LAND COVER, RAINFALL AND LAND-SURFACE ALBEDO
IN WEST AFRICA
DOUGLAS O. FULLER and CHRISTIAN OTTKE
Department of Geography, The George Washington University, Washington, DC 20052, U.S.A.
E-mail: [email protected]
Abstract. Land surface albedo is an important variable in General Circulation Models (GCMs).
When land cover is modified through anthropogenic land use, changes in land-surface albedo may
produce atmospheric subsidence and reduction of rainfall. In this study we examined albedo time
series and their relationships with rainfall, land cover, and population in West Africa. This particular
region was selected because it has become a focal point in debates over biophysical impacts of
desertification and deforestation. Our analyses revealed that albedo and rainfall were related only
modestly at short time scales (monthly and annual) and that mean annual albedo values remained
relatively stable from 1982–1989 over a wide range of climatic and vegetation zones in West Africa.
The relationship between long-term mean rainfall and mean albedo was strong and curvilinear
(r 2 = 0.802). The same was true for the relationship between percent tree cover and mean albedo
(r 2 = 0.659). These results suggest that long-term climate patterns, which control vegetation type
and canopy structure, have greater influence on albedo than short-term fluctuations in rainfall. Our
results reinforce other recent studies based on satellite data that have questioned the extent and
pervasiveness of desertification in West Africa.
1. Introduction
During the 1980s and 1990s numerous reports of rapid loss of vegetation cover in
West Africa raised widespread concerns about biophysical impacts of deforestation
and desertification in the region (Gornitz, 1985; Sayer et al., 1992; FAO, 1993;
WRI, 1988). In its 1990 assessment of forest cover in Africa, the FAO (1993)
reported that the percentage of total forest lost per year during the 1980s in the
humid parts of the region ranged from 0.54 in Liberia to 1.6 in Benin. During a brief
period from 1981–1985 the annual deforestation rate in Cote d’Ivoire is reported
to have reached 7.3 percent, nearly ten times the average for all tropical countries
during the 1980s (Fairhead and Leach, 1998). Similarly, in the semi-arid Sahelian
zone of West Africa, several United Nations organizations identified anthropogenic
desertification as the cause of widespread famine and drought that affected the
region in the late 1960s, 1970s and again in the mid-1980s (Thomas and Middleton, 1994; Nicholson et al., 1998). Since these early reports of widespread
desertification and deforestation, however, a number of studies have emerged that
challenged the validity and magnitude of estimated changes in West Africa’s vegetative cover (Helldén, 1991; Thomas and Middleton, 1994; Fairhead and Leach,
1998; Mortimore, 1998; Prince et al., 1998).
Climatic Change 54: 181–204, 2002.
© 2002 Kluwer Academic Publishers. Printed in the Netherlands.
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DOUGLAS O. FULLER AND CHRISTIAN OTTKE
The linkage between vegetative and climatic change is well established in landsurface climatology as well as in General Circulation Models (GCMs). The loss of
vegetation in tropical humid and dry regions caused by land uses such as logging,
overgrazing, or intensive cultivation is thought to produce an increase in landsurface albedo and a decrease in surface roughness, which lead to an increase
in atmospheric subsidence and drought in regions such as the Sahel (Charney et
al., 1975; Nicholson et al. 1998). While few would dispute that vegetative loss produces changes in the radiative properties of the surface, particularly the partitioning
of latent and sensible fluxes, questions remain over the magnitude and duration of
albedo change necessary to induce a biophysical feedback to climate (Allen et al.,
1994). Numerous studies have been conducted on climate-biosphere interactions
in the region including recent work by Xue and Shukla (1993, 1996), Cook (1997),
and Wang and Eltahir (2000a,b). Comparison of modeling approaches reveals that
land surface conditions for West Africa range from extremely simple with uniform
albedo of 0.10 (Cook, 1997) to more complex, realistic values dependent upon soil
type and vegetation (Xue and Shukla, 1996). Rowntree (1991) reviewed several
early GCM experiments based on Sahelian conditions that involved changing land
cover types having albedos of 0.12 to 0.22 respectively, which approximated complete removal of vegetation. Xue and Shukla (1993) conducted GCM experiments
with a more sophisticated coupled land-surface climate model that involved land
cover change with a corresponding albedo change of 0.10 (i.e., from 0.20 to 0.30).
Their study revealed decreased moisture flux convergence and southward shift of
negative rainfall anomalies related to albedo increases in the Sahel.
While such experiments have provided useful insights on potential climatic
feedbacks, several studies have suggested that albedo increases induced by landcover change may play only a minor role in anthropogenic climatic modification.
In part, this is because some researchers may have overestimated the increase in
albedo associated with land-cover change. Williams and Balling (1996), for example, questioned the assumptions of albedo change made by Charney et al. (1975)
because their model included a large change of albedo from 0.14 to 0.35, which
generally exceeds observed values obtained from ungrazed and grazed locations.
Further, Giambelluca et al. (1997) found that differences in albedo between primary forest and pasture in Amazonia were less than those used in GCM-based
deforestation scenarios. Based on field studies in a semi-arid region in Niger, Allen
et al. (1994) concluded that a maximum change in albedo of 0.09 in mean annual
albedo could occur only if closed-canopy bush cover were completely removed.
Thus, for land-cover change to exert a biogeophysical feedback, other surface
characteristics such as soil moisture and evapotranspiration may be more important in explaining fluctuations in climate (Lare and Nicholson, 1994; Williams and
Balling, 1996).
With the possible exception of localized patches of denuded ferrugenous soils,
known as ‘plaques des sols nus’ in French-speaking West Africa (Puech et al.,
2000), the literature suggests that most land surfaces under both intensive and ex-
LAND COVER, RAINFALL AND LAND-SURFACE ALBEDO IN WEST AFRICA
183
tensive land uses possess anthropogenically modified vegetation cover consisting
of shrubs, scattered trees, and a more or less continuous herbaceous layer (Donfack et al., 1995). Interestingly, reports from several field studies suggest that in
some instances, intensive land use may lead to an increase in vegetative cover,
particularly where land available for cultivation is scarce in dryland environments
(Mortimore, 1998). As land holdings are generally not extensive in the region and
nearly all agriculture is rain-fed, it is very common to find small agricultural plots
mixed with natural or anthropogenically modified vegetation within a given area
(Rasmussen, 1996).
To estimate properly the effect that changes in land cover may have on albedo
and rainfall, it is important to quantify relations between these variables. Moreover,
because most studies of land-surface climatology are limited to a few points in
space and time, it is useful to evaluate the seasonal, interannual, and longer-term
variability in land-surface albedo. This is particularly true in areas of highly modified vegetation, cultivation, and human habitation, which are typical of intensive
land uses in the tropics generally and in West Africa in particular (Mortimore,
1998). Several co-occurring factors are likely to influence land surface albedo in
complex ways in this region. Precipitation itself may act by darkening the soil
surface temporarily and by providing soil water for plant growth. Over relatively
long time spans (e.g., a decade or more), positive rainfall anomalies may increase
production of woody canopies, which would enhance shadowing effects (Ringrose
et al., 1992), as well as increasing the amount of canopy litter and humus. Humic substances and soil wetness also are likely to decrease land-surface albedo in
significant ways (Huete, 1989; Irons et al., 1989). Conversely, grazing may have
an important impact on albedo of grasslands and savannas by reducing vegetation cover and height (e.g., NRC, 1992; Xue, 1996). However, previous empirical
(Fuller and Prince, 1996; Fuller, 1998) and modeling studies (e.g., Claussen, 1995;
Lu et al., 2001) show that grasslands are highly responsive to changes in rainfall
and may recover quickly from perturbations, particularly in semi-arid regions.
The most expedient way to obtain multitemporal data on land-surface properties
over large areas such as West Africa is through the use of polar or geostationary
satellites, which measure shortwave and longwave fluxes to space. While recent
work on satellite-based albedo estimation has emphasized the development of
improved albedo products (Toll et al., 1997; Lucht et al., 2000; Strugnell et al.,
2001), few studies have evaluated the multitemporal nature of land-surface albedo
and its relation to different variables associated with land cover and land use. In
this study, we use time-series data from the polar-orbiting NOAA Advanced Very
High Resolution Radiometer (AVHRR) to study seasonal, interannual, and multiyear variations in West African albedo. We selected this region for study because
it contains a wide range of vegetation and climate zones over a relatively small
latitudinal range from 5–20 degrees N (Figure 1). Further, the region has become
a focal point of debates over desertification, deforestation, and climate change as
outlined above. The decade of the 1980s was the driest decade on record for West
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Table I
Gridded data sets used to analyze relationships between albedo, land cover, and rainfall
Data product
Source(s)
Years
represented
Reference
Data type
Albedo
AVHRR
Pathfinder
1982–1990
James and Kalluri (1994)
Continuous
Land cover
AVHRR
Landsat
1992–1993
Hansen et al. (2000)
Categorical
Percent tree cover
AVHRR
Landsat
Air photos
(Senegal only)
1992–1993
DeFries et al. (2000)
Rasmussen (1998)
Continuous
Climate zone
Interpolated
data from
meteorological
stations
1920–1990
UNSO (1997)
Categorical
Rainfall
Rain gauge data
1980–1990
Nicholson et al. (1988)
Continuous
Population
Roads
Slope
Land cover
Nighttime lights
1997
Dobson et al. (2000)
Continuous
Africa (Nicholson, 1993). Therefore, we expected to find drought-related increases
in albedo through the period. We analyze temporal variability of albedo in light
of rainfall data as well as several new land cover data sets (Table I) including tree
cover, land cover type, and population to determine which independent environmental variables may drive observed albedo changes at a variety of time scales. As
land cover type, tree cover, and human population are all influenced to some extent
by precipitation in the region, we expect some interaction among these independent variables. Following previous studies of land-surface-climate feedbacks cited
above, our hypothesis was that increased vegetation cover, rainfall, and intensification of land use, as measured by population density, should be associated with
decreases in land-surface albedo in the study area.
Figure 1. Map of the study area showing the location of sites used to analyze albedo and rainfall time series. The length of the series indicates the number
of continuous months for which total monthly rainfall was reported. Rainfall data were compiled by S. E. Nicholson et al. (1988) and are available through
http://carpe.umd.edu/. Stations were selected to represent a range of climatic zones found in West Africa (see text for more details).
LAND COVER, RAINFALL AND LAND-SURFACE ALBEDO IN WEST AFRICA
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DOUGLAS O. FULLER AND CHRISTIAN OTTKE
2. Materials and Methods
To derive a land surface albedo data set for West Africa we utilized the AVHRR
Pathfinder image archive for the period 1981–1994, which NASA and NOAA produce as imagery mapped at 8-km spatial resolution (James and Kalluri, 1994).
These data consist of five channels from three polar-orbiting satellites – NOAA 7
(August 1981 – December 1984), NOAA 9 (January 1985 – October 1988), and
NOAA 11 (November 1988 – December 1994). The Pathfinder data are improved
in several respects over previous AVHRR time-series products in that the data
processing included enhanced navigation, inter-satellite calibration, and partial
correction for Rayleigh scattering (Kaufmann et al., 2000). We utilized AVHRR
Pathfinder channel 1 (0.58–0.68 µm) and channel 2 (0.72–1.1 µm) data, which
were processed as monthly composites according to the maximum value of the
Normalized Difference Vegetation Index (NDVI) rule described by Holben (1986).
Despite compositing and compensation for changes in solar zenith angle (SZA),
sensor drift, and atmospheric conditions, channels 1 and 2 retain several anomalies
related to aerosol loading. In particular, stratospheric aerosols from the eruption of
Mount Pinatubo in June 1991 produced a notable increase in channel 1 reflectance,
which lasted throughout much of 1992 within the NOAA 11 period. Moreover,
positive trends in channel 1 and 2 reflectance from 1985–1988 have been noted
and are most likely due to the changes in SZA over the NOAA 9 period (Gutman,
1999). Nevertheless, recent time series analysis of AVHRR channels 1 and 2 in the
Pathfinder data indicated stationarity for most biomes (Kaufmann et al., 2000).
There are several ways to estimate broadband albedo from narrow-band reflectance data from AVHRR channels 1 and 2. The most common approach is
to express broadband albedo as a linear combination of visible and near infrared
reflectances (Toll et al., 1997; Csiszar and Gutman, 1999). In our study, we used
coefficients developed by Wydick et al. (1987), who compared AVHRR shortwave
channels with Earth Radiation Budget Experiment (ERBE) data from the Nimbus-7
satellite to produce a narrow-to-broad (NTB) formula:
α = 0.347(Ch. 1) + 0.650(Ch. 2) + 0.0746
(1)
where α is broad-band albedo in percent and Ch. 1 and Ch. 2 are the reflectance values in AVHRR channels 1 and 2 respectively. Equation 1 resulted from a regression
involving NOAA-7 AVHRR and the Nimbus-7 ERB whose viewing conditions and
scene types were matched as closely as possible (Taylor, 1990). Broadband albedo
values produced from this linear formula have been applied successfully in several algorithms to identify burned area in AVHRR imagery (Barbosa et al., 1998;
Fuller and Fulk, 2001). Csiszar and Gutman (1999) recently evaluated several NTB
techniques and they concluded that ERBE-based approaches compared favorably
to other methods although the broad field of view of the ERBE (2.5◦ × 2.5◦ ) may
include residual cloud cover that increases the retrieved albedo values relative to
higher spatial resolution data of the AVHRR (1.1 km at nadir). Although more so-
LAND COVER, RAINFALL AND LAND-SURFACE ALBEDO IN WEST AFRICA
187
phisticated AVHRR-based albedo retrieval algorithms are now becoming available
(e.g., Strugnell et al., 2001), these may not be applicable to the time period we are
investigating for which we have the most continuous, monthly rainfall data.
We obtained our rainfall data through Central African Regional Program for
the Environment data archive (http://carpe.umd.edu/). These data originate from
the work of S. Nicholson, of Florida State University, who compiled raingauge
measurements from several hundred stations in Sub-Saharan Africa from 1980–
1990 (Nicholson et al., 1988). Because the rainfall record (1980–1990) did not
coincide completely with the AVHRR Pathfinder records (1981–1994) we did not
extend our analysis of albedo beyond 1990. In addition, we avoided analysis of
albedo after 1990 due to the effect of aerosols from the Mount Pinatubo, which
could confound time series analysis of the Pathfinder data.
The station locations in West Africa were used as a template for the extraction of
albedo time series data, which was done using Idrisi GIS software (Eastman, 1999).
Initial data extraction yielded albedo time series for 173 sites across West Africa.
To ensure broad geographic sampling, we used a climate zonation map of Africa
that contains six major classes ranging from hyperarid to humid (UNSO, 1997).
The UNSO classes are based on the ratio of mean annual precipitation to evapotranspiration (Table II). Of the 173 meteorological station sites that fell within our study
window (Figure 1), we excluded those in the hyperarid class since we assumed that
land cover change is negligible in the Saharan belt of Northwest Africa. We also
removed stations with short or highly fragmented records (i.e., < 36 continuous
months) so that cross correlations between rainfall and albedo could be calculated
over a range of ± 12 lags. Moreover, due to positional uncertainties we eliminated
sites along the coastline to avoid sampling of extensive water surfaces. The resultant subset of 43 time series is shown in Figure 1 and their climate zone, land cover,
and mean rainfall are listed in Table III. Data on land cover were obtained from
an archive maintained by the University of Maryland http://glcf.umiacs.umd.edu),
which were produced at 1-km spatial resolution (Hansen et al., 2000). Note that
only one of the 43 sites (Kumasi, Ghana) fell within the ‘Urban/built-up’ category
thus most sites probably represent a mixture of natural and modified vegetation as
well as habitation typical of the region. Similarly, data on percent tree cover were
obtained from the same archive, which are described by DeFries et al. (2000). In
the discussion below, land cover and tree cover data sets are referred to as the
‘Hansen’ and ‘DeFries’ data sets respectively. Finally, to evaluate the effect that
anthropogenic influence may have on albedo, we utilized a new gridded global
population data set based on a conflation of diverse input variables in a geographic
information system (Dobson et al., 2000).
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Table II
Aridity zones and corresponding ratios of mean annual precipitation to mean annual potential evapotranspiration for each zone as
defined in UNSO (1997)
Aridity zone
Ratio of mean annual precipitation to
mean annual potential evapotranspiration
Arid
Semi-arid
Dry sub-humid
Moist sub-humid
Humid
0.05–0.2 (5–20%)
0.2–0.5 (20–50%)
0.5–0.65 (50–65%)
0.65–1 (65–100%)
> 1 (> 100%)
3. Results and Discussion
3.1. LAND COVER , POPULATION , AND TREE COVER IN THE REGION
The land cover, population, and tree cover data sets (Table I) provide several insights on the nature of the anthropogenic influences in West Africa. Nine of the
fourteen global land cover classes in the Hansen classification are found in the
study region. If we exclude the “bare” category, the most common land cover type
in West Africa is wooded grassland (WG), which occupies about 38 percent of
the region followed by open shrubland (OS), which occupies another 16.3 percent
(Figure 2). Only about 10 percent of the region is covered by evergreen broadleaved (EGB) vegetation, although studies suggest that more extensive tropical
moist forest cover existed across the humid belt in Guinea, Sierra Leone, Liberia,
Ghana, Cote d’Ivoire, and Nigeria prior to the 20th century (Fairhead and Leach,
1998). Closed shrubland (CS) (10.1 percent), woodland (WL) (8.27 percent), grassland (GL) (8.79 percent) and cropland (CL) (8.51 percent) are the next most
common land cover types. The remaining classes, deciduous broad-leaved (DBL)
and urban/built-up (UB) cover less than one percent of the study region.
Combining the Hansen and DeFries data sets, we find that the mean tree cover
per class is modest, ranging from approximately 70 percent in the EBL class to
about 5 percent in the OS class (Figure 3). The error bars in Figure 3 show the
standard deviation of the mean per class, which indicate that there is significant
class overlap with respect to percent tree cover. Further, the CL and UB classes,
which are presumably the most intensively used land areas in West Africa, possess
higher mean tree cover than the CS and OS classes. These latter two classes are
typically associated with pastoral land use, which is generally considered to be
less intensive than either UB or CL (Mortimore, 1998).
LAND COVER, RAINFALL AND LAND-SURFACE ALBEDO IN WEST AFRICA
Table III
Sites used to study the relations between albedo, land cover, and rainfall. Climate
zonation from UNSO (1997), land cover from Hansen et al. (2000), and mean rainfall
from Nicholson et al. (1988)
Site
Climate zone
Land cover
Mean annual
rainfall (mm)
Agadez, Niger
Dagana, Senegal
Gao, Mali
Hombori, Mali
Louga, Senegal
Nguigmi, Niger
Arid
Open shrubland
Open shrubland
Open shrubland
Open shrubland
Open shrubland
Bare
93
89
140
229
191
163
Diourbel, Senegal
Gaya, Niger
Kano, Nigeria
Katsina, Nigeria
Koutiala, Mali
Linguere, Senegal
Maradi, Niger
Mopti, Mali
Segou, Mali
Sokoto, Nigeria
Tambacounda, Senegal
Tivaouane, Senegal
Zinder, Niger
Semi-arid
Open shrubland
Wooded grassland
Closed shrubland
Closed shrubland
Closed shrubland
Open shrubland
Grassland
Open shrubland
Open shrubland
Closed shrubland
Wooded grassland
Closed shrubland
Open shrubland
361
761
646
480
761
360
414
388
317
546
523
309
372
Bauchi, Nigeria
Bobo-Dioulasso, Burkina Faso
Garoua, Cameroon
Kaduna, Nigeria
Kandi, Benin
Mango, Togo
Sikasso, Mali
Velingara, Senegal
Wa, Ghana
Zaria, Nigeria
Dry sub-humid
Closed shrubland
Wooded grassland
Wooded grassland
Closed shrubland
Cropland
Wooded grassland
Wooded grassland
Wooded grassland
Wooded grassland
Grassland
843
952
754
1138
884
1101
1047
826
847
935
Atakpame, Togo
Ilorin, Nigeria
Jos, Nigeria
Lokoja, Nigeria
Minna, Nigeria
Oshogbo, Nigeria
Sedihou, Senegal
Wenchi, Ghana
Moist sub-humid
Wooded grassland
Cropland
Closed shrubland
Grassland
Closed shrubland
Cropland
Woodland
Wooded grassland
1323
1166
1219
1140
1124
1320
882
1306
Bo, Sierra Leone
Enugu, Nigeria
Kabala, Sierra Leone
Kumasi, Ghana
Ondo, Nigeria
Warri, Nigeria
Humid
Cropland
Grassland
Wooded grassland
Urban/builtup
Cropland
Cropland
2387
1525
1968
1406
1639
2806
189
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DOUGLAS O. FULLER AND CHRISTIAN OTTKE
Figure 2. Proportion of the study area occupied by the different land cover types found in the Hansen
et al. (2000) classification determined with GIS software.
Figure 3. Percent tree cover determined by overlay of the DeFries et al. (2000) tree cover data set
with the Hansen et al. (2000) land cover classification. Means of percent tree cover for each class are
given as are error bars, which show the standard deviation of the mean for each type.
LAND COVER, RAINFALL AND LAND-SURFACE ALBEDO IN WEST AFRICA
191
Figure 4. Population density obtained from Dobson et al. (2000) plotted against percent tree cover
from DeFries et al. (2000) for different land-cover types found in West Africa. Population density
is given as the log10 value and is used to infer anthropogenic influence on the land cover zones and
sites studied. EBL = evergreen broad-leaved, DBL = deciduous broad-leaved, WL = woodland, WG
= wooded grassland, OS = open shrubland, CS = closed shrubland, CL = cropland, GL = grassland,
UB = urban/built-up.
To assess how population density may affect tree cover we evaluated the relationship between these variables (Dobson et al., 2000), by showing where the
Hansen classes fall within a tree cover-anthropogenic space (Figure 4). Figure 4
reveals that five classes (EBL, DBL, WL, WG, and OS) fall within a narrow range
of low population densities, but that these classes vary greatly in their mean tree
cover (also shown in Figure 3). The CS, CL, and GL classes are intermediate in
their population densities, whereas the UB population density is approximately an
order of magnitude greater than CS, CL, and GL. Figure 4 shows that despite high
population density, mean tree cover in urban areas does not differ greatly from CS,
CL, and GL classes. This finding is consistent with Mortimore’s analysis (1998),
which showed that increasing levels of land-use intensity and population may lead
to increased or stable woody vegetation cover in tropical drylands of Africa.
Normally standing crop and primary production are related positively to rainfall.
However, utilizing the data from 43 sites (Table III), Figure 5 reveals that the relationship between percent tree cover and rainfall, while linear over the range from
0–800 mm, is highly variable in areas where mean annual precipitation exceeds
this upper limit. Presumably, this is because land use options (e.g., cropping type
and pattern) increase in more humid environments such that woody biomass and
land are allocated efficiently to met the needs of adjacent human populations.
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DOUGLAS O. FULLER AND CHRISTIAN OTTKE
Figure 5. Mean annual precipitation from Nicholson et al. (1988) versus percent tree cover from
DeFries et al. (2000). Each point represents one of the study locations shown in Figure 1 (See Figure 4
for abbreviations.). Note that the scatter in the relationship between tree cover and precipitation
increases significantly beyond 800 mm annual rainfall.
Figure 6. Time series of albedo derived from the NOAA AVHRR Pathfinder data set for the years
1982–1989. Each series shows monthly mean values for each series averaged by climate zone.
LAND COVER, RAINFALL AND LAND-SURFACE ALBEDO IN WEST AFRICA
193
3.2. ALBEDO OF DIFFERENT CLIMATIC AND LAND COVER TYPES
Figure 6 shows the time series of monthly albedo averaged by climate zone. This
figure reveals a substantial difference in albedo between arid and semi-arid zones,
but significant overlap in values between the remaining climate zones. In addition,
all series in Figure 6 reveal a weak, periodic pattern consistent with the seasonality
of rainfall in the region (Nicholson et al., 1988). We expect that rainfall produced
both an increase in canopy greenness (Justice et al., 1986) as well as wetting of
the soil surface (Allen et al., 1994). These two factors probably combine to reduce
the land-surface albedo each wet season (defined here generally one or more consecutive months where total rainfall exceeds 50 mm). Generally, the wet period
may vary from approximately 1–2 months in the arid and semi-arid areas (August–
September) to nine months in humid sites, such as Bo, Sierra Leone (Figure 1 and
Table III). During dry periods albedo may increase substantially as seen by the high
values displayed for arid sites in Figure 6. Albedo values for bright desert environments are generally reported to range between approximately 35–45 percent (Oke,
1987), although Figure 6 reveals fluctuations within and between years ranging
approximately from 40-60 percent. Several factors may explain the relatively high
values observed over arid sites. First, non-Lambertian effects are likely especially
during periods of high solar zenith angle (Csiszar and Gutman, 1999). These effects
are more pronounced for bright surfaces (Strugnell et al., 2001) such as those found
in the sandy soils of the semi-arid Sahel region. The aforementioned NTB conversion may be influenced by the inclusion of clouds as well as reflective aerosols,
which are present in the extensive dust plumes that cover parts of the region during
the dry season (N’Tchayi Mbourou et al., 1997). Data published by Nicholson et
al. (1998) indicated an increase in dust occurrence at Gao (Table II) coincident
with negative rainfall anomalies during the period of 1982–1987. We note a slight
positive trend in albedo for arid and semi-arid sites (Figure 6) consistent with this
finding as well as with drought throughout the 1980s.
Notwithstanding probable atmospheric and bi-directional effects, Nicholson et
al. (1998) reported similar albedo values for sites in West Africa based on Meteosat data. In addition, using atmospheric correction and typical bi-directional
reflectance (BDRF) shape parameters applied to AVHRR data, Strugnell et al.
(2001) reported mean albedo values for open shrublands of 35 percent in Africa
north of the equator. Their data suggest that West Africa has some of the highest
measured albedo of snow-free locations on earth. Figures 7 and 8 reveal the mean
annual albedo, mean range, and coefficient of variation summarized by climate
zone and land cover type respectively. The latter two statistics represent the intraannual variation (seasonality) and the inter-annual variation by climate zone and
land cover type. Figure 7 provides a summary of the data shown in Figure 6 and
reveals that intra-annual variation (i.e., the mean range) was larger than interannual variation calculated using the standard deviation of the annual means for
each location.
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DOUGLAS O. FULLER AND CHRISTIAN OTTKE
Figure 7. Albedo statistics, including the interannual coefficient of variation, mean annual albedo,
and mean range. Data summarized using digital, georeferenced climate zones provided by UNSO
(1997). Error bars show the standard deviation for each of the five climate zones covered.
The variation between wet and dry season albedo shown in Figures 7 and 8 is
somewhat greater than that reported in previous studies. For example, in arid to
dry sub-humid locations, Nicholson et al. (1998) reported an intra-seasonal range
of 4–6 percent, which agrees generally with field data summarized by Allen et al.
(1994). The higher seasonality found in our study suggests that BRDF, aerosols,
and residual clouds are not well accounted for in either the Pathfinder data or the
Wydick NTB conversion (Equation 1). Inter-annual variability reported in Figure 7
was relatively low, ranging from 3–8 percent semi-arid to arid climate zones, which
is comparable to values cited by other authors such as Nicholson et al. (1998)
and Courel et al. (1984). Moreover, in a separate analysis, Ottke (2001) found
no statistically significant linear trends in the monthly albedo data for 1982-1992
except in a few scattered locations.
An interesting finding shown in Figure 8 is the relatively high interannual
variability obtained for grassland sites. Presumably this is because the albedo of
grass-dominated sites is more sensitive to rainfall, which controls green leaf area
and production of shallow-rooted growth forms in areas of seasonal water deficit
(Fuller and Prince, 1996). Another interesting finding is the low mean annual
LAND COVER, RAINFALL AND LAND-SURFACE ALBEDO IN WEST AFRICA
195
Figure 8. Albedo statistics from monthly time series data summarized by land cover type. Land-cover
data from Hansen et al. (2000). Error bars show the standard deviation for each of the five climate
zones covered.
albedo (22.9 percent) for CL sites. Note that cropland sites tend to be in more
humid zones, which generally can support rain-fed agriculture at higher levels
of production than arid and semi-arid zones. Moreover, the low mean for sites
classified as cropland and urban/built-up (24.2 percent) suggests that the more
intensively land is used in West Africa the lower its albedo. Several factors may
explain the darkening of the surface in urban and cropped environments including
shadowing caused by buildings and the use of dark building materials (e.g., mud),
use of animal manure to fertilize soil, and burning of agricultural residue and natural vegetation to improve forage and soil fertility. Burning is an important factor
in West Africa (Kennedy et al., 1994) that likely influences the annual minimum
albedo value observed in time series of AVHRR composites (Barbosa et al., 1998;
Fuller and Fulk, 2001).
3.3. RAINFALL - ALBEDO RELATIONS AT MULTIPLE TEMPORAL SCALES
To further understand what may be controlling variation in albedo time series
(independent of BRDF and the atmosphere), we calculated the cross correlation
coefficient (ρ) between monthly rainfall and albedo for each site. In each cal-
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Figure 9. Cross-correlation coefficients (ρ) plotted against lag for the sites shown in Figure 1 indicating the relationship between total rainfall and albedo at monthly time scales. Cross correlations
were calculated for ± 12 lags without seasonal detrending.
culation of ρ, rainfall was lagged by albedo as the former was assumed to be
driving a portion of the albedo variation through time. Table IV gives the correlation values at zero lag, ρ(0) (the Pearson correlation coefficient), the value
of the maximum (positive or negative) correlation and the corresponding lag in
months of the maximum correlation. The absolute values of ρ(0) were generally
low as were the maximum values, which ranged typically from 0.35 to 0.60 for
most sites. The sign of ρ varied within and between land cover types and climatic
zones (Table IV). Figure 9 reveals two broad types of albedo behavior with respect
to monthly rainfall. With the exception of several outliers shown in this figure,
ρ was moderately positive (0.4–0.6) at lags from –5 - +1 or moderately negative
(−0.6 – −0.4) at lags from 0 – +4. This result suggests that rainfall exerts a modest
influence on albedo 3–5 months in the future, but that this influence is not likely to
be greater than that of the previous year’s precipitation (negative lag).
Compared with cross-correlation coefficients reported from NDVI-rainfall
times series from Southern Africa (Fuller and Prince, 1996), which revealed maximum cross correlations of around 0.80 and 0.60 for raw and seasonally detrended
series respectively, these data suggest that rainfall explains less than a third of the
albedo variation at the monthly time scale. We also examined the zero-lag relationship between mean annual albedo and total annual rainfall for 19 sites with
continuous rainfall-albedo time series of 61-96 months and found low correlations
at the annual scale (not shown). Overall, this suggests that rainfall is a poor predic-
LAND COVER, RAINFALL AND LAND-SURFACE ALBEDO IN WEST AFRICA
197
Table IV
Cross-correlation coefficients for sites shown in Table III. Zero lag correlation is the Pearson
product moment correlation. Maximum lag correlation and the corresponding lag number are
also reported as are land cover (Hansen et al, 2000) and climate zone (UNSO, 1997) for each
site.
Site
Zero
lag
correlation
Maximum
lag
corelation
Lag #
Land cover
Climatic zone
Agadez, Niger
Dagana, Senegal
Gao, Maili
Hombori, Mali
Louga, Senegal
Nguigni, Niger
−0.466
−0.273
0.169
−0.199
−0.057
−0.101
−0.466
−0.400
−0.360
−0.501
−0.471
−0.305
0
3
4
1
3
−4
Open shrubland
Open shrubland
Open shrubland
Open shrubland
Open shrubland
Bare
Arid
Diourbel, Senegal
Gaya, Niger
Kano, Nigeria
Katsina, Nigeria
Koutiala, Mali
Linguere, Senegal
Maradi, Niger
Mopti, Mali
Segou, Mali
Sokoto, Nigeria
Tambacounda, Senegal
Tivaouane, Senegal
Zinder, Niger
−0.207
−0.154
0.083
−0.266
−0.204
−0.320
−0.200
−0.102
−0.207
−0.242
0.160
0.154
−0.272
−0.616
−0.395
0.484
0.561
0.316
−0.603
−0.356
−0.526
0.453
0.466
0.482
−0.550
−0.410
3
2
−3
−4
−5
1
3
2
−2
−4
3
−8
−4
Open shrubland
Wooded grassland
Closed shrubland
Closed shrubland
Closed shrubland
Open shrubland
Grassland
Open shrubland
Open shrubland
Closed shrubland
Wooded grassland
Closed shrubland
Open shrubland
Semi-arid
Bauchi, Nigeria
Bobo-Dioulasso, BF
Garoua, Cameroon
Kaduna, Nigeria
Kandi, Benin
Mango, Togo
Sikasso, Mali
Velingara, Senegal
Wa, Ghana
Zaria, Nigeria
0.039
0.084
0.068
0.055
0.056
0.231
−0.082
−0.063
0.370
−0.127
−0.475
−0.307
−0.321
−0.429
−0.104
−0.450
0.126
0.358
0.502
0.437
2
−4
4
3
5
3
6
−4
−1
−4
Closed shrubland
Wooded grassland
Wooded grassland
Closed shrubland
Cropland
Wooded grassland
Wooded grassland
Wooded grassland
Wooded grassland
Grassland
Dry sub-humid
Atakpame, Togo
Ilorin, Nigeria
Jos, Nigeria
Lokoja, Nigeria
Minna, Nigeria
Oshogbo, Nigeria
Sedihou, Senegal
Wenchi, Ghana
0.466
0.267
−0.101
0.315
0.139
0.484
−0.523
0.386
0.497
0.336
0.424
−0.371
−0.346
0.484
0.636
0.432
1
−2
9
−7
4
0
1
1
Wooded grassland
Cropland
Closed shrubland
Grassland
Closed shrubland
Cropland
Woodland
Wooded grassland
Moist sub-humid
Bo, Sierra Leone
Enugu, Nigeria
Kabala, Sierra Leone
Kumasi, Ghana
Ondo, Nigeria
Warri, Nigeria
0.357
0.233
0.484
0.417
0.345
−0.372
0.357
0.448
0.484
0.444
−0.358
0.372
0
−1
0
1
1
0
Cropland
Grassland
Wooded grassland
Urban/builtup
Cropland
Cropland
Humid
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DOUGLAS O. FULLER AND CHRISTIAN OTTKE
Figure 10. Mean annual precipitation obtained from Nicholson et al. (1988) plotted against mean
annual albedo given in percent. The mean albedo was calculated as the grand mean of each time
series. A negative exponential function was fitted to the data with an r 2 = 0.805.
tor of monthly albedo even when lags are considered. Although we do not reject
Charney’s hypothesized relationship between albedo and rainfall, it would appear
that short-term fluctuations in albedo or rainfall are not sufficient to induce changes
in either variable. Nicholson et al. (1998) came to a similar conclusion for a series
of sites that she and her colleagues analyzed in the semi-arid Sahelian zone for the
period 1983-1988.
3.4. LONGER - TERM INFLUENCES ON ALBEDO
Low cross correlations at the monthly and annual scale prompted us to investigate
whether long-term average values improve the strength of the relationship. Figure 10 shows the scatter diagram of mean annual rainfall over the time series versus
long-term mean albedo, i.e., the average value of all albedo observations over the
time series. This reveals that long-term mean albedo drops sharply between 0–
500 mm rainfall and that further increases in mean annual rainfall produce little
further change. Figure 10 shows the goodness of fit (r 2 = 0.805) for a negative
exponential function fit to the data. This suggests that albedo may be controlled by
long-term (i.e., decadal scale) variations in climate.
Decadal-scale climatic shifts between dry and wet phases (Nicholson, 1993)
may also affect the rates of recruitment, production, and mortality of trees and
LAND COVER, RAINFALL AND LAND-SURFACE ALBEDO IN WEST AFRICA
199
Figure 11. Relationship between percent tree cover (y-axis) and mean annual albedo given in percent.
The mean albedo was calculated as the grand mean of each time series for which corresponding tree
cover data were available in either DeFries et al. (2000) or Rasmussen (1998). A negative exponential
function was fitted to the data with an r 2 = 0.659.
shrubs, which are long-lived growth forms. Although woody cover in the region
has been extensively modified, some anthropogenic and climatically induced vegetation changes are likely to occur very slowly (Schlesinger and Gramenopoulos,
1996; Fairhead and Leach, 1998; Tappan et al., 2000). Moreover, when climatic shifts do occur, these are likely to affect long-lived growth forms gradually
(Goward and Prince, 1995; Fuller and Prince, 1996). Percent tree cover, therefore,
is an expression in part of long-term climate, rather than short-term (i.e., monthly)
rainfall fluctuations. Figure 11 shows long-term mean annual albedo versus percent
tree cover for 34 or the 43 sites shown in Figure 1 and Table III. It was necessary
to reduce the number of observations in this analysis because the DeFries data
set is continuous over the range from 10–80 percent. However, many of the sites
in our study possessed values below this lower bound. For sites in Senegal we
used a more complete set of gridded data provided by Rasmussen (1998), who
produced continuous tree cover data from an interpolation of 12,000 interpreted air
photographs covering the country. One clear advantage to using finely gridded data
(e.g., 1 km spatial resolution) such as these over biome-level data is that within
biome variation may be evaluated.
Figure 11 reveals that the relationship is very similar to that shown in Figure 10,
although the goodness of fit (r 2 = 0.659) was somewhat lower. Figure 11 also
200
DOUGLAS O. FULLER AND CHRISTIAN OTTKE
shows that mean albedo dropped sharply from 50–25 percent over the range of 0–
15 percent tree cover and changed little with increasing amounts of tree cover. This
sensitivity of albedo to changes in tree cover suggests that tree crowns and their
shadows are likely to influence pixel-level albedo, particularly in the arid and semiarid parts of our study region. Further, it should be borne in mind that according to
the DeFries data set, tree cover is modest (generally below 50 percent) even in the
humid regions of West Africa. We expect therefore that soil and other sub-canopy
constituents in the background (e.g., ash, char, litter, and other debris) may be as
important overall than tree cover in determining mean albedo.
4. Conclusions
Our analyses have shown that albedo and rainfall are related only modestly at short
time scales (monthly and annual) and that albedo values remained relatively stable
over the time period from 1982–1989 over a wide range of climatic and vegetation
zones in West Africa. If a change in albedo of 0.10–0.20 is needed to induce a
biophysical feedback to climate as suggested by numerous GCM scenarios, then
we conclude from our analyses of individual sites that such changes have most
likely not occurred in the region in the recent past. While such changes may have
occurred over several decades since the 1950s (Nicholson et al. 1998), the period
from the 1982–1992 showed little change in albedo. Furthermore, while vegetation
greenness may vary greatly between years as shown by number of studies based on
the semi-arid Sahelian zone (Tucker et al., 1991; Nicholson et al., 1998; Prince et
al., 1998), albedo appears to be more complex and less likely than NDVI to change
abruptly with short-term fluctuations in rainfall. This conclusion is supported by
the relationships between long-term average albedo and rainfall (Figure 10) and
between albedo and tree cover (Figure 11).
Based on previous studies of relations between NDVI and rainfall (e.g., Nicholson et al., 1990; Fuller and Prince, 1996) we expected to find somewhat higher
correlations between rainfall and albedo in our study. Our results suggest that factors other than rainfall influence the temporal behavior of land-surface albedo in
West Africa including land use and woody vegetation cover. Data taken from studies done at Kano, Nigeria (Mortimore, 1998) suggest that as population increases
so, too, does cultivated area, percent tree cover, and use of fertilizers. The relatively
low mean annual albedo found for sites classified as cropland or urban suggests that
land cover change resulting in these forms of intense land uses may ultimately exert
relatively modest influences on climate. However, overall Figures 8 and 11 indicate
that loss of woody vegetation would enhance albedo significantly. This conclusion
stems from the fact that population and tree cover are negatively related (Figure 4)
and that built-up areas in these countries would not tend to decrease albedo relative
to broad-leaved and woodland vegetation.
LAND COVER, RAINFALL AND LAND-SURFACE ALBEDO IN WEST AFRICA
201
Because our study revealed low interannual variability in albedo, it tends to reinforce the conclusions of others who have questioned the extent and pervasiveness
of desertification in West Africa (Helldén, 1991; Thomas and Middleton, 1994;
Nicholson et al., 1998; Prince et al., 1998). However, it should be borne in mind
that land degradation is complex and its definition depends largely on context. For
example, rangeland specialists may consider rain-use efficiency, forage quality, and
animal carrying capacity as much more relevant indicators of land degradation than
albedo per se (Bremen and de Wit, 1983; Le Houerou, 1984). Moreover, owing
to small temporal differences among data sets (Table I), we expected some unexplained variation due to land-cover changes that may have occurred after the
albedo time series that we examined. In addition, our albedo data have shown
utility in studying relative differences between different land cover and climatic
classes, although more accurate albedo data sets are now becoming available for
use in long-term climate simulations (Strugnell et al., 2001). Future coupling of
these data with GCMs and field-based information on land-cover transformation in
Africa (Mortimore, 1998) should lead to improved predictions of how biophysical
changes will affect future climate change in the region.
Acknowledgements
This research was supported by a grant from The George Washington University
Facilitating Fund (UFF). The authors wish to thank three anonymous reviewers for
their constructive comments on the original manuscript.
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