An assessment of land degradation in the Save catchment of ...degradation and the spatial scale of...

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(2007) 39.3, 380–391

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Vol. 39 No. 3, pp. 380–391, 2007ISSN 0004-0894 © The Authors.

Journal compilation © Royal Geographical Society (with The Institute of British Geographers) 2007

Blackwell Publishing Ltd

An assessment of land degradation in the Save catchment of Zimbabwe

Julia Mambo and Emma Archer

School of Geography, Archaeology and Environmental Science, University of the Witwatersrand,

Johannesburg, South Africa

Email: [email protected]

Revised manuscript received 12 December 2006

The lack of reliable baseline information on land degradation is a hindrance towards itsmonitoring and mitigation. Of particular interest is the identification of areas susceptibleto degradation. In this study, remote sensing and GIS technologies were applied todetect and map susceptibility to land degradation in Buhera district, in Save catchment,Zimbabwe. Data used included Landsat TM and ETM imagery for 1992 and 2002, agro-ecological zones, vegetation cover and population density. The study identified fivepreliminary categories of degradation susceptibility ranging from very high to low.

Key words:

Save catchment, land degradation, remote sensing, baseline data, NDVI, GIS

Introduction

Political, economic and environmental factors haveinteracted in complex ways to bring about the currentstate of land degradation in the communal areas ofZimbabwe (Whitlow 1988; Stocking 1996). Previousstudies on this phenomenon have indicated wide-spread degradation and soil erosion in the country,particularly in the communal areas, primarily attributedto farming methods, livestock practices as well ashistorical factors. This paper presents research onproduction of baseline information on geographicalpatterns of susceptibility to land degradation in theSave catchment, Zimbabwe. Baseline informationis essential for stakeholders to monitor, control andmanage land degradation. The absence of such relevantand reliable catchment-wide baseline information,necessary for assessing the severity and extent ofdegradation, has made its monitoring difficult.

Understanding and monitoring land degradation

Land degradation is a major factor in the progressivedeterioration of African livelihoods and economies(Eswaran

et al.

2001). The Dahlem Report (Reynolds

and Stafford Smith 2001) suggests that land degradationis caused by a complex combination of socioeconomicand biophysical factors. These interact and exertinfluences that vary in space and time, resulting inreduced biological productivity of land (Barrow 1991;Dregne 1998). Population increase, poverty and over-grazing are often cited as major causes of degradation,particularly in sub-Saharan Africa (Booth

et al.

1994;Chapman and Mather 1995; Young 1998; Reynoldsand Stafford Smith 2001). Inequitable land distributionand societal/cultural conflicts have exacerbated theproblem (Whitlow 1988; Moyo

et al

. 1991; Booth

et al.

1994). Climate change, in arid and semi-arid eco-regions, especially the reduction in total precipitationduring crucial growing seasons or even slight shifts inthe seasonal distribution of rainfall, may contributeto land degradation, by reducing vegetation cover andthus increasing soil erosion (Reynolds and StaffordSmith 2001).

The complexities of the dynamics involved in theonset and progression of land degradation complicateits assessment. Young (1998) observes the lack oftechniques to assess the extent and severity of landdegradation. This lack of baseline data makes moni-toring difficult and hinders policy formulation tomanage the problem (Barrow 1991; Young 1998).

Land degradation in the Save catchment of Zimbabwe

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Vol. 39 No. 3, pp. 380–391, 2007ISSN 0004-0894 © The Authors.Journal compilation © Royal Geographical Society (with The Institute of British Geographers) 2007

Nonetheless recently developed remote sensingtechniques and geographic information systems havebeen employed to bridge this gap (Prince 2002;Eastman 2003; Lillesand

et al.

2004). Remotely senseddata can be integrated into a GIS, which provides aplatform for storage, retrieval and updating of largedatasets for analysis and presentation. The measure-ment of spatially continuous variables linked to landdegradation and the spatial scale of land degradationcan be done using remote sensing (Prince 2002).Further, the availability of time series data allows formonitoring of land cover change (Lillesand

et al.

2004).In particular, remote sensing facilitates the assessmentof vegetation, soil and water, which are three essentialbiophysical components of land degradation (Barrow1991; Lambin and Strahler 1994; Reynolds and StaffordSmith 2001; Lillesand

et al.

2004).While acknowledging limitations of techniques

used, this study demonstrates in part the effectivenessof GIS and remote sensing as tools for the produc-tion of baseline data for assessment and monitoringof land degradation in the Save catchment.

Monitoring land degradation using remote sensing

Vegetation cover, in particular the change in speciescomposition of vegetation, is often used as anindicator for susceptibility to degradation as well asits severity (Barrow 1991; de Vreede 2001; Lambinand Ehrlich 1996; Prince 2002; Scoones 1992).However, Tucker

et al.

(1991) have argued that inarid and semi-arid regions, such as the Sahel and theSahara, which are influenced by short-term drought,vegetation cover is not a good indicator of long-termdynamics of degradation. In contrast, a study con-ducted by Lambin and Ehrlich (1996), on land coverchanges in sub-Saharan Africa from 1982 to 1991,showed vegetation as a good indicator of vegetationdynamics and degradation in many countries in theregion including the arid and semi-arid regions.

The distinct reflectance spectrum of vegetation,low in the red and high in the infra-red, has beenexploited in a large number of spectral indices, as aremote sensing-based technique (Prince 1998;Reynolds and Stafford Smith 2001). The NormalizedDifference Vegetation Index (NDVI), a spectralvegetation index which measures soil and vegetationmoisture (Singh 1989; Lyon

et al.

1998), has beenwidely used for environmental change monitoring(Young 1998; Eastman 2003; Lillesand

et al.

2004).The index can be used to identify areas showingdistressed or degraded vegetation, leading to identi-

fication of possible degraded areas (Barrow 1991;Booth

et al.

1994). Alternatively, the Soil AdjustedVegetation Index (SAVI) measures soil status, partic-ularly soil colour, moisture, texture and presenceof organic matter, which influences the spectralreflectance of vegetation thus influencing calculatedvegetation indices (Huete

et al.

1985). The SAVI hasbeen highly recommended for monitoring vegetationin semi-arid and arid regions with sparse vegetationcover and little rainfall as well as monitoring land-scapes with different soil backgrounds (Huete

et al.

1985; Huete 1988). Whilst not produced for monitor-ing land degradation, spectral indices have beensuccessfully applied in such studies.

While there has been a wide application of spectralindices in the assessment of ecological processes suchas land degradation, Asner and Lobell (2000) alludeto the difficulties of measuring indicators such asthe spatial extent of vegetation and bare soils usingremote sensing techniques. This has been attributedto the scale at which these variations occur. Ofparticular interest is the fact that spatial and temporalvariation in greenness can be measured through theNDVI. However, the greenness does not separatethe effects of changing vegetation conditions relativeto vegetation cover (Asner and Lobell 2000). This isparticularly relevant in the arid and semi-arid eco-systems which, due to limited vegetation cover,have limited spectral information, insufficient spatialresolution and the influence of soil brightness, all ofwhich hinder improved mapping in these eco-regions(Asner and Heidebrecht 2005).

Measuring the decline of vegetation productivitythrough Net Primary Production (NPP) is another remotesensed based method used to assess degradation.NPP measures the accumulation of biomass overtime (Prince 2002). Based on the carbon cycle, NPPmeasures soil nutrients. While it has been noted thatdesertification normally reduces NPP (Asner andHeidebrecht 2005) and reductions in NPP below acertain range may indicate degradation (Prince 19982002), Huenneke

et al.

(2002) have argued that thestructural pattern and biological material of vegetationtypes changes, while the NPP does not. While NPPalone is directly related to rainfall, Rain Use Efficiency(RUE), calculated as a ratio of NPP and rainfall overa five-year period, provides a useful index of degra-dation (Reynolds and Stafford Smith 2001).

Change detection

Image differencing of NDVI, which entails a pair-wise comparison of spectral reflectance values, is a

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commonly used technique for change detection(Singh 1989; Lyon

et al.

1998; Sohl 1999). Althoughconsidered simplistic, simple differencing has yieldedcomparatively good results when compared with morecomplex methodologies (Singh 1989). However,one of the limitations of this method is the processof deciding the level of suitability or the minimumsize of sites needed for the final selection, which istermed thresholding. This process is used to ascertainthe upper and lower limits of normal variation, toidentify positive, negative and no change areas (Singh1989; Eastman and McKendry 1991; Linderman

et al.

1998). Since there is no automated method for thisprocess, thresholding is rather dependent on theanalyst (Sohl 1999; Eastman 2003). Sensitivity of theprocedure to geo-registration is another limitation(Singh 1989). Sohl (1999) cautions that imagedifferencing should not be solely relied upon forchange analysis.

Change vector analysis (CVA) is another commonlyused technique for monitoring land use change, defor-estation and land degradation. Change vector analysismeasures spatial patterns of change to characterizethe magnitude and direction in spectral space froma first to a second date. The technique has beenwidely used in environmental change studies invarious African countries, including Zambia andMalawi (Lambin and Strahler 1994; Eastman 2003).

Various change detection methods have been pro-posed and two basic approaches have been identi-fied, namely comparative analysis of independentlyproduced classifications, and simultaneous analysisof multi-temporal data (Singh 1989; Lambin andStrahler 1994). This study uses both approaches toassess land cover change in the Save catchment.

Study site and background

Degradation studies in Zimbabwe have indicatedestimated soil loss due to sheet erosion to be asmuch as 50 tonnes/ha (Elwell 1987). Whitlow andCampbell (1989) estimated that 25 per cent of thecommunal areas were severely eroded compared to2 per cent in the commercial areas. Whitlow (1988)found that soil erosion was prevalent in all agro-ecological zones, but more pronounced in zonesIII, IV and V, with zones IV and V characterizedby unreliable rainfall and poor soils. The agro-ecological zones as defined by Vincent and Thomas(1960) are based on seasonal rainfall, quantity andreliability, and on soil types. Zone I is classified asthe most productive with highest rainfall and deep

fertile soils and zone V as the least agriculturallyproductive with very little rainfall and poor soils. Landdegradation is prevalent, particularly in the communallands, where an estimated 80 per cent of degradedland in the country was prevalent (Scoones 1992).Despite the assumed contribution of overgrazing andlivestock overstocking to degradation, studies byScoones (1992) in the communal lands indicated thatthe erosion patterns at the time were not concentratedin key grazing areas and degraded areas did notaffect livestock production.

The Save catchment in Zimbabwe, which is consti-tuted primarily of agro-ecological zones III, IV andV and is predominantly communal, comprises twomajor river systems – the Runde and the Save (seeFigure 1). Three tenure systems exist within thecatchment: communal areas, resettlement schemesand freeholds for small to medium scale commercialfarming. Of the total catchment area in 1982, 39 percent was under communal tenure, inhabited by 44 percent of the communal population of Zimbabwe, whichconsisted mainly of subsistence or peasant farmers.With a then estimated population density of 40 to70 persons per km

2

, population growth has been citedas a factor contributing to the deterioration of theenvironment in the catchment (Eschweiler 1992).

1

The catchment is of national importance to theagricultural development of the country, with largeareas of economically important irrigation schemes(Eschweiler 1992). Most of its over 8.4 millionhectares of land falls within agro-ecological regionsIV and V, which are characterized by unreliablerainfall and poor quality soils. As in most communalareas in the country, human and animal populationsexceed carrying capacity (Whitlow 1988; Scoones1992). Nonetheless, in the more resilient savannahecosystems, separation of the natural versus theman-made influences has been difficult, given thatvegetation change and soil erosion are part of theecosystem changes (Scoones 1992). Moreover, evi-dence of adverse impacts of land degradation oncattle production was lacking and the indicatorsmeasured, such as vegetation change and soil ero-sion, could not support the widely held contentionthat communal areas in Zimbabwe are severelydegraded (Scoones 1992). This paper will continue touse the term ‘degradation’, but the authors arecognizant of cautions such as those raised byScoones as to its use.

Land degradation in the catchment is widespreadand severe, especially in communal areas, whichare characterized by devegetated landscapes, poor


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quality pasture and soil infertility. Degradation ismostly manifest as gullies that render large tracts ofland virtually unusable, threatening water supplyand quality (Booth

et al

. 1994). While gullies are anassumed indicator of severe erosion or degradation,Stocking (1996) argues that gullies are only a symptomof a degraded catchment not the cause, and theirsediment contribution is minimal.

The severity of land degradation in the catchmenthas, however, attracted the attention of more than15 organizations, including government departments,local community groups, non-governmental organiza-tions, the private sector and research organizations(Eschweiler 1992; IUCN-ROSA 1996). Despite regis-tering significant success in initiating interventionsto mitigate degradation, these organizations have notperformed to expectation. Minimal coordination anda piecemeal approach to implementation resultedin duplication of effort, inefficiency and competitionamong the stakeholders. The mitigation methods usedby the organizations did not take into account thediversity of land tenure systems within the catch-ment, thus affecting the success of some projects(IUCN-ROSA 1996).

The uncoordinated response to the degradationproblem is symptomatic of a weak basis upon whichpolicies and actions for managing natural resources inthe catchment are premised. The problems includea lack of reliable information indicating the severity ofand susceptibility to degradation across the catch-ment; weak institutional frameworks that perpetuateuncoordinated responses for monitoring and impactassessment; and a lack of suitable interventionsappropriate for the catchment. The availability ofreliable baseline information influences the other twoproblematic areas. It is therefore essential for suchinformation to be in place as an initial requirementtowards addressing the problem of uncoordinatedresponses to the land degradation problem in thecatchment. This does not, however, imply that thedevelopment of this information should proceed inisolation of initiatives towards addressing the otherelements.

The study area

The study was based in Buhera, a district mostrepresentative of the Save catchment in terms ofclimate, land use and population density (see Figure 2).

Figure 1 Location and extent of the Save catchment in Zimbabwe2

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It is entirely under communal land tenure and subjectto a combination of several factors that acceleratedegradation. Figure 2 provides an indication of theseriousness of land pressure for cultivation.

Buhera district falls under three agro-ecologicalzones, III, IV and V. As stated earlier, zones IV andV are characterized by unreliable rainfall and poorsoils. Zone I is classified as the most productive, withhighest rainfall and deep fertile soils, and zone V as

the least agriculturally productive, with very littlerainfall and poor soils. According to 1992 data,70.7 per cent of the total area in Buhera district wasunder cultivation, while only 27.7 per cent was underwoodland (see Figure 2). There are indications thatwoodland has continued to be cleared to expandagricultural activities. The lack of baseline dataprior to 1992 has made monitoring of degradationprior to that date difficult.

Methods used

A conceptual framework for assessing severity andsusceptibility of land degradation was developed forthis study, based on assessment of changes in vegetationcover, manifestation of degradation and biophysicalcharacteristics such as climate, soils and topography.Figure 3 highlights elements of the conceptual modeland Table 1 lists the data elements that were usedto assess susceptibility to land degradation.

Image processing

Buhera straddles three Landsat satellite scenes, namely169/74, 169/73 and 168/74. Two sets of the threeLandsat TM and ETM scenes for the years 1992 and2002 were georeferenced using image to imagerectification with SPOT images projected in UTM(Zone 36S) and referenced onto the WGS84 ellipsoid.An average of 12 ground control points per image

Figure 2 Main land uses/cover in Buhera district (in 1992)3

Figure 3 Flow diagram showing assessment of severity of degradation in the Save catchment


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were used, with a residual error of approximately2.1 metres. The three scenes were mosaiced and animage enclosing Buhera district was extracted, uponwhich subsequent image processing was undertaken.Two bands, 3 and 4, were used for change detectionanalysis. Band 3 (Red) is absorbed by chlorophyll,assisting in vegetation differentiation and the reflectanceof Band 4 (Near Infra Red) helps to determinevegetation types, particularly biomass (Lillesand

et al

. 2004).

Vegetation change analysis

Two NDVI images were used to identify areas ofchange between the two dates 1992 and 2002. NDVI,expressed as a ratio between measured reflexivityin the red and the infra-red bands is calculated asNDVI = (NIR

−

R)/(NIR + R), where NIR in TMimagery is near infra-red band 4 and R is red band3. The advantage of using NDVI is that it not onlyidentifies the areas of change but also assesses thestate of vegetation. However, it is important to notethat non green vegetation (not in leaf) will not givea high NDVI and thus may not be measured.

To identify the spatial patterns of change, simpleimage differencing was done on the NDVI images.Thresholding, to ascertain the upper and lower limitsof variation beyond which true change is consideredto have occurred, was performed using the RECLASScommand in IDRISI

™

. Threshold values, based onthe standard deviation and the mean, were derivedfrom a histogram of each image and a standarddeviation of 2 was used. The reclassed image depictedareas in three categories, positive (more vegetation),negative (less vegetation) and no-change areas, overthe ten years.

The second comparative method adopted forchange analysis was CVA, which analyses eachpixel value in multi-band data, using two bandsfrom different years. The resulting image highlightsthe direction (expressed as an angle) and magnitude

of change (Singh 1989). If used with the red andinfra-red bands (as in the NDVI process), the CVAdirection of change highlight areas of vegetationgrowth or reduction (Sohl 1999). The accuracy ofresults is highly dependent on the availability ofanniversary date data.

Limitations of the chosen methodologies

Many remote sensing based methods have limitationsassociated with the different environments, the analyt-ical methods and the scales of application (Reynoldsand Stafford Smith 2001). This study encounteredlimitations on all these fronts. A notable limitation wasaccess to anniversary data. 1992, being a droughtyear, was not ideal to serve as a baseline formonitoring, which is ordinarily based on a normalyear (Lambin and Ehrlich 1996).

Results

A comparison of the NDVI images reveals that in1992, higher NDVI values (from 0.11 to 0.73)indicate areas with healthy vegetation mainly in thewoodland land-cover class despite 1992 being adrought year. The other different colour tonesidentified, with lower NDVI values (from

−

0.38 to

−

0.51) indicate high moisture, given the low NDVIreflectance of moisture and also given the similarityof the tones to water bodies. These are low-lyingareas, characterized by riverine vegetation. Incontrast, 2002 shows a general reduction in NDVIvalues (from 0.25 to 0.63), indicating a possiblereduction in healthy vegetation across the district,but the area to the north of the district falling underwoodland class shows high NDVI values (from0.60 to 0.75). Tones indicating moisture patterns, asidentified in 1992, have completely disappeared in2002, except for the water body with an NDVIvalue of

−

0.63.

Change analysis using NDVI

Simple differencing and reclassification of NDVI imagesproduced an image highlighting areas of positive,negative and no change, as shown in Figure 4. Thedifference image clearly highlighted three hotspotsof negative change that would constitute priority areasfor intervention and further detailed investigation.

Change vector analysis (CVA)

The CVA process produced two images showing thedirection and magnitude of change. In Figure 5, thelarger directions of change, ranging from 246.51 to

Table 1 Data elements used to assess susceptibility to land degradation

Susceptibility

Extent of loss of vegetation coverIndications of manifestation of degradationLand use and populationLandscape characteristicsClimate (rainfall) and soils (AEZ)

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359.01 (degrees), are prevalent in the hotspot areasidentified by the NDVI image differencing. Thisimplies a reduction in vegetation cover in these areas.The smaller directions of change could indicate thatlittle or no change in vegetation growth has occurred.The image also shows a general reduction invegetation across the whole image. The results arecompatible with the image showing magnitude ofchange, even though the degree of change wasmoderate (between 132 and 170), given that thehighest magnitude of change would be 303. Areasof high magnitude of change correspond to areas ofhigh direction of change.

The results of the change vector analysis and theNDVI image differencing identified three correspond-ing areas of vegetation reduction or hotspots. These areasare considered to be at greatest risk of degradation.

Ancillary data

Areas that had experienced greatest change, possiblevegetation stress and likely degradation, or where

degradation is likely to occur, were identified above.To analyse for susceptibility to land degradation,additional data such as agro-ecological zones (soilsand climate) and existing data on vegetation coverwere used. Vegetation cover and type is a goodindicator of soil conditions and thus can be used inconjunction with agro-ecological zones data toidentify fragile areas.

The negative change image (Figure 4) was digitizedto delineate the different degradation classes, high,moderate and low. It should be noted that this layeris somewhat subjective as it depended on the judge-ment of the analyst. The negative change map and twoother maps, agro-ecological zones and vegetationcover, were rasterized to allow for analysis in a GIS(Figure 6).

The degradation map and the agro-ecological zoneswere analysed using cross tabulation. The resultantmap, showing patterns of land degradation in thethree agro-ecological zones, was further analysedusing cross tabulation again, with a vegetation map to

Figure 4 A reclassed image generated by differencing the NDVI images


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Figure 5 CVA image showing direction of change

Figure 6 Description of GIS analysis procedure

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characterize the vegetation cover in the degradationhotspots. The resultant map from the three layers(Figure 7) indicated the following classes:

1 Very High Susceptibility – areas of high risk in thedegradation map, falling under agro-ecologicalzone 5 (extensive farming and regarded as arid) andunder woodland in the vegetation map.

2 High Susceptibility – areas of high risk in thedegradation map, falling under agro-ecologicalzone 3 (semi-intensive farming) and under wood-land in the vegetation map.

3 Moderate Susceptibility – areas of moderate risk onthe degradation map, falling under agro-ecologicalzone 5 (extensive farming and regarded as arid) andunder any vegetation or land class type, includingbushland and cultivation.

4 Low Susceptibility – these are areas of moderaterisk in the degradation map, falling under any agro-ecological zone, mainly zone IV (semi-extensivefarming and semi-arid) and any vegetation or landclass type.

Table 2 demonstrates how the four classes arecoded in the shades of grey depending on severity.Susceptibility to degradation required investigationof other factors such as population pressure, anassumed contributor of land degradation. A mapwhich classifies population density by ward was

used. The lowest density was recorded as being22.07 and the highest 80.00.

A visual comparison of Figure 7 and the populationdensity map produced the following results:

• Areas of very high indication of degradation werewards with the lowest population density of 22.07–24.00 and 24.01–35.00.

• Areas of high indication to degradation wereidentified in wards of moderate population densityof 45.01–50.

• Areas of moderate to low indication were identifiedin low and moderate to high population density of24.01–35 and up to 60.00.

The six categories of population density were regroupedinto three classes: high, moderate and low, foranalysis with the land degradation indication data.Using cross tabulation, a map representing areassusceptible to degradation was produced (Figure 8).The following findings were made and are codeddepending on susceptibility.

Zone 1 (the darkest grey) represents areas of veryhigh susceptibility, with high indication (from Figure 6)despite being in low population areas, and highpopulation in any class of indication (except nochange areas). Zone 2 represents high susceptibilityareas with high to moderate degradation indicationbut with medium population density. Zone 3 repre-sents moderate susceptibility areas, with moderateto low indication and medium population density.Zone 4 (white) was classified as low susceptibilityareas, with no indication and mainly under cultivationas well as in any agro-ecological zone. These areasare not a priority but should nonetheless be targetedfor suitable interventions.

Figure 8 shows that most areas of very high sus-ceptibility (Zone 1) were located along the majorrivers and their tributaries in both the northern andsouthern parts of the district. This clearing could beattributed to expansion of agricultural activities by the

Figure 7 Land degradation indication map

Table 2 Illustration of how severity classes were derived

Severity

AE zone

3 4 5

Severity High AE zone woodland woodlandModerate Any vegetationLow


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smallholder farmers in these areas. In the northernpart of the district, some areas of high susceptibilitywere located along the major highway, suggestingeither firewood trading or deforestation for curiobusinesses. All these possible suggestions would needto be substantiated through further studies.

Discussion

The lack of information about the severity and sus-ceptibility of land degradation can be partlyaddressed through utilization of technologies suchas remote sensing and GIS. Although all the datarequired for undertaking the necessary analysis is notreadily available, enough data exists to potentiallyimprove the basis for decisions aimed at addressingthe land degradation problems in the catchment.

The data produced, showing geographical patternsof severity and susceptibility to land degradation, canbe utilized for decisionmaking by identifying thewards where susceptibility is greatest. The methodused for identifying patterns of vegetation changebetween 1992 and 2002, change vector analysis andimage differencing of the NDVI images, highlightedareas of greatest change where degradation is likely(degradation hot spots) and where interventions formitigation of degradation should be targeted. Theprocess further assisted in identifying land degrada-

tion hot spots that may require urgent attention,namely areas showing possible vegetation stress andvegetation cover loss, as a possible manifestation ofland degradation. GIS analysis enabled comparison ofthe identified areas with topographic and vegetationdata to identify the fragile areas. It is important tonote that spatial and temporal scales should takeninto consideration, since the time scale over whichmeasurements are made is critical for detectinglong-term trends of degradation against the short-term variations (Scoones 1992). This data is not,however, conclusive as additional data is requiredto justify the areas chosen.

Techniques used in this study are by no meansexhaustive, given the debates on the suitability ofvegetation as an indicator for susceptibility to landdegradation, as well as the use of NDVI versus SAVI orNPP for assessment of degradation in arid and semi-aridareas such as Buhera. In addition, lack of access to themost recent data on land tenure, influenced by theongoing land reform programme in the country, alsohindered further analysis of the possible dynamics anddrivers of change in the district. Specific biophysicaldata, such as a detailed vegetation map showingvegetation types or species as well as a detailed soilmap of the area, were problematic to obtain.

Conclusion

The study has demonstrated that the problem of lackof information about the severity and susceptibilityof land degradation can be partly addressed byharnessing technologies such as remote sensing andGIS. The data produced provides an indication ofpriority areas (degradation hot spots), where interven-tions to prevent further change and where practicesfor mitigating the associated degradation shouldbe targeted. The generated data may be useful forenvironmental organizations, government and localcommunities in mainstreaming land degradation inter-ventions and improved coordination of its mitigation.Information produced would also be used to targetwhere further studies should be undertaken to gain adetailed understanding of the dynamics of degradationand the mitigation measures appropriate for these areas,particularly given the findings showing populationdensity as less of a factor than might be assumed.

Acknowledgements

We would like to thank Dr A. Agumya (ICRAF) for theprovision of the population data set and his assistance, and

Figure 8 Rivers indicated in black line and highways in double black broken line

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Forestry Commission of Zimbabwe Staff for the LandsatImagery and other data sets as well as their assistance.

Notes

1 According to Prince (1998), 1992 census data showedthat out of a total population of 11.4 million, 98.8 percent were black and of this 51.4 per cent still resided inthe communal areas.

2 Map compiled by author from VegRIS data (ForestryCommission) and Surveyor General Topographic Data.

3 Map compiled by author from VegRIS data (ForestryCommission) and Surveyor General Topographic Data.

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