Monitoring and modelling open savannas using multisource
information: analyses of Kalahari studies
Andrew Dougill1 and Nigel Trodd2
1
2
Leeds Environment Centre, University of Leeds, Leeds, LS2 9JT, UK
Telford Institute of Environmental Systems, Department of Geography, University of
Salford, Manchester, M5 4WT, UK.
Contact author: adougill@lec.leeds.ac.uk
Abstract
In the Kalahari of Botswana, as in many other open savannas, the main ecological change
following cattle-based agricultural intensification is one of grass removal and bush
encroachment. Changes to vegetation communities in Kalahari rangelands have been
expressed in terms of a state-and-transition model. However, there remain uncertainties to the
mechanisms and conditions for ecological change. In part, this is due to previous
(inadequate) spatial and temporal scales of data collection. This paper describes the results of
ongoing ecological studies in the Makoba ranches and analyses the contribution that finescale ground-based surveys and interpretation of Earth observation data can make to reducing
uncertainties in state-and-transition models. This detailed case study provides a mechanism
for evaluating the role of multisource information for monitoring and modelling open
savannas.
Key words:
bush encroachment, environmental monitoring, open savanna, Kalahari, remote-
sensing, spatial patterns, resilience, state and transition.
1
Introduction: bush encroachment on savanna rangelands
Intensification of domestic livestock production in the Kalahari of Botswana in the last thirty
years has been associated with significant ecological changes, notably the shift in vegetation
dominance from herbaceous to woody plant species (Ringrose et al., 1990; Skarpe, 1990;
Perkins & Thomas, 1993a,b; Ringrose, Vanderpost & Matheson 1995). This process, termed
bush encroachment, has been experienced similarly in open savannas across the globe, for
example elsewhere in southern Africa (Bosch, 1989; Adams, 1996), Sahelian Africa (Warren
& Agnew, 1988), North America and Mexico (Archer, 1990), Australia (Andrew, 1988) and
South America (Medina & Silva, 1990). Indeed, land degradation studies highlight that for
many savannas the most widespread problem affecting sustainable agricultural production
remains invasion by thorn scrub (Warren & Agnew, 1988; Scoones, 1995), not the more
widely cited examples of complete vegetation removal which have perpetuated myths over
the extent and nature of dryland degradation (Thomas & Middleton, 1994; UNEP, 1997).
Studies reported here have a dual focus. Firstly, we review current scientific understanding
on the extent and causes of bush encroachment in the Kalahari. Secondly, we provide a
detailed case study of ecological studies at the Makoba ranches of the Central District,
Botswana (2159 S, 2539 E), to demonstrate how data collected by fine-scale ecological
survey and satellite remote sensing studies are being used jointly to develop an ecological
state-and-transition model. This conceptual model summarises ecological understanding of
the processes leading to vegetation community structure changes within the dynamic
Kalahari environment. In addition, the model highlights remaining uncertainties caused by
complex interactions of grazing intensities, rainfall variability and fire regimes.
The
application of multiple studies at a single site provides the basis for a meaningful comparison
of different data sources and permits an evaluation of the role of multisource information for
monitoring and modelling open savannas.
Kalahari rangelands: models of ecological change
2
Previous research in the Kalahari of Botswana has described the nature of ecological changes
occurring since the widespread adoption of intensive livestock ranching, as encouraged by
the national Tribal Grazing Lands Policy (TGLP) of the 1970s. These studies have
demonstrated that bush encroachment generally remains confined to an area within 2 km of
the boreholes on which cattle production is centred (Perkins & Thomas, 1993a,b). Given the
initial adoption of an ‘eight kilometre rule’ for borehole spacing within the TGLP, there
remained extensive grass-dominant areas unaffected by bush encroachment (termed ‘grazing
reserve’ by Perkins & Thomas, 1993a,b). The bush-encroached zone - grazing reserve duality
which characterises TGLP ranch blocks, such as those in the Makoba ranch block, maintains
vegetation community diversity on ranch blocks by providing both bush- and grass-dominant
areas. This community diversity helps to explain the minimal impacts on overall agricultural
productivity noted on TGLP ranch schemes to date (Vossen, 1990; White, 1993). Recent
ecological and socio-economic studies in many savanna settings have shown that such mixed
bush and grass communities provide the greatest flexibility for land managers in maximising
agricultural outputs through matching the inherent spatial and temporal dynamism of dryland
ecosystems (Behnke, Scoones & Kerven, 1993; Scoones, 1995). Possible land degradation
concerns still exist in the Kalahari because, as Perkins & Thomas (1993b) note, bush
encroachment is an expansive process with its greatest spatial extent occurring around older
boreholes. Given this observation, and development pressures leading to a relaxation of the 8
km spacing between boreholes (White, 1993), there is potential for the coalescence of bushdominated areas, and a real concern that this will reduce future livestock yields.
Thus far, the progress in quantifying the extent of bush encroachment has not been paralleled
by equivalent advances in our understanding of the mechanisms and processes operating to
cause bush encroachment. Analysis of soil hydrochemical factors have shown that counter to
a number of early assertions (e.g. Skarpe, 1990; Perkins & Thomas, 1993a) ecological change
has not been caused by, or associated with, significant changes in profile patterns of soil
water and nutrient availability (Dougill, Heathwaite & Thomas, 1997; 1998a). These
conclusions have been combined with ground-based observations of patterns of ecological
3
change by Dougill, Thomas & Heathwaite (in press) to provide a conceptual ‘state and
transition’ model (Fig. 1) that summarises current understanding of factors causing changes
in vegetation community structure. This model highlights several remaining uncertainties (x1
- x4 on Fig. 1), chiefly regarding the combined effects of biotic pressures (grazing) and
natural abiotic variability (fire and rainfall regimes) in leading to the transitions between
vegetation community states. At present, these uncertainties limit model precision and inhibit
its use to support changes in management strategies. Studies reported here attempt to reduce
ecological uncertainties by using ground-based survey and remote-sensing methods to assess
combinations of biotic and abiotic factors which cause transitions between different
vegetation community ‘states’.
Information content of monitoring techniques
Ranch scale studies in the Eastern Kalahari (e.g. Perkins & Thomas, 1993a,b) have identified
ecological changes and current ecological heterogeneity at this scale. However, such studies
are unable to explain fully the processes operating to cause the patterns of change noted (Fig.
1). Uncertainty remains because the factors causing ecological transitions operate at different
scales to these ranch-scale analyses and due to interactions between these processes. Biotic
effects of increased herbivore use (i.e. cattle grazing) operate at the fine-scale of impacts on
individual plants (Mott et al., 1992) which then combine to influence wider-scale vegetation
communities. Conversely, rainfall varies at a broader regional scale transcending ranch-scale
patterns of grazing pressure. Similarly, changes to fire regimes are regulated by a
combination of regional driving forces, notably rainfall and lightning occurrence, and finescale grazing-induced changes to vegetation communities which control the herbaceous fuel
load (Frost & Robinson, 1987). This complexity highlights the interaction of factors
operating at a range of scales in determining vegetation community structure and
productivity. Consequently, studies at multiple scales are required to extend understanding of
key processes influencing the extent and permanence of bush encroachment.
Ground-based ecological survey
4
Recent ecological debates (e.g. O’Connor, 1991, 1994; Scholes & Walker, 1993; Belsky,
1994; Jeltsch et al., 1996, 1997) have switched attention from the necessity for long term
ecological monitoring, to the additional information available from fine-scale spatial pattern
studies of bush and grass communities. Patterns of bush and grass cover are determined by
grazing, fire and rainfall regimes, in addition to competition for soil water and nutrients
resources. Consequently, it is recognised that differences in fine-scale patterns of bush and
grass cover could yield information on the role of changes in such ecological determinants in
causing bush encroachment. For example, Jeltsch et al. (1996) conclude that increased
grazing alone will lead to increased bush cover distributed evenly through an area. However,
where bush encroachment is reversed by occasional fires, bushes will be clumped into
patches creating thickets which exclude fire (Skarpe, 1991; Jeltsch et al., 1996). Similarly,
distinct patterns of grass cover have been linked to changes in grazing pressures and interannual rainfall variability (Belsky et al., 1993; O’Connor, 1994), in addition to competition
with shrubs for light, water and nutrients (Amundson, Ali & Belsky, 1995). Consequently,
recent ecological investigations at Makoba ranch block have focused on fine-scale patterns of
bush and grass cover. Analyses of these patterns aim to reduce uncertainty in the relative
roles played by changes in grazing and fire regimes in causing bush encroachment.
Ecological studies have been conducted in the Makoba ranch block in the eastern Kalahari
(Fig. 2) since 1988. This is an area of deep Kalahari sand soils, traditionally dominated by
mixed savanna communities, with grassy plains dominated by tufted perennials including
Eragrostis, Pogonarthia and Stipagrostis species and mixed shrubs, typically Lonchocarpus,
Acacia, Grewia, Rhigozum and Terminalia species. Studies reported here focus on one ranch,
Uwe Aboo, chosen because of previous ranch-scale ecological (Perkins & Thomas, 1993a,b)
and soil hydrochemical studies (Dougill et al., 1997, 1998a). Bush encroachment here was
characterised by an increased dominance of Acacia species and Grewia flava (Dougill, Trodd
& Shaw, 1998b), within 1 km of the borehole, an area that has experienced intensive grazing
for over twenty years.
5
Studies were based on a grazing gradient approach, with spatial patterns of bush and grass
cover being sampled at set distances along a study transect radiating from the borehole (50,
200, 400, 800 and 2600 m from the borehole – matching the exponential decline in grazing
intensity with distance from the borehole) and at an ungrazed control site within the confines
of the neighbouring Makoba veterinary cordon fence. At each site, a 30 m by 30 m plot was
demarcated and the location of all bush species (over 0.2 m height) was mapped by the coordinates of their rooting points and two canopy diameters (N-S and E-W). Where canopies
of individual bushes overlapped boundaries were drawn of clumps containing the numerous
coalescing bushes. Patterns of grass biomass were measured around selected bushes (at least
4 bushes from each of the main bush species present) within these 30 m by 30 m plots at the
end of the 1995 dry season. Change in grass biomass was quantified by harvesting above
ground herbaceous biomass from 0.5 m by 0.5 m quadrats placed on two transects (N-S and
E-W) away from a bushes central rooting point until an unprotected ‘open’ setting was
reached. The dry season timing of this study is vital, as this is the period when grass biomass
is at a minimum and grasses are at the greatest risk of grazing-induced mortality (Mott et al.,
1992). Consequently, such studies provide the best indicator of the ecological resilience
provided by residual sub-bush-canopy grass cover (and the associated seed resource) and of
the continuity of grass cover, a vital factor determining the probability of intense fires (Frost
& Robinson, 1987). Unfortunately, the dry season analysis precludes detailed species
identification such that ecological differences between open and sub-bush-canopy niches
remains an important variable to be quantified for this area, together with preliminary
analysis of the grass seed bank, a factor which has only been superficially studied for this
region (O’Connor & Pickett, 1992; Veenendaal, Ernst & Modise, 1996).
Analysis of bush patterns showed that there were significantly greater number of bushes per
clump at ‘bush-encroached’ sites (within 1 km of borehole) compared to ‘grazing reserve’
(2600 m from borehole) and ‘control’ (ungrazed veterinary fence) sites (Table 1), displaying
the increased spatial clumping of bushes with their increased density. Recognition that bush
patterning displayed a distinct lack of uniformity within sampled plots, matches analysis of
6
aggregated patterns described for mixed bush-encroached stands in the western Kalahari
(Skarpe, 1991).
The ecological resilience implied by the transition back to grass-dominance following fires
(transition x4 – Fig. 1) requires residual grass biomass and therefore a grass seed resource, to
be present in the landscape. At sites in the Makoba ranch block, residual grass biomass is
concentrated in the protected sub-bush-canopy niche of low growing dense bushes, such as
Acacia ataxacantha and Grewia flava. At bush encroached sites, sub-bush-canopy niches
support significantly higher (p = 0.009; n = 45 assessed by two-tailed t-test) grass biomass
than neighbouring gaps between clumps, a pattern opposite to that found at ungrazed
‘control’ sites (Fig. 3). Findings of ecological studies in other regions, such as around
Lethlakeng (Ringrose et al., 1995) and the mid-Boteti region (Ringrose et al., 1996) imply
that mechanisms exist to diminish the physical protection provided by bush canopies.
Reduction in this protection is likely as bushes mature or through changes in canopy form as
a result of livestock browsing, such that this sub-bush-canopy refuge for grasses can be
removed enabling a transition to a degraded, almost completely bush-dominant state
(transition x3).
Extending ground-based studies is essential to provide an opportunity for improving our
understanding of how intensive cattle grazing links to fire frequency and ecosystem
resilience. However, resolving the uncertainty about the conditions under which irreversible
ecosystem changes occur (transition x3) would take many years of extensive field analysis.
Furthermore, it is unlikely that spatially limited ground studies will be able to investigate
fully the various combinations of biotic and abiotic factors that lead to a retention of grassdominant areas within landscapes. It is to this end that extending the spatial and temporal
coverage of ecological change monitoring by investigating archives of satellite Earth
observation data is considered.
Earth observation by remote sensing
7
Earth observation data have been widely used to highlight variations in vegetation
community characteristics from their reflectance characteristics. Many studies have observed
the ability of Landsat TM and MSS imagery to assess vegetation abundance (e.g. Smith et
al., 1990) or to estimate plant size and density in semi-arid environments (e.g. Franklin &
Turner, 1992). However, vegetation community structure (ratio of bush:grass cover) is the
key dependent variable in the state-and-transition model. Importantly, ground radiometry
experiments show differences in the multispectral reflectance of grass and bush canopies that
have been attributed to their different life-forms and phenologies (Franklin, Duncan &
Turner, 1993). However, the main difficulty in assessing savanna vegetation is caused by the
limited radiometric dimensionality of optical Earth observation data (Graetz & Gentle, 1982).
Trodd & Dougill (1998) report the results of ground radiometry experiments on bush and
grass canopies at the Uwe Aboo ranch which show that during the dry season there is limited
dimensionality in visible and near-infrared reflectance data. These results imply that the
presence of any vegetation - bush or grass, green or senescent - on the bright Kalahari sand
soils has the effect of decreasing the reflected radiance from that surface. Subsequently, a
radiometric difference between bush and grass canopies cannot be distinguished from other
spectral effects and it follows that it is impossible to make direct estimates of the relative
proportions of grass and bush cover from single date optical remotely sensed data.
An alternative strategy has been to exploit information contained in a multitemporal sequence
of Earth observation data. These studies have tended to use time series of spectral vegetation
indices, rather than the reflectance in individual wavebands, because they are more sensitive
to variations in shrub type and phenology (Duncan et al., 1993). Analysis of temporal
changes in vegetation indices based on high temporal frequency remote sensing data allows
us to monitor vegetation phenology and biome seasonality (Justice et al., 1985). Vegetation
indices selected for such studies are those that represent the absorption of photosynthetically
active radiation, but they are seriously affected by atmospheric conditions, sensor calibration,
sensor viewing conditions, solar illumination geometry and soil moisture, colour and
brightness (Bannari et al., 1995; Lambin, 1996). Fortunately, some of these effects are
8
minimised in the preparation of global data sets such as International Geosphere Biosphere
Programme Global 1 km AVHRR (IGBP G1K) data (Eidenshink & Faundeen, 1994).
Consequently, assuming that the soil background at a site is invariant, temporal variations in
the Normalised Difference Vegetation Index (NDVI - the normalised difference of the
bidirectional reflectances in the red and near-infrared bands) are the product of either
variations in the angular distribution of incoming radiation or of phenological characteristics
of the vegetation.
Annual and seasonal variations in AVHRR NDVI have been used to monitor regional
primary productivity (e.g. Tucker, Dregne & Newcombe, 1991). In a study of Botswana,
Nicholson & Farrar (1994) observed different levels of productivity associated with different
vegetation community types and studies have observed differences in the phenological phase
of different vegetation community types from AVHRR NDVI data (Fuller & Prince, 1996;
Peters et al., 1997). A subsequent study within Central District, Botswana investigated
seasonal patterns of monthly NDVI values for 3 bush-dominant sites in the Makoba ranch
block and 2 grass-dominant sites of the Central Kalahari Game Reserve (Trodd, Dougill &
O’Connell, 1997). AVHRR NDVI 10-day maximum value composites for the period from
July 1992 to June 1993 were extracted from the IGBP G1K data set accessible through the
World Wide Web - http://edcwww.cr.usgs.gov/landdaac/1km/. NDVI values were sampled
from 5 pixels at each site and, to reduce the effects of contamination by cloud cover
(Gutman, 1987), maximum monthly values were calculated from the fifteen 10-day values for
each site. Daily rainfall data that had been recorded at Uwe Aboo ranch during the same
period were aggregated into monthly totals. Total rainfall for the study period was 317 mm,
with rainfall concentrated between October 1992 and April 1993, peaking in December 1992.
The analysis focused on the relationship between rainfall and NDVI for each of the sites (Fig.
4). At grass-dominant sites the NDVI increased rapidly following first rainfall. In contrast, at
bush-dominant sites the NDVI increased in the month preceding rainfall. Peak NDVI values
occurred simultaneously at both site types, but were found to decrease more rapidly at grassdominant sites compared to bush-dominant sites. These patterns correspond to seasonal
9
dynamics of above ground biomass production proposed in an ecological growth response
model (Fig. 4a) devised for southern African savannas by Scholes & Walker (1993). In this
model, trees and bushes exhibit signs of growth prior to first `effective’ rainfall due to their
ability to access subsurface moisture whereas grasses respond to rainfall events and exhibit a
shorter, more intensive growth pulse (Scholes & Walker, 1993). Inverting these relationships
will permit the use of coarse resolution remote sensing data to estimate vegetation
community structure providing that they can be validated by ground-based measurements.
This is not an easy task, but concerted action in acquiring large area ground data (e.g.
Loveland et al., 1991; De Fries & Townshend, 1994; Prince et al., 1995) and the
development and application of geostatistical techniques (Chappell, 1998; Stein et al., 1998)
suggest it is possible. Furthermore, new sensors such as ESA’s Along Track Scanning
Radiometer, and its successor the Advanced ATSR, SPOT VEGETATION and the EOS
Moderate Resolution Imaging Spectrometer will acquire optical Earth observation data of
greater geometric accuracy and precision and at the necessary temporal frequency to drive
phenologically based models for image interpretation. These new data sources in association
with the archive of AVHRR data will therefore provide an information source capable of
monitoring changes in savanna vegetation community structure.
Of the other remaining uncertainties in the ecological model (Fig. 1) it is apparent that
ground-based surveys cannot provide data on the frequency, intensity and spatial extent of
fires with the required spatial and temporal coverage. Conversely, Earth observation data can
map fire scar patterns (e.g. O’Neill, Head & Marthick, 1993) and archives are being created
(e.g. Arino & Melinotte, 1998). If we accept that wildfires operate at a return interval in the
order of 3 x 103 days (Graetz, 1987) then it suggests that fire regimes could be constructed for
areas, such as the Kalahari, for which Landsat MSS and TM data are available from 1972 to
present. In addition, problems exist with spatially representative measurement of rainfall
variability patterns due to the paucity of the ground-based raingauge network and inherent
spatial and temporal variability of rainfall. In this regard, Cold-Cloud-Duration data, derived
from Meteosat thermal infrared images with a spatial resolution of 5 km, have been used to
10
estimate 10 day rainfall for southern Africa since 1993 (Thorne, Grimes & Dugdale, 1997),
an operation that will reduce a major uncertainty in the use of temporal datasets caused by
the lack of, and uneven distribution of, raingauges in the Kalahari (Farrar, Nicholson & Lane,
1994). Such advances display the increasingly positive contribution that Earth observation
data adds to ecological change monitoring and modelling when this is carefully integrated
with thorough and extensive ground truthing and affiliated fine scale ecological studies. The
requirements for such integrated studies are discussed below.
Integration of ground-based and remotely-sensed information
This discussion of recent research at the Makoba ranch block has identified information
requirements to reduce uncertainties in an ecological state-and-transition model and has
demonstrated the strengths and limitations of ground-based and remote sensing sources. It is
evident that the information content of either source alone is unable to meet the information
requirements for assessing the combination of processes that cause transitions to a bushdominant state. However, by integrating these sources, ecologists can expect to acquire most
of the data required to test and develop ecological models. Possible mechanisms for such
integration are illustrated conceptually in Fig. 5 based on experiences of studies in the
Makoba ranches and the overall need for a clear input into agricultural management
strategies. Success will be most likely if integration occurs in study formulation, as opposed
to attempting integration at a later stage.
Ecologists desire to improve understanding of how fine-scale vegetation patterns relate to
different processes and Earth observation scientists appreciate that sub-pixel vegetation
patterns influence the level of reflectance (Begue, 1993). These patterns may be the presence
of sub-bush-canopy grass or clumping of bushes and in developing new algorithms for image
interpretation, such as canopy reflectance models (Franklin & Turner, 1992), remote sensing
studies will benefit by representing these ecological variables as parameters of the model.
Similarly, change detection in remote sensing requires an appreciation of land cover
11
dynamics that is embedded in ecological growth response models. Therefore, there exists a
symbiotic relationship between these two fields of research.
Conclusion
Ecological models of savannas have advanced in the last decade but current models still
exhibit uncertainties regarding the conditions under which transitions are likely to occur and
rates of change. Reducing these uncertainties, refining and validating models all require data,
and ground-based ecological surveys combined with existing and new Earth observation
missions are - and are likely to remain - the major sources of that data. Data are required to
characterise changes in the landscape at a range of spatial and temporal scales, matching the
range of scales at which determinant processes affect vegetation communities, such that
ground-based studies alone are insufficient. Equally, Earth observation data are only a
surrogate measure and require ground truthing and interpretation to yield ecologically
meaningful information. The synthesis of studies in part of the Kalahari detailed here has
shown that the full benefits will only materialise with improved understanding of Earth
observation data and that, in part, this will be achieved by greater input from fine scale
ecological studies.
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Tables and Figures.
Table 1. Bush clumping indices for 30 m by 30 m plots in bush encroached zone (50, 200,
400 and 800 m from borehole), grazing reserve (2600 m from borehole) and ungrazed
control site on Uwe Aboo Ranch, Makoba ranches.
Figure 1. Ecological state and transition model for Kalahari open savannas (modified from
Dougill et al., in press).
Figure 2. Location of study ranch.
Figure 3. Pattern of mean grass biomass with distance from bush rooting points for bushes
sampled at bush encroached plots (50, 200, 400 and 800 m from borehole), grazing
reserve (2600 m from borehole) and control (Makoba veterinary fence) sites on Uwe
Aboo ranch (adapted from Dougill et al., 1998b).
Figure 4. Seasonal patterns of (a) simulated water use by grasses and trees in a broad-leafed
savanna (based on Scholes & Walker, 1993), and (b) NDVI for grass- and bush-dominant
vegetation communities in the Kalahari.
Figure 5. Monitoring, modelling and management of open savannas: an integrated research
framework using multisource information.
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Table 1. Bush clumping indices for 30 m by 30 m plots in bush encroached zone (50, 200,
400 and 800 m from borehole), grazing reserve (2600 m from borehole) and ungrazed
control site on Uwe Aboo Ranch, Makoba ranches.
Study Sites
Mean no. of distinct
Mean no. of bushes
clumps
Bush encroached
Bush clumping
index (bushes /
clump – mean and
95% confidence
interval
76.3
193
2.62 ± 0.45
90.5
164
1.80 ± 0.28
51.0
83
1.63 ± 0.51
zone (n = 16)
Grazing reserve
(n = 8)
Ungrazed control
(n = 4)
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