Extent of Woody Plant Invasion Along a Riparian Zone of the Molopo River, North West Province, South Africa

NORTH -WEST UMVER'1Y I YUNIBESITI YA BOKONE -BOPHIRIMA NOORl:M'ES-UNIVERSITEIT

Mafikeng Campus

Extent of woody plant invasion along a riparian zone of the Molopo River, North West Province, South Africa.

By: Alvino Abraham Comole Student No: 18045529 BSc Hons in Biology (North-West University)

Submitted in the fulfilment of the requirements for the degree Master of Science

In the Faculty of Agriculture, Science and Technology (Department of Biological. Sciences) North-West University 2014 Supervisor: Prof. P.W. Malan Co-supervisor: Prof. C. Munyati

2021 -02- 0 3 NORTH-WEIi UNIVER511Y O l!1Ill YUN IBESITI YA BOKON E-BOPHIRIMA u NOOR()NES-UNIVERSITE IT

Mafikeng Campus

DECLARATION

I, Alvino Abraham Comole (18045529), hereby declare that the dissertation titled: Extent of woody plant invasion along a riparian zone of the Molopo River, North West Province, South Africa, is my own work and that it has not previously been submitted for a degree qualification to another University.

Signature: ·-~ -·-· ···· ...... Date: ... ~.?..-::.. ?..'f: ..-:..~~!.~ Alvino A. Comole

This thesis has been submitted with my approval as a university supervisor and I certify that the requirements for the applicable M.Sc degree rules and regulations have been fulfilled.

Signed .. ... ~...... Prof. P.W. Malan (Supervisor) Date -~·<:?.'.~ :-:~~:-.RP..

Signed ...... Prof. C. Munyati (Co-Supervisor) Date ...... DEDICATION

I dedicate this research to God Almighty, who protected and guided me through and who made it possible for me to reach this level of my academia. May only His name be praised and glorified at all times.

t iii } ACKNOWLEDGEMENTS

• I owe lot of credit to my supervisor, Prof. P.W. Malan, for guiding, supporting and exposing me to the world of research. • I would like to thank my co-supervisor, Prof. C. Munyati, for guidance and supporting me especially on Remote Sensing. • I would also like to acknowledge the North-West University, (Mafikeng Campus), for funding this research. • To the late Mr. Lawrence Makhoba, thank you for your support and time spent on drawing maps needed for this research. Rest in peace. • To my friends, Mr. Ndou and Sammy (Department of Geography and Environmental Sciences, Mafikeng Campus), for your assistance on GIS and remote sensing. • A great appreciation to my parents, Fernando Orlando Comole and my late mom lzaura Antonio Cossa, for bringing me into this world. • Credits go to my wife Thandeka Precious Masuku and my son Loyiso Miguel Comole, for the support and encouragement. • To Mr T .D Kawadza for language editing this research. LIST OF FIGURES AND TABLES

FIGURES:

CHAPTER 2: Figure 2.1: Map of the Study area, showing the location of the study sites Figure 2.2: Monthly mean temperatures for Mahikeng (1990-2009) (South African Weather Service, Station [05080447 O] - MAI-IlKENG WO - 25.8080 25.5430 1281 M) Figure 2.3: Monthly mean rainfall for Mahikeng (1984-2012) (South African Weather Service, Data for station [0508047 O] - MAFIKENG WO Measured at 08:00 Figure 2.4: Mean Annual Rainfall of the North West Province (Department of Agriculture, Conservation, Environment and Tourism, 2002) Figure 2.5: Geology of the North West Province (Department of Agriculture, Conservation, Environment and Tourism, 2002) Figure 2.6: Soil Degradation in the North West Province per Magisterial District (Department of Agriculture, Conservation, Environment and Tourism, 2002) Figure 2.7: Vegetation Types of the North West Province (Department of Agriculture, Conservation, Environment and Tourism, 2002) Figure 2.8: Main land uses of the North West Province (Department of Agriculture, Conservation, Environment and Tourism, 2002) Figure 2.9: Percentage area of magisterial districts managed under a communal land tenure system in the North West Province (Department of Agriculture, Conservation, Environment and Tourism, 2002) Figure 2.10: Main land uses in the North West Province (Department of Agriculture, Conservation, Environment and Tourism, 2002) Figure 2.11: Mean groundwater recharge per year of the North West Province (Department of Agriculture, Conservation. , Environment and Tourism, 2002)

CHAPTER 3: Figure 3.1: Procedure by Coetzee & Gertenbach (1977) for determining quadrant size for a height class, e.g. 1 m high plant. Test squares are enlarged in steps until at least one plant is included Figure 3.2: Woody species density in Tshidilamolomo benchmark Figure 3.3: Woody plant densities in Tshidilamolomo benchmark according to height classes Figure 3.4: Woody species density in Makgori benchmark Figure 3.5: Woody plant densities in Makgori benchmark according to height classes Figure 3.6: Woody species density in Loporung benchmark Figure 3.7: Woody plant densities in Loporung benchmark according to height classes Figure 3.8: Woody species density in Tshidilamolomo Village (Site 1) Figure 3.9: Woody plant densities in Tshidilamolomo Village (Site 1) according to height classes Figure 3.10: Woody species density in Tshidilamolomo Village (Site 2) Figure 3.11: Woody plant densities in Tshidilamolomo Village (Site 2) according to height classes Figure 3.12: Woody species density in Tshidilamolomo Village (Site 3) Figure 3.13: Woody plant densities in Tshidilamolomo Village (Site 3) according to height classes Figure 3.14: Woody species density in Loporung Village (Site 1) Figure 3.15: Woody plant densities in Loporung Village (Site 1) according to height classes Figure 3.16: Woody species density in Loporung Village (Site 2) Figure 3.17: Woody plant densities in Loporung Village (Site 2) according to height classes Figure 3.18: Woody species density in Loporung Village (Site 3) Figure 3.19: Woody plant densities in Loporung Village (site 3) according to height classes Figure 3.20: Woody species density in Makgori Village (Site 1) Figure 3.21: Woody plant densities in Makgori Village (Site 1) according to height classes Figure 3.22: Woody species density in Makgori Village (Site 2) Figure 3.23: Woody plant densities in Makgori Village (Site 2) according to height classes Figure 3.24: Woody species density in Makgori Village (Site 3) Figure 3.25: Woody plant densities in Makgori Village (Site 3) according to height classes

CHAPTER 4: Figure 4.1: An example ofNDVI calculation Figure 4.2: Green vegetation reflectance across various portions of the electromagnetic spectrum Figure 4.3: 19 May 1988 NDVI image Figure 4.4: 19 May 1988 supervised classification Figure 4.5: 01 March 1996 NDVI image Figure 4.6: 01 March 1996 supervised classification Figure 4.7: 03 May 2013 NDVI image Figure 4.8: 03 May 2013 supervised classification Figure 4.9: Overall trend change of vegetation in percentage

~ vii o;:; Figure 4.10: Mahikeng monthly rainfall (mm) during the year 1988, 1996 and 2013 (South African Weather Service, Data for station [0508047 O] - MAFIKENG WO Measured at 08:00) Figure 4.11: A comparison between intact and degraded vegetation in Loporung Village: (a): Intact vegetation (b) Degraded vegetation Figure 4.12: Prosopis infested sites within Botswana along the Molopo River Figure 4.13: Cutting of trees in the study area Figure 4.14: Overgrazing along the riparian zones of the Molopo River in the study area Figure 4.15: A water hole created in the Molopo River for domestic uses Figure 4.16: Cattle kraal made from woody plants

TABLES:

CHAPTER 2: Table 2.1: Location of the study area and the benchmark sites

CHAPTER 3: Table 3.1: Indication of the extent of bush encroachment (TE/ha) (Moore and Odendaal, 1987; Bothma, 1989; National Department of Agriculture, 2000) Table 3.2: The sampling framework of the study area

CHAPTER 4: Table 4.1: Accuracy Assessment Results LIST OF ACRONYMS

GPS: Global Positioning System KIA: Kappa Index of Agreement NDVI: Normalized Difference Vegetation Index NIR: Near Infrared NWP: North West Province R:Red TVI: Transformed Vegetation Index VI: Vegetation Indices W-fW: Working for Water ABSTRACT

River systems play a pivotal role in the functioning of the ecosystem. The frequent and intense disturbances of these systems create problems for conservation of biodiversity and ecosystem functioning. As in most parts of the world, the riparian zones of the Molopo River are highly modified. Changes are caused by alien plant invaders such as Prosopis velutina and human induced overgrazing and other human activities such as cutting down of indigenous trees and creating boreholes along and within the Molopo River system. The extent of woody plant invasion was quantified at selected sites and reference sites along the riparian zones of the Molopo River and up-stream, in-land in the vicinity of the villages of the study area. The prominent species identified in the selected sites included Prosopis velutina, Senegalia mel/ifera and Vachellia tortilis. The other woody species observed included Vache Ilia erioloba, V. hebeclada, Grewia jlava, Ziziphus mucronata and Tarchonanthus camphoratus. The exotic, non-woody cactus, Opuntia .ficus-indica was also present. All the selected sites, except the benchmark sites, had woody plant densities, exceeding 2 000 TE/ha that will almost totally suppress grass growth. Remote sensing techniques were used to analyse the overall trend of vegetation in the study area. Landsat TM images of 1988, 1996 and 2013, were used to monitor change detection of vegetation. Land cover maps were established, comprising five classes of land cover, viz. intact vegetation, degraded vegetation, grassland, bare surface and water body. The classification of the images was achieved using the supervised K-nearest neighbour algorithm. Analysis of vegetation condition trends revealed a decline in intact vegetation with an increase in degraded vegetation especially in the vicinity of the villages in the study area. However, it was further observed that water as an important source of life was also decreasing. TABLE OF CONTENTS

CHAPTER 1 1 Introduction 1 1.1 Background 1 1.2 Problem Statement 4 1.3 Literature Review 6 1.3 .1 Models of Bush Encroachment 10 1.4 Aim and Objectives 12 1.4.1 Aim 12 1.4.2 Objectives 12 CHAPTER2 13 Study Area and Climatic Conditions 13 2.1 Study Area 13 2.2 Climatic Conditions 16 2.2.1 Temperature 17 2.2.2 Rainfall 17 2.3 Geology and Soil Types 20 2.3.1 Geology 20 2.3.2 Soils 22 2.4 Vegetation of the Study Area 25 2.4.1 Sour Mixed Bushveld 25 2.5 Main Land Use in the North West Province 26 2.6 Alien Plant Invasion in the North West Province 28 2. 7 Mean Ground Water Recharge 29 CHAPTER3 31 Woody Plant Invasion in the Study Area 31 3 .1 Introduction 31 3.2 Methods 31 3 .3 Data Analysis 3 5 3.3.1 Ground Trothing Analysis 35 3 .4 Results 3 5 3.4.1 Woody Plant Invasion in the Tshidilamolomo Village (Benchmark) 35 3.4.2 Woody Plant Invasion in the Makgori Village (Benchmark) 39 3.4.3 Woody Plant Invasion in the Loporung Village (Benchmark) 40 3.4.4 Woody Plant Invasion in Tshidilamolomo Village (Site 1) 42 3.4.5 Woody Plant Invasion in Tshidilamolomo Village (Site 2) 45 3.4.6 Woody Plant Invasion in Tshidilamolomo Village (Site 3) 46 3.4.7 Woody Plant Invasion in Loporung Village (Site 1) 48 3.4.8 Woody Plant Invasion in Loporung Village (Site 2) 50 3.4.9 Woody Plant Invasion in Loporung Village (Site 3) 52 3 .4.10 Woody Plant Invasion in Makgori Village (Site 1) 52 3.4.11 Woody Plant Invasion in Makgori Village (Site 2) 57 3.4.12 Woody Plant Invasion in Makgori Village (Site 3) 59 3.5 Discussion 60 3.5.1 Woody Plant Invasion in Tshidilamolomo 60 3.5.2 Woody Plant Invasion in Loporung 62 3 .5 .3 Woody Plant Invasion in Makgori 64 3.6 Conclusion 64 CHAPTER4 67 Remote Sensing 67 4.1 Introduction 67 4.2 Literature Review 68 4.2.1 Image Rectifying 71 4.2.2 Vegetation Indices 72 4.2.3 Remote Sensing Image Classification 74 4.2.3.1 Unsupervised Classification 75 4.2.3.2 Supervised Image Classification 75 4.2.4 Accuracy Assessment 76 4.3 Remote Sensing Methods 77 4.3.1 Study Site Description 77 4.3.2 Field Data Collection 77 4.3.3 Image Data and their Dates 77 4.3.4 Image Pre-Processing 78 4.4 Image Data Processing 78 4.4.1 Image Classification Methods 78 4.4.2 Accuracy Assessment 79 4.5 Results 80 4.5.1 Classification Accuracy Assessment 80 4.5.2 NDVI and Vegetation Density Analysis 80 4.6 Conclusion 92 CHAPTERS 94 General Discussion, Conclusion and Recommendations 94 5.1 General Discussion 94 5 .2 Conclusion 96 5 .3 Recommendations 97 References 99 Appendix A CHAPTER I

Introduction

1.1 Background

Alien plants are defined as those species that are not indigenous to a particular region or country (Hoffman and Ashwell, 2001). In general, it is the herbaceous alien invasive species, shrubs and trees that are of concern as they are able to dominate and transform the natural vegetation. This relates to the impact of thorn trees on both the productivity of agricultural land and ecosystem process (Hoffinan and Ashwell, 2001 ).

Alien plant invasion is the establishment and spread of non-indigenous plants in disturbed or natural vegetation at the expense of indigenous species (V ersveld et al., 1998). In the past, poor veJd management sometimes made it necessary to introduce fodder plants such as Opuntia sp. and Prosopis sp. into arid areas where stock had depleted the natural veld. Many weeds were also introduced accidentally in imports of fodder for horses during the Anglo Boer War (Versveld et al., 1998). Alien plants like jacaranda (Jacaranda mimisifolia), lantana (Lantana camara), pepper trees (Schinus mo/le) and syringa (Melia azedarach) were introduced as ornamental plants, but have subsequently become naturalized as environmental weeds (Hoffman and Ashwell, 2001 ). I NWU · I LIBRARY_ The need to stabilize driftsands, mainly in coastal areas of the former Cape Province, led to the introduction of especially Australian acacias from 1875 onwards. Acacia cyclops (Rooikrans) and Port Jackson Willow (Acacia saligna) proved to be extremely efficient as sand binders. By 1934, the government had reclaimed nearly 11 000 hectares of driftsands, not to mention work done by private landowners. Black wattle (Acacia meamsil) was introduced to provide bark for an expanding tanning industry. The use of bark reached a peak in the 1960's, with an estimated 290 000 hectares under black wattle production. By the end of the 1990's, however, this area had decreased to approximately 110 000 hectares as synthetic materials replaced the need for black wattle products (Hoffman and Ashwell, 2001).

1 Another reason to bring alien plants to South Africa arose from the shortage of natural timber. The first plantations were planted soon after colonization in the 1600's. In the late 1880's, much research took place, notably at the Tokai plantation in Cape Town, to determine which species to cultivate. With the development of gold and diamond mining, plantations were also established in the former Transvaal to supply poles for mineshafts. The demand for wood continues unabated and pine (Pinus spp.) and Eucalyptus plantations continue to be planted for commercial purposes (Hoffinan and Ashwell, 2001 ). Because the source area is expanding, the problem of Pinus and Eucalyptus invasions are likely to continue in the future.

After successful introduction of animals, livestock intermittently dispersed the seeds into the wetland ecosystems (Otsamo et al., 1993). This included the riverine ecosystems where they were originally not intended to extend (Ngunjiri and Choge, 2004; Anderson, 2005; Mwangi and Swallow, 2005). Species such as Prosopis sp. are, therefore treated as invasive in the wetlands, a trend that is consistent with other global introductions of Prosopis species (Pasiecznik et al., 2001).

It is especially alien plants, such as mesquite (Prosopis velutina) that predominates in riparian habitats. Prosopis species have increased water usage as compared to native vegetation (V ersveld et al., 1998). Most of these alien invasive species produce large numbers of seed which are wind or bird dispersed or they have developed highly efficient means of vegetative reproduction, which results in aggressive encroachment (Brooks et al., 2004).

In general, alien plants invade land with open soil more readily than they do veld with a good vegetation cover. Disturbance may take place as a result of floods, fire, construction or overgrazing (Hoffinan and Ashwell, 2001 ).

Aliens usually become established in places where there is adequate water. Riparian zones are particularly susceptible to alien plant invasions as they are physically dynamic environments where floods may expose areas of bare soil on which weeds can start to grow. The seeds of alien plants may be dispersed by the river itself or by birds that roost in the trees and shrubs in the riparian zones (Hoffinan and Ashwell, 2001 ). In some cases, riverine biodiversity is also naturally low (Wyant and Ellis, 1990; Stave et al., 2003). Therefore,

2 riverine forests are highly susceptible to invasions due to resource fluctuations, disturbance and low biodiversity.

Two of the most significant effects of alien plant invasions are their impacts upon water resources and disturbing ecosystem functioning (Hoffman and Ashwell, 2001 ). Alien trees use much more water than natural fynbos or grassland vegetation. It is estimated that woody aliens use approximately 3 300 million cubic meters of water per year, which is equivalent to 6. 7 % of South Africa's total mean annual runoff (Hoffman and Ashwell, 2001 ). In the arid interior, invasions of alien plants are threatening ground water supplies (Hoffman and Ashwell, 2001). For example, Prosopis (mesquite) extracts about 192 million cubic metres of water each year (Hoffman and Ashwell, 2001 ). Alien plants change the species composition and vegetation structure of an area, which in tum has an effect on many other organisms, such as pollinators and the decomposers that live in the soil. In the main agricultural areas, alien plants can significantly reduce the total grazing area, while some species are poisonous to livestock. Woody aliens also change the natural fire regime of an area. According to Simmons et al. (2007), Prosopis glandulosa is not damaged by low intensity surface fires, on the contrary, they generally expand by increasing their growth and foliar nutrient concentration.

South Africa has a long history of problems associated with invasion of arable land or invasion of land by alien plants, rating amongst the worst in the world (Richardson and Van Wilgen, 2004). Although the full extent of invasion by alien plants in riparian zones countrywide has not been documented, regional information indicates that the proportion of rivers invaded is likely to be very high as riparian zones are among the most densely invaded habitats in all biomes and many alien species spread along watercourses (Richardson et al., 1997; Richardson et al., 2000). The cost of alien invasion includes both the loss of productivity of the land and the cost of eradicating the invaders (Hoffman and Ashwell, 2001).

This study aims to indicate that there is an increase in invasive woody plants along the riparian zone of the Molopo River, as woody alien species become established in places where there is adequate water (Hoffman and Ashwell, 2001). Thorny indigenous woody species, such as Black Thom (Senegalia mellifera), Umbrella Thom (Vachellia tortilis), Candle Thom (Vachellia hebeclada) and Camphor Bush (Tarchonanthus camphoratus) are

3 also invading certain areas along the Molopo River. The situation whereby the density of woody plants e.g. trees and shrubs, increases in an area is called bush encroachment (Tainton, 1999). This study will therefore investigate this phenomenon. The study will also explore the possible causes of the findings (increase of invasive woody plants) and their impact on the natural flow of the river and on the diversity of indigenous vegetation in the Molopo District

1.2 Problem Statement

River ecosystems are highly prone to invasion by alien plants because of their dynamic hydrology and opportunities for recruitment, following floods (Decamps et al., 1995; Hupp and Osterkamp, 1996). Efficient dispersal of alien propagules in water and continuous access to water resources facilitates alien plant invasions (Thebaud and Debussche, 1991; Pysek and Prach, 1993; Planty Tabacchi et al., 1996; Richardson, 2001).

Many alien invaders of riparian zones in South Africa are tall trees with higher water consumption than the indigenous vegetation (Dye and Poulter, 1995; Dye and Jermain, 2004). While much of South Africa is semi-arid, invasive alien trees impact negatively on the country's scarce water resources by reducing run-off (Van Zyl, 2003). Prosopis species are rated within the top ten most invasive species in riparian habitats (Zachariades et al., 20 I I). Prosopis species are deep-rooted desert phreatophytes and it competes for resources with the native grassy and dwarf shrublands which are the dominant vegetation types in South Africa's most arid Province (Mucina and Rutherford, 2006). Prosopis invasions interact with ground and soil water in different ecosystems in South Africa (Versfeld et al., 1998; Fourie et al., 2002).

Prosopis species now occur over several million hectares in the Northern Cape, Western Cape, Free State and North West Province (Zachariades et al., 2011). Other researchers also rank Prosopis species among the most problematic of the invasive plant species in South Africa (Robertson et al., 2003; Nel et al., 2004). According to Le Maitre et al. (2000), Mesquite invasions alter the hydrology of water-impoverished ecosystems, and have been estimated to use approximately 192 million m3 of water annually, which is an equivalent of 1100 mm of rainfall (V ersfeld et al., 1998; Zimmermann et al., 2006). The spread of mesquite and the formation of expansive infestations have been enhanced by livestock and game which consume the ripe seed pods and disperse scarified seeds. In South Africa, the

4 size of seed banks varies across the distributional range of mesquite and is affected by the presence or absence of livestock, with accumulations of as many as 2500 seed/m2 in some areas (Roberts, 2006).

Riparian zones along the Molopo River undergo degradation due to human related disturbance (Figures 4.10 and 4.12, Chapter 4), overgrazing (Figure 4.11, Chapter 4) and trampling by livestock. Other impacts on riparian vegetation recorded elsewhere, such as grazing and trampling by livestock (Hancock et al., 1996; Mathooko and Kariuki, 2000; Robertson and Rowling, 2000) also take place in South African river ecosystems (Fleischner, 1994).

The influence of alien trees on water resources increases with proximity to water courses (Gorgens and Van Wilgen, 2004). For this reasons, the national Working for Water (WfW) programme is targeting the invaded riparian zones and their immediate sub-catchment for alien clearance since 1995, in order to increase water production, conserve biodiversity, and improve water quality (Van Wilgen et al. 1998). Working for Water (WfW) programme was launched by the South African government to control the invasive alien plants (Van Wilgen et al. 1998; Binns et al. 2001; Le Maitre et al. 2002; Anonymous 2006). For some reasons, the Working for Water programme did not reach the invaded riparian zones of the Molopo River in the Molopo District. As a result, Prosopis velutina is aggressively invading these areas. Because of this invasion, woody species, especially aliens, are increasing inland.

Thorny indigenous woody species such as Senega/ia mel/ifera (Black Thorn), Vachellia tortilis (UmbreJJa Thorn) and shrubs such as Vachellia hebeclada (Candle Thorn) are also invading certain areas along the Molopo River. The study area is a water-limited ecosystem and an increase in woody plant density (bush encroachment) invariably results in the suppression of herbaceous plants. As a result, most land owners view Senegalia mellifera as a serious threat due to its invasive habits and ability, at high densities, to suppress the herbaceous layer almost completely (Donaldson and Kelk, 1970; Richter et al., 2001 ).

5 1.3 Literature Review

Riparian zones form the interface between aquatic and terrestrial ecosystems (Gregory et al., 1991) and, except in broad floodplains, are relatively narrow, linear features across the landscape. Riparian zones support distinctive vegetation that varies in structure and function from adjacent aquatic and terrestrial ecosystems (Naiman and Decamps, 1997). Riparian vegetation is shaped by disturbance regimes of the surrounding landscape, by wind and fire for example, and by disturbances associated with aquatic systems, such as flooding, debris flows and sedimentation processes (Tang and Montgomery, 1995). The distribution of riparian vegetation types is primarily determined by gradients of available moisture and oxygen, and plant communities can be stratified by height above the river channel (Tickner et al., 2001; Boucher, 2002). Variations also exist owing to the post-disturbance successional phase of the vegetation (Kallio la and Puhakka., 1988).

Rivers are dynamic ecosystems and while active channels generally are hostile to vegetation establishment, the adjacent riparian zones are colonized by specialized disturbance-adapted species (Naiman and Decamps, 1997). Riparian plants are adapted to fluctuations in the water table, as river levels alternate between low base flows and floods. Riparian vegetation provides habitat, stabilizes riverbanks and filters sediments and nutrients from the surrounding catchment (Barling and Moore, 1994). These ecosystems may be considered 'critical transition zones' as they process substantial fluxes of materials from closely connected and adjacent ecosystems (Ewel et al., 2001).

Riparian zones throughout the world have been the focus of human habitation and development for many centuries (Washitani, 2001), resulting in direct and indirect degradation of their ecological integrity. Direct degradation includes deforestation of the vegetation for agriculture (Kentula, 1997; Patten, 1998), grazing and trampling by livestock (Robertson and Rowling, 2000), pollution from the surrounding catchment (Kentula, 1997; Washitani, 2001), and the planting of alien species (Rowntree, 1991; Viljoen and Groenewald, 1995; Hood and Naiman, 2000; Tickner et al., 2001).

Riparian zones in South African rivers have experienced a lot of degradation as a result of human-related activities (Acocks, 1988; Rogers, 1995).

6 Over 8 percent(> 10 million ha) of South Africa is covered by invasive alien plants (Binns et al., 2001 ; Van Wilgen et al., 2001) and much of the affected area is natural rangeland (Richardson and Van Wilgen, 2004). A species is termed invasive if it causes negative ecological and socio-economic impacts outside its natural range (Pimentel, 2001, Shackleton et al. 2007; Mwangi and Swallow, 2008; Aguilera et al., 2010). The negative ecological impacts of invasive trees include biodiversity loss, reduction of ecosystem productivity and a reduction of nutrient cycling. On the other hand, negative socio-economic impacts may consist of the cost of managing invasive plant species or loss of environmental services provided by the habitat before invasion occurred. Often, invasion is associated with alien species when they cause natural imbalance amongst species previously found in the invaded environment (Lockwood et al., 2007).

Pastoral farming is the primary economic activity in dry lands, whereas agro-pastoralism is also practiced but has high incidents of crop failures (Darkoh, 1998; Speranza et al., 2008; Eriksen and Lind, 2009). As livestock depend on the natural range resource and crop failures are frequent, the local communities tend to rely more on natural resources such as the collection of firewood etc. for livelihoods than on crop production (Eriksen and Lind, 2009; Sietz et al., 2011). Furthermore, in the communal areas, farmers keep animals for many purposes other than meat or wool production (Hoffinan and Ashwell, 2001 ). As a result of overstocking, the rangeland resources are often degraded.

Invasive alien plants can drastically suppress livestock production by lowering rangeland grazing capacity through suppressing and displacing important indigenous forage species (Milton et al., 2003; Richardson and Van Wilgen, 2004). Some species of Prosopis, native to North and Central America, were introduced into the area in the late 1880's to provide shade, fodder and fuel wood (Zimmermann 1991, Zimmermann and Pasiecznik, 2005). However, they have had serious negative environmental impacts (Zimmermann and Pasiecznik, 2005). One such impact has been the widespread coalescing of infestations into large dense thickets that are thought to have suppressed and displaced indigenous forage species and reduced rangeland grazing capacity (Roberts, 2006). Very few studies have attempted to assess and quantify the impact of such invasions on rangeland composition and grazing capacity (Ndhlovu, 2011 ).

7 Large areas in the Nama-Karoo have been cleared of Prosopis trees under a government-led invasive alien plants control programme (Zimmermann and Pasiecznik, 2005). The programme, called Working for Water (Wtw), is principally aimed at securing threatened water resources by clearing invasive alien plants from South Africa's major watersheds (Le Maitre et al., 2000; Binns et al., 2001; Le Maitre et al., 2002). Although the justification for the WfW programme has been explicitly based on its potential to deliver socio-economic benefits through increased water supply and employment (Van Wilgen et al., 1998; Binns et al., 2001; Anon 2006; Hope, 2006) there is an implicit assumption that invasive alien plants' removal will also facilitate recovery of agricultural productivity in affected areas (Ndhlovu, 2011).

This assumption has not, however, been empirically evaluated for Prosopis clearing activities in Nama-Karoo rangelands. Invasive alien plants are costly to clear and most private and state efforts have proved to be inadequate (Turpie, 2004). In the Nama Karoo, where costs of Prosopis clearing often exceed the value of land (Zimmermann and Pasiecznik 2005), WfW provides the sole means of adequately tackling the invasive alien plants problem. However, the future extent of WfW clearings is uncertain as the WfW programme has to compete with other pressing government initiatives for funding (Turpie, 2004). As the competing proposals are mostly developmental rather than environmental, WfW activities have to demonstrate their full socioeconomic worth to compete effectively (Turpie, 2004).

The benefits of clearing invasive Prosopis trees from Nama-Karoo rangelands have not been adequately described in financial and economic terms (Ndhlovu, 2011 ). Ecological studies focused on assessing and quantifying the impact of Prosopis invasion and clearing on rangeland grazing capacity could provide a basis (Richardson and Van Wilgen, 2004; Turpie, 2004; Blignaut, 2010) for such economic and financial descriptions.

Bush encroachment affects the agricultural productivity and biodiversity of 10 to 20 million hectares in South Africa (Ward, 2005). Accumulating evidence shows that in the past 50 years, savannas throughout the world are being transformed due to bush encroachment (Ward, 2005). The reduction of agricultural productivity occurs because of the low value of thorn trees to livestock, while a decrease of biodiversity occurs because a multi-species grass sward is replaced with a single tree species (Ward, 2005). Some of the encroaching species are indigenous trees such as Dichrostachys cinerea, Senegalia mellifera subsp. detinens,

8 Terminalia prunioides, T. sericea and Vachellia nilotica (Strohbach, 1998). The alien woody species invading in the Molopo Area are Prosopis spp., especially P. velutina, Acacia mearnsii, three hakea species namely, Hakea drupacea, H gibbosa and H sericea (Henderson, 2001 ).

In South Africa, Senega/ia me//ifera is found widely distributed in the drier western parts (Hagos and Smit, 2005), particularly in areas located in the North-West Province and north eastern part of the Northern Cape of South Africa, known as the Kalahari Thomveld (Acocks, 1988). Senega/ia mellifera, commonly known as Black Thom also occurs in Angola, Namibia and Botswana, extending northwards to Tanzania (Smit, 1999). This encroacher grows especially well in the deep sandy soils of the Kalahari region (Hagos and Smit, 2005).

Bush encroachment is a global phenomenon that has been studied mainly in North America (e.g. Archer et al., 1995; Van Auken, 2000; Van Auken, 2009), Australia (Tothill and Mott, 1985) and Africa (O'Connor et al., 2014). Colonialism and subsequent political changes have resulted in a pattern of land tenure and consequently of land use, that is not commonplace in other areas experiencing bush encroachment (O'Connor et al., 2014). A conceptual framework for reviewing bush encroachment in African savannas is provided by study of the tree-grass 'balance' of savannas (O'Connor et al., 2014). Bush encroachment is a consequence of this balance being perturbed (Scholes and Archer, 1997 a & b; Sankaran et a1., 2004). I NWU- • JBRARY Rainfall and soil type are the primary determinants of the African savanna, while fire and herbivory (grass-eating) are secondary determinants (Frost et al., 1986). However, climate is a primary determinant of fire frequency and intensity (Archibald et al., 2009; Archibald et al., 2010). Increasing atmospheric carbon dioxide concentration recently is a key factor influencing bush encroachment across the globe (Polley et al., 1992; Archer et al., 1995). In the southern African context, there is compelling evidence for the effect of atmospheric carbon dioxide concentration on increased woody growth (Bond, 2008; Bond and Midgley, 2012). A number of southern African workers have concluded that landscape-level bush encroachment, in which the potential role of other factors has been investigated, is attributable to increasing atmospheric carbon dioxide concentration (Wigley et al., 201 O; Buitenwerf et al., 2012; Russell and Ward, 2014; Ward et al., 2014).

9 In the late nineteenth century and the first half of the twentieth century, a number of ecologists identified bush encroachment as an emerging problem which might have adverse consequences for livestock production (O'Connor et al., 2014). Bush encroachment by Senegalia me/lifera was recorded along stock-transport roads in the southern Kalahari as early as the 1860's (Fritsch, 1868; Gillmore, 1882, as cited by Jacobs (2000)). Grasslands around Pietermaritzburg, KwaZulu-Natal, South Africa became invaded by Vachellia nilotica from protected sites in ravines as a response to wattle (Australian Acacia spp.) cultivation and the suppression of fire (Bews, 1917; Bayer, 1933). Conversely, demand for fuel wood had denuded some areas close to settlements (O'Connor et al., 2014).

Botswana is the only country for which a reliable national estimate has been made of the extent of encroachment (Moleele et al., 2002). The estimate suggests that 37 000 km2 or 6.8 % of the country's area had become encroached by 1995 (O'Connor et al., 2014) and that encroachment is still taking place. This will also be the case in South Africa, especially in the riparian zones, if some mechanisms are not implement to, at least slow down the rate of invasion. The rate of increase in woody cover has differed across southern Africa (O'Connor et al., 2014). Extension officers judged that the main species accounting for the most bush encroachment and invasion throughout southern Africa are six legumes, namely Vachellia hebeclada, V. ka"oo, V. nilotica, V. tortilis, Senegalia me/lifera and Dichrostachys cinerea, as well as Rhigozum trichotomum and Tachonanthus camphoratus in the Northern Cape, South Africa (Hoffinan et al., 1999).

1.3.1 Models of Bush Encroachment

Management responses to bush encroachment in savannah are based on our conceptual understanding of the tree-grass interaction (O'Connor et al., 2014). Sankaran et al. (2004) identified two main models for explaining tree-grass coexistence in savannas: competition based models and demographic-bottleneck models. Competition-based models emphasise competitive interactions in determining tree-grass co-existence, with co-existence resulting from spatial or temporal niche separation (O'Connor et al., 2014). The best-known is Walter's (1939) root niche separation (two-layer soil water) model based on soil water as the limiting factor, with shallow-rooted grasses and deep-rooted trees having differential 'access' to this resource.

10 Although grasses are better at abstracting water in the upper soil layer, trees are able to persist because they have exclusive 'access' to water in the deeper soil layers (O'Connor et al., 2014). Sustained heavy grazing reduces grass cover, thereby favouring the woody component by allowing more water to infiltrate to deeper soil layers (Walker et al., 1981). Sandy soils can become more easily encroached than heavy soils because their greater rate of water infiltration can potentially promote greater percolation to deeper layers (Walker and Noy-Meir, 1982). Conversely, heavy-textured soils favour grass growth by virtue of their fertility and by retaining water in the upper soil layers for longer (Dye and Spear, 1982). The model appears most applicable to semi-arid savannas because upper soil layers of mesic or moist savannas may contain sufficient water for supporting both trees and grasses (O'Connor et al., 2014). Neither a temporal nor spatial competition-based model considers the seedling stage (O'Connor et al., 2014).

In contrast, demographic bottleneck models (Higgins et al., 2000) emphasised the impact of climatic variability and disturbance on germination, growth and mortality of trees, as opposed to competitive interactions between trees and grasses, in determining tree-grass co-existence (O'Connor et al., 2014). The fire-trap model of Higgins et al. (2000) further suggests that woody cover increases in mesic or moist savannas when the fire return period increases temporarily allowing individuals to grow beyond the flame zone. Seedling recruitment is limited mainly by drought but adult recruitment from saplings is considered to be the limiting population process (O'Connor et al., 2014). Frequent, intense fires in these productive systems limit opportunities for saplings to escape the flame zone, thus creating a demographic bottleneck. Bush encroachment, therefore, depends on growth rate in relation to fire-return period and would be promoted by those factors that reduce fire frequency or intensity and those that promote sapling escape from the fire trap (O'Connor et al., 2014).

Increased atmospheric carbon dioxide concentration favours woody encroachment because it enhances woody growth rates following loss of biomass to disturbance because it increases root growth and storage (Bond, 2008; Kgope et al., 2010). Support for the fire-trap model in southern Africa is centred on mesic moist savannas (O'Connor et al., 2014).

11 1.4 Aim and Objectives

1.4.1 Aim

This aim of the study was to monitor and quantify the extent of invasive woody species along the riparian zones of the Molopo River; Molopo District; North West Province; South Africa.

1.4.2 Objectives

To quantify invasive tree densities in selected sites;

To use satellite images ranging from 1980-2013 to detect change of succession over time.

12 CHAPTER2

Study Area and Climatic Conditions

2.1 Study Area

This study was conducted in the semi-arid Savanna Biome in the North West Province, South Africa. The semi-arid savanna is characterised by a range of physiognomic vegetation types of the tropical and sub-tropical summer rainfall regions of Africa (Barnes, 1976).

The Savanna Biome has an upper stratum of rather low trees, many of which provide useful browse, scattered in the grass-dominated undergrowth. Tree density varies greatly, from conditions approaching forest at one extreme to almost open grassland at the other. Most of these trees are deciduous (Tainton, 1999). Throughout these savanna areas, there is a delicate balance between the tree and grass components of the vegetation (Tainton, 1999). The vegetation locally is sour mixed bushveld, dominated by thorn trees species.

This study was conducted along the riparian zones of the Molopo River, which forms part of the border between South Africa and Botswana (Figure 2.1 ). The locations of the study sites and the respective reference sites (benchmark sites) located within the greater Molopo District are indicated in Table 2.1. The Molopo District is located within the Kalahari Desert ecological zone (Department of Agriculture, Conservation, Environment and Tourism, 2002). The study area falls within the Eastern Kalahari Bushveld (SVK l, Mahikeng Bushveld Vegetation types) (Mucina and Rutherford, 2006).

13 Table 2.1: Location of the study area and the benchmark sites

Site number Location Latitude Longitude 1 Botswana 1 -25.810325 24.701703 2 Botswana 2 -25.811495 24.696559 3 Botswana 3 -25.813701 24.693695 4 Botswana4 -25 .817508 24.69239 5 Tshidilamolomo (Site 1) -25.813665 24.696428 6 Tshidilamolomo (Site 2) -25 .81354 24.694967 7 Tshidilamolomo (Site 3) -25.813097 24.696994 8 Tshidilamolomo Benchmark -25.811805 24.703953 9 Loporung (Site 1) -25.73681 24.060764 10 Loporung (Site 2) -25.737433 25.065274 11 Loporung (Site 3) -25.737383 25.066903 12 Loporung Benchmark -25.744288 24.996523 13 Makgori (Site 1) -25.823002 24.790603 14 Makgori (Site 2) -25.821662 24.789903 15 Makgori (Site 3) -25.82153 24.788048 16 Makgori Benchmark -25.82806 24.796581 17 Phitsane -25.732232 25.081137

The mean monthly maximum and minimum temperatures for the North West Province are 35.6 °C and -1.8 °C for November and June respectively (Mucina and Rutherford, 2006). Annual rainfall is approximately 360 mm, with the highest rainfall during the summer months, between October and April. Due to the low precipitation levels, the NWP is considered to be an arid region. A large amount of precipitation occurs as thunderstorms, which are associated with events such as heavy gusts of wind, lightning, hail and flash floods. Water drains through three primary river systems: The Vaal in the south, Molopo in the West and Crocodile / Marico in the east (Department of Agriculture, Conservation, Environment and Tourism, 2002).

14 The Molopo River rises from the Molopo Eye near Mahikeng (New name for "Mafikeng"), flows westwards to form the northern border of the North West Province with Botswana. The Molopo River was once a tributary of the Orange River system, but being blocked by high dunes, it no longer reaches the Orange River (Midgley et al. , 1994). The Molopo River is currently non-perennial as its water is heavily abstracted at source. This river has a number of tributaries which fall within the province, namely the Ramatlabamaspruit, Ganyesaspruit, Setlagolespruit and Papanaespruit, all of which are non-perennial. In South Africa, most of the rivers are dammed for irrigation and agriculture. As a result, inflow of the Molopo River, which forms part of the boundary between Botswana and South Africa, has become heavily reduced and even non-existent in some years. The Molopo River within Botswana is formed as clear channels draining from the Goodhope, Phitsane Molopo and the Karst-Dolomitic around the Kanye area in southern Botswana (Department of Agriculture, Conservation, Environment and Tourism, 2002).

Due to the local arid conditions, there is heavy reliance on ground water resources to meet water demand.

23•3o•o·E 24°0'0"E 24•3o•o·E 2s· o·o·E 25•3o•o"E 2s· o·o·E 2s•3o•o·E 2ro·o·E 21•3o•o·E

Legend Cl) Cl) 6 4 Benchmarks Sites Roads / 6 g Luporung lll •• ··• g • Waypoints -- Railroad Phitsanet ~ "' Rivers c= North_West_Province ?' C/J - Dams Cl) b 0 ....b b.... "' Tshidilamolomo "' • 'Ill----······· Makgori y.> .__ y 0 - , / ,-..~ - ---- "' 0 2 75 5 5 11 16 5 22 Kitomelers 23•3o•o·E 24•o·o"E 24•3o•o·E 2s·o·o·E 2s•3o•o·E 2s· o·o·E 2s•3o•o·E 21· o·o·E 21•3o•o·E

Figure 2.1: Map of the Study area, showing the location of the study sites

15 2.2 Climatic Conditions

Climate, in the broad sense, is a major determinant of the geographical distribution of plants and animals. Within any area of general climatic uniformity, local conditions of temperature, light, humidity and moisture vary greatly and these factors play an important role in the production and survival of plants (Tainton, 1999). However, the most important climatic variables are temperature and rainfall.

The Savanna Biome is the largest biome in South Africa and also in Africa (Scholes, 1997), occupying approximately 65 % of the continent (Huntley and Walker, 1982). The Savanna

2 Biome represents 32.78 % of South Africa (399 600 km ) (Mucina and Rutherford, 2006). This biome extends beyond the tropics to meet the Nama-Karoo Biome on the central plateau. More specifically, the Savanna Biome occupies most of the far-northern part of the Northern Cape, the western and the north-eastern parts of the North-West Province (Mucina and Rutherford, 2006). Areas of the Savanna are largely tropical and occupy the greater area of the southern continents (Huntley and Walker, 1982) as well as some parts of the northern continents (Mucina and Rutherford, 2006).

Precipitation in the Savanna Biome is seasonal (alteration of wet summer and dry winter periods) (Mucina and Rutherford, 2006). The Savanna Biome in South Africa does not occur at high altitudes and is found mostly below 1 500 m and extends to 1 800 m on parts of the Highveld, mainly along the southern most edges of the Central Bushveld (Mucina and Rutherford, 2006). Temperatures are higher than those of the adjacent Grassland Biome at higher altitudes (Mucina and Rutherford, 2006). The mean daily maximum temperature for February rarely drops below 26 °C in the Kalahari region and some low-altitude parts of the savanna in the east (Schulze, 1997). In July this temperature remains above 20 °C for the most of the area, with some temperatures at the highest altitudes dropping to 18 °C (Mucina and Rutherford, 2006). The mean daily minimum temperature in February rarely 16 °C, with the temperature of substantial parts of lower lowveld remaining above 20 °C (Mucina and Rutherford, 2006).

16 Mahikeng falls within the Mafikeng bushveld (Mucina and Rutherford, 2006) and has a summer rainfall with very dry winters. The mean annual precipitation (MAP) varies from 350 mm in the west to 520 mm in the east (Figure 2.4). Frost occurs frequently in winter. The mean monthly maximum and minimum temperatures for Mahikeng are 35.6 °C and -1.8 °C for November and June respectively (Mucina and Rutherford, 2006).

According to Figure 2.2, the months of October to December and January to March are the hottest, while June and July are the coldest. The Molopo District has a semi-arid climate, characterised by high day temperatures during the summer months and cool daily temperatures during winter months.

2.2.1 Temperature

Monthly Mean Temperature (°C) for Mahikeng 20 ~ =-=------17.8 17.3 ,....._ 18 15.6 15.8 ~ 16 13.5 ~ 14 = 10.9 "t;;.. 12 ~ 10 7.6 0 ~ 8 ■ Mean Temp ( C) E-- 6 C ~ 4 ~ 2 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month

Figure 2.2 Monthly mean temperatures for Mahikeng (1990-2009) (South African Weather Service, Station [05080447 O] - MAHIKENG WO - 25.8080 25.5430 1281 M)

2.2.2 Rainfall

Soil moisture has been cited as a primary determinant of the structure of a plant community in the savanna (Scholes and Walker, 1993; Moleele et al., 2002). Annual precipitation over the country as a whole is relatively low and evaporation losses are high. Moisture stress is a factor which exerts a major influence on plant survival (Tainton, I 999). Rainfall is, therefore,

17 the factor which most clearly determines the distribution of plant communities in the study area, as well as the potential productivity of these communities. Bush encroachment in the arid areas is influenced by variation of rainfall because plants respond to rainfall events (Teague and Smit, 1992). Changes in vegetation type and amount have been attributed to deeper rainfall penetration into the soil, favouring deeper-rooted trees (Ward, 2005). The invasive proliferation of Prosopis species has been reported to be a result of long-term changes in the pattern of precipitation (Ramakgwale, 2006).

Figure 2.3 shows the highest mean rainfall of 110. 7 mm during the month of January, followed by 90 mm in December, for the period of 28 years. Mahikeng receives a relatively low rainfall during the winter season (May to July, Figure 2.3). This limited rainfall and the long-term overgrazing may contribute to the proliferation of woody plant species.

Monthly mean rainfall for Mahikeng (mm)

120 ,...... _ 100 ~ '-' 80 4'! c; 60 ~ c; 40 ~ Rainfall (mm) 03

0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Months

Figure 2.3 Monthly mean rainfall for Mahikeng (1984-2012) (South African Weather Service, Data for station [0508047 O] - MAFIKENG WO Measured at 08:00)

Climatic conditions in the North West Province (NWP) vary significantly from the west to the east. The far western region is arid (receiving less than 300 mm of rainfall per annum) (Figure 2.4), encompassing the eastern reaches of the Kalahari Desert (Department of Agriculture, Conservation, Environment and Tourism, 2002). The central region of the Province is dominated by typical semi-arid conditions, with the eastern region being predominantly temperate (Department of Agriculture, Conservation, Environment and Tourism, 2002).

18 The rainfall pattern is highly variable both spatially and temporally and largely mirrors the prevailing climatic conditions of the Province (Department of Agriculture, Conservation, Environment and Tourism, 2002). On average, the western region receives less than 300 mm per annum, the central region around 550 mm p.a., while the eastern and south-eastern region receives over 600 mm per annum (Figure 2.4). The dominant rainfall season in the central region is mid-summer (peaking in January) (Department of Agriculture, Conservation, Environment and Tourism, 2002). Western parts of the province typically receive rain in the late summer (peaking in February), while the eastern parts typically peak in early summer (December) (Department of Agriculture, Conservation, Environment and Tourism, 2002).

In the south-eastern region of the province, evaporation exceeds precipitation, a further (indicator and) contributor to the arid and semi-arid conditions, which dominate much of the province (Department of Agriculture, Conservation, Environment and Tourism, 2002). Hail from convective storms does occur sporadically in summer, with the southeast receiving an average of 3-5 hailstorms per year and the rest of the province approximately 1-3 per year (Department of Agriculture, Conservation, Environment and Tourism, 2002). A major source of veld fires are lightning strikes and in the far east of the province the typical ground flash density is 8-9 flashes/km 2/year, reducing to around 5-6 flashes/km 2/year in the central parts and 2-3 flashes/km 2/year in the west (Department of Agriculture, Conservation, Environment and Tourism, 2002).

19 North West Province : Mean Annual Ra infall

...a... ·- ...,_ ...,_

LEGEND

MUN. A·N-:NUAL. HlNfAU. - S00·6.»n:,n 0 -

Map 2

Figure 2.4: Mean Annual Rainfall of the North West Province (Department of Agriculture, Conservation, Environment and Tourism, 2002)

2.3 Geology and Soil Types

The condition of the soil is another factor which may prevent the optimum development of the vegetation. A terminal community determined by soil condition is known as an edaphic climax. An edaphic climax could result from a permanent limitation of soiJ depth, as in the iron-pan soils of many parts of South Africa, or from an inherent lack of soil nutrients. This is another situation where the climax community may not provide any clear indication of the area (Tainton, 1999).

2.3.1 Geology

The geology of an area influences the topography and thus influencing the climate, present materials, soil and vegetation (Van Riet, 1990). The condition of the soil is another important factor which may prevent the optimum development of the vegetation. A terminal community

20 determined by soil condition is known as an edaphic climax. An edaphic climax could result from a permanent limitation of soil depth, as iron-pan soils of many parts of South Africa, or from an inherent lack of soil nutrients. This is another situation where the climax community may not provide any clear indication of the climate of the area (Tainton, 1999).

In South Africa, the Savanna Biome is located mostly in the north-eastern part of the country (Mucina and Rutherford, 2006). The geology of this area is dominated by a very stable block of ancient continental crust, known as the Kaapvaal Craton (Mucina and Rutherford, 2006). The kaapvaal craton began to form by a process of accretion over 3.5 billion years ago (bya) and has been largely unaffected by crustal processes for the past 2 giga years, except on its fringes (Mucina and Rutherford, 2006). The craton also hosts a number of significant sedimentary basins and igneous intrusions, thus preserving a geological record spanning most of geological time (Mucina and Rutherford, 2006).

The North West Province has an ancient geological heritage, rich in minerals and paleontological artefacts. The north-eastern and north-central regions of the Province are largely dominated by igneous rock formations, as a result of the intrusion of the Bushveld Complex. Ancient igneous volcanic rocks dating back to the Ventersdorp age (more than 2000 million years) appear to be the dominant formations in the western, eastern and southern regions of the Province (Department of Agriculture, Conservation, Environment and Tourism, 2002). According to Department of Agriculture, Conservation, Environment and Tourism, (2002), sedimentary rocks dating back to the Quaternary period (65 million years) occur in the north-western part of the Province (Figure 2.5).

The Bushveld Complex is largely igneous in origin and occurs in the north-eastern region of the Province, from Brits and Rustenburg in the east to north of Zeerust and Swartruggens into Botswana (Department of Agriculture, Conservation, Environment and Tourism, 2002).

21 North West Province Geology

Map 1

Figure 2.5: Geology of the North West Province (Department of Agriculture, Conservation, Environrpent and Tourism, 2002) I

2.3.2 Soils

Different soil types as well as soil depth determine the productivity and the palatability of grazing in the long term. Soil colour, texture and structure are the most important characteristics of the soil (Mogodi, 2009).

There is a much closer relationship between soils and vegetation in dry regions, such as much of the savanna areas, than in higher-rainfall regions. In low-rainfall areas where water is the main limiting growth factor, it is those physical factors that determine the rainfall efficiency, that have the greatest influence on the vegetation composition. Local influences of soil properties (e.g. variation on a scale of as small as 0.25 ha) may have a pronounced influence on the pattern and type of tree-grass coexistence in an area. Such properties may be so il crust formation (Mills, 2003) on a certain soils (enhancing water runoff and therefore less available soil water in the profile, but possibly raising available nutrient levels (Dougill and Thomas,

22 2004), swelling (cracking) clay soils that fonned on basic parent materials (high storing capacity for soil water but with seasonal root pruning taking place), duplex soils with a root impenetrable clay pan below or skeletal soils (shallow, usually also stony soils, but with fissures and cracks in the saprolite where some water may be stored and roots may penetrate).

Due to the low rainfall experienced by the Province (Figure 2.4), soils in the North West Province are deemed to be only slightly leached over much of the western region (Department of Agriculture, Conservation, Environment and Tourism, 2002). With high evaporation rates, there is a predominance of upward movement of moisture in the soils. This often leads to high concentrations of salts such as calcium and silica in soils, which sometimes lead to the formation of hard pans or surface duricusts (Department of Agriculture, Conservation, Environment and Tourism, 2002). As result, high levels of salinity or alkalinity may develop in these areas. Consequent to this, the organic content of the soil is reduced.

Extent of soil degradation per magisterial district

L 0

Map 10

Figure 2.6: Soil Degradation in the North West Province per Magisterial District (Department of Agriculture, Conservation, Environment and Tourism, 2002)

23 Under these extreme conditions of a high evapotranspiration and low annual rainfall (e.g. Kalahari), deep soils are needed for trees and shrubs to survive. As the rainfall increases and the evapotranspiration decreases, shallower soils can also support the growth of trees and shrubs. Where duplex soils with a prismacutanic B-horizon (e.g. Estcourt soil form) are occurring, grass will dominate over trees and shrubs. Similarly, where high clay content swelling soils occur, despite favourable climate-soil water conditions, grasses will dominate because they can adapt to seasonal root pruning better than the perennial trees and shrubs. Grass quality, i.e. foliar nutrients, can also be closely related to soil texture (Mutanga et al. , 2004).

Van Rooyen (1971) reported that the strongest relation between soil and vegetation in the broad Kalahari area was found on the deep red sandy soils (Hutton soil form) (Orthic A-Red apedal B). The soils are base-saturated and have a considerable water storage capacity. The thorn trees, Vachellia erioloba and V. haematoxylon, serve as distinct indicators of vegetation associated with these soils. On the same soils but on slightly higher elevations and on the northern slopes, dense communities of Senegalia mellifera and Vachellia tortilis dominate. In contrast to vegetation on the deep red soils, on yellow-coloured, sandy but calcareous soils [Augrabies (Orthic A-Neocarbonate B-Unspecified) and Addo soil form (Orthic A Neocarbonate B-Soft carbonate B)], treeless grass plains dominate. Very little soil covers the dolomite formation of the Ghaap Plateau. The soils are usually calcareous [Coega soil form (Orthic A-Hardpan Carbonate)], with Tarchonanthus camphoratus, Olea europaea subsp. africana and Searsia spp. occurring (Department of Agriculture, Conservation, Environment and Tourism, 2002).

The Mafikeng Bushveld has Aeolian Kalahari (what sort of sand is this?) sand of the Tertiary to recent age on flat sandy plains, sandy soil deep (<1.2 m). Clovelly and Hutton soil forms are the most prominent. Ae (red, high base status, (>300 mm deep, with dunes), Ai (yellow, high base status and Ah (red and yellow, high base status) land types are the only land types present (Department of Agriculture and Water Supply Republic of South Africa, 1998; Mucina and Rutherford, 2006). The meaning here is also obscure. In the study area, soil degradation is moderately high (Figure 2.6). This implies that not only vegetation degradation, but also soil degradation is taking place in the study area. The Kalahari region has predominantly sandy soil and this is especially sensitive to water and wind erosion (Department of Agriculture, Conservation, Environment and Tourism, 2002). The low

24 rainfall (Refer to Figure 2.4) of the study area, together with the high soil degradation index (Figure 2.6) will eventually contribute to the general degradation of the area.

2.4 Vegetation of the Study Area

The broad patterns of geology, soil types and climate are the major governing factors in the distribution of the Province's vegetation types (Department of Agriculture, Conservation, Environment and Tourism, (DoACET, 2002). The description of the vegetation patterns (Figure 2. 7) is based on Low and Rebelo (1998). However, approximately 60 % of natural vegetation types in the North West Province have been transformed through anthropogenic activities. Approximately 72 % of the NWP falls within the Savanna Biome, with the following major vegetation types (percentage cover shown in parentheses) (Department of Agriculture, Conservation, Environment and Tourism, 2002). The study area was situated within the Sour Mixed Bushveld (Figure 2.7) as described by Mucina and Rutherford (2006).

2.4.1 Sour Mixed Bushveld

Combretum apiculatum, Vachellia cajfra, Dichrostachys cinerea, Lanea discolor, Sclerocarya birrea, and various Grewia spp. form the main tree and shrub components within this vegetation type (Mucina and Rutherford, 2006) (Refer to Figure 2. 7). Local thorn trees include Vachellia hebeclada, V. lwrroo, V. tortilis and Senegalia mellifera as well as Tarchonanthus camphoratus. The dominant grasses are Digitaria eriantha, Schmidtia pappophoroides, Anthephora pubescens, Stiptagrostis uniplumis and various Eragrostis spp. and Aristida spp.

25 North West Province : Vegetation types

..., ...

. ,_,, vtCKt an n,u

I.an• dd ~"-."n1Ca~1~) Orr ~Thmda -A .. .,...,, knwid - l,l"r\,.b o..zr--di "'n:tbusnot"IO-- Map 17

Figure 2.7: Vegetation Types of the North West Province (Department of Agriculture, Conservation, Environment and Tourism, 2002)

2.5 Main Land Use in the North West Province

In many cases, the rapid rate of population growth is consistent with the trends in environmental degradation and rapid depletion of natural resources. The North West Province is the sixth largest of South Africa's provinces, and has the third lowest population density (North West Province Vegetation Types (Department of Agriculture, Conservation, Environment and Tourism, DoACET, 2002). Most of the rural communities are found in the Bophirima District followed by the Central Eastern, Rustenburg and Southern Districts. Rural villages in the west and north are small and scattered, and the majority of which are located in the former Bophuthatswana areas (Department of Agriculture, Conservation, Environment and Tourism, DoACET, 2002). Most of these communal areas are also over-crowded, giving rise to squatter settlements. The study area is located within the communal areas (Figure 2.9) where livestock farming (Figure 2.8) plays an important role.

26 Most of the farmers keep livestock for barter-trading, milk-production and slaughter. Most farmers keep cattle but do not take part in any grazing system. There was no sign in the study area where farmers invested in any type of fencing. This is due to security concerns. Meaning here vague. To fence an area is expensive and newly erected fences are stolen virtually overnight. Communal areas are not privately owned by the farmers, but are under control of the tribal authority (chief). The latter has the final say as to what can and cannot be done in the area.

Main land uses in the North West Province

....:..... "".t.-

LEGEND • r-. LAHO UU c-nogar,t""""9 co,,wr,-.on ~·"' -Wfttllf.N... ~19 Map4

Figure 2.8: Main land uses of the North West Province (DoACET, 2002)

27 Percentage area of magisterial districts managed under a communal land tenure system ~

LEGE ND • T

,., Uflld C0HHUNAl LANO lUUlf - "',ao--.-. - S,.I ..~ .. Map 27 0 ,..,

Figure 2.9: Percentage area of magisterial districts managed under a communal land tenure system in the North West Province (Department of Agriculture, Conservation, Environment and Tourism, 2002) NWU· ·· ·1 2.6 Alien Plant Invasion in the North West Province •JBRARY_

The introduction of alien plants and animals have had a significant impact on indigenous biodiversity, ecosystems and habitat quality throughout South Africa. The net result is a loss of species richness, through the replacement or suppression of indigenous species and alteration in ecosystem functioning (Breytenbach, 1986). Many alien plant invader species tend to be more successful in establishing viable populations in disturbed areas such as cultivated lands and urban environments (Le Maitre, 1999). Other alien woody species have competitive advantages over indigenous species as they have no predators and have a faster growth rate (Figures 3.8, 3.10 and 3.12, Chapter 3). The alien plant invasion of the study area is within 0.5 % - 5.0 % (Figure 2.10).

28 Alien plant invasions in the catchments of the North West Province

/.• ., I ~ ... \- -... \ -~ \ ~ ' .....' ,

LEGEND .,.....,.,_, IIMIU_

- "11lryC&tnl!S

• ,o._o"' !-0 - 1D0,. 0!,-5..0~ Map 12 •0$'-

Figure 2.10: Main land uses in the North West Province (Department of Agriculture, Conservation, Environment and Tourism, 2002)

2.7 Mean Ground Water Recharge

The North West's surface water comprises rivers, dams, pans, wetlands and dolomitic eyes fed by underground water sources (DoACET, 2002). In the North West, ground and surface water are integrated and interdependent as dolomitic eyes or springs are the sources of several major rivers which rise within the boundaries of the Province, such as Groot Marico, Mooi and Molopo Rivers (DoACET, 2002). Therefore, water quality and quantity issues affecting groundwater also have implications for surface waters.

The riverine systems within the North West are susceptible to a range of climatic conditions, particularly rainfall and evaporation. Rainfall is highly variable, both in space and time, often resulting in severe droughts and extreme flooding. In the eastern part of the Province, rainfall varies from 500 to 700 mm per annum (DoACET, 2002). In the central regions (where the study area is situated), this drops to between 400 and 600 mm (DoACET 2002). In the

29 western part of the Province, annual rainfall varies between 100 and 400 mm (DoACET, 2002). Coupled to these figures is a corresponding increase in evaporation from east to west. The net effect is a reduction in runoff of 5.50 mm in the east and 0.25 mm in the west. In all catchments in the North West Province, evaporation exceeds rainfall (DoACET, 2002).

The mean groundwater recharge of the study is within 3.0 - 8.0 per annum (Figure 2.11).

Mean groundwater recharge per annum in the North West Province

Map 14

Figure 2.11: Mean groundwater recharge per year of the North West Province (DoACET, 2002)

30 CHAPTER3

Woody Plant Invasion in the Study Area

3.1 Introduction

Woody plant invasion (bush encroachment) describes a progression whereby a grass dominated vegetation is replaced by a woody species dominated one (Donaldson, 1967). According to Hoffman and Ashwell (200 l ), bush encroachment is mainly caused by the proliferation of native trees and shrubs in response to poor management practices.

In the study area, indigenous bush encroachment and alien (exotic) plant invasion was recognized. Indigenous bush encroachment is the proliferation of indigenous woody species at the expense of grazing grass. Alien plant invasion is the establishment and spread of non indigenous plant in disturbed or natural vegetation at the expense of indigenous species. The causes of bush encroachment are not fully understood (Ward, 2005), but high grazing intensity and fire suppression are thought to be primary causes (Van Vegten, 1983; Archer, 1995; Moleele et al., 2002).

3.2 Methods

In order to quantify the woody plant densities in the study area the variable quadrant method was used (Coetzee and Gertenbach, 1977). Woody structure and composition were recorded per species, height class and canopy regime at different height levels. Quadrant size was determined independently at each height class to describe the density and distribution of plants (Figure 3.1). Figure 3.1 illustrates the procedure for 1 m high plants. Figure 3. 1 shows that 5 m x 5 m squares were large enough to include a l m high plant in three quadrants but in the fourth, a IO m x IO m square is required to include such a plant. The quadrant size for 1 m high plants was therefore four contiguous, nested, IO m x 10 m squares, i.e. one square

2 of 20 m x 20 m with an area of 400 m • The procedure was repeated to determine a suitable quadrant size for each height class. A specially designed data sheet was used to record the data.

31 Rtchmgulm· Cl'Oss c11libratr11 at f, m 1.utrrvnls

Trst S(flllll'tS, onr hi r11d1 ffH!llll'llllt tlrllmitrd by thr (TOSS. h1chults at lrilsf onr 1•lm1t (ltl' qunch·nut

t .. .fr I . * • • I •-- - '

Imlh111trnls btlo11g;t11g to Ollt hdghf rhus F h111l qm1d1·n11t. Four funrs thr slzr or tilt' l!ll'grst trst sqi,m·r

Figure 3.1 : Procedure by Coetzee and Gertenbach (1977) for determining quadrant size for a hei ght class, e.g. I m high plant. Test squares are enlarged in steps until at least one plant is included

The densities of woody plants in all height classes was determined as Tree equivalents per hectare (TE/ha) (Hagos and Smit, 2005). A tree of 1.5 m metres tall, represents 1 tree equivalent (TE) (Hagos and Smit, 2005). Thus, a plant of 3 m in height represents 2 TE/ha. The total of each class was thus calculated separately.

The data collected by using the variable quadrant method (Coetzee and Gertenbach, 1977) was used as baseline data to confirm the remote sensed data to identify and map the different categories of bush encroachment (Chapter 4). The guidelines adopted by the department of Agriculture used to describe the extent of bush encroachment are shown in Table 3.1 adapted from (Moore and Odendaal, 1987; Bothma, 1989; National Department of Agriculture, 2000).

32 Table 3.1: Indication of the extent of bush encroachment (TE/ha) (Moore and Odendaal, 1987; Bothma, 1989; National Department of Agriculture, 2000)

Tree equivalents per hectare Extent of bush encroachment (TE/ha) 200-457 Encroached (grass production declines linearly) 458-714 Highly encroached (Grass suppressed further)

715-1099 Severely encroached (More grass suppression) >2000 Grass growth almost totally suppressed

Each of the research sites was allocated a reference or benchmark site (Figure 3.2 and Figure 3.7 respectively). A benchmark site is a site whose veld condition is considered to be outstanding in terms of management objectives (Tainton, 1999). A benchmark site is used for comparison with the veld in the same ecological zone and with the same macroclimatic conditions to differentiate between the influences of climate and management (Tainton, 1999; De Klerk, 2003). The subjective selection of benchmark sites involves the selection of sites which are productive and stable. These sites would support long-term animal production while conserving the water and soil resources or show the healthiest ecological processes and are best protected from erosion under the prevailing macroclimatic conditions (Tainton, 1999; De Klerk, 2003). Hence, a benchmark was selected for each study site to compare and determine the severity of woody plant invasion along the riparian zones of the Molopo River.

33 Table 3.2: The sampling framework of the study area

TopographicNillage name No. of survey sites No. of benchmarks

Tshidilamolomo 3 1

Figures 3.8 & 3.9 Figure 3.2 Figures 3.10 & 3.11 Figures 3.12 & 3.13 Tshidilamolomo 4 None (Botswana site) Makgori 3 1 Figures 3.20 & 3.21 Figure 3.4 Figures 3.22 & 3.23 Figures 3.24 & 3.25 Loporung 3 I Figures 3.14 & 3.15 Figure 3.6 Figures 3.16 & 3.17 Figures 3.18 & 3.19 Makgobistad 1 None (Botswana site)

This survey was carried out where all sites were selected along the riparian zones of the Molopo River. • Tshidilamolomo. Three survey sites were selected and compared with a reference site (benchmark site) (Table 3.2 and Figure 2.1). • Tshidilamolomo (Botswana site). This site was located on the Botswana side of the border and was thus not accessible. Only reference points were taken using a GPS apparatus and no ground thruthing was done in this site. However, remote sensing was the only way of monitoring and detecting change of vegetation over time (Table 3.2) and (Figure 2.1) • Makgori. Three survey sites were selected and a benchmark site (Table 3.2) and (Figure 2.1).

34 • Loporung. Three survey sites were selected and compared with a benchmark site (Table 3.2 and Figure 2.1). • Makgobistad (Botswana site). This site was located on the Botswana side of the border and was thus not accessible. Only reference points were taken using GPS apparatus and no ground thruthing was done in this site. However remote sensing was the only way of monitoring and detecting change of vegetation over time (Table 3.2 and Figure 2.1).

3.3 Data Analysis

3.3.1 Ground Trothing Analysis

Data collection started by enumerating the woody species in each site per height class. This was done, because woody plants within different height classes do not contribute equally towards bush encroachment. Individual large trees will eventually contribute more to bush encroachment than individual small trees.

The data were analysed using bush equivalent factors at different height classes by multiplying the number of individuals recorded by the factor for the individual height classes

(1 TE = 1 tree of 1.5 m; Thus 2 TE = 1 tree of 3 m etc.). The term "bush equivalent" is used to describe the tree population of different sites in a common currency (Tainton, 1999).

The bush equivalent factor off = 0.33 was used for species at heights of less than 0.5 m and a factor off = 0.59 was used for species height at 0.5 - 1.0 m. A bush factor off = 1.00 was used for species height of 1.0 - 2.0 m, bush factor off = 1.67 used for species at heights of 2. 0 - 3.0 m, f = 2.33 used for bush heights of 3.0 - 4.0 m and lastly f = 3.00 was used for bush heights of more than 4 m (Tainton, 1999).

3.4 Results

3.4.1 Woody Plant Invasion in the Tshidilamolomo Village (Benchmark)

35 This site was chosen as a benchmark because it had limited veld disturbance. The trees were scattered and the grasses were limited in this site. The benchmark was located within the same ecological zone as the research sites. The woody plant density was determined at 1 110 TE/ha locally (Figure 3.2).

Vachellia erioloba was the most abundant woody species present and was recorded at a density of 367 TE/ha (Figure 3.2). The National Department of Agriculture (2000) does not consider Vachellia erioloba to be an encroaching species. It is slow growing woody species (Coates Palgrave, 1990). Vachellia erioloba was first declared protected in South Africa in 1941 and was again included on a revised list that was produced in 1976 that still valid (Seymour and Milton, 2003). It is a declared protected species, in terms of section 12 of the National Forest Act, 1998 (Act No. 84, 1998). I NWU-.\.l When a tree species is declared protected, no person may: LIBRARY (a) cut, disturb, damage, destroy or remove any protected tree; or (b) collect, remove, transport, export, purchase, sell, donate or in any other manner acquire or dispose of any protected tree, except under a licence granted by the minister of Water Affairs and Forestry (Act No. 84, 1998).

This Act does not distinguish between dead and living trees. This implies that removal of dead specimens without a permit is illegal. Vachellia erioloba (previously known as Acacia erioloba) has the potential of suppressing growth of other acacias. This is because most acacias have been shown not to grow under the canopies of other acacias (Tainton, 1999). Senegalia mellifera (previously known as Acacia mellifera) the most "problematic" species in the Molopo District for decades (Donaldson, 1967) is the exception. It is capable of growth beneath canopies of other acacias (previous botanical name of thorn trees) and was identified at this site.

Senegalia mellifera was determined at 321 TE/ha (Figure 3.2) within this reference site (benchmark site). According to Coates Palgrave (1990), Senegalia mellifera is an indicator of overgrazing. The presence of Senegalia mellifera at this density will linearly suppress grass production (Table 3.1), implying that grasses will not be able to grow here, because of the intense intra species competition between the Senegalia mellifera (Black Thom) trees. This

36 implies that the spreading of Senegalia mellifera locally will soon transform the local vegetation in this site into impenetrable thickets (Coates Palgrave, 1990).

Vachellia hebeclada was also identified in this site and occurred at a density of 273 TE/ha (Figure 3.2). Vachellia hebeclada that grows in deep, sandy and shallow rocky soil (Coates Palgrave, 1992) has a characteristic of growing low, normally not higher than 2 m (Coates Palgrave, 1992). It usually occurs as a prostrate, spreading, multi-stemmed shrub with dense branches near ground level or with stems rising from branching underground stems and can achieve a diameter several times its height which is often reminiscent of a large flattened bush in appearance (Coates Palgrave, 1992). According to Table 3.1, grass production will decline linearly when bush density exceeds 200 TE/ha. It was noticed by the researcher, that Boer goats prefer to browse on Vachellia hebeclada, especially during the dry season or where grazing was scarce.

The other woody species observed, included Tarchonanthus camphoratus (Camphor Bush), Grewia flava (Raisin Bush) and Ziziphus mucronata (Buffalo Thorn) but were at "non threatening" levels in terms of Table 3.l(Figure 3.2).

One metre high Vachellia hebeclada trees occurred at a density of 218 TE/ha and contributed the most to bush encroachment locally (Figure 3.3). Also noteworthy, was the occurrence of 4 metre high Vachellia erioloba trees, which occurred at 178 TE/ha (Figure 3.3).

37 Total woody species density per species (TE/ha) 1400

,e---= 1200 1110 l;,;i r- '-' 1000 ·.c;;; 800 =~ -c 600 ·u"'~ ~ 367 Q.. 321 400 273 "'>. -c 0 200 124 0 8 17 ~ 0 Senegalia Vachellia Vachellia Tarchonanthus Grewiajlava Zi::.iphus Total mellifera erioloba hebeclada camphoralus mucronata

Woody density

Figure 3.2: Woody species density in Tshidilamolomo benchmark

Density of woody species (TE/ha) per height class m 250 218 .e---= l;,;i 200 178 r--...,

"'~ ·u 150 ~ Q.. "'>. -c 0 100 0 ...~ 0 50 .-::>. "' 0 0 0 =~ Q 0 0.5 m 0.5-1 m 1-2 m 2-3 m 3-4m 4-5 m Height class (m)

■ Senegalia mel/ifera ■ Vache/lia erioloba ■ Vache/lia hebec/ada ■ Tarchonanthus camphoralus ■ Grewia /lava ■ Ziziohus mucronata

Figure 3.3: Woody plant densities in Tshidilamolomo benchmark according to height classes

38 3.4.2 Woody Plant Invasion in the Makgori Village (benchmark)

Woody plant population of Makgori benchmark (Figure 2.1 , Chapter 2) was limited and recorded a density of 297 TE/ha (Figure 3.4). Senegalia mellifera was the most abundant woody species present (Figure 3.4). This species is known to cause impenetrable thickets and is also an indicator of overgrazing (Coates Palgrave, 1990). According to Table 3.1 , grass production declines linearly when bush densities exceeds 200 TE/ha.

From Figure 3.4, it is clear that Senegalia mellifera (with a density of 201 TE/ha) contributed the most to bush encroachment. Vachellia tortilis, V. hebeclada and Ziziphus mucronata were also present locally but they were all at "non-threatening" levels in terms of Table 3.1.

Senegalia mellifera was the most dominant encroacher in this reference site (benchmark site) and was present within all the height classes (Figure 3.5). 0.5 metre high Senegalia mellifera trees occurred at 48 TE/ha while 5 metre high trees only recorded at 13 TE/ha (Figure 3.5).

Total woody species density per species (TE/ha)

350

c!:!,;-- 300 la;l t, 250 >, .-:: 201 5"' 200 -= .!!"' 150 t Q, ;, 100

-=0 38 37 ; 50 21

0 Senegalia mel/ifera Vache/lia hebec/ada Vache /lia tortilis Zi=iphus mucronata Total Woody species

Figure 3.4: Woody species density in Makgori benchmark

39 Density of woody species (TE/ha) per height class (m) ,-._ e,: 60 .c liW--- 48 E-- '-' so "'Q> ·;:; a, 40 C. "'.... "O 0 30 0 ...~ ....0 20 ·;;;- =Q> 10 ~ 0 0 0 0 0.5 m 0.5-1 m 1-2 m 2-3 m 3-4 m 4-5 m Height (m) class

■ Senegalia mel/ifera ■ Vachellia hebec/ada ■ Vachellia tortilis ■ Ziziphus mucronata

Figure 3.5: Woody plant densities in Makgori benchmark according to height classes

3.4.3 Woody Plant Invasion in Loporung Village (benchmark)

Woody plant population ofLoporung benchmark, which is situated along the riparian zone of the Molopo River in Loporung Village (Figure 2.1 , Chapter 2), was recorded at a total density of I 077 TE/ha (Figure 3.6). The most prominent woody species within this benchmark site, were Vachellia hebeclada (324 TE/ha), V erioloba (267 TE/ha) and Tarchonanthus camphoratus (326 TE/ha) (Figure 3.6).

Vachellia erioloba is a slow growing tree that is generally not considered an encroaching species and has a nutritional value as fodder for livestock and used for various purposes by local people (Coates Palgrave et al., 1987; Smit, 1999; National Department of Agriculture (NDA), 2000). Vachellia erioloba has a deep taproot (Smit, 1999) that makes it easy to inter act positively with grasses because it does not compete directly with the shallow roots of grasses for available resources. According to Scholes and Archer (1997a), large trees are able to promote grass growth beneath its canopies but the nett result is dependent on tree density. This was not observed in this site because large trees were limited (Figure 3.7).

40 Tarchonanthus camphoratus is an encroacher species and may replace other vegetation in grasslands (Wells et al., 1986) and in the riparian zones of the Molopo River, lead to a decline in biodiversity. According to Coetzee et al. (2007), Tarchonanthus camphoratus has the potential to be a significant encroacher in disturbed and semi-arid savanna habitats. Encroachment by Tarchonanthus camphoratus has the potential to reduce grazing capacity, with due consequences to both wildlife and pastoralists, dependent on the habitat. Overgrazing is the most important facilitator for the encroachment of Tarchonanthus camphoratus.

Encroaching species that occurred locally were limited to Tarchonanthus camphoratus, Vachellia hebeclada and Ziziphus mucronata (Figure 3.6 and Figure 3.7). Vachellia erioloba trees with a maximum height of 0.5 metre and 3 metre high Tarchonanthus camphoratus trees were the most abundant in this benchmark site (Figure 3.7).

Total woody species density per species (TE/ha)

~ 1200 0:: 1 7 ~ t, 1000 ·.c;;; C 800 ~ "O

324 326 267

so 110 0 Vachellia tortilis Vachellia Vachellia Tarchonanthus Zi::iphus Total erioloba hebeclada camphoratus mucronata Woody secies

Figure 3.6: Woody species density in Loporung benchmark

41 Density of woody species (TE/ha) per height class (m) ~300 ~ ...;i 239 233 t,2so

"'~ f ·2 200 Q. "' -S1so 0 0 t 100 0..., .<:: so "'C ~ Cl 0 0.5 m 0.5-1 m 1-2 m 2-3 m 3-4 m Height class (m)

■ Vachel/ia erioloba ■ Vache/lia hebeclada ■ Tarchonanthus camphoratus ■ Ziziphus mucronata ■ Vachel/ia tortilis

Figure 3.7: Woody plant densities in Loporung benchmark according to height classes

3.4.4 Woody Plant Invasion in Tshidilamolomo Village (Site 1)

The total woody plant density in this site (Tshidilamolomo Village (Site 1), was recorded at 3 473 TE/ha (Figure 3.8), which almost completely suppressed grass growth (Table 3.1). The woody species that contributed most to the woody plant encroachment, was Prosopis velutina that was present at a density of 3 465 TE/ha (Figure 3.8). The only other woody species observed was Vachellia hebeclada (8 TE/ha, Figure 3.8). This site was allocated within the same ecological zone as the Tshidilamolomo benchmark (Figure 2.1 , Chapter 2).

Prosopis velutina is an alien (exotic) species that has been naturalized over considerable areas, including the border regions of the North West Province (Coates Palgrave, 1990). Prosopis species grows in areas with relatively deep soil with water close to the surface such as river banks, pans and depressions (Le Maitre, 1999). According to Le Maitre (1999), Prosopis species is considered an 'aggressive grower' and its rate of expansion was found to be 18 % per annum or doubling in area in 5 years. This implies that, if we use the data from Le Maitre (1999), the density of Prosopis velutina may increase from the current 3 4 73 TE/ha to 6 946 TE/ha in just 5 years.

42 Prosopis velutina trees with a maximum height of 3 m were recorded at 2 600 TE/ha (Figure 3.9) and were the most abundant locally (Figure 3.9). Vachellia hebeclada with a maximum height of 0.5 metres were only present at a density of 8 TE/ha (Figure 3.9). The presence of Prosopis velutina at these densities almost completely suppressed grass growth (Table 3.1) and the growth of other plant species (Figure 3.9). This was observed from the lower density of Vachellia hebeclada (8 TE/ha) (Figure 3.8 and 3.9).

Apart from its invasive characteristics, Prosopis species is a big "problem" in South Africa because of its extensive use of water (Le Maitre, 1999). Prosopis is able to develop a deep root system that can enable it to reach the water table at depths of 10-15 m. A root depth of 53 m for Prosopis spp. has been recorded (Le Maitre, 1999). Furthermore, Prosopis species can use water ranging from 135- 930 mm per year with the typical water use being from 350- 500 mm per annum (Le Maitre, 1990). According to Hoshino et al. (2012), Prosopis spp. are well known for their high adaptability to arid and semi-arid conditions and are characterised by their very high water use efficiency. Prosopis can detect even very tiny soil moisture and grow to various conditions. The deep taproot of Prosopis can adapt to a wide variety of soil conditions and can grow upwards towards the soil surface to capitalize on little rainfall and can generally grow to depths down to 80 metres (Hoshino et al., 2012). This is the most extensive root system of any plant in the world (Thorp and Lynch, 2001 ).

It is clear from Figure 3.8 that this site was severely encroached and had a substantially higher woody plant density than the reference site (benchmark site) (Figure 3.2). The latter recorded a woody plant density of 1 110 TE/ha compared to the 3 473 TE/ha locally. Prosopis velutina was absent in the benchmark site (Figure 3.2), while it recorded at density of 3 465 TE/ha in this site (Figure 3.8). This is a clear sign of woody plant invasion. Vachellia erioloba (367 TE/ha), V hebeclada (273 TE/ha) and Senegalia mellifera (321 TE/ha) were the evident woody species within the reference site (Figure 3.2). NWU-··1 ILIBRARY

43 Total woody species density per species (TE/ha)Where? 4000 3465 3473 .-- 3500 T T ell l l ~ 3000 I""',_., 2500 ·;;;c C ~ 2000 -C 'a 1500 ..... "g 1000 0 ~ 500 8 0 Prosopis velutina Vache//ia hebeclada Total Woody species

Figure 3.8: Woody species density in Tshidilarnolomo Village (Site l)

Density of woody species (TE/ha) per height class (m)

3000 ---ell 2600 cE:! t, 2500 rI- ,, ' -~C,I 2000 ~ Q. "' ~ 1500 0 0 ....~ 1000 . 0 432 ...,, .£,,, 500 C I 208 ~ B 107 0 97 Ci 0 0 0 r, 0 21 0 n 0.5 m 0.5-1 m 1-2 m 2-3 m 3-4 m 4-5 m Height class (m) I ■ Vachel/ia hebeclada ■ Prosopis ve/utina I

Figure 3.9: Woody plant densities in Tshidilarnolomo Village (Site 1) according to height classes

44 3.4.5 Woody Plant Invasion in Tshidilamolomo Village (Site 2)

The density of woody plants was recorded at 3 913 TE/ha (Figure 3. l 0) which will almost completely suppress grass growth (Table 3.1). Prosopis velutina (3 128 TE/ha) was the most prominent woody encroacher recorded in this site (Figure 3.10). Other woody species observed, include Vachellia hebeclada (34 TE/ha) and Senegalia mellifera (58 TE/ha) (Figure 3.10) which were present at "non-threatening" levels in terms of Table 3.1. According to Hoshino et al. (2012), Prosopis sp. often grows "aggressively" and suppresses even the development of other woody species. This was clearly visible in this site where Vache/Zia hebeclada and Senegalia me/lifera were the only other woody species present (Figure 3.10).

This was substantially higher than the woody density (1 110 TE/ha) in the benchmark site (Figure 3.2) where 1 metre high trees were the most prominent (218 TE/ha, Figure 3.3).

Three metres high woody species occurred at a density of I 600 TE/ha (Figure 3.11). Woody species within a maximum height of 5 m were limited locally and were present at a density of only 37 TE/ha (Figure 3.11).

Total woody species density per species (TE/ha) 4500 3821 3913 ~ 4000 T ~ .l. t., 3500 r-t- .f' 3000 "' ~= 2500 -~... 2000 Q,j ~ 1500 f 1000 0 ~ 500 34 58 0 Prosopis velutina Vache l/ia hebeclada Senegalia mellifera Total Woody species

Figure 3.10: Woody species density in Tshidilamolomo Village (Site 2)

45 Density of woody species (TE/ha) per height class (m) 1800 16()0 ,-;- 1600 ~ fl ._,~ 1400 .i! 1200 CJ Q,l ~1000 .... 798 OJO "g 800 T 0 .&. 3: ..., 600 0 .... 369 ·'5 400 I""' = 149 0 200 17 0 0 0 0 0 0 0 0 37 0 0 0 17r~ - 0.5m 0.5-1 m 1-2 m 2-3 m 3-4 m 4-5 m Height class (m)

■ Vachellia hebeclada ■ Prosopis velutina ■ Senega/ia mellifera

Figure 3.11: Woody plant densities in Tshidilamolomo Village (Site 2) according to height classes

3.4.6 Woody Plant Invasion in Tshidilamolomo Village (Site 3)

The total woody species density in this site was recorded at 2 139 TE/ha (Figure 3.12). This woody density will almost suppress grass growth (Table 3.1). From Figure 3.12, it is clear that Prosopis velutina completely dominated here. It was present at 1 981 TE/ha for all height classes inclusively (Figure 3.12). This density was high, compared to the total woody density of 1 110 TE/ha in the reference site (benchmark site, Figure 3.2).

The results of this site are uniform to that ofTshidilamolomo (Site 2). Senegalia mellifera (73 TE/ha) and Vachellia hebeclada (85 TE/ha) were also present at "non-threatening" levels in this site (Figure 3.12 and Figure 3.13) and thus also confirms that dense stands of Prosopis spp. will suppress the development of other species (Hoshino et al., 2012).

Three metre high woody plants were the most evident and occurred at a density of 750 TE/ha (Figure 3.13), while three metre high trees in the benchmark were limited (Figure 3.3).

46 Woody species with a maximum height of 5 metres were limited locally and it was recorded at 37 TE/ha (Figure 3.13).

Total woody species density per species (TE/ha) 2500 ,-.. 2139 ell 1981 ~ - ~ 2000 r--r- '-" .£ ~ 1500 4,1 'O "' -~ 1000 4,1 C. "' ~ 500 0 0 73 85 ~ r---, 0 Prosopis velutina Senegalia mel/ifera Vachel/ia hebec/ada Total Woody species

Figure 3.12: Woody species density in Tshidilamolomo Village (Site 3)

Density of woody species (TE/ha) per height class (m)

,-.. 900 ell 750 ~ 800 ~ 665 t, 700 rf rt . .;u 600 4,1 ~500 ..... "g 400 -- 0 rI~ ~ 300 ...... 200 ~ 112 ·' 83 <;R .. -~ 100 <;') - .: -- - JJ -- :• 4,1 1 0 0 0 0 0 0 ..-,J 0 C 0 o p-7 al I\l --5 0.5 m 0.5-1 m -1-2 m 2-3 m 3-4 m 4-5 m Height class (m)

■ Vache/Jia hebeclada ■ Prosopis ve/utina ■ Senega/ia mel/ifera

Figure 3.13: Woody plant densities in Tshidilamolomo Village (Site 3) according to height classes

47 3.4.7 Woody Plant Invasion in Loporung Village (Site 1)

The total density of woody plants in Loporung was recorded at a density 3 933 TE/ha (Figure 3.14) which, according to Table 3.1, will almost totally suppress grass growth. The most abundant encroacher encountered here was Senegalia mellifera (1 839 TE/ha), Grewiajlava (1 535 TE/ha), Ziziphus mucronata (355 TE/ha) and Vachellia tortilis (196 TE/ha) also present but at lower densities (Figure 3.14). Other woody plants encountered include Searsia lancea which was relatively limited at 8 TE/ha (Figure 3.14).

The total woody plant density locally exceeded the density in the benchmark site, that was recorded at 1 077 TE/ha (Figure 3.6)

In many cases, Grewiajlava is harvested to make brushwood fencing and shelter for cooking in communal areas and also used as laths for roofing traditional houses (Mainah, 2006). In some cases, Grewia jlava plants are uprooted to obtain the roots, which are considered to have medicinal value (Mainah, 2006). This species is browsed on mainly by donkeys and goats, both of which are considered less selective feeders. Grewiajlava was not present in the benchmark site (Figure 3.6).

It was reported that when green palatable vegetation cover becomes scarce, Grewia jlava becomes a more preferred browse (Mainah, 2006). The major products obtained from Grewia jlava include berries (fruits), roots, stems and leaves (Mainah, 2006). Different parts of the plant are used for direct consumption, commercial trading, beer brewing, firewood (kindling), stimulant and/or for building fences and houses. Leaves are mainly used to a relatively lower scale for making "tea" when boiled (Mainah, 2006). The use of berries was reported as the most important product of Grewia jlava for direct consumption, commercial selling and making beer (Kgadi) (Mainah, 2006).

Senegalia mellifera is known to cause impenetrable thickets and is also an indicator of overgrazing in the savanna (Coates Palgrave, 1990). The shallow root system of Senegalia mellifera enables it to compete directly with grasses (Smit, 1999) and at a density of 1 839 TE/ha (Figure 3.14) can completely suppress grass growth (Moore and Odendaal, I 987;

48 Belsky and Amundson, 1992; Richter et al., 2001). Senegalia mellifera is described as "acting like windmills, pumping large amounts of water into the air everyday" (De Klerk, 2003). According to Donaldson (1967), 0.5 metre high Senegalia mellifera trees transpires approximately 12.8 litres per day, implying that the total loss of water through transpiration by Senegalia mellifera alone will be approximately 7 000 litres of water daily here.

Woody plants with a maximum height of 2 metres (2 m height class) had the highest densities and were recorded at 1 064 TE/ha (Figure 3.15).

Total woody species density per species (TE/ha) 4500 ,-, ~ 3933 ,!:: 4000 i;a;l C 3500 c ·a 3000 ~ ":: 2500 ~ -~ 2000 1839 C. 1535 "' ,S 1500 0 ; 1000 355 500 196 8 0 Senegalia Vache llia tortilis Grewiajlava Zi::iphus Searsia /ancea Tota l mel/ifera mucronata Woody species

Figure 3.14: Woody species density in Loporung Village (Site 1)

49 Density of woody species (TE/ha) per height class (m)

,-._ 1200 -.------1064 i 1000 +------~------I E- '-'

Jic,; 800 +------1 Q,> Q, 625 -6'"' 600 +----"'36'------tll-t ------1 0 0 ...:t; 400 +-----ffl-➔.}5------t~ 1----,.,,,_------f 0..., ~ 200 +-----filll''l4 .__ _.,.._, ~----~ 1----,;--,---- JA,S...------l =Q,> i:::i

0.5 m 0.5-1 m 1-2 m 2-3 m 3-4 m 4-5 m 5-6 m Height class (m)

■ Senegalia me/lifera ■ Vache Ilia tortilis ■ Grewiajlava ■ Ziziphus mucronata ■ Searsia lancea

Figure 3.15: Woody plant densities in Loporung Village (Site 1) according to height classes

3.4.8 Woody Plant Invasion in Loporung Village (Site 2)

The total densities of woody plants in Loporung Village (site 2) was 2 791 TE/ha (Figure 3.16) which almost totally suppressed grass growth (Table 3.1). In contrast with the previous site (Loporung, Site 1) where Senegalia mellifera dominated (Figure 3.14), Vachellia tortilis dominated here and was present at a density of 1 693 TE/ha (Figure 3.16). Ziziphus mucronata (479 TE/ha) and Grewiajlava (100 TE/ha) were also present in this site (Figure 3.16). Vachellia tortilis was limited within the benchmark site and occurred at a density of 50 TE/ha (Figure 3.6 and Figure 3.7).

Vachellia tortilis trees within the 2 m and 3 m height classes contributed the most to woody plant invasion locally (Figure 3.17). Vachellia tortilis is adapted to dry conditions and is often one of the first plant to establish on disturbed areas like old lands (Coates Palgrave, 1990). This species also occurred along dry riverbeds and is cold tolerant (Coates Palgrave, 1990; Smit, 1999).

so Total woody species density per species (TE/ha) 3500 ,-.. ~ ,e 3000 2791 ~ T ~ ';:'. 2500 .-:: ~ 2000 1693 "C -~ 1500 '-' " .....~1000 "C 519 479 g 500 ~ 100 0 Senegalia me/lifera Vache/lia tortilis Grewiajlava Zi:::iphu mucronata Total Woody species

Figure 3.16: Woody species density in Loporung Village (Site 2)

Density of woody species (TE/ha) per height class (m)

900 ~------,e= 800 -l------>------1 I:: 700 -l------~~- ---- 1------1 '-' -~ 600 '-' "~ 500 -+------t ..... "g 400 +------➔ ·~---~ C t 300 ------1· ______, C

.£ 200 --~----~ 80 "' O= 100 -t----,.:-r--- 0 0 13 0 0 -'--'_ .....__ 0.5 m 0.5-1 m 1-2 m 2-3 m 3-4 m 4-5 m Height class (m)

■ Senegalia mellifera ■ Vache Ilia tortilis ■ Grewia jlava ■ Ziziphus mucronata

Figure 3.17: Woody plant densities in Loporung Village (Site 2) according to height cJasses

51 3.4.9 Woody Plant Invasion in Loporung Village (Site 3)

The woody plant density in this site was recorded at 3 423 TE/ha (Figure 3.18). Unlike other study sites in Loporung Village, Senegalia mellifera was the sole abundant encroacher identified locally (Loporung Village, Site 3), occurring at 3 008 TE/ha (Figure 3.18). Other woody plants present included Vachellia erioloba, V. hebeclada, V. tortilis, Grewiajlava and Ziziphus mucronata (Figure 3.18).

Furthermore, Senegalia mellifera was most abundant at all height classes (Figure 3.19). However, 1 metre high trees contributed the most to woody plant invasion (Figure 3.19) occurring at 2 010 TE/ha (Figure 3 .19). Senegalia mellifera has a shallow root system and also suppresses development of other woody plants (Smit et al., 1999) but plays an important role in soil enrichment (Hagos and Smit, 2005).

Total woody species density per species (TE/ha) 4000 3423 ~ 3500 T ~ 3 8 IT ~ 3000 '-'

-~~ 2500 =a> "C 2000 ~ a> "[ 1500

~ ,S 1000 0 0 ~ 500 199 33 33 33 117 r--, 0 r7 Senega/ia Vachellia Vachellia Vache l/ia Grewiaflava Zi=iphus Total me/lifera tor ti/is erioloba hebeclada mucronata Woody species

Figure 3.18: Woody species density in Loporung Village (Site 3)

3.4.10 Woody Plant Invasion in Makgori Village (Site 1)

Wood plant densities in this site in Makgori Village was recorded at 6 129 TE/ha (Figure 3.20) which will almost completely suppress grass growth (Table 3.1). The woody species

52 that contributed most to woody plant invasion in this site were Senegalia mellifera (3 447 TE/ha), Vachellia hebeclada (821 TE/ha), Grewia jlava (797 TE/ha), Ziziphus mucronata (500 TE/ha) and the non-woody Opuntia ficus-indica (564 TE/ha) (Figure 3.20). Senegalia mellifera was the most dominant encroacher locally (Figure 3.20). This species is known to cause impenetrable thickets and is an indicator of overgrazing (Coates Palgrave, 1990). The presence of Grewiaflava locally, made this site economically productive.

Density of woody species (TE/ha) per height class (m)

2500 ,..._ c-: e!: 2010 ~ 2000 '-' "'Q,l -~ 1500 Q. ;.,"' -0 g 1000 ....~ 0 594 E 500 233 "' 67 =Q,l 150 99 67 Q 33J , o 33 33 0 ,.., 0 0 0 l o 33- o o a oo.:'.'._oo 2 1 0 0 0 0 O 0 0 0 0 0 0 0 0.5m 0.5-1 m 1-2 m 2-3 m 3-4m 4-5 m Height class (m)

■ Senegalia mellifera ■ Vachel/ia tortilis ■ Grewiajlava ■ Ziziphus mucronata ■ Vachellia hebec/ada ■ Vachel/ia erioloba

Figure 3.19: Woody plant densities in Loporung Village (site 3) according to height classes

Mainah (2006) reported that negative human impacts such as cutting of plants, settlement establishment and livestock tracks, contribute to the decline in Grewia flava abundance. However, in this site, the cutting of Grewiaflava was not evident.

It was reported by Mainah (2006), that when green palatable vegetation cover becomes scarce, Grewia. jlava shrubs become more preferred as browse (Mainah, 2006). The major products obtained from Grewia flava include berries (fruits), roots, stems and leaves (Mainah, 2006). Different parts of the plant are used for different purposes, such as direct consumption, commercial trading, beer brewing, firewood (kindling), as a stimulant and/or for use in fence building and house building. Leaves are mainly used on a relatively lower

53 scale for making 'tea' when boiled (Mainah, 2006). The use of berries was reported as the most important product of Grewia flava for direct consumption, commercial selling and making beer (Kgadi) (Mainah, 2006).

Vachellia hebeclada occurred at a density of 821 TE/ha (Figure 3.20) and has a characteristically short growth, normally not higher than 2 m. It usually occurs as a prostrate, spreading, multi-stemmed shrub with dense branches near the ground level or with stems rising from branching underground stems and can achieve a diameter several times its height which is often reminiscent of a large flattened bush in appearance (Coates Palgrave, 1992). The spread of this species could restrict movement of animals and the thickness of this shrub restricts light for understory vegetation (Coates Palgrave, 1992).

The exotic cactus, Opuntia sp. have been used by humans for thousands of years for a variety of purposes (Smith, 1967). Besides being consumed as food or beverages, most portions of the plants have been used as medicine and in modern times have also been commercially prepared as capsules, drinks, pills or powders. Prickly pear are cultivated and wild-harvested as fruit (tunas), vegetables (nopalitos-the young green cladodes or pads) and are used as forage for livestock in many countries (Wilson, 2007), especially as a supplemental feed in times of drought (Stintzing and Carle, 2005). Opuntia ficus-indica occurred at a density of 564 TE/ha in this site (Figure 3.20). NWU· I LIBRARY Ziziphus mucronata (also known as Buffalo Thorn) is a small to medium-sized deciduous tree, up to 10 metres in height, with an irregular spiky canopy (Shackleton et al., 2005). Ziziphus mucronata occurred at a density of 500 TE/ha (Figure 3.20). It has a single trunk that is often crooked; its branches spread and droop, branching above ground or sometimes near the base (World Agroforestry, 2010). Ziziphus mucronata has distinctive angular zigzag branchlets and twigs, together with the hooked and stinging thorns. The thorns of this species, usually present at the base of the leaf, are often in pairs, reddish brown, one straight (up to 2 cm) (World Agroforestry, 2010) and the other shorter, stronger and hooked. Leaves of

Ziziphus mucronata are shiny and light green, simple (30-80 mm x 20-50 mm), alternate or in tufts, with the blade prominently 3- 5 veined from the base (Schmidt et al., 2002).

Ziziphus mucronata is commonly associated with barren soils (International Centre for Underutilized Crops, 2001 ; Liu and Zhao, 2009) and drought resistant species distributed

54 throughout the summer rainfall areas of sub-Saharan Africa, extending from South Africa northwards to countries such as Eritrea, Ethiopia, Ghana and Senegal (Schmidt et al., 2002). According to Rutherford and Westfall (2003), Ziziphus mucronata is a common tree species in the Southern African Savanna and Nama Karoo Biome. In some studies, Ziziphus mucronata regenerates naturally from seeds in various habitats with a mean annual temperature range of 12- 30 °C and a mean annual rainfall of 446-1200 mm (Orwa et al., 2009). However, it is more common on flat and open woodlands, in alluvial soils along rivers, around pans as well as on termite mounds (Coates Palgrave et al. , 2002; Azam-Ali et al. , 2006).

In addition to Ziziphus mucronata, several other Ziziphus species such as Ziziphus jujuba, Z. mauritiana and Z. spina-christi are exploited for medicinal use in other parts of the world (Mokgolodi et al. , 2011 ).

Woody species with a maximum height of 0.5 m (0.5 m height class) occurred at 2 013 TE/ha (Figure 3.21). Trees with a maximum height of 6 metres (6 m height class) were also present at 100 TE/ha (Figure 3.21). According to Smit (1999), many thorn trees fail to grow beneath canopies of established individuals, irrespective of its species, while Senegalia mellifera is able to grow under canopies of other Senegalia mellifera (Tainton, 1981). According to Meyer et al. (2005), Senegalia mellifera encroaches regularly in open savannas and has a great re-sprouting ability after the intense fires although little is known about the relationship between total shrub height and height of regrowth after fire. However, younger Senegalia mellifera trees generally have a greater ability to re-sprout than older trees (Meyer et al. , 2005).

55 Total woody species density per species (TE/ha)

,..._ 7000 (, "0 821 797 8 1000 564 500 ~ 0 Senega/ia Vache/lia Grewiaflava Opunliajicus- Zi=iphus Total mel/ifera hebeclada indica mucronata Woody species

Figure 3.20: Woody species density in Makgori Village (Site 1)

Density of woody species (TE/ha) per height class (m)

2500 ,..._ (

"'Q,I 11500

"'>, "0 8 1000 ...~ 0 >, .-:= 500 "'Cl Q,I Q 0 0.5m 0.5-1 m 1-2 m 2-3 m 3-4 m 4-5 m 5-6 m Height class (m)

■ Senegalia me/lifera ■ Grewiajl.ava ■ Ziziphus mucronata

Figure 3.21: Woody plant densities in Makgori Village (Site 1) according to height classes

56 3.4.11 Woody Plant Invasion in Makgori Village (Site 2)

The woody plant population was recorded at 4 933 TE/ha (Figure 3.22) which will almost completely suppress grass growth (Table 3.1). The woody species that contribute most to woody plant composition locally were Senegalia mellifera (2 143 TE/ha), Grewia flava (2 087 TE/ha) and Ziziphus mucronata (686 TE/ha) (Figure 3.22). The reference site (Figure 3.4) recorded a woody plant density of 297 TE/ha with Senegalia mellifera the most evident (201 TE/ha, Figure 3.4). Furthermore, 6 metre high Senegalia mellifera trees were recorded at 400 TE/ha locally (Figure 3.22). This species is an indicator of overgrazing (Coates Palgrave, 1990). This also indicated that this site had undergone long-term overgrazing. The only other woody species observed, include Vachellia karroo that occurred at a density of 17 TE/ha (Figure 3.22). Senegalia mellifera and Grewia flava were the most prominent encroachers in this site at a combined density of 4 230 TE/ha (Figure 3.22). The presence of Grewia flava locally, is indicative of some economic uses of plants by the local inhabitants.

Grewiaflava with a maximum height of 0.5 m (0.5 m height class) contributed the most to woody plant encroachment and occurred at a density of 1 221 TE/ha (Figure 3.23). This implies that Grewia flava is spreading locally to make this site more economically productive. Grewia flava was absent in the benchmark site (Figure 3.4). However, the presence of Senegalia mellifera implies that more grasses will be suppressed locally and non accessible because this species cause impenetrable thickets (Coates Palgrave, 1990) and will eventually thicken up to a stage where it will suppress the development of a herbaceous layer.

57 Total woody species density per species (TE/ha) 6000 4933 l sooo -rT ~ E-< ~ 4000 ·;;; C Q,l -o 3000 "'Q,l ·2 2143 2087 r+--- r+--- ....~2000 "0 0 ; 1000 686 17 0 n Senegalia mellifera Vache/lia karroo Grewiajlava Zi:::iphus mucronata Total Woody species

Figure 3.22: Woody species density in Makgori Village (Site 2)

Density of woody species (TE/ha) per height class (m)

1400 122 1 ';'1200 ..c --~ t,1000

"'Q,l ';j Q,l 800 0. "'.... "0 0 600 0 ....~ 400 0 400 .... 267 ·;;;- C 200 Q,l Q 0 0 13 O O O 0 0 0 0 0.5 m 0.5-1 m 1-2 m 2-3 m 3-4 m 4-5 m 5-6 m Height class (m)

■ Senegalia mel/ifera ■ Grewiajlava ■ Ziziphus mucronata ■ Vache Ilia karroo

Figure 3.23: Woody plant densities in Makgori Village (Site 2) according to height classes

58 3.4.12 Woody Plant Invasion in Makgori Village (Site 3)

The total density of woody plants in Makgori Village (site 3) was recorded at 3 773 TE/ha (Figure 3.24) which almost totally suppressed grass growth (Table 3.1). The most abundant encroacher species encountered locally was Senegalia mellifera (3 029 TE/ha) (Figure 3.24), while Grewia flava (233 TE/ha) and Ziziphus mucronata (310 TE/ha) were - spreading locally (Figure 3.24). Both these species made this site economically productive especially to local people. Other species observed here (Makgori Village, Site 3) include Vachellia erioloba (17 TE/ha), V. tortilis (17 TE/ha) and V. hebeclada (167 TE/ha). The reference site (benchmark site) recorded a total woody plant density of 297 TE/ha where Senegalia mellifera was also the sole dominant (Figure 3.4).

Senegalia mellifera with a maximum height of 0.5 metres were the most evident and occurred at a density of 2 244 TE/ha and contributed most to woody species encroachment locally (Figure 3.25). Six metre high trees were not so many locally and were recorded at a density of 32 TE/ha (Figure 3.25). This is a clear indication that bush encroachment is commencing in this site.

Total woody species density per species (TE/ha)

4500

,-_ 4000 37 3 ~ ~ 3500 f--, 3029 ';'. 3000 .-.:: "'5 2500 "O .i 2000 (,I ~ ~ 1500 ;a.. "g 1000 0 310 ~ 500 167 233 17 17 0 Senega/ia Vachellia Vachellia Vache//ia Grewiajlava Zi=iphus Total mellifera erioloba tortilis hebeclada mucronata Woody species

Figure 3.24: Woody species density in Makgori Village (Site 3)

59 Density of woody species (TE/ha) per height class (m)

2500 --;- 2244 ~ l:: 2000 '-' ·.:;..."' ~ 1500 "'>, "O ...~ 1000 0 .f' 500 ..."'= Q 32 0 0 0 0 0 0 0.5 m 0.5-1 m 1-2 m 2-3 m 3-4 m 4-5 m 5-6 m Height class (m)

■ Senegalia mel/ifera ■ Grewiajlava ■ Ziziphus mucronata ■ Vachellia hebeclada ■ Vachellia erio/oba ■ Vachellia tortilis

Figure 3.25: Woody plant densities in Makgori Village (Site 3) according to height classes

3.5 Discussion

3.5.1 Woody Plant Invasion in Tsbidilamolomo

As was expected, the densities of woody plants along the riparian zone of the Molopo River in the selected sites ofTshidilamolomo Village (Figures 3.8; 3.10 and 3.12) were recorded at "threatening" levels, according to Table 3.1. According to Table 3.1 , grass growth will be totally suppressed when the bush densities exceed 2 000 TE/ha (Moore and Odendaal, 1987). Prosopis velutina was the most abundant invader (with an average of 3 089 TE/ha) in the three selected sites respectively (Figures 3.8; 3.10 and 3.12). Woody encroachers within the 3 m height class (3 m height class) contributed the most to bush encroachment in the selected site (Figures 3.9; 3.11 and 3.13). This implies that intra species competition takes place in these sites, as such, trees of the same species compete for available resources such as water, nutrients and space. Prosopis sp. has the potential to form dense thickets and often put other species "under severe pressure" (Van Wilgen et al. , 1998). This species competes effectively for available water, space, soil nutrients and sunlight (Van Wilgen et al., 1998, 2001). As a

60 result, the establishment and growth of grasses and other woody species was suppressed. This was observed from the absence of grasses and lower densities of Vachellia hebeclada and Senegalia mellifera recorded in these sites (Figures 3.8; 3.10 and 3.12).

The diversity, and productivity of herbaceous species was also reportedly to be lower under closed canopies and isolated trees of Prosopis species, than under indigenous tree species canopies (Kahii et al., 2009; Muturi et al., in prep.) or in more "open" areas (Schade et al., 2003; El-Keblawy and Al-Rawai, 2007). The declining occurrence of Vachellia hebec/ada and Senegalia mellifera may suggests that Prosopis invasion is potentially a greater threat to Vachellia hebeclada and Senegalia mellifera than any other species locally. These findings are in agreement to other reports which stated that in those areas where Prosopis invade, it tends to have a negative impact on the understory vegetation (Muturi, 2012).

Woody plants with a maximum height of 0.5 m (0.5 m height class) (Prosopis velutina included) were present at lower densities in the selected sites. This finding may suggest that Prosopis velutina has a negative effect on its own growth. This corresponds with Muturi (2012), that Prosopis litter have a stronger inhibiting effect on its own germination than on Acacia germination. This suggests that seeds that have the best chance of germinating are those that occur far away from the canopies or litter of established Prosopis species.

The absence of Prosopis velutina in the Tsidilamolomo benchmark (Figure 3.2) site created an opportunity for diversity of vegetation to increase as compared to research sites. However, the presence of Senegalia mellifera (321 TE/ha) and Vachel/ia hebeclada (273 TE/ha) (Figure 3.2) resulted in a decline in grass production. The emerging scenario is one in which this site may be overwhelmed by woody plants leading to total loss of grass vegetation and thus a "crash" in the grazing herbivore population. Furthermore, the spread of Senegalia mellifera and Vachellia hebeclada could restricts movement of animals in the bush area and the thickness of this shrub may restricts light for understory vegetation. lI: :Ai;l

Vachellia erioloba in the Tshidilamolomo benchmark was recorded at 367 TE/ha (Figure 3.2). This species is not considered an encroacher species (National Department of Agriculture, 2000) and is slow growing (Coates Palgrave, 1990). The presence of this species at the sampling site renders the site ecologically productive due to its potential to suppress growth of other acacias (Tainton, 1999).

61 Tarchonanthus camphoratus, a "declared invader," is competitive and replaces other vegetation in grasslands, on stream banks and in ruderal situations (Wells et al. , 1986). Splinters of wood are poisonous and cause septic sores which are slow to heal. It also taints milk and is a skin irritant. As an invasive species with great resistance to control (Ivens, 1967), it would pose serious risk to a range of habitats in this area. However, the presence of this species in this site can be useful as a source of firewood for the locals because it burns well even when green (Coates Palgrave, 1990).

The emerging scenario is one in which these sites may be overwhelmed by invasive plants leading to total loss of grass vegetation and thus a "crash" in the grazing herbivore population (Ng'weno et al., 2009).

Senegalia mellifera spreads rapidly, both from seed and vegetative growth and may result in the formation of impenetrable thickets (Coates Palgrave, 1990). Its shallow root system allows it to compete directly with grasses (Smit, 1999). As a result, these sites may be overwhelmed by this encroacher, leading to a total loss of grasses and may eventually influence the grazing-herbivore population (Ng'weno et al., 2009). However, it has been suggested that, when Senegalia mellifera is controlled, a couple of individual bushes or trees must always be left in order to maintain the soil nutrient supplementation of these trees (Hugo and Smit, 2005). This, nevertheless, will be difficult, due to the "aggressive" growing nature of Senegalia mellifera (Hagos and Sm it, 2005).

3.5.2 Woody Plant Invasion in Loporung

The woody species recorded density were 3 933 TE/ha; 2 791 TE/ha and 3 424 TE/ha respectively in the three survey sites in Loporung (Figures 3.14; 3.16 and 3.18). From Figure 3.18, it was clear that the dominance of Senegalia mellifera (3 005 TE/ha) affected the growth of other woody species present locally. Furthermore, grazing capacity was reduced. Senegalia mellifera was also recorded abundant in site 1 ( 1 839 TE/ha, Figure 3 .14).

Vachellia tortilis (I 693 TE/ha), however, was dominant instead (Site 2, Figure 3.16). Vachellia tortilis is drought resistant (Coates Palgrave, 1990), can tolerate strong salinity and

62 seasonal waterlogging and generally forms open, dry forests in pure stands or mixed with other species (Orwa et al. , 2009). The long taproot and numerous lateral roots enable it to utilize the limited soil moisture available in the arid areas. It tolerates a maximum temperature of 50 °C and a minimum temperature close to O °C (Orwa et al., 2009). Due to its drought hardiness and fast growth, the species is considered more useful than many indigenous species growing in the arid zone of India. It is a promising species for afforesting shifting sand dunes, refractory sites, hill slopes, ravines and lateritic soils (Orwa et al., 2009). The research site where it was recorded abundantly was characterised by stones with no grasses. The presence of Vachellia tortilis in Loporung limits the growth of grasses and other vegetation that have a long tap root system. As a result, diversity and grazing capacity is suppressed.

The presence of Grewia jlava and Ziziphus mucronata in Loporung made this site economically productive. In many cases, Grewia flava is harvested to make brushwood fencing and shelter for cooking in communal areas and also used as laths for roofing traditional houses (Mainah, 2006). In some cases, Grewia flava is uprooted to obtain the roots, which are considered to have some medicinal value (Mainah, 2006). The species is browsed on mainly by donkeys and goats, both of which are considered less selective feeders.

Grewiaflava was absent in the benchmark site (Figures 3.6 and3.7). The research sites were located in hilly and stony areas where people may find it difficult to reach (Figure 2.1). Mainah (2006) reported that negative human impacts such as cutting of the plant/bush/tree, settlement establishment and livestock tracks contribute to the decline in Grewia flava abundance.

According to Pareek (2001), most parts of Ziziphus have medicinal value. The roots, bark, leaves and/or fruits of Ziziphus mucronata can be used as medicines and are applied or taken in various ways, usually as drinks, food and even as poultice (Mokgolodi et al., 2011). Common ailments treated using Ziziphus mucronata include chronic cough, boils, toothache, rheumatism and swellings (Roodt, 1998; lwalewa et al., 2007; Orwa et al., 2009). Ziziphus mucronata extracts are also used by traditional medical practitioners to treat bacterial infections such as gonorrhea, syphilis, cholera, dysentery and boils (Moloto, 2004). Thus, the presence of this species at this site is economically favourable.

63 3.5.3 Woody Plant Invasion in Makgori

The woody plant densities recorded at the selected sites in Makgori Village were above acceptable levels (Table 3.1). An aggressive encroacher (Senegalia mel/ifera) contributed 56.24 % (3 447 TE/ha) to the total densities (6 129 TE/ha) in site 1 (Figure 3.20). Senegalia me/lifera was the most abundant encroacher and occurred at a density of2 143 TE/ha in site 2 (Figure 3.22). Senegalia mellifera trees, shorter than 1 m were the most evident (Figure 3.25). Infestation of this species, reduces vegetation diversity by direct competition for soil nutrients, soil moisture and space. In addition, grazing capacity was reduced (Table 3.1). (In a similar study) in another area of the Kalahari Thornveld where highly significant negative correlation between the density of Senegalia mellifera trees and grass density was found, the grass phytomass was reduced with 82 % at tree densities of 2 500 TE/ ha (Richter et al., 2001).

The densities recorded in the benchmark of Makgori Village also exceeded accepted densities (> 200 TE/ha) (Table 3.1). Senegalia mellifera was also recorded amply at 201 TE/ha in thi s site (Figure 3.4 and Figure 3.5 respectively). This implies that this species may double its current density within approximately five (5) years and transform this site into impenetrable thickets (Coates Palgrave, 1990) because of the aggressive nature of its growth.

The presence of Grewia jl.ava, Ziziphus mucronata and especially Opuntia species in the selected sites of the Makgori Village makes this village economically favoured. Furthermore, at these high densities (Grewia flava (797 TE/ha), Opuntia sp. (564 TE/ha) and Ziziphus mucronata (500 TE/ ha, Figure 3.21) these shrubs/trees will induce more grass suppression (Table 3.1 ). In addition, the spread of this species may restrict movement of animals and further thickening would restrict light penetration for understory vegetation (Mogodi, 2009).

3.6 Conclusion

The findings of this study are consistent with similar studies where it was found that dense woody trees resulting from overgrazing, rainfall patterns and other factors have a competitive advantage over grasses (Moore et al., 1985). Woody plant densities exceeding 2 000 TE/ha were observed in all survey sites except the benchmark sites. According to Moore and

64 Odendaal (1987), woody plant densities exceeding 2 000 TE/ha will almost totally suppress grass growth.

An almost total absence of grasses was observed in all survey sites except for the benchmark sites. Grass establishment and growth are correlated to woody species density, given that abiotic factors, such as rainfall and temperature are primary determinants. This was seen in the Makgori benchmark site which recorded a woody plant density lower than 300 TE/ha (Figure 3.4).

The results obtained in Tshidilamolomo survey sites were comparable to results of similar studies by Schade et al. (2003), EI-Keblawy and AI-Rawai (2007), Kahii et al. (2009), Muturi, (2012) and Muturi et al. (in prep.). Diversity, density and productivity of herbaceous species was lower under closed canopies and isolated trees of Prosopis species, than under indigenous tree canopies.

Prosopis velutina has an impact on water resources by disturbing ecosystem functioning, changing the species composition and vegetation structure which in tum has an effect on pollinators and decomposers that live in the soil and threatening ground water supplies (Le Maitre et al., 2002; Gorgens and Van Wilgen, 2004; Naumberg et al., 2005; Doody et al. , 2011),. Tshidilamolomo is a communal area and inhabitants living in this village depend on ground water and livestock farming. Thus, Prosopis velutina significantly reduces the total grazing area (Milton et al., 2003, Richardson and Van Wilgen, 2004) and also directly competes with humans for ground water.

However, the woody invader, Prosopis velutina, is used as fuel for firewood in Tshidilamolomo Village and in some instances people eat the pods or give it to their cattle as fodder. Pods of this species are boiled in water to make honey syrup. Prosopis sp. is, however, difficult to control due to the low growing branches and the thorny nature of the plant.

For these reasons, the national Working for Water (WfW) programme is targeting the invaded riparian zones and their immediate sub catchment for alien clearance across the country (Van Wilgen et al. , 1998).

65 For some reason, the Working for Water programme (WfW) did not target the invaded riparian zones of the Molopo River in the Molopo District. As a result, Prosopis velutina, is aggressively invading these areas. Because of this invasion, woody species, especially aliens, are spreading inland.

The Molopo River is blocked by high dunes and no longer reaches the Orange River (Midgley et al., 1994). The Molopo River is currently non-perennial as its water is heavily utilised. As a result, inflow of the Molopo River, which forms the boundary between Botswana and South Africa (Figure 2.1) , has become heavily reduced and even non-existent in some years. Due to the arid conditions, there is heavy reliance on ground water resources to meet the water demand.

Besides the occurrence of the encroaching woody species, the exotic cactus Opuntia ficus indica was also considered as an encroacher in certain sites. Opuntia sp. is well known for its tasty fruits. The usage of these plants requires proper and well-structured plans that can easily be accessed by inhabitants for the protection and conservation. In addition, the proliferation of this species at highest densities could suppress grass production which in turn could affect livestock production.

The current research gap regarding the usage of encroaching species for economic purposes such as selling of wood, fruit production etc. needs to be conducted. It can thus be concluded that local people will only eradicate woody encroachers if they will gain direct useful advantage from these actions.

Chapter 4 of this study aims to investigate or monitor change of vegetation over time. This chapter will also show the extent of woody plant invasion along the riparian zone of the Molopo River especially the two sites that are located in Botswana (refer to Table 3.2).

66 CHAPTER4

Remote Sensing

4.1 Introduction

As it was mentioned in Chapter 1, Riparian zones worldwide have been the focus of human habitation and development for many centuries (Washitani, 2001 ), resulting in direct and indirect degradation of their ecological integrity. Direct degradation includes vegetation clearance for agriculture (Kentula, 1997; Patten, 1998), grazing and trampling by livestock (Robertson and Rowling, 2000), pollution from the surrounding catchment (Kentula, 1997; Washitani, 2001) and the planting of alien species (Rowntree, 1991 ; Viljoen and Groenewald, 1995 ; Hood and Naiman, 2000; Tickner et al. , 2001).

However, environmental changes are often the result of anthropogenic pressure (e.g. population growth) and natural factors such as variability in climate and intrinsic vegetation dynamics (Guerra et al., 1998; Janetos and Justice, 2000). Due to increasing population growth rate, there have been increasing rates of livestock rearing and farming; and increased rates of deforestation of woodland for building kraals and fences in the villages. Increasing livestock may lead to overgrazing which in turn leads to bush encroachment.

Furthermore, the cutting down of woody plants may lead to land degradation and loss of biodiversity. River ecosystems are highly prone to invasion by alien plants because of their dynamic hydrology and opportunities for recruitment following floods (Decamps et al., 1995; Hupp and Osterkamp, 1996). Efficient dispersal of alien propagules in water and continuous access to water resources facilitates alien plant invasions (Thebaud and Debussche, 1991 ; Pyek and Prach K., 1993; Planty Tabacchi et al., 1996; Henderson, 1998; Richardson, 2001).

Bush encroachment is a process whereby the density of woody plants e.g. trees and shrubs, increases in an area at the expense of grasses (Tainton, 1999). The process is thought to be a result of demand above ground or below ground competitive capacity of grasses subjected to grazing (Brown and Archer, 1999). Bush encroachment is also considered as the suppression of palatable grasses and herbs by encroaching woody species often unpalatable to domestic

67 livestock (Ward, 2005). It is thus important to monitor the magnitude of encroachment of woody plants in the riparian zone of the Molopo River. The monitoring of woody plant density along the riparian zones of the Molopo River, may increase biodiversity and quantity of surface and ground water. In this regard, the utility of remote sensing in the assessment of bush encroachment will be applied, as demonstrated by a number of authors (e.g. Hudak and Wessman 1998; O'Connor and Crow 1999; Hudak and Wessman 2001 ; Laliberte et al., 2004; Martin and Asner 2005; Munyati et al., 2011).

4.2 Literature Review

Remote sensing may provide a viable way to monitor (and quantify) the changes caused by bush encroachment and alien invasion. The information generated by remote sensing could inform government and stake holders about the magnitude of encroachment (Edem and Duadze, 2004). The mapping of change in vegetation cover over time is needed to manage or monitor alien plants and bush encroachment effectively. As it was discussed in Chapter 3, bush encroachment and alien plant invasion can be so fast that even local authorities do not know its extent and impact, hence the need for the information. Thus, in order to ensure timely and accurate information, remote sensing is needed (Edem and Duadze, 2004).

Remote sensing provides a viable source of data from which updated land cover information can be extracted efficiently and cheaply for effective change detection (Edem and Duadze, 2004). Changing land cover patterns necessitates frequent updating of land use and land cover maps, especially in developing regions; where the updates are often inadequate and outdate rapidly (Rogers et al., 1997). Remote sensing provides the most accurate means of measuring the extent and patterns of changes in the landscape conditions over time (Miller et al., 1998); Defries and Belward, 2000). I NWU- ·1 1. .IRRARY Satellite data have become a major application in change detection because of the repetitive coverage of the satellites at short intervals (Mas, 1999). With the help of remotely sensed satellite data and ground truthed verifications, the specific vegetation characteristics of the study area are were extracted and analysed.

68 The interpretation of remotely sensed data for land use and land cover can be done visually (manually) or automatically (Erdas, 1999). However, consistency and reproducibility are considered to be important (Cihlar, 2000). The usage of remote sensing in the assessment of bush encroachment has been demonstrated by a number of authors. A variety of imagery has been utilised in the process, ranging from aerial photographs (Hudak and Wessman 1998; O'Connor and Crow 1999), satellite imagery (Hudak and Wessman 2001), combinations of aerial photographs and satellite images (Hudak and Wessman 2001 ; Laliberte et al. 2004) to airborne hyperspectral images (Martin and Asner 2005).

Although the goal in such remote sensing studies of bush encroachment is generally to map and quantify areas affected (i.e. 'classify' such areas as bush-encroached), the classification algorithms utilised in detecting and mapping bush encroachment on remotely sensed images are varied (Munyati et al., 2011), with varying associated classification accuracies. For example, Hudak and Wessman (2001) utilised image texture-based algorithms to classify savannah vegetation into densities on which interpretation of bush encroachment was based, with a weak but significant correlation between image texture and field percent woody cover

(R 2 = 0.2, P = 0.0086). Laliberte et al. (2004) used object-based classification on a Quick Bird and aerial photograph image dataset to monitor the vegetation changes over time, with

2 detection accuracies of about 87 % for all shrubs >2 m , although the classification scheme underestimated shrub and grass cover due to spatial resolution constraints.

Martin and Asner (2005), on the other hand, used spectral mixture analysis of A VIRIS hyperspectral imagery to estimate the fractional surface cover of green woody canopies (indicative of bush encroachers), senescent herbaceous vegetation, and bare soil in each pixel, with the resulting fractional cover estimates of woody vegetation being well correlated to field measurements (R2 = 0. 84, P < 0.01 ).

However, high spatial resolution is important in detecting bush encroachment on remotely sensed images (Hudak and Wessman 2001 ; Laliberte et al., 2004), primarily because the encroaching bushes have small crown diameters (Munyati et al., 2011). From a simulation of the 10-m spatial resolution SPOT panchromatic images, Hudak and Wessman (1998) established that the 10-m textural images from aerial photographs correlated to woody stem count for bush encroaching shrubs at savanna study sites, indicating that not only aerial photography could be used to characterise bush densities but also high spatial resolution

69 satellite images. In this study, Landsat TM/ETM+ images were used as they satisfied the requirements of relatively high spatial resolution (30 m) and large spatial coverage.

Most wet-season images are often cloud-obscured and are therefore, difficult to use for classification, while the dry-season images, though much better, miss the observation of growing vegetation. Dry-season images were thus used in this study. The differentiation of vegetation on satellite images is also not easy, because of varying effects of dry weather, bush fires, different stages of fallow regrowth and background soils, and causes differences in spectral responses of the same features over relatively short distances (Edem and Duadze, 2004).

The unique spectral characteristics of vegetation are what make biophysical remote sensing possible. Because of its chemical and structural characteristics, vegetation absorbs, reflects, and transmits electromagnetic radiation in a different manner than other natural and anthropogenic surfaces (Laidler et al., 2008). The contrast between chlorophyll absorption of visible wavelengths and strong reflectance in the near infrared (NIR) aids in discriminating plant types and have resulted in the development of numerous vegetation indices (Vl's) that provide a means of quantitatively measuring certain biophysical parameters (Laidler and Treitz, 2003; Jensen, 2007 as cited by Laidler et al., 2008).

The catalyst to understanding biophysical trends, usmg remote sensing data, is the investigation of relationships between spectral vegetation indices, how they vary across landscapes, and how these fluctuations are related to vegetation composition, biomass, and ecological site factors (Walker et al., 1995; Boelman et al. , 2003). Vegetation indices are mathematical derivatives of spectral reflectance that are designed to provide a single value representative of the amount or vigour of vegetation within a pixel (Laidler et al., 2008). Vl's are generally less sensitive to external variables (e.g., solar zenith angle) than individual image channels (Laidler and Treitz, 2003; Jensen, 2007). The normalized difference vegetation index (NDVI) (Rouse et al., 1974) is one of the most widely used index (Laidler et al. , 2008).

Satellite imagery, particularly Landsat, has been widely used for assessing vegetation condition (Hunt et al. 2003). It has been introduced as an important tool for understanding and monitoring various components of rangeland function and health (Palmer and Fortescue,

70 2006). The benefit of using remote sensing m detecting bush encroachment is that high spatial resolution remote sensing enables direct imagining of plant individuals that are at least the size of the ground resolution of the remote sensing image (McGlynn and Okin, 2006).

4.2.1 Image Rectifying

Often, remotely sensed imagery contains "noise" or error introduced by the environment or the sensor, which requires radiometric rectification for further analysis (Jensen, 2005). Radiometric rectification represents another form of image rectification, where temporal variation of multitemporal remotely sensed data is rectified (Gao, 2009). This form of rectification involves improving the accuracy of surface spectral reflectance, emittance and back-scattered measurements (Jensen, 2005). This process may take the form of normalizing data or the resampling of data to the same spatial resolution (Gao, 2009). Once these processes are complete, images are ready for further analysis. According to Gao (2009), it is important to geometrically rectify images for three reasons:

(a) Firstly, most remote sensing applications end products about the Earth's environment and resources do occur in thematic map form. The maps produced needs to conform to certain geometric mapping standards. Image rectification ensures that geometric distortions occurring within the remote sensing imagery are eliminated or reduced to an acceptable level;

(b) Secondly, raw satellite images and aerial photographs have to be projected to a common ground reference system in order to be analysed with data from other sources in a GIS. This enables images, obtained at different times, to be spatially registered with one another and;

(c) When used to detect changes or update existing maps, remotely sensed data must be re-projected to a coordinate system with a known geometry identical to that of digital maps to be revised.

71 4.2.2 Vegetation Indices

The assessment and monitoring of vegetation condition, using remote sensing techniques, have required the use of vegetation indices (Vi' s) (Amiri and Tabatabaie, 2009) as a precise radiometric measure of the spatial and temporal change in photosynthetic activity (Huete et al. , 1997). These are feature extraction operations intended to yield estimates of vegetation cover from imagery (Philpot, 2011).

Different indices are used to measure vegetation, whereas the NDVI is the most commonly used and is commonly used as an estimator of vegetation productivity (Nett Primary Productivity) (Kerr and Ostrovsky 2003; Ulrich, 2005). Vegetation indices, derived from remotely sensed data in general, are useful in indicating presence of vegetation, including monitoring its dynamics. These include the vegetation index (VI), transformed vegetation index (TYi) and the normalized difference vegetation index (NDVI) (Lillesand et al., 2008 a and b) , as well as related derivative indices. These vegetation indices enhance vegetation on multispectral images primarily on the basis of the high reflectance in the near infrared (NIR) and strong absorption in the red (R) regions of the electromagnetic spectrum by "healthy" vegetation (Lillesand et al., 2008 a and b). All these indices basically depend on the characteristic of vegetation to reflect the different wavelengths (especially near infrared and red) of light in a specifically distinguished manner (Ulrich, 2005).

The reflectance of these wavelengths is influenced by particles in the air and the ground cover below the vegetation, thus creating distortion in the satellite scene (Ulrich, 2005). However, the NDVI is thought to be a suitable indicator of relative biomass and greenness (Boone et al., 2000). The NDVI ranges between -1 and + I, with positive values indicative of vegetated areas. "Healthy" vegetation generally has high NDVI values. In general, though, higher NDVI values correspond to an increase in vegetation 'greenness' or vigor, controlled by a combination of vegetation type, health, photosynthetic activity and canopy density (Mennis, 2001). NDVI values for vegetated land are generally greater than 0.1 , and values exceeding 0.5 indicate dense vegetation (Budde et al. 2004).

The visible red light between 0.4 and 0.7 µm is significantly absorbed by the plant leaves' pigments, the chlorophyll, and used in the process of photosynthesis (Ulrich, 2005). The mesophyllic leaf structure reflects strongly the incoming sunlight between 0.7 and 1.1 µm

72 (NIR) (Tucker, 1979). Thus, the NDVI of a satellite scene provides a pattern of the vegetation's phytoactivity (Ulrich, 2005). "Healthy" vegetation will show a high absorption in the red and a high reflectance in the near infrared spectrum of light (Ulrich, 2005).

The NDVI value is calculated as follows (Figure 4.1):

NDVJ= [NIR-RED] 7 [NIR+RED]

n introre visi

0.50- 0.08 = 0. 72 0.40-0.3 0 = 0.41 0.50+0.08 0.40+0.30 Figure 4.1: An example of NDVI calculation. Source: http//earthobservatory .nasa.gov/Li brary

The NIR and RED portions range from 0.72 to l.10µm and 0.58 to 0.68µm respectively (Figure 4.2). Green vegetative surfaces result in positive NDVI values (NIR> RED) (Weiss et al. , 2004). An increase in green vegetation results in higher NDVI values and lower NDVI values imply less or non-vegetated areas (Weiss et al. , 2004; Jin et al. , 2008).

73 Le I I C II ' Dominant factor p1gme ts : s ucture Water con ent .. . ,.. • ► C ntr lling • •I I I I ., leaf r flee ance 80 I I .., Primary 70 Chlorop II Water absorp ion absorption r absorption _ 60 _, bands - 0 ' 0 NIA plateau u 50 C t ~ 40

QI a: 30 ... red odgo 20

0 1 T .. 04 06 0.8 1.0 1.2 1.4 .8 2.0 2.4 2.6 Na"'f!leng I ◄ .,. .,. I I Vii I NIA I WIR - 0 C: "() I ::, Q) I in ~ a: : CJ I ------

Figure 4.2: Green vegetation reflectance across various portions of the electromagnetic spectrum (Adapted from Bogonko, 2005)

The NDVI is useful in arid and semi-arid environments due to its ability to differentiate between vegetation and soil in cases where there is sparse vegetation cover. It was on this basis, that the NDVI was preferred for use in this study.

4.2.3 Remote Sensing Image Classification

Image classification is a process that converts satellite data into information based on pixel values within the image. This process has made it possible for researchers to study the earth's surface, using satellite data (Goodchild, 1994; Gao, 2009). Monitoring vegetation conditions is sometimes accompanied by the classification of remote sensing images (Van Til et al., 2004). The main purpose of image classification is to analyse the distribution of vegetation types and determine their ecological relationships (Ndou, 2013). Information is extracted based on assigning pixels to classes (Smith, 2012). Each pixel is treated as an individual unit composed of values in various spectral bands. Classification makes it possible to gather groups of similar pixels into classes that are associated with informational categories of interest, by comparing pixels to one another and to pixels of known identity. Image

74 classification can be applied to various remote sensing applications, used for image analysis and pattern recognition (Campbell, 2006).

This process assigns pixels which have similar spectral characteristics and which are assumed to belong to the same class are identified and assigned a unique colour. Once an image is classified, the dataset can be interrogated and the area of different classes can be set (Gibson and Power, 2000; Campbell, 2006).

Image classification is separated into supervised classification and unsupervised classification. Supervised classification requires significant interaction with the analyst, who guides the classification by identifying areas on the image that belong to a certain category. Unsupervised classification, however, requires minimal interaction with the analyst and searches for natural groups of pixels within the image (Campbell, 2006).

4.2.3.1 Unsupervised Classification

Unsupervised classification involves the examination of unknown pixels in an image and combining them into a number of classes on the basis of the natural groupings or clusters present in an image (Babykalpana and ThanushKodi, 2010). This method is designed to automatically group the pixels without prior knowledge of the features represented by each pixel (Babykalpana and ThanushKodi, 2010).

Unsupervised classification has the following advantages: human error is minimized, no extensive prior knowledge of the region is required and unique classes are recognized as distinct units. However, to its disadvantage, unsupervised classification identifies spectrally homogeneous classes within the data that do not necessarily correspond to the information categories that are of interest to the analyst. The analyst also has limited control over the menu of classes and their specific identities (Campbell, 2002).

4.2.3.2 Supervised Classification

Supervised classification, on the other hand, involves the need for prior knowledge of the ground cover of the study site (Hasmadi et al. , 2009). The user decides on the number and

75 types of classes to be extracted. It requires "training" data to generate classes defined by the user. The same generated ''training" data is used to "train" a classification algorithm (Kamaruzaman et al. , 2009). It is on this basis that the supervised classification was preferred ahead of unsupervised approach in the current study.

4.2.4 Accuracy Assessment

Accuracy assessment may be defined as the degree of closeness of results to the value accepted as true or correct (Zavoianu et al., 2001). Accuracy assessment is performed to provide an index of the degree of correctness of a map or classification (Foody, 2002). In remote sensing, this is achieved by overlaying a reference image and simulated image (Geri et al., 2011). Accuracy assessment in remote sensing is achieved by using the error matrix (ERRMAT) method and the Kappa Index of Agreement (KIA).

The error matrix measures the relationship between image classification results and measured ground conditions (Collingwood et al. , 2009). This is regarded as a standard procedure for accuracy assessment (Foody, 2002). The KIA is a discrete multivariate technique which is used to statistically evaluate the accuracy of classified data (Collingwood et al. , 2009). It is commonly used to quantify the agreement between two images on a categorical basis. For this agreement to be obtained, two images must be categorized into equal groups (Schouten, 1982). It considers the effect of chance agreement in the error matrix (Collingwood et al. , 2009).

The advantage of the KIA over the standard error matrix is that it measures both overall and individual class accuracy (Collingwood et al., 2009). Several studies that evaluated the efficiency and effectiveness of both the error matrix and KIA for accuracy assessment have recommended the KIA for accuracy assessment (Rossiter, 2004; Pontius, 2000; Geri et al. , 2011). It was on this basis that this study also used KIA for accuracy assessment.

There is no map which provides completely accurate information. Mapping and interpretation of vegetation conditions are limited to certain adjacent scales of observation (Ndou, 2013). Integrating remotely-sensed data and ground observations, to reduce uncertainties and increase the reliability of the results is recommended (Arieira et al., 2011).

76 4.3 Remote Sensing Methods

4.3.1 Study Site Description

The study area falls within the Savanna Biome (see Chapter 2) described by Mucina and Rutherford (2006) as Mafikeng Bushveld and has a summer rainfall with very dry winters (see Figure 2.3, Chapter 2). The study area falls in the western part of the province and its mean annual precipitation (MAP) does not exceed 350 mm (Figure 2.4, Chapter 2). Frost is frequent in winter. The mean monthly maximum and minimum temperatures for Mahikeng are 35.6 °C and -1.8 °C for November and June respectively while its altitudes is between 1 100-1 400 m (Mucina and Rutherford, 2006).

4.3.2 Field Data Collection

Eighteen (17) plots were established for intensive study (Table 2.1, Chapter 2). The spatial location of each plot was determined using a Garmin eTrex 10 Global Positioning System (GPS) for ease of identification on satellite imagery. Field work (Ground thruthing) was performed from these sites. Ground thruthing was not done on the remaining 5 plots as it required consultations and permission (Table 3.2, Chapter 3). Among these 5 plots, 4 plots were selected at a Botswana site near Tshidilamolomo village and another was selected at a Botswana site near Phitsane village (Figure 2.1, Chapter 2).

4.3.3 Image Data and their Dates NWU · I ILIBRARY_ Landsat TM/ETM+ images were chosen for use as they satisfied the requirements of relatively high spatial resolution (30 m) and large spatial coverage. Three recent (19 May 1988, 01 March 1996, and 03 May 2013) cloud-free TM and ETM+ scenes were obtained for use for detecting and mapping vegetation density.

77 4.3.4 Image Pre-Processing

All images used in this study were downloaded from http: earthexplorer.com. The downloaded images were imported to su it Idrisi Taiga software. The quantitative . use of remote sensing satellite images in many applications requires that the geometric distortion inherent in these images be corrected to a desired map projection. Thus, the process of geometric correction was achieved using the image resample module in Idrisi Taiga remote sensing software. This technique is the most preferred if the images are to undergo classification (Richards and Jia, 2006). The images used in this study were projected to the Universal Transverse Mercator (UTM) coordinate system, using the World Geodetic System 1984 ellipsoid.

The shape file of the study area was created by digitising and was created from the 2013 image. The 1996 and 1988 shape files were created from the 2013 image shape file. These shape files were established to represent villages and sample points within the study area, along the riparian zone of the Molopo River. These shape files were projected into UTM, for easy use with satellite images.

4.4. Image Data Processing

4.4.1 Image Classification

Vegetation condition categories were classified on the basis of the NDVI analyses. Five classes, namely intact vegetation, degraded vegetation, grassland, bare area and water body were extracted from the multi-temporal NDVI images. The classification of these multi temporal images was achieved by employing the supervised classification algorithm built in the Idrisi Taiga GIS software. This supervised classification method simply groups pixels according to their homogeneous spectral properties. Satellite images for the respective years were classified independently.

Supervised classification was performed in which images were classified into vegetation cover categories that enable detection of extent of change. In the process, the methods of image classification range from use of hard pixel classifiers to object-oriented classification

78 and spectral mixture analysis (e.g. Brandt and Townshend, 2006; Numata et al., 2007). In this regard K-Nearest Neighborhood (KNN) Classification was used for the image classification.

K-nearest neighbour algorithm is a method for classifying objects based on closest training examples in the feature space. K-nearest neighbour algorithm is among the simplest of all machine learning algorithms (Cover and Hart, 1967; Bremner et al. , 2005). Training process for this algorithm only consists of storing feature vectors and labels of the training images. In the classification process, the unlabelled query point is simply assigned to the label of its k nearest neighbours. Typically, the object is classified based on the labels of its k nearest neighbours by majority vote. If k=l , the object is simply classified as the class of the object nearest to it. When there are only two classes, k must be an odd integer. However, there can still be ties when k is an odd integer when performing multiclass classification.

4.4.2 Accuracy Assessment

An accuracy assessment was undertaken, to validate the vegetation classes. Ten sample points per class, were verified in the field, using the stratified (purposeful) random sampling method. Their GPS readings were taken, using the centimetre level precision Ashtech®ProMark2™ Global Positioning System (GPS) receiver. The sample points constituted features (particularly vegetated surfaces) common to the respective imagery sets. The collected field verification points were overlaid on the NDVI images for each year and vector layers were digitised around these points, to create a training file. Two images classified and based on ground truth data were then compared for all years, utilising the error matrix analysis algorithm, built into the Idrisi Taiga software. Estimates of error were provided by module results, i.e.:

• Error of Omission - occurs when pixels of Class I were wrongly assigned to Class 2, and from Class 1 perspective, these pixels should have been assigned to Class 1 but were omitted. • Error of Commission - occurs when pixels of Class 2 were wrongly assigned to Class 1, and from Class 1 perspective, these pixels shouldn't have been assigned to Class 1 but were included.

79 4.5 Results

4.5.1 Classification Accuracy Assessment

A classification accuracy assessment of the imagery is presented first as a basis for establishing the reliability of the temporal and spatial overall trend in image classification classes. Classification accuracy assessment is considered significant if KIA is greater than 0.70 (Jansen, 2005). According to results obtained overall accuracies ranged between 0.82624 and 0.83515 and KIA between 0.7306 and 0.83804. A summary of overall accuracies and KIA for 1988, 1996 and 2013 are presented in the table (Table 4.1) below. Given the KIA values obtained the classification accuracy is reliable (Jensen, 2005). A full error matrix for each year is presented in Appendix A.

Table 4.1: Accuracy Assessment Results

YEAR OVERALL ACCURACY KIA 1988 0.82624 0.7306 1996 0.80313 0.7649 2013 0.83515 0.83804

4.5.2 NDVI and Vegetation Density Analysis

Following image sub-setting and NDVI enhancement, all NDVI enhanced images were classified using the K-nearest neighbour algorithm classifier. Classification of vegetation density in this study categorised five major classes namely: intact vegetation, degraded vegetation, grassland, bare surface and water body (Figures 4.3, 4.5 and 4.7). NDVI values from three dates (l 988, 1996 and 20 \3) (as calculated by equation 4. l) were classified into five major classes namely: < -2 as water; < 0.1 as bare surface area; 0.1- 0.2 as grassland; 0.3 to 0.5 as degraded vegetation density and > 0.5 as intact vegetation density. NOVI values for the 19 May 1988 image (Figure 4.2) show high reflectance NDVJ values indicating that there was high vegetation density (Intact vegetation) for the area during this period. The reflectance NDVI values ranged from 0.724638 (highest) to - 0.484848 (lowest) values (Figure 4.2). The eastern areas of the study area show high NDVJ values while the western part show the

80 lowest values. The high NDVI values for this year can be explained by the fact that there was above normal rainfall in the years prior to the image acquisition ( e.g. 1987 - May 1988) (Figure 2.3, Chapter 2).

Legend ~ Border NOVI Value N Htgh O 7 24638 Kilometers W+ E 0 5 10 20 LOW O 484848 s 2-1 ~so·o·E 2 s • 11•0"E

Figure 4.3: 19 May 1988 NDVI image

24. 38'30"E 2,4• 51 '30"E 2s • 11'0°E ~ t

Legend

~ Border Vegetation condition - Intact vegetation - Degraded vegetateon Grassland Kilo m eters Bare surface 0 5 1 0 20 W+E - Water s 24 "45'0 " 24"51 '30" 24" 58"0"E 25"11'0"'E

Figure 4.4: 19 May 1988 supervised classification

81 Results from the K-nearest neighbour classification (Figure 4.4) reveals that in 1988, degraded vegetation density occupied the largest total area of 57.039 % followed by the grassland class 29.745 % and the intact vegetation density occupying 12.205 % of the total area (Figure 4.8). Finally, bare surfaces and water bodies occupied the lowest total area 0.713 % and 0.298 % respectively (Figure 4.8).

On the contrary, the 1996 image shows a marked increase in intact vegetation density and an increase in the area occupied by the degraded vegetation density. However, NDVI values show a slight decrease compared to the 19 May 1989 values. The 1996 NDVI values range between 0.648855 and -0.451613 (Figure 4.5) while the 03 May 2013 values range from 0.350346 to -0.114 734 (Figure 4. 7).

24•3a•30•E 24•45'Q"E 24"58'0.E 25•4'3Q"E 25" 11 '0"E

Legend

BOfder NOVI Value "' High : 0.648855 t Kilometers r,:i 0 5 10 20 Low : -0.451613 s 24"38'30"E 24"45'0"E 24"58'0"E 25"4'30"E

Figure 4.5: 01 March 1996 NDVI image

82 2◄ " 38'30"E 2◄ "5 1 '30"E 24"58'0"E 25"4 '30"E

Legend

l7 Border Vegetation condition - Intact vegetation - Degraded vegetation - Grassland Kilometers Bare surface 0 5 10 20 - Water

24"38'30"E 24 " ◄ 5 '0"E 24 "51'30"E 24"58'0"E 25 " ◄ '30"E

Figure 4.6: 01 March 1996 supervised classification

An interesting result of the image acquired in 2013 is that it displayed high vegetation density in the arid area (western part of the study area) and low vegetation density in the eastern part, which receives more rainfall. However, the highest NOVI value is 0.350346 and the lowest for the period being -0.114 734 (Figure 4. 7). In spite of the fact that the western part shows high vegetation density, this period had the lowest NOVI values of all the years. This image displayed a major decrease in intact vegetation density from 28.645 % during the year 1996 to 0.112 % in 2013 (Figure 4.9). Furthermore, the highest NOVI values were reflected in the Prosopis infested sites (Tshidilamolomo research sites, sites within Botswana "border" near Tshidilamolomo (Figure 4.12) and Senegalia mellifera encroached sites of Makgori and Loporung (Figure 4.6). The degraded vegetation density class occupied the highest total area of 82.25 % followed by grassland class which occupied 14.797 % (Figure 4.9). The bare surface area showed an increasing trend of 0. 713 % recorded in 1988, 0.861 % in 1996 and 2.835 % recorded in 2013. The reasons behind these "rather unexpected" results, lie within the species composition, environmental factors, climate (Figure 4.10), topography, geology, human disturbances (Figure 4.13), soil and grazing patterns (Figure 4.14).

83 The dramatic decline in the NDVI values from 1988 to 2013 may be explained by the climate variation in the area in particular the rainfall pattern (Figure 4.10) which is erratic and unpredictable as well as the gradual global warming. This has an influence on the vegetation density of the area particularly indigenous vegetation as the lush greens are found in the eastern parts of the NDVI classified images which correspond with the fact that South African rainfall decreases from east to west (Figure 2.4, Chapter 2). Another possible reason for the decline in the reflectance values is anthropogenic activities taking place in the catchment such as overgrazing (Figure 4.14), fire wood harvesting (Figure 4.13) and cutting of woody plants for making kraals (Figure 4.16). Almost all cattle kraals observed in the villages in the study area were made from woody plants, especially Vachellia erioloba.

Vachellia erioloba is declared a protected species, in terms of section 12 of the National Forests Act, 1998 (Act No. 84 of 1998). The protected species Act 84, 1998 states that if a tree is declared protected, no person may:

(a) cut, disturb, damage, destroy or remove any protected tree; or (b) collect, remove, transport, export, purchase, sell, donate or m any other manner acquire or dispose of any protected tree, except under a licence granted by the minister of Water Affairs and Forestry.

The Act does not distinguish between dead and living trees. This implies that removal of dead specimens without a permit is illegal. However, local people continue to cut these trees for various purposes. The cutting of other woody plants such as Tarchonanthus camphoratus, Senegalia mellifera and Vachellia tortilis was observed in some areas within the study sites.

84 24'38'30"E 24'45'0"E 24' 51'30"E 25' 4'30"E 25'11'0"E ~

Legend

r--- Border NOVI Value High : 0 .350346

Kilometers Low : -0 .114734 0 5 10 20

24' 38'30"E 24'45'0"E 2◄ '51'30"E 24'58'0"E 25' 4'30"E 25' 11 '0"E

Figure 4.7: 03 May 2013 NDVI image

24'38'30"E 24' 45'0-E 2s· 11·o·E !"'

Legend

Border Vegetation condition

- Intact vegetation

- Degraded vegetaHon

Grassland

Kilometers Bare surface 0 5 10 20 - water

24'38'30"E 24. 51'30"E 25'4'30-E 25'11'0"E

Figure 4.8: 03 May 201 3 supervised classification

85 Water bodies occupied the smallest total area of 0.091 % (Figure 4.9). The classes that did not change much during this period was the degraded vegetation density and bare surface which increased by only 1.543 % and 0.148 % from 1988 to 1996 (Figure 4.9). The only class that recorded a major decline between 1988 and 1996 is the grassland which occupied 29.745 % of the total area in 1988 and 11.822 % in 1996 which is a decrease of 17.923 %. Intact vegetation class recorded an increasing trend (12.205 % in 1988 and 28.645 % in 1996) as illustrate by Figure 4.9. ' NWU--~'l LIBRARY_

Overall trend in image classification in (%)

90 82.25 80 70 58.58 57.039

~ 50 :,i ~ 40 28.645 29.745 c 30 14.797 20 2.835 10 0.091 0.861 006 0.713 0.298 0 °- Intact vegetation Degraded Grassland Bare area \\ ater vegetation CLASSES

1996 2013

Figure 4.9: Overall trend in image classification classes

86 Mahikeng monthly rainfall (mm) during the years 1988, 1996 and 2012 160 ,...._ 140

.,_,~ 120 = 100 :! .5 80 i,: I. ~... 60 § 40 2: 20

0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Months

~ 1988 ~ 1996 2012

Figure 4.10: Mahikeng monthly rainfall (mm) during the year 1988, 1996 and 2012 (South African Weather Service, Data for station [0508047 OJ - MAFIKENG WO Measured at 08:00)

From Figure 4.10, it is clear that there is a positive correlation between the rainfal I pattern and the vegetation fluctuation in the study area. This correlation can be seen from the highest NOVI calculated from the 1988 image (Figure 4.3) and the highest rainfall received between the February to April. The acquisition date of the 1988 image was 19 May 1988. It is clear from Figure 4.10, which was a sufficient rainfall that could influence the growth of vegetation prior the acquisition date of the 1988 image. The highest rainfall recorded from the 1988 image was approximately 148 mm in February and approximately 85 mm in march respectively (Figure 4.10). Furthermore, the l st of March 1996 NDVI image calculated the slightly lower NOVI values (0.648855; Figure 4.5) as compared to 19th of May 1988 NOVI values (0.724638; Figure 4.3). This may be due to the amount of rainfall (approximate 141 mm and 62 mm recorded in January and February respectively; Figure 4.10) received prior the 1996 Image acquisition date (01 March 1996). Rainfall data of the year 2013 was incomplete, in this regard, the 2012 rainfall data was used instead. From Figure 4.10, it is clear that there was a rainfall stress during the year 2012. However, it cannot be concluded that the lowest NOVI calculated from the May 2013 NOVI image resulted from the significant lowest rainfall recorded during the year 2012. The decline of rainfall (Figure 4.10) in the study area contributed to the decrease of water class calculated from 1988 1996 and 2013 images (Figure 4.9).

87 Figure 4.11: A comparison between intact and degraded vegetation in Loporung Village: (a): Intact vegetation (b) Degraded vegetation

Intact vegetation in the high areas of Loporung Village along the riparian zone of the Molopo river and the Botswana border (Figure 4.11 (a)) and Degraded vegetation in the vicinity of the Loporung village (Figure 4.11 (b)). The intact vegetation in Figure 4.11 (a) may be due to the fact that it is far from human settlement and it is monitored. According to Figure 4.11 (a) and (b ), it is clear that local people have an effect on the local vegetation. Overgrazing and deforestation play a major role in some areas of the study area.

88 Figure 4.12: Prosopis infested sites within the Botswana along the Molopo River

Figure 4.13: The tree cutting in the study area

89 Figure 4.14: Overgrazing along the riparian zone of the Molopo River in the study area

.·

2014 O:i O?

Figure 4.15: A water hole created in the Molopo River for domestic uses

90 Boreholes and self-dug water holes (Figure 4.15) in the Molopo River may have negative effects on the natural flow of the river. Furthermore, these boreholes may result to livestock death as young animals may drown while drinking water.

Figure 4.16: Cattle kraal made from woody plants

In the riparian zone of the Molopo River, the interaction of rainfall variability and anthropogenic activities such as grazing and expansion of alien plants strongly influences vegetation diversity as well as water and sediment retention which explain in the main the variation in distribution and diversity of vegetation. Alien plants depend on ground water and flood water that infrequently occur in the western part of the Molopo River due to the relatively flatter terrain compared to the relatively steeper terrain of the eastern part (Figure 2.1 , Chapter 2). These trees are multi-stemmed and often have extensive root systems which may contribute towards the infiltration and percolation of rain water into the soil. This may

91 cause an increase in soil moisture and alter the water table which allows these trees to remain physiologically active under drought conditions.

The cutting of some indigenous woody plants (Figure 4.13) and overgrazing (Figure 4.14) in Tshidilamolomo and inside the Botswana (Figure 4.7) made it easy for the expansion and encroachment of Prosopis velutina (Figure 4.12).

4.6 Conclusion

The results obtained from the NDVI images showed an increase in vegetation density along the riparian zone of the Molopo River especially in Tshidilamolomo and Botswana sites (Figure 4.12). However, the NDVI's calculated from the three images decreased from 1988 to 2013. This implies that the overall vegetation is transforming from intact ("healthy vegetation") to degraded vegetation (Figure 4.11 (a) and (b)). It can therefore, be concluded that woody plants are invading the riparian zone of the Molopo River while decreasing in some areas especially areas that are in the vicinity of the human settlement. Rainfall fluctuation, overgrazing and deforestation account for the transformation of vegetation and loss of biodiversity in some areas of the study area.

Furthermore, overgrazing and accompanied human activities such as cutting of indigenous trees is the primary cause of the current status of Prosopis velutina infestation along the riparian zone of the Molopo River (Figure 4.12). ln the communal grazing areas, the continual usage of the area by livestock over the years, as well as wood collection by residents of nearby villages, was reported as the main causative factor of the opening up and increase in bare soil (Munyati, 2011). Woody plants such as Senegalia mellifera and Vachellia tortilis along the riparian zone of the Molopo River are also increasing (Figure 4.11 (a) and (b)). Senegalia mellifera and Vachellia tortilis were mainly observed along the riparian zone of the Molopo River near Loporung and Makgori Villages.

Water bodies in the study area continue to decline. This was observed from the classes that were digitised from the NDVI results calculated from the 1988, 1996 and 2013 images (Figure 2.9). The situation was worse in the Tshidilamolomo and Botswana sites where Prosopis velutina invaded. Prosopis invasion is a big problem in South Africa because of its

92 extensive water use (Le Maitre, 1999) and needs to be eradicated on a full time basis to improve the sustainability of these riparian zones.

93 CHAPTERS

General Discussion, Conclusion and Recommendations

5.1 General Discussion

The North West Province is the sixth largest province in South Africa (Department of Agriculture, Conservation, Environment and Tourism (DoACET, 2002). There are nine major biomes recognized in South Africa (Mucina and Rutherford 2006), but only the Savanna and Grassland biomes occur in the North West Province (NWP) (Department of Agriculture, Conservation, Environment and Tourism, 2002). Approximately 71 % of the North West Province falls within the Savanna Biome with its associated Bushveld vegetation, the remainder falls within the Grassland Biome comprising a wide variety of grasses typical of arid and semi-arid areas (Department of Agriculture, Conservation, Environment and Tourism, 2002).

Climatic conditions in the North West Province vary significantly from west to east. The western region is arid, encompassing the eastern frontier of the Kalahari Desert. The central region of the province is typically semi-arid, with the eastern region being predominantly sub-tropical climate (Department of Agriculture, Conservation, Environment and Tourism, 2002).

On average, the western region of the NWP receives less than 300 mm of rainfall per annum, the central region approximately 550 mm p.a., while the eastern and south eastern region receives more than 600 mm of rainfall per annum (Figure 2.4, Chapter 2). Mahikeng falls within the Mafikeng Bushveld and has a summer rainfall with very dry winters. The average annual precepitation for Mahikeng is 520 mm/p.a. Frost frequently occurs in winter. The mean monthly maximum and minimum temperatures for Mahikeng are 35.6 °C and -1.8 °C for November and June respectively.

As result of the low rainfall in the NWP, the soils are deemed to be only slightly leached over much of the western region. Due to high evaporation rates, there is a predominance of

94 upward movement of moisture in the soils. This results in high levels of soil salinity or alkalinity (Department of Agriculture, Conservation, Environment and Tourism, 2002) and will thus affect the soil organic matter.

Given the arid and semi-arid conditions of the western part of the North West Province, the vegetation of this region largely comprises plants that are adapted to these conditions (xerophytes, mostly thorn trees) ((DoACET, 2002). As a result, low biomass, productivity and species richness of plants tend to prevail in this region.

The study area was situated along the riparian zones of the Molopo River, which arises from the Molopo Eye near Mahikeng. The Molopo River flows westwards to form the northern border of the North West Province with Botswana. This river is currently non-perennial as its water is heavily abstracted at its origin, the Molopo Eye. This implies that, water quality and quantity issues affecting groundwater also have implications for surface waters.

The situation is made worse by the presence of the alien invader, Prosopis velutina, which commonly occurs in riparian and adjacent areas included in this study. Invasion by Prosopis velutina in the study area, especially in the regions along the riparian zones of the Molopo River, poses a severe threat to biodiversity and water quantity. Prosopis velutina dominated in most areas to the extent that the natural vegetation has almost been lost. Alien plants pose a threat to ecosystem functioning, because they use larger amounts of water than the natural vegetation, and therefore reduce the amount of runoff that reaches the streams and rivers. These impacts reduce the diversity and cover of indigenous plant species. The development of herbaceous vegetation was also limited, due to the invasion of, especially Prosopis velutina. Other ecological impacts of the invasion process include the alteration of soil nutrient cycling, increased river bank erosion, altered fire intensity and reduction of light to the forest floor or near to the ground.

Thorny trees such as Senega/ia mellifera, Vachellia tortilis, V. hebeclada and the non-thorny Grewia _/lava and Tarchonanthus camphoratus were also considered a threat in the study area, especially in the Makgori and Loporung study sites. Non-woody plants, which were also encroaching, include the exotic cactus, Opuntiaficus-indica.

95 Satellite data have become very useful in change detection because of the repetitive rapid coverage of the satellites at short intervals. With the help of remotely sensed satellite data (1988, 1996 and 2013) and ground trothed verifications, the specific vegetation characteristics of the study area could be extracted and analysed.

The highest NDVI values were observed in the Prosopis sp. infested sites (Thsidilamolomo research sites), those sites on the RSA-Botswana border near Tshidilamolomo and the Senegalia mellifera encroached sites of Makgori and Loporung. Results obtained from the NDVI images showed the increase in vegetation density along the riparian zone of the Molopo River especially in Tshidilamolomo and the Botswana sites. However, the NDVI's calculated from the three images decreased from 1988 to 2013. This implies that the overall vegetation is transforming from intact (healthy) vegetation to degraded vegetation. It can, therefore, be concluded that woody plants are invading the riparian zone of the Molopo River while decreasing in some areas. The reasons behind this rather unexpected result, lie within the species composition, environmental factors, climate, topography, geology, human disturbances, soil and grazing patterns that induce heterogeneity over space and time (Jiao et al., 2011). Communal farming is common-place in the study sites. Local farmers (cattle farmers) do not have property rights and farm on a self-sustaining basis. Cattle were used for the purposes of meat, milk and barter trading. The land belongs to the local chief (tribal office) and as such the farmers, thus, do not take any responsibility of rehabilitating their land. No proper fencing system, and no proper grazing system, were observed in the study area. This has eventually led to long-term overgrazing and eventually no field resting periods.

5.2 Conclusion

Overgrazing has transformed much of the natural vegetation of the central, southern and south eastern regions of the NWP (Department of Agriculture, Conservation, Environment and Tourism, 2002). Overgrazing was evident in the study area and contributed towards the establishing of woody encroachers in areas where they did not occur before. However, global warming and the gradual increase in atmospheric carbon dioxide concentration are believed to be the major drivers of woody plant encroachment.

96 The Working for Water (WfW) Project is active in the North West, but is currently concentrating on alien infestations of wetlands and dolomitic eyes particularly in the Mafikeng and Rustenburg districts (Department of Agriculture, Conservation, Environment and Tourism, 2002). However, the WfW did not reach the Molopo River, especially where the study area was situated. NWU-· 1 5.3 Recommendations t_LIBRARY _

• The Working for Water Project must eradicate the exotic Honey mesquite (Prosopis velutina) along the riparian zones of the Molopo River. This requires eradicating the trees and monitoring them. Indigenous woody encroacbers such as Senegalia mellifera (Black Thom) also need to be eradicated. • The Molopo River must be regularly monitored for the prevention of human impact such as the creation of boreholes in the river stream as shown in Chapter 4. • The Department of Agriculture, Conservation, Environment and Tourism (DACET) must regularly educate the village inhabitants regarding bush encroachment and alien plant invasion. This can be done through regular tribal meetings. • The DACET must educate the farmers of the strategies to combat bush encroachment. Farmers must be trained and supported especially on using good grazing practices to prevent overgrazing. • Communal farming practices must be limited. Farmers must own their land. In this way, they will protect the natural resources. • Village inhabitants must be educated especially about protected plant species, such as Vachellia erioloba for the making of cattle kraals. • Economic use of the exotic, Opuntia ficus-indica needs to be investigated. • More research must be done especially on the extent of woody plant invasion along the rivers of the North West Province.

The findings of this study will be disseminated to the villagers, especially in areas where this study was conducted. These findings will probably add value and knowledge to the farmers, especially with regard to the status of bush encroachment in their vicinity. Information dissemination to the villagers will be done through the tribal meetings preferably next year

97 2015. The findings of this study will be also presented to the Department of Agriculture and Environmental Affairs.

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128 APPENDIX A

ACCURACY ASSESSMENT RESULTS

Error Matrix Analysis of Ground (columns : truth) against 1988_image (rows : mapped)

1 2 3 4 5 Total ErrorC

------1 I 190548 146354 25 0 538 I 337465 0.4354 21 862 484416 3270 0 0 I 488548 0.0085 31 1596 101474 166629 5391 62 I 275152 0.3944 41 0 112 7005 24640 1 I 31758 0.2241 51 0 0 0 0 44671 4467 0.0000

------Total I 193006 732356 176929 30031 5068 I 1137390 ErrorO I 0.0127 0.3386 0.0582 0.1795 0.1186

ErrorO = Errors of Omission (expressed as proportions) ErrorC = Errors of Commission (expressed as proportions)

90% Confidence Interval = +/- 0.0007 (0.2338 - 0.2351) 95% Confidence Interval = +/- 0.0008 (0.2337 - 0.2353) 99% Confidence Interval = +/- 0.0010 (0.2335 - 0.2355)

KAPPA INDEX OF AGREEMENT (KIA) Using 1988_image as the reference image ...

Category KIA

------1 0.6757

2 0.9762 3 0.5329

4 0.7698

5 1.0000

Ground truth

Category KIA

------1 0.9819

2 0.7065

3 0.9232 4 0.8153 5 0.8809

Overall Kappa = 0.7306 Error Matrix Analysis of Ground (columns: truth) against 1996_image (rows: mapped)

1 2 3 4 5 Total ErrorC

------1 I 242509 32049 309 0 01 274867 0.1177 21 7510 495959 1359 87 01 504915 0.0177

31 851 231 100083 0 01 101165 0.0107 41 0 42 70 7286 01 7398 0.0151

51 0 0 0 30 768 I 798 0.0376

------Total I 50870 528281 101821 7403 7681 889143 ErrorO I 0.0333 0.0612 0.0171 0.0158 0.0000 I

ErrorO = Errors of Omission (expressed as proportions) ErrorC = Errors of Commission ( expressed as proportions)

90% Confidence Interval = +/- 0.0007 (0.2338 - 0.2351) 95% Confidence Interval = +/- 0.0008 (0.2337 - 0.2353) 99% Confidence Interval = +/- 0.0010 (0.2335 - 0.2355)

KAPPA INDEX OF AGREEMENT (KIA)

Using 1996_image as the reference image ...

Category KIA

--·------1 0.9625 2 0.7911 3 0.7434 4 0.9761 5 0.7337

Ground truth

Category KIA

-----·--·------1 0.9221 2 0.6747 3 0.9398 4 0.6397 5 1.0000

Overall Kappa = 0.80313 Error Matrix Analysis of Ground (columns: truth) against 2013_image (rows: mapped)

1 2 3 4 5 Total ErrorC

1 I 1116 516 187 0 01 1819 0.3865 21 816 818687 6609 10731 51 836848 0.0217 31 203 21507 147281 207 01 169198 0.1295 41 2 367 105 28214 01 28688 0.0168 51 0 0 0 4 62 I 66 0.0606

------Total I 2137 841077 154182 39156 67 I 1036619 ErrorO I 0.4778 0.0266 0.0448 0.2794 0.07461 '

ErrorO = Errors of Omission (expressed as proportions) ErrorC = Errors of Commission (expressed as proportions)

90% Confidence Interval = +/- 0.0007 (0.2338 - 0.2351) 95% Confidence Interval = +/- 0.0008 (0.2337 - 0.2353) 99% Confidence Interval = +/- 0.0010 (0.2335 - 0.2355)

KAPPA INDEX OF AGREEMENT (KIA)

Using 2013_image as the reference image ...

Category KIA

------1 0.9388 2 0.7319 3 0.7792 4 0.9281 5 0.7833

Ground truth

Category KIA

1 0.9821 2 0.9603 3 0.9171 4 0.6203 5 0.7104

Overall Kappa = 0.83515