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

Applied

SOl43-6228(%)00005-7

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

Geography,

The use of integrated remotely sensed and GIS data to determine causes of vegetation cover change in southern Botswana Susan Ringrose Department of Environmental Gaborone, Botswana

Science, University of Botswana, Private Bag 0022,

Cornelis Vanderpost Department of Environmental Gaborone, Botswana

Science, University of Botswana, Private Bag 0022,

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 225

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 226

Vegetation change in southern Botswana: S Ringrose et al

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 sandy loam to silt loam

Ferralic arenosols

Deep to very deep, well to excessively sands to loamy fine sands

Chromic

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

luvisols

Source: Soil Mapping

and Advisory

well to well drained, greyish brown to pale brown, fine drained, yellowish

brown to dark red, coarse drained, strong

Services Project (1990) 221

Vegetation change in southern Botswana: S Ringrose et al Table 2 Details of natural vegetation cover Location

Designation

Natural vegetation description

Fossil valleys

A5

Northern microphyllous 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

savanna: Acacia erioloba, A. mellifera,

(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 (villagebased) 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 Sensor

Waveband

TM2

0.52-0.60

of visible and near infrared bands Thematic Mapper (TM) imagery (pm)

General interpretation Mainly grey coloured

soils-some

TM3

0.63-0.69

Mainly Fe-oxide rich soils-darkened

TM4

0.76-0.90

Actively growing

Source: Ringrose 228

et al. (1989, 1990)

vegetation

darkened

vegetation

vegetation

and highly reflective

soil

Vegetation change in southern Botswana: S Ringrose et al

.

Village Roads o Borehole + Coordinate Land-use Category: Fields Communal Grazing Mixed Fields/Grazing Controlled Grazing

Iv

Figure 2 grazing

Main land use categories

0

10

20

30

km

in the Malwelwe area, showing dominance of communal

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 229

Vegetation change in southern Botswana: S Ringrose et al

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.

I

TM2

-it

Unit

+

Unit 2

Medium

dense

&

Unit 3

Medium

sparse

*

Unit L

Sparse

I

TM3

1

Dense

vegetation

vegetation

cover

cover

vegetation

cover

vegetation

I

TM&

cower

I

Figure 3 Spectral characteristics of main vegetation classes on Thematic Mapper bands 230

Vegetation change in southern Botswana: S Ringrose et al

Table 4 Results of correlation variables (n = 42)

analysis

between Thematic

Mapper

bands and major

Waveband or transform

Woody vegetation cover

Alive berbaceous

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

-0.292*

-0.162 -0.388* -0.327* -0.299* -0.312* - 0.022 - 0.024 -0.255 -0.260

-0.099 0.065 0.040 0.104 -0.220 0.057 0.129 -0.014 - 0.047 - 0.063

cover

vegetation-soil

Bare soil 0.412* 0.257 0.451* 0.353* 0.287 0.404 0.216 0.218 -0.240 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. 231

Vegetation change in southern Botswana: S Ringrose et al

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 averages per unit)

Woody weeds < 2m high

Forbs c OSm high and average herb. cover

Trees >3m high

Shrubs

1 (n = 6)

A. leuderitzii B. africanum L. nelsii

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

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

Aloe (25-50%)

2 (n = 11)

A. leuderitzii

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

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

Aloe (lo-30%)

3 (n = 21)

No trees

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

M. tenuispina D. cinerea B. petersiana (45%)

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

4 (n = 2)

No trees

0. A. A. T

D. cinerea (60%)

Elephantorizza spp. (5-10%)

5 (n = 2)

No trees

Lycium spp. 0. pulchra A. fieckii

B. petersiana (10%)

Succulents Aloe Asparagus spp. (< 5%)

6

A. erioloba Boscia spp. I: sericea

A. fleckii A. tortilis

D. cinerea (15%)

No forbs (< 5%)

Map unit

232

c 3m

(herbaceous cover is given as

high

pulchra tortilis erioloba sericea

Vegetation change in southern Botswana: S Ringrose et al Table 6 Spatial extent of vegetation classes by village and borebole buffer zones Village @km) Vegetation class

Borebole @km)

Village (3 km)

Borebole (3 km)

%

km’

%

km*

%

Other

64.6 12.3 58.4 41.8 2.2

9 1 7 32 8

132.8 48.6 145.4 59.3 3.4

18 6 18 45 13

128.5 171.0 201.5 36.9 8.6

18 20 26 28 33

258.7 336.2 365.8 65.7 14.1

35 40 46 50 54

Total

179.3

7

389.5

15

546.5

22

1040.5

41

Dense Medium dense Medium sparse SpW2

km2

km*

Total %

km2 732.1 845.1 787.9 132.2 26.2 2523.5

% 29.0 33.5 31.2 5.2 1.1 100.0

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 transformations 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%

2

MedlUrn-dense 2536%

3

Medium-spars*

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

6

R

1526%

Figure 4

RockoutcmP

9B

Distribution

of main vegetation

areas of bare soil (Sefe et al., 1996). While cattle constant, boreholes are increasingly being installed around the country. To determine which, if any, of the above agencies present savanna mosaic, mapped vegetation data and from image interpretation were converted to polygons

classes

numbers have remained relatively in close proximity to one another may be regarded as affecting the broad land use categories resulting for GIS-based analysis, using PC-

Vegetarinn change in southpm Botswana: S Ringrose ct al

F@ure 5 Woody weed areas near Malwelwe (bush = 2m high)

Figure 6 1.5 ml

showing the dominance

Bare soil areas subject to erosion in the southwest

of Mayfenus

part of the Malwelwe

tenuispina

area (pole =

235

Vegetation change in southern Botswana: S Ringrose et al 251O’E +

2391s

2S7DE

+

25JYE

+

t

24CO'St

241WS

t

A/9

Roads Village 0 2km Village Buffer + Coordinate Vegetation density m dense medium dense medium sparse sparse a other

Figure 7 GIS-derived vegetation distribution

map showing

20

30

km

the extent of 2-km buffers around villages in relation to

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 50 236

Vegetation change in southern Botswana: S Ringrose et al

% of total

sparse vegetation

40

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. 237

Vegetation change in southern Botswana: S Ringrose et al

N

a

+

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

-I-

0

10

20

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 238

Vegetation change in southern Botswana: S Ringrose et al

% of total

woody

weeds

50

0 oto2km

2to4lun

distance Figure 10

over 6 km

4t.08km

from borehole

(km)

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/ 239

Vegetation change in southern Botswana: S Ringrose et al

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. 240

Vegetation change

in southern

Botswana:

S Ringrose

et al

References Amtzen,

J (1990)

Development

‘Economic

policies

and rangeland

degradation

in Botswana’,

Journal

of International

2, 441 l-4499

Briggs, J, Dickinson, G, Murphy, K, Pulford, I, Belal, A E, Moalla, S, Springuel, I, Ghabbour, S I and Mekki, A-M (1993) ‘Sustainable developments and resource management in marginal environments: natural resources and their use in the Wadi Allaqi region of Egypt’, Applied Geography 13, 259-284 Chavez, P and MacKinnon, D J (1994) ‘Automatic detection of vegetation changes in the Southwestern United States using remotely sensed images’, Photogrammetric Engineering and Remote Sensing 60, 567-585 Central Statistics Office (1992) Population of Towns, Villages andAssociated Localities Ministry of Finance and Development Planning, Gaborone Fiorella, M and Ripple, W J (1993) ‘Analysis of conifer forest regeneration using Landsat Thematic Mapper data’, Photogrammetric Engineering and Remote Sensing 59, 1383-1388 Franklin, J (1986) ‘Thematic Mapper analysis of coniferous forest structure and composition’, International Journal of Remote Sensing 7, 1287-1301 Frost, P, Menaut, J-C, Walker, B, Medina, E, Solbrig, 0 and Swift, M (1986) Responses of Savanna to Stress and Disturbance: A Proposalfor a Collaborative Programme of Research Biology International Special Issue 10, International Union of Biological Sciences, Paris Ghosh, T K (1993) ‘Environmental impacts analysis of desertification through remote sensing and land based information systems’, Journal of Arid Environments 25, 141-150 Graetz, R D, Walker, B H and Walker, P A (1988) ‘The consequence of climatic change for seventy percent of Australia’, in Pearson, G I (ed.) Greenhouse Planning for Climatic Change CSIRO Division of Atmospheric Research, Canberra, Australia Grunblatt, J, Ottichlo, W K and Sinange, R K (1992) ‘A GIS approach to desertification and mapping’, Journal of Arid Environments

23, 81-102

Hastings, D A and Di L (1994) ‘Modelling of global change phenomena with GIS using the global change data base II prototype synthesis of the AVHRR-based vegetation index from terrestrial data’, Remote Sensing of Environment

49, 13-24

Hendzel, L (1981) Range Management Handbook for Botswana Ministry of Agriculture, Gaborone Lee, C T and Marsh, S E (1995) ‘The use of archival Landsat MSS and ancillary data in a GIS environment to map historical change in an urban riparian habitat’, Photogrammetric Engineering and Remote Sensing 61, 999-1008.

Magadza, C H D (1994) ‘Climate change: some likely impacts in Southern Africa’, Food Policy 19(2). 165-191 Matheson, W (1994) ‘The use of Landsat MSS imagery to determine plant characteristics and community spectral separability from sub-tropical to arid zones in Northern Australia’, Geocarto International 2, 5-13

Matheson, W and Ringrose, S (1994a) ‘The development of image processing techniques to assess changes in green vegetation cover along a climatic gradient through Northern Territory, Australia’, International Journal of Remote Sensing

15, 17-47

Matheson, W and Ringrose, S (1994b) ‘Assessment of degradation features and their development into the postdrought period in the west-central Sahel using Landsat MSS’, Journal of Arid Environments 26, 181-199 Ministry of Agriculture (1991) Agricultural Statistics, 1990 Ministry of Agriculture, Gaborone Niemann, 0 (1993) ‘Automated forest cover mapping using Thematic Mapper images and ancillary data’. Applied Geography 13, 86-95 O’Niell, A L, Head, L M and Marthick, J K (1993) ‘Integrating remote sensing and spatial analysis techniques to compare Aboriginal and pastoral fire patterns in the East Kimberley, Australia’, Applied Geography 13. 67-85

Palgrave, K C (1981) Trees of Southern Africa C Struik Publishers, Cape Town Passini, M F, Delgadillo, J and Salazar, M (1989) ‘L’ecosystem forestier de Basse Califomie: composition florestique, variables ecologiques, principals dynamique’, Acta Oecologica/Oecologica Plantarum 10, 275-293 Perkins, J S and Thomas, D S G (1993) ‘Spreading deserts or spatially confined environmental impacts? Land degradation in the Kalahari desert of Botswana’, Land Degradation and Rehabilitution 4, 179-194 Peuquet, D, Davis, J R and Cuddy, S (1993) ‘Geographic information systems and environmental modelling’, in Jakeman, A J, Beck, M B and McAleer, M J (eds) Modelling Change in Environmental Systems Wiley, New York, pp 543-556 Ringrose, S and Matheson, W (1987) ‘Desertification in Botswana: progress towards a viable monitoring system’, Desertification Control Bulletin 13, 6-l 1 Ringrose, S and Matheson, W (1992) ‘Visual analysis of Landsat MSS imagery to determine the aerial extent of woody vegetation cover and indications of change over time in the west-central Sahel’, Global Ecology and Biogeography Letters 1, 176-l 8 1

241

Vegetation change in southern Bobwana: S Ringrose et al Ringrose, S, Matheson, W, Mogosti, B and Tempest, F (1989) ‘Nature of the darkening effect in drought affected savanna woodland environments relative to soil reflectance in Landsat and Spot wavebands’, Remote Sensing of Environment 25, 519-524 Ringrose, S, Matbeson, W, Tempest, F and Boyle, T (1990) ‘The development and causes of range degradation features in south-east Botswana using multi-temporal Landsat MSS imagery’, Phorogrammerric Engineering and Remote Sensing 56, 1253-1262 Ringrose, S, Matheson, W, O’Neill, T and Werner, P A (1994) ‘Vegetation spectral reflectance along a nortlsouth vegetation gradient in northern Australia’, The Journal of Biogeography 21, 419-433 Ringrose, S, Chanda, R, Musisi-Nkambwe, S and Sefe, F (1996) ‘Environmental change in the mid-Boteti area of north central Botswana’, Environmental Management Rutherford, M C (1983) ‘Growth rates, biomass and distribution of selected woody plant roots in Burkea africana-Ochna pulchra savanna’, Vegerario 52, 45-63 Scholes, R and Walker, B (1993) An African Savanna: Synthesis of the Nylsvey Study Cambridge University Press, Cambridge Sefe, F, Ringrose, S and Matheson, W (1996) ‘Desertification in north central Botswana: causes, processes and impacts’, Journal of Soil and Water Conservafion Soil Mapping and Advisory Services Project (1990) Soil Map of the Republic of Botswana, Scale 1: I 000 000 FAO/BOT/85/011 Soil Mapping and Advisory Services Project (1991) Vegetation Map of the Republic of Botswana, Scale I: 2 000000 AG:DP/BOT/85/011 Timberlake, J (1980) Vegetation Map of South East Botswana Ministry of Agriculture, Gaborone UN General Assembly (1994) Elaborarion of an Inrernational Convention to Combat Desertification in Countries Experiencing Serious Drought and/or Desertification, Parricularly in Africa United Nations, New York Vanderpost, C (1995) ‘Population change and environmental problems in the mid-Boteti region of Botswana’, Geo.loumal34, 521-529 Weare, P R and Yalala, A (1971) ‘A provisional vegetation map of Botswana’, Botswana Nores and Records 3, 131-147 (Revised manuscript received 13 March 1996)

242