and the Negev) exhibit different yearlyvariations of the phenological patterns two seasons in Sinai movingalong the LST axis and three seasons in the Negev where the NDVI axisrepresents the growing season

The Sinai has an ecosystem similar to that found in the Sahara while theNegev only a few kilometers away has an ecosystem similar to that ofthe Sahel

Drought indicators were derived in terms of three geometricalexpressions based on the two extreme points of the NDVI-LSTscatterplots Evaluation of the suggested indicator shows only one thatcan successfully separate between the drought and the wet years It isconcluded that the AVHRR imagery can provide valuable information fordrought monitoring and characterization

References

Asrar G Fuchs M Kanemasu ET and Hatregeld JL (1984) ordfEstimating absorbedphotosynthetic radiation and leaf area index from spectral remacrectance of wheatordmAgronomy J Vol 76 pp 300-6

Brown OB Brown JW and Evans RH (1985) ordfCalibration of advanced very-high resolutionradiometer infrared observationsordm J Geophys Res Vol 90 C6 pp 11667-77

Buschmann C and Nagel E (1993) ordfIn vivo spectroscopy and internal optics of leaves as basisfor remote-sensing of vegetationordm Int J Remote Sens Vol 14 pp 711-22

DallrsquoOlmo G and Karnieli A (2002) ordfFollowing phenological cycles of desert ecosystems usingNDVI and LST data derived from the advanced very high resolution radiometerordm IntJ Remote Sens Vol 23

Ehrlich D and Lambin EF (1996) ordfBroad scale land-cover classiregcation and interannualclimatic variabilityordm Int J Remote Sens Vol 17 pp 845-62

Elvidge CD and Chen ZK (1995) ordfComparison of broad-band and narrow-band red and near-infrared vegetation indexesordm Remote Sens Environ Vol 54 pp 38-48

MEQ141

36

Figure 6Trajectories of conmined

NDVI and LST valuesfor each of the fourhydrological years(October (10)

September (9))

Desertiregcationphenology and

droughts

33

Figure 7Lines connected betweenmaximumLSTminimum NDVIand minimumLSTmaximum NDVI forthe Sinai and the Negev

Figure 8Schematic illustration ofthree geometricalexpressions forindicating droughts

Year Sinai Negev

Angle 19951996 8036 748519961997 8320 706319971998 8331 720619981999 8323 7778

Area 19951996 043 08319961997 038 13119971998 041 14519981999 024 050

Length 19951996 2238 247519961997 2516 273119971998 2642 299519981999 2022 2117

Table IIICalculated valuesfor the threedrought indicators

MEQ141

34

indicator is able to show this phenomenon The other two indicators plusmn slopeand angle plusmn produce mixed results Consequently the ordflengthordm indicator will beused for further analysis

Using the ordflengthordm indicator Figure 10 presents the DFj values as a functionof the yearly rainfall of each year Despite the fact that only eight points areinvolved in the regression analysis a clear trend is exhibited between thedrought-year cluster (negative values) and the wet-year cluster (positive

Figure 9Results of the

discrimination functionprocedure for eachdrought indicator

Figure 10Discrimination function

values vs the yearlyrainfall amounts of

each year

Desertiregcationphenology and

droughts

35

values) The crossing point between the regression line and the DF= 0 line canbe used to quantify the threshold between wet and drought years in terms ofrainfall amount (54mm in the current example)

Summary and conclusionsResults from the current study show that desertiregcation phenology anddroughts processes can be detected and characterized by using four out of regveNOAAAVHRR spectral bands The NDVI is derived from the red and the NIRbands and the LST from the two thermal bands Combined use of these twoproducts provides more information than any product alone

Time series presentation of NDVI and LST reveals that the NDVI valuescorrespond to the reaction of the vegetation to rainfall and that LST valuesrepresent seasonal climatic macructuation Scatterplot analysis of LST vs NDVIdemonstrates the following

The two different biomes (Sinai and the Negev) exhibit different yearlyvariations of the phenological patterns two seasons in Sinai movingalong the LST axis and three seasons in the Negev where the NDVI axisrepresents the growing season

The Sinai has an ecosystem similar to that found in the Sahara while theNegev only a few kilometers away has an ecosystem similar to that ofthe Sahel

Drought indicators were derived in terms of three geometricalexpressions based on the two extreme points of the NDVI-LSTscatterplots Evaluation of the suggested indicator shows only one thatcan successfully separate between the drought and the wet years It isconcluded that the AVHRR imagery can provide valuable information fordrought monitoring and characterization

References

Asrar G Fuchs M Kanemasu ET and Hatregeld JL (1984) ordfEstimating absorbedphotosynthetic radiation and leaf area index from spectral remacrectance of wheatordmAgronomy J Vol 76 pp 300-6

Brown OB Brown JW and Evans RH (1985) ordfCalibration of advanced very-high resolutionradiometer infrared observationsordm J Geophys Res Vol 90 C6 pp 11667-77

Buschmann C and Nagel E (1993) ordfIn vivo spectroscopy and internal optics of leaves as basisfor remote-sensing of vegetationordm Int J Remote Sens Vol 14 pp 711-22

DallrsquoOlmo G and Karnieli A (2002) ordfFollowing phenological cycles of desert ecosystems usingNDVI and LST data derived from the advanced very high resolution radiometerordm IntJ Remote Sens Vol 23

Ehrlich D and Lambin EF (1996) ordfBroad scale land-cover classiregcation and interannualclimatic variabilityordm Int J Remote Sens Vol 17 pp 845-62

Elvidge CD and Chen ZK (1995) ordfComparison of broad-band and narrow-band red and near-infrared vegetation indexesordm Remote Sens Environ Vol 54 pp 38-48

MEQ141

36

Figure 7Lines connected betweenmaximumLSTminimum NDVIand minimumLSTmaximum NDVI forthe Sinai and the Negev

Figure 8Schematic illustration ofthree geometricalexpressions forindicating droughts

Year Sinai Negev

Angle 19951996 8036 748519961997 8320 706319971998 8331 720619981999 8323 7778

Area 19951996 043 08319961997 038 13119971998 041 14519981999 024 050

Length 19951996 2238 247519961997 2516 273119971998 2642 299519981999 2022 2117

Table IIICalculated valuesfor the threedrought indicators

MEQ141

34

indicator is able to show this phenomenon The other two indicators plusmn slopeand angle plusmn produce mixed results Consequently the ordflengthordm indicator will beused for further analysis

Using the ordflengthordm indicator Figure 10 presents the DFj values as a functionof the yearly rainfall of each year Despite the fact that only eight points areinvolved in the regression analysis a clear trend is exhibited between thedrought-year cluster (negative values) and the wet-year cluster (positive

Figure 9Results of the

discrimination functionprocedure for eachdrought indicator

Figure 10Discrimination function

values vs the yearlyrainfall amounts of

each year

Desertiregcationphenology and

droughts

35

values) The crossing point between the regression line and the DF= 0 line canbe used to quantify the threshold between wet and drought years in terms ofrainfall amount (54mm in the current example)

Summary and conclusionsResults from the current study show that desertiregcation phenology anddroughts processes can be detected and characterized by using four out of regveNOAAAVHRR spectral bands The NDVI is derived from the red and the NIRbands and the LST from the two thermal bands Combined use of these twoproducts provides more information than any product alone

Time series presentation of NDVI and LST reveals that the NDVI valuescorrespond to the reaction of the vegetation to rainfall and that LST valuesrepresent seasonal climatic macructuation Scatterplot analysis of LST vs NDVIdemonstrates the following

The two different biomes (Sinai and the Negev) exhibit different yearlyvariations of the phenological patterns two seasons in Sinai movingalong the LST axis and three seasons in the Negev where the NDVI axisrepresents the growing season

The Sinai has an ecosystem similar to that found in the Sahara while theNegev only a few kilometers away has an ecosystem similar to that ofthe Sahel

Drought indicators were derived in terms of three geometricalexpressions based on the two extreme points of the NDVI-LSTscatterplots Evaluation of the suggested indicator shows only one thatcan successfully separate between the drought and the wet years It isconcluded that the AVHRR imagery can provide valuable information fordrought monitoring and characterization

References

Asrar G Fuchs M Kanemasu ET and Hatregeld JL (1984) ordfEstimating absorbedphotosynthetic radiation and leaf area index from spectral remacrectance of wheatordmAgronomy J Vol 76 pp 300-6

Brown OB Brown JW and Evans RH (1985) ordfCalibration of advanced very-high resolutionradiometer infrared observationsordm J Geophys Res Vol 90 C6 pp 11667-77

Buschmann C and Nagel E (1993) ordfIn vivo spectroscopy and internal optics of leaves as basisfor remote-sensing of vegetationordm Int J Remote Sens Vol 14 pp 711-22

DallrsquoOlmo G and Karnieli A (2002) ordfFollowing phenological cycles of desert ecosystems usingNDVI and LST data derived from the advanced very high resolution radiometerordm IntJ Remote Sens Vol 23

Ehrlich D and Lambin EF (1996) ordfBroad scale land-cover classiregcation and interannualclimatic variabilityordm Int J Remote Sens Vol 17 pp 845-62

Elvidge CD and Chen ZK (1995) ordfComparison of broad-band and narrow-band red and near-infrared vegetation indexesordm Remote Sens Environ Vol 54 pp 38-48

MEQ141

36

indicator is able to show this phenomenon The other two indicators plusmn slopeand angle plusmn produce mixed results Consequently the ordflengthordm indicator will beused for further analysis

Using the ordflengthordm indicator Figure 10 presents the DFj values as a functionof the yearly rainfall of each year Despite the fact that only eight points areinvolved in the regression analysis a clear trend is exhibited between thedrought-year cluster (negative values) and the wet-year cluster (positive

Figure 9Results of the

discrimination functionprocedure for eachdrought indicator

Figure 10Discrimination function

values vs the yearlyrainfall amounts of

each year

Desertiregcationphenology and

droughts

35

values) The crossing point between the regression line and the DF= 0 line canbe used to quantify the threshold between wet and drought years in terms ofrainfall amount (54mm in the current example)

Summary and conclusionsResults from the current study show that desertiregcation phenology anddroughts processes can be detected and characterized by using four out of regveNOAAAVHRR spectral bands The NDVI is derived from the red and the NIRbands and the LST from the two thermal bands Combined use of these twoproducts provides more information than any product alone

Time series presentation of NDVI and LST reveals that the NDVI valuescorrespond to the reaction of the vegetation to rainfall and that LST valuesrepresent seasonal climatic macructuation Scatterplot analysis of LST vs NDVIdemonstrates the following

The two different biomes (Sinai and the Negev) exhibit different yearlyvariations of the phenological patterns two seasons in Sinai movingalong the LST axis and three seasons in the Negev where the NDVI axisrepresents the growing season

The Sinai has an ecosystem similar to that found in the Sahara while theNegev only a few kilometers away has an ecosystem similar to that ofthe Sahel

Drought indicators were derived in terms of three geometricalexpressions based on the two extreme points of the NDVI-LSTscatterplots Evaluation of the suggested indicator shows only one thatcan successfully separate between the drought and the wet years It isconcluded that the AVHRR imagery can provide valuable information fordrought monitoring and characterization

References

Asrar G Fuchs M Kanemasu ET and Hatregeld JL (1984) ordfEstimating absorbedphotosynthetic radiation and leaf area index from spectral remacrectance of wheatordmAgronomy J Vol 76 pp 300-6

Brown OB Brown JW and Evans RH (1985) ordfCalibration of advanced very-high resolutionradiometer infrared observationsordm J Geophys Res Vol 90 C6 pp 11667-77

Buschmann C and Nagel E (1993) ordfIn vivo spectroscopy and internal optics of leaves as basisfor remote-sensing of vegetationordm Int J Remote Sens Vol 14 pp 711-22

DallrsquoOlmo G and Karnieli A (2002) ordfFollowing phenological cycles of desert ecosystems usingNDVI and LST data derived from the advanced very high resolution radiometerordm IntJ Remote Sens Vol 23

Ehrlich D and Lambin EF (1996) ordfBroad scale land-cover classiregcation and interannualclimatic variabilityordm Int J Remote Sens Vol 17 pp 845-62

Elvidge CD and Chen ZK (1995) ordfComparison of broad-band and narrow-band red and near-infrared vegetation indexesordm Remote Sens Environ Vol 54 pp 38-48

MEQ141

36

values) The crossing point between the regression line and the DF= 0 line canbe used to quantify the threshold between wet and drought years in terms ofrainfall amount (54mm in the current example)

Summary and conclusionsResults from the current study show that desertiregcation phenology anddroughts processes can be detected and characterized by using four out of regveNOAAAVHRR spectral bands The NDVI is derived from the red and the NIRbands and the LST from the two thermal bands Combined use of these twoproducts provides more information than any product alone

Time series presentation of NDVI and LST reveals that the NDVI valuescorrespond to the reaction of the vegetation to rainfall and that LST valuesrepresent seasonal climatic macructuation Scatterplot analysis of LST vs NDVIdemonstrates the following

The two different biomes (Sinai and the Negev) exhibit different yearlyvariations of the phenological patterns two seasons in Sinai movingalong the LST axis and three seasons in the Negev where the NDVI axisrepresents the growing season

The Sinai has an ecosystem similar to that found in the Sahara while theNegev only a few kilometers away has an ecosystem similar to that ofthe Sahel

Drought indicators were derived in terms of three geometricalexpressions based on the two extreme points of the NDVI-LSTscatterplots Evaluation of the suggested indicator shows only one thatcan successfully separate between the drought and the wet years It isconcluded that the AVHRR imagery can provide valuable information fordrought monitoring and characterization

References

Asrar G Fuchs M Kanemasu ET and Hatregeld JL (1984) ordfEstimating absorbedphotosynthetic radiation and leaf area index from spectral remacrectance of wheatordmAgronomy J Vol 76 pp 300-6

Brown OB Brown JW and Evans RH (1985) ordfCalibration of advanced very-high resolutionradiometer infrared observationsordm J Geophys Res Vol 90 C6 pp 11667-77

Buschmann C and Nagel E (1993) ordfIn vivo spectroscopy and internal optics of leaves as basisfor remote-sensing of vegetationordm Int J Remote Sens Vol 14 pp 711-22

DallrsquoOlmo G and Karnieli A (2002) ordfFollowing phenological cycles of desert ecosystems usingNDVI and LST data derived from the advanced very high resolution radiometerordm IntJ Remote Sens Vol 23

Ehrlich D and Lambin EF (1996) ordfBroad scale land-cover classiregcation and interannualclimatic variabilityordm Int J Remote Sens Vol 17 pp 845-62

Elvidge CD and Chen ZK (1995) ordfComparison of broad-band and narrow-band red and near-infrared vegetation indexesordm Remote Sens Environ Vol 54 pp 38-48

MEQ141

36

Goward SN and Hope AS (1989) ordfEvaporation from combined remacrected solar and emittedterrestrial radiation preliminary FIFE results from AVHRR dataordm Adv in Space ResVol 9 pp 239-49

Holben BN (1986) ordfCharacteristics of maximum-value composite images from temporalAVHHR dataordm Int J Remote Sens Vol 7 pp 1417-34

Holben BN Tanre D Smirnov A Eck TF Slutsker I Abuhassan N Newcomb WWSchafer JS Chatenet B Lavenu F Kaufman YJ Castle JV Setzer A Markham BClark D Frouin R Halthore R Karneli A OrsquoNeill NT Pietras C Pinker RT VossK and Zibordi G (2001) ordfAn emerging ground-based aerosol climatology aerosol opticaldepth from AERONETordm J Geophys Res-Atmos Vol 106 D11 pp 12067-97

Holling CS (1973) ordfResilience and stability of ecological systemsordm Ann Rev of Ecology andSystematics Vol 4 pp 1-23

Huete A (1988) ordfA soil-adjusted vegetation index (SAVI)ordm Remote Sens Environ Vol 25pp 295-309

Justice CO Townshend JRG Holben BN and Tucker CJ (1985) ordfAnalysis of the phenologyof global vegetation using meteorological satellite dataordm Int J Remote Sens Vol 6pp 1271-318

Karnieli A and Tsoar H (1995) ordfSpectral remacrectance of biogenic crust developed on desert dunesand along the Israel-Egypt borderordm Int J Remote Sens Vol 16 pp 369-74

Lambin EF and Ehrlich D (1996) ordfThe surface temperature-vegetation index space for landcover and land-cover change analysisordm Int J Remote Sens Vol 17 pp 463-87

Lambin EF and Ehrlich D (1997) ordfLand-cover changes in Sub-Saharan Africa (1982-1991)application of a change index based on remotely sensed surface temperature andvegetation indices at a continental scaleordm Remote Sens Environ Vol 61 pp 181-200

Lambin EF and Strahler AH (1994) ordfChange-vector analysis in multitemporal space a tool todetect and categorize land-cover change processes using high temporal-resolution satellitedataordm Remote Sens Environ Vol 48 pp 231-44

Lieth H (1974) Phenology and Seasonality Modeling Springer-Verlag New York NY

Mainguet M (1994) Desertiregcation plusmn Natural Background and Human Mismanagement 2nd edVol 1 Springer Heidelberg

Nemani RR Peirce L Running SW and Goward S (1993) ordfDeveloping satellite derivedestimates of surface moisture statusordm J Appl Meteorology Vol 32 pp 548-57

Otterman J (1974) ordfBaring high-albedo soils by overgrazing a hypothesized desertiregcationmechanismordm Science Vol 186 pp 531-3

Otterman J (1996) ordfDesert-scrub as the cause of reduced remacrectances in protected versusimpacted sandy arid areasordm Int J Remote Sens Vol 17 pp 615-9

Price JC (1984) ordfLand surface temperature measurements from the split window channels ofthe NOAA-7 advance very high resolution radiometerordm J Geophysical Res Vol 89pp 7231-7

Qin Z (2001) ordfA study of temperature change on both sides of the Israeli-Egyptian borderremote sensing and micrometeorological modelingordm PhD thesis Ben Gurion University ofthe Negev Beer-Sheva

Qin Z and Karnieli A (1999) ordfProgress in the remote sensing of land surface temperature andground emissivity using NOAA-AVHRR dataordm Int J Remote Sens Vol 20 pp 2367-93

Qin Z DallrsquoOlmo G Karnieli A and Berliner P (2001) ordfDerivation of split window algorithmand its sensitivity analysis for retrieving land surface temperature from NOAA-AVHRRdataordm J Geophysical Res Vol 106 pp 22655-70

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Rao CRN and Chen J (1998) ordfRevised post-launch calibration of channels 1 and 2 of theadvanced very high-resolution radiometer on board the NOAA-14 spacecraftordm availableat httppsbsgi1nesdisnoaagov8080EBBmlniccal1html

Rouse JW Haas RH Schell JA Deering DW and Harlan JC (1974) Monitoring the VernalAdvancements and Retrogradation (Greenwave Effect) of Nature Vegetation NASAGSFCFinal Report NASA Greenbelt MD

Sellers PJ (1985) ordfCanopy remacrectance photosynthesis and transpirationordm Int J Remote SensVol 6 pp 1335-72

Tsoar H and Karnieli A (1996) ordfWhat determines the spectral remacrectance of the Negev-Sinaisand dunesordm Int J Remote Sens Vol 17 pp 513-25

Tucker CJ (1979) ordfRed and photographic infrared linear combination for monitoringvegetationordm Remote Sens Environ Vol 8 pp 127-50

Tucker CJ Fung IY Keeling CD and Gammon RH (1986) ordfRelationship betweenatmospheric CO2 variations and a satellite-derived vegetation indexordm Nature Vol 319pp 195-9

UNEP (1992) World Atlas of Desertiregcation Edward Arnold Publishers Sevenoaks

Vermote E TanreAcirc D Deuze JL Herman M and Morcette JJ (1997) ordfSecond simulation of thesatellite signal in the solar spectrum (6S)ordm IEEE Trans Geosci Remote Sens Vol 35pp 675-85

Vogelmann JE (1990) ordfComparison between two vegetation indices for measuring differenttypes of forest damage in north-eastern United Statesordm Int J Remote Sens Vol 11pp 2281-97

Weinreb MP Hamilton G Brown S and Koczor RJ (1990) ordfNonlinearity corrections incalibration of advanced very-high resolution radiometer infrared channelsordm J GeophysRes Vol 95 C5 pp 7381-8

Wilhite DA and Glantz MH (1985) ordfUnderstanding the drought phenomenon the role ofderegnitionsordm Water International Vol 10 pp 111-20

MEQ141

38