The use of integrated remotely sensed and GIS data to determine … and... · 2011. 3. 30. · SOl43-6228(%)00005-7 The use of integrated remotely sensed and GIS data to determine

Applied Geography, Vol. 16, No. 3, pp. 225-242, 1996 Copyright 0 1996 Else&x Science Ltd

Printed in Great Britain. All rinhts reserved 0143-6228/96 Sk00 + 0.00

SOl43-6228(%)00005-7

The use of integrated remotely sensed and GIS data to determine causes of vegetation cover change in southern Botswana

Susan Ringrose

Department of Environmental Science, University of Botswana, Private Bag 0022,

Gaborone, Botswana

Cornelis Vanderpost

Department of Environmental Science, University of Botswana, Private Bag 0022,

Gaborone, Botswana

Wilma Matheson

Westwood International School, PO Box 2446, Gaborone, Botswana

The characteristics and dynamics of dry savanna vegetation cover are receiving considerable attention from the perspectives of both global change and range degradation studies. Problems include the establishment of major savanna determinants and the floristic response of vegetation cover to given stimuli. Basic work on determinants is required to assess the nature and causes of natural resource depletion, particularly in the Kalahari region. Use of image processing techniques involving the association of pizel values and field data have resulted in the development of a vegetation map indicating floristic content and structure. Results indicate that a clear distinction can be made between classes containing high proportions of taller woodland species and those that contain mainly woody weeds. Degraded areas with sparse vegetation cover and large areas of bare soil were also identified. The GIS technique of buffer analysis was applied to determine the extent to which herbivory (livestock) and the gathering of bush products by the local population were directly involved in the spatial distribution of savanna types. Results indicate that most of the degraded areas are within 2 km of villages and boreholes. Most of the woody weed areas fall within a 2-4&m zone around boreholes. Spatial association indicates that uncontrolled bush product harvesting and goat grazing are primarily responsible for village-centred degradation, while cattle grazing around numerous boreholes is a primary cause of woody weed development. These kinds of savanna adaptive responses are dlfflcult to reverse in rural Botswana because of increasing population pressure and concomitant poverty. Copyright 0 1996 Elsevier Science Ltd

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Vegetation change in southern Botswana: S Ringrose et al

Changes in human land-related activities in combination with drought-prone conditions have had a severe impact on the state of the vegetative natural resources in dryland areas worldwide, but particularly in sub-Saharan Africa (see, for example, Briggs et al., 1993; UN General Assembly, 1994). Discussion surrounding a number of these issues concerns the nature of dry savanna vegetation and how the floristic characteristics of natural vegetation cover are modified through time under various influences. A number of these modifications appear to result in vegetation change and decline. This can be a particularly acute problem in marginal semi-arid areas where the livelihood of the rural poor is adversely affected. The dry and/or broadleaved savannas typical of semi-arid areas are regarded as being a dynamic mosaic, resulting from anthropogenic and/or natural causes in the form of fire and herbivory (livestock grazing), along with available nutrients and moisture (Frost et al., 1986). To many ecologists, change in savanna areas has been documented as a decline in relatively open woodland with commensurate increases in the proportion of woody weeds (bush encroachment) (e.g. Graetz et al., 1988; Passini et al., 1989). Some of these changes in southern Africa are believed to result from global warming phenomena (Magadza, 1994). These changes take the form of natural resource decline as areas of dense woody weeds tend to exclude cattle and minimize grass regrowth. Perhaps even more critical, especially in developing countries, is the overall decline of any kind of vegetation cover, resulting in an increase in the area of bare soil. This means that the natural bush-derived (vezdproduct) resources on which local people rely to supplement their livelihood are becoming depleted (Sefe et al., 1996). The increase in bare soil also brings increased potential for moisture reduction, erosion and compaction, which decreases organic content and inhibits future regrowth (Ringrose et al., 1996).

Whereas it is assumed that savanna type is related to the main determinants (Scholes and Walker, 1993), few studies have considered the relative involvement of herbivory and human veZdproduct use (e.g. Vanderpost, 1995) in determining savanna types. Relatively little previous work has been undertaken using Landsat Thematic Mapper data, in combination with GIS techniques, to map floristic composition and vegetation structural components to ascertain major savanna determinants in the context of natural resource depletion (Fiorella and Ripple, 1993; Chavez and MacKinnon, 1994). Increasing importance is being attached to mapping vegetation cover using Thematic Mapper data, although this has mainly taken place in wetter climatic belts (e.g. Franklin, 1986; Niemann, 1993). Other studies have concentrated on the use of multispectral scanner data in relation to species content and vegetation structure (Matheson, 1994) and how they change over time (Lee and Marsh, 1995). Increasing emphasis is being placed on the use of geographic information systems (GIS) to attempt to develop an increased understanding of complex vegetation-human interrelationships in a spatial context. General environmental change has been assessed using GIS techniques (Grunblatt et al., 1992; Peuquet et al., 1993). Alternatively, mapped data derived from satellite imagery are used as inputs to a GIS database (Ghosh, 1993; O’Neill et al., 1993; Hastings and Di, 1994; Ringrose et al., 1996).

The aims of this paper are, first, to provide spatial information on vegetation structure and floristic composition derived from Thematic Mapper imagery to identify change in terms of natural resource depletion in an area of dry savanna. Secondly, the study considers the main determinants of the resulting savanna mosaic in terms of the impacts of herbivory and direct human-related activity using GIS analysis techniques.

Study area

The location of the Malwelwe area in south-central Botswana is shown on Figure 1. The area is 2523.4 km*, bounded by 23”45’-24”15’ south and 25”00’-25”40’ east. For decades

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L


Figure 1 Location of the Malwelwe area in southeast Botswana

some form of cultivated agriculture or livestock herding has been prevalent. Along with frequent fires, this has had an impact on the nature of the vegetation cover. Ground level varies from 1220 m as1 in the southeast to 1100 m as1 in the northwest and northeast. Much of the area is underlain by Kalahari sand (sandveld) which thins in the vicinity of fossil valleys on whose linear axes most of the villages occur. The southeast quadrant comprises more loamy soils (hardveld). General soils data (Table 1) were obtained from the Soil

Table 1 Description of main soils in the Malwelwe area

Soil type (FAO) Soil description

Haplic calcisols Moderately deep to very deep, imperfectly to well drained, grey to brawn, sandy loams to clays

Petric calcisols Moderately deep, moderately well to well drained, greyish brown to pale brown, fine sandy loam to silt loam

Ferralic arenosols Deep to very deep, well to excessively drained, yellowish brown to dark red, coarse sands to loamy fine sands

Chromic luvisols Moderately deep to very deep, moderately well to slightly excessively drained, strong brown to dark red, tine and fine medium sandy loam

Source: Soil Mapping and Advisory Services Project (1990)

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Table 2 Details of natural vegetation cover

Location Designation Natural vegetation description

Fossil valleys A5 Northern microphyllous savanna: Acacia erioloba, A. mellifera, Combretum hereronse

Main sandveld area A2 Eastern broadleaved savanna: Burkea africana, Ochna pulchra, Terminalia sen’cea, A. jleckii

Rock outcrops in hardveld Cl Croton gratissimus woodland: Croton gratissimus, Combretum apiculatum, Pappea capensis, Combretum molle, Ximenia americana

Main hardveld area A2 + D2 AZ-Eastern broadleaved savanna, as above with Acacia grandicomuta thicket: A. grandicomuta, A. mellifera. A. tortilis, A. leuderitzii

Source: Mainly from Timberlake (1980)

Mapping and Advisory Services Project (1990). Natural vegetation (Table 2) has also been mapped regionally by Soil Mapping and Advisory Services Project (1991), Weare and Yalala (1971) and Timberlake (1980). Mostly the area is shown as comprising natural woodland vegetation typical of the central Kalahari region.

Four major land use categories, typical of much of rural Botswana, have been mapped as part of the present work. These include arable agriculture, open communal (village- based) grazing and grazing controlled by fences in the sandveld. Mixed arable and grazing take place concurrently in the hardveld (Figure 2). By far the largest proportion of the area is under communal, unmanaged grazing and subject to heavy grazing pressure. The average stocking rate in the communal areas was approximately 23 ha per livestock unit in 1990 (Ministry of Agriculture, 1991), which is close to carrying capacity (Arntzen, 1990). The overall population is scattered and averages approximately three persons per km2. Individual settlements vary in type and size from small hamlets with a population of less than 10 people to fairly large villages with up to 1000 people (Central Statistics Office, 1992).

Satellite imagery and fieldwork

Digital Thematic Mapper (TM) imagery bands 2, 3 and 4 were obtained because these were considered most appropriate for vegetation cover analyses (Fiorella and Ripple, 1993). The imagery is a subscene of WRS 172-077 comprising 2000 X 2000 pixels from

Table 3 General interpretation of visible and near infrared bands Thematic Mapper (TM) imagery

Sensor Waveband (pm) General interpretation

TM2 0.52-0.60

TM3 0.63-0.69

TM4 0.76-0.90

Mainly grey coloured soils-some darkened vegetation

Mainly Fe-oxide rich soils-darkened vegetation

Actively growing vegetation and highly reflective soil

Source: Ringrose et al. (1989, 1990)

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. Village

Iv Roads o Borehole + Coordinate

Land-use Category: Fields Communal Grazing Mixed Fields/Grazing Controlled Grazing 0 10 20 30 km

Figure 2 Main land use categories in the Malwelwe area, showing dominance of communal grazing

May 1994. The data were chosen because of the timing of the wet season (October-May) and to correspond as closely as possible to the fieldwork period. The spectral definition of the three bands used and their potential with respect to interpretative detail are given in Table 3.

Fieldwork was conducted during March-April 1994. Initially this consisted of reconnaissance vegetation pattern mapping using 1: 50 000 and 1:250000 topographic maps. Later a series of 46 sites was chosen to represent vegetation groupings in the area. Each site was located using a Sony Pyxis Global Positioning System such that each location was regarded as being accurate to * 30-50 m. All woody species types were

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identified (Palgrave, 1981) along with canopy cover, dead and live herbaceous cover along three 80-m transects at each of the 46 sites. Soil colour and texture were recorded at nine locations, along the three transects at each site. The general state of the bush was also recorded in terms of savanna type, degree of trampling, evidence of erosion and calcrete formation. Results were calculated into percentages per unit area using the programs TRANSECT and TRANSCALC for statistical comparison with spectral reflectance data (Ringrose and Matheson, 1987).

Image processing

In terms of spectral reflectance characteristics, two specific vegetation types have been recognized as being prevalent in semi-arid areas: near infrared reflective vegetation (i.e. those species exhibiting a high near infrared reflectance) and darkened vegetation (those with a low near infrared reflectance) (Ringrose et al., 1989, 1990; Chavez and MacKinnon, 1994). This is a species-specific and/or phenological phenomenon related to leaf structure and shape, the adaptability of plants to withstand drought conditions and the interplay of soil/plant combined spectral reflectance characteristics (Ringrose et al., 1994). Both these spectral responses are normally prevalent in wet-season imagery in Botswana. Grasses and broader multi-leaf vegetation cover produce higher near infrared values and more woody species such as the microphyllous Acacia spp. and Bosciu spp. produce darkening in both the visible and near infrared bands (Figure 3). Similar reflectance characteristics have also been observed in semi-arid Australia and the Sahel (Matheson and Ringrose, 1994a,b).

70

60.

-it Unit 1 Dense vegetation cover

+ Unit 2 Medium dense vegetation cover

& Unit 3 Medium sparse vegetation cover

* Unit L Sparse vegetation cower

I I I I TM2 TM3 TM&

Figure 3 Spectral characteristics of main vegetation classes on Thematic Mapper bands

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Table 4 Results of correlation analysis between Thematic Mapper bands and major vegetation-soil variables (n = 42)

Waveband or transform Woody vegetation cover Alive berbaceous cover Bare soil

TM2 TM3 TM4 TM4 -TM2 TM4 - TM3 TM4 + TM3 TM4/TM2 TM4/TM3 NDVI T -NDVI

-0.292* -0.099 0.412* -0.162 0.065 0.257 -0.388* 0.040 0.451* -0.327* 0.104 0.353* -0.299* -0.220 0.287 -0.312* 0.057 0.404 - 0.022 0.129 0.216 - 0.024 -0.014 0.218 -0.255 - 0.047 -0.240 -0.260 - 0.063 0.250

*significant at p = 0.05 level

Image processing intended to define these different vegetation types was undertaken using ERDAS IMAGINE 8.1, running on a SPARClO workstation. The TM imagery was georectified at source (Satellite Applications Centre, Pretoria). A number of image processing techniques were undertaken to assist in the detection of specific species or community groupings. These included the development of various vegetation indexes and linear regression analyses based on known pixel sites to identify spectral patterns in the vegetation cover. Principal component analysis, intended to minimize between-band variation, was also used for vegetation cover differentiation and overall species mapping.

To determine relationships between pixel values and vegetation cover and structural characteristics, 42 sample sites were located on the imagery by obtaining the original pixel values in a 3 X 3 pixel square using the Inquire Cursor function. A table was drawn up with three pixel values on TM2, TM3 and TM4-the percentages of green woody vegetation cover, alive herbaceous cover, dead herbaceous cover and bare soil. The data were transferred into the SPSS environment for statistical analyses. Linear regression and correlation were performed on each dataset to determine which, if any, of the initial TM bands or band combinations could be used to predict either bare soil or vegetative characteristics. The relationships show that most plant and soil data are found in the near infrared band with some in the reflective green visible band (Table 4). Interestingly, the correlation with green woody vegetation cover is negative and that with bare soil is positive. This strongly suggests that in the Malwelwe area a high near infrared response is indicative of soil reflectance (from the ferralic arenosols), whereas a low near infrared response is indicative of green vegetation cover (darkening).

Results

Vegetation cover and species mapping

A total of about 60 species were identified in the field. The savanna mosaic varies considerably with respect to size of individual species and their canopy diameters. The results of field data collection show that woody vegetation cover varies throughout the study area from 7.4 to 41.4 per cent, alive herbaceous cover from 0 to 48.9 per cent, dead herbaceous cover from 0.3 to 56.4 per cent and bare soil from 26.3 to 74 per cent.

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Vegetation data from field locations were used directly to map the cover mosaic, initially using the reflectance characteristics of TM4; then this was extended to the three-band TM dataset and the results of principal component analysis. A number of vegetation categories were identified on the basis of structure and floristic composition (Figure 3 and Table 5). The main structural classes were grouped according to the relative density of the main components. These are described as being: dense ( > 35 per cent woody vegetation cover + alive herbaceous cover: Unit l), medium dense (25-35 per cent woody vegetation cover + alive herbaceous cover: Unit 2), medium sparse (15-25 per cent woody vegetation cover + alive herbaceous cover: Unit 3) and sparse or degraded ( < 15 per cent woody vegetation cover + alive herbaceous cover: Unit 4). Spectral characteristics of the four major class types are shown on Figure 3.

Table 5 Plant species in the vegetation units in order of relative abundance (herbaceous cover is given as averages per unit)

Map unit Trees >3m high

Shrubs c 3m

high

Woody weeds < 2m high

Forbs c OSm high and average herb. cover

1 (n = 6) A. leuderitzii B. africanum L. nelsii

2 (n = 11) A. leuderitzii

3 (n = 21) No trees

4 (n = 2) No trees

5 (n = 2) No trees

6 A. erioloba Boscia spp. I: sericea

T sericea A. fieckii 0. pulchra G. flava E. undulata R. brevispinosum

0. pulchra C. angolensis I: sericea G. flava A. tortilis A. fleckii Bauhinia spp.

G. flava T sericea 0. pulchra A. fleckii R. teninervis G. retinervis A. mellifera A. tortilis Tephrosia spp.

0. pulchra A. tortilis A. erioloba T sericea

Lycium spp. 0. pulchra A. fieckii

A. fleckii A. tortilis

M. tenuispina D. cinerea B. petersiana (10%)

Aloe (25-50%)

M. tenuispina D. cinerea B. petersiana (25%)

M. tenuispina D. cinerea B. petersiana

(45%)

Aloe (lo-30%)

Elephantorizza spp. Aloe Asparagus spp. (5-20%)

D. cinerea (60%)

Elephantorizza spp. (5-10%)

B. petersiana (10%)

Succulents Aloe Asparagus spp. (< 5%)

D. cinerea No forbs (15%) (< 5%)

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Table 6 Spatial extent of vegetation classes by village and borebole buffer zones

Vegetation class

Village Village Borebole Borebole

@km) (3 km) @km) (3 km) Total

km2 % km’ % km* % km* % km2 %

Dense 64.6 9 132.8 18 128.5 18 258.7 35 732.1 29.0 Medium dense 12.3 1 48.6 6 171.0 20 336.2 40 845.1 33.5 Medium sparse 58.4 7 145.4 18 201.5 26 365.8 46 787.9 31.2 SpW2 41.8 32 59.3 45 36.9 28 65.7 50 132.2 5.2 Other 2.2 8 3.4 13 8.6 33 14.1 54 26.2 1.1

Total 179.3 7 389.5 15 546.5 22 1040.5 41 2523.5 100.0

The dense class (Unit 1) comprises mostly trees dominated by Acacia, Burkea and Lonchocarpus species which are apparently indicative of original savanna conditions (Rutherford, 1983) and covers 29 per cent of the area (Table 6). Unit 1 includes areas protected by fences (‘delineated features’ on Figure 4) to prevent unwanted communal livestock grazing and veldproduct harvesting. The medium-dense class (Unit 2) comprises mainly savanna shrubs dominated by Ochna pulchra and Commiphoru angolensis and covers 33 per cent of the area. The medium-sparse class (Unit 3) comprises a high proportion of low woody weeds such as Maytenus tenuispina, Bauhinia petersiana and Dichrostachys cinerea (Figure 5). The latter species has been cited as a significant indicator of disturbed land (Hendzel, 1981). Unit 4 consists of no large trees and relatively few shrubs and is mainly characterized by expanses of bare soil (Figure 6). Much of this unit is subject to either wind or water erosion, especially in the vicinity of fossil valleys and is regarded as degraded rangeland. Three minor categories include naturally sparse vegetation in some of the fossil valleys where growth is restricted by near-surface calcrete (Unit 5). Locally dense linear vegetation is also found along parts of the valleys (Unit 6). The spatial extent of each mapped class is shown on Table 6.

Vegetation mapping in terms of structure and floristic composition is of interest in terms of savanna determinants as a number of species/cover changes appear to be operative. First, the pre-existing Kalahari woodland or tree-shrub mosaic which is still prevalent in the area is being increasingly out-competed by woody weed species such as Maytenus tenuispina (cf. Rutherford, 1983). Secondly, Unit 4 areas appear to represent transforma- tions either from former woodland areas directly, or through an intermittent stage of woody weed growth. In either case, the current very low-density cover is being maintained through years of relatively good and poor rainfall without the regenerative capability of the savanna being able to develop back into denser growth. This is presumably because of sustained and effective human-induced pressure (cf. Ringrose et al., 1996).

Geographical information system analysis

GIS analysis was undertaken to indicate the extent to which human and/or livestock activity may be implicated in the nature and distribution of the present savanna mosaic. The number of goats has increased substantially in recent years throughout Botswana and most are kept in the villages (Ministry of Agriculture, 1991). Fuelwood gathering and goat grazing in combination with land clearing for crop cultivation close to villages have been implicated elsewhere as contributing to the creation of extensive

233

Vegetation change in southern Botswana: S Ringmse et al

1 Dens0 >36% 6

2 MedlUrn-dense 2536%

3 Medium-spars* 1526% 9B

Valby ve@emtion ~v.dense bush) abo . . . . . . .

R RockoutcmP

Figure 4 Distribution of main vegetation classes

areas of bare soil (Sefe et al., 1996). While cattle numbers have remained relatively constant, boreholes are increasingly being installed in close proximity to one another around the country.

To determine which, if any, of the above agencies may be regarded as affecting the present savanna mosaic, mapped vegetation data and broad land use categories resulting from image interpretation were converted to polygons for GIS-based analysis, using PC-

Vegetarinn change in southpm Botswana: S Ringrose ct al

F@ure 5 (bush =

Woody weed areas near Malwelwe showing the dominance of Mayfenus tenuispina

2m high)

Figure 6 1.5 ml

Bare soil areas subject to erosion in the southwest part of the Malwelwe area (pole =

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251O’E + 2S7DE + 25JYE +

2391s t

24CO'St

241WS t

A/ Roads 9 Village

0 2km Village Buffer + Coordinate

Vegetation density m dense

medium dense medium sparse sparse

a other 20 30 km

Figure 7 GIS-derived map showing the extent of 2-km buffers around villages in relation to

vegetation distribution

ARC/INFO software. Topographic and infrastructure data were also digitized from available maps, which ranged in scale from 1: 50 000 to 1: 250 000. This dataset includes roads, boreholes and village locations (see Figure 2). The distribution of villages represents the population distribution taken from the 1991 census records (Central Statistics Office, 1992). The census classifies settlements into either villages, lands-areas, cattle-posts or mixed lands-cattle areas. In this work a distinction is made between villages, regarded as settlements with a substantial residential population (minimum 50

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40 % of total sparse vegetation

36 32

25

20

15

10

5

0 otQ2km 2to4kDl 4to6km OV0T 6 km

distance from village (km)

Figure 8 The proportion of sparse vegetation cover (degraded land) in relation to distances from villages

residents), and boreholes (watering points), which lack a substantial residential population and are also referred to as cattle-posts.

The villages and boreholes were considered as surrogate depletion nuclei providing a central location for combined impacts of human pressure (in the form of wood harvesting and land clearance) as well as cattle and goat grazing. The intensity and nature of human pressure was considered a function of the distance from village and borehole locations. Previous work has suggested that natural vegetation cover may be altered at distances up to 6 km from a watering point or settlement site, particularly where the affected haloes overlap or coalesce (Ringrose and Matheson, 1992). Buffers of 2, 3,4 and 6-km radii were therefore created around village and borehole locations separately and overlaid on the vegetation map using the PC-ARC/INFO UNION command. The buffer radii were designated as zones of high, medium and low impact, depending on the relative distance from the disturbance source.

A breakdown of total mapped vegetation units in both village and borehole buffer zones at 2 and 3-km radii is shown on Table 6. The 2-km buffer zone around villages occupies 7 per cent of the total study area while the 3-km buffer occupies 15 per cent. Comparable data around boreholes are 22 per cent and 41 per cent, respectively. Since they are scattered throughout the study area, the area of influence of boreholes is far greater than that around the relatively few nucleated villages (cf. Perkins and Thomas, 1993). Consideration of the amount of sparse degraded land (5.2 per cent of the study area) within the 2-3-km buffers indicates that 32 per cent of degraded land is located within 2km of villages and 45 per cent in the 3-km zone (Figure 7 and Table 6). However, in terms of overall distance from villages, the proportion of degraded land initially decreases to about 4-6 km and then increases again beyond 6 km in this area (Figure 8). This further increase may be explained by the elongate configuration of the intensively used fossil valleys in which the villages are situated. This elongate area of intermittent depletion means that, relative to a village site, a portion of the degraded area extends through the 6-km buffer and beyond.

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a Borehole 2 km Zone 4 km Zone Roads Woody Weeds Other vegetatior

+ Coordinate 0 10 20

N

-I-

30 km

Figure 9 GIS-derived map showing the extent of 2 and 4-km buffers around boreholes in relation to medium-sparse (woody weed) vegetation cover

The borehole buffer results include similar proportions of sparsely vegetated areas in the 2 and 3-km buffers (Table 6). Borehole results indicate that 28 per cent of the sparse degraded area is found within 2 km of cattle-posts and 50 per cent within 3 km. Consequently, about 60 per cent (due to buffer overlap) of the total sparse degraded area is found within 2 km of either a village or a borehole and about 95 per cent within the 3&m buffer. These results establish a strong relationship between the spatial pattern of both human and livestock population distribution and the occurrence of degraded land.

Similar analyses were undertaken with respect to the extent of woody weeds, which are most prevalent in the extensive (3 1.2 per cent of the study area) medium-sparse vegetation class. Of the woody weed areas, 18 per cent are found within the 3-km buffer around

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50 % of total woody weeds

0 oto2km 2to4lun 4t.08km over 6 km

distance from borehole (km)

Figure 10 The proportion of medium-sparse vegetation cover (woody weeds) in

relation to distances from boreholes

villages and 46 per cent around boreholes (Table 6). Thus around 60 per cent of the medium sparse vegetation class in which woody weeds dominate is found within 3 km of either a borehole or a village. However, the highest concentration of woody weed areas lies 2-4km from boreholes (Figures 9 and 10).

Discussion

Vegetation mapping in the Malwelwe area of south-central Botswana has demonstrated the presence of four main vegetation classes on the basis of cover density and predominant species content. While some of these classes may be remnants of original Kalahari woodland and shrub savanna, others represent significant modifications in terms both of structure and species content. In order to implicate human and/or livestock activities directly in the distribution of the present savanna mosaic, an assumption is made that greater disturbance takes place in close proximity to settlements. This is where people keep their livestock or smallstock at night and from where individuals disperse daily to gather fuelwood and other veldproducts. The results of GIS analysis show that most of the natural vegetation cover has now been removed within walking distance (2-3 km) of villages. This zone is subject to intense firewoodlveldproduct collection and browsing/grazing, mainly by goats. The combined effects of these uses result in the reduced incidence of vegetation regeneration while the consumption is maintained. Hence this part of the savanna mosaic may be said to be either in some kind of balance with the external pressures exerted on it or in a state of semi-perpetual decline. These areas are relatively small at present, occupying only about 5 per cent of the study area.

Cattle grazing is mostly centred around boreholes and, to a lesser extent, around villages. Such boreholes are usually without a substantial residential population and therefore the immediate area is less subject to intensive goat grazing and fuelwood/

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veldproduct gathering. Nevertheless, degraded areas were found close to boreholes, due to the trampling effect of large numbers of animals gathering daily for watering. At greater distances from boreholes the effect of intense cattle grazing manifests itself in the form of significant increases in woody weed species (bush encroachment). Spatial analysis has indicated that a strong positive relationship exists between the occurrence of woody weeds and cattle-post (borehole) locations. The savanna transformation into predominantly woody weeds is spatially significant as the cattle-posts themselves are both numerous and scattered, such that their zones of influence on the surrounding vegetation cover often overlap. The result is that presently the vegetation class containing the highest proportion of woody weeds covers about one-third of the study area.

Conclusions

The results show that two types of vegetation cover changes are taking place in the southeastern Botswana savanna. First, about 5 per cent of the natural savanna cover is being converted to resource-poor, almost bare soil areas, particularly in the vicinity of villages but also in areas adjacent to boreholes. Secondly, extensive areas of woody weeds (about 30 per cent of the study area) are replacing natural woodland. Both these changes represent a major natural resource loss as potentially both grazing and browse are reduced in terms of both quality and quantity. Results of GIS analysis show a positive correlation between the location of villages and boreholes and the location and extent of depleted land (sparse vegetation). Areas depleted of vegetation cover appear to be predominantly anthropogenic in origin, with local people and goats causing vegetation depletion, mostly within walking distance of their residences. People and cattle living close to boreholes cause a similar kind of depletion, but this is not so extensive. The main vegetation change around boreholes was found to be a zone of woody weeds mostly 2-4km from the borehole. The results of spatial analysis strongly implicate cattle grazing as the major cause of savanna change around boreholes, while direct human influence in terms of veldproduct gathering and goat grazing is a primary cause of savanna change around villages.

The practical implications of spatial analysis suggest that while human and livestock numbers are increasing, these kinds of savanna changes will continue; they appear to have become established as a temporary equilibrium form in terms of the pressures currently exerted on them. Since similar pressures are being increasingly evidenced throughout the Kalahari, it appears likely that increasingly large areas of woodland and shrubland will be transformed to areas of woody weeds. Smaller areas are in danger of becoming devegetated because of anthropogenically induced pressures. Policy changes will be needed to encourage the use of alternative energy supplies, to regulate access to communal rangelands or to increase distances between boreholes so as to limit the spatial extent of change and depletion. In Botswana, however, there is little indication that veldproduct harvesting and herd sizes can be reduced, because an increasing proportion of the rapidly growing rural population has to rely on rural land resources in the absence of employment growth in the formal sector of the economy.

Acknowledgments

The imagery used in this work was purchased as a result of a groundwater research project (GRES II) funded by the Botswana Geological Survey Department. Mr B Makwiti and Mr G Koorutwe are thanked for their contribution to the diagrams.

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