INT. J. REMOTE SENSING,
VOL.
25,
NO.
10
FEBRUARY,
2004,
3, 573–592
Monitoring desertification and land degradation over
sub-Saharan Africa
E. SYMEONAKIS
Departamento de Geografia, Universidad de Valencia, Avenida Blasco Ibáñez
28, Valencia 46010, Spain; e-mail: [email protected]
and N. DRAKE
Department of Geography, King’s College London, Strand, London
WC2R 2LS, UK; e-mail: [email protected]
(Received 25 March 2002; in final form 5 February 2003 )
Abstract. A desertification monitoring system is developed that uses four
indicators derived using continental-scale remotely sensed data: vegetation cover,
rain use efficiency (RUE), surface run-off and soil erosion. These indicators were
calculated on a dekadal time step for 1996. Vegetation cover was estimated using
the Normalized Difference Vegetation Index (NDVI). The estimation of RUE
also employed NDVI and, in addition, rainfall derived from Meteosat cold cloud
duration data. Surface run-off was modelled using the Soil Conservation Service
(SCS) model parametrized using the rainfall estimates, vegetation cover, land
cover, and digital soil maps. Soil erosion, one of the most indicative parameters
of the desertification process, was estimated using a model parametrized by
overland flow, vegetation cover, the digital soil maps and a digital elevation
model (DEM). The four indicators were then combined to highlight the areas
with the greatest degradation susceptibility. The system has potential for nearreal time monitoring and application of the methodology to the remote sensing
data archives would allow both spatial and temporal trends in degradation to be
determined.
1.
Introduction
Desertification has been seen as a major cause of problems in large parts of the
African continent; however, there is a general lack of suitable monitoring methods.
The United Nations Conference on Desertification (UNCOD) held in 1977
provided the first map of desertification hazard. A lack of data meant that the
best that could be done was an ‘educated guess’ at the true extent of the problem
(Thomas and Middleton 1994). Desertification hazard was assessed as moderate,
high or very high through an evaluation of climatic conditions, the inherent
vulnerability of the land and the pressure put upon it by human or animal action.
The assessment was made by a limited number of consultants with experience in
dry lands world-wide (Thomas and Middleton 1994). According to UNEP’s own
words: ‘the map was based on existing geographical data which was not precise
International Journal of Remote Sensing
ISSN 0143-1161 print/ISSN 1366-5901 online # 2004 Taylor & Francis Ltd
http://www.tandf.co.uk/journals
DOI: 10.1080/0143116031000095998
574
E. Symeonakis and N. Drake
enough to assist future action in planning and guiding anti-desertification activities
either at a national or international level’ (UNEP 1990). This study promoted the
recognition that there was a need for improved methods of mapping and
monitoring desertification and for a better understanding of the processes involved.
The flowchart in figure 1 shows the way natural processes – such as drought –
and human activities – such as irrigation, arable agriculture, deforestation and
Figure 1. The causes and development of desertification (modified from Kemp 1994). The
light grey ellipses are those that involve vegetation cover reduction, while the dark
grey ones involve soil erosion. Note that all processes involve vegetation cover
changes and soil erosion.
Monitoring desertification over sub-Saharan Africa
575
urbanization – initiate desertification. The complexity of the processes has so far
precluded the development of a comprehensive model and methods of monitoring
have involved the use of indicators in the attempt to assess and monitor
desertification. Indicators usually describe one or more aspects of desertification
and provide data on threshold levels, status and evolution of relevant physical,
chemical, biological and anthropogenic processes. However, there is a clear
distinction between the indicators that are useful to have and those which are
practical to obtain (Warren and Khogali 1992). This is scale dependent as it is
relatively simple to collect degradation data from individual fields but another
matter to do so for whole regions, countries and continents.
In the 1980s, the importance of using indicators to monitor desertification at
local, regional and global scales was recognized by the Food and Agriculture
Organisation (FAO) and by the United Nations Environment Programme (UNEP),
who developed a Provisional Methodology for Assessment and Mapping of
Desertification consisting of a total of 22 indicators (table 1). Grunblatt et al. (1992)
evaluated the methodology at a regional scale through a pilot project carried out in
the Lake Baringo district in Kenya. They attempted to map all 22 indicators using
field methods and concluded that most of the indicators proposed by UNEP could
only be used at the local scale, because the costs at the regional scale would be
prohibitive and the process of data collection time-consuming. They concluded that
the use of remotely sensed data was necessary to monitor desertification over large
areas. Thus, remote sensing-derived indicators provide an attractive way to map
and monitor the desertification processes.
Though there has since been some notable studies that have developed methods
for deriving individual indicators of desertification from remotely sensed data (e.g.
Prince et al. 1998), studies that have used more than one indicator have tended to
persevere with approaches based on field sampling and observation. For example,
Sharma (1998) approached the problem by cutting down the number of indicators
needed by only considering hydrological processes of desertification. He attempted
to establish the severity of desertification through such hydrological indicators as
‘reduced area of water bodies’, ‘increased run-off’, ‘decreased infiltration’,
‘accelerated soil erosion and sedimentation’, and ‘deteriorated ground-water
resources’ (table 1). Krugmann (1996) suggested a novel method of defining
grassroots indicators of desertification that are simple and inexpensive and can be
readily defined and implemented by the inhabitants of a region (table 1). These
indicators are much less complex and demanding than those of FAO/UNEP but
they are only applicable to local scales. Rubio and Bochet (1998) used an
alternative approach to define desertification indicators for Europe. They identified
a list of core indicators (table 1) from which study-specific indicators could be
drawn according to a list of criteria, such as reliability, measurability, applicability,
cost-effectiveness, interpretability. This framework can assist in the development
of a desertification assessment and monitoring system but the indicators they
developed were for European desertification problems and are not directly
applicable to the problems found in sub-Saharan Africa.
It is clear that a number of attempts have been made to use indicators in order
to assess the magnitude of the desertification problem, and to provide a baseline for
monitoring, but there is still a lack of a practical and comprehensive long-term
measurement system applicable at a regional scale. Furthermore, it is evident that
FAO/UNEP
Indicators FOR STATUS OF D:
Surface affected by
soluble salts
Rubio and Bochet (1998)
Sharma (1998)
PHYSICAL:
SOIL e.g. run-off rate,
soil loss rate, compaction,
organic matter content,
salinization, acidification
Reduced area of
water bodies
Rainfall variability
cover of perennial plants
Percentage of optimum natural
level of organic matter
FOR RATE OF D:
Increase in salt-affected area
Amount of transported
sand in m3a21
21 21
Soil loss in t ha a
Soil salinization
and alkalinization
Water erosion areas
Water erosion potential
BIOLOGICAL:
Vegetation degradation
Range carrying capacity
Desirable and
undesirable plant species
SOCIO-ECONOMIC:
Growth rate of areas affected
by wind erosion as percent Human settlements
of the total productive land Land use
Increase of areas where
subsoil is exposed or of
surface affected gullies
Sediment deposits in dams
Decline in percent of biomass
Decrease in area of woodland
Run-off
Infiltration
CLIMATE e.g. rainfall amount,
intensity, frequency, variability, Soil erosion
wind speed, temperature,
Sedimentation
evapo-transpiration
Evapo-transpiration
VEGETATION e.g. percentage
cover, density, morphology,
Sequential changes in
root depth, richness, endemism,
depth to groundwater
growth rate, LAI
Groundwater quality
TOPOGRAPHY e.g. slope
angle, length, shape, aspect
SOCIO-ECONOMICS
e.g. abandonment
of land, conservation practices,
risk of forest fire, unsuitable
practices, emissions, human
density
Tripathy et al.
(1996)
Le Houerou (1984, 1989),
Prince et al. (1998)
SOIL
VEGETATION –
RAINFALL
Soil erosion
Water use efficiency~net
primary productivity/
evapo-transpiration
VEGETATION
Soil moisture
Albedo
NDVI
Rain use efficiency~net
primary productivity/rain
E. Symeonakis and N. Drake
Appearance of sand sheets,
hummocks, nebkas or dunes Wind deposition and
deflection areas
Affect on the surface by rain
Wind
erosion potential
splash, rills and gullies
Crusting and compaction
Reduction of the canopy
Exposure to crusting
and compaction
Indicators of desertification.
Krugmann (1996)
Aridity index
576
Table 1.
Table 1.
Krugmann (1996)
Range cover and range
condition trend line
Rubio and Bochet (1998)
Fuel wood consumption
Nutritional status
FOR INHERENT
RISK OF D:
Migration
Number of dry months
Environmental perception
Average depth of
groundwater table
Salt content in irrigation water
Physiography
Drainage
Soil management practices
Potential soil loss
FOR HAZARD OF D:
Combined effect of
interactions of status, rate,
inherent risk, animal
and population pressures
Sharma (1998)
Tripathy et al.
(1996)
Le Houerou (1984, 1989);
Prince et al. (1998)
Monitoring desertification over sub-Saharan Africa
FAO/UNEP
(Continued ).
D, desertification.
Adapted from Rubio and Bochet (1998).
577
578
E. Symeonakis and N. Drake
long-term observations from space provide a practical way of implementing a
regional monitoring system in order to derive broadly applicable and cost-effective
indicators of desertification over large areas. Ideally such a monitoring system
needs to provide a comprehensive set of indicators in order to allow evaluation of
the numerous different processes that contribute to desertification. In order to
provide a starting point, we have selected indicators that are of crucial importance
in all aspects of desertification. Figure 1 shows that changes in vegetation cover
characteristics and soil erosion are important indicators of desertification as they
both result from all desertification processes. Thus, methods of estimating these
two indicators were developed. Another two indicators, rain use efficiency (RUE)
and surface run-off, have been estimated as they can be derived using the diverse
array of data needed to estimate soil erosion, but also provide valuable insights
into the status of desertification. In the future the aim is to add more indicators
to the system.
The indicators are tested over sub-Saharan Africa for 1996 on a ten-daily
temporal and a 0.1‡ spatial resolution using remotely sensed data to derive
parameters that change over short time-scales (i.e. rainfall, net primary production
(NPP) and vegetation cover), and ancillary Geographical Information System (GIS)
data for those parameters that can be considered invariant over the short term
(i.e. soil properties, land use and slope). All four indicators are then combined in
order to identify areas that are defined as suffering intense desertification by all
four indicators. These areas are prime targets for field surveys in order to verify
results and consider alleviation measures.
2.
Implementation and evaluation of indicators
The following sections introduce the potential of each indicator, provide an
outline of how they were derived and assess their utility for regional land
degradation monitoring.
2.1. Estimation of vegetation cover
The flow diagram in figure 1 shows the control of vegetation cover on all the
processes of erosion and desertification. It is, therefore, an important indicator that
needs to be considered when attempting to measure the extent of desertification.
Vegetation cover was estimated with the use of Advanced Very High Resolution
Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) data
extracted from a continental database generated by the National Aeronautics and
Space Administration (NASA) Global Inventory Monitoring and Modelling
Studies (GIMMS) group (Los et al. 1994; http://edcintl.cr.usgs.gov/ftp2/ndvi).
The raw NDVI data contained numerous blank areas where there was cloud cover
for the whole of the ten-day period. The method of averaging (Gutman et al. 1994)
was used to fill in the cloud-covered areas with the average values from the
preceding and following dekads.
A number of techniques have been used in the literature to determine vegetation
cover from NDVI, such as the scaled NDVI or N*:
NDVI{NDVI0
ð1Þ
N ~
NDVIs {NDVI0
where NDVIs is the value of NDVI at 100% vegetation cover (N*~1.0) and NDVI0
Monitoring desertification over sub-Saharan Africa
579
is that value for bare soil (N*~0) (Choudhury et al. 1994, Carlson et al. 1995).
Other authors have used a linear relationship between green vegetation cover (Vc)
and NDVI by measuring vegetation cover in the field and comparing it to satellite
NDVI measurements. For estimating vegetation cover, a regression relationship,
calculated by Zhang (1999) using only the AVHRR-derived NDVI and the
vegetation cover information, was applied in this research:
Vc ~1:333z131:877 NDVI
ð2Þ
with fit statistics of R2~0.73, F~241.63 and p~4.26610227. Figure 2 shows the
initial scaled NDVI image with the cloud contamination shown in white
(figure 2(a)), the respective NDVI image with the clouds removed (figure 2(b))
and the percentage vegetation cover image (figure 2(c)) for the first dekad of March
1996.
Although a useful indicator of desertification, estimates of vegetation cover
should be treated with caution since it is not yet clear how best to exploit the
information with regard to desertification. A reduction in vegetation cover alone
does not necessarily indicate desertification since cover also fluctuates between and
within years according to factors such as phenology, natural variations in rainfall
and cropping that are unrelated to the desertification process. Rain use efficiency,
Figure 2. An example of the processing of NDVI to vegetation cover for the 1st dekad of
March 1996: (a) scaled NDVI image (0–255) with cloud cover; (b) scaled NDVI
image (0–255) with cloud removed; (c) vegetation cover (%) image.
580
E. Symeonakis and N. Drake
the indicator described in the next section, overcomes these problems as it combines
information on vegetation productivity and the amount of rainfall received.
2.2. Rain use efficiency
Rain use efficiency (RUE) is the ratio of NPP to precipitation. It is calculated
on a yearly time-step and thus does not suffer from short-term fluctuations. RUE
tends to decrease when aridity and potential evapotranspiration increase. However,
all other conditions remaining equal it has also been shown that RUE is lower in
degraded arid lands than in equivalent un-degraded areas (Le Houérou 1984, 1989,
Tyson 1996). Thus, negative deviations from the conservative value of RUE have
the potential to provide a useful index of desertification independent of the rainfall
(Prince et al. 1998). The indicator is an attractive one as desertification is a
phenomenon with a time-scale longer than a year, yet precipitation and NPP in
semi-arid and arid areas typically vary strongly both within and between years.
Since in these areas NPP is usually more strongly affected by rainfall than any other
factor, including degradation, NPP values alone may not serve as an indicator of
desertification without taking the rainfall into account (Prince et al. 1998). Hence,
RUE is a useful indicator of degradation because it relates an important aspect of
the function of arid lands (NPP) to the principal controlling factor.
To derive estimates of NPP the NDVI dekadal archive, described in § 2.1, was
used to derive the annual integral of NDVI for 1996 (figure 3(a)). NPP was then
calculated using a simple regression model calibrated by Prince et al. (1998) with
data for peak, above-ground, standing crop measured in field sites in Sénégal, Mali
and Niger between 1984 and 1988:
X
NDVI{3:852
ð3Þ
NPP Mg ha{1 a{1 ~3:139
n~239, r~0.684, pv0.001
RUE (figure 3(d )) was derived by dividing NPP (figure 3(b)) by rainfall
(figure 3(c)). Rainfall was provided by the United States Agency for International
Development (USAID), Famine Early Warning System (FEWS) (Herman et al.
1997; http://edcintl.cr.usgs.gov/adds/adds.html). To estimate rainfall, FEWS uses a
preliminary estimate of precipitation based on the Geostationary Operational
Environmental Satellite (GOES) Precipitation Index (GPI) (Arkin and Meisner
1987). The GPI uses the duration of cold cloud tops over a region for the
determination of precipitation by assigning 3 mm of precipitation for each hour that
cloud top temperatures are measured to be less than 235K. The GPI estimate is
corrected using Global Telecommunications System (GTS) rain gauge data thus
producing an estimate of convective precipitation. For the regions over which
precipitation is due to orographic lifting and the clouds are relatively warm the
rainfall rate is estimated using a process which combines the relative humidity, wind
direction and the terrain slope. Wind vectors and relative humidity from the
analyses of the 1‡ horizontal resolution Environmental Modelling Centre (EMC)
Global Data Assimilation System (GDAS). Therefore, the FEWS technique
incorporates rainfall from both the convective and stratiform cloud types,
producing a final estimate of total precipitation (Herman et al. 1997).
As anticipated, there appears to be a strong relationship between average
annual rainfall (figure 3(c)) and NPP throughout sub-Saharan Africa: areas that
received the highest amounts of rainfall also scored the highest figures of NPP
Monitoring desertification over sub-Saharan Africa
(a)
581
(b)
(c)
(d )
Figure 3. Flow diagram illustrating the calculation of rain use efficiency (RUE) with the
different parameter maps inset: (a) mean annual NDVI; (b) net primary production (kg mm21); (c) precipitation (mm); and (d ) RUE (kg DM ha21 mm21, where DM
is dry matter) for 1996.
582
E. Symeonakis and N. Drake
and vice versa. The majority of the estimated RUE values in figure 3(d ) are between
1 kg of dry matter (DM) ha21 a21 mm21 and 8 kg of dry matter ha21 a21 mm21 but
there seems to be a trend towards higher RUE in very dry areas of Mali, Chad,
Sudan, Namibia and Somalia. The white ‘no data’ area on the border between
Chad and Sudan in figure 3(d ) is the outcome of the division of NPP values
(figure 3(b)) with zero precipitation (figure 3(c)). This may be due to technical
problems with the use of NDVI as it can overestimate very low productivities.
In general, though, the results are in good agreement with measurements from
field plots in various African locations (table 2), according to which RUE values
Table 2.
Location
Annual rainfall, annual primary above-ground production and RUE in some arid
and semi-arid areas of Africa.
Type
of
study
Senegal
E
Senegal
E
Senegal
E
South Africa
S
Senegal
–
Chad
E/S
Chad
E/S
Chad
E/S
Chad
E/S
Upper Volta,
E
Sahel
Tanzania,
E
Serengeti
Mali,
E
South Sahel
Mali,
E
South Sahel
Mali,
E
South Sahel
Mali,
E
South Sahel
Sudano-Sahelian E/S/Sy
zone
Sudano-Sahelian E/S/Sy
zone
Senegal
E
Tanzania
E
E
South Africa,
Pretoria
Kenya
E/S
Zimbabwe,
E
Matopo
Tanzania,
–
Serengeti
Sahel
Estim.
Mean annual
primary
production
RUE
(kg DM ha21 a21) (kg DM ha21 a21 mm21)
Duration
of years
Mean
annual
rainfall
(mm)
7
7
7
Lt
–
–
–
–
–
4
247
247
247
250
300
320
320
320
320
369
573
1677
2902
1200
420
420
1200
1380
3180
955
2.32
6.79
22.75
4.80
1.40
1.31
3.75
4.31
9.94
2.59
2
400
1600
4.00
4
449
1875
4.18
4
449
1975
4.40
4
449
5000
11.13
4
449
11000
24.49
20
500
1350
2.70
20
500
18000
3.70
10
2
–
500
600
607
1650
7000
900
3.30
11.62
1.48
6
–
628
650
3096
1380
3.0–9.6
2.12
–
700
5220
7.45
9
100–800
NPP: 60–3500
0–10
E, experimental, S, survey, Lt, long-term survey, Sy, synthesis.
After Le Houérou (1984).
Monitoring desertification over sub-Saharan Africa
583
are usually of the order of 1.7–8.0 kg DM ha21 a21 mm21 in 350 mm annual mean
rainfall zone, 1.0–4.0 kg DM ha21 a21 mm21 in the transition between the Sahara
and the Sahel and 1.6–6.0 kg DM ha21 a21 mm21 in the Sudano-Sahelian transition
(Le Houérou 1989). The biological limit is probably reached with RUE of about
30.0 as suggested from small plot experiments under ideal conditions of seasonal
rain distribution and soil fertility (Van Keulen 1975).
In general, the RUE values of this research for 1996 seem to be slightly lower
than those reported by Prince et al. (1998) who estimated RUE over the Sahel
from 1982 to 1990 using rain gauge measurements and satellite NDVI data. This
reduction could indicate desertification; however, it has to be stressed that it might
simply be due to the fact that two methodologies utilize different precipitation
data. Prince et al. (1998) used rain gauges while we used satellite estimates. The
latter data are not flawless, underestimating the higher and overestimating the
lower values (Symeonakis 2001, Symeonakis et al. 2000). Nevertheless, this study
provides a method of calculating RUE that can be readily implemented on an
operational basis using the seven-year long archive of the FEWS ten-daily
precipitation estimates and the 19-year long dekadal NDVI archive of GIMMS. In
addition, the methodology presented here produces RUE estimates for the entire
sub-Saharan Africa and with a much finer spatial resolution (0.1‡) than the Prince
et al. (1998) methodology, which produces results for a limited number of pixels
and with a resolution of 0.5‡.
2.3. Overland flow
There is a well-established tendency for water run-off to increase with land
degradation. Overgrazing, for example, leads to trampling and compaction of the
soil which reduces the infiltration and thus increases the amount of water that
leaves as run-off. Deforestation also leads to increased overland flow since it
removes the vegetation which probably affects rates of run-off more than any other
single factor. The rate of run-off is therefore a useful indicator of the desertification
process and was estimated in the present study with the use of the Soil
Conservation Service (SCS) model.
The Soil Conservation Service (1972) developed a simple empirical method
known as the SCS Run-off Curve Number model for computing abstractions from
storm rainfall. The model was developed by studying overland flow in many small
experimental watersheds and it is one of the most widely used methods to compute
run-off. The basis of the model is that the ratio of overland flow (OF) to effective
storm rainfall (Pe~P2Ia, where P is rainfall and Ia is initial abstraction) is equal to
Fa/S where Fa is the water retained in the watershed and S the potential retention
(SCS 1972). The model is expressed by the following equation:
.
ð4Þ
OFp ~ðPi {Ia Þ2 ðPi z0:8S Þ
where OFp is overland flow in a rainfall event, Pi the rainfall, Ia the initial
abstraction (Ia~0.2S), and S the potential retention. In order to make the SCS
model easier to apply, the Soil Conservation Service expressed the potential
retention, S, in the form of a dimensionless run-off curve number (CN), which can
be extracted from published tables and is dependent on factors such as land cover
and soil texture and, for some land cover types, the percentage vegetation cover
584
E. Symeonakis and N. Drake
(Pilgrim and Cordery 1993, Rawls et al. 1993). CN and S are related by:
S~ð25400=CN Þ{254ðmmÞ
ð5Þ
The International Geosphere Biosphere Project (IGBP) Land Cover Data Base,
available for free on the Internet at http://edcwww.cr.usgs.gov/landdaac/glcc/
tablambert_af.html, was used to extract the necessary land cover information and
the soil textural data of the FAO Soil Map of the World (FAO 1995) was used to
create the map of the SCS soil groups. Vegetation cover was estimated as described
in § 2.1 and FEWS rainfall data were used to parametrize the model (§ 2.2).
However, as the SCS is an event-based model it needs to be modified before it
can be applied to estimate dekadal run-off. Zhang (1999) developed such a
modification. If it is assumed that the daily rainfall amounts approximate an
exponential frequency distribution within each dekad, then Ji, the rain day
frequency density, is:
n {rri
ð6Þ
e o
Ji ~
ro
where n is the number of rain days per dekad, ro is the mean rain intensity per rain
day (mm day21), and ri is the daily rainfall (mm). The rainfall distribution per
dekad in equation (6) was used to adjust the SCS model. Therefore the dekadal
overland flow can be described as (Zhang 1999):
?
ð
ð7Þ
OFi ~ OFp Ji dr
Ia
where OFp is the overland flow in a rainfall event, and OFi is dekadal overland
flow. The OFi can be calculated using an approximate method, that is:
X
DrOFp Ji
ð8Þ
OFi ~
where Dr~ðrimax {Ia Þ=n, ri~IaziDr and supposing rimax ~250 mm per rain day as a
basic simplification. This extension to the SCS model was used in this research to
estimate overland flow on a dekadal time stop over sub-Saharan Africa. The
number of rain days per dekad in equation (6) was estimated by applying indicator
kriging to daily GTS rain gauge data from 527 sub-Saharan stations (Symeonakis
2001, Symeonakis et al. 2000). The output of indicator kriging is a field valued
between 0 and 1, which can be interpreted as the probability of an (x, y) location
being wet. For each dekad and for every location (x, y), the algebraic sum of the
daily outputs of indicator kriging is the sum of the thresholded daily probabilities
(0ƒpƒ1) of that location being wet. Therefore, this dekadal sum is valued from 0
to 10 and can be interpreted as the number of rain days per dekad. Figure 4(a)
shows the mean annual overland flow estimated by summing the dekadal estimates
of the extension of the SCS model.
Although overland flow estimates can be useful to study the potential for land
degradation they need to be interpreted with caution as not all areas that produce
high amounts of run-off are degrading, some are simply receiving very large
amounts of rainfall. Thus the run-off coefficient, computed by dividing the amount
of run-off by the amount of rainfall, might be a more useful indicator of
degradation (figure 4(b)). Areas with higher coefficients are more likely to be
degrading than those with low values as they are producing more run-off per unit
Monitoring desertification over sub-Saharan Africa
585
Figure 4. Maps of yearly overland flow and run-off coefficient. (a) Overland flow (mm)
produced by summing the dekadal estimates of the extended SCS model. (b) Run-off
coefficient. Note that the encircled area in northern Sudan appears to have very high
run-off coefficient values in (b), but relatively low overland flow values in (a), and can
be considered prone to degradation.
of rainfall. For example the encircled area in northern Sudan in figure 4(b) appears
to have very high run-off coefficient values but has relatively low overland flow
values (figure 4(a)) and thus can be considered prone to degradation. In contrast,
586
E. Symeonakis and N. Drake
Madagascar which appears to have high overland flow rates (figure 4(a)) has
relatively low run-off coefficient values (figure 4(b)).
2.4. Soil erosion
Figure 1 clearly depicts the importance of soil erosion in desertification since it
appears to be the end result of all desertification processes. Drought, natural or
human-induced reduction in vegetation cover, poor agricultural practices leading to
soil aggregate breakdown and soil organic matter losses, poor irrigation practices
leading to salinization, all lead to an increase in soil erosion rates and, ultimately,
desertification. Erosion, therefore, seems to be the single most important indicator
of the desertification process. It was estimated on a ten-daily time step with the use
of the Thornes model (Thornes 1985, 1990) – a simple model that combines
sediment detachment and vegetation protection in the following equation:
E~kOF 2 s1:66 e{0:07Vc
ð9Þ
where E is erosion (mm), k is the soil erodibility coefficient, OF is the overland flow
(mm), s is the slope (%), and Vc is the vegetation cover (%). The methodologies for
the estimation of OF, and Vc have been described in § 2.3 and § 2.1 above. For
calculating slope the GTOPO30 global DEM was used. GTPO30 is freely available
at http://edcwww.cr.usgs.gov/landdaac/gtopo30/gtopo30.html, and has a horizontal
grid spacing of approximately 1 km at the Equator. Recently, attention has been
devoted to examining the scaling of slope in global DEMs as it can seriously affect
the accuracy of erosion models at coarse scales (Drake et al. 1999). Zhang et al.
(1999) compared the slopes derived from DEMs at various scales, revealed a
relationship between slope and topographic fractal properties and developed a
method of estimating high resolution slopes from coarse-scale DEMs by using
fractal parameters. This method was used to estimate a 30 m resolution slope map
from the 30 arc seconds DEM. The soil erodibility factor k is a quantitative
description of the inherent erodibility of a particular soil. It is controlled primarily
by soil texture and soil organic matter content and, to a lesser extent, by structure
and permeability. The FAO Soil Map of the World (FAO 1995) and the table
developed by Mitchell and Bubenzer (1980, table 3) which provides values of k for
different soil textural classes and percentages of organic matter content, were used
in this study for the estimation of k. The Thornes model was run for the 36 dekads
of 1996.
In order to compare the soil erosion rates in different environments we
computed the yearly sum of erosion (figure 5). It appears that soil erosion is most
serious in the humid subtropics, tropical Savannah and semi-arid environments.
The erosion rate is relatively low in the tropical rainforests and in arid areas due
to the high vegetation cover in the former and little to no rainfall in the latter. It is
possible to gain some insight into those factors that are causing high erosion by
comparing the time series of erosion to that of other model parameters at specific
locations. For example, in central Eritrea (figure 6(a) and (b)), at the beginning of
the wet season, vegetation cover is low (3%) and thus a relatively small amount
of rainfall (70 mm) is enough to cause the highest rates of erosion for the year
(16 mm). Two months later, in July, the same amount of precipitation leads to less
overland flow and erosion (5 mm and 9 mm) due to the doubling of the vegetation
Monitoring desertification over sub-Saharan Africa
Table 3.
Textural class
Soil erodibility coefficient (k) values.
0.875v%OM
Sand
Loamy sand
Sandy loam
Loam
Silt loam
Silt
Sandy clay loam
Clay loam
Sandy clay
Silty clay
Clay
587
0.875v~
OMv1.625
1.625v~
OMv2.5
2.5v~
OMv3.5
OMw~3.5
0.04
0.11
0.255
0.36
0.45
0.56
0.26
0.27
0.28
0.24
0.17
0.03
0.1
0.24
0.34
0.42
0.52
0.25
0.25
0.13
0.23
0.21
0.025
0.09
0.215
0.315
0.375
0.47
0.23
0.23
0.125
0.21
0.25
0.02
0.08
0.19
0.29
0.33
0.42
0.21
0.21
0.12
0.19
0.29
0.05
0.12
0.27
0.38
0.48
0.6
0.14
0.25
0.13
OM, organic matter.
After Mitchell and Bubenzer (1980).
Figure 5.
Annual erosion (mm) for 1996 computed by summing the dekadal maps.
cover in this period (6%). In the semi-arid area of Gonder in the Ethiopian
highlands (figures 6(c) and (d )), erosion closely follows the time series of precipitation and overland flow, reaching a maximum in August when precipitation is
at its highest (mean of 280 mm) and there is a dip in vegetation cover (30%),
probably due to agricultural practices such as harvesting.
588
E. Symeonakis and N. Drake
(a)
(b)
(c)
(d )
Figure 6. Monthly variation of precipitation (mm/10), vegetation cover (%), overland flow
(mm) and erosion (mm) in an area in Eritrea and in Gonder: (a, c) maximum values;
(b, d ) mean values. (‘FEWS’ is the Famine Early Warning System precipitation
estimate and ‘eros’ the erosion estimate).
3.
Combination of indicators
Although each individual indicator is helpful in describing an aspect of
desertification, it is useful to combine the results of vegetation cover, rain use
efficiency, the run-off coefficient and erosion, to determine which areas appear to be
suffering from land degradation for all indicators. For example, by performing a
Boolean combination of the 20% of the most highly eroded areas, the 20% of pixels
with lowest vegetation, the 20% with the highest run-off coefficient and the lowest
RUE values, the areas with the greatest degradation potential can be highlighted.
Figure 7(a) shows this for the entire sub-Saharan Africa whereas figures 7(b)–( f )
show some examples from arid and semi-arid areas (Eritrea, Ethiopia, Kenya,
Namibia, Somalia, South Africa and Sudan) but also from wet tropical areas (D. R.
Congo, Rwanda and Burundi) that potentially need to be targeted for
desertification and land degradation respectively.
These results show localized land degradation and do not support the ‘marching desert’ approach whereby desertification is the expansion that occurs along a
defined front. The concept of deserts progressively moving over productive land in
the form of a readily measurable front of mobile sand dunes has, in recent years,
been increasingly criticized (Warren and Agnew 1987, Binns 1990, Hellden 1991,
Pearce 1992, Thomas 1997). Our results support this criticism. Like Goudie (1996)
we find that desertification is more like a ‘sporadic rash than an advancing tide’.
Monitoring desertification over sub-Saharan Africa
589
Figure 7. Areas with the highest erosion and run-off coefficient and lowest RUE and
vegetation cover that could be targeted for land desertification/degradation
monitoring: (a) sub-Saharan Africa, (b) Kenya, (c) Sudan, (d ) D. R. Congo,
Rwanda and Burundi, (e) South Africa and Namibia and ( f ) Ethiopia, Eritrea and
Somalia.
4.
Conclusions
With the increase in the complexity of human impacts on the environment, in
the number of stressors and in the importance of cumulative impacts, there is a
need to improve monitoring and evaluation methodologies ‘with a view to more
590
E. Symeonakis and N. Drake
efficient environmental management strategies’ (Rubio and Bochet 1998). It is
unlikely that there is one single index or variable which can represent the complex process of desertification (Sanders 1992). We have developed methods of
implementing four important indicators of desertification and land degradation at a
region scale. By combining these estimates of vegetation cover, RUE, run-off and
soil erosion we have defined areas that all indicators suggest are suffering the
detrimental effects of land degradation and desertification. These areas are
considered to be prime targets for desertification that warrant further study.
Desertification indicators, however, are dynamic at time-scales longer than a
year. Thus, to use the methods of this study to identify progressive desertification
and land degradation, a longer record needs to be examined. All the data needed to
implement the monitoring system are readily available on the internet in near-real
time. Thus, it would be relatively simple to establish monitoring systems of
desertification and land degradation in sub-Saharan Africa that could keep abreast
of the situation and gradually build up a time series of data that would allow the
analysis of temporal trends in desertification. Alternatively, use of online archives
would allow examination of inter-annual variability of these factors for the last six
years.
Another area ripe for further research would be to develop and implement more
indicators of desertification in order to provide a more comprehensive picture of
this complex phenomenon. One important area that needs to be improved is soil
erosion as we need to consider the effects of wind as well as water. This could
potentially be achieved using products such as the TOMS Aerosol Index or the
MODIS aerosol products.
Acknowledgments
This research was largely funded by the Hellenic State Scholarships Foundation
(IKY) and partly by the Research Training Project financed by the European
Community with contract no: EVK2-CT-2001-50014.
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