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MAPPING OF COLOPHOSPERMUM MOPANE USINGLANDSAT TM IN EASTERN BOTSWANAREUBEN J. SEBEGO a , WOLTER ARNBERG b , BENGT LUNDEN c & SUSAN RINGROSE da University of Botswana , P/Bag UB00704, Gaborone , Botswanab Department of Physical Geography and Quaternary Geology , StockholmUniversity , SE—106 91 , Stockholm , Swedenc Department of Physical Geography and Quaternary Geology , StockholmUniversity , SE—106 91 , Stockholm , Swedend Harry Oppenheimer Okavango Research Centre , P/Bag 285, Maun , BotswanaPublished online: 01 Feb 2012.

To cite this article: REUBEN J. SEBEGO , WOLTER ARNBERG , BENGT LUNDEN & SUSAN RINGROSE (2008) MAPPING OFCOLOPHOSPERMUM MOPANE USING LANDSAT TM IN EASTERN BOTSWANA, South African Geographical Journal, 90:1,41-53, DOI: 10.1080/03736245.2008.9725310

To link to this article: http://dx.doi.org/10.1080/03736245.2008.9725310

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ABSTRACT

The spatial distribution of a tree species, Colophospermum mopane is mapped at its southern limits in eastern Botswana. Its relationship with soil type distribution is also investigated. Methods applied in the exercise used the possibilities of integrated applications of geographic information systems and remote sensing to detect and delineate spectral classes of C. mopane and of soils. Landsat TM data for the wet and dry seasons were analysed and interpreted for soil and vegetation classification. Supervised classification of the wet season (19/04/93) image was carried out using land usekover classes derived from Colour infrared (CIR) aerial photographs, flown during the wet season (April, 1996) over the study area. Classification of the dry season (19/10/84) image on the other hand was based on the existing Food and Agricultural Organisation (FAO) soil maps of the same area. Results show that CIR photographs can be reliably used in selecting training areas for the classification of Landsat TM imagery. Secondly, results also indicate that Landsat TM can be used to map the distribution pattern of mopane. The methods applied in this study and the tools used prove that the much needed inventorying of vegetation in Botswana can be carried out using remote sensing. Lastly, the study shows that spectral classes of soil and vegetation have a positive relationship.

Introduction In Botswana, the livelihood of most people is highly

dependent on the natural forest and rangeland resources upon which the agropastoral system is based (Prince and Astle, 1986; Abel et al., 1987). Natural forests and rangeland resources form an important resource base for food and medicinal products that form part of people's subsistence as well as their economic base and well being. It has become the policy of the Botswana Government to preserve natural resources (Government of Botswana, 1991). In part, this policy has led to the inventory and monitoring of forest and rangeland resources countrywide. To date, inventories of these resources have been carried out at national and regional scales using conventional methods involving direct field measurements and aerial photo interpretation. Only in a few cases has satellite data been used (Norwegian Forestry Association, 1993; Botswana Range Inventory and Monitoring Project (BRIMP), 1995). Most of the published vegetation maps are at small scales and therefore generalised and lacking detailed information of vegetation distribution patterns. The authors believe that more detailed maps of key forest resources need to be produced for the management and conservation of these resources using improved and up-to-date methods of landscape data acquisition and analysis. This study was undertaken to overcome some of the shortcomings identified on the existing vegetation maps of Botswana.

The advent of satellite and remote sensing technologies have in the past decades brought-up a milestone development in automated land usehand cover mapping techniques, making it possible to improve natural resources management practices @ellis, 1988; Zonneveld and Kannegieter, 1988; Boles et al, 2004; Lu et al., 2004 ). Internationally vegetation distribution mapping projects are underway in response to the Convention on Biological Diversity (Dymond and Sheperd, 2004). The preferred use of satellite data for this purpose is based on the fact that remote sensing has become the unheralded technique in acquisition and analysis of natural resource data (Trotter, 1991). This observation is based on the wide range ofusage and

advantages of satellite remote sensing over similar methods of data acquisition like aerial photography. The former is now commonly used for validation of course resolution maps and imagery (Scepan, 1999). Direct interpretation and analysis of satellite data for land cover classification is normally a straight-forward technique but it is necessary to exercise caution when dealing with semi-arid landscapes because of posibilities of mixed signals from trees, grasses and bare soil (Matheson, 1994; Shoshany et al., 1995; Schultz and Engman, 2000; Boyd and Foody, 2004).

Contemporary vegetation mapping, monitoring and assessment techniques have been eminently boosted through the advancement of computer based technologies (Mulder, 1988). In this study the integration of remote sensing and Geographic Information Systems (GIS) technologies are used to produce a distribution map of a species locally known as Mophane (Colophospermum mopane) (Kirk ex Benth.) Kirk ex J. Leonard, in the PalapyeITswapong area, Botswana. The species is now referred to by its English name, mopane. Mopane belongs to the family Leguminosae (pod bearing family) (Coates-Palgrave, 1983). The generic name in Greek is translated as 'seed inhabiting the light', most likely referring to its botanical make up. The seed is said to be always enclosed in its pod and as a result, even germination takes place on the ground surface (Van Voorthuizen, 1976). Mopane is found only in the north, north-west and along the eastern part of the country commonly known as the hardveld (Moganane, 1989; Thomas and Shaw, 1991). The distribution of this species is such that it is locally dominant and has its most southern limit at latitude 23" S, south of Palapye village.

The aim of this study was to map the spatial distribution pattern ofmopane in a test area using Landsat Thematic Mapper (TM) images and to relate the distribution pattern of mopane to the distribution pattern of soils. The rationale for selecting mopane was based on the following observations: The species is abundant and provides a protein rich diet (browse) for both cattleandsmallstock(Macala, 1996). Itisalsoahostofanedible

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worm (Arytaina mopane) or mopane worm. Economically, the mopane worm provides a protein rich diet (Macala, 1996), as well as being a financial base for those communities where the tree is found. For many subsistence coummunities along the mopane belt harvesting of mopane worm has become the 2nd important economic activity after arable farming. Commercial trade in mopane worms has grown to a stage where livelihoods can be sustained; in Botswana up to US$3.3million is earned annually from sales of mopane worms (http://www.mopane. org/economics.htm). This is necessitated by the unpredictable nature of the country's rainfall pattern that makes the rain-fed agricultural system un-sustainable. Mopane is also used for the production of charcoal, timber and commercialized sale of fire wood (Sebego, 1999).

The Study Area The study area lies between longitudes 27'00' and 27' 33'

E and latitudes 22" 23' and 22'52' S. It is situated along the main north-south highway (Figure 1) and is about 230 km north of Gaborone, the capital city. Soils around the area are well developed and variable. Because of this, the eastern part of the country is locally referred to as the hardveld region in comparison to the western sand-laden two thirds, which is known as the sandveld (De Wit and Bekker, 1990; Moganane, 1989; Thomas and Shaw, 199 1). A long range of hills, as well as river channels, intermittently dissect the area. Rainfall and temperature regimes are highly variable, both spatially and temporally and are both characterized by seasonality in their occurrence (Bhalotra, 1987). Mopane is the dominant species in the study area as it is in the rest of the northern hardveld. The area immediately west of Palapye is part of the sandveld, dominated by Terminalia serisea. Here, mopane is found only in small 'island' patches of hard soil. The hill ranges around the study area are also conspicuously free of mopane except in a few isolated areas where the tree is found on the alluvial soils along the foothills and lower slopes. Some few kilometers north of Radisele lies the southern limit of mopane, a delimitation commonly known as the mopane line.

The selection of this area was based on two major reasons: Firstly, it is an area with typical pockets of mopane woodland where the species is found in all its various growth forms (tree, tall shrubs and short shrubs). The intermittent distribution pattern ofmopane in its various growth forms could be the basis for stratification and mapping. Secondly, in the area just north of Radisele lies the southern limit of mopane in Botswana. This area was therefore viewed as the most important, as an ecotonal area for mopane.

Two vegetation types have been described across the study area (Weare and Yalala, 1971). The area around Radisele and southwards is described as the Acacia nigrescens/Combretum apiculatum tree Savannah. Tall A. nigrescens trees growing in association with Sclerocarya birea, Peltophorum afiicanum, Acacia erioloba, and Combretum imberbe conspicuously mark this type of vegetation. The Palapye/Tswapong area covered by this study is described as mopane bushveld. In this area the mopane tree is found in all its growth forms and is locally monospecific. Mopane also grows in association with other species such as S. birea, Terminalia prunioides, Commiphora mossambicensis, C. apiculatum, C. imberbe and Adonsonia digitata. These species may also be found to be locally dominant. In addition to these, various types of acacias

Figure 1 : Map of study area showing an insert of a strip flown with

0 10 20 Kilometers Legend

,"~. ,' vdlage R m d ii '/..'. m d C J R moerage

together with Ziziphus mucronata form riverine vegetation along the drainage channels (Weare and Yalala, 1971).

Soil mapping and profile nutrient analyses for the area have been undertaken by the Soil Mapping and Advisory Services of the Ministry of Agriculture (MoA) and the Food and Agricultural Organisation soil survey team (Verbeek and Remmelzwaal, 1990). Soils around Palapye and Tswapong Hills area are mostly Alluvial deposits; described as generally being imperfectly to moderately drained greyish brown to dark brown sandy clay loams. From Radisele southwards, across Mahalapye, is a group of soils found on igneous and metamorphic rocks; namely, Dystric Regosols, and Dystric Leptosols. These are described as very shallow to shallow, moderately well to somewhat excessively drained, greyish brown to yellowish red coarse sands to coarse sandy loams. The transition from the nutrient rich alluvial soils to the more rugged Regosols in the south may have some ecological significance in the southward movement of mopane.

Satellite data When acquiring the imagery data, efforts were made

to select dates with cloud free skies. Landsat TM satellite images were acquired for two seasons (wet and dry). The wet season image is a quarter scene (Track and frame 17 1-76 lower left quadrant) dated 19 April, 1993. At this time of the year in Botswana vegetatiodcrop phenological development is at its peak and appropriate for species identification, and consequently, vegetation classification (Campbell, 1987). The dry season image is a full scene (Track and frame 171- 76) from 19 October, 1984. Being a dry season image, it was appropriate for soil type mapping and analysis. This is because during the dry season (July-October) most of the deciduous woodlands are free of any green leaves as a result of their seasonal senescence; grass cover is at its minimum, often leaving the surface bare. The low vegetation canopy cover prevalent in arid and semi-arid ecosystems such as those found in Botswana favour digital soil type mapping procedures

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because of the high influence of soil surface reflectance (Huete and Tucker, 1991; Korolyuk and Shcherbenko, 1994).

The use of satellite image data for vegetation classification is normally preceded by reconnaissance surveys or detailed field checks. Sequentially, image processing is preceded by the selection of representative training sites of the vegetation communities of the area studied.

Pre-processing Pre-processing of remotely sensed data is a pre-requisite

when multi-temporal data or different band combinations are used (Landsat 7 Science and data handbook, 2004; Lillesand et al., 2004). The radiometric procedures required for the processing of the data are described in detail in Robinove (1982). Landsat data in its raw form is acquired in the digital number (DN) format ranging from 0-255. In processing image data, analysis is often done between spectral bands as well as between multi-temporal images. This may create problems. The multi-spectral sensors on board Landsat satellites are calibrated differently hence their data are not comparable. Standardisation or calibration procedures are necessary steps to correct for the differences. In this study, radiometric corrections were made to convert image digital numbers (DN) to radiance, a physical parameter that is more representative of the radiant energy recieved by the sensor than digital numbers.

The satellite data were callibrated using the following formula: L = (Lmax - Lmin)/255 x DN+Lmin where L= raw radiance expressed as watts per square steradian.

Methods Selection of training areas

Even though Landsat TM presents fine resolution data, it can still be regarded as course especially when used for single

species identification. Hence we used finer resolution data aerial photography to play that critical role in validating the images (Scepan, 1999; Serneels et al., 2001). Colour infrared (CIR) aerial photographs (scale, 1:30 000) were used to select training areas for satellite image classification (Sebego and Arnberg, 2002). The aerial photographs were acquired by flying a single-photo wide strip (3 1 x 7 km) cutting through the study area as shown in Figure 1. One of the objectives of the flying mission was to determine the accuracy at which mopane can be detected from CIR photographs and their suitability as ground truth material for satellite image classification. The land cover map derived (Figure 2) was digitised and used as a template for the selection of training areas on the wet season image. Accuracy assessments on the use of CIR photography have shown that mopane can be mapped with an accuracy of about 84% (Sebego and Arnberg, 2002). Soil distribution was inferred from a dry season image, Landsat TM 5 and compared with existing digital soil maps and associated attribute data. During the dry season the deciduous vegetation would have shed all of its leaves, leaving the surface exposed. Landsat TM 5 is best suited for soil reflectance characteristics (Lillesand et al., 2004). Studies that involve land use and land cover mapping have been carried out both in Botswana and internationally (e.g. Johnston and Bonde, 1989; Ringrose and Matheson, 1991a), but often not supported by the use of CIR photography for the selection of training areas to be used for image classification.

Fifty (50) sample sites were selected as vegetation units based on the stratified approach (Mueller-Dombois and Ellenberg, 1974; Kent and Coker, 1992). A vegetation unit is here described as an area of uniform land cover identifiable on both air photos and on the ground. Site location was done on both black and white and CIR photographs and was done

Figure 2: The land cover map derived from CIR aerial photographs (species abbreviations in Appendix 1). Map coordinates are in decimal degrees.

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both before and during field data sampling. Within the strata, homogeneous and hence representative vegetation units were selected. The sampling technique was that of randomly placed 100 x 20 m plots across the units.

Field baseline data collection primarily involved the identification of vegetation classes and the acquisition of soil samples (Table I ) . The use of topographic maps and a Global Positioning System assisted in locating the selected sites in the field. At each sample site, tree cover was estimated by the Bitterlitch method (Bonham, 1989), while soil samples were acquired by augering. These conventional methods have some limitations particularly with regard to the practicality of spatial

coverage; the methods on their own are particularly slow and cumbersome.

Data extraction Data were extracted from three primary sources namely, in

situ field measurements, soil and vegetation maps and satellite images. Tree canopy cover (TCC) was estimated in the field as the total percentage of ground area covered by tree canopy. Canopy cover was thought to have a direct relationship with reflectance values, especially in the red and near-infrared region where vegetation reflectance is normally low and high respectively. Soil properties were characterised in terms of soil colour and texture.

Table 1. A listing of sample points and their attributes

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A coding system for the soil characteristics was adopted to standardise the data and also to facilitate computerisation. Texture

was grouped into three categories, sandy, loamy and clayey and coded 1 , 2 and 3 respectively. Along the same lines, colour was

described as a range from light to medium to dark and similarly coded 1 ,2 and 3 respectively. Lastly, sample sites were digitised

on screen (Figure 3) against which values were extracted from respective Landsat TM bands

Image processing and GIs database preparation Classification of the satellite images was carried out

in E ~ A S I M A G ~ E (version 8.2) ( E ~ A S I ~ ~ . . 1994).

as the main parameters for statistical spectral analysis because of the influence of soils characteristics on the reflectance values in band 5 of the dry season image and the relatively

I \ ’ Vegetation spectral classes were obtained using the maximum likelihood classification sub-module. All the spectral bands Of were used in the classification, but only selected bands (TM band3 and TM

high vegetation reflectance values displayed in band 3 of the wet season image. Single band images were exported into IDRISI GIS for spatial data analysis. Output maps resulting from the classification were also transferred to a GIS database. The purpose for a single CIS database was to run correlation

TM with the exception Of band

band5) were used to extract values related to vegetation and soil characteristics respectively. The two bands were chosen

and spatial analysis of soils, vegetation and their reflectance attributes.

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Figure 3: A Landst TM band 5 dry season image with vector file overlay of the FAO/MoA soil map and field sample points

Processing of the wet season image From the CIR photography, a total of twenty two (22) land

cover classes were distinguished with up to 78% accuracy (Sebego and Amberg, 2002), (Table 2). In order to ensure that the classification was based on geographically accurate training areas, the land cover map was resampled to match the TM image and map coordinates were used to pick up the training areas. It is expected that vegetation of different type and form would have varying reflectance characteristics and can therefore be spectrally separated. Applying the image alarm functions on training area statistics helped in testing separability between species. Image alarm verifies the selected signature data as ‘the true representation of the pixels to be classified’ (Erdas Field Guide, 1997). Separability by bands between species was also tested through the plot of mean reflectance (signature curves) of TM bands for mopane and non-mopane classes. It should be noted here that other vegetation classes were included in the analysis in order to compare their spectral characteristics with those of mopane. The analysis of the signature curves made it possible to identify any spectral overlaps between the classes; to give the analyst a chance to evaluate and drop, add or merge classes as may have been necessary.

Table 2: Ecological zones visually interpreted and used to classify the wet season image

Processing of the dry season image A spatial correlation of the dry season image and MoAIFAO

soil map was carried out. Again for the purpose of controlled matching of the two data sets, georectification (resampling in Idrisi) was used to match the vector layer containing soil classes on to the dry season image. For this purpose, only major soil units were used (and to facilitate this, soil names were modified). A cut off point was made at the second level to reduce the number of classes or categories. For example, A4b-9-3 was reduced to only A4b (Table 3). The first level or main unit distinguished by a capital letter connotes the type of parent material whilst the second level or soil unit is based on a number of soil characteristics such as texture, depth, drainage and colour (Verbeek and Remmelzwaal, 1990). Grouping of the soil classes up to second level reduced the number of classes from 30 to 26. Analysis of the dry season image was therefore on these 26 classes (Table 3).

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Table 3: A description of the major soil categories abbreviated accordingly by soil units (FA0 classification system).

Analysis of data Soils and vegetation data were integrated into a single GIS

database for statistical and spatial data analysis. A summary of the various attributes of both vegetation and soils are as shown in Table 1. Classes of mopane and the different soil types were subjected to spatial and non-spatial statistical analysis. Overlay analysis was carried out to show the relationship between the spatial distribution of mopane and the distribution of soil type. Raw data extracted from wet and dry season images and contained in the IDRISI values files were, together with the field data, subjected to the Pearson correlation analysis. This was done specifically to determine any relationship between the following variables: TM3, TM4, (of the wet season image), TM5, TM7, (of the dry season image), Tree Canopy Cover, NDVI and soil texture.

Results In contrast to what has been reported elsewhere (mostly

in wetter climates) the spectral reflectance characteristics of vegetation in these savannah woodlands display a different spectral reflectance curve (Figures 4, 5 and 6). This difference in the spectral response of vegetation is due to the “darkening effect” (Otterman 198 1,1982; Pech and Davis, 1987; Ringrose et al., 1989; Ringrose and Matheson, 1991a; Sellers et al., 1992). Darkening is said to occur as a result of a number of field conditions. In areas of limited soil moisture, stressed plants exhibit higher reflectance in the red than in the near infrared (NIR) band mainly due to the change in leaf structure and orientation. Results from this study show that in general, the spectral response of vegetation displays lower values in both the red and the NIR band than what has been reported in other areas, especially in the northern hemisphere. Values recorded in Landsat TM band 3 (red) were higher than those recorded in Landsat TM band 4 (NIR) except for the categories dense riverine Cmo, medium dense Cmo, riverine acacia and vegetated Hills where the reverse is true.

Figure 4: Landsat TM signature curves for the classes medium dense mopane and sprase mopane (wet season)

)25 -

0.2

J.15

0.05 1

Rand j l

Figure 5 : Landsat TM signature curves for the classes dense mopane and dense riverine mopane (wet season). CMo=Colophospermum mopane, D/R=Dense Riverine, D=Dense

WR CMO -- URCMO r DCMO

-8- D CMO -DRCMO --O-UCMO

I

D/RCMO +UCMO + D CMO

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Figure 6: Radiance values extracted from the dry season image

A logical explanation for the variation between the different classes is that the spectral response could be moisture related. Higher moisture content along river channels is anticipated, which means that plant leaves in the dense riverine vegetation are therefore turgid and healthy, resulting in a higher spectral response in the NIR than in the red band. Recorded band 4 reflectance values were lower than band 3 reflectance values in low to medium dense vegetation. This agrees with the second and third field condition for darkening. In areas of low to medium vegetation cover, there is a higher spectral response in the red than in the NIR band. This is due to the high red reflectance of soils, and that shadowing is prevalent resulting in the lowering of reflectance especially in the near infrared band.

Plants on other land cover classes away from water collecting points are normally less healthy and also wilted during hot days resulting in low reflection in the NIR as well as the red band. Similar results have been reported in Botswana by Ringrose et al. (1 997) in the Okavango area. Peculiar to these areas is also the band separation of species; separability is better achieved in bands 3 and 5. High reflectance values in band 5 show the prevalence of soil reflectance in these savannah ecosystems (Figure 6). Soils rather than vegetation characteristics are the highest contributors to the high spectral response values displayed in this band.

Accuracy assessment results for the classification of mopane show a range from 60-83% (Table 4). By far the best classification of mopane is the class dense riverine Cmo at 83% accuracy, this is probably because it represents a highly homogeneous class and also that its high spectral reflection allows for a clear separation from other low reflecting classes. Second is the class medium dense mopane on shale with 80% accuracy. A possible explanation for high values in this class could be the unique spectral response characteristics of the frequent rock outcrops as compared to other exposed surfaces. The lowest accuracy of mopane classes were that of medium dense and sparse mopane at 62.2% and 61.3% respectively. This is possibly a result of the high influence on reflectance by the various soil types in the area. Generally, classification results were best in depicting cultivated areadfields and very dense monospecific vegetation types (Figure 7). Stands of mopane were accurately classified at least in the category dense Cmo and dense riverine Cmo (Table 4).

Table 4: A contingency matrix for the assessment of accuracy in classifying vegetation types. Sampling units are as described in the methods section. Eoc: Error of commission, Ua: User’s accuracy.

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Figure 7: A classified wet season image

Problems of inaccuracy were encountered regarding the classification of other classes of mopane. Figure 8 shows that three out of six classes show mopane beyond its actual zone. This is possibly a result of some spectral overlaps between tree species. For example, there is a possibility of spectral overlaps between mopane and other species, especially the broad-leafed type found at the bottom of the image, around Radisele and along some river channels. A clear indication of this kind of misclassification is shown where the class dense riverine

Figure 8: Final classification result showing mopane distribution in the

Cmo appears along three river channels on the mid-left side of Figure 8. The river channels are in fact covered by a mixture of broad leafed species other than mopane. A positive feature with respect to the classification of mopane however is that it is ‘ritghtfully ommitted’ on two major land units namely the hills (regosols) and the deep Kalahari sands (arenosols). Mopane does not appear on these two land units.

As a result of the influence of soil characteristics on the reflectance values in band 5 of the dry season image and the

study area

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relatively high vegetation reflectance values displayed in band 3 of the wet season image, the two bands were chosen as the main parameters for statistical spectral analysis. The correlation between the spectral classes of ‘soil’ and ‘vegetation’ is very high, showing a correlation coefficient of r = 0.90. This suggests that the distribution of the vegetation can be attributed to the differences in soil type (Figure 9). Regression analysis indicates that 81% (R2) of the variation in the distribution of vegetation classes are accounted for by the variation in soil.

A summary of the analysis is shown in Tables 5 and 6. Table 5 is a general overview of the degree of correlation between all the vegetation classes combined, and soil types as assumed from the “soil reflecting bands” (TM 5 and TM 7). Table 6 on the other hand shows the degree of correlation between the variables within the six classes of mopane. In fact, the two “soil bands” (TM5 and TM7) from the dry season image are both correlated with wet season TM3 as well as TM 4 (Table 5). In Table 6 the degree of association between the variables is shown but with the input classes being only those of mopane. Of particular interest is the high correlation between band 3 and band 5 at 0.01 significant levels. According to this result, mopane may be correlated with soil type.

Some of the results reflected in Tables 5 and 6 may be viewed as anomalies. For example, the correlation between TM band 4 and tree cover is negative. The correlation between tree cover and NDVI also resulted in a negative value. A negative correlation between TM 4 and tree cover is due to the

darkening effect explained earlier. As long as the vegetation cover is not dense, and shadows and bare ground are visible to the sensors, the reflectance from TM 3 will be higher than that of TM 4. The effect is a negative correlation. Since NDVI is a ratio between the red and the near infrared bands, it is bound to be negative as long as the red reflectance is higher than near infrared reflectance. As mentioned earlier, there are some areas where the near infrared reflectance is higher than the red reflectance, hence a positive NDVI. This means that there is a threshold tree cover at which higher TM 3 than TM 4 values are inverted to show the direct opposite relationship.

Figure 9: Regression analysis between dry season TM band and wet season TM band3 images (standard e r ro~0.006 1)

0 3

n is

0 2

0 15

0 1

0 05

U

/-

0 I n 02 00.1 noh n n8 n i

TM 3

Table 5: Correlation coefficients showing the relationship between the variables: (TM3, TM4) wet season, (TM5, TM7) dry season, TCC, NDVI and soil texture on all sample sites. (TCC = Total Canopy Cover; STX = Soil Texture)

** Correlation is significant at the 0.01 level (2-tailed). * Crrelation is significant at the 0.05 level (2-tailed).

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Table 6: Correlation coefficients showing the relationship between the variables: (TM3, TM4) wet season, (TM5, TM7) d y season, NDVI, TCC and soil texture on mopane classes. (TCC = Total Canopy Cover; STX = Soil Texture)

** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed).

Conclusion The classified map of mopane (Figure 8) is not 100%

accurate. The main problem is that mopane is shown as being present beyond its actual geographical limits. This is due to some spectral overlaps between mopane and some other species. However, one positive aspect of the classification is that Figure 8 shows that mopane is absent on all the hills. Overall, results of accuracy assessment show that the use of Landsat TM imagery provides a viable alternative for the mapping of mopane. On soil type-mopane relationship, it is concluded that the former forms a close relationship to the distribution pattern of mopane. This confirms our earlier indication that the mopane transition area south of Palapye follows a shift from the Alluvials to Dystric regosols and Dystric Leptosols.

But as a general concluding remark, it should be stressed that even though the use of Landsat TM has been recommended by some researchers (van Heist, 1991; Norwegian Forestry Association, 1993) as possible alternative mapping tools, the instrument should be used with great caution. Landsat TM does not provide an absolute solution to vegetation mapping at species level in areas of mixed species. This specifically applies to the classes CL3 and CL4 (sparse and medium dense categories). In addition to the problem of mixed species, all the classes with the exception of the dense categories are clouded with the problem of high soil background influence on the spectral response pattern of vegetation. Acceptable results for species mapping can be achieved for stands of mopane at categories Dense riverine and Dense Cmo.

Appendix 1 Species and class abbreviations: Adi Ael Aer Afl Ame Ane Ato Bal

Cim Cmo Cms

Dci D cmo D/R Gfl Md cmo Mse Paf Sbi Sp cmo TPr Tse Xam Zmu

Cap

CPY

Adonsia digitata Acacia erioloba Acacia erubescens Acacia fleckii Acacia mellifera Acacia nigrescens Acacia tortilis Boscia albitrunca Combretum apiculatum Combretum imberbe Colophospermum mopane Commiphora mossambicensis Commiphora pyracanthoides Dichrostachys cinerea dense mopane cmodense riverine mopane Grewia Java Medium dense cmo Maytenus senegalensis Peltophorum apicanum Sclerocarya birea Sparse mopane Term inalia prun io ides Terminalia sericea Ximenia americana Ziziphus mucronata

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Acknowledgements The authors would like to thank the Swedish International

Development Co-operation Agency (Sida) for their financial support throughout the duration of the study.

REFERENCES Abel, N. 0. J., Flint, M. E., Hunter, N. D., Chandler, D. and

Maka, G., 1987: Cattle Keeping, Ecological Change and Communal Management in Ngwaketse. Vol. I & 11. Integrated Farming Pilot Project. Ministry of Agriculture, Gaborone.

Bhalotra, Y. P. R., 1987: Climate ofBotswana. Part 2: Elements of Climate. Botswana Meteorological Services, Gaborone.

Boles, S.H., Xiangming, X., Jiyuan, L., Qingyuan, Z., Sharav, M., Siqing, C., and Dennis, O., 2004: Land cover characterization offemperate East Asiausing multi-temporal Vegetation sensor data. Remote Sensing of Environment, 90, 477-489.

Bonham, C. D., 1989: Measurements of Terrestrial Vegetation. John Wiley and Sons, London.

Botswana Range Inventory and Monitoring Project, 1995 Inception Report. Ministry of Agriculture, Gaborone.

Boyd, D.S., and Foody, G.M., 2004: Changing Land Cover. In: Harris, F. (ed.), 2004: Global Environmental Issues. Wiley, Hoboken, NJ.

Campbell, J. B., 1987: Introduction to Remote Sensing. Guilford Press, London.

Coates-Palgrave, K., 1983: Trees of Southern Africa. Struik, Cape Town.

De Wit, P.V., and Bekker, R.P., 1990: Explanatory Notes on the Land Systems of Botswana. FAO/UNDP/Government of Botswana Project BOT/85/011, Field Document 31. Ministry of Agriculture, Gaborone.

Dymond, J.R., and Sheperd, J.D., 2004: The spatial distribution of indigenous forest and its composition in the Wellington region, New Zealand, from ETM+ satellite imagery. Remote Sensing of Environment, 90, 116-125.

ERDAS Inc., 1994: Imagine Production Tour Guides. Atlanta, Georgia.

FAOKJNDPIGovernment of Botswana, 1984: Soil Mapping and Advisory Services Project AG: Bot/85/011. Gaborone.

Government of Botswana, 1991 : National Policy on National Resources Conservation and Development, Government Paper No. 1 of 1990. Gaborone.

Http:l/www.gsfc.govlIASlhandbook/handbook~htmls~ chapter1 llchapterl 1 .html

Huete, A., R., and Tucker, C., J., 1991: Investigations of soil influences in AVHRR red and near-infrared vegetation index imagery. International Journal of Remote Sensing, 12, 1223-1242.

Johnston, C. A., and Bonde, J., 1989: Quantitative analysis of ecotones using a Geographic Information System. Photogrammetric Engineering and Remote Sensing, 55, 1643- 1647.

Kent, M., and Coker, P., 1992: Vegetation Description and Analysis: A Practical Approach. Belhaven Press. London.

Korolyuk, T. V., and Shcherbenko, H.V., 1994: Compiling soil maps on the basis of remotely sensed data digital processing: soil interpretation. International Journal of Remote Sensing,

Lillesand, T. M., Kiefer, R.W. and Chipman, J.W., 2004: Remote Sensing and Image Interpretation. Wiley, New York.

Lu, D., Mausel, P., Brondizio, E. and Moran E., 2004: Change detection techniques. International Journal of Remote Sensing, 25,2365-2407

Macala, J. P., 1996: Colophospermum mopane: a fodder tree for feeding livestock. Proceedings of the First Multidisciplinary Symposium on Phane. University of Botswana. Gaborone

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, 9, 5- 13.

15, 1379-1400.

Moganane, B. G., 1989: The Soils and Land Suitability for Arable Farming in the Gaborone area. Ministry of Agriculture, Gaborone.

Mueller-Dombois, D., and Ellenberg, H., 1974: Aims and Methods of Vegetation Ecology. Wiley, New York.

Mulder, C., 1988: Digital image processing, computer- aided classification and mapping. In: Kuchner, A. W., and Zonneveld, I. S. (eds.), Vegetation Mapping. Kluwer Academic Publishers, Dordrecht.

Nellis, D., 1988: The application of remote sensing technology to rural resource and land use planning in the United States. In: International Yearbook of Rural Planning. Elsevier Applied Science, London.

Norwegian Forestry Association, 1993: Chobe Forests Inventory and Management Plan, Final Report. Ministry of Agriculture, Gaborone.

Otterman, J., 198 1 : Satellite and field studies of man’s impact on the surface of arid regions. Tellus, 33, 77.

Otterman, J., 1982: Baring high albedo soils by overgrazing: a hypothesised desertification mechanism. Science, 186,53 1- 533.

Pech, R. P., and Davis, A. W., 1987: Reflectance modelling of semi-arid woodlands. Remote Sensing of Environment, 23, 365-377.

Prince, S. D., and Astle, W. L., 1986: Satellite remote sensing of rangelands in Botswana I. Landsat MSS and herbaceous vegetation. International Journal of Remote Sensing, 7, 1533-1 553.

Ringrose, S., and Matheson, W., 1991a: A Landsat analysis of range conditions in the Botswana Kalahari drought. International Journal of Remote Sensing, 12, 1023-1 05 1.

Ringrose, S., and Matheson, W., 1991 b: Initial characterisation of woody vegetation cover in the south-east Botswana Kalahari. Global Ecology and Biogeography Letters 2, 18- 22.

52

Dow

nloa

ded

by [

Nor

th D

akot

a St

ate

Uni

vers

ity]

at 0

9:57

28

Oct

ober

201

4

Ringrose, S., Matheson, W., and Mogotsi, B., and Tempest, F., 1989: Nature of the darkening effect in drought affected savannah woodland environments relative to soil reflectance in Landsat and Spot Wavebands. Remote Sensing of Environment, 25: 5 19-524,

Ringrose, S., Vanderpost, C., and Matheson, W., 1997: Use of image processing and GIS techniques to determine the extent and possible causes of land management lfenceline induced degradation problems in the Okavango area, northern Botswana. International Journal of Remote Sensing, 18, 2337-2364.

Robinove, C. J., 1982: Computation with physical values from Landsat digital data. Photogrammetric Engineering and Remote Sensing, 48,781-784.

Sebego, R. J. G., 1999: The ecology and distri-bution limits of Colophospermum mopane in southern Africa. Botswana Notes and Records, 3 1,53-72.

Sebego, R. J. G., and Arnberg, W., 2002: Interpretation of mopane woodland using air photos with implications on satellite image classification. International Journal of Applied Earth Observation and Geoinformation, 4, 1 19- 135.

Serneels, S., Said, M.Y., and Lambin, E.F., 2001: Land cover changes around a major east African wildlife reserve: the Mara Ecosystem (Kenya). International Journal of Remote Sensing, 22,3397-3420

Schultz, G.A. and Engman, E.T (eds.) 2000: Remote Sensing in Hydrology and Water Management. Springer-Verlag, Berlin.

Scepan, J., 1999: Thematic validation of high-resolution global land-cover data sets. Photogrammetric Engineering and Remote Sensing, 65, 105 1- 1060.

Sellers, P. J., Berry, J. A., Collatz, G. J., Field, C. B., and Hall, F. G., 1992: Canopy reflectance, photosynthesis and transpiration. 111. A reanalysis using improved leaf models and a new canopy integration scheme. Remote Sensing of Environment. 42, 187-216.

Shoshany, M., Lavee, H., and Kutiel, P., 1995: Seasonal vegetation cover changes as indicators of soil types along a climatological gradient: a mutual study of environmental patterns and controls using remote sensing. International Journal of Remote Sensing, 16,2 137-2 15 1.

Thomas, D. S . G., and Shaw, P. A., 1991: The Kalahari Environment. Cambridge University Press, Cambridge.

Trotter, C. M., 1991: Remotely sensed data as an information source for geographical information systems in natural resource management: a review. International Journal of Geographic Information Systems, 5,225-239.

Van Heist, M., 1991: Mapping Woody Biomass Classes in Southeast Botswana using Landsat MSS. Forestry Association of Botswana, Gaborone.

Van Voorthuizen, E.G., 1976: The mopane tree. Botswana Notes and Records, 8,223-230.

Verbeek, K., and Remmelzwaal, A., 1990: Revised General Soil Legend of Botswana: FAO/UNDP/Government of Botswana Project BOT/85/011. Field Document No.32. Gaborone.

Weare, P. R., and Yalala, A., 197 1 : Provisional vegetation map of Botswana. Botswana Notes andRecords, 3, 131-148.

Zonneveld I. S . , and Kannegieter, 1988: Composition and structure ofvegetation. In: Kuchner, A. W., and Zonneveld I. S. (eds), Vegetation Mapping. Kluwer Academic Publishers, Dordrecht.

REUBEN J. SEBEGO University of Botswana

P/Bag UB00704 Gaborone, Botswana

WOLTER ARNBERG Department of Physical Geography and Quaternary

Geology Stockholm University

SE- 106 91 Stockholm, Sweden

BENGT LUNDEN Department of Physical Geography and Quaternary

Geology Stockholm University

SE - 106 91 Stockholm, Sweden

SUSAN RINGROSE Harry Oppenheimer Okavango Research Centre

PIBag 285 Maun. Botswana

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