A comparison of the vegetation response to rainfall in the ......rainfall and what hydrologic variables, such as soil moisture, are most closely linked with NDVI. Similarly, little

A C O M P A R I S O N O F T H E V E G E T A T I O N R E S P O N S E

T O R A I N F A L L I N T H E S A H E L A N D E A S T A F R I C A ,

U S I N G N O R M A L I Z E D D I F F E R E N C E V E G E T A T I O N I N D E X

F R O M N O A A A V H R R

S H A R O N E. N I C H O L S O N , M I C H A E L L. D A V E N P O R T * and A D A R. M A L O * *

Department of Meteorology, Florida State University, Tallahassee, FL 32306, U.&A.

Abstract. This article presents the results of a study of the relationship between rainfall and Normalized Difference Vegetation Index (NDVI) in East Africa and the Sahel. Monthly data for the years 1982 to 1985 have been analyzed. We have evaluated NDVI-rainfall relationships by vegetation type, using the major formations described by White (1983). In the article, a comparison of the differential response of vegetation growth to rainfall in the two study regions is emphasized.

The most important conclusions of our research are as follows: (1) The spatial patterns of annually-integrated NDVI closely reflect mean

annual rainfall. (2) There is a good relationship between rainfall variations and NDVI on

seasonal and interannual time scales for areas where mean annual rainfall ranges from approximately 200 to 1200 ram.

(3) In most cases, NDVI is best correlated with the rainfall total for the concurrent plus two antecedent months; the correlation is better in the Sahel than in East Africa.

(4) The ratios of NDVI to rainfall are considerably higher in East Africa than in the Sahel.

(5) Mean annually-integrated NDVI is linearly related to mean annual rainfall in the Sahel. In East Africa the relationship is approximately log-linear; above some threshold value of rainfall, NDVI values level off and vary minimally with rainfall.

Two possible explanations of this last conclusion are suggested: above this threshold, rainfall is no longer the limiting factor in vegetation growth and/or NDVI is not a good indicator of vegetation growth. The latter is a likely possibility since NDVI directly reflects photosynthetic activity and becomes a poor indicator of biomass (i.e., growth) as high canopy densities are reached. The NDVI-rainfall relationship for East Africa is markedly similar to the relationship between NDVI and Leaf Area Index demonstrated by Sellers (1985) and Asrar et al. (1984).

1. Introduct ion

T h e A f r i c a n c o n t i n e n t is p r e d o m i n a n t l y s e m i - a r i d l a n d w h i c h is h igh ly sens i t i ve to

b o t h c l i m a t i c f l u c t u a t i o n s a n d h u m a n e n c r o a c h m e n t o n t h e n a t u r a l e n v i r o n m e n t . In

r e c e n t years , t h e c o m b i n e d e f fec ts o f d r o u g h t and h u m a n - i n d u c e d l a n d d e g r a d a -

* Current Address: Air Force Engineering and Services Center, Staff Meteorologist, Tyndall AFB, FL 32403-6001, U.S.A. ** Current Addresss: U.S. Weather Bureau, San Juan, Puerto Rico.

Climatic Change 17: 209-241, 1990. �9 1990 Kluwer Academic_Publishers. Printed in the Netherlands_

210 Sharon E. Nicholson et al.

tion, termed desertification, have taken an immense toll on the environment. In the Sahel, where drought conditions have been most severe, there is increasing evidence that these two factors may be related, that land surface conditions may help to reinforce and sustain the meteorological drought.

A better understanding of these problems requires large-scale monitoring of the land surface, quantification of surface characteristics, and an assessment of vegetation response to moisture availability. A recently developed satellite methodology has potential applicability to all of these issues. This method is based on the differential reflection of green vegetation in the visible and infrared portions of the spectrum (Figure 1). The Normalized Difference Vegetation Index (NDVI) is com- monly used to express this difference (Justice, et al., 1985, 1986; McGinnis and Tarpley, 1985; Tucker, et al., 1985a, b; Goward, et al., 1985; Townshend and Tucker, 1984). NDVI is calculated from Advanced Very High Resolution Radi- ometer (AVHRR) data from NOAA-8 and NOAA-9 polar orbiting satellites and defined as

NDVI = (Ch 2 - Ch 1)/(Ch 2 + Ch 1),

where Ch 1 and Ch 2 are the reflectances in the visible (0.58 to 0.68/~m) and near infrared channels (0.725 to 1.1 ktm), respectively.

Ultimately the NDVI is determined by the degree of absorption by chlorophyll in the red wavelengths, which is proportional to leaf chlorophyll density, and by the reflectance of near infrared radiation, which is proportional to green leaf density (Tucker et al., 1985a). Thus NDVI correlates well with such variables as green leaf biomass, leaf area index (LAI), total dry matter accumulation and annual net primary

, 5 0 I i [ f - 1 I I i I i 1 i |

--I

1 ~ 30 DRY SOtL --

n SAME SOIL, ~ . .-.--'-

w 20 ONLY WET \ L--"''-'" --.---'-- " \ . .- .- .----"- '- -

] 0 " . . . - " " " " " "

0 I I I l I l I i I l I i 400 500 600 700 800 900 I000

WAVELENGTH (nm)

Fig. l. Spectral reflectance curve for green vegetation, and dry and wet soil (from Tucker and Miller, 1977).

Climatic Change December 1990

Comparison of Vegetation Response to Rainfall in the Sahel 211

productivity (Curran, 1980; Tucker, et al., 1981, 1983, 1985a; Asrar, et al., 1984). However, this index most directly represents the absorption of photosynthetically active radiation (PAR) and hence is physiologically a measure of the photosynthetic capacity of the canopy and bulk stomatal or canopy resistance to water vapor transfer (Sellers, 1985; Tucker and Sellers, 1986).

A large number of studies have examined NDVI patterns over Africa. Tucker et

al. (1985a) used it to classify continental land-cover and Townshend and Justice (1986) have used NDVI to describe vegetation dynamics on a continental scale. Regionally, NDVI has been applied to assessing herbaceous biomass and dry matter accumulation in African grasslands and rangelands (Tucker et al., 1983, 1985b; Justice and Hiernaux, 1986; and Prince and Tucker, 1986).

Study of NDVI in relationship to moisture availability would lead to a better understanding of the environmental constraints on vegetation growth. Ideally, the comparison would involve soil moisture, but observations of this parameter on a large scale are scarce and accurate models of soil moisture often require input data which are hard to obtain for developing countries such as those in Africa. A useful alternative is rainfall. The relationship of NDVI to rainfall has been established in a number of studies (e.g., Tucker et al., 1985a, b; Hielkema et al., 1986; Tucker and Dregne, 1988; and Choudhury and Tucker, 1987) and hence NDVI can be applied to large-scale monitoring of drought conditions (Hellden and Eklundh, 1988 and Henricksen and Durkin, 1986). The NDVI-rainfall relationship has not yet been investigated in great depth. For example, it remains to be established whether and under what conditions NDVI is a sensitive indicator of the interannual variability of rainfall and what hydrologic variables, such as soil moisture, are most closely linked with NDVI. Similarly, little is known about the timing of NDVI response to rainfall.

Establishment of the relationship between rainfall and vegetation growth has several applications. For one, models of crop and vegetation growth or primary productivity (e.g., Hielkema et al., 1986) are often based on rainfall. The relationship can also help to determine the sensitivity of various vegetation formations to climate variability and to establish the climatological limits in which NDVI is a useful indicator of vegetation growth. Moreover, in many parts of the world there are few rainfall observations or stations; the data are hard to obtain; and in some countries, war or economic crises lead to multi-year breaks in the records. Thus, the NDVI data may be useful substitutes, especially for the purpose of drought monitoring. A related use might be in monitoring the spatial variability of rainfall, which is large in the tropics. Few observing networks are dense enough to provide such information, but NDVI is quasi-continuous in space, and easily subjected to analyses of spatial variability. Finally, the ratio of NDVI to rainfall may be useful in evaluating changes of land productivity due to factors other than climatic fluctuations.

Our recent studies focus on the NDVI-rainfall relationship, especially the spatio- temporal variability of NDVI for various vegetation types and its dependence on rainfall variability. Various analyses describe the degree and nature of the associa-



Forests

16a 16b 19a

Woodlands

26

Bushland/Thicket/ Shrub-Grassland

42 45 54b 59

tion between NDVI and rainfall both in the mean and on interannual time scales. The correlation, timing and magnitude of the NDVI response to rainfall and how these differ among various vegetation formations are emphasized. In the current article we compare and contrast the results of these analyses for two geographically distinct areas, the West African Sahel and East Africa. The Sahel study is limited to Niger and Mali, the East African to Kenya and Tanzania.

2. Comparative Geography of East Africa and the Sahel

The climatic and topographic conditions of East Africa and the West African Sahel differ greatly. The regions contrast sharply in the insolation regime, the timing of the rainy season and the degree to which topography and other geographic factors influence climate. Environmental conditions are considerably more complex in East Africa than in the Sahel.

In East Africa, large-scale topography and lake and maritime effects produce a great diversity of regional and local scale climates. Traversed by the Rift Valley, much of the region is highland or mountainous, or covered by large lakes such as Victoria, Turkana, Malawi, and Tanganyika. The seasonal displacements of several wind convergence zones, the trade winds, and the Atlantic and Indian Oceans all



20 I 15 /=v , r'

- 1 0 - 5 0 5 10 15

Iiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiilil

l . . . . . t ~ : ' a 4 1. t -

I ] f.:./x'-:2.:';:I

Sudanian woodland with abundant Isoberlinia

Undifferentiated Sudanian Woodland

Sahel Acacia wooded grassland

Semi-desert grassland and shrubland

Absolute desert

Edaphic grassland mosaic

Herbaceous swamp

Fig. 2. UNESCO vegetation classification (from White, 1983. (a) East Africa; (b) Mali and Niger.

influence the region's climate. These geographic and meteorologic factors create a pattern of rainfall which changes markedly, both in amount and seasonality, over relatively small distances. Semi-arid climates prevail throughout most of the region, but the seasonal timing of the rains is highly localized.

In the Sahel there are few areas of significant topographic relief and rainfall is generally associated with only one major system, the disturbances linked with the Intertropical Convergence Zone (ITCZ). Thus, rainfall patterns are relatively simple. The ITCZ influences the region on its northward excursion during northern hemisphere summer and its northern-most position marks the transition between the Sahel and the Sahara. In West Africa, the length of the rainy season and total annual rainfall are primarily determined by the duration of the ITCZ's influence.



Hence, rainfall and the season length are a function of latitude and progressively decrease northward, and rainfall is generally limited to summer or 'high-sun' months.

The vegetation formations are consequently quite different in the two regions. Over eastern Africa, a complex array of vegetation types reflects the manifold influences of climate and topography (Figure 2a). The five dominant biomes include forest, wet and dry miombo (deciduous woodland), bushland/thicket, and grassland/shrubland, in general progression from the wettest to the driest environments. The forests tend to be in the highest regions and along the coast and shore- lines, the woodlands, in the southern lowland areas with pronounced wet and dry seasons. Semidesert grassland and shrubland is confined to the arid north. In the Sahel, vegetation, like rainfall, is primarily a function of latitude and changes from woodland to grassland then desert going progressively northward (Figure 2b). However, the Niger River and Lake Chad are associated with edaphic formations which interrupt the zonal patterns.

3. Data

Our study utilizes monthly rainfall totals and monthly composited NDVI. The analysis period includes the four years 1982-1985. Rainfall data derive from a long-term continental archive described elsewhere (Nicholson, 1986). The analysis incorporates 37 stations in Kenya, 46 in Tanzania, 28 in Mali and 21 in Niger (Figure 3). NDVI is calculated from NOAA-AVHRR global area coverage (GAC) data, with a resolution of 3 x 5 kin. These data were provided and processed by the GIMMS group at the Goddard Space Flight Center. A cloud mask was applied by using channel 5 and labelling everything colder than 12 ~ as cloud. Daily values were formed into monthly composite images by using for each pixel the maximum NDV! within the compositing period, as described by Holben (1986). This minimizes the effects of atmosphere, scan angle and cloud contamination. The defini- tion of NDVI as a ratio minimizes the influence of varying solar zenith angles and surface topography on the derived data set.

Vegetation classification is based on the UNESCO map produced at 1:5,000,000 by White (1983). The map delineates eleven major formations in East Africa; one is referred to White for details about their characteristics. Two subgroups of the coastal forest mosaic were evaluated separately, the true forest patches and the diverse forest mosaic, which has been extensively modified by man. Our study neglects two edaphic zones and thus distinguishes a total of ten vegetation formations, seven of which correspond to those used in a comparable study of East Africa by Justice e t al. (1986). These are referred to, in short, as coastal, upland and lowland forest; coastal mosaic; wet and dry miombo; shrub-thicket mosaic; shrub and Itigi thicket; and semi-desert shrub-grassland (Table I).

In our study area of Mali and Niger, White (1983) delineates six major vegetation formations and a large area of absolute desert (Figure 2, Table II). These in-



(a)

0

- 5

- 1 0

I

�9 �9149 �9 �9 �9 Y

30 35 4-0

(b)

15

10

- 1 0 - 5 o 5 lO 1.5

Fig. 3. Locations of rainfall stations. (a) East Africa; (b) Mali and Niger.

clude two woodland zones (Sudanian and undifferentiated woodland), a bushland/

thicket zone (Sahel Acacia wooded grassland with deciduous woodland), a semi- desert grassland and shrubland zone, and two edaphic vegetation types (herba-

ceous swamp near Lake Chad and a grassland mosaic near the Niger River). The two edaphic types are not inclutted in this study but are discussed elsewhere (Malo and Nicholson, 1990).

Although the entire analysis region in West Africa is generally termed the 'Sahel', that name is properly applied only to the semi-desert grassland, where mean annual rainfall ranges from about 100 to 300 mm, concentrated in 2 to 3 months. The two woodland zones roughly correspond to what is referred to as the Soudan-



TABLE I: Vegetation classification used in the study and stations in each formation

UNESCO UNESCO Reference Number of Typical Vegetation formation number name in stations station

article

Forest Transitions~Mosaics Guinea-Congolian lowland, 11 rainforest woodland and secondary grassland mosaic

E. African coastal mosaic: 16a Zanzibar-Inhambane

E. African coastal mosaic: 16b forest patches

Afromontane (undifferentiated 19a montane vegetation)

Woodlands Wetter Zambezian miombo 25 woodland

Drier Zambezian miombo 26 woodland

Bushland/Thicket/ Shrub-Grassland Ifigi deciduous thicket 40

Lowland 4 Kitale forest

Coastal 4 Utete mosaic

Coastal 1 Amani forest

Upland 8 Kericho forest

Wet 4 Biharamulo miombo

Dry 12 Kondoa miombo

Itigi 1 Manyoni thicket

Somali-Mosaic Acacia 42 Shrub 20 Commiphoro deciduous thicket bushland and thicket

E. African evergreen busfiland 45 Shrub- and secondary Acacia thicket grassland mosaic

Wajir

Somalia-Mosaic semi-desert grassland and shrubland

Miscellaneous Edaphic grassland on volcanic soils

13 Mandera

54b Shrub 4 Habbaswein grassland

59 - 0

Mangrove 77 - 12

ian zone in o ther publ ica t ions (e.g., Nicholson, 1980, 1983), with m e a n annua l

rainfall ranging f rom abou t 600 m m in the no r th to 1200 m m in the south dur ing a

4 to 5 m o n t h period. The bush l and / th i cke t zone ( U N E S C O n u m b e r 43) is the

Sahe l -Soudan ian t rans i t ion in those sources and it is phys iognomica l ly akin to the

three shrub- th icket zones of Eas t Afr ica ( U N E S C O n u m b e r s 40, 42 and 45). The


Comparison of Vegetation Response to Rainfall in the Sahel

TABLE II: Vegetation classification used in the study and stations in each formation

217

UNESCO UNESCO Reference Number of Vegetation formation number name in stations

article

Typical station

Woodlands Sudanian woodland with 27 southern abundant lsoberlinia woodland

Undifferentiated Sudanian 29a northern 19 woodland woodland

Bushland and Thicket Sahel Acacia wooded 43 grassland/ grassland with deciduous woodland woodland transition

Desert~Semi-Desert North Sahelian semi-desert grassland and shrubland

3 Bougouni

11 Tera

Kolikoro

54a grassland 7 Menaka

Absolute desert 71 desert 1 Bilma

75 swamp 1 Nguigmi Edaphic Vegetation Herbaceous swamp with aquatic vegetation

Edaphic grassland mosaic with semi-aquatic vegetation

64 edaphic 7 Djenne grassland

Sudanian woodland zones ( U N E S C O numbers 27 and 29a) are physiognomically

comparab le to the miombo woodlands of East Africa ( U N E S C O numbers 25 and

26). The Sahel and Somali semi-desert grasslands ( U N E S C O numbers 54b and 5 4a) are likewise similar.

Initially, each rainfall station was assigned to one of these formations; the number of stations in each category and one representative station for each are listed in

Tables 1 and 2. Preliminary analysis showed that the NDVI data for some coastal or lakeshore stations in East Africa reflected water rather than land. These stations,

six in all, were thus eliminated f rom the analyses.

4. The Spatial Variability of NDVI and Rainfall

The spatial variability of NDVI and its association with rainfall patterns are illustrated in Figures 4 and 5. These show the mean annual rainfall for the analysis

period and the mean annual integrated NDVI (sum of twelve mean monthly corn- posited NDVI values) for East Africa and the Sahel of Mali and Niger, respectively.

In the Sahel, both rainfall and NDVI decrease rapidly f rom south to north and the contours of both are generally zonally oriented; i.e., bo th tend to be primarily a

function of latitude (Figure 4). An annual NDVI of 2.0 roughly corresponds to



20

15

10

20

15

10 - 1 0 - 5 0 5 10 15

Fig. 4. Annual integrated NDVI and annual rainfall (mm) for Mali and Niger: four-year mean for 1982-85 (shading, NDVI greater than 2.0 or rainfall greater than 500 ram/an; stipling, NDVI less than 1.0 or rainfall less than 200 mm/an; dashed line, NDVI = 0.6, approximately the lower limit for vegetation growth).

mean annual rainfall of 600 mm, while 1.0 corresponds to approximately 250 mm of rainfall per year. In the drier regions, the relationship is less consistent, but the 50 mm isohyet lies close to the NDVI isopleth of 0.6, roughly the lower limit for vegetation (Townshend and Justice, 1988). This compares favorably with Dubief's (1963) finding that generally 50 ram/an is required to sustain vegetation growth in

the Sahel/Sahara transition zone. Figure 5 shows the mean annual integrated NDVI (i.e., the sum of the twelve

monthly values) and mean annual rainfall over Kenya and Tanzania. This figure clearly illustrates the relative complexity of both NDVI and rainfall patterns and the effects of topography and the lakes. This contrasts sharply with the latitudinal patterns in the Sahel. The high rainfall in southeastern and coastal Tanzania, near Lake Victoria and in the central highlands clearly corresponds to maximum NDVI values. Likewise, low rainfall in the northern and eastern portions of Kenya is reflected as low values of NDVI. Minimum values of both are seen in the northwest near Lake Turkana and in the eastcentral lowlands. Rainfall below 500 mm/year generally corresponds to annual integrated NDVI of less than 2.0 or 3.0. The 1000 mm/year isohyet runs fairly close to the NDVI = 4.0 isopleth.



5

0

-5

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NDVI

iii

' i iiiii i .............. iii ,

:50 35 40

RAIN

::~::;iiir .... -~- / . : : : z

/;,%1 j

3 0 3 5 4 (

. . .

Fig. 5. (a) Annual integrated NDVI for East Africa: four-year mean for 1982-85 (shading, NDVI greater than 4.0; stipling, NDVI less than 3.0). (b) Mean annual rainfall (mm) for the four-year period 1982-85 (shading, rainfall greater than 1000 ram/an; stipling, rainfall less than 500 mm/an).

The contrast between East Africa and the Sahel is seen both in the magnitude of NDVI for various rainfall amounts and in the complexity of the spatial patterns. For example, 500 mm/an rainfall in East Africa corresponds to an annual integrated NDVI of 2.0 to 3.0, but at least 600 mm/an are required for annual NDVI to reach 2.0 in the Sahel. Annual integrated NDVI of 4.0 corresponds to N 1000 mm/ an in East Africa but in the Sahel the annual integrated NDVI at most attains N2.8, although rainfall exceeds 1000 mm/an in southern areas. This clearly shows that a certain amount of rainfall will produce considerably lower growth in the Sahel than in East Africa. This might result from several factors: higher insolation (and hence potential evapotranspiration) during the rainy season in the Sahel, lower inherent soil fertility in the Sahel, or intense desertification (i.e., land degradation) in that region (Glantz, 1977).

5. Phenology of Individual Vegetation Formations and the Seasonal Cycle of Rainfall

The relationship between the phenology of vegetation and the seasonal cycle of rainfall is illustrated by temporal plots for the 1982-1985 analysis period. Figures 6 through 9 present station plots for each vegetation formation; each figure summar- izes one major physiognomic category.

The four forest types (Figure 6) are all found in East Africa. These are charac- terized by relatively high NDVI year-round and a generally unimodal seasonal pattern. Monthly composited NDVI falls below 2.5 in at most 2 or 3 months, if at all,



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200

100

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4-00 _~

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Fig. 6. Temporal plots of monthly composited NDVI (solid line) versus rainfall (dashed line, in ram) for stations within the forest zone (mean annual rainfall in mm is indicated at top of graph; all stations are in East Africa).


Comparison of Vegetation Response to Rainfall in the Sahel 2 2 1

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982 1983 1984 1985

TIME

Fig. 7. Temporal plots of monthly composites of NDVI (solid line) versus rainfall (dashed line, in ram) for stations within the woodland zone (mean annual rainfall in mm is indicated at top of graph; top two stations are East Africa, lower two are Sahel).



BUSHLAND/THICKET

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1982 1983 19B4 1985

TIME Fig. 8. Temporal plots of monthly composites of NDVI (solid line) versus rainfall (dashed line, in mm) for stations within the bushland/thicket zone (mean annual rainfall in mm is indicated at top of graph; top three stations are East Africa, Nara is Sahel).



SEMI-DESERT GRASSLAND

400 ..j ,4 ..j

300 '<

.2 200

.1 l O O

o o

" M E N A K A 1 9 1 - 500 .5

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" 3 0 0 ,,< ,3 Z

Z .2 2 0 0

" ~ - 1 O0 m ~ t

- # % -

1 g B 2 1 9 8 3 1 gB4- 1 g B 5

T I M E

Fig. 9. Temporal plots of monthly composites of NDVI (solid line) versus rainfall (dashed line, in ram) for stations within the semi-desert grassland zone (mean annual rainfall in mm is indicated at top of graph; Habbaswein is in East Africa, Menaka is in the Sahel).

and it always exceeds 2.0. Maximum NDVI values are about the same at all four stations, although mean annual rainfall ranges from 987 mm at Utete to 2025 mm at Kericho. This maximum lies at about 0.50-0.55. The wetter and drier stations differ primarily in the magnitude of the NDVI seasonal minimum and the number of months with low NDVI. The seasonal amplitude of NDVI appears to be roughly proportional to mean annual rainfall and shows little relationship to the amplitude of the seasonal rainfall cycle.

Throughout the forest regions rainfall generally shows a distinctly bimodal seasonal cycle and the two rainfall peaks manifest themselves as small, secondary NDVI peaks superimposed upon the basic unimodal pattern. NDVI shows a clear response to the cycle of rainfall, but there is little relationship between the total amount of rainfall and the integral of NDVI. There is also little year-to-year change of NDVI even when rainfall varies significantly. Monthly composited values seldom exceed 0.50-0.55 and are markedly similar at Kitale and Kericho, although mean annual rainfall for these two stations is 1288 and 2006 mm, respectively. Highest ND.VI values are attained at Utete, by far the driest of the four forest stations. The one anomalous station is Amani, representative of the coastal forest. There NDVI



shows little month to month variation and almost always exceeds 0.4. This may reflect anomalously high soil moisture associated with runoff from the highlands to the west.

The phenology of NDVI at the woodland stations differs from that of the forest in several ways (Figure 7). The month-to-month variations are less erratic and the period of minimum NDVI is longer and more pronounced, with monthly composited values often falling below 0.2. The peak monthly values seldom exceed 0.4, compared to 0.5 in the forest zones. At most stations there is little change of the minimum value from year to year and that minimum seems nearly invariant from one location to another, even when rainfall conditions are quite diverse. Of the four representative stations in Figure 7, the dry season minimum is close to 0.1 at three of them, although mean annual rainfall ranges from 634 to 1009 ram. This suggests that a certain minimum of photosynthetic activity is maintained by the evergreen species of the woodland even in the dry season. It is interesting to note the strong resemblance of the NDVI phenology at East African and Sahelian woodland stations in terms of maximum and minimum values. The two regions differ clearly, however, in the number of months in which maximum or minimum NDVI is sus- tained and hence in annual integrated NDVI. These characteristics appear to be a function of the length and magnitude of the rainy season at the various locations.

The phenology of vegetation in the bushland/thicket formations is considerably different (Figure 8). Except for the Itigi thicket, represented by Manyoni, the seasonal cycles of NDVI and rainfall in the East African bushland formations are highly irregular. However, NDVI fluctuations closely correspond to rainfall fluctuations in both timing and magnitude. This is the case for other stations of that formation which were examined. In the Sahel a similar correspondence is evident in both the magnitude and timing of the fluctuations, but there is a more regular seasonal pattern of both NDVI and rainfall. The temporal pattern of NDVI in the Sahel differs from that of East Africa also in the extended 'plateau' in NDVI after the rainy season and the sharp minimum just prior to the onset of the rains. This does not reflect patterns of vegetation growth, but appears instead to be background 'noise' related to patterns of atmospheric aerosols and humidity (Malo and Nicholson, 1990).

The patterns of both rainfall and NDVI in the Itigi thicket formation (Manyoni) much more closely resemble woodland patterns (Figure 7) than those of the other thicket zones (Figure 8). This anomaly has two possible explanations. One is that although White's (1983) map shows Manyoni to be part of the thicket, the region may actually be predominantly woodland; the Itigi thicket formation is very small and surrounded by woodland which extends to within N 10 km of Manyoni. It is also possible that the relatively high NDVI response is characteristic of the Itigi thicket formation; it has a closed canopy, similar to the forest zones, and the canopy is so thick that sunlight does not penetrate through it to the ground (White, 1983).

The phenological signature of the two semi-desert grassland formations is hard to distinguish from that of the woodland formations (Figure 9). The main differ-



ence is in the minimum values of NDVI. A monthly composited value of 0.05 is

approximately the limit of vegetation growth (Townshend and Justice, 1986). In the woodland zones, NDVI falls below this threshold only at Sahelian stations, where

its value appears to be reduced by aerosols and atmospheric water vapor (Holben, 1986). The woodland formations all include a number of evergreen or semi-evergreen species which are photosynthetically active during the dry season of minimum growth. This is not the case in the grassland and NDVI falls below 0.05 much of the year.

Two other locations merit comment, as their phenological signatures are

markedly differ from that associated with the dominant vegetation formation in these locations (Figure 10). Both are West African stations: Ke Macina, which is classified as woodland, and Kidal, which is classified as semi-desert grassland. At Kidal, NDVI seldom exceeds 0.5 during the four years 1982-1985 and a response

to rainfall is suggested only in the two wettest years 1984 and 1985. This is also true for other stations classified as grassland (e.g., Iferouane). These low values imply an absence of green vegetation, an observation which Townshend and Justice (1986) likewise made for the northern Sahel and even parts of the Acacia wooded

grassland. The absence of vegetation is probably a result of the long prevailing

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drought conditions in the region (Nicholson, 1989). All four years of our analysis were abnormally dry: the four-year mean at Kidal is 82 mm compared to a long- term mean of 133 mm. The fact that some response of NDVI to rainfall is apparent in the wetter years suggests that the drought has not permanently altered the region's vegetation patterns.

The situation might be different at Ke Macina. White (1983) classifies the region as woodland and the long-term mean of annual rainfall is 557 mm, a value typical of other woodland stations. Rainfall was considerably high prior to the mid-1960s. During the analysis period 1982-1985, the NDVI phenology closely resembles that of the semi-desert grassland stations. The mean annual rainfall for that period is 456 mm. The two decades of drought which have affected the region may have permanently altered its landscape, allowing ephemeral grassland species, which are resilient to fluctuating moisture conditions, to encroach southward onto the former woodland.

6. Interannual Fluctuations of Rainfall and NDVI

Most of Africa suffers from recurrent drought. The unavailability of real-time climatic and environmental data makes it difficult to monitor its development, in- tensity and impact. The problem is most acute in the dryland regions because drought is most frequent and severe there and because meteorological stations are relatively scarce. If a sensitive and consistent NDVI response to interannual fluctuations of rainfall can be demonstrated, this index can provide an indicator of drought conditions in data-sparse regions (Hellden and Eklundh, 1988). Our analyses suggest that vegetation is directly responsive to the interannual variability of rainfall in many regions but that limits to sensitivity exist. A few examples are given in Figures 6 through 10.

The temporal plots in Figures 6 through 9 depict this variability during the years 1982 to 1985 for the various vegetation formations. These formations differ noticeably in their response to rainfall variability. In the forest zones, the year-to- year changes of NDVI are minimal (Figure 6) and the maximum NDVI that is attained is nearly invariant. However, annual integrated NDVI shows some variation with rainfall because the duration of the low-growth period and in the minimum values of NDVI attained during that period change in response to rainfall. In the woodlands (Figure 7) a somewhat greater response of NDVI to the interannual variability of rainfall is apparent. Both maximum and minimum monthly composite values of NDVI are relatively constant but the annual integral, as represented by the area under the NDVI curve, does seem to vary as rainfall conditions change. For example, this is apparent at Bougouni in 1984, which was an abnormally dry year, and at both Kondoa and Biharamulo during 1984/85, when rainfall was relatively high. Again it generally appears to be the number of months with low vegetation growth (and hence low NDVI) which changes. In some of the drier woodland locations (e.g., Kolikoro), the maximum NDVI also decreases notably in the driest



years. In the two driest vegetation formations, the bushland/thicket (Figure 8) and grassland locations (Figure 9), there is a clear and immediate response to rainfall variability; it is apparent in both the maximum monthly values of NDVI which are attained during the year and in the annual integrated NDVI. Each rainfall peak clearly manifests itself in the NDVI cycle. There is no apparent change in minimum NDVI, however. This seems reasonable since in these formations there is little green vegetation during the dry season.

Thus, the temporal plots for specific locations suggest that for most vegetation types the annual integral of NDVI will vary with annual rainfall but that the maximum or minimum NDVI may be insensitive to rainfall variability. This conclusion is tested by examining the geographic patterns of annual NDVI and rainfall for the analysis period.

Figure 11 shows the spatial patterns of rainfall and NDVI over East Africa for three 12-month periods beginning in November of 1982. This period, rather than

NOV 82 - OCT 83 NOV 83 - OCT 84- NOV 84- - OCT 85

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Fig. 11. Annual integrated NDVI and annual rainfall (mm) for the years 1982-1985 in East Africa (stipling, rainfall below 500 mm/year and NDVI below 3.0; light shading, rainfall above 100() ram/ year and NDVI greater than 4.0; dark shading, rainfall above 2000 mm/year; from Davenport and Nicholson, 1990).


228 Sharon E. Nichotson et al.

the calendar year, was chosen because the calendar year bisects the rainy season in much of East Africa. The interannual fluctuations of rainfall are not uniform throughout the region, but some generalizations can be made. In the driest areas of the north and east, rainfall was relatively good in 1983, well below normal in 1984 and highest in 1985. The contrast between the last two years is particularly apparent from the size of the areas bounded by the 200 mm and 500 mm isohyets. Large areas received less than 200 mm in 1984, while in 1985, no stations received less than 200 mm and generally rainfall was above 500 mm for the year. These same changes are reflected in the annual integrated NDVI. For example, a vegetation response to the low rainfall near Lake Victoria in 1984 and high rainfall in 1985 is likewise apparent in NDVI. In the wetter southern areas, however, NDVI patterns differ considerably from those of rainfall. The most vigorous vegetation growth is in 1985, the driest of the three years. This might reflect either the insen- sitivity of vegetation to interannual rainfall fluctuations in humid regions or a lagged soil moisture response to rainfall.

Rainfall isohyets over Mali and Niger (Figure 12a) for each of the four years 1982-1985 indicate that 1983 and 1984 were considerably drier than 1982 and 1985. The positions of most isohyets were further south in 1984 than 1983, sug- gesting that 1984 was generally the drier year. Wettest conditions in most locations occurred in 1985. These trends are consistent with rainfall trends observed in the Sahel as a whole (Nicholson, 1989).

The isopleths of NDVI in the four years (Figure 12b) suggest these same trends: vegetation growth was apparently lowest in 1984 and considerably lower in both 1983 and 1984 than in the other two years. In general, the spatial patterns of NDVI departures in the four years (Figure 12b) closely correspond to those of rainfall (Figure 12a), except in the northern areas where NDVI values are too low to repre- sent vegetation growth. However, NDVI was generally about the same in both 1985 and 1982, despite higher rainfall in 1985. This may indicate an incomplete recovery of vegetation from the extreme drought conditions of the two previous years.

Analyses for both East Africa and the Sahel suggest that a rainfall threshold exists, above which NDVI is insensitive to rainfall fluctuations. In both regions this threshold appears to lie at -1000-1100 mm/an or 200 mm/mo (Malo and Nichol- son, 1990; Davenport and Nicholson, 1990). There is a comparable minimum threshold, below which no relationship with rainfall variability exists because NDVI represents a background signal of bare ground and atmospheric effects and not vegetation growth. This is probably N150 mm/an or 75-100 mm/mo in the Sahel. The upper threshold appears to suggest that above -1000 mm/an or 200 mm/mo, rainfall is not the limiting factor in vegetation growth. Such a conclusion is somewhat premature, however, since NDVI directly measures photosynthetic activity and not vegetation amount (Tucker and Sellers, 1986). This question is examined in the next section.



7. General Relationships Between NDVI and Rainfall

For each of the vegetation formations, the linear correlation between monthly NDVI and rainfall was calculated for the formations as a whole and for representative locations within them. The correlation was assessed for concurrent monthly values of rainfall and NDVI and for various one-, two- and three-month averages of rainfall concurrent with and/or prior to the NDVI month. For the Sahel, the long dry season dramatically skews the data distribution. In order to obtain more representative correlations for this region, the dry season months (i.e., those in which mean rainfall falls below 10 mm) were eliminated from the calculations.

In both East Africa and the Sahel (Table III), the best relationship is with multi- month rainfall totals and with NDVI lagging rainfall. The correlations are higher for individual stations than for the formations as a whole and are generally higher in Mali and Niger than in East Africa. In most cases, the highest correlation is with rainfall in the concurrent plus two previous months. This is true for both East and West Africa. The lag appears to relate to the fact the vegetation does not respond directly to rainfall, but rather to soil moisture, which is a multi-month integral of rainfall (Malo and Nicholson, 1990).

Scatter diagrams of NDVI versus rainfall, using the best correlated rainfall parameter as indicated in Table III, further illustrate the relationship. Diagrams for one station representing each of the vegetation formations and for the formations as a whole are shown in Figure 13. Although considerable scatter exists, these diagrams tend to suggest a log-linear relationship between NDVI and rainfall in East Africa and a somewhat more linear relationship in West Africa. In general, NDVI levels off when monthly rainfall reaches N200 mm/mo and shows little change as rainfall increases above that amount. It was likewise noted in the temporal plots (Figures 6 through 9) that rainfall above 200 mm/mo appears to have little influence on NDVI. To some extent, the large scatter in these diagrams represents the high temporal and spatial variability of rainfall on monthly time scales. As also suggested by the correlation analysis (Table III), the association between NDVI and rainfall is better for individual stations than for the zones as a whole. This is not surprising since the specific locations are more homogeneous than the multi-station zone with respect to additional environmental factors such as topography and soils.

Figure 14 shows similar plots of the four-year averages of both NDVI and rainfall, based on all stations. For East Africa, this clearly demonstrates a log-linear relationship. The linear correlation between rainfall and NDVI is 0.82 and the log- linear correlation (i.e., between NDVI and the log of rainfall) is 0.89. There is little change of NDVI with rainfall when the latter exceeds -1000-1200 mm/an (Figure 14a). This is in good agreement with the previous analyses, which suggested that NDVI is insensitive to the interannual variability of rainfall when rainfall exceeds -1000 mm/an. The plot for West African stations (Figure 14b) shows a highly linear relationship between NDVI and rainfall, in sharp contrast to that for East



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TABLE III: Correlation between monthly NDVI and rainfall in various time intervals for each vegetation formation and for one representative station within each formation. N is the number of correlation pairs and the highest correlations are indicated in boldface. Columns, from left to right, denote correlation of NDVI with rainfall in concurrent month (0), one month earlier (1), two months earlier (2), in the two previous months (2 + 1), and in the concurrent plus two previous months (2 + 1 + 0)

Vegetation zone/station 0 1 1 + 0 2 + 1 + 0 2 + 1 N

EAST AFRICA Lowland forest 0.44 0.65 0.64 0.72 0.65 72

-Kitale 0.31 0.65 0.58 0.70 0.70 36

Coastal mosaic 0.33 0.46 0.50 0.57 0.50 72 -Utete 0.61 0.74 0.80 0.88 0.76 36

Coastal forest 0.31 0.47 0.52 0.73 0.67 36 -Amani 0.31 0.47 0.52 0.73 0.67 36

Upland forest 0.33 0.56 0.52 0.65 0.66 288 -Kericho 0.16 0.58 0.46 0.66 0.69 36

Wet miombo 0.49 0.63 0.63 0.70 0.68 108 -Biharamulo 0.54 0.55 0.67 0.63 0.47 36

Dry miombo 0.52 0.66 0.66 0.72 0.71 432 -Kondoa 0.50 0.82 0.75 0.87 0.89 36

Itigi thicket 0.53 0.71 0.72 0.82 0.81 36 -Manyoni 0.53 0.71 0.72 0.82 0.81 36

Shrub-thicket 0.56 0.69 0.73 0.79 0.73 684 -Wajir 0.61 0.76 0.80 0.81 0.66 36

Shrub-thicket mosaic 0.33 0.56 0.54 0.65 0.63 468 -Mandera 0.42 0.93 0.87 0.82 0.76 36

Semi-desert shrub 0.37 0.60 0.61 0.72 0.70 144 -Habbaswein 0.52 0.82 0.79 0.87 0.80 36

WEST AFRICA

Northern woodland 0.63 0.79 0.82 0.88 0.78 92 -Bamako 0.67 0.90 0.91 0.95 0.86 28

Southern woodland 0.54 0.78 0.79 0.86 0.80 408 -Koutiala 0.53 0.90 0.82 0.97 0.93 23

Grassland/woodland transition 0.39 0.72 0.72 0.76 0.68 175 -Nara 0.71 0.85 0.95 0.90 0.71 16

Grassland 0.22 0.61 0.57 0.64 0.64 71 -Menaka 0.31 0.65 0.66 0.77 0.72 16


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Africa. The correlation is 0.96. This linearity may simply reflect the fact that at the West African stations we analyzed mean annual rainfall barely exceeds the -1000 mm/an 'threshold'. The plots of monthly data for the Sahel (Figure 13b) do suggest some tendency for a log-linear relationship.

The log-linear relationship between NDVI and rainfall (Figure 14a) is comparable to that between leaf area index and vegetation index (Figure 15), as described by Asrar et al. (1984) and Sellers (1985). As the canopy density increases, addi-



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tional growth contributes progressively less to the photosynthetic process because leaves which are lower in the canopy receive a continually diminishing amount of photosynthetically active radiation (PAR). A point of saturation is reached such that PAR absorption, and therefore vegetation index, are constant while leaf area index increases. As rainfall increases, NDVI and presumably canopy density increase until this saturation is reached and NDVI thereafter remains constant des-



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pite increasing rainfall. NDVI might therefore be a sensitive indicator of rainfall in drier regions but not humid ones and might potentially serve as an indicator of abnormally dry conditions but not wet ones.

The relationship between leaf area index and NDVI thus poses two possible explanations for the apparent 'threshold', above which NDVI is relatively constant while rainfall increases. One is that maximum photosynthetic capacity is reached, even if growth continues. The other is that above this threshold, rainfall is not the limiting factor in vegetation growth. We cannot yet distinguish between the two possibilities. However, the former is suggested by the fact that maximum monthly composited NDVI during the course of the year is relatively invariant over the various formations, with NDVI rarely exceeding -0.5.

One additional aspect of the NDVI-rainfall relationship merits further discus- sion. The slope of the association between rainfall and NDVI varies with plant formation. Figure 13 shows, for example, that in the Acacia wooded grassland the slope of the NDVI-rainfall relationship is considerably higher than in the woodland formation. This suggests that the formations differ in the rate of growth, or primary productivity, per unit rainfall, a concept which Le Houerou (1984) terms rain-use efficiency. It is well known that vegetation types differ markedly in this respect. Now, however, NDVI may provide a method to quantify this characteristic on a large scale.

We have attempted to examine rain-use efficiency in both analysis regions by calculating an index termed the rain-greenness ratio or RGR. This is defined as the ratio of mean annual integrated NDVI to mean annual rainfall, multiplied by 100 to produce values greater than unity. Davenport and Nicholson (1990) found that the map of RGR over East Africa bears a striking resemblance to White's (1983)

Climatic Change Dec ember 1990


vegetation map of the region (Figure 2a). This was not the case for the Sahel because of the relatively high 'noise' level in the NDVI signal in the Sahel (Malo and Nicholson, 1990). Nonetheless, a comparison of RGR for the various vegetation formations and for the two analysis regions produces some interesting results.

In East Africa (Table IV) this ratio is lowest in the forest zones, ranging from 2.8 to 3.9. It is, on the average, only slightly higher in the two woodland formations, being 3.5 and 4.3 for the wet and dry miombo, respectively. The values for the thicket formations are quite high (4.6 to 5.6) but for the driest region (the grassland) it is surprisingly low (3.5). The growth efficiency of dryland vegetation is generally considered to be higher than for the species dominant in wetter regions, because the former have developed numerous physiological and structural adapta- tions to cope with limited and irregular water supply (Noy-Meir, 1985). This issue is complex, however, since the ratio of productivity to rainfall also depends on soil composition and textural properties, land productivity and other factors.

In the Sahel, the rain-greenness ratio increases steadily as rainfall decreases and the vegetation formations of the driest regions appear to be the most efficient with respect to moisture utilization. The ratios for the Sahel (Table IVb) are considerably lower on the whole than those for comparable conditions in East Africa. For example, a ratio of 3.9 is exceeded only in the very arid grassland zone. A similar conclusion was reached in Section 4 from Figures 4 and 5. This may indicate a relatively low productivity of the West African landscape, possibly a result of the extended drought (Nicholson, 1989) or intensive desertification (Glantz, 1977). Alterna- tive explanations are that the evaporative losses of the rainfall are greater over West Africa, thus resulting in lower soil moisture availability for the same amount of rainfall, or that the dominant soil types in the Sahel are inherently less fertile than those of East Africa.

8. Summary and Conclusions

This study has demonstrated a strong statistical association between rainfall and NDVI in the West African Sahel and in East Africa on climatic time and space scales. The mean spatial patterns of the two variables are remarkably similar. The phenology of vegetation in all formations closely reflects the seasonal cycle of rainfall. On a monthly scale the best association is generally between NDVI and a three-month average of rainfall in the concurrent and two previous months. This lagged response suggests that NDVI is a better indicator of soil moisture than of rainfall. This conclusion was supported by soil moisture calculations at Niamey (Malo and Nicholson, 1990).

The seasonal phenology of NDVI appears to be a distinguishing feature of broad physiognomic classes of vegetation. The response of a given formation in East Africa, and its relationship to rainfall, is similar to that of the corresponding West African formation. The two regions are similar in two other respects. First, the upper limit of monthly composited NDVI appears to be -0.5 to 0.55 in both



TABLE IVa: Annual integrated NDVI (four-year mean), annual rainfall (four- year mean in mm), and rain-greenness ratio (RGR) for vegetation formations in East Africa

Zone Annual NDVI Mean annual RGR rainfall

Lowland forest 4.3 1320 3.2 Coastal mosaic 4.0 1014 3.9 Coastal forest 5.2 1962 2.7 Upland forest 4.3 1157 3.9

Wet Miombo 3.7 1102 3.5 Dry Miombo 3.8 948 4.3

Itigi thicket 3.9 700 5.6 Shrub-thicket 2.5 546 5.1 Shrub-thicket mosaic 3.3 803 4.6 Semi-desert shrub 1.1 297 3.5

TABLE IVb: Annual integrated NDVI (four-year mean), annual rainfall (four-year mean in mm), and rain-greenness ratio (RGR) for vegetation formations in West Africa

Zone Annual NDVI Mean annual RGR rainfall

Southern woodland 2.6 951 2.8 Northern woodland 1.5 460 3.5 Grassland/woodland transition 0.9 249 3.9 Grassland 0.6 110 5.8

regions. Also, in bo th regions a clear associat ion be tween N D V I and rainfall

appears to be limited to rainfall condi t ions of less than - 2 0 0 m m / m o or N1000

m m / a n . There is a general log-linear o r exponential associat ion be tween m e a n N D V I

and mean rainfall. N D V I exhibits a ' saturat ion ' response to rainfall, levelling off as

rainfall increases and remaining relatively cons tant after a certain threshold value

of rainfall is reached. T he most likely reason for this is f ound in the relationship be tween N D V I and vegetat ion growth, which is also exponential . NDVI , which is

physiological ly a measure of the photosynthe t ic capaci ty of the plant canopy, increases in p ropo r t i on to leaf-area index (i.e., plant growth) until some threshold

value of N D V I (and canopy density) is reached. A b o v e that, N D V I is relatively con-

stant because the m a x i m um photosynthe t ic capaci ty is at tained even though growth

may continue. This threshold is apparent ly attained at - 1 0 0 0 m m / a n or N 2 0 0 - 2 5 0 m m / m o . The annual NDVI-ra infa l l relationship is l inear in the Sahel because this

threshold is not exceeded. However , the month ly plots of N D V I and rainfall (which



often exceeds 200 mm/mo) do show some resemblance of an exponential response curve (Figure 13).

In most of the analysis region, NDVI is a sensitive indicator of interannual fluctuations of rainfall. The year-to-year geographical displacement of NDVI isopleths closely reflects that of the isohyets of rainfall. The interannual variation in timing and magnitude of the seasonal phenology of vegetation at individual locations are markedly similar to the timing and magnitude of the seasonal cycle of rainfall. The driest years are clearly those of lowest vegetation growth, as indicated by NDVI. The strong response to rainfall fluctuations is evident as long as annual rainfall lies between approximately 200 mm and 1000-1200 mm. This agrees well with the conclusions of ecological field studies of Lamotte and Bourlibre (1983). It is note- worthy that the NDVI values are sufficiently sensitive to rainfall variations that they can distinguish among four anomalously dry years. This suggests that NDVI would be a useful tool for reconstructing rainfall conditions in relatively dry locations where conventional rainfall measurements are sparse or completely unavailable.

Our analysis has detected several Sahelian stations whose spectral signature does not match those of the vegetation formations indicated by White (1983) and instead match those of drier regions. These suggest a southward shift of the vegetation patterns in West Africa, in response to the long sequence of drought years. The grassland appears to recover during wetter years, but there is some evidence of long-term change in the northern woodland. NDVI is well suited for monitoring such long-term changes.

The ratio of NDVI to rainfall (RGR) provides a rough quantitative measure of the efficiency of water use, a characteristic which varies among vegetation types. Notable differences in this ratio are observed among the various plant formations and between East Africa and the Sahel. In our study, the highest efficiencies were generally found in the plant formations of the driest environments.

The ratio of NDVI to rainfall is considerably higher for East Africa than for the Sahel. For example, annual rainfall of 1000 mm roughly corresponds to annual integrated NDVI of 4.0 in East Africa, but only -2 .5-2 .8 in West Africa. This suggests a higher growth rate per unit rainfall, i.e., a greater water-use-efficiency in East Africa. If differences in evaporative losses in the two regions can be discount- ed, the different ratios might be a result of the different soil fertility in the two regions or of the intense drought and desertification in the Sahel. The ratio also declined in the Sahel in 1985, when rainfall increased following two years of extreme drought. This would suggest an incomplete recovery from the drought. Thus, the combination of NDVI and rainfall might ultimately be useful for monitoring and quantifying desertification in semi-arid regions.

Acknowledgements

We would like to acknowledge the support of the U.S. Air Force and NASA-grants NAG 5-764 and NGT 70062 in this project. C. J. Tucker of NASA-GSFC kindly



provided the NDVI data used in this study. We would like to thank Brent Holben, Chris Justice and Steve Prince for critical discussions of our results. The assistance of ES.U. colleagues J.-Y. Kim and U Easter is also much appreciated.

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(Received 13 June, 1989; in revised form 16 April, 1990)


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