Remote Sensing of Environment 113 (2009) 1046–1057
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Remote Sensing of Environment
j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / r s e
Detection and mapping of long-term land degradation using local net production
scaling: Application to Zimbabwe
S.D. Prince ⁎, I. Becker-Reshef, K. Rishmawi
Geography Department, University of Maryland, College Park, MD 20742-8225, USA
a r t i c l e
i n f o
Article history:
Received 12 August 2008
Received in revised form 28 January 2009
Accepted 31 January 2009
Keywords:
Dryland degradation
Desertification
Zimbabwe
Communal land
Net primary production (NPP)
Local NPP scaling (LNS)
MODIS
Sustainability
a b s t r a c t
Degradation of vegetation and soils in drylands, sometimes called desertification, is thought to be a serious
threat to the sustainability of human habitation, but maps of the extent and severity of degradation at
country and global scales do not exist. Degraded land, by definition, has suffered a change relative to its
previous condition set by its climate, soil properties, topography and expectations of land managers. The
local net production scaling (LNS) method, tested here in Zimbabwe, estimates potential production in
homogeneous land capability classes and models the actual productivity using remotely-sensed observations. The difference between the potential and actual productivities provides a map of the location and
severity of degradation. Six years of 250 m resolution MODIS data were used to estimate actual net
production in Zimbabwe and calculate the LNS using three land capability classifications. The LNS maps
agreed with known areas of degradation and with an independent degradation map. The principal source of
error arose because of inhomogeneity of some land capability classes caused by, for example, the inclusion of
local hot-spots of high production and differences in precipitation caused by local topography. Agriculture
and other management can affect the degradation estimates and careful inspection of the LNS maps is
essential to verify and identify the local causes of degradation. The Zimbabwe study found that
approximately 16% of the country was at its potential production and the total loss in productivity due to
degradation was estimated to be 17.6 Tg Cyr− 1, that is 13% of the entire national potential. Since the locations
of degraded land were unrelated to natural environmental factors such as rainfall and soils, it is clear that the
degradation has been caused by human land use, concentrated in the heavily-utilized, communal areas.
© 2009 Elsevier Inc. All rights reserved.
1. Introduction
Land degradation has mainly been studied at a local scale and from
the perspective of the farmer and pastoralist, but it also has effects at
the country, continental and global scales (Prince, 2002). For example,
the cumulative costs of degradation in Zimbabwe through siltation of
dams and waterways has been estimated to have a major impact on
Gross Domestic Product (GDP) (Gore et al., 1992; Grohs, 1994). Other
regional effects include: reduction of food security; disruption of the
surface water balance; reduced carbon sequestration and release of
carbon through soil erosion; impacts on regional climate through
changes in the evaporation ratio, roughness, albedo and increased
atmospheric dust loads (Reynolds & Stafford Smith, 2002).
Drylands are particularly susceptible to degradation and, although
they cover about 41% of Earth's land surface (Safriel & Adeel, 2005),
estimates of the extent and severity of degradation vary greatly
(Lepers et al., 2005). There are estimates that 70% of the world's
drylands are affected and that at least one third of present deserts are
⁎ Corresponding author. Tel.: +1 301 405 4062; fax: +1 301 314 9299.
E-mail address: sprince@umd.edu (S.D. Prince).
0034-4257/$ – see front matter © 2009 Elsevier Inc. All rights reserved.
doi:10.1016/j.rse.2009.01.016
man-made (UNCED, 1993). Set against these high estimates are
studies that find no widespread dryland degradation (desertification),
at least in the Sahel (e.g. Prince et al., 1998; Niemeijer & Mazzucato,
2002). Nevertheless, most observers agree that there are significant
areas of land degradation and these have large effects on the environment and human well-being.
In view of the far-reaching consequences of degradation and the
large areas that are said to be affected there is a need for inventories
and monitoring at the country to global scales using consistent,
objective, repeatable, and spatially explicit measures (Prince, 2004).
Objective measurement of degradation for large areas has, however,
proved extremely difficult, mainly due to multiple criteria and the lack
of reliable methods (Verstraete, 1986; Prince, 2002; WMO, 2005).
Existing global maps such as GLASOD (Thomas & Middleton, 1992),
the USDA NRCS Desertification Vulnerability map (Eswaran & Reich,
2003), the United Nations World Atlas of Desertification (UNEP, 1997)
and, more recently, Lepers et al. (2005) all depend on coarse resolution soils maps and indicate vulnerability to degradation, rather
than actual degradation.
Part of the definition of desertification, or dryland degradation, used
by the United Nations (UN) and others is a reduction of the productive potential of the land (Reynolds, 2001). While productivity may be
S.D. Prince et al. / Remote Sensing of Environment 113 (2009) 1046–1057
measured in many ways, the growth of vegetation per unit area per unit
time (net primary production, NPP) is used here. The processes that lead
to land degradation involve the interaction of environmental, social,
economic, and historical factors (Gore et al., 1992; Reynolds et al., 2007)
and are typically induced when cropping and grazing exceed this
potential which can happen, for example, during droughts. Degradation
manifests itself in characteristics other than productivity, for example
reduced biodiversity (Pickup, 1998; Adeel et al., 2005). Nevertheless,
reduced NPP is a consistent symptom of degradation relative to the
potential in the site and can be used as an index whether or not the effect
on productivity is the objective. In order to detect degradation, however,
a reference value for the non- or less-degraded condition is required
(O'Malley & Wing, 2000; Stoms & Hargrove, 2000; Boer & Smith, 2003;
Prince et al., 2007), and a fundamental difficulty is to determine that
reference value (Wessels et al., 2007). Here the potential NPP, the NPP
that would be expected in the absence of human land use, is used as the
reference. The magnitude of the difference between actual and potential
NPP provides both a quantitative measure of degradation and the
associated loss of carbon fixation.
Potential NPP can be estimated using mechanistic biogeochemical
models but the climate and soils data that are required to drive the models
are often available only at a resolution of 0.5°–2° (0.25–4×104 km2), an
area that cannot contribute to policy applications at a country scale
(Prince, 2002). In order to circumvent the use of coarse resolution climate
and soils maps, satellite measurements of net primary production (NPP)
are used here to estimate both actual and potential NPP (Prince, 2004).
Techniques for measurement of net primary production (NPP) using
Earth-observing satellite data were first developed in the mid 1980s
(Prince, 1991) but it is only now that a satellite data archive has
accumulated with a long enough record (N25 years) to allow degradation
studies at appropriate time scales (Prince et al., 2000; Prince, 2002). The
remotely sensed data that can be used to model productivity includes
MODIS (from 2000 to present), SPOT VEGETATION (1998 to present),
SeaWifs (1997 to present), MERIS (1995 to present) and NOAA Advanced
Very High Resolution Radiometer (AVHRR, 1981 to present).
The purpose of the present work is to assess the Local NPP Scaling
(LNS) method (Prince, 2004) for quantification and mapping of degradation in areas from a few km2 to country scales. In LNS multitemporal satellite data are used to calculate the annual NPP of each
pixel, then the difference between the potential and actual NPP for
each pixel is calculated. Variation in potential NPP can be caused by
differences in land use, land cover and physical factors. The variation is
reduced by stratification into homogeneous regions (Soriano &
Paruelo, 1992; Prince, 2004). The procedure is similar in concept to
the use of land classification to determine appropriate uses of land for
agriculture or livestock production and for land valuation (FAO, 1976).
Within each land capability class (LCC), all pixels are assigned the
same potential NPP — the productivity that would have been attained
were it not for human factors (Soriano & Paruelo, 1992; Tappan et al.,
2004). Without the use of the LCCs, regions having a lower potential
production would be confused with degraded areas of high potential.
The potential NPP is estimated from the highest NPP found in the
LCC to which that pixel belongs. LCCs are derived from climate, soils,
land cover and land use, and are independent of actual NPP. In
another approach, Residual Trend (RESTREND) analysis (Wessels
et al., 2007), the rainfall–NPP relationship is used to obtain the
difference between potential and actual NPP using the actual rainfall
each year. Both methods model the potential and compare it with
the observed NPP but, in RESTREND, the results apply only within
the time series of satellite data used (although previous years may
influence the starting NPP); LNS, however, should be able to detect
preexisting degradation.
Methods that have some similarities to LNS have been proposed.
Budde et al. (2004) compared the NDVI of a pixel with surrounding
pixels using a moving window (31 × 31 km in their Senegal study).
The size of the window will affect the result depending on the spatial
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patterns of degradation and on whether there are differences in NDVI
that are unrelated to degradation. Compared with LNS, this approach
will blur abrupt changes in degradation, for example at the boundaries
between communal and commercial areas. Another study (Stoms &
Hargrove, 2000) used national parks as reference sites to estimate
potential NDVI but this is only valid for areas that have the same soils,
climate and land use as the park. Lambin and Ehrlich (1997) used a
time-series of NDVI to express each year's NDVI relative to the
maximum observed for each pixel; while this was intended as a
change detection technique, it could be used to monitor active
degradation. Boer and Puigdefabregas (2005) estimated potential NPP
from NDVI by calculation of the maximum actual evapotranspiration
(AET) consistent with the rainfall and potential evapotransiration in
each pixel. Unfortunately, as in the case of many more detailed NPP
models, the paucity of meteorological stations limits the spatial
resolution of the meteorological data to such an extent that key local
variations cannot be resolved. While the aim of all these studies was to
estimate the potential productivity for comparison with the actual, the
use of homogeneous areas (LCC) in LNS clearly has some advantages.
LNS is tested here in Zimbabwe (Fig. 1a) where there is a very
appropriate, if regrettable in human terms, opportunity to observe land
that is indisputably degraded. Degradation was already far advanced in
some areas of Zimbabwe in the 1980s (Muller, 1983; Whitlow, 1988),
mostly concentrated in areas of subsistence farming, and it has
accelerated since that time. These heavily degraded areas have, for
most of the 20th century, been occupied exclusively by indigenous
peoples (Moyo, 1995). They were established in the colonial period
when land was appropriated by Europeans for commercial farming and
the indigenous populations were increasingly confined in what became
known as “communal”, in contrast with “commercial” (or “general”)
and “other” (parks, reserves) lands (Fig.1b, Zimbabwe,1979a). The same
tenure system, mostly with similar consequences, is also found in South
Africa (Wessels et al., 2008).
What started as an inevitable consequence of two divergent forms
of land use, became increasingly inequitable as indigenous population
densities increased (Fig. 1c) (Roder, 1964; Moyo et al., 2000), ultimately leading to a conspicuous contrast between land degradation
in the communal areas and highly productive commercial land. While
it is often assumed that communal areas were placed in areas with
low land capability, in fact both commercial and communal lands are
found in all LCCs (Vincent et al., 1960). Owing to the spatial coherence
of these two types of land tenure in Zimbabwe, degradation can be
seen even in continental-scale satellite imagery (Fig. 1d). The
indisputable difference in degradation between communal and
neighboring commercial land that occupy similar environments is
used here to test the LNS technique.
2. Methods
2.1. Data
Land-cover was obtained from Hansen et al. (2000), precipitation
from NOAA (2008), and soils from the SOTER (SOTER, 2002) and the
Zimbabwe soil map (Zimbabwe, 1979b).
MODIS data at a resolution of 250 × 250 m (6.25 ha) for 2000–2005
were used to estimate NPP. MOD13Q1 16 day normalized difference
vegetation index (NDVI) composites of the sums of the NDVI (ΣNDVI)
for each southern hemisphere growing season (September–May)
were calculated. MODIS has been operational since only 2000, a
shorter time series than is available in other data sets, but it was
preferred to test LNS owing to its higher spatial resolution and improved radiometric properties. Conclusions reached using MODIS
should be applicable to similar sensors.
The 30-year average rainfall for Zimbabwe, from gauge data, is
750 mm and, from reanalysis results (GPCP, 2008), for the 10 years
from 1997, was 677 mm (s.d. 123 mm). GPCP data were used to
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Fig. 1. Maps of Zimbabwe; a – locations of all place names used in text (Map no. 4210 Rev. 1, United Nations, 2004) ; b – administrative districts and land tenure type (white – commercial,
black – communal, grey – other); c – Landsat TM mosaic of Zimbabwe with land tenure type boundaries. Note the unusual regional pattern of vegetation in Zimbabwe caused by land cover
differences between communal (lighter) and commercial (darker) areas; d – population density in 1982 (Zimbabwe, 1982) with land tenure type boundaries. Note higher population in
Communal areas.
calculate these climatological values since NOAA (2008) were not
available for 30 years. The average GPCP annual rainfall for 2000–2005
was 679 mm (s.d. 129 mm), consisting of 1 year with a high total
(991 mm) and several years below the 30-year average. Thus the
average and interannual variability of rainfall in the study period were
not unusual. NDVI was converted to NPP using the products of the
CASA model (Imhoff et al., 2004).
The LNS results were assessed in various comparisons with
independent data. These included comparison of the LNS LCC map
with the Agro-Ecological Survey map of Natural Regions and Areas in
Zimbabwe (Vincent et al., 1960). This survey used soils, rainfall and
topography alone, not considering current NPP, and is therefore directly
comparable with the LCCs used here. LNS potential NPP was compared
with CENTURY model results (Parton et al., 1993; Cramer et al., 1999).
Landsat ETM+ bands 7, 4, and 2 false color composites were made from
GeoCover Orthorectified Landsat Compressed Mosaics (MDA_Federal,
2000) and were used in visual comparisons with the LNS map, as was
the Zimbabwe National Land Degradation Survey (Whitlow, 1988).
2.2. Land capability classification (LCC)
The LCCs were defined by stratification of the digital maps
of rainfall, soils and land use. The classes were derived using a
k-prototypes clustering technique (Huang, 1997; Hargrove & Hoffman, 2004), which is an extension of the k-means method but with
the added advantage that it can cluster large data sets consisting of
both numeric and categorical variables.
The steps were as follows:
i. Digital soil and land cover maps were gridded to 250 × 250 m
cells, each cell geographically registered with the appropriate
MODIS 250 × 250 m pixel.
S.D. Prince et al. / Remote Sensing of Environment 113 (2009) 1046–1057
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Fig. 2. k-prototype stratifications of Zimbabwe and potential NPP in each class derived using the Local NPP Scaling method; a — LCC from the Zimbabwe soil map (Zimbabwe, 1979b)
and rainfall (NOAA, 2008) (ZSOL–PPT), b — using land cover (Hansen et al., 2000) and rainfall (LULC–PPT). Box in a shows area enlarged in b.
ii. Three LNS analyses were carried out using different combinations of variables: precipitation with land cover (LULC–PPT);
SOTER soils map classes with precipitation (SOTR–PPT); and
Zimbabwe soils map classes with precipitation (ZSOL–PPT).
iii. The number of initial LCC clusters was defined using all permutations of the soils (31classes for the SOTER, and 27 for the Zimbabwe
soil map), land cover (7 classes), and four equal ranges of rainfall.
The number of rainfall classes was arbitrary, based on an approximate target number of final LCCs. Thus there were 28 initial
clusters for LULC–PPT, 124 for SOTR–PPT, and 108 for ZSOL–PPT.
iv. k-prototype classification of all grid cells was carried out using
Euclidean distances for numeric attributes and the number of
mismatches between pixel values for categorical attributes
(Huang, 1997). The weighting function in the classification was
used to weight the categorical and numeric variables equally.
The prototype properties were recalculated after each allocation to clusters and, after all the pixels were allocated, all cells
were tested in relation to the updated classification and those
found to be closer to a different prototype were reallocated.
This procedure was repeated recursively until no more cells
changed clusters or until the iterations reached a pre-set limit
(Huang, 1997), whichever was reached first.
v. The final k-prototypes clusters were classified using a decision
tree (Breiman, 1984) to create definitions of the cluster in terms
of the input data. Unreasonable classes were removed by
pruning the tree, which led to fusion of some classes.
2.3. Measurement of net primary production (NPP) using satellite data
The ΣNDVI for each growing season was used as a surrogate for
NPP (Prince, 1991), since there is a near-linear relationship between
NPP and ΣNDVI in tropical grassland, cropland and sparse woodland
and light use efficiency has been shown not to improve accuracy
(Fensholt et al., 2006). In order to estimate NPP in terms of carbon, a
simple scaling of annual ΣNDVI into NPP was made by regressing the
Fig. 3. Effectiveness of the three land capability classifications (LCC) as indicated by potential NPP from the Century model (Parton et al., 1993; Cramer et al., 1999). The classes for the
three LCC are shown: light grey is the PPT–LULC, dark grey, the PPT–ZSOL; and black, the PPT–SOTR classifications. The size of the symbol indicates the coefficient of variation within
each class and the bars indicate ± 1 standard deviation.
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ΣNDVI on NPP from the CASA model (Imhoff et al., 2004), thus the
NPP used here is a simple linear transform of ΣNDVI.
correlations and also by comparison with the potential NPP from
CENTURY (Parton et al., 1993; Cramer et al., 1999).
2.4. Local net primary production scaling (LNS)
2.5.3. Local NPP scaling (LNS)
The three LNS maps were compared with independent maps of
degraded areas.
The potential and minimum NPP of each LCC class were estimated
by the 90th and 5th percentiles of the frequency distribution of
ΣNDVI, respectively. The results of parametric and non-parametric
estimates of the percentiles were similar and so the parametric
frequency distribution was used.
The reduction of the actual 5-year interannual mean NPP below the
potential NPP was calculated by subtraction of the actual from the
potential, and this negative quantity is the LNS metric of degradation. This
procedure assumes that there is land in its potential state within each LCC
class: if not, the differences between actual and potential NPP and the
degree of degradation would be underestimated. The differences between
potential and actual productivity of each pixel provided a map of the
impact of degradation on productivity — that is the NPP lost as a result of
degradation. The percentage of the potential NPP of each pixel can also be
used, but each percentage value applies only to the LCC to which it belongs.
2.5. Assessment
2.5.1. Land capability classification (LCC)
The LCC was assessed in three ways: first, by estimating the extent
to which the LCCs reduced the correlation between the environmental
factors that were used in the classification; second, by comparison of
the ranking of the LCCs with potential NPP and the rankings using two
independent estimates of NPP — rainfall and interannual mean
ΣNDVI; and, third, by comparison of the LCCs with the Agro-Ecological
Survey map of Natural Regions and Areas in Zimbabwe.
2.5.2. Potential net primary production (NPP)
The maps of potential NPP were assessed by comparison with soils
and land use (communal or commercial) in order to detect any
i. LNS results in areas that had a wide range of LNS values were
compared visually with Landsat images. The high spatial
resolution of Landsat (30 m) allowed qualitative interpretation
of some aspects of land use and land cover and any relationships with the modeled degradation.
ii. Comparison was made with the Zimbabwe National Land
Degradation Survey (Whitlow, 1988) which was derived from
the density of erosion features shown by aerial photographs. The
survey assessed the frequency of erosion features, an aspect of
degradation that is independent of observations of NPP. The aerial
photographs were mainly acquired in 1980, although some were
for as early as 1979 and others as late as 1984. The survey map is
gridded at 0.5°× 0.5° and has six classes: no erosion (class 1);
0.1–4% (class 2); then in 4% steps (classes 3–5); and N16%
(class 6). Although the survey predated the MODIS satellite
data by approximately 20 years, persistence of degraded
conditions is a key aspect of desertification (Prince, 2002;
Wessels et al., 2004) thus coincidences of severely eroded areas
in 1980 and low LNS values in 2000–5 were a test of the skill of
the LNS approach in detecting pre-existing degradation. In
addition to visual comparison, the means and frequency
distributions of LNS for each National Land Degradation Survey
class were calculated and the values for each class compared.
iii. The user accuracy of prediction of degradation by LNS for each
LCC was calculated by logistic regression in order to predict the
binomial probability of the identification of degraded or not
degraded (Knoke et al., 2002). Two independent measures of
degradation were predicted: first, the communal (assumed to
Table 1
Similarities in rainfall between the land capability classes (LCC) derived from land cover and precipitation (LULC–PPT).
The density of the fill in each cell indicates the probability of similarity in mean rainfall between the row and column classes: white, p b 0.001; light gray, p b 0.05; dark grey, p N 0.05;
black, self-comparisons. Filled row and column labels indicate classes with multiple similarities with other cells that could have been fused.
S.D. Prince et al. / Remote Sensing of Environment 113 (2009) 1046–1057
be degraded) and commercial (assumed not to be degraded)
land uses (Zimbabwe, 1979a) and, second, the National Degradation Survey degradation classes grouped into two ranges
(1–2 and 3–6). The logistic regression results were mapped to
allow assessment of the spatial variability in predictive ability
of LNS.
iv. The LNS results were compared with the use of NDVI alone,
without the application of any further processing. Logistic
regression of ΣNDVI on the two independent degradation
metrics (iii) was used to estimate its user accuracy.
3. Results
3.1. Land capability classification (LCC)
At the countrywide scale all three LCCs stratifications produced
similar maps but with local differences (Fig. 2). SOTR–PPT (not
shown) gave similar results to the ZSOL–PPT. LULC–PPT had much
smaller polygons than those based on the soils which tend to be more
coherent. There were 28 final classes for ZSOL–PPT, 124 for SOTR–PPT
and 124 for ZSOL–PPT.
The degree to which the three LCCs reduced within-class variation
was assessed by ranking the classes according to potential NPP from
the CENTURY model (Parton et al., 1993) (Fig. 3) and with rainfall. The
differences between the LCCs that used soil (ZSOL–PPT and SOTR–
PPT) were smaller than in the case of land cover (LULC–PPT),
nevertheless, all three classifications gave satisfactory results. In a
significant ranges test of LULC–PPT (Table 1) the majority of classes
had statistically significant differences in rainfall. A few classes had
multiple similarities with other classes and could have been fused.
The three LCCs were compared with the Zimbabwe Natural Regions map (Vincent et al., 1960), an independent classification of land
suitability. All three LCCs placed most of the 22 Natural Regions in the
same rank order of productivity as the Natural Regions map (Fig. 4).
3.2. Estimation of potential net primary production (NPP)
Pixels above the 90th percentile of a LCC class were found in both
commercial and communal areas. There was little visual correlation
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between potential NPP and land tenure type (communal, commercial), nor with rainfall, but there was a strong correlation of both with
actual NPP. Although lower potential areas were more commonly
found in communal than commercial areas (Fig. 5), there were sizable
areas of both in all potential production classes. Communal areas,
however, are generally at lower altitudes than commercial land. The
National Land Degradation Survey map (Whitlow, 1988) showed no
correspondence of degraded land and natural regions. Similarly the
potential NPP maps show no association between communal land use
and the climate, soils and terrain slope constraint classes in the Global
Agro-Ecological Zone map (Fischer et al., 2000; Plate 28). Thus
the association of degradation with communal land and the abrupt
changes in vegetation cover across commercial–communal boundaries showed that communal lands are not degraded simply because
they have low potential.
There were some differences in environmental factors that were
not removed by stratification, for example, in some LCCs the highest
NPP occurred on locally higher land, presumably associated with
higher rainfall or unusable land. As a result the potential NPP was set
unrealistically high for other parts of the class with lower rainfall. It
might be argued that fertilized croplands and especially irrigation also
set the potential too high and so over-estimate degradation, but these
management treatments may also be regarded as the potential for
cropped land.
A comparison of potential NPP estimated by the LNS with NPP from
CENTURY (Parton et al., 1993) gave a much higher correlation than
with the actual NPP (Table 2). Since the model can only estimate
productivity of undisturbed land, that is potential NPP, this confirms
that the LNS technique provided reasonable estimates of potential
NPP. Among the three LNS analyses, LULC–PPT accounted for the most
variation in NPP.
3.3. Local NPP scaling
There was general agreement between the LNS maps calculated for
SOTR–PPT (Fig. 6) and ZSOL–PPT at both the country and local scales.
The LNS map shows some very clear patterns in the location of
degradation, similar to those reported by Prince (2004). Land in good
condition relative to its potential was found in the commercial areas:
Fig. 4. Potential production (g− 2 year− 1) in the three land capability classifications (LCC) compared with the potential for agricultural production from the Zimbabwe Natural
Regions and Areas map (Vincent et al., 1960). The potential of the Natural Regions for agricultural production is indicated by a numeral followed by letter, 1A (maximum) to 5A
(minimum). Region XX is omitted since it is unsuitable for any use other than reserves.
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Fig. 5. The area of land allocated to commercial and communal use in the five main Natural Regions as a percentage of the total land area occupied by each tenure type. Natural
Regions are ranked according to their potential productivity from highest to lowest. Modified from map of Natural Regions and Areas in Vincent et al. (1960).
along the broad SW to NE sweep of the higher land from Plumtree on
the Botswana border (see Fig. 1 for place names), to Bulawayo through
Gweru and Chivhu and north to Harare; from Gweru northeast
through Marondera; from Harare NW through Chinhoy and Karoi; and
E–W from Gwanda (Matabeleland South) to Chiredzi (Masvingo).
Extensive parks and reserves were also close to potential, for example:
the large Hwange reserve S and W of Dete (Matabeleland North);
Gonarezhou along the SE border with Mozambique; and below the
Zambezi escarpment in the northern part of Mashonaland West
between Chirundu and Zumbo, including Mana Pools National Park.
In marked contrast, communal land was almost everywhere
degraded, often in striking contrast to neighboring commercial land.
Particularly severe examples were: in the Save catchment in Manicaland; an area centered on Mutoko (Mashonaland East); along the
Shashe river (Matabeleland South); and centered on Gokwe (Midlands).
The LNS maps were remarkably similar to the National Degradation
Survey country-wide degradation map (Whitlow, 1988) (Fig. 7), despite
the difference in methods and the 20-year time difference. Areas
mapped as eroded in 1980 almost everywhere had low LNS values. For
example: in the Save catchment, around Mutoko (Mashonaland East);
along the Shashe (Matabeleland South), east of Chegutu (Mashonaland
West) and around Gokwe (Midlands) were all identified in the LNS
maps.
A detailed comparison of the LNS and National Degradation Survey
maps provides some indication of trends in degradation between 1980
and 2000. The mean LNS values for each National Degradation Survey
class were calculated and the mid values between the means were
used to classify the LNS percentages into six classes (Fig. 8). Increases,
decreases or no change in each National Degradation Survey class grid
cell were labeled. Because the two maps are based on quite distinct
measures of degradation, a comparison depends on the assumption
that the absolute values and ranges of the two were unchanged over
the 20 years. With this qualification, there were areas of reduced
degradation but much larger areas of increase.
The degree of agreement of the LNS and the Degradation Survey
map was highly significant (p b 0.01 of difference). The LNS values
declined with increasing erosion in the Degradation Survey (Fig. 9a)
and in land use classes in the order commercial, parks and communal
(Fig. 9b). In contrast to the LNS, the annual mean ΣNDVI showed no
such decline across Degradation Survey classes or the land use categories (Fig. 9a,b), and was therefore less able to detect degradation.
Several areas of Zimbabwe that were very little affected by erosion
in 1980 had very large negative LNS values in 2000 (Fig. 9c). While the
LNS and degradation maps measure different properties of the land,
the greater area identified by the LNS may also indicate some
extension of degradation over the 20-year gap between the aerial
photography used by the National Land Degradation Survey and the
MODIS data used for LNS. This speculation is supported by the skewed
distributions of LNS in the higher erosion classes (Fig. 9c).
For each LCC class the accuracy of the LNS designation of degradation was calculated using logistic regressions. Two independent
criteria for degraded/not degraded were used, communal/not
communal, and the National Degradation Survey classification divided
into two ranges, 1–2 and 3–6. The results indicate that the user
accuracy of prediction of degradation, judged by comparison with the
Survey, was: SOTR–PPT 73%, ZSOL–PPT 73% and LULC–PPT 64%
(Fig. 10). For comparison, the accuracy of prediction of degradation
in the two Survey class ranges using ΣNDVI values alone, without
stratification by the LCC, was 69%.
Table 2
Comparison of LNS with potential NPP from an independent global model that uses
climate forcing data alone (Parton et al., 1993; Cramer et al., 1999).
Degrees of
freedom
NPP
Land capability
classification
Correlation with
independent measure
of potential NPP (r2)
LNS potential NPP
Zimbabwe soil map
and precipitation
(ZSOL–PPT) classes
SOTR soil map and
precipitation (SOTR–PPT)
classes
Land cover and precipitation
(LULC–PPT) classes
All pixels in Zimbabwe
46.1%
16,815
49.1%
16,502
55.0%
16,967
22.5%
610,514
Remotely sensed
(actual) NPP
The correlation between the potential NPP from the global model and potential NPP
calculated with LNS was higher for all three LNS analyses than it was with the actual
NPP.
S.D. Prince et al. / Remote Sensing of Environment 113 (2009) 1046–1057
Fig. 6. Local NPP Scaling (LNS) of Zimbabwe using the ZSOL soils map and precipitation
(ZSOL–PPT) land capability classification. Communal and Commercial area boundaries
shown in black. Inset, higher resolution segment SW of Gweru showing communal area
degradation (top left) and commercial area degradation (lower right).
Qualitative, visual comparisons of the LNS maps with Landsat
(MDA_Federal, 2000) generally showed coincidence of the boundaries
of communal lands with low LNS (Fig. 11). The higher spatial resolution of the Landsat data (30 m) showed innumerable examples of
stark contrasts in land condition across commercial–communal
boundaries, often separated by only a fence line. It was very clear at
1053
Fig. 8. Change in degradation between 1980 Zimbabwe land degradation survey
(Whitlow, 1988) and 2000 LNS degradation map.
this fine scale that low LNS values were associated with sparse
vegetation in communal land. In addition to identifying the degraded
parts of the communal areas, the LNS maps suggested variation in the
degrees of degradation within communal areas, and also identified
degraded areas in commercial land (Fig. 6).
In a vegetation change study in Buhera District (Manicaland)
Mambo and Archer (2007) used Landsat data to identify areas that
Fig. 7. Comparison of: a — LNS with SOTR soils map and precipitation (SOTR–PPT); and b — National Land Degradation Survey (Whitlow, 1988). Legend indicates the LNS values in
units of loss of NPP and the degradation class in the National Survey.
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S.D. Prince et al. / Remote Sensing of Environment 113 (2009) 1046–1057
had become more degraded over the period from 1992 to 2002.
Interestingly, these change areas coincide closely with areas where
LNS indicated less degradation. Two suggestions are offered that may,
individually or together, account for this apparent paradox. First, the
Buhera study measured vegetation change, while LNS measures static
vegetation condition. Second, it may be that the areas identified as
severely degraded by LNS were not susceptible to further degradation,
rather only in the areas where degradation was moderate could
further decline take place. This latter point is speculative and further
study of this important comparison is warranted.
4. Discussion
Land capability classification to create classes of uniform productive potential is fundamental to LNS. The same concept is widely used
in land evaluation (FAO, 1976; McRae & Burnham, 1981; Wessels et al.,
2004), however, there is no global, comprehensive and consistent
method for definition of land capability. Rainfall and land cover have
been used for LNS (Prince, 2002) and, since soils are an important
determinant of productivity and risk of degradation (Eswaran et al.,
1997, 1999), soil type was added here.
Residual inhomogeneities in LCC classes may cause errors in
estimation of the potential NPP, as was found in some cases in
Zimbabwe. Similar problems are likely if classes contain more and less
productive soils or highly productive “hot-spots” such as wetlands,
riparian features, irrigated or fertilized crops. The effect of such hotspots is that actual productivity is scaled according to the potential set
by unrepresentative parts of the class, thus overestimating the degradation. By definition, unrepresentative features are small and the
effect is greatly reduced if the individual satellite observations used
to calculate NPP are large relative to these features. Potential NPP
estimation for LCC classes using the productivity of the upper 90th
percentile assumes that each class contains some non-degraded areas.
If this is not the case, degradation will be underestimated. The selection of the 90th percentile was arbitrary and a formal test of the
effect of changing this would be useful, however, the main purpose in
Fig. 9. LNS values for three different stratifications of Zimbabwe; a — mean of the three LNS analyses (SOTR–PPT, ZSOL–PPT, and LULC–PPT) in each National Degradation Survey class,
and mean ΣNDVI for comparison, b — LNS and NDVI values in three land use classes, c — National Degradation Survey and LNS scores of municipal wards (4th level administrative
units; 1200 total). The number of municipal wards falling into each of 5 equal ranges of the National Degradation Survey (Whitlow, 1988) and, within each range, the numbers of
municipalities in five ranges of ZSOL–PPT LNS values.
S.D. Prince et al. / Remote Sensing of Environment 113 (2009) 1046–1057
Table 3
Effect of degradation on country-wide net primary production (NPP) in Zimbabwe for
local NPP scaling applied to three land capability classifications; a — mean loss of NPP in
g cm− 2 year− 1) below potential and statistics, b — percentage losses and at three levels
(0–1, 1–2, N2 standard deviations).
a.
Land capability
classification
Loss of net primary production compared with potential
(g cm− 2 year− 1)
LULC–PPT
SOTR–PPT
ZSOL–PPT
Mean
Median
Mode
Standard deviation
57.4
58.8
60.9
51.1
50.0
55.0
34.6
33.3
40.8
38.3
40.0
39.2
b.
Land
capability
classification
LULC–PPT
SOTR–PPT
ZSOL–PPT
At potential NPP
(% country)
18.1
16.2
16.1
Below potential NPP (% country)
0–68%
(0–1 std)
below
68–95%
(1–2 std)
below
N 95%
(2 std)
below
31.2
31.8
29.7
29.7 (60.9)
30.2 (62.1)
29.5 (59.2)
21.0 (81.9)
21.8 (83.8)
24.8 (84.0)
Figures in brackets are the cumulative percentages.
the use of the upper frequency range rather than the maximum value,
was to minimize the effects of extreme outliers. In the application to
Zimbabwe, the mean of the annual sums of per-pixel NDVI were
averaged over 5 years. There are other possibilities, for example averaging annual LNS values.
Factors other than degradation may also reduce or increase productivity, such as removal of woodland for agriculture or other management that is not incorporated in the derivation of the LCC, or
enhancement of productivity by run-on in addition to local rainfall.
Careful inspection of the results can mitigate these problems and the
LCC can and should be adapted to the specific purpose of the analysis.
LNS focuses attention on areas that may contain degradation, but
detailed investigation is needed to confirm the diagnosis and assign a
cause (Botha, 2000).
k-prototypes clustering allowed for a consistent, quantitative, and
rigorous classification scheme, using a combination of numeric and
categorical data. The computation resources needed, however, were
large, mainly because of the iterative reassignment of pixels (Hargrove
& Hoffman, 2004). The implementation of the algorithm for this
application could be improved by adding functions to control the
number of classes produced when multiple data layers are used and to
prevent the formation of very small classes.
1055
In LNS, the productive potential of the land cover is the reference for
assessment of the degree of degradation. Productive potential, however,
can be defined in different ways. To be useful for land management, the
potential is relative to the specific land use, not the productivity of the
pristine, natural vegetation cover, sometimes called potential vegetation. In applications to degradation the use of potential, natural
vegetation, in many cases, would map all agricultural land as degraded.
In the case of agriculture the most productive areas are generally
fertilized and possibly irrigated, and the selection of these as the
estimator of potential NPP in LNS is not necessarily inappropriate in a
predominantly agricultural region, it is in fact indicative of the
maximum NPP of the LCC. LNS could be adapted for other applications
by selection of different values of the potential NPP. For example, in
dynamic global vegetation models where disturbance is not accounted
for, the potential natural vegetation in the absence of land cover
alteration, past or present, is appropriate. Clearly the choice of a
reference, potential NPP must match the particular application. The
choice in selection of a definition for “potential” might seem to be a flaw,
but it can provide flexibility in application of LNS.
This study in Zimbabwe found severe degradation in large areas of
the country: only approximately 16% was at its potential NPP and over
80% was up to two standard deviations of the LNS frequency
distribution (95%) below the potential (Table 3). An advantage of
the use of NPP to detect degradation is that lost production can be
expressed directly in units of loss of carbon fixation per year. For
Zimbabwe the loss in 2000–5 was estimated to be 17.6 Tg Cyr− 1 of the
entire national potential NPP (132.5 Tg Cyr− 1) giving an actual NPP of
115.0 Tg Cyr− 1. It is interesting to note that (Imhoff et al., 2004)
estimate that 24.5 Tg Cyr− 1 is appropriated for human use in Zimbabwe, approximately the same as is lost by degradation. Since the
locations of land with low NPP relative to the potential were unrelated
to environmental factors such as rainfall and soils (and land cover in
LUCC–PPT), it is clear that the degradation has been caused by human
land use, concentrated in communal areas. Nevertheless, the proportion of NPP lost (~13%) is quite small in view of the appearance of the
communal lands, but it agrees with Scoones (1992) finding that there
was no detectable reduction in livestock production in Zimbabwe's
communal areas.
This assessment of LNS was made possible by the large areas of
indisputable degradation in Zimbabwe and the stark contrast
between degraded and non-degraded land. The importance of nondegraded reference sites has been recognized by the Committee for
the Review of the Implementation of the United Nations Convention
to Combat Desertification (UNCCD) who emphasized the need for
“quantifiable and readily verifiable benchmarks” (Convention to
Combat Desertification, 2002). Where similar circumstances occur,
Fig. 10. User accuracy of three LNS analyses in prediction of degradation. Degradation was determined by presence of communal land. Accuracies in percentages; a — SOTR–PPT,
b — ZSOL–PPT, c — LULC–PPT.
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S.D. Prince et al. / Remote Sensing of Environment 113 (2009) 1046–1057
Fig. 11. Visual comparison of Landsat (a) and LNS (b) for Chiwundura, 20 km N of Gweru, Zimbabwe. Communal area and surrounding commercial areas are all in natural region IIIB
(Semi-intensive livestock production: with support from small grain crop production). Lighter tones represent brightness for Landsat and degradation in the LNS image. Note overall
uniformity of the communal area shown by Landsat (a), but differences in the degree of degradation indicated by LNS (b). Landsat data from GeoCover 2000 true color composite
(MDA Federal 2000).
such as in the northern provinces of South Africa (Wessels et al.,
2004), similar tests may be undertaken. The establishment of a
network of such paired sites throughout global drylands would be an
important step towards UNCCD's goals.
Acknowledgement
This work was partly supported by NASA grant NAG5 9329 and the
World Food Programme.
References
Adeel, Z., Safriel, U., Niemeijer, D., & White, R. (Eds.). (2005). Millennium ecosystem
assessment. Ecosystems and human well-being: Desertification synthesis. Washington,
D.C.: World Resources Institute.
Boer, M. M., & Puigdefabregas, J. (2005). Assessment of dryland condition using spatial
anomalies of vegetation index values. International Journal of Remote Sensing, 26,
4045−4065.
Boer, M., & Smith, M. S. (2003). A plant functional approach to the prediction of changes
in Australian rangeland vegetation under grazing and fire. Journal of Vegetation
Science, 14, 333−344.
Botha, J. H. (2000). An assessment of land degradation in the Northern Province from
satellite remote sensing and community perception. South African Geographical
Journal, 82, 70−79.
Breiman, L. (1984). Classification and regression trees. Belmont, California: Wadsworth
International Group.
Budde, M. E., Tappan, G., Rowland, J., Lewis, J., & Tieszen, L. L. (2004). Assessing land
cover performance in Senegal, West Africa using 1-km integrated NDVI and local
variance analysis. Journal of Arid Environments, 59, 481−498.
Convention to Combat Desertification (2002). Recommendations and conclusions of the
African regional conference preparatory to the first session of the Committee for the
Review of the Implementation of the United Nations Convention to Combat
Desertification (UNCCD–CRIC1), Windhoek, Namibia: Secretariat of the Convention
to Combat Desertification, 12 pages.
Cramer, W., Kicklighter, D. W., Bondeau, A., Moore, B., Churkina, C., Nemry, B., et al.
(1999). Comparing global models of terrestrial net primary productivity (NPP):
Overview and key results. Global Change Biology, 5, 1−15.
Eswaran, H., Almaraz, R., van den Berg, E., & Reich, P. (1997). An assessment of soil
resources of Africa in relation to productivity. Geoderma, 77, 1−18.
Eswaran, H., & Reich, P. (2003). World soil resources map index. Web page available online on http://www.nrcs.usda.gov/technical/worldsoils/mapindx/#regional: United States Department of Agriculture, Natural Resources Conservation Service.
Accessed 3 June 2003.
Eswaran, H., Reich, P., & Beinroth, F. (1999). Global desertification tension zones. Sustaining the global farm, Purdue University and the USDA–ARS National Soil Erosion
Research Laboratory.
FAO (1976). A framework for land evaluation, Soils Bulletin 32, M-51. Soil Resources
Development and Conservation Service, Land and Water Development Division
Rome: Food and Agriculture Organization of the United Nations.
Fensholt, R., Sandholt, I., Rasmussen, M. S., Stisen, S., & Diouf, A. (2006). Evaluation of
satellite based primary production modelling in the semi-arid Sahel. Remote
Sensing of Environment, 105, 173−188.
Fischer, G., van Velthuizen, H., Nachtergaele, F., & Medow, S. (2000). Global agroecological zones (Global-AEZ). Web page available on-line on http://www.fao.org/
ag/AGL/agll/gaez, Rome & Laxenberg: Food and Agriculture Organization of the
United Nations (FAO) and International Institute for Applied Systems Analysis
(IASA). Accessed January 12 2007.
Gore, C., Katerere, Y., Moyo, S., Mhone, G., Mazambani, D., Ngobese, P., et al. (1992). The case
for sustainable development in Zimbabwe: Conceptual problems, conflicts, and contradictions, report prepared for the United Nations Conference on Environment and
Development (UNCED), 159pp. Harare: Environmental and Development Activities
(ENDA–Zimbabwe) and Regional Network of Environmental Experts (ZERO).
GPCP (2008). Global analyses of monthly precipitation derived from satellite and surface
measurements. Web page available on-line on http://lwf.ncdc.noaa.gov/oa/wmo/
wdcamet-ncdc.html, Asheville, NC: Global Precipitation Climatology Project , NOAA
World Data Center for Meteorology, National Climatic Data Center. Accessed 24 July.
Grohs, F. (1994). Economics of soil degradation, erosion and conservation: A case study in
Zimbabwe. Kiel: Wissenschaftsverlag Vauk.
Hansen, M. C., DeFries, R., Townshend, J. R. G., & Sohlberg, R. (2000). Global land cover
classification at 1 km spatial resolution using a classification tree approach. International Journal of Remote Sensing, 21, 1331−1364.
Hargrove, W. W., & Hoffman, F. M. (2004). Potential of multivariate quantitative methods for
delineation and visualization of ecoregions. Environmental Management, 34, S39−S60.
Huang, Z. (1997). Clustering large data sets with mixed numeric and categorical values.
First Pacific Asia Knowledge Discovery and Data Mining Conference. Singapore: World
Scientific.
Imhoff, M. L., Bounoua, L., Ricketts, T., Loucks, C., Harriss, R., & Lawrence, W. T. (2004).
Human appropriation of net primary productivity (HANPP). Web page available online on http://sedac.ciesin.columbia.edu/es/hanpp.html: Socioeconomic Data and
Applications Center (SEDAC). Accessed 25 July.
Knoke, D., Bohrnstedt, G. W., & Mee, A. P. (2002). Statistics for social data analysis, 4th ed :
Thomson Wadsworth.
Lambin, E. F., & Ehrlich, D. (1997). Land-cover changes in sub-saharan Africa (1982–1991):
Application of a change index based on remotely sensed surface temperature and
vegetation indices at a continental scale. Remote Sensing of Environment, 61, 181−200.
Lepers, E., Lambin, E. F., Janetos, A. C., DeFries, R., Achard, F., Ramankutty, N., et al.
(2005). A synthesis of information on rapid land-cover change for the period 1981–
2000. BioScience, 55, 115−124.
Mambo, J., & Archer, E. (2007). Assessment of land degradation in the Save catchment of
Zimbabwe. Area, 39, 380−391.
McRae, S. G., & Burnham, C. P. (1981). Land evaluation. Oxford: Clarendon Press.
MDA_Federal (2000). GeoCover. Web page available on-line on http://glcf.umiacs.umd.
edu/portal/geocover/edition.shtml: Accessed 19 January 2009 from Global Land
Cover Facility, University of Maryland.
Moyo, S. (1995). The land question in Zimbabwe. Harare (Zimbabwe): SAPES Trust.
Moyo, S., Rutherford, B., & Amanor-Wilks, D. (2000). Land reform & and changing social
relations for farm workers in Zimbabwe. Review of African Political Economy, 27,181−202.
S.D. Prince et al. / Remote Sensing of Environment 113 (2009) 1046–1057
Muller, T. (1983). A case for a vegetation survey in a developing country based on
Zimbabwe. Bothalia, 14, 721−723.
Niemeijer, D., & Mazzucato, V. (2002). Soil degradation in the West African Sahel how
serious is it? Environment, 44, 20−31.
NOAA (2008). FEWS-NET/MFEWS/AFN Data Archive. RFE1.0 10-Day Rainfall Estimates.
Web page available on-line on http://www.cpc.ncep.noaa.gov/products/fews/
data.shtml: NOAA/ National Weather Service National Centers for Environmental
Prediction Climate Prediction Center. Accessed 24 July 2008.
O'Malley, R., & Wing, K. (2000). Forging a new tool for ecosystem reporting. Environment, 42, 20.
Parton, W. J., Scurlock, J. M. O., Ojima, D. S., Gilmanov, T. G., Scholes, R. J., Schimel, D. S.,
et al. (1993). Observations and modeling of biomass and soil organic matter dynamics
for the grassland biome worldwide. Global Biogeochemical Cycles, 7, 785−809.
Pickup, G. (1998). Desertification and climate change — The Australian perspective.
Clomate Research, 11, 51−63.
Prince, S. D. (1991). A model of regional primary production for use with coarseresolution satellite data. International Journal of Remote Sensing, 12, 1313−1330.
Prince, S. D. (2002). Spatial and temporal scales of measurement of desertification. In M.
Stafford-Smith & J. F. Reynolds (Eds.), Global desertification: Do humans create
deserts? (pp. 23−40). Berlin: Dahlem University Press.
Prince, S. D. (2004). Mapping desertification in southern Africa. In G. Gutman, A.
Janetos, C. O. Justice, E. F. Moran, J. F. Mustard, R. R. Rindfuss, D. Skole, & B. L. TurnerII
(Eds.), Land change science: Observing, monitoring, and understanding trajectories of
change on the Earth's surface (pp. 163−184). Dordrecht, NL: Kluwer.
Prince, S. D., Brown de Colstoun, E., & Kravitz, L. (1998). Evidence from rain use
efficiencies does not support extensive Sahelian desertification. Global Change
Biology, 4, 359−374.
Prince, S. D., Goward, S. N., Goetz, S., & Czajkowski, K. (2000). Inter-annual atmosphere–
biosphere variation: Implications for observation and modeling. Journal of
Geophysical Research Atmospheres, 105, 20,055−20,063.
Prince, S. D., Wessels, K. J., Tucker, C. J., & Nicholson, S. E. (2007). Desertification in the
Sahel: A reinterpretation of a reinterpretation. Global Change Biology, 13,
1308−1313.
Reynolds, J. F. (2001). Desertification. In S. Levin (Ed.), Encyclopedia of Biodiversity
(pp. 61−78). San Diego, CA: Academic Press.
Reynolds, J. F., Smith, D. M. S., Lambin, E. F., Turner, B. L., II, Mortimore, M., Batterbury,
S. P. J., et al. (2007). Global desertification: Building a science for dryland development. Science, 316, 847−851.
Reynolds, J. F., & Stafford Smith, M. (Eds.). (2002). Global desertification: Do humans
create deserts? Berlin: Dahlem University Press.
Roder, W. (1964). The division of land resources in Southern Rhodesia. Annals of the
Association of American Geographers, 54, 41−52.
Safriel, U., & Adeel, Z. (2005). Dryland systems. Ecosystems and human well-being:
Current state and trends (pp. 948). Washington, D.C.: Island Press.
1057
Scoones, I. (1992). Land degradation and livestock production in Zimbabwe's
communal areas. Land Degradation and Rehabilitation, 3, 99−114.
Soriano, A., & Paruelo, J. M. (1992). Biozones — Vegetation units defined by functional
characters identifiable with the aid of satellite sensor images. Global Ecology and
Biogeography Letters, 2, 82−89.
SOTER (2002). Soil and terrain database for Southern Africa (SOTRSAF) 1:2.5 million,
version 1.0. Rome: FAO Web page available on-line on http://www.fao.org/ag/agl/
agll/soter.stm. Accessed 8 August 2008.
Stoms, D. M., & Hargrove, W. W. (2000). Potential NDVI as a baseline for monitoring
ecosystem functioning. International Journal of Remote Sensing, 21, 401−407.
Tappan, G. G., Sall, M., Wood, E. C., & Cushing, M. (2004). Ecoregions and land cover
trends in Senegal. Journal of Arid Environments, 59, 427−462.
Thomas, D.S.G., & Middleton, N.J. (Eds.) (1992). World atlas of desertification. London:
United Nations Environment Programme, Edward Arnold.
UNCED (1993). Agenda 21: Earth Summit — The United Nations programme of action from
Rio: Managing fragile ecosystems: Combating desertification and drought. United
Nations Conference on Environment and Development (pp. 294).
UNEP (Ed.) (1997). World atlas of desertification. London & New York: Arnold & Wiley,
on behalf of UNEP.
Verstraete, M. M. (1986). Defining desertification: A review. Climatic Change, 9, 5−18.
Vincent, V., Thomas, R. G., & Staples, R. R. (1960). An agricultual survey of Southern Rhodesia:
Part I agro-ecological survey. Salisbury: Federation of Rhodesia and Nyassaland.
Wessels, K. J., Prince, S. D., Frost, P. E., & van Zyl, D. (2004). Assessing the effects of
human-induced land degradation in the former homelands of northern South Africa
with a 1 km AVHRR NDVI time-series. Remote Sensing of Environment, 91, 47−67.
Wessels, K. J., Prince, S. D., Malherbe, J., Small, J., Frost, P. E., & VanZyl, D. (2007b). Can
human-induced land degradation be distinguished from the effects of rainfall
variability? A case study in South Africa. Journal of Arid Environments, 68, 271−297.
Wessels, K. J., Prince, S. D., & Reshef, I. (2008). Mapping land degradation by comparison
of vegetation production to spatially derived estimates of potential production.
Journal of Arid Environments, 72, 1940−1949. doi:10.1016/j.jaridenv.2008.05.011.
Whitlow, R. (1988). Land degradation in Zimbabwe, 62+Appendices. Harare: Department of Natural Resources, Government of Zimbabwe/Department of Geography,
University of Zimbabwe.
WMO (2005). Climate and land degradation. Geneva: World Meteorological Organization.
Zimbabwe (1979a). Land classification. Harare, Zimbabwe: Department of the Surveyor
General.
Zimbabwe (1979b). Provisional soil map of Zimbabwe–Rhodesia. Edition 2. Salisbury,
Zimbabwe Surveyor-General, Chemistry and Soil Research and Specialist Services
Section, Pedology and Soil Survey.
Zimbabwe (1982). Map of rural population density: August 1982. Based on enumeration
areas. Harare: Zimbabwe Surveyor-General, Central Statistical Office.