The Invasion of Pteronia Incana (Blue Bush) Along a Range of Gradients In

Home , Pteronia

The invasion of Pteronia incana (Blue bush) along a range of gradients in the Eastern Cape Province: It’s spectral characteristics and implications for soil moisture flux

JOHN ODHIAMBO ODINDI

Submitted in fulfilment of the requirement for the degree of

PHILOSOPHIAE DOCTOR

in the Faculty of Science at the Nelson Mandela Metropolitan University

January 2009

Promoter: Professor Vincent Kakembo

Abstract

Extensive areas of the Eastern Cape Province have been invaded by Pteronia incana (Blue bush), a non-palatable patchy invader shrub that is associated with soil degradation. This study sought to establish the relationship between the invasion and a range of eco-physical and land use gradients. The impact of the invader on soil moisture flux was investigated by comparing soil moisture variations under grass, bare and P. incana invaded surfaces. Field based and laboratory spectroscopy was used to validate P. incana spectral characteristics identified from multi-temporal High Resolution Imagery (HRI).

A belt transect was surveyed to gain an understanding of the occurrence of the invasion across land use, isohyetic, geologic, vegetation, pedologic and altitudinal gradients. Soil moisture sensors were calibrated and installed under the respective surfaces in order to determine soil moisture trends over a period of six months. To classify the surfaces using HRI, the pixel and sub-pixel based Perpendicular Vegetation Index (PVI) and Spectral Mixture Analysis (SMA) respectively were used.

There was no clear trend established between the underlying geology and P. incana invasion. Land disturbance in general was strongly associated with the invasion, as the endemic zone for the invasion mainly comprised abandoned cultivated and overgrazed land. Isohyetic gradients emerged as the major limiting factor of the invasion; a distinct zone below 619mm of mean annual rainfall was identified as the apparent boundary for the invasion. Low organic matter content identified under invaded areas was attributed to the patchy nature of the invader, leading to loss of the top soil in the bare inter-patch areas.

The area covered by grass had consistently higher moisture values than P. incana and bare surfaces. The difference in post-rainfall moisture retention between grass and P. incana surfaces was significant up to about six days, after which a near parallel trend was noticed towards the ensuing rainfall episode. Whereas a higher amount of moisture was recorded on grass, the surface experienced moisture loss faster than the invaded and bare surfaces after each rainfall episode.

There was consistency in multi-temporal Digital Number (DN) values for the surfaces investigated. The typically low P. incana reflectance in the Near Infrared band, identified from the multi-temporal HRI was validated by field and laboratory spectroscopy. The PVI showed clear spectral separability between all the land surfaces in the respective multi-temporal HRI. The consistence of the PVI with the unmixed surface image fractions from the SMA illustrates that using HRI, the effectiveness of the PVI is not impeded by the mixed pixel problem. Results of the laboratory spectroscopy that validated HRI analyses showed that P. incana’s typically low reflectance is a function of its leaf canopy, as higher proportions of leaves gave a higher reflectance. Future research directions could focus on comparisons between P. incana and typical green vegetation internal leaf structures as potential causes of spectral differences. Collection of spectra for P incana and other invader vegetation types, some of which have similar characteristics, with a view to assembling a spectral library for delineating invaded environments using imagery, is another research direction.

ACKNOWLEDGEMENTS

I would like to thank the many people whose support in different ways made this thesis possible, Prof. Vincent Kakembo for his enthusiasm, inspiration, guidance and support throughout my doctoral studies. This study could also not have been possible without the NRF grant holders funding he secured.

Dr. Jenipher Gush and the Amakhala Game Reserve Conservation Centre team (Shahid Razzaq, Dr. Nathalie Razzaq, Lauren Le Roux and Giles Gush) for the support during field work.

Dr. Jaques Petersen for statistical support

Mr. Peter Bradshow and Ms Phozisa Mamfengu of SANParks – Park Planning and Development for providing GIS data and advice.

Staff in the Geosciences department for always being there for me. Special thanks goes to Willy Deysel and Paul Baldwin for making sure that everything I needed for my laboratory work was available.

My Colleagues and friends (Mhangara, Nyamugama, Manjoro, Dhliwayo, Mengwe, Nohoyeka, Mamfengu, Onyancha, Kleyi and Gitonga) for providing a stimulating environment to learn and grow.

My brother Dr. Mak’Ochieng and his family for all the sacrifices.

My immediate and extended family, particularly my parents and my first cousin Peter Were for making me who I am.

Generous funding from NRF grant holder bursary and the NMMU postgraduate funding from the Research Office is hereby appreciated.

To God for the strength and determination iii

TABLE OF CONTENTS

Page ABSTRACT i ACKNOWLEDGEMENTS iii TABLE OF CONTENTS iv APPENDICES viii LIST OF FIGURES ix LIST OF TABLES xii LIST OF ACRONYMS xiii

Chapter 1: General introduction 1.1 Introduction 1 1.2 The research problem 2 1.3 Aim of the study 3 1.4 Specific objectives 3 1.5 Chapter outline 5

Chapter 2: Plant invasions across gradients, hydrological response and spectral characteristics: A theoretical background 2.1 Introduction 7 2.2 Plant invasions across ecological and physical gradients 7 2.3 P. incana : Origin, floristic structure and invasion implications 9 2.4 Relationship between soil moisture and vegetation patchiness 10 2.4.1 Moisture retention: Implications for invasion control and restoration of invaded areas 12 2.4.2 Techniques for monitoring soil moisture flux 12 2.4.2.1 Capacitance moisture probes 13 2.5 Classification of P. incana invaded surfaces using pixel and sub-pixel based techniques 14 2.5.1 Separation of P. incana using ratio based indices 14 2.5.2 Perpendicular Vegetation Indices 15 2.5.3 Pixel and sub-pixel based techniques 16

2.5.4 Endmember selection, validation and applicable resolutions 18 2.6. The use of spectroscopy for validation of surface reflectance 20 2.6.1 Role of spectroscopy in remote sensing 20 2.6.2 The spectroscopy process 21 2.6.3 In-situ versus laboratory spectroscopy 22 2.6.4 Spectral reflectance at different wavelengths 23 2.6.5 Importance of spectral derivatives 26 2.7 Summary 26

Chapter 3: P. incana occurrence across a range of gradients 3.1 Introduction 28 3.2 Major gradients within the transects 29 3.2.1 Geological formations 30 3.2.2 Land use types 30 3.2.3 Vegetation types 30 3.2.4 Rainfall 31 3.3 Methods 33 3.4 Results 35 3.5 Discussion 39 3.5.1 P. incana invasion and the underlying geology 39 3.5.2 Land use and P. incana invasion 39 3.5.3 Disturbance as a cause of invasion 40 3.5.4 Isohyet gradient and P. incana invasion 41 3.5.5 P. incana invasion and soil characteristics 42 3.6 Conclusion 43

Chapter 4: Hydrological response of P. incana invaded areas: implications for landscape functionality 4.1 Introduction 44 4.2 The study area 45 4.3 Materials and methods 47 4.3.1 Capacitance sensor: Theory and instrumentation 47 4.3.2 Sensor calibration 48

4.3.3 Field installation 49 4.3.4 Data presentation and analysis 50 4.4 Results and discussion 50 4.4.1 Moisture variations 50 4.4.2 Episodic moisture flux 51 4.4.3 Soil moisture trends 54 4.4.4 Day/night moisture oscillations 58 4. 4.5 Implications of P. incana invasion for landscape function 62 4.5 Conclusion 63

Chapter 5: A comparison of pixel and sub-pixel based techniques to separate P. incana invaded areas using multi-temporal High Resolution Imagery 5.1 Introduction 64 5.2 The study area 66 5.3 Methods 68 5.3.1 High Resolution Imagery acquisition 68 5.3.2 Image rectification 69 5.3.3 Image enhancement 70 5.3.4 Multi-temporal image analyses 70 5.3.5 Surfaces sample spectroscopy 76 5.4 Results 77 5.5 Discussion and conclusion 85

Chapter 6: The use of laboratory spectroscopy to establish Pteronia incana spectral trends and its separability from bare surfaces and green vegetation 6.1 Introduction 87 6.2 The study area 89 6.3 Materials and methods 91 6.4 Results and discussion 94 6.5 Conclusion 101

Chapter 7: Synthesis 7.1 Introduction 102 7.2 P. incana invasion correlation with macro-scale gradients 102 7.3 P. incana invasion and soil moisture flux 103 7.4 P. incana spectral characteristics 104 7.5 Application of pixel and sub-pixel based classifications in P. incana invaded areas 104

References 107

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APPENDICES

Appendix A: P. incana canopy mixtures with respective leaves to branch ratios 141

Appendix B: Green vegetation, bare soil and P. incana monthly sample reflectance spectra 142

Appendix C: First order derivatives of the monthly reflectance spectra 145

Appendix D: Portion of calibrated sensor moisture logs for the three episodes at 1hr interval 148

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LIST OF FIGURES

Figure 2.1: Pteronia incana invader shrub 10

Figure 2.2: The influence of terrain on moisture infiltration 11

Figure 2.3: Perpendicular Vegetation Index (PVI) 15

Figure 2.4: Vegetation spectra and portions that react to different plant components 22

Figure 2.5: Spectral response of soils at oven dried, 0.03, 0.12, 0.20, 0.30 and 0.42 gravimetric water contents (g/g) 25

Figure 3.1 Transects and GPS invasion nodes 34

Figure 3.2: P. incana invaded nodes on underlying geology 35

Figure 3.3: P. incana invaded nodes on landuse types 36

Figure 3.4: P. incana invaded nodes on vegetation types 37

Figure 3.5: P. incana invaded nodes on mean annual precipitation 38

Figure 4.1: The study site at Amakhala Game Reserve 46

Figure 4.2: Correlation between Volumetric Water Content ( θv)

using oven-dried weights and the probe outputs 49

Figure 4.3: Probe response to precipitation episodes during the six months study period 51

Figure 4.4 a-c: Soil moisture flux for the selected rainfall episodes 52-53

Figure 4.5 a-c: Moisture measurements at rainfall onset, break point and lowest amount recorded 54-55

Figure 4.6 a-f:Day and night soil moisture oscillations before and after break-points 59-61

Figure 5.1: Location of the study area 67

Figure 5.2: Densely invaded patches around the study area 68

Figure 5.3 a-c: Geo-rectified band composites 70-71

a: 2001 green, red and NIR band composite 70

b: 2004 green, red and NIR band composite 71

c:2006 green, red and NIR band composite 71

Figure 5.4 : A flow-diagram of image data acquisition and processing 75

Figure 5.5: Residual values based on P. incana residual images 76

Figure 5.6 a-f: PVI images and respective classes 77-78

Figure 5.7a: A 2001 image of different surfaces DN clusters in a NIR-red plot 79

Figure 5.7b: A 2004 image of different surfaces DN clusters in a NIR-red plot 79

Figure 5.7c: A 2006 image of different surfaces DN clusters in a NIR-red plot 80

Figure 5.8 a – c: Multi-temporal surface endmembers 80-81

Figure 5.9a – f:Multi-temporal P. incana image fractions and P. incana boolean images with training sets from PVI images 82-83 x

Figure 5.10a – c: Spectral samples measurements between October 2007 and January 2008 83-84

Figure 6.1: Pteronia incana (Blue bush) invasion in the study area 89

Figure 6.2: Location of the study area 90

Figure 6.3 a and b: Sample ratios and canopy surfaces reflectance values 94

Figure 6.4: The influence of increasing proportion of leaves on reflectance at 0.55 µm, 0.65 µm and 0.88 µm wavelengths 96

Figure 6.5: Green vegetation, bare soil and P. incana monthly interval samples reflectance 97

Figure 6.6: Reflectance differences between the respective surfaces 98

Figure 6.7: Surface reflectance means for the six months data set 99

Figure 6.8: Spectra for the six months reflectance 1 st order derivative 100

LIST OF TABLES

Table 3.1: Major characteristics of the invaded nodes within transect A 32

Table 3.2: Physical and chemical characteristics of soils at invaded and uninvaded sites 39

Table 4.1: Surface soil moisture value threshold ranges and break-points 57

Table 4.2: Surface moisture slope angles and y-intercepts before and after breakpoints 58

Table 4.3: Day/night moisture standard deviations before and after break-points 59

Table 5.1 a - c: Error matrices 2001, 2004 and 2006 imagery 74

Table 6.1: Leaf to branch weights and proportions 93

Table 6.2: Branch to leaf proportions and P. incana canopy reflectance at different wavelengths 97

Table 6.3: Sample reflectance t-tests, p-values, means and standard deviations 98

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LIST OF ACRONYMS

AGR - Amakhala Game Reserve APAR - Absorbed Photosynthetic Active Radiation ASTER - Advanced Spaeceborne Thermal Emission Radiometer CASI - Compact Airborne Spectrographic Imager CLSMA - Constrained Linear Spectral Mixture Analysis DEM - Digital Elevation Model EMS - Electromagnetic Spectrum FD - Frequency Domain GCPs - Ground Control Points GIS - Geographical Information System GPS - Global Position System GV - Green Vegetation HRI - High Resolution Imagery IFOV - Instantaneous Field of View KIA - Kappa Index of Agreement LAI - Leaf Area Index LMM - Linear Mixture Model LPU - Linear Pixel Unmixing LSMA - Linear Spectral Mixture Analysis LSU - Linear Spectral Unmixing MLC - Maximum Likelihood Classification MSAVI - Modified Soil Adjusted Index MSE - Mean Square Error NDVI - Normalised Difference Vegetation Index NIR - Near Infrared NP - Neutron Probe NPV - Non-Photosynthetic Vegetation PCA - Principal Component Analysis PVI - Perpendicular Vegetation Index RMS - Root Mean Square RMSE - Root Mean Square Error SAVI - Soil Adjusted Vegetation Index xiii

SMA - Spectral Mixture Analysis SR - Simple Ratio TDR - Time Domain Reflectometry VIs - Vegetation Indices WI - Wetness Index VWC - Volumetric Water Content

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Chapter 1: General introduction

1.1 Introduction

Plant species invasion has been identified as a major threat to ecosystems worldwide (see; Wilcove et al. , 1998; Richardson et al. , 1998; van Wilgen, 2001). Diverse local effects have been identified as causes of broad landscape implications, which include among others transformation of forests to grasslands in the Amazon (D’Antonio and Vitousek, 1992), increased surface runoff causing massive erosion like P. incana (Blue bush) in the Eastern Cape, South Africa (Kakembo, 2003) and change in fire regimes in western Australia (Christensen and Burrows, 1986). Such ecosystem threats have led to an increased search for control and restoration methods that may enhance ecological and socio-economic stability. However, due to diversity in invader species and invaded environments, there are no standard methods for invasion control and management. Consequently, different scale dependent invasion scenarios and species will require different management approaches (van Wilgen et al ., 2001; Kakembo, 2003).

Due to diverse interacting variables that determine an ecological process, it is difficult to determine a specific scale at which an ecological phenomenon can be investigated (Farina, 1998). A multiplicity of scales is therefore often preferred to better understand an ecological process (Rouget and Richardson, 2003). Ultimately however, proposed methods for mitigation of plant species invasion and tools to meet information needs for invasion management at both micro and macro scales have to be site and case specific (Waring and Running, 1999; Rouget and Richardson, 2003). In this study, a combination of geo-information techniques and ground based methods at local and landscape scales are used to provide an in-depth understating of the invasion of the Eastern Cape environments by P. incana , a patchy vegetation species indigenous to the dry Karoo conditions.

Earlier studies by Kakembo et al. (2006) and Palmer et al . (2005) using advanced Space-borne Thermal Emission Radiometer (ASTER) and High Resolution Imagery provided clear distinction between green vegetation and other surface cover types.

However areas invaded by P. incana in both studies were not readily separable from bare surfaces. Whereas there is a possibility to further explore existing remote sensing techniques that can be used to delineate surfaces invaded by P. incana , coupling geo- information techniques with investigations of physical and ecological factors associated with P. incana invasion would provide a better understanding of the invasion dynamics . Besides, a survey of P. incana invasion across a range of gradients viz : land-use, precipitation, vegetation, geology and soil would provide the basis for management of the invasion . The present study seeks to separate P. incana from other surfaces using remote sensing techniques and spectroscopy, establish its occurrence across a range of gradients and determine the hydrological response of the invaded surfaces. As pointed out by Hobbs and Hopkin (1990), an understanding of conditions that promote invasions and encourage the establishment of native species, forms an important component for developing procedures to manage plant species invasion.

1.2 The research problem

A number of areas in the rangelands of the Eastern Cape Province have been adversely affected by invasive plant species. P. incana in particular has steadily invaded several catchments in communal areas, commercial and game farms. The first step to the management and restoration of such areas would be a reliable delineation of invaded surfaces from other land cover types. Due to P. incana’s apparent spectral uniqueness, earlier efforts to use Normalised Difference Vegetation Index (NDVI) commonly used for above ground green biomass mapping were unsuccessful (see Kakembo, 2003, Kakembo et al . 2006, Palmer et al ., 2005). Although the Perpendicular Vegetation Index (PVI) has previously been successful in separating P. incana invaded areas from other surface types using a once off scene (Kakembo, 2003), its multi-temporal consistency and replicability has not been tested. Besides pixel based techniques, pixel un-mixing, field and laboratory spectroscopy are other techniques that need to be explored.

In addition to problems associated with separating P. incana invaded surfaces, changes in surface vegetation cover caused by the invasion may engender a range of biophysical deteriorations. These changes may include among things, the alteration of 2 surface and sub-surface moisture budgets which may in turn inhibit the competitiveness of resident species. Whereas observations have shown that changes in moisture budgets typify P. incana invaded surfaces, the hydrological response of these surfaces to rainfall events has not been established. On the basis of the gaps in knowledge outlined above, the key research questions to be addressed by this study are:

i) What is the pattern of P. incana occurrence across a range of gradients?

ii) What is the hydrological response of P. incana invaded surfaces as compared to grass and bare surfaces?

iii) What is the ideal wavelength for separating P. incana from bare surfaces and green vegetation cover types?

iv) Can consistency be achieved in separating P. incana invaded areas using multi-temporal High Resolution Imagery (HRI)? Are sub-pixel techniques more effective than pixel ones in P. incana separation using HRI?

1.3 Aim of the study

The aim of the study is three pronged viz : to establish the spectral characteristics of P. incana , assess its relationship with a range of variables and determine its impacts on the soil moisture regime.

1.4 Specific objectives i) To establish the occurrence of P. incana across a range of gradients.

This objective was achieved by surveying a transect across land use, isohyetic, geologic, vegetation, pedologic, topographic and altitudinal gradients. Presence/absence invasion nodes across each gradient were then recorded using Global Position System (GPS) co-ordinates. Additional information on soil pH

and organic matter was used to determine the relationship between local soil conditions and P. incana invasion. ii) To compare soil moisture flux under a P. incana invaded surface, grass cover and bare areas and assess the implications for landscape function.

To achieve this objective, capacitance moisture sensors were used to compare moisture flux on a P. incana invaded surface, a bare area and a grass patch over six months. Moisture flux was monitored after each rainfall episode and duration to inflection points between moisture gain and loss during wet and dry periods were determined. iii) To compare pixel and sub-pixel based techniques to separate P. incana invaded areas using multi-temporal imagery

A DCS 420 high resolution colour infra-red camera on an aerial platform was used to acquire multi-temporal high resolution imagery from P. incana invaded surfaces and associated cover types. Several image correction techniques were adopted and a pixel based technique (Perpendicular Vegetation Index-PVI) and sub-pixel based technique (Constrained Linear Spectral Mixture Analysis – CLSMA) were used to compare consistency in P. incana separation. Spectroscopy and Kappa Index of Agreement (KIA) were used to validate the results. iv) To determine appropriate wavelengths for separating P. incana from other cover types using spectroscopy.

This objective was achieved by laboratory and field based spectroscopy of P. incana , bare soil and green vegetation over a six months period. Different P. incana spectral responses were simulated using a diverse range of branch to leaf ratios. Monthly P. incana canopy reflectances were then compared with bare soil and green vegetation reflectance. First order derivatives of reflectance were further used to separate spectra for different cover types.

1.5 Chapter outline

Chapter 1: General introduction

This chapter provides an overview of issues related to P. incana invasion as well as remote sensing and ground based techniques applicable to P. incana invasion. Major research questions arising from the research problem are presented, as well as the overall aim of the study, specific objectives and a brief description of how they were achieved. The section concludes by providing a chapter outline.

Chapter 2: Plant invasions across gradients, hydrological response and spectral characteristics: A theoretical background

The chapter reviews literature on landscape invasion processes and implications. Literature on methods applied to aerial platform high resolution imagery and hyperspectral remote sensing techniques used in separating P. incana surfaces from other land cover types is provided. Major factors determining plant species existence and sustainability are identified.

Chapter 3: P. incana occurrence across a range of gradients

P. incana occurrence was surveyed across land use, isohyetic, geologic, vegetation, pedologic and altitudinal gradients. Nodes across each gradient were recorded using Global Position System (GPS) co-ordinates. Additional information on soil pH and organic matter was used to determine the relationship between local soil conditions and P. incana invasion.

Chapter 4: Hydrological response of P. incana invaded areas: implications for landscape functionality

Soil moisture flux trends were monitored over a period of six months and wet - dry points of moisture inflection between selected rainfall episodes were presented. The chapter concludes by discussing the implications of P. incana invasion for landscape

5 function and provides options for restoration and management of P. incana invaded surfaces.

Chapter 5: A comparison of pixel and sub-pixel based techniques to separate P. incana invaded areas using multi-temporal High Resolution Imagery

This chapter explains the procedures used to acquire, rectify, analyse and validate High Resolution Imagery (HRI) from aerial platforms. The Perpendicular Vegetation Index (PVI) and Spectral Mixture Analysis (SMA) techniques were applied. Spectroscopy was used to validate spectral trends identified from HRI.

Chapter 6: The use of laboratory spectroscopy to establish Pteronia incana spectral trends and its separability from bare surfaces and green vegetation

P. incana spectral trends at different wavelengths as determined by changes in branch to leaf ratios are presented in this chapter. The chapter also presents a comparison of green vegetation, bare surfaces and P. incana spectra over a six months period. The chapter is concluded by presenting P. incana , bare surfaces and green vegetation spectral separation using first order derivatives of reflectance.

Chapter 7: Synthesis

The chapter brings the different strands of the respective chapters together and provides conclusions based on the findings of the study. Directions for further research are also suggested.

Chapter 2: Plant invasions across gradients, hydrological response and spectral characteristics: A theoretical background

2.1 Introduction

In order to provide insights into existing literature on plant invasions, this review focuses on vegetation remote sensing and bio-physical aspects related to plant species invasion. This chapter is made up of four parts. The first part reviews literature on factors influencing plant species invasion at regional scales as well as the use of transects as means of gaining an understanding of invasion occurrence across a range of gradients. The second part looks at the implication of invasion processes for soil moisture; it reviews existing perspectives on plant species invasion and moisture flux on grass, bare surfaces and P. incana invaded areas. The third part looks at remote sensing using imagery, particularly the use of Perpendicular Vegetation Index (PVI) and Spectral Mixture Analysis (SMA) in surface cover analysis. Endmember selection processes as well as possible spatial resolutions for SMA are also discussed. The last part of this chapter reviews the use of spectroscopy in separating different vegetation types and moisture influence on surface spectra. This part also reviews the use of first order derivatives in identifying spectral differences between surfaces. Notwithstanding brief re-assessment of relevant literature in each of the subsequent chapters modelled on publication format submissions, this chapter provides extended reviews of literature relevant to the study. A stand alone review chapter is therefore provided to bring together the different strands of the theoretical frameworks relevant to respective chapters. It is therefore inevitable that some aspects covered in this chapter are repeated in subsequent chapters.

2.2 Plant invasions across ecological and physical gradients

The effects of plant species invasion have led to an increased search for causative and best possible ways of invasion management. Invasive plant species have been identified by a number of authors as one of the biggest causes of habitat transformation and consequent threat to species biodiversity (Walker and Vitousek, 1991; Le Maitre et al ., 1996; Burgman and Lindenmayer, 1998; Mack et al., 2000;

Alvarez and Cushman, 2002; Hoffman et al., 2004). In an attempt to gain insights into plant species invasion at different scales, some researchers have identified species data (e.g. Freitag et al ., 1997) or plants species and habitats (e.g. Fairbanks and Benn, 2000; Reyers et al ., 2001) as two major classes for landscape analysis. However, methods that combine both species and habitat data are increasing in popularity (see; Noss et al ., 1990; Cowling et al., 1999).

A number of studies (Woodward and Cramer, 1996; Smith et al ., 1997; Diaz and Cabido, 1997; Pyankov et al., 2000) have emphasised the relationship between plant functional types at regional level and along specific ecological and physical gradients. Comparisons of areas within a region are principally important in understanding the ecological processes, as differences in topography, micro-climate, or previous land use can create distinct ecological patterns (Pauchard et al ., 2004). As opposed to experimental studies that emphasise the isolation of variables, holistic consideration at broader scales provide better understanding of invasion processes (Hiero et al., 2005). Such holistic approaches shed light on plant response to varying ecological, physical, climatic and anthropogenic variables (Ni, 2003).

Recent developments in geo-information techniques, as well as increasing availability of environmental and geophysical digital data have led to better testing and improvement of qualitative and quantitative mapping of species distribution (Brotons et al., 2004). Consequently, a number of studies have taken advantage of growth in geo-information techniques for resource mapping, monitoring and management (see: Amissah-Arthur et al., 2000; Gough and Rushton, 2000; Neke and Du Plessis, 2003; Muñoz and Felicísimo, 2004).

Within a biome, plant species invasion is rarely continuous and is influenced by a range of factors like edaphic, microclimatic and human disturbance regimes (Fox and Fox, 1986; Lindenmayer and McCarthy, 2001). According to Wace (1977), identification of the most important factors influencing variations in invasion therefore becomes the first step in understanding current and future invasion trends that can be used to design mitigation programs. Following the identification of factors influencing invasion at a given scale is the choice of appropriate data collection methods. The use of quadrats and belt transect sampling methods have become useful 8 in a wide range of vegetation studies (Cox, 1990). These methods are popular because they can be used to acquire data more rapidly in their natural setting (Fidelbus and MacAller, 1993). Since plant distribution is often in patch form, the use of transects in large scale studies is one of the feasible approaches (Buckland et al ., 2007). Whereas random systematic sampling methods are commonly used within transects (Eberhardt and Thomas, 1991), the technique chosen will often depend on the type of data required, sample sizes and the available manpower (Eberhadt and Thomas, 1991).

Cover density and frequency form an important part in the belt transect sampling data acquisition process. Density is often determined by the number of plants in a specified area and can be determined by mean vegetation cover per surface (Fidelbus and MacAller, 1993). Frequency on the other hand is the relative presence or absence of a given species and will be affected by the size of the quadrat or belt transect used (Fidelbus and MacAller, 1993). A number of studies (Davis et al , 1998; Dukes, 2001; Küffer et al ., 2003; Fu et al ., 2003) have identified precipitation and moisture as important factors influencing vegetation density and frequency.

2.3 P. incana : Origin, floristic structure and invasive implications

P. incana is a perennial shrub belonging to the Asteraceae family and indigenous to the dry Karoo biome. It is officially documented in South Africa as a harmful plant invader species. The shrub has a thick lower woody stem with highly dendrite branches (Figure 2.1). Leaves are generally green with hairy bluish white covering hence common name Blue bush. The shrub is commonly propagated by seeds that are easily dispersible by wind or animals. According to Smith (1966) as quoted by Kakembo (2003), P. incana was sited in Albany district as early as 1850s and is suspected to have originated from Klein Karoo. The shrub was however first declared an invader in the 1930s in Alexandria division. Current field observations show that P. incana thrives across a diverse range of gradients in the Eastern Cape.

Figure 2.1: Pteronia incana invader shrub

Based on field observations, P. incana has a range of known undesirable characteristics among others un-palatability to browsers and superior competition in rangelands leading to single species dominance. P. incana invaded sites have also been identified as niche areas for rill and gully formations and subsequent degeneration into badlands.

2.4 Relationships between soil moisture and vegetation patchiness

A number of theories have been put forth to explain invasion processes. These include among others resource ratio theory (MacAthur, 1972; Tilman, 1982; Levine and D’Antonio, 1999; Miller et al ., 2005), species diversity theory (Elton, 1958) and fluctuating resource theory (Davis et al ., 2000). In reference to resource ratio and fluctuating resource theories, “resources” are emphasized as one of the key factors that determine plant species recruitment and invasion processes. These resources include among others soil nutrient, soil moisture and sunlight. However, soil moisture availability is regarded as the most important resource that determines plant species invasion (Fu et al ., 2003; ICT227, 2007).

At local scales, soil moisture’s influence on ecological and species coverage is influenced by a wide array of physical characteristics like sedimentation (Cammeraat 10 and Imeson, 1999), slope gradient (Eddy et al. , 1999; Zonneveld, 1999; Dunkerley and Brown, 1995), aspect (Leprun, 1999), soil surface conditions (Dunkerley and Brown, 1995) and amount of vegetation cover (Galle et al. , 1999; MacDonald et al ., 1999). At broader scales, soil moisture is determined by climate, geology, topography, soils, vegetation and land-use (Hawley et al. , 1983; Burt and Butcher, 1985; Le Roux et al ., 1995; Fu et al ., 2003). Slope gradient and surface disturbance to a large extent determine surface runoff, infiltration and therefore stability or replacement of existing resident vegetation (Tongway and Hindley, 1995; Pauchard and Alaback, 2004; Kakembo, 2007).

According to Tongway and Hindley (1995), run-on enhances resident species stability due to high water infiltration and therefore higher nutrient cycling while areas of runoff gives rise to instability of resident species due to low infiltration and high erosion (Figure 2.2). It is such areas of low infiltration and consequent low soil moisture content that are highly vulnerable to invasion (Kakembo et al., 2007).

Figure 2.2: The influence of terrain on moisture infiltration (Adapted from Tongway and Hindley, 1995).

Soil surface moisture variations in P. incana invaded areas have been analysed using the Wetness Index (WI), a component of the TOPMODEL extracted from a Digital Elevation Model (DEM) (see; Kakembo et al ., 2007). Whereas very little is understood on short and medium term moisture fluxes between P. incana invaded sites and grass patches, a number of studies (Seghieri et al ., 1997; Galle et al., 1999; Valentin and d’Herbés, 1999) have documented the relationship between soil moisture and vegetation patchiness.

2.4.1 Moisture retention: Implications for invasion control and restoration of invaded areas

Manual clearance and burning are not appropriate rehabilitation options on P. incana invaded rangelands, as they expose crusted soil surfaces to longer run-off trajectories and consequent erosion (Kakembo, 2003). According to Palmer et al. (2005), use of fire reduces biodiversity by destroying species seed banks and standing vegetation. Sediment and litter trapping on the other hand has been known to significantly increase soil moisture levels (Kakembo, 2003; Ludwig et al., 1999) and was identified by Kakembo (2003 and 2007) as a key factor in grass species recruitment on P. incana invaded surfaces. Whereas it is acknowledged that moisture entrapment and accumulation may lead to recruitment of grass species and maintenance of grass patches, a gap still exists in our understanding of the hydrological implications of P. incana invaded surfaces. Recommendation of moisture elevation as a means of P. incana invasion management can only be validated after medium to long-term monitoring using appropriate soil moisture measurement equipment and techniques.

2.4.2 Techniques for monitoring soil moisture flux

Thermo-gravimetric soil moisture measurement, one of the most accurate methods for determining soil water content, involves comparison of the ratio of mass of water in a soil sample after it has been oven dried at 100-110oC to a constant weight (Muñoz- Carpena, 2004). Whereas this method is highly accurate and inexpensive, it is destructive, slow, and does not allow for same-site repetitive sampling (Baumhardt et al ., 2000; Muñoz-Carpena, 2004). This makes it inappropriate for most moisture measurement applications including multi-temporal single site moisture monitoring.

Since their establishment in 1980s, soil moisture sensors have emerged as important soil moisture measurement tools (Evett and Parkin, 2005). Soil moisture sensors are broadly categorized as either volumetric or tensiometric methods (Muñoz-Carpena, 2004). Both sensor types are used to determine the volume of water in a specified amount of undisturbed soil and can easily be compared with other hydrologic variables like precipitation (Mayamoto et al. , 2003; Muñoz-Carpena, 2004). Both forms of instrumentation are grouped as Neutron Probes (NP), Time Domain 12

Reflectometry (TDR) or Frequency Domain (FD) – Capacitance (see; Robinson et al. , 1999, Muñoz-Carpena, 2004 and ICT227, 2007).

The biggest advantage of the existing commercial moisture sensors in comparison to traditional oven drying techniques is their ability to measure temporal moisture fluxes with minimal soil disturbance (Evett and Parkin, 2005). Consequently, use of moisture sensors has become the most practical means of soil moisture measurement (Robinson et al ., 1999).

2.4.2.1 Capacitance moisture probes

Capacitance probes (used in this study) are among a suite of available indirect moisture measurement techniques. These probes make use of the dielectric permittivity of a medium as a function of its charge time (McMichael and Lascano, 2003). Since the electric constant of water, solid soil and air are about 80, 4 and 1 respectively, capacitance probes are highly sensitive to soils with varying degrees of water (Dean, 1994; Geesing et al. , 2004; Decagon Devices Inc., 2007). However, due to low operating frequencies of capacitance devices, soil specific calibration is often recommended as readings may change with temperature, salinity, bulk density and amount of clay (Dean, 1994; Baumhardt et al ., 2000; Czarnomski et al ., 2005).

Capacitance probes have several advantages over many existing soil moisture sensors; they are accurate with soil specific calibration, relatively inexpensive, sensitive to high salinity levels, usable with conventional data loggers and are more robust and flexible than most other moisture measurement devices (Muñoz-Carpena, 2004). However, capacitance sensors need soil specific calibration and careful installation to avoid air gaps (Muñoz-Carpena, 2004). For a better understanding of moisture flux on a mosaic of bare surfaces, invaded areas and remnant grass patches, parallel moisture measurements are necessary. Whereas a number of existing techniques can be used to delineate the above mentioned surfaces, the use of remote sensing, which is fast emerging as an important tool in surface cover mapping is reviewed in the section below.

2.5 Classification of P. incana invaded surfaces using pixel and sub-pixel based techniques

2.5.1 Separation of P. incana using ratio based indices

Measurement of bio-physical form, type and status is an important process in land use planning, landscape monitoring and species mapping (Brookes et al. , 2000; Jensen, 2005). Since the 1960s, scientists have successfully developed mathematical models that can be used to determine different states of vegetation (Asner et al ., 2003; Lillesand et al ., 2004; Jensen, 2005). The most commonly used techniques under such models are the various types of Vegetation Indices (VIs) (Asner et al. , 2003; Jensen, 2005). These indices estimate among others leaf area (LAI), percentage green cover, chlorophyll content, green biomass, Absorbed Photosynthetic Active Radiation (APAR) and canopy type and architecture (Jensen, 2005; Piwowar, 2005). Commonly used VIs are often dimensionless and used for radiometric applications on remote sensing imagery that distinguish vegetation abundance and condition from other materials (Jensen, 2007). The growing popularity of remote sensing applications has seen an increase in VIs (see; Running et al. , 1994, Lyon et al ., 1998, Asner et al ., 2003 and Jensen, 2005).

Most of the commonly used vegetation indices make use of the unique healthy vegetation reflectance in the visible (VIS) and near infrared (NIR) sections of the electromagnetic spectrum. The application of popular vegetation indices like Simple Ratio (SR) and Normalized Difference Vegetation Index (NDVI) are limited to isolation of green biomass from other background material (Asner et al . 2003; Lillesand et al., 2004). However, trial applications of these indices and other similar ratio based indices failed to separate P. incana from wet and dry bare surfaces and senescing vegetation (see Kakembo, 2003; Kakembo et al ., 2007). On the other hand, separation of P. incana from other surfaces was achieved using Perpendicular Vegetation Indices (PVI) in the same studies. The PVI is explored further in the section below.

2.5.2 Perpendicular Vegetation Indices (PVI)

Perpendicular vegetation indices (PVI) originated from the works of Kauth and Thomas (1976) and Richardson and Wiegand (1977) who used a red and near infrared band correlation from Landsat imagery to differentiate vegetation from soil. It is based on Gram-Schmidt orthogonalization and identifies points of maximum greenness that is perpendicular to the soil line (Sunar and Taberner, 1995; Akkartal et al. , 2004). According to Akkartal et al. (2004) the efficacy of PVI (Figure 2.3) in differentiating vegetation cover from background soil effects is due to the red/infrared band combination absorption of iron oxide present in many soils.

Figure 2.3: Perpendicular Vegetation Index (PVI), showing a perpendicular measure of vegetation from the soil base line. In this example point A has a higher PVI and therefore higher vegetation density than point B. (Source: Canadian Centre for Remote Sensing website)

Perpendicular Vegetation Index (PVI) is analytically superior to the SR index and NDVI as it fully accounts for the background soil, reduces the effects of differences in solar zenith and accounts for topographic differences (Jensen, 1996; Asner et al., 2003). It is expressed as:

PVI = ()NIR − aR + b a 2 +1 (1)

Where a and b are the slope and offset of the soil line respectively.

2.5.3 Pixel and Sub-pixel based techniques

Conventional indices like NDVI, Modified Soil Adjusted Vegetation Index (MSAVI) and PVI display pixel content by aggregating the spectra for all the components within a pixel (Pu et al. , 2003; Lu et al ., 2003a). Such indices fail to fully account for landscape mosaics as smaller land cover types are concealed (DeFries et al ., 2000). Consequently, pixel based techniques offer reliable but less accurate estimation of land cover surfaces, as heterogeneous classes within pixels are grouped to single classes (Foody, 1996; Huguenin et al. , 1997; Tompkins, 1997; DeFries et al ., 2000; Wu et al. , 2002; Pu et al. , 2003). According to Adams and Gillespie (2006), almost all landscape reflectance values are a mixture of different spectra with visual purity emanating from dominance of a single or a few spectra over others. This can be accounted for by the reflectance of different materials within an Instantaneous Field of View (IFOV) (Lillesand et al ., 2004). Since the determination of precise proportions is often daunting, Clark’s law often applies (see Adams and Gillespie, 2006).

The Spectral Mixture Analysis (SMA) also referred to as Linear Spectral Unmixing (LSU), Linear Spectral Mixture Analysis (LSMA), Linear Mixture Model (LMM) or Linear Pixel Unmixing (LPU) (Bateson and Curtiss, 1996; Lu et al ., 2003b; Zhu, 2005; Omran et al., 2005) is one of the existing sub-pixel based or “soft classifier” techniques used to decompose pixels into their components and is often suggested for more accurate land cover mapping (Smith et al ., 1990; Tompkins, 1997; Erol, 2000; McGwire et al ., 2000; Pu et al. , 2003; Omran et al., 2005; Palaniswami et al. , 2006). This model involves de-convolution of proportional cover based on spectral reflectance of endmembers used as references (Zhu and Tateishi, 2001; Omran et al. , 2005). The output is endmember image fractions and residual images with root mean- square of each pixel fit (Huguenin et al ., 1997; Gross and Schott, 1998). These results provide an estimation of a pixel’s ground area represented by each reference classes (Lillesand et al ., 2004). Whereas accurate reference class measurements are possible, SMAs are mainly used as aids to imagery analysis and interpretation (Adams and Gillespie, 2006).

Spectral Mixture Analysis (SMA) differs from PVI in three ways; it can be used to establish wide variety of major imagery cover types fairly easily (Bateson and Curtiss, 1996; Asner et al ., 2003) and sub-pixel spectrally unique materials can be distinguished (Bateson and Curtiss, 1996). Spectral Mixture Analysis (SMA) has several advantages over pixel based classification methods; it changes Digital Number (DN) values to specific elements within a pixel, can be used to identify various elements within a pixel and can be used to provide individual land cover distribution within an image (Tompkins et al ., 1997; Adams and Gillespie, 2006). According to Adams and Gillespie (2006), it is easier to correlate field covers to unmixed pixel fractions as DNs can be converted to numerical fraction using representative endmembers. Unlike VIs, SMA can be used on any band combinations from multispectral (Lu et al ., 2004), hyper spectral (Miao et al. , 2006; Chen and Vierling, 2006) and even thermal data (Collins et al ., 2001) and are not restricted to any particular wavelengths.

Spectral Mixture Analysis (SMA) model assumes that the reflectance spectrum is a linear combination of the endmembers of materials present in a pixel weighted by their fractional abundance (Adams et al. , 1995; Lu et al. , 2003a; Asner et al ., 2003; Jensen, 2005). It is expressed as:

Ri = ∑ f k Rik + ε i , (2) k =1

Where i is the spectral band used; k = 1, ……., n (number of endmembers); Ri is the spectral reflectance of band i of a pixel which contains one or more endmember; fk is the proportion of endmember k within the pixel; Rik is spectral reflectance of endmember k within pixel on band i and εi is the error of band i.

The proportion of each endmember which is usually between zero and one is the fractional area occupied by each material within a pixel and sums to one (Settle and Drake, 1993; Asner et al. , 2003; Lu et al., 2003a; Adams and Gillespie, 2006). To reflect true abundance fractions of endmembers, constrained unmixing solution is applied where fk is restricted and expressed as:

∑ f k = 1 and 0 ≤ f k ≤ 1. (3) k =1

To test the accuracy and pixel fit of the land cover fractions to the reference image, root mean-square (RMS) or mean square error (MSE) residual images (Miao et al , 2006) are often used. This may be expressed as;

n p 1 2 2 Γx = ∑ ∑ q jσ ij β i (4) p j =1i = 1

where Γx is the mean square error (MSE), n is the number of spectral bands, p is the number of endmembers, q j are the main diagonal elements in the symmetric matrix + + T -1 T 2 (X )T, X =(X X) X , and σ ij are the variance of residual ε. Low RMS or MSE indicate better fractional fit to the reference image (Huguenin et al ., 1997; Mather, 1999; Lu et al ., 2003a).

2.5.4 Endmember selection, validation and applicable resolutions

A proper choice of endmembers determines the reliability of an SMA process (Zhu and Tateishi, 2001; Lu et al , 2003b; Lu et al , 2004). Since only few surface materials can accurately be spectrally distinguished, it is recommended that an SMA process should involve a selection of endmembers that represent few major surface materials (Small, 2001; Sobal et al ., 2002; Lass et al ., 2005). A large body of literature on different endmember selection methods exists (see; Tompkins et al ., 1997; Mustard and Sunshine, 1999; van der Meer, 1999; Maselli, 2001; Lu et al ., 2003b; Theseira et al ., 2003 for summaries of the most commonly used endmember selection methods).

Despite the large number of endmember extraction methods in existence, most researchers prefer image based endmember extraction techniques because they are collected under conditions similar to those of the image (Plaza et al ., 2005) and are easier to extract (Bateson and Curtis, 1996; Roberts et al ., 1998; Palaniswami et al ., 2006). Image based endmembers are also at same scale as imagery to be processed

(Roberts et al., 1998) and eliminate the need for ground measurements, which are often impossible as in case of forest canopy (Asner et al ., 2003). However, several authors (Bateson and Curtiss, 1996; Asner et al ., 2003; Jensen, 2005) observe that it is often difficult to identify sufficiently pure pixels from available remote sensing data scales.

The number of endmembers to be used in an SMA process is determined by data spectral information and the imagery sensors field of view (Asner et al ., 2003; Adam and Gillespie, 2006). Surface materials complexity will increase with an expansion in field of view (Adam and Gillespie, 2006). According to Ustin et al. (1996), two to six endmembers are required for an SMA process regardless of the data spectral detail. Depending on the heterogeneity of land surfaces, two endmembers can be used to provide reliable fractions on most image datasets (Roberts et al ., 1998). Too many endmembers however reduce fractional accuracy as they can easily simulate one another (Adam and Gillespie, 2006). To enhance image classification accuracy, the number of endmembers can be reduced by ignoring the unique spectral data that are not required in the final fractions. The ignored endmembers can then be accommodated in the Root Mean Square (RMS) residual image. Whereas the choice of endmembers may vary from one application to another (see; Plaza et al ., 2004; Amorós-López et al., 2006; Robichaud et al ., 2007), three or four endmembers [i.e. green vegetation (GV), shade and soil or GV, shade, soil and non-photosynthetic Vegetation (NPV)] are commonly used (Sobal et al ., 2002; Pu et al., 2003; Lu et al., 2004; Uenishi et al., 2005).

The SMA procedure starts with qualitative image mapping that may be followed by quantitative image analysis (Adam and Gillespie, 2006). Establishing precise quantitative endmember fractional covers is often difficult, as extractions are based on complex heterogeneous landscapes (Plaza et al ., 2005; Adam and Gillespie, 2006). Since output validation is often a difficult process, factors like the number, quality and spectral variability of endmembers, as well as atmospheric distortions of data become important in determining the accuracy of image fractions (Tompkins et al ., 1997; Asner and Lobell, 2000; Adam and Gillespie, 2006; Palaniswami et al ., 2006). Several researchers however suggest several SMA groundtruthing methods like the use of Maximum Likelihood Classifier (MLC) (Lu et al . 2004), Root Mean Square 19

Error (RMSE)-(Uenishi et al ., 2005), Principal Component Analysis (PCA) - (Miao et al ., 2006), and correlation of GPS registered points with image fractional abundance (Palaniswami et al., 2006).

Spectral Mixture Analysis (SMA) has commonly been used on low spatial resolution remote sensing data (see; van der Meer, 1999; Zhu and Tateishi, 2001; Lu et al., 2004; Uenishi et al ., 2005; Amorós-Lopez et al., 2006 among others). However, the application of SMA on high and medium spatial resolution remote sensing data has been found to be capable of yielding good mapping results (see: Plaza et al. , 2005 - mapping oil spills on sea water using Compact Airbone Spectrographic Imager (CASI) and Miao et al . 2006 – mapping yellow star thistle invasion using CASI-2). According to Jensen (2005), SMAs are applicable to imagery data of any spatial resolution. In addition to more apparent fractions that can be obtained from image data, Jensen (2005) observes that high resolution imagery of 1 x 1m resolution may sometimes require more applicable endmembers to help account for subtle features like pure shadow, sun-glint water or bare soil’s mineral differentiation.

Whereas PVI and SMA have proved valuable in separating surface materials, different distortions caused by un-ideal conditions during image acquisition may give inaccurate reflectance or DN values. The identification, separation and comparison of spectral trends require separation and analysis of individual land surface materials. Field based or laboratory spectroscopy can be used to validate separate reflectance patterns at desired wavelengths. This process which involves the isolation of a material of interest and measuring its reflectance is reviewed below.

2.6 The use of spectroscopy for validation of surface reflectance

2.6.1 Role of spectroscopy in remote sensing

Remote sensing applications in vegetation mapping rely heavily on different materials’ visible (VIS) and near-infrared (NIR) spectral characteristics. Such characteristics have been widely used in remote sensing for both imagery analysis and materials spectroscopy (see: Gitelson et al ., 2002; Van Til et al., 2004 for

20 applications). Field spectroscopy or laboratory measurements have emerged as important remote sensing tools for data acquisition and field validation (Smith et al ., 1990; Everitt et al. , 2002; Jensen, 2005; Piekarczyk, 2005; Leuning et al., 2006; Adams and Gillespie, 2006). This method can be used to convert imagery radiance to reflectance, improve mapping analysis and modelling accuracies (Goetz and Srivastava, 1985; McCoy, 2005), and for image acquisition reconnaissance purposes (Analytical Spectral Devices, 2008).

2.6.2 The spectroscopy process

Field spectroscopy is made possible by the absorption, transmission radiance and irradiance process (Jensen, 2007). Under clear atmospheric conditions, solar radiation accounts for 90% of the incident irradiance; the rest comes from nearby structures, vegetation, clouds or even the instrument operator (McCoy, 2005). To achieve reliable results, it is necessary to maximise solar radiation and minimise radiation from surrounding materials (McCoy, 2005). Materials reflectance values are achieved by measurement of target radiance and reflectance from a standard white unglazed ceramic panel with about 98.2% average reflectance (McCoy, 2005). Due to its high diffuse reflectance of any material, a spectralon standard panel is the most commonly used reference material for field and laboratory applications Jensen (2007). It assumed that the standard plate is a lambertian reflector with independent zenith and azimuth angles of incident radiation (Jackson et al ., 1992). A target material reflectance ( r) is calculated as ratio of a materials reflectance to standard white reflectance panel;

r = (radiance of target/radiance of panel) k (5) where the constant k is the panel correction factor which is a ratio of the solar irradiance to the standard white plate and should be close to 1 (McCoy, 2005).

Spectral reflectance data can be obtained by comparing materials spectral radiation and wavelength vis a vis its chemical and physical properties (Kokaly et al ., 2003). In vegetation remote sensing for instance, green vegetation can be distinguished from other materials due to their unique spectral curves determined by leaf pigments, internal scattering and leaf water content (Jensen, 2007). Figure 2.4 below depicts 21 areas that react to different leaf components within the 0-2.5 µm wavelength regions. However, due to differing leaf attributes, spectra of different leaves and grass types may be located above or below the one shown below (Smith, 2001; Kokaly et al. , 2003; Lillesand et al., 2004).

Figure 2.4: Vegetation spectra and portions that react to different plant components shown at the top (from Smith, 2001).

2.6.3 In-situ versus laboratory spectroscopy

In situ spectral reflectance measurement equipment makes use of electromagnetic radiation and materials’ unique chemical and/or physical properties (Kokaly et al., 2003). According to Jensen (2007), such equipment can be used to acquire more information about materials, calibrate data from other platforms and generate spectra for better separation of materials from multi-spectral or hyperspectral data. In situ spectral measurement also allows for monitoring of spectral response based on change in conditions (see; Laudien et al., 2003; Van Til et al., 2004; Aldakheel et al ., 2004; Foley et al ., 2006; Thorhaug et al ., 2006). However, major disadvantages of field measurements include influence of atmospheric scattering, heterogeneity of field materials and difficulty of moving spectroscopy equipment (Adams and Gillespie, 2006). Better spectral data can therefore be achieved by reducing the effects posed by the above mentioned challenges (Foley et al ., 2006).

Laboratory spectroscopy has become popular with the scientific community, particularly for portable samples, as conditions can easily be designed, monitored or altered (Adams and Gillespie, 2006; Foley et al ., 2006). Several authors (Curtiss and Goetz, 1994; Milton, 1987; McCoy, 2005) provide a number of guidelines that must be taken into account to achieve reliable laboratory reflectance data: • Sensor Instantaneous Field of View (IFOV) must be known • The reference panel should fill the IFOV • The target should fill the IFOV • Irradiance should be constant when taking both panel and target measurements and • The linear response changes to radiation and standard panel reflectance should be known and should be kept constant during reflectance measurement.

Laboratory based measurements have several advantages over field spectral measurements viz : it allows for control of viewing and illumination geometry; secondly, measurements can be done any time and thirdly problems arising from wind and haze are eliminated (McCoy, 2005; Jensen, 2007). However, measurements under such artificial conditions do not allow for fair comparison with aerial data acquired from solar energy. It is also impractical to carry out some measurements like tree canopies or large rocks in the laboratory (see: Adams and Gillespie, 2006). In cases where calibration with imagery data is not required, laboratory based spectral measurement remains the best possible option for spectral data acquisition (Adams and Gillespie, 2006).

2.6.4 Spectral reflectance at different wavelengths

At the visible section of the Electro Magnetic Spectrum (EMS), the spectral behaviour is determined by chlorophyll and other plant pigments. The 0.45 – 0.52 µm and 0.63- 0.69 µm in the visible portion of the EMS are known to be regions that show greatest chlorophyll absorption and are often referred to as chlorophyll absorption bands (Jensen 2007; Lillesand et al., 2004). The 0.45 – 0.52 µm portion is highly sensitive to both carotenoids and chlorophyll while 0.63 – 0.69 µm portion is highly sensitive to

23 chlorophyll (Jensen 2007). In this section, more blue and red wavelengths are absorbed than green wavelengths. Consequently, a small reflectance peak is generated within the visible portion (Smith, 2001; Lillesand et al. , 2004; Jensen, 2007). In the case of senescing vegetation and a consequent decrease in chlorophyll absorption, there is often an increase in reflectance values at both the blue and red wavelengths (Lillesand et al., 2004). This portion can thus be used to detect changes in internal leaf structure as well as vegetation health (Jensen, 2007).

The reflectance of healthy green vegetation increases sharply between red and near infrared wavelengths (around 0.7 µm) – red edge (Smith, 2001; Lillesand et al ., 2004; Jensen, 2007). In most plants, the distinctive red edge peak into the near infrared wavelength persists to around 1.3 µm where 40 to 60 percent of incident near infrared energy is reflected (Jensen, 2007). In this wavelength, the reflectance scatter is dictated by the internal leaf cellular structures (Smith, 2001). Due to high variability in leaf cellular structures of different plants, this wavelength can be used to distinguish between different species (Lillesand et al ., 2004). Vegetation stress or senescence often leads to reduction in near infrared reflectance, making this region useful for mapping stressed vegetation (Lillesand et al ., 2004; Jensen, 2007). Other important applications of this section include general vegetation mapping, crop condition monitoring, yield estimation, and biomass measurement (Aronoff, 2005). Generally, reflectance decreases with an increase in wavelength beyond 1.3 µm, as leaf incident energy is either absorbed or reflected (Kokaly et al., 2003; Lillesand et al , 2004). However, there are two conspicuous water absorption bands at 1.4 µm and 1.9 µm within this wavelength (Smith, 2001; Lillesand et al ., 2004).

Spectral reflectance curves for soil, rocks and mineral are not markedly dissimilar from those of vegetation (Lillesand, 2004; McCoy, 2005; Aronoff, 2005; Adams and Gillespie, 2006; Jensen, 2007). Richardson and Wiegand (1977) also provide red and near infrared reflectance distinctions between grass, dense vegetation, dry soil, wet soil and water. A typical soil or rock spectral response shows a steady rising curve in the visible and near infrared but may rise less steeply after the near infrared wavelength (Figure 2.4) (McCoy, 2005). Soil reflectance may depend on factors like the soils moisture, texture, organic matter and mineralogy (Jensen, 2007). The influence of these factors on soil reflectance is often interrelated, for instance, coarse 24 sandy soils – often well drained – usually have higher reflectance in comparison to poorly drained soil types (Lillesand et al. , 2004; Jensen, 2007). Similar to water absorption bands in vegetation reflectance trends, the effects of moisture on soil spectral response are often apparent around 1.4 and 1.9 µm (Figure 2.4). In dry sandy soils however, coarse particles have lower reflectance than fine textured soils (Lillesand et al. , 2004). According to McCoy (2005), dry soils are characterised by two reflectance effects; firstly, reflectance increases and secondly water absorption bands become less apparent (Figure 2.5), or may even disappear for extremely dry sandy soils. Drying of clay or silt also leads to a reduction in depth of moisture absorption bands. However, unlike sandy soils, the water absorption band dips may still be visible even after extremely dry conditions (McCoy, 2005).

Figure 2.5: Spectra response of soils at oven dried, 0.03, 0.12, 0.20, 0.30 and 0.42 gravimetric water contents (g/g) (Adapted from Whiting et al ., 2004).

An increase in organic matter leads to a decrease in spectral reflectance (Jensen, 2003). According to McCoy (2005), only up to 5% of soil organic matter can affect spectral response often restricted to the visible wavelengths. Reflectances generally increase with increased soils salinity content in the visible and near infrared wavelengths (Jensen, 2007). In iron oxide rich soils, noticeable increase between 0.6- 0.7 µm and a slight dip between 0.85 and 0.9 µm in comparison to soil types without iron oxide are often visible (Jensen, 2007).

2.6.5 Importance of spectral derivatives

Whereas it may be easy to distinguish some materials like water from other surfaces using spectral reflectance, some materials have been known to have near similar or overlapping spectra (Curran et al. , 1991). Other materials’ spectra like senescing vegetation for instance can be heavily influenced by background soil, shadows or litter (Curran et al. , 1991). Derivatives can be used to enhance clarity of such spectra at specific wavelength ranges or within the entire range of wavelengths under investigation (Elvidge and Chen, 1995; Chen et al ., 1999). Derivatives aim at identifying inflection points from zero order reflectance curves for different materials. These inflection points can then be compared against each other (Chen et al ., 1999).

Derivatives are achieved by dividing the reflectance difference by an interval of contiguous wavelength, which yields interval slopes of the original spectrum (Becker et al. , 2005). According to Becker et al. (2005), areas of sudden change in the spectrum provide better spectral differences than gentle curves. Derivatives have been found to be useful in suppressing background signals, distinguishing closely related signals and reducing differences caused by changes in illumination (Demetriades- Shah et al. , 1990; Elvidge and Chen, 1995; Chen et al ., 1999). The use of derivatives has also been useful in the identification of the red edge and amount of chlorophyll content by locating its position in the reflectance spectrum (Chen et al ., 1999; Blackburn, 2007).

2. 7 Summary

Investigations of plant invasions along specific ecological and physical gradients provide a better understanding of the invasion process in terms of plant response to varying ecological, physical, climatic and anthropogenic variables. Given that soil moisture flux is heavily influenced by P. incana invasion, moisture regulation can be used to control the invader and restore landscapes whose degradation is a result of the invasion. Notwithstanding the efficacy of pixel based techniques like the PVI, sub- pixel based techniques, for instance the SMA can provide better surface separation. Image based endmember extraction techniques are preferred by most researchers, as endmembers mirror image conditions. Spectroscopy is important as a data validation 26 tool in land cover mapping. Laboratory based spectroscopy under a controlled environment provides better results than field based spectral measurements. In cases where materials have closely related spectral reflectance, the use of derivatives can be used to provide clarity in spectral differences.

Chapter 3: P. incana occurrence across a range of gradients

3.1 Introduction

The effects of plant species invasion are currently considered a serious threat to biodiversity in many parts of the world (Williams and Gill, 1995; Mack and D’Antonio, 1998; Grove and Willis, 1999). This has led to increased research towards unravelling causative factors and developing mitigation measures (D’Antonio et al ., 1999; Mack et al ., 2000; van Wilgen et al. , 2001; Rejmánek et al. , 2004). Since varying conditions interact to determine invasion at different scales (Farina 1998), investigations under diverse physical and natural settings at broader landscapes offer a feasible research option for understanding plant species invasion (Richardson et al ., 2004). According to Byers and Noonburg (2003), such scales are often made up of heterogeneous ecological and environmental conditions that may provide a better understanding of invasion processes. In such cases, the identified variables can then be used to determine how the invader interacts with local physical, ecological and climatic conditions which in turn can be used to identify sensitive landscapes (Kruger et al ., 1989). Since it is often difficult to identify replica land-use, topographic or micro-climatic conditions occurring in multiple areas, identification and investigation of a wide array of existing variables within the landscapes remain the most viable approach to identifying factors influencing species invasion (Pauchard et al ., 2004).

The effects of eco-physical and environmental variables as filters to vegetation sustainability can be well understood by considering the filters independently (Keddy, 1992). Plant invaders are often discontinuous, as determined by eco-physical and environmental variables within a biome (Carr et al ., 1992). Wace (1977) suggests that such variables can be used to classify landscape sensitivity that can in turn be used to design invasion management and rehabilitation programs.

Previously, studies on P. incana invasion have concentrated on identification of conditions that determine invasion at patch, hillslope and catchment scale (see Kakembo, 2003; Kakembo et al ., 2006; Kakembo, et al ., 2007). Generally, studies carried out at such localised scales are crucial to identifying micro-scale co-variables

28 that may be associated with P. incana invasion. However, since eco-physical and environmental factors often differ spatially, conditions that limit or encourage invasion may also differ across these factors (Higgins and Richardson, 1996). Consequently, whereas fine scale studies may provide an understanding on specific factors influencing invasion, such an approach may ignore processes outside the affected site, making landscape extrapolations based on local findings inappropriate (Pauchard et al ., 2003). To gain a holistic understanding of P. incana invasion at landscape scale, it would therefore be imperative to gain insights of its occurrence across a range of gradients.

Climatic conditions, underlying lithology, and ecological disturbance have been identified as variables associated with plant species distribution (see; Fox and Fox, 1986; Woodward, 1987; Carr et al ., 1992; Mackey, 1993). In addition to these variables, local soil physical and chemical properties have been known to determine the type and form of vegetation (Dukes and Mooney, 1999; Küffer et al. , 2003; Echeverria et al . 2004). Changes in soil organic matter (OM) have for instance been associated with an increase in soil temperature, change in trace gases and an alteration in the soil microbial activity all of which directly affect plant sustainability (Buckley and Schmidt, 2001; Küffer et al. , 2003). Soil pH on the other hand determines the type and amount of nutrients available in the soil. Since different vegetation types thrive on different types of nutrients, vegetation type and form are therefore often determined by local soil pH (Goldberg, 1985; Spies and Harms, 1988; Hironaka et al ., 1990; Gardiner and Miller, 2004; Hillel, 2004; Moody, 2006). Against the background of variations in controlling variables, P. incana invasion was investigated across a range of gradients.

3.2 Major gradients within the transects

In order to determine the relationship between P. incana and a range of gradients, one major transect from the coast (near Port Elizabeth) to just beyond Grahamstown and two north-easterly and westerly prongs across Ngqushwa District were surveyed (see Figure 3.1). The transect traversed five major gradients namely: geological formations, land use, vegetation, altitudinal and isohyetic zones. Soil physical

29 properties viz : particle sizes, OM and pH for samples collected along the transect were also analysed.

3.2.1 Geological formations

The transect traversed a variety of geological formations and consequent heterogeneous soils. The formations range from Kirkwood, Sundays river, Alexandria, Nanaga, Lake Mentz, Grahamstown, Weltevrede, Dwyka, Fort Brown to Adelaide and Escourt. P. incana invaded nodes were recorded along the transect (see Figure 3.2). The association of the invaded nodes with specific geologic formations was identified by overlaying GPS coordinates on the geology map as described in the methods section 3.3.

3.2.2 Land use types

The transect transcended land use types ranging from game farms, grazing land, cultivated communal and commercial farms, and abandoned lands. Generally, the area covered by the major transect had fewer land-use types compared to the north-easterly and westerly prongs across Ngqushwa district. The two prongs traversed communal villages characterised by dense rural settlements, fragmented cultivated and grazing land, and extensive abandoned land, large tracts of which are affected by severe forms of soil erosion. It is these abandoned and overgrazed lands that constitute the endemic zone of the invasion.

3.2.3 Vegetation types

Sixteen vegetation types are traversed by the transect (Figure 3.4). The major transect and two prongs crossed twelve and four vegetation types respectively. It is noteworthy however that the natural vegetation has been tremendously modified by man’s activities, particularly in the communal lands. Many of the vegetation types indicated by Figure 3.4 exist in remnant form. Apart from the invader investigated in the present study, other alien invader species have gained a footprint, replacing vast areas of the indigenous vegetation types. The deviation from the natural vegetation was evident at the different P. incana invaded nodes surveyed along the transect.

3.2.4 Rainfall

The major transect and two prongs traversed five and two isohyetic zones respectively. The mean annual precipitation within the zones ranges from 363 mm in a part of the Algoa Bay to 950 mm around Grahamstown (Figure 3.5). Soil moisture variations within micro-topographic features were identified by Kakembo et al . (2007) as one of the key factors influencing P. incana invasion. A survey of invasion nodes along a transect from the coast into the interior should provide insights into the pattern of the invasion in relation to isohyetic zones. It should also be possible to identify the threshold precipitation limit beyond which the invasion is not prevalent. Major characteristics of the main transects’ invaded sites are summarised in the table below.

Table 3.1: Major characteristics of the invaded nodes within transect A.

Invaded site Geological Lithology/ Vegetation MAR 1 Slope MASL 2 Estimated. P. formation rock material zone (Range) in degrees incana cover(%)

1 Alexandria Conglomerate, Coega 218-353 2 47 30 Calcareous Bortveld Sandstone Cocquinite 2 Nanaga Calcareous Albany 353-487 3 357 35 Sandstone coastal belt Sandy limestone 3 Quaternary Aeolian sand Kowie Thicket 218-353 7 192 70

4 Weltervrede Shale Tarkastad 218-353 5 360 50 Quartzite montane Shrubland 5 Dwyka Shales Kowie Thicket 218-353 3 290 40

MAR 1 – Mean Annual Rainfall MASL 2 – Metres above Sea Level

3.3 Methods

P. incana invaded nodes were surveyed along a transect from the coast into the interior along the N2 main road and across Ngqushwa district. Digital shapefiles from SANParks (Park Planning and Development Division) validated with relevant data from hard copy maps were used to identify the respective gradients traversed by the transect. Using a centimetre level precision Ashtech ®ProMark2 ™ Global Position System (GPS) receiver and hard copy maps, one major continuous belt transect (A) spanning 135 x 8 km with numbered P. incana invaded nodes was surveyed from the first invaded area near Port Elizabeth. Subsequent invaded nodes were identified along the N2 major road that runs inland in a north-easterly direction (Figure 3.1). The occurrence of P. incana across the eco-physical, land use, latitudinal and isohyetic gradients that the transect traversed was investigated. The numbered GPS points along the transect were also used as sampling sites to determine soil physical properties. Differences in pH and OM between invaded and un-invaded sites were sought. Two other prongs of the transect (B and C) were established in north-east (55 x 5 km) and north-west (30 x 5 km) directions up to last known nodes of the invasion (Figure 3.1). Based on previous preliminary field surveys, the first invaded node within the major transect was located 30km north-east of Port Elizabeth (Figure 3.1). Subsequent GPS points representing P. incana invaded surface nodes within the major transect were consecutively numbered 2 to 5 (Figure 3.1).

Figure 3.1: Transects and GPS invasion nodes.

At each invaded surface GPS node, slope and altitude were recorded. Surrounding P. incana percentage cover within a 20m radius of each of the invaded node was also estimated. The nodes’ underlying geology, surrounding vegetation, landuse and mean annual precipitation were identified from the relevant digital and hardcopy maps. All the GPS invasion points surveyed were then overlaid on the digital maps of the respective variables.

At each node along the major transect, soil samples were collected to determine the soil pH and OM on invaded and un-invaded surfaces. Twenty soil samples were randomly collected around each invaded centroid. A similar number of samples was collected from respective adjacent un-invaded sites using the same procedure. Soil pH was determined in the laboratory using a calibrated HANNA HI 991300 pH meter (HANNA Instruments, Woonsocket - USA). To establish the soil percentage OM, the same sampling procedure above was used. At each invaded area, average OM content from twenty samples was determined using Loss on Ignition (LOI) method (Heiri et al., 2001). Soil particle analysis was also carried out using the hydrometer method

(Day, 1965). Unlike pH and OM that were compared between invaded and un- invaded surfaces, soil particle analysis was restricted to invaded surfaces.

3.4 Results

There was no P. incana invasion recorded in areas underlain by Sundays River, Kirkwood, Lake Mentz and Grahamstown geological formations within transect A (Figure 3.2). A higher frequency of invasion nodes was recorded on the two minor prongs of the transect (B and C) than the main transect A. Invasions were recorded on Alexandria, Fort Brown, Adelaide and Escourt geological formation on transect B and Adelaide and Escourt geological formation on transect C (Figure 3.2). There were no P. incana invasion nodes recorded beyond the last nodes on the north-east and northwest directions of transect prongs B and C respectively. Areas beyond prong B were dominated by Adelaide and Escourt with strips of Karoo dolerite while areas beyond prong C were dominated by Adelaide and Escourt with strips of Karoo dolerite and Tarkastad geological formations.

Figure 3.2: P. incana invaded nodes on underlying geology.

Invasion nodes 2, 3, 4 and 5 recorded within this transect fell within land described on the map as vacant/unspecified land use. However, during the transect survey process, nodes 3 and 5 were identified as lying on grazing land, while node 4 was located on a game farm. Node 1 lay on cultivated land (Figure 3.3). Transects B and C traversed mainly grazing, cultivated and abandoned lands in the communal areas (Figure 3.3).

Figure 3.3: P. incana invaded nodes on land use types.

P. incana invasion nodes were recorded in areas covered by Coega Bontveld, Albany Coastal belt, Kowie and Bhisho Thornveld thicket types (Figure 3.4). However, the original vegetation in these zones has been substantially modified. Transect B covered six vegetation types. Two invaded nodes were on the Great Fish River Thicket while one node was on the Suuberg, Buffels and Bhisho Thornvelds. Generally, the vegetation types affected by P. incana invasion were diverse. By implication, there is no specific vegetation zone with which the invasion is associated.

LEGEND Alluvial Vegetation Bhisho Thornveld Freshwater Wetlands Mistbelt Forest Albany Broken Veld Buffels Thicket Freshwater Lakes Sundays Thicket Albany Coastal Belt Coastal Lagoons Great Fish Noorsveld Quartzite Fynbos Albany Dune Strandveld Salt Marshes Great Fish Thicket Suurberg Shale Fynbos Algoa Dune Strandveld Inland Salt Pans Groot Thicket Montane Shrubland Algoa Sandstone Fynbos Lowland Wetlands Escarpment Grassland Tsomo Grassland Mistbelt Grassland Seashore Vegetation Kowie Thicket Montane Grassland Coega Bontveld Southern Coastal Forest GPS point in a transect Dry Grassland Escarpment Thicket Southern Karoo Riviere

Figure 3.4: P. incana invaded nodes on vegetation types.

The five invasion nodes along main transect A clearly lie in an isohyetic zone with a precipitation range of 218 - 619mm of rain (Figure 3.5). There is a conspicuous absence of invasion in the zone between nodes 4 and 5 (Grahamstown area) which receives well over 619mm. All the nodes within transects B and C were in areas with less than 619mm, with most of them lying in the 218 – 487 mm isohyetic zone (Figure 3.5). There was no invasion recorded to the north-easterly and north-westerly directions beyond the last nodes of transect prongs B and C respectively. These directions are towards the wetter higher altitude Amatola Mountains (Figure 3.5).

Figure 3.5: Invaded nodes on mean annual precipitation.

The soils in the five invaded GPS nodes had diverse contents of sand, silt and clay ranging between 73.9-87.1%, 4.9-11.5% and 4.9-11.6% respectively (Table 3.2). Based the soil’s physical characteristics on table 3.2 below, the textural triangle showed the soil classes as loamy sand for sites 1, 2 and 5 and sandy loam for site 3 and 4. The soil pH in all the recorded sites was higher in invaded sites than un- invaded sites (Table 3.2). However, none of the soil samples exhibited extreme acidity conditions. In most cases, pH conditions were ideal for normal plant life. With exception of site 4, all sites had very low OM content (Table 3.2). Soils in most sites within invaded areas had <1.5% OM. Site 1 and 3 had <1% soil OM content while site 2 and 5 had >1% but <1.5% OM in invaded and un-invaded sites. The highest OM content was recorded on site 4 (>3%) on both invaded and un-invaded sites.

Table 3.2: Physical and chemical characteristics of soils at invaded and un-invaded sites.

Site Sand Silt Clay pH pH OM OM <2 µm 2-50 µm 50-2000 µm invaded un-invaded invaded un-invaded (%) (%) (%) site site site (%) site (%) 1 86.9 6.5 6.5 7.7 7.9 0.85 0.89 2 83.6 6.5 9.8 6.6 6.7 1.12 1.22 3 75.3 11.5 11.6 5.6 6.2 0.72 0.84 4 73.9 9.8 16.3 6.7 6.9 3.02 3.31 5 87.1 4.9 4.9 5.8 6.1 1.41 1.50

3.5 Discussion

3.5.1 P. incana invasion and the underlying geology

The underlying lithology from which diverse soil types derive has been known to influence vegetation types, as it influences among other things soils chemical composition, topography and biological and nutrient cycling (Kruckeberg, 1985; Christopherson, 2003; Burek and Potter, 2006). In this study however, there was no clear trend of P. incana invasion identified on any of the diverse geological formations within transect A (Figure 3.2). The most frequent P. incana invasion nodes were recorded in transect B and C which were dominated by the Adelaide and Escourt geological formation. However, there were no invaded sites sighted beyond the last nodes in the north-eastly and north-westly directions of the two respective transects dominated by a similar geological formation. Therefore, geology alone cannot explain P. incana invasion.

3.5.2 Land use and P. incana invasion

As noted from Figure 3.3, P. incana invasion is mostly prevalent on land categorised as vacant/unspecified land-use. These are areas dominated by open grasslands used as grazing land. Some of the areas within this land-use category were previously under crop farming. As was noted during transect survey, the invaded nodes lie on disturbed surfaces used for livestock and prevously for crop farming. A case in point is the invasion node 3 (Figure 3.3) which for a long time was under livestock and crop

farming but currently under private game farming. The association of the invasion with land disturbance is duscussed in section 3.5.3 below.

There was no clear pattern descernible between vegetation types and invasion. Since vegetation types are often closely related to precipitation, an interplay of the three is expected to influence P. incana invasion. As was noted earlier, there was an absence of the invasion with an increase in precipitation regardless of the vegetation type. The role of precipitation is discussed in section 3.5.4 below. Regarding the topographic influence, all the invaded nodes were recorded on gentle slope angles ranging between 2 0 to 7 0. It is notetworthy however that slope angle measurements along a transect are inconclusive. Catchment surveys by Kakembo et al . (2006) indicated a clear spatial correlation between P. incana invasion and steep slope angles.

3.5.3 Disturbance as a cause of invasion

A number of authors (see; Crawley, 1987; Hobbs, 1989; Richardson and Cowling, 1992; Bergelson et al ., 1993; DeFarrari and Naiman, 1994; Smith and Knapp, 1999) have reported landscape disturbance as a major cause of plant species invasion. According to Cross (1981), the success of Rhododendron ponticum invasion in the oakwoods understorey of Ireland is attributed to the disturbance caused by herbivores. Rhododendron ponticum gains a competive advantage over native species becausue it is unpalatable to herbivores and can survive under the shade (Cross, 1981). West of the Yellowstone area, Olliff et al. (2001) notes that Linaria vulgaris, Centaurea maculosa, Linaria dalmatica, Malilotus ofifcinalis, Cirsium arvense and Verascum thapus invasion is prevalent on heavily disturbed agricultural and rangelands. Whereas their spread is often localised due to clonal propagation, offsite dispersal of their winged seeds allows for dispersal by wind and animals (Saner et al ., 1995).

Similarly, communal areas in the Eastern Cape with previous or current disturbance and with depleted indigenous vegetation have been known to be most vunerable to P. incana invasion (Kiguli et al ., 1999; Palmer et al ., 2005; Kakembo et al ., 2006). On heavily grazed communal rangelands such as those around transect prongs B and C, selective browsing of the existing native vegetation further gives P. incana a

competive advantage over resident vegetation. Whereas former commercial farms can be described as having high resource productivity, the subjection of communal rangeland to excessive resource exploitaion has led to more rapid trasnformation of vegetation types (Tanser, 1997).

According to Higgins and Richardson (1996), interaction between an invader and the recipient environment determines invasion success. Generally, invasion resistant environments have the ability to filter the invader at establishment, growth, reproduction and dispersal (Keddy, 1992). The competitive advantage provided by established vegetation beyond the last nodes of transect prongs B and C provides conditions unsuitable for P. incana invasion. The reasons for absence of P. incana invasion in these areas is further discussed in the relevant sections below. Elton (1958) suggests that communities with diverse species are more resistant to invasion than those with limited diversity. This has been demonstrated by a number of authors (e.g. Case, 1990, Hector et al ., 2001; Lyons and Schwartz, 2001; Troumbis et al. , 2002). Case (1990) further suggests that resident community attributes strongly influence the success of plant invasion. This argument is based on the hypothesis that species rich communities capture resources more efficiently leaving fewer resources to the invaders than species poor comminities (Knops et al ., 1999; Symstad, 2000). The high prevalence of P. incana invasion in disturbed communal rangelands suggests that disturbance of the resident vegetation through overgrazing and fuel wood extraction (eg along transect prongs B and C) has a greater influence on P. incana invasion than the invader attributes.

3.5.4 Isohyet gradient and P. incana invasion

Whereas a variation in altitude was noted along the transect, the invasion seemed to be limited to below 360masl within transect A (Table 3.1). Areas within the transect with higher altitude and higher amount of rainfall (for intance between points 4 and 5 - around Grahamstown and towards Amatola Mountains – Figure 3.5) had no invasion recorded. Generally, two rainfall patterns are descernible in the region; coastal precipitation as determined by proximity to the sea and inland precipitation as determined by altitude. The latter seems to have a bearing on invasion trends. Generally, nine out of the twelve invaded nodes were located on the edge of 363- 41

487mm to 487-619mm rainfall categories (Figure 3.5), which were lower than the increasing precipitation towards Amatola Mountains. The combined effect of low precipitation and disturbance must be taken note of, as the areas where P. incana invasion is endemic lie in the low precipitation zone where disturbance in the form of land abandonment and overgrazing are widespread.

Whereas some invaders like Melinis manutiflora have shown a positive correlation between precipitation amounts and invasion (Baruch, 1985), others have shown that reduced precipitation interacts with other variables like landuse to determine invasion success (Archer et al ., 1988; Alpert et al ., 2000). It is clear from the transect survey that there is a distinct isohyet boundary (>619mm) beyond which P. incana invasion does not occur. This observation is in keeping with the finding by Kakembo et al . (2007) that areas of high wetness within the landscape are not ideal sites for P. incana invasion.

3.5.5 P. incana invasion and soil characteristics

Studies by Dukes and Mooney (1999) and Küffer et al. (2003) suggest that differences in soil nutrients may influence vegetation invasion. In this study, invaded areas had consistently lower OM than un-invaded areas. The loss of soil OM is attributed to the patchy nature of P. incana . The intershrub bare areas are typically crusted, impeding infiltration and promoting runoff connectivity. The removal of the top soil layer from bare areas inevitably results in OM depletion in the intershrub areas. In cases where surface OM has been depleted due to surface erosion, P. incana will have better establishment rates than shallow rooted grasses. Several authors (Bryan and Brun, 1999, Leprun, 1999, Cameraat and Imeson, 1999) have reported a decline in the soils OM with reduced vegetation resulting from excessive land use. As demontrated by Cameraat and Imeson (1999) in Stipa tenacissima invaded surfaces, increased surface run-off is common in exposed soils with reduced infiltration due to a decline in OM from disturbed vegetation. At local scales, soil OM and its difference after invasion is determined by a combination of several factors such as slope angle, soil texture and surface vegetation cover (Cammeraat and Imeson, 1999). Similar factors are identified by Kakembo (2003) as key drivers to soil nutrient loss and conversion to dysfunctional states in P. incana invaded landscapes. Whereas a variety 42

of factors interact to determine loss of OM at the initial stages of P. incana invasion, a number of studies (Dunkerley and Brown, 1995, Eddy et al ., 1999, Zonneveld, 1999) observe that slope angle combines with precipitation amounts to determine the severity of soil erosion and consequent decline in soil OM.

The dominance of sandy loam soils as noted from the particle size analyses exercerbates soil OM loss, as such soils are rated as having a high erodibility potential given their low aggregate stability. OM loss in P. incana invaded areas can therefore be perceived as a post-invasion process. It is also noteworthy however, that OM loss can pre-date the invasion, for example on abandoned lands, where OM is depleted, promoting the invasion by the resilient P. incana at the expense of indigenous vegetation.

3.6 Conclusion

Whereas the underlying geological formations and related topography, lithology and soils should determine P. incana invasion, there was no clear trend established between P. incana invasion and the underlying geology. Land use types on the other hand greatly influence P. incana invasion, particularly in communal lands characterised by disturbance in form of cultivation, abandonment and overgrazing. Precipitation has been identified as the most important factor in P. incana invasion, as the propensity for the invasion decreased with increasing precipitation. P. incana invasion can therefore not be expected in areas with more than 619mm mean annual rainfall. The higher precipitation is also likely to increase the native vegetation density and resilience and therefore better competitive ability. Soil OM was noted as higher on un-invaded surfaces than invaded surfaces. The patchy nature of P. incana impedes infiltration and promotes runoff connectivity and hence OM depletion.

1Chapter 4: Hydrological response of P. incana invaded areas: implications for landscape functionality

4.1 Introduction

Plant species invasion is one of the greatest threats to rangelands (Kiguli et al. , 1999; Kakembo, 2003). Invasions have been known to transform soil moisture and nutrient status (Musil, 1993), decrease recruitment of native species (Walker and Vitousek, 1991) and affect surface hydrological flows (van Wilgen et al ., 1992; Kakembo et al ., 2006). South Africa’s grassland and savanna biomes for instance are reported by Henderson and Wells, (1986) as invaded by shrubs indigenous to the Karoo and unpalatable tussocky rass species that cause deterioration of soil and associated biophysical attributes. Several authors (Pimentel et al. , 2000; Sala et al. , 2000; Alvarez and Cushman, 2002; Dukes, 2002) note that plant species invasion is often inconsistent with ecological and socio-economic ideals.

Changes to soil surface cover may alter the output of environmental envelope so that the availability of water and nutrients over time is insufficient for some vegetation species to persist. An example is the loss of perennial grasses from a landscape (Tongway and Hindley, 1995). In the Eastern Cape, rangelands have been severely affected by land degradation directly linked to P. incana invasion (Kakembo, 2003; Palmer et al ., 2004). The invaded patches are often characterised by inter shrub spaces with grass and bare surfaces at initial and advanced stages of invasion respectively. The invasions often cause shrinkage of grass patches, crusting of soils and severe soil erosion (Kakembo, 2003; Kakembo et al . 2006; Kakembo et al. 2007). Crusted surfaces for instance inhibit infiltration and promote runoff generation and connectivity leading to nutrient and soil loss (Kakembo et al. 2007). This may ultimately transform invaded environments to what is referred to by some authors (Ludwig et al. , 1997; Kakembo, 2003; Palmer et al ., 2004) as movement towards a dysfunctional state.

Soil moisture is one of the most important abiotic factors that determine vegetation growth, variability and regeneration (Walker and Peet, 1983; Isard, 1986; Breshears

44 1This chapter is based on a paper in preparation for submission to the Ecohydrology Journal - Authors, Odindi, J. O. and Kakembo, V.

and Barnes, 1999; Knapp et al. , 2002; Fu et al. , 2003; Flanagan and Johnson, 2005; Chen et al ., 2007). Whereas P. incana is a native of the dry South African Karoo environments, Kakembo (2003) and Palmer et al ., (2005) have shown that it can successfully invade more mesic environments. Kakembo (2003) for instance points out that a combination of drought and overgrazing that affected resident vegetation between mid 1950s and 1970s created an enabling environment for P. incana invasion in the lower Great Fish region.

Using the Wetness Index, Kakembo (2003) demonstrated that grass patches persisted in areas with higher moisture content than those invaded by P. incana . Similar findings have also been recorded by Pärtel and Helm (2007) on alvar grasslands in western Estonia and Farley et al . (2004) on different ages of Pinus radiata (Monterey pine) in páramo grassland in Cotopaxi province, Ecuador. These observations are in keeping with suggestions by Wilson (1998) and Pärtel and Wilson (2002) that grass species may acquire and retain soil moisture resources more efficiently than young woody species in environments with relatively poor but homogeneously distributed moisture.

A number of studies on moisture flux and retention on vegetated and bare patterned environments have however been biased towards run-off/run-on moisture movements (Tongway and Hindley, 1995; Peugeot et al ., 1997; Seghieri et al ., 1997; Galle et al ., 1999). There is a general paucity in literature on moisture retention in environments invaded by plant species and P. incana surfaces in particular. Consequently, the hydrological response of P. incana invaded areas and grass surfaces remains speculative. This study intended to compare soil moisture flux under P. incana patchy invader shrub with grass and bare areas. Trends in soil moisture conditions under the respective surfaces and their response to rainfall episodes were monitored between 1 st November 2007 and 1 st May 2008.

4.2 The study area

The study was conducted in Amakhala Game Reserve, Eastern Cape, South Africa (Figure 4.1). The game reserve has an area of about 4800 hectares with an altitudinal variability ranging from 186 to 232 metres above sea level. 45

Figure 4.1: Study site in Amakhala Game Reserve.

Due a long history of goat farming as a major land use, the area’s natural vegetation has been transformed to open grasslands with isolated patches of standing thicket and P. incana invasion on some degraded hill-slopes. The existing thicket vegetation types and P. incana are perennial, while the C 4 and C 3 savanna grass types dominate the warm growing season and winter rains respectively. The area has a wet-dry seasonal climatic variation. Annual rainfall is highly variable, ranging between 380- 570mm with monthly rainfall peaks in September/October and March. The least amount of precipitation is received in mid-summer (December/January) and mid winter (June/July). Summer temperatures range from 16 o-30 oC while winter temperatures are between 5 o-22 oC. The entire game reserve falls within a convergence of different geologic formations. The experimental site is however underlain by the Schelmhoek rock formation of the Algoa group. This formation comprises mainly the calcareous sandstone and shale middens lithology. The soils are well developed and consolidated with high proportions of clay that vary in thickness in ridges and valleys.

4.3 Materials and methods

Soil moisture flux was monitored for a period of six months under a P. incana invaded surface, grass and inter-patch bare area. Whereas oven-drying is probably the most commonly used method to determine soil moisture content, it relies on destructive sampling and does not allow for long term moisture monitoring. With site specific calibration, capacitance moisture sensors on the other hand can be used to reliably determine on-site moisture measurements over time.

4.3.1 Capacitance sensor: Theory and instrumentation

Capacitance soil moisture measurement technique dates back to early 1930s (Smith- Rose, 1933). However, it was not until the 1980s that it was commercialised and tested under laboratory and field conditions (Dean et al. , 1987; Bell et al ., 1987). The technique is based on changes on a given medium’s dielectric constant ( K) to determine its Volumetric Water Content ( θv) (Dean, 1994; Gawande et al. , 2003). In this technique, a probe with a specific voltage is inserted into a medium and the rate of voltage change measured. Change in a mediums K is directly proportional to the sensor’s voltage change which in turn determines the probes raw count. Capacitance soil moisture measurement is based on the K of soil-water- air combination. The K of water is large (80) in comparison to 3-5 and 1 for soil and air respectively.

Consequently, a change in soil K will be proportional to the change in soil θv. Since absolute soil permittivity is difficult to achieve, capacitance sensor output is often referred to as apparent permittivity, as a measure of soil water content (Robinson et al. , 2005).

In this study, high frequency (50MHz) ECH 2O EC-20 soil moisture probes (Decagon Devices Inc., Pullman, WA) were used. These probes require 10ms of 10Ma at 2.5V o o excitation and can be used to measure between 0 – 100% θv within -40 to 60 C. The probes were connected to a 5–channel Em5 data logger and ECH 2O utility software, which allowed for automated soil moisture logs and readings.

4.3.2 Sensor calibration

The manufacturer’s calibration for ECH 2O sensors can be used in soil types with low to moderate sand and salinity content with an accuracy of ± 0.03m 3/m 3. This accuracy drops to ± 10% for coarse and highly saline soils (Cobos, 2007). Several authors (Tardif, 2003; Geesing et al ., 2004; Blonquist et al. , 2005; Czarnomski et al. , 2005; Yashikawa and Overduin, 2005; Campbell, 2006; Cobos, 2007) note that site specific soil moisture sensor calibration improves θv measurements substantially. More accurate soil moisture measurements can therefore be achieved by using site specific calibration by relating an equation between the actual θv (achieved by the oven-drying method) and the sensor voltage output (Tardif, 2003; Fares et al. , 2004). Soil samples were collected from a section of a south facing hillslope of 8 0 that had adjacent grass and P. incana , and inter-shrub bare areas. It was at this site that moisture sensors were installed.

In the laboratory, the samples were passed through a 2mm sieve and oven dried for 24hrs at 105 0C. Dry samples were then packed in calibration containers and EC 20 moisture probes inserted until they were completely buried. Raw probe counts were taken using an Em50 data logger connected to a computer with an ECH 2O utility software (Decagon Devices Inc.). Medians of every 10 readings were preferred to means of similar readings (Dean, 1994). The above procedures were repeated several times to establish probe output consistency.

A home made volumetric sampler with inner diameter of 6cm and height of 21cm giving a sample volume of 594cm 3 was used to extract soil samples for moisture measurement. The sample volume was taken from the calibration container using a volumetric sampler, emptied in weighed oven drying jars and quickly sealed. The jars with the volumetric soil samples were then weighed using a Mettler PE 3600 Delta- range with a 0.01 accuracy balance. Water was then added to the air dry volumetric sample, thoroughly mixed and raw sensor output recorded from the data logger. This procedure was repeated until the sample was near saturation point. The weighed moist o samples were oven dried at 105 C for 24 hours, left to cool and reweighed. The θv in cm 3/cm 3 were determined using the mass of moist and oven dried samples. These were used to develop a trend line and a mathematical equation (Figure 4.2) to be 48

applied to the moisture probe readings from the data logger installed on grass, P. incana and bare surfaces.

Figure 4.2: Correlation between Volumetric Water Content ( θv) using oven- dried samples and probe outputs. The equation used to determine

onsite θv is shown in the graph.

4.3.3 Field installation

A mid section of the short gentle (8 o) sloping south facing aspect covered by P. incana , grass patches and bare patches was identified for sensor installation. The sensors were connected to the data logger and set to log raw moisture data after every 60minutes. The raw moisture probe readings were downloaded from the data logger fortnightly between 1 st November 2007 and 1 st May 2008. Since less variability is expected below a depth of 30cm due to less effects of evapo-transpiration and sub- surfaces water flows (Hamdhani et al ., 2005; De Lannoy, 2006), 20cm moisture probes were used for measurement of moisture on the three surfaces. To determine on site θv, all the raw moisture probe counts were subjected to the correlation equation from oven-drying method and raw probe counts shown in Figure 4.2 above. To establish consistency between precipitation received and moisture probe response, onsite θv moisture measurements were compared to the rainfall data from a nearby rain gauge.

4.3.4 Data presentation and analysis

Three moisture episodes were identified from the study duration and piecewise regression used to determine the rate of moisture loss and inflection points between the wet soil moisture loss (immediately after a rainfall event) and dry soil moisture loss (after rapid surface moisture loss). Threshold ranges and break-points were determined using functions:

Y = a 1 + b 1*X+ b 2*(X – breakpoint)*(X>breakpoint) (6)

In each case, the wet moisture loss (immediately after a rainfall event) and dry moisture loss (approx. six days after a rainfall event) independent regression lines were defined as:

Wet moisture loss: Y = a 1+b 1x (7)

Dry moisture loss: Y = {a 1+break-point (b 1-b2)}+b 2x (8)

where: a 1 is the origin of the wet moisture loss

b1 is the end of wet moisture loss and origin of the dry moisture loss

b2 is the end of the dry moisture loss.

The actual break-points were determined as mid-points of piecewise regression thresholds. To establish moisture retention/loss rates, moisture slopes and their respective Y-intercepts were calculated for the different surfaces. Standard deviations were also used to determine day/night moisture oscillations.

4.4 Results and discussion

4.4.1 Moisture variations

On the basis of moisture probe response to rainfall events, there were nine moisture elevation episodes during the six months of data collection. No rain fell during the first twenty days of sensor installation. The highest soil moisture content peak was 50

experienced on the twenty first day of January (Figure 4.3). The month of December had the highest cumulative moisture content during the study period due to two major rainfall episodes on the second and the twenty fourth day of the month. The data gathered during this period was not considered for analysis as it didn’t provide for sufficient drying periods between the precipitation events for moisture flux analysis. The relevant data was categorised into three portions based on the durations between rainfall episodes and labelled episodes 1, 2 and 3 (see Figure 4.3). To accommodate the entire data range, hourly moisture readings were converted to monthly durations

1 0 0.9 10 0.8 20 0.7 Episode 3 30 ) )

3 0.6 Episode 1 40 /cm

3 0.5 Episode 2 50

(cm 0.4

60 (mm) Rainfall 0.3

0.2 70 Volumetric Water Content Content Water Volumetric 0.1 80 0 90

1/11/'07 1/12/'07 1/01/'08 1/02/'08 1/03/'08 1/04/'08 1/05/'08 Date Grass surface P. incana surface Bare surface Figure 4.3: Probe response to precipitation episodes during the six months study period – arrows show episodes selected for analysis.

There were different moisture peaks as determined by the amount of precipitation. Within the entire duration of the study, the area covered by the grass patch had consistently higher moisture readings than the P. incana and the grass surfaces. A similar trend was recorded as the surfaces dried out.

4.4.2 Episodic moisture flux

In all the rainfall episodes, a considerable difference in moisture retention between grass and P. incana is noticeable up to about six days, after which near parallel moisture content within the two surfaces prevailed uptill the ensuing rainfall episode 51

(Figure 4.4 a-c). There was also near parallel moisture reduction trends between P. incana and bare surfaces in all the rainfall episodes. In all cases, the grass patch lost moisture more rapidly than P. incana and bare surfaces (Figure 4.4 a-c).

0.4 Grass surface P. incana surface 0.35 Bare surface 0.3 ) 3 0.25 /cm 3 0.2

(cm 0.15 0.1 VolumetricWaterContent 0.05 0 1 6 12 18 24 28 Days from 16/01/'08 a) Episode 1

0.25 Grass surface P. incana surface 0.2 Bare surface ) 3 0.15 /cm 3 0.1 (cm

0.05 Volumetric Water Content

0 1 6 12 18 24 Days from 13/02/'08 b)Episode 2

0.5 Grass surface 0.45 P. incana surface 0.4 Bare surface 0.35 ) 3 0.3 /cm

3 0.25 0.2 (cm 0.15 0.1 Volumetric Water Content VolumetricWater 0.05 0 1 6 12 18 24 30 36 Days from 12/03/'08 b) Episode 3

Figure 4.4 a-c: Soil moisture flux for the selected rainfall episodes.

The differences in surface moisture retention based on surface condition in this study are consistent with findings by Fu, et al . (2000) and Qiu, et al. (2001) who identified infiltration, surface run-off and evapo-transpiration as the key factors determining moisture content at small scales. According to Dekker and Ritsema (1996), such differences can be highly randomised, as determined by vertical fluxes leading to boundaries between different moisture regimes influenced by evapo-transpiration. De Lannoy et al. (2006) further clarify that since a reduction in moisture content leads to a decrease in evapo-transpiration, wetter vegetation patches will experience more rapid soil moisture loss than bare surfaces. Whereas wetter surfaces may retain higher minimum moisture values than drier surfaces, the difference in surface moisture variability between grass and bare surfaces declines as the surfaces dry out (Monteny et al . (1997).

Similar findings are also noted by Oakley (2004) who found higher moisture readings in unburned as compared to burned sites. According to the study, the interception of rainfall by vegetation canopy and moisture retention by the rooting system greatly influences soil’s moisture levels. However, less moisture is lost in areas with complete grass cover as the surface is protected from solar radiation. In related studies, Pärtel and Helm (2007) recorded higher moisture values on surfaces covered by alvar grass than adjacent woody vegetation, while Farley et al . (2004) found higher moisture retention on grass patches than Pinus radiata stands of different ages. In the

latter study, there was a consistent reduction in soil moisture with increasing age of pine stands. Wilson (1993) and Stark et al . (2003) have found that encroaching shrub and woody species increase soil moisture and other resources. On the contrary, Jobbagy and Jackson, (2001) observe that such invaders take advantage of already existing higher moisture values.

4.4.3 Soil moisture trends

There was a stepwise moisture reduction for the studied episodes on the three surfaces (Figure 4.5 a-c). Typically, the variability in soil moisture readings at different stages were determined by the amount of precipitation received. Relevant to this study however were the higher grass surfaces moisture values at each precipitation episode at onset, break-point and just before the ensuing precipitation episode (Figure 4.5 a-c).

0.4 0.35 Highest VWC Break point 0.3 Low est VWC ) 3 0.25

/cm 0.2 3 0.15 (cm 0.1 0.05

Volumetric Water Content Volumetric Content Water 0 Grass P. incana Bare

Surfaces a) Episode 1.

0.25 Highest VWC 0.2 Break point Low est VWC ) 3 0.15 /cm 3 0.1 (cm

0.05 Volumetric Water Content Content Water Volumetric 0 Grass P. incana Bare

Surfaces b) Episode 2.

0.5 0.45 Highest VWC Break point 0.4 Low est VWC 0.35 ) 3 0.3

/cm 0.25 3 0.2 (cm 0.15 0.1 0.05 Volumetric Water Content Content Water Volumetric 0 Grass P. incana Bare Surfaces

c) Episode 3.

Figure 4.5 a-c: Moisture measurements at rainfall onset, break point and lowest amount recorded. (VWC-Volumetric Water Content).

The wet/dry thresholds were used to determine the break-points between the two moisture loss regimes. The grass surface had the highest moisture loss after each rainfall episode and took longer to reach wet/dry threshold than P. incana (Table 4.1). The dense grass surface impedes surface flow during and soon after precipitation leading to higher moisture retention and consequent higher moisture reading (Tongway and Hindley, 1995). Galle et al. (1999) note that in areas covered by grass

and thicket, up to 30mm of rain may be trapped by leaves and roots creating longer surface reservoir for infiltration. However, the higher amount of moisture may rapidly be lost through rooting system and micro-fauna that open up the surface (Chase and Boudouresque, 1989). As seen in the sub-section on moisture loss slopes (Table 4.2), higher moisture retention capacity on grass after rainfall also means a higher amount of moisture is lost through direct solar energy evaporation, evapo-transpiration and sub-surface infiltration before a wet/dry threshold is reached. Consistently low moisture values and longer moisture retention before breakpoints prevailed on bare surfaces than the P. incana and grass surfaces (Table 4.1). The lower moisture readings and longer moisture retention can be attributed to surface crusting and sealing. The bare crusted surfaces as seen in the P. incana invaded areas cause a reduction in hydraulic gradient and infiltration which enhance excessive surface flow (Le Bissonnais et al ., 1995; Thiery et al ., 1995). According to Coran et al. (1992), hardening from surface cementation and hydrophobic processes cause higher mechanical resistance and consequent higher surface run-off. Whereas little water infiltrates under bare crusted surfaces, absence of vegetation that may lead to evapo- transpiration and the crusted sealing that locks moisture under the surface can be used to account for longer moisture retention than grass and P. incana surfaces (Table 4.1).

The P. incana invaded surface retained more moisture than the bare surface but less moisture than the grass patch. Higher moisture values than the bare surface can be attributed to the surface root opening that allows infiltration, above surface shading that keeps the surface cool and moist and P. incana stems and litter that reduce runoff. Lower moisture values than the grass patch on the other hand can be attributed to partial P. incana canopy cover that allows for solar penetration and consequent surface moisture loss through evapo-transpiration. These reasons can also be used to account for the durations taken before wet/dry break points (Table 4.1).

Table 4.1: Surface soil moisture value threshold ranges and break-points.

Rain Surface Highest θv Lowest θv θv at Break-point Actual 3 3 3 3 episode (cm /cm ) (cm /cm ) Break-point threshold break-point (cm 3/cm 3) range (Hrs) (Hrs) 1 Grass 0.381 0.1139 0.1745 154.011 - 157.189 155.600 P. incana 0.2315 0.0989 0.1397 153.100 - 157.945 155.523 Bare 0.1753 0.0565 0.0817 158.039 - 160.943 159.491 2 Grass 0.2015 0.1157 0.1427 102.554 - 112.051 107.302 P. incana 0.1541 0.0989 0.1163 100.458 - 111.781 106.120 Bare 0.1116 0.0738 0.0923 106.384 - 116.582 101.285 3 Grass 0.4667 0.1163 0.2003 118.393 - 122.358 122.376 P. incana 0.2699 0.1013 0.1475 118.678 - 123.446 121.062 Bare 0.192 0.052 0.0844 120.558 - 125.826 123.192

The higher moisture content on grass than the other two surfaces confirms earlier findings by Ritsema et al . (1993) and Dekker and Ritsema (1996), which showed that micro-scale moisture variation in areas covered by grass in comparison to other surfaces is caused by downward preferential channelling below the patches. According to the authors’ findings, more water from subsequent surface precipitation is accumulated on grass under- patches preferential paths while bare and drier areas persist due to their higher water repelling characteristics and low hydraulic conductivity.

Different but consistent soil moisture versus time slopes trends were observed on the three rainfall episodes (Table 4.2). The trends for each of the surface slope values before and after the break-points were similar in the three rainfall episodes (Table 4.2). There was a general decrease in the highest amount of moisture retained after a rainfall on grass, P. incana and bare surfaces respectively. However, there was an increase in slope steepness values before and after the breakpoint on the bare, P. incana and grass surfaces, indicating a higher rate of moisture loss on the grass patch than on the bare surface. However, as shown in Table 4.1, the lowest moisture value recorded on grass was higher than P. incana and bare surfaces.

Table 4.2: Surface moisture slope and y-intercepts before and after breakpoints.

Rain Surface Slope before* y-intercept Slope after* y-intercept Episode Break-point break-point

1 Grass - 0.00152 0.381 -0.0001 0.1763 P. incana -0.00073 0.231 -0.00007 0.1409 Bare -0.00066 0.175 -0.00004 0.0817 2 Grass -0.00046 0.201 -0.00006 0.1515 P. incana -0.00031 0.154 -0.00004 0.1157 Bare -0.00023 0.111 -0.00003 0.0837 *Slope 3 before Grass break-point -0.00208- Wet slope 0.466 -0.0001 0.1985 P. incana -0.00106 0.269 -0.00007 0.1463 *Slope after break-pointBare – Dry-0.0007 slope9 0.192 -0.00004 0.0835

4.4.4 Day/night moisture oscillations

Moisture logs for the entire study duration showed day/night moisture oscillations. Table 4.3 shows standard deviations of the three episodes for high (day) and low (night) moisture values before and after the wet/dry thresholds. Consistently high and low deviations for grass and bare surfaces respectively were recorded in all rainfall episodes, while the deviation values for P. incana lay between the two surfaces. The lower day-time moisture readings can be attributed to higher temperatures that lead to higher surface evaporation rates. Similarly, higher night-time moisture readings on the other hand can be explained by low temperatures that lead to low evaporation. Generally, the deviations were higher before the wet/dry threshold than after the thresholds.

Table 4.3: Day/night moisture standard deviations before and after break-points.

Rain episode Surface θv STDEV before θv STDEV after Break-point (cm 3/cm 3) break-point (cm 3/cm 3) 1 Grass 0.0687 0.0161 P. incana 0.0333 0.0108 Bare 0.0301 0.0058 2 Grass 0.0157 0.0081 P. incana 0.0107 0.0056 Bare 0.0079 0.0038 3 Grass 0.0765 0.0227 P. incana 0.0392 0.0140 Bare 0.0295 0.0084

Due to lower moisture retention capacity, the bare surface was less affected by the day/night moisture fluctuations. Conversely, the grass surface “mulch” and lower night temperatures explain the higher moisture readings at night (Figure 4.6 a-f). The P. incana invaded surface can be considered an intermediate zone; neither lost too much moisture during day due to P. incana shrub shading nor showed high moisture values like grass surfaces due to its lower moisture retention capacity. The oscillations and deviations are also determined by the amount of moisture in the soil. It is expected that the standard deviations and oscillation will continue decreasing as a surface dries out (see Menziani et al ., 2003).

0.45 Grass surface 0.4 P. incana surface Bare surface 0.35

) 0.3 3 0.25 /cm 3 0.2

(cm 0.15 0.1 0.05 Volumetric Water Content 0 0 30 60 90 120 150 Time (h)

a) Episode 1 oscillation before break-point. 0.2 0.18 Grass surface P. incana surface 0.16 Bare surface 0.14 ) 3 0.12 /cm

3 0.1 0.08 (cm 0.06 0.04

Volumetric Water Content Content Water Volumetric 0.02 0 150 250 350 450 550 650

Time (h) b) Episode 1 oscillation after break-point.

0.25 Grass surface P. incana 0.2 Bare surface ) 3 0.15 /cm 3 0.1 (cm

0.05 VolumetricWater Content 0 0 20 40 60 80 100 Time (h) c) Episode 2 oscillation before break-point.

0.18 Grass surface 0.16 P. incana 0.14 Bare surface 0.12 ) 3 0.1 /cm 3 0.08 (cm 0.06 0.04 VolumetricWaterContent 0.02 0 110 160 210 260 310 360 410 460 510 Time (h) d) Episode 2 oscillation after break-point.

0.5 0.45 Grass surface 0.4 P. incana surface Bare surface 0.35 ) 3 0.3 /cm

3 0.25 0.2 (cm 0.15 0.1 Volumetric Water Content Water Volumetric 0.05 0 0 20 40 60 80 100 120

Time (h) e) Episode 3 oscillation before break-point.

0.25 Grass surface P. incana surface 0.2 Bare surface ) 3 0.15 /cm 3 0.1 (cm

0.05 Volumetric Water Content VolumetricWater

0 120 220 320 420 520 620 720 Time (h) f) Episode 3 oscillation after break-point.

Figure 4.6 a-f: Day and night soil moisture oscillations before and after break-points.

The day/night moisture oscillations in this study were consistent with findings by Menziani et al . (2003). Simulating day and night conditions in the laboratory, the authors found a negative correlation between soil temperature and θv where an increase in temperature led to a decrease in θv and a decrease in soil temperature led to

an increase in the soils θv. In the same study, field observations showed that day/night

θv deviations from the mean decreased as the soils dried out.

4.4.5 Implications of P. incana invasion for landscape function

That the highest, intermediate and lowest soil moisture was consistently recorded under grass, P. incana and bare areas clearly indicates infiltration and runoff conditions under the respective cover conditions. Soil moisture trends indicate that alteration of vegetation cover to P. incana -bare surface mosaic leads to reduced infiltration and increased runoff.

As pointed out earlier, monitoring was conducted on a gentle slope (8 0) where P. incana invasion was at a stage when wide bare areas had not developed between the shrubs. Worst case scenarios of the invasion characterised by patchiness loss and hence extremely wide bare areas are common on degraded steep slopes in many communal areas. Under such conditions, soil moisture flux conditions would significantly be different. Such scenarios where patchiness loss is significant imply far greater soil moisture loss and runoff. As observed by Cammeraat (2004), once individual bare patches produce more runoff than can be absorbed by vegetation clumps lower in the hydrological pathway, runoff will concentrate and initiate rills. Exacerbation of such conditions could lead to the conversion of landscapes to dysfunctional systems.

P. incana’s resource capture capability was demonstrated by the significantly greater moisture retention under its tussocks than under bare zones. Even under conditions of considerable patchiness loss, individual tussocks do remain resource islands. This must be taken cognisance of when developing strategies to rehabilitate highly degraded P. incana -bare zone mosaics.

As already mentioned, P. incana invaded surfaces are often characterized by low moisture content and high vulnerability to soil erosion. Consequently, rehabilitation measures such as manual clearance and burning are inappropriate, as they expose the soil surface to erosion and moisture loss through direct solar heating (Kakembo et al , 2006; Palmer et al ., 2005). Restoration of P. incana invaded areas should therefore 62

focus on reducing surface run-off and evaporation caused by solar energy and increasing infiltration. As demonstrated by Tongway and Ludwig (1996), spreading of brush piles on bare slopes can be used to capture and maintain moisture and other nutrients. In P. incana invaded environments, experiments by Kakembo (2007) that entailed the use of brush piles have shown remarkable recovery of grass species in P. incana invaded areas. Similar success has also been observed on private farms around Amakhala Game Reserve using this method.

According to Kakembo (2007), cleared P. incana used as brush piles increases surface litter, captures sediments, acts as mulch that traps and maintains soil moisture and protects the soil from surface moisture loss caused by solar heating. Reduced competition from cleared patches and elevated soil moisture provides a conducive environment for grass re-establishment. Due to P. incana’s robust seed bank and inherent high resilience, Kakembo et al . (2006) suggests that follow-up clearances and grazing controls are necessary during the rehabilitation process.

4.5 Conclusion

Significant soil moisture retention and flux variations between grass, P. incana , and bare areas were identified. The invasion process shows an alteration of the soils moistures regimes so that the availability of water and nutrients over time is insufficient for some vegetation species to persist. Despite their greater retention capability, grass surfaces lose soil moisture more rapidly than P. incana and bare surfaces. Bare areas on the other hand recorded longer moisture retention before breakpoints than the P. incana and grass surfaces. This could be attributed to soil surface crusting that locks moisture in the soil. P. incana’s resource capture capability was demonstrated by the significantly greater moisture retention under its tussocks than under bare zones. Cognisance of this must be taken when rehabilitating highly degraded invaded areas.

2Chapter 5: A comparison of Pixel and sub-Pixel based techniques to separate Pteronia incana invaded areas using multi-temporal High Resolution Imagery

5. 1 Introduction

The impacts of invasions by non-native plant species are increasingly attracting attention in ecological studies. Whereas some non-native species are known to enhance local ecological diversity (Loreau and Mouquet, 1999), others have been detrimental to natural and socio-economic systems (Mack and D’Antonio, 1998; Grove and Willis, 1999; Mack et al ., 2000; Pimental et al. , 2000; Stachowicz, et al. , 2002). One of the rapidly growing ecological research and application tools is the use of Remote Sensing techniques, as it offers a range of additional research benefits in comparison to traditional ground based mapping and analysis methods (see, Buiten, 2000).

Vegetation Indices (VIs) are probably the most widely used remote sensing measure in ecological research. These indices provide a measure of photosynthetically active above ground green biomass and are often used as surrogates for rainfall and vegetation density (Tucker et al., 1985; van Dijk, 2000). Medium spatial resolution imagery have been the most successful tool for VIs applications at regional, sub- continental or even global scales (Tucker et al., 1985; Billington, 2000). Whereas large land cover plant invasions are not an exception (Le Maitre et al ., 2001), most ecological invasions originate and sometimes exclusively occur at localised spatial scales as dictated by conditional suitability. These make coarse spatial resolution imagery unsuitable for spatially precise identification and mapping of invader species (Nilsen et al. , 1999).

Fine spatial resolution satellite remote sensing imagery provides reliable Normalised Difference Vegetation Index (NDVI) measurements at localised scales. However, not all vegetation types yield positive NDVI values (de Boer, 2000). In comparison to other vegetation types, Palmer et al. (2005) found that areas invaded by Pteronia incana (Blue bush) had very low NDVI values in both Advanced Spaceborne Thermal

2This chapter is based on a paper under review by the Journal of Applied Remote Sensing - Authors, Odindi, J. O. and Kakembo, V.

Emission Radiometer (ASTER) and colour infrared high spatial resolution imagery. This supported earlier findings of a study by Kakembo (2003) that investigated spectral characteristics of P. incana invaded areas in comparison to other vegetation types.

Using single date High Resolution Imagery (HRI), Kakembo et al. (2007) noted that slope based VIs like the NDVI, Soil Adjusted Vegetation Index (SAVI) and Modified Soil Adjusted Vegetation Index (MSAVI) could be used to separate robust green vegetation from P. incana but failed to separate P. incana invaded areas from bare surfaces. The Perpendicular Vegetation Index (PVI) on the other hand provided reliable separability between P. incana invaded areas and other land surfaces, particularly bare areas. However, the observations were based on a one-off scene. Multi-temporal HRI as well as spectroscopy would therefore be required to establish temporal and seasonal spectral variations for P. incana .

Like other commonly used VIs, the PVI is based on pixel level analyses that involve an aggregation of pixel content. At this level of analysis, the influence of dominant features within pixels often overshadows minor covers. Consequently, pixel based methods may provide unreliable land cover classifications (Cracknell, 1998; Lu et al. , 2004). Owing to its patchy nature, areas invaded by P. incana are often interspersed by grass patches and bare surfaces at early and advanced stages of invasion respectively. Under such scenarios, a mixed pixel problem arises whereby multiple land covers occur within pixels. The use of pixel based techniques in such cases may ‘force’ heterogeneous classes within pixels to belong to single classes (Foody, 1996; Huguenin et al. , 1997; Tompkins, et al. , 1997; Defries et al., 2000; Pu et al., 2003).

Spectral Mixture Analysis (SMA) methods that de-convolve pixel content have enhanced land cover mapping and classification accuracy (see; Smith et al., 1990; Tompkins, 1997; Novo and Shimabukuro, 1997; Erol, 2000; McGwire et al., 2000; Elmore et al. , 2000; Small, 2001; Pu e t al. , 2003; Lu et al ., 2003b; Lu et al ., 2004; Omran et al., 2005; Palaniswami et al. , 2006). Originally designed for hyperspectral imagery analysis (see Tseng, 1999; Lobell and Asner, 2004; Lass et al ., 2005; Miao et al ., 2006; Robichaud et al., 2007), SMA has been equally useful in multispectral image analyses (see van der Meer and Jong, 2000; Pu et al., 2003; Lu et al., 2004; 65

Piwowar, 2005; Omran et al., 2005; Uenishi et al., 2005; Palaniswami et al ., 2006). Spectral unmixing offers two major benefits: changing spectral values to specific elements within a pixel and providing a single land cover distribution within an image for each class (Tompkins et al ., 1997). This chapter therefore sought to compare P. incana separation using the pixel based PVI and SMA’s Linear Spectral Un-mixing (LSU). Multi-temporal HRI and laboratory spectroscopy were used to establish P. incana’s spectral characteristics.

5.2 The study area

The study area lies in the upper section of one of the catchments fringing the lower Great Fish River in the Eastern Cape Province of South Africa (Figure 5.1). The area has for a long time been under communal land ownership and has a long history of livestock grazing, cultivation and its subsequent abandonment. Annual rainfall that ranges between 480 – 550 mm is bimodal with peaks in October-November and March-April. Less than 25% of the annual rainfall is received between May to Sept. (the winter period). Average minimum and maximum temperatures are 5 oC and 31 0C respectively.

Fig. 1: Location of the study area.

The topography of the area is characterised by slopes that rise steeply before they even out into gentle and extensive interfluves. It is underlain by a mixture of sandstones and shale of the Rippon formation belonging to the Ecca group. The area is dominated by the Karoo super group’s Shallow litholic soils that belong to the Mispah form (the equivalent of Entisols in the USDA classification). Ephemeral streams whose channels are clogged with sediment owing to severe soil erosion dissect the area. Blanket invasion by P. incana predominantly occurs on abandoned lands, most of which are severely eroded.

The area falls within a semi-arid region of the Eastern Cape plateau. According to Cowling (1984), vegetation in the study area can be described as Subtropical Transitional Kaffrarian Thicket. This description is however, based on historical pristine conditions, as most vegetation cover has undergone significant transformation. Like most of the communal areas in the Eastern Cape, vegetation in 67

the study area has been greatly transformed due to past and present injudicious land use practices. The invasion by P. incana has given rise to the conversion of extensive areas to a single species dominance scenario (Figure 5.2). Efforts by the local community to control the invader are noticeable from Figure 5.3b and c in the form of parallel strips.

Figure 5.2: Densely invaded patches around the study area.

5. 3 Methods

5. 3. 1 High Resolution Imagery acquisition

Infra-red HRI acquired using a DCS 420 colour infrared camera on an aerial platform was used in this study. The camera records energy from approximately 0.3 µm to just above 1.0 µm portrayed on the film as false colours (Kodak, 1999). The study area was flown in a light aircraft at an altitude of about 2700 m on 21 March 2001, 14 October 2004 and 18 July 2006. In each case, three spectral bands (green – 052 - 0.62 µm, red- 0.63 - 0.69 µm and NIR - 0.7-1.0 µm) were captured. The images were taken during different seasons to address P. incana ’s phenological variations across the year, which might influence its spectral response.

Although hillslopes with blanket invasion of P. incana are common in the study area (Figure 5.2), field surveys were done to locate sites with clear reference points where the main cover types of the area co-exist. A site with four land cover types (Bare surfaces, Grass patches, P. incana invaded areas and Riparian vegetation) was therefore identified. Using this site as a reference point, multi-temporal digital HRI with a spatial resolution of 1 m x 1 m, pixel array dimension of 1012 x 1524 and about 1.5 km x 1.2 km spatial coverage were selected for processing. The selected images were then exported to the Idrisi Kilimanjaro GIS and remote sensing software.

5. 3. 2 Image rectification

The images were separated into three bands; the infrared, red and green. Fourty Ground Control Points (GCPs) uniformly distributed across each image were acquired from the field using a centimetre level precision Ashtech ® proMark2 TM Global Position System (GPS). Information relating to the respective cover types was also recorded in the form of circular GPS waypoints in the vicinity of each GCP. This information was used in the image classification process as described in the section on temporal image analysis. The nearest neighbour algorithm was used to resample the imagery. A Root Mean Square (RMS) error as low as 0.006 m confirmed geo- referencing accuracy. Further accuracy was established by way of digitizing vector polygons and lines from the 2001 composite image and overlaying them on the 2004 and 2006 counterparts. All the vector layers digitized from the 2001 images overlaid perfectly on the latter images’ corresponding features.

Inconsistencies in brightness values of multi-temporal imagery may affect image quality and interpretation. These inconsistencies may be due to the sensor signal or environmental factors during image acquisition (Jensen, 2005; Eckhardt et al. , 1990). Atmospheric and sensor properties were not available during image capture, as the infra-red camera sensors were not calibrated. In the absence of these details, relative radiometric correction as recommended by Jensen (2005) and Janzen et al. (2006) was used.

It is noteworthy that spectral units for the imagery are Digital Number (DN) values spanning a range of 0-255. Since the infra-red camera sensors were not calibrated, DN 69

could not be converted to reflectance values. As pointed out by Lillesand et al ., (2004), a general linear correlation exists between DN integers and absolute radiance and hence reflectance, such that 0 and 255 represent minimum and maximum radiance respectively. All the multi-temporal imagery bands had different DN values, necessitating atmospheric correction. The 2001 imagery was used as a base image for normalisation due to its greater visual clarity. Using the CALIBRATE module in Idrisi Kilimanjaro, the images were adjusted using the offsets and gains from the fitted regression intercept and slope.

5. 3. 3 Image enhancement

The key advantage that low altitude HRI has over satellite sensor imagery is that atmospheric condition that can degrade image quality can be avoided when planning a flight mission. The resulting images were of good visual quality and virtually noise free. Nevertheless, an attempt was made to further improve their quality by use of filters that accentuate or suppress image data of different frequencies in relation to the surrounding pixel brightness (Lillesand et al ., 2004). A 5 x 5 filter kernel was passed over the images, which considerably enhanced visual image quality.

5.3.4 Multi-temporal image analyses

Colour composites (Figure 5.3 a-c) were created from the green, red and NIR bands (bands 1, 2 and 3 respectively) to yield green vegetation sensitive NIR false colour rendition for the 2001, 2004 and 2006 images.

a: 2001 green, red and NIR band composite.

b: 2004 green, red and NIR band composite.

c:2006 green, red and NIR band composite.

Figure 5.3 a-c: Geo-rectified band composites.

These provided a platform for identifying bare soil areas from which samples were extracted in order to perform a linear regression on bare soil pixels in the red and infrared bands. Using the REGRESS module in Idrisi Kilimanjaro, the slope and intercept were obtained in order to generate PVI images. The PVI is analytically preferable to most simple ratio indices, as it fully accounts for the background soil, reduces the effects of differences in solar zenith and accounts for topographic differences (Asner et al. , 2003). It is expressed as:

PVI = ()NIR − aR + b a 2 +1 (9)

Where a and b are the slope and offset of the soil line respectively (Asner et al., 2003).

As pointed out earlier, the PVI provided distinct separability of P. incana from other surfaces in a study that used single date HRI. The consistency of the PVI to provide separability under a multi-temporal setting was tested by digitising point features on the respective cover surfaces identified from multi-temporal PVI imagery and corresponding DN values were extracted from the red and NIR bands. In line with the

soil-line concept, the latter band values were plotted against the former. The resultant scattergrams served to validate the separability of the respective surfaces.

Image classifications of the multi-temporal imagery based on the PVI images were conducted using the maximum likelihood algorithm. For purposes of accuracy assessment, the PVI derived classifications were compared with those based on GPS waypoint records of the corresponding vegetation cover types. An error matrix was then created for the ground data (column: truth) against the PVI classification (rows: mapped). Using the ERRMAT module in Idrisi Kilimanjaro, correctly classified pixels (diagonal bold), error of commission (ErrorC) and error of omission (ErrorO) were generated. The overall Kappa Index of Agreement (KIA) values were 0.78, 0.84, and 0.85 for 2001, 2004 and 2006 respectively, signifying high classification accuracy (Table 5.1 a-c). Boolean images representing P. incana were then created and compared with sub-pixel P. incana image fractions.

Table 5.1 a - c: Error matrices 2001, 2004 and 2006 imagery (1 - P. incana , 2 - Grass and 3 - Bare surfaces) a) Error Matrix Analysis of 2001 classification 1 (columns: truth) against 2001 classification 2 (rows: mapped)

1 2 3 Total ErrorC ------1 | 530008 48106 3863 | 581977 0.0893 2 | 4717 788749 565 | 794031 0.0067 3 | 6049 121096 39135 | 166280 0.7646 ------Total | 540774 957951 43563 | 1542288 ErrorO | 0.0199 0.1766 0.1016 | 0.1196

Overall Kappa = 0.7806. (Diagonal bold values are correctly classified pixels ).

b) Error Matrix Analysis of 2004 classification 1 (columns: truth) against 2004 classification 2 (rows: mapped)

1 2 3 Total ErrorC ------1 | 262576 3615 0 | 266191 0.0136 2 | 15276 817193 9272 | 841741 0.0292 3 | 5836 95597 332923 | 434356 0.2335 ------Total | 283688 916405 342195 | 1542288 ErrorO | 0.0744 0.1083 0.0271 | 0.0840

Overall Kappa = 0.8555 . (Diagonal bold values are correctly classified pixels).

c) Error Matrix Analysis of 2006 classification 1 (columns: truth)against 2006 classification 2 (rows: mapped)

1 2 3 Total ErrorC ------1 | 644553 5 7788 | 652346 0.0119 2 | 78936 554055 38142 | 671133 0.1744 3 | 8271 2378 208160 | 218809 0.0487 ------Total | 731760 556438 254090 | 1542288

ErrorO | 0.1192 0.0043 0.1808 | 0.0879

Overall Kappa = 0.8580. (Diagonal bold values are correctly classified pixels).

Owing to the mixed pixel problem pointed out earlier, multi-temporal endmembers for the three dominant land surfaces (bare surfaces, grass and P. incana ) were extracted from the processed imagery. This was achieved using the image based “purest pixels” identification technique (Adams and Gillespie, 2006) based on a priori GPS field surveys. The endmembers were used to generate P. incana invaded surface fractions using the LSU (Adams et al ., 1995; Eastman, 2003). The LSU model is one of the SMA models and is based on reflectance spectrum linear combination of the endmembers of materials present in a pixel weighted by their fractional abundance (Jensen 2005). It is expressed as:

Ri = ∑ f k Rik + ε i (10) k =1

Invaded surfaces were unmixed using Constrained Linear Spectral Unmixing (CLSU) and image fractions. In this technique, the proportion of each endmember is between 0 and 1, and the fractional area occupied by each material within a pixel sums to 1

(Lu, et al., 2003a). To reflect true abundance fractions of endmembers, constrained unmixing solution was applied where fk is restricted and expressed as:

∑ f k = 1 and 0 ≤ f k ≤ 1. (11) k =1

The Spectral Mixture Analysis (SMA) works well with few and spectrally distinct surface types (Lu et al. , 2003a; Lass et al., 2005). Therefore, riparian vegetation as a surface not central to this study was excluded from this test. A summary of the steps followed in the multi-temporal image processing is shown in Figure 5.4.

Data acquisition (HRI) and surface cover validation

Image pre-processing -Band separation -Image georectification -Image enhancement

-GPS co- ordinates, field surface data and colour composites PVI generation

Signature development Endmember extractions

Classes -P. incana -Grass Image fractions -Bare surfaces -P. incana -Grass -Bare surfaces classifications Supervised Accuracy assessments

unmixing Pixel Discard grass and bare surface fractions

Mask grass and bare surfaces

P. incana fractions

P. incana boolean images

P. incana Boolean images and fractions comparisons

Figure 5.4. A flow-diagram of image data acquisition and processing.

Global Position System waypoints around P. incana invaded surfaces surveyed at the time of image acquisition and P. incana residual images were used to determine the reliability of P. incana image fractions. The plots were overlaid onto the P. incana fraction residual images and values were extracted from points digitised within the

plots. Most of the field samples had low residual values when extracted from the residual image, implying high classification accuracy (Figure 5.5).

Figure 5.5: Residual values based on P. incana residual images.

5.3.5 Surfaces sample spectroscopy

Besides the pixel unmixing of P. incana invaded areas, it would be useful to establish the invader species’ unique spectral values using laboratory spectroscopy. Given that a spectrometer provides direct reflectance values, in situ or laboratory reflectance measurements would play an important role in validating the spectral patterns identified from the HRI. Owing to the impracticality of spectral measurement during image acquisition (Anderson and Milton, 2006), laboratory spectral measurements were done between October 2007 and January 2008. An EPP 2000 concave grating spectrometer (StellarNet Inc., Tampa - Florida) was used to take reflectance measurements under laboratory conditions and compared with HRI data. The spectrometer has a wavelength range of 0.28 – 0.9 µm in the visible and NIR, a resolving resolution of less than 1 nm for the 25 µm slit and an aberration corrected concave grating (StellarNet Inc., Tampa-Florida). Its wavelength was scaled to the vegetation sensitive 0.45 – 0.9 µm range and set to store a single average reading for five individual data scans. Four sets of samples comprising soil from bare surfaces, Gwarrie - Euclea undulata (a dominant species in the area to represent green 77

vegetation) and P. incana were collected from the study site and their reflectances measured in the laboratory within one hour of collection. A total of four average data files were recorded for each cover type.

Whereas grass surfaces were considered for generating image fractions, their reflectance are highly depended on their greenness as determined by moisture availability. Grass responds quickly to changes in moisture availability and it can be expected that small amounts of precipitation can cause significant changes in reflectance. To minimise this discrepancy, E. undulata green broad leaves were used for reflectance measurements instead of grass.

5. 4 Results

The use of the PVI as a basis for the respective supervised classifications provided a clear distinction between all the land-cover types in the respective sets of imagery (Figure 5.6 a - f).

a: A PVI for the 2001 image. b: A reclass for the 2001 PVI image

c: A PVI for the 2004 image. d: A reclass for the 2004 PVI image

e: A PVI for the 2006 image. f: A reclass for the 2006 PVI image .

Figure. 5.6 a-f: PVI images and respective classes (lower values in the PVI image indicate P. incana invaded surfaces).

The NIR-Red scatterplots (Figure 5.7 a - c) depict low NIR spectral values for P. incana invaded areas and conform to PVI models by Richardson and Wiegand (1977) and Elvidge and Lyon (1985). The multi-temporal consistency serves to confirm that the PVI provides a reliable spectral separation of P. incana from the other surface cover types.

Figure 5.7a: A 2001 image of different surfaces DN clusters in a NIR-red plot.

Figure 5.7b: A 2004 image of different surfaces DN clusters in a NIR-red plot.

Figure 5.7c: A 2006 image of different surfaces DN clusters in a NIR-red plot.

Endmembers from the three surfaces (bare areas, grass and P. incana ) showed a consistent pattern of DN values in all three temporal images (Figure 5.8 a - c). Whereas areas invaded by P. incana had the lowest DN values in the NIR band, grass and bare surfaces distinctly showed the highest DN values in NIR and red bands respectively.

250 Bare surfaces 200 Grass P. incana 150

100 DN values

0 Green Red NIR

Image band Figure 5.8 a: 2001 endmembers.

200

Bare surfaces 150 Grass P. incana

100 DN values 50

0 Green Red NIR

Image band Figure 5.8 b: 2004 endmembers.

200

Bare surfaces 150 Grass P. incana

100 DN values 50

0 Green Red NIR

Image band Figure 5.8 c: 2006 endmembers.

Figure 5.8 a - c: Multi-temporal surface endmembers.

P. incana surface fractions were compared with Boolean images generated using training sets from multi-temporal PVI images (Figure 5.9 a - f). Values for the fractions range from 0 to 1, indicating absence and presence of P. incana respectively. In the Boolean image, 0 shows P. incana invaded areas and 1 shows other surfaces.

This comparison shows a clear visual spatial correlation between the surface fractions and boolean images.

Figure 5.9a: 2001 P. incana surfaces Fig. 5.9b: 2001 P. incana Boolean fraction. image.

Figure 5.9c: 2004 P. incana surfaces fraction. Figure 5.9d: 2004 P. incana boolean image.

Figure 5.9e: 2004 P. incana surfaces fraction. Figure 5.9f: 2006 P. incana boolean image.

Figure 5.9a - f: Multi-temporal P. incana image fractions and P. incana boolean images with training sets from PVI images.

It can be noted from reflectance measurements (Figure 5.10 a - c) that there are indistinctive reflectance differences in the green band (around 0.55 µm) in the three sets of measurements. The 0.69 - 0.87 µm wavelengths provided the greatest distinction between the respective surfaces, with P. incana showing the lowest reflectance in all three imagery sets. The wavelengths between 0.56 µm and 0.71 µm also clearly discriminated the reflectance of bare surfaces from green vegetation, and bare surfaces from P. incana .

0.9 Bare surfaces 0.8 Green vegetation P. incana 0.7

0.6

0.5

0.4 Reflectance 0.3

0.2

0.1

0 0.45 0.55 0.65 0.75 0.85 Wavelength ( µm) a: Spectral measurements 28/10/ 07.

0.9 Bare surfaces 0.8 Green vegetation P. incana 0.7

0.6

0.5

0.4 Reflectance 0.3

0.2

0.1

0 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 Wavelength ( µm) b: Spectral measurement 11/12/2007.

0.7 Bare surfaces 0.6 Green vegetation P. incana

0.5

0.4

0.3 Reflectance 0.2

0.1

0.0 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 Wavelength ( µm)

c: Spectral measurements 19/01/2008.

Figure 5.10a - c: Spectral samples measurements between October 2007 and January 2008.

5. 5 Discussion and conclusion

High reflectance in the NIR in comparison to the red band is often determined by vegetation density, stage of growth and internal leaf water content (Walkie and Finn, 1996; de Boer, 2000; Jensen, 2000). On the contrary, a significant reduction of the red edge, as well as low NIR reflectance have been identified as spectral attributes in P. incana (Figure 5.6 a - c). According to de Boer (2000), the downward shift in the NIR DN values can often be attributed to the waxy nature of the leaf surface and hair cover or internal leaf pigmentation. Whereas the internal leaf pigmentation was not investigated in this study, the hairy and waxy leaf surface which is a typical characteristic of P. incana should influence its spectral response. This surface is readily visible in the form of whitish grey cover often different from surrounding vegetation, hence the “Blue bush” as it is locally known.

The PVI showed clear spectral separability between all the land surfaces in the respective temporal imagery taken in different seasons (Figure 5.6 a - c and 5.7 a - c). This consistency under a multi-temporal setting confirms the PVI’s suitability to establish P. incana’s temporal pattern and spectral response under different seasonal situations. It also confirms the observation by Kakembo et al. (2007) that using HRI, the PVI is best suited for the identification of perennial shrubs with characteristics similar to P. incana . That the PVI is consistent with the unmixed surface image fractions from CLSU demonstrates that using HRI, the effectiveness of the PVI is not impeded by the mixed pixel problem.

Like in other heterogeneous land surfaces (Asner et al. , 2003), the use of sub-pixel analysis has potential to provide better P. incana and other surface type classifications than existing VIs. However, comparisons of SMA and PVI classifications did not show any significant differences in this study. This can be attributed to the fine spatial resolution of the imagery used in this study where much of a single surface is accommodated within a pixel. In P. incana invaded surfaces, the bare areas usually span more than 1 x 1 m spatial dimensions, making it possible to be classified as bare in both PVI and SMA. On the other hand, P. incana individual patches often occupy more than 1 x 1 m spatial dimensions. For these reasons, sub-pixel based techniques

like SMA are unlikely to increase the accuracy of classifications achieved by pixel based methods.

Clear separability between bare surfaces, green vegetation and P. incana was achieved using spectral reflectance measurements of the different wavelengths. A short rise at around 0.67 µm (considered P. incana’s red edge) that was recorded in all the spectral measurements could be used to differentiate bare surfaces from P. incana . A clear red edge distinction for green vegetation after 0.68 µm is discernible. P. incana ’s typically low reflectance in the NIR region (0.75-0.87) is also clearly evident. That distinct separability between all the surfaces was achieved in the NIR region validates spectral trends identified from HRI.

The spectral response of most annuals and in many cases perennial vegetation types change with seasonal variations. Consequently, it is often desirable that the timing of imagery acquisition be in tandem with specific stages of vegetation growth. That notwithstanding, the present study confirmed that, under favourable atmospheric conditions during imagery capture, seasonal variation seem not to significantly influence P. incana’s temporal spectral response trends. The consistency of P. incana separability on a multi-seasonal basis is therefore useful for P. incana monitoring using remote sensing techniques.

Multi-temporal trends show unique spectral characteristics in areas covered by P. incana in comparison to other vegetation surfaces. Sub-pixel classifications using SMA can further be used to compare and refine pixel based PVI classes and results validated by field or laboratory spectral measurements. Results from this study show that using HRI, a combination of the two techniques can reliably provide data for monitoring and management of invasions by P. incana and other invader shrubs with similar spectral characteristics.

3Chapter 6: The use of laboratory spectroscopy to establish Pteronia incana spectral trends and separation from bare surfaces and green vegetation

6.1 Introduction

The invasion of rangelands by unpalatable dwarf shrubs indigenous to the Nama- semi-arid Karoo region of South Africa has become a serious environmental problem in many parts of the country’s Eastern Cape Province. In an effort to understand the dynamics of its invasion, Pteronia incana (Blue bush), the most undesirable among the invader shrubs, has been investigated in different studies (see Kakembo, 2004; Palmer et al , 2005; Kakembo et al , 2006, 2007). Besides reducing grazing capacity, the shrub is associated with severe forms of soil erosion and eventual conversion of rangelands to dysfunctional systems (Kakembo, 2007). Remote sensing techniques are one of the tools that have been used in the respective studies for purposes of mapping P. incana distribution. Despite the strides made in characterising its distribution, a number of gaps remain in the quest to achieve its unmistakable separation from other vegetation surfaces and bare areas. Ascertaining the shrub’s distinct spectral trends would facilitate reliable delineation and restoration of invaded areas.

Remote sensing systems make use of vegetation spectral characteristics in the visible and Near Infra-Red (NIR) sections of the electromagnetic spectrum to differentiate it from other surfaces. However, Digital Number (DN) value extractions and conventional land cover classification methods using imagery from satellite and aerial platforms have shown that P. incana has a subtle spectral response dissimilar to the typical vegetation reflectance patterns (see: Kakembo, 2004; Palmer et al ., 2005).

Whereas image DN values are widely accepted as reliable surrogates for reflectance (Jensen, 2005; Lillesand et al ., 2004), the quality and accuracy of DN values and classification representations are dependent on various factors that include among others the quality of the imagery and correction methods adopted. Surface classifications using DN values are also pixel based and therefore susceptible to either

3This chapter is based on a paper accepted by the South African Geographical Journal – Authors, Odindi, J. O. and Kakembo, V.

the influence of background materials, for instance soil and organic material or a mixture of the same species at different phenological stages within a pixel (Miller et al ., 1991; Droesen, 1999). Whereas these shortcomings can be overcome by unmixing the pixel contents (Huguenin et al ., 1997; Lu et al. , 2003), testing for accuracy of unmixed image fractions still remains a challenge (Plaza et al. , 2005; Adams and Gillespie, 2006).

In comparison to imagery DN value analysis, laboratory or in-situ spectral measurements using a spectrometer is often considered a more reliable method of achieving accurate reflectance values for materials. Unlike aerial or satellite based imagery data acquisition techniques, this method’s reflectance accuracy is enhanced by less technical requirements, a reduced sensor to target distance, sensor stability and an increased dwell time (see; Rundquist et al. , 2004; Milton et al. , 2007). The main parameters that determine vegetation spectral reflectance comprise floristic composition (Schmidt and Skidmore, 2002), bare surface or dead organic matter that may include bark or branches (Droesen, 1999) and response to seasonal changes (Miller et al ., 1991). Their consideration under laboratory conditions can therefore be used to provide a better understanding of P. incana spectral trends. A typical characteristic of P. incana individual shrubs is the high branch to leaf ratio (Figure 6.1). Caution is necessary when dealing with such shrubs because of possibilities of mixed signals from grasses and bare soil (Sebego et al , 2008), as well as background branch reflectance. This problem is further compounded by the vertical orientation of P. incana in relation to an imagery acquisition sensor system.

Figure 6.1: Pteronia incana (Blue bush) invasion in the study area.

Since green vegetation is known to have low and high reflectance in the red and NIR bands respectively, this study hypothesises that low P. incana reflectance identified in earlier studies using Advanced Spaceborne Thermal Emission Radiometer (ASTER) and High Resolution Imagery (HRI) (see; Kakembo, 2004; Palmer et al ., 2005) were a consequence of high P. incana branch to leaf ratios. The present study therefore attempts to compare the effect of background reflectance ( P. incana branches) to P. incana leaves of different proportions using laboratory spectroscopy. It also attempts to establish P. incana’s spectral separability from green vegetation and bare soil between 0.45 – 0.88 µm wavelengths.

The study area

Samples were acquired from P. incana invaded sites in Amakhala Game Reserve 90km north-east of Port Elizabeth (Figure 6.2). The area was under commercial livestock farming and crop cultivation for over 80 years until 1999 when it was converted to a private game reserve. According to the 80-year long rainfall records on the Game Reserve, the area receives between 380-570mm of rain per year. Annual rainfall in the area is bi-modal with most of it falling in the summer month of March and spring months of September and October. Its temperature, based on records at

neighbouring Shamwari Game Reserve, ranges from 7.1 0C to 19.5 0C in winter and 18.6 0C to 32.4 0C in summer.

Figure 6.2: Location of the study area.

According to Vlok and Euston-Brown (2002) vegetation classification, the area falls within the broader Albany thicket with a mosaic of both Albany dune thicket and Albany valley thicket. These thicket types comprise leaf and stem succulents, shrubs, trees, lianas, succulent herbs, grasses and forbs. The dominant thicket species identified in the field were gwarrie - Euclea undulate , ribbed kunibush – Rhus pallens , spiny currant bush – Rhus longispina while dominant grasses were kikuyu grass – Pennisetum clandestinum and rooigras – Themeda triandra . According to field observations, hillslopes that have experienced some form of disturbance, for instance overgrazing or cultivation abandonment are densely invaded by P. incana (refer to Figure 6.1). Such hillslopes have been identified as highly vulnerable to invasion (Kakembo et al ., 2007). The soils of the study site hillslopes are predominantly clays and sands derived from mudstones and sandstones of the Kirkwood formation. A flat alluvium terrace that lies to the west of the Bushman’s river (Figure 6.2) traverses the game reserve.

Materials and methods

The study was conducted between September 2007 and March 2008 using samples collected once every month. The invader exhibits a distinctive winter carryover effect in September, as opposed to its photosynthetically active summer appearance in March. This phenological variation permitted a clear identification of the invader’s inter-seasonal spectral differences. In-situ reflectance measurements for adjacent P. incana , bare surfaces and gwarrie-Euclea undulata (a green vegetation species interspersing P. incana and bare soil surfaces) were taken from three sampling sites. E. undulata is evergreen deciduous vegetation (De Winter, 1963), unlike grass which quickly responds to intra-seasonal fluctuations in moisture availability. Such fluctuations would give rise to discrepancies in reflectance. The sites were identified in the field as having similar slope angle, position, aspect and soil surface characteristics, hence ensuring consistency in sample collection. During each field visit, P. incana samples were cut at stem height and branches of E. undulata were acquired to represent green vegetation. Blocks of bare soil samples were collected in rectangular plastic containers of 10x6x4cm dimensions. This ensured the collection of intact soil surfaces, hence providing consistent reflectance measurements. To ensure that the respective samples maintained their field status, they were packed into dark plastic papers that were covered in a dark plastic container. The samples were then transported within two hours to the laboratory for spectral measurements. In keeping with Smith et al . (2004), an initial comparison between in-situ and laboratory spectral measurements showed no significant difference.

To determine spectral reflectance values of leaf to branch ratios, P. incana leaves were stripped. Using a precision digital scale (Mettler PE 3600 Delta-range), five sample proportions (1:0, 3:1, 1:1, 1:3 and 0:1) of leaves to branches, comprising 100g, 75g, 50g 25g and 0g of leaves respectively were separated (Table 6.1). The respective proportions were mixed and spread to a height of 5cm in a container of 45cm diameter .

Table 6.1: Leaf to branch weights and proportions.

Sample Leaves (g) Cut branches (g) Total weight (g) Approx. ratio 1 100.37 0 100.31 1:0 . 2 75.06 24.92 99.98 3:1 . 3 49.98 49.98 99.96 1:1 . 4 25.06 75.25 100.31 1:3 . 5 0 99.08 99.8 0:1

Spectral reflectance for P. incana leaf to branch ratios, green vegetation ( Euclea undulata ) and soil samples were measured in the laboratory using a high resolution EPP 2000 concave grating spectrometer (StellarNet Inc., Tampa - Florida). The spectrometer has a wavelength range of 0.28 – 0.88 µm in the visible and NIR, a less than 1nm uniform resolution over the entire spectral range and an aberration corrected concave grating (StellarNet Inc., Tampa-Florida). In the present study, the spectrometer’s wavelength was scaled to vegetation sensitive range of 0.45 – 0.88 µm and readings taken from visible blue (0.45 – 0.5 µm), visible green (0.5 – 0.6 µm), visible red (0.6 – 0.7 µm) and near infrared (0.7 – 0.88 µm). Illumination calibration for reflectance spectra was achieved using a thermoplastic resin Spectralon ® (LabSphere, Inc., North Sutton, NH) standard white panel.

The spectrometer fibre optic sensor head measuring 0.64cm and an Instantaneous Field of View (IFOV) of 15cm in diameter was fixed 40cm above the target samples at nadir position. Containers with wide circumferences were used to avoid reflectance from non-target materials. The surface reflectance factors (R λ) were calculated as ratios between the reflected radiant flux from the standard white panel and the reflected radiant flux from the samples using formula:  L   λ  Rλ =  Rp λ (12)  LP λ 

Where Lλ is the flux from the surface, LP λ is the flux from the panel, Rp λ is the bi- conical reflectance of the panel under constant view geometry and illumination (Schaepman-Strub et al ., 2004).

The spectrometer was configured to acquire ten individual scans which were averaged within the system and recorded as a single data file. A total of five readings were taken from each sample. SpectraWiz ® software (StellarNet Inc., Tampa-Florida) was set at 0.0005 µm wavelength increment and used to view and save data from the spectrometer.

Means ( Ms ) and Standard Deviations ( SDs ) for leaf to branch ratio spectral values at the green and red band mid-points – 0.55 µm and 0.68 µm respectively – (Lillesand et al ., 2004) were obtained. Due to wavelength limitation of the spectrometer used, its upper limit value of 0.88 µm instead of the 1.0 µm mid-point was used for the near infrared band. To determine the effect of increasing leaf percentage in branch samples, regression analyses were performed at 0.55 µm, 0.68 µm and 0.88 µm. The t- test was used to determine whether the difference between the mean reflectance of the respective surfaces was statistically significant. Nine sets of t-tests were required (green vegetation vs. bare surfaces, bare surface vs. P. incana and P. incana vs. bare surfaces) at 0.55 µm, 0.68 µm and 0.88 µm.

To determine green vegetation, bare surface and P. incana spectral trends between 0.45 to 0.88 µm, a 0.015 µm interval was used to extract the first order derivative from surface spectral measurements. Derivatives of spectra have a long history in remote sensing (Becker et al ., 2005). Spectral derivatives have several advantages over reflectance values, which include among others the ability to reduce spectral differences caused by variability in illumination, removal of background signals and distinction of closely related spectra (Demetriades-Shah et al. , 1990; Curran et al ., 1991; Elvidge and Chen, 1995; Smith, et al ., 2004). In this study, a 0.45 µm to 0.88 µm wavelength range was used to show points of inflection and to determine spectral trends of the surface spectrum at 0.015 µm interval using the formula:

1st d = (ρn+1 − ρn ) (λn+1 − λn ) (13) Where d 1st is 1 st derivative, ρ is reflectance, n is band number and λ is wavelength in µm (Becker et al ., 2005).

Results and discussion

The P. incana sample reflectance for the respective leaf to branch ratios showed a steady increase with wavelength; a prominent rise is noticeable from 0.65 µm (Figure 6.3). Despite some overlaps identified in the low leaf proportions (1:3 and 0:1, see Figure 6.3), a very strong correlation value between average spectral measurements and increasing percentage of leaves was identified (refer to Figure 6. 4).

0.14 1 : 0 3 : 1 P. incana canopy 0.12 1 : 1 1 : 3 0 : 1 0.1

0.08

0.06 Reflectance

0.04

0.02

0 0.45 0.55 0.65 0.75 0.85 Wavelength ( µm)

Figure 6.3: Sample ratios and canopy surfaces reflectance values

There was a general increase in reflectance at 0.55 µm and 0.65 µm with an increase in the proportion of leaves. However, a decline in reflectance at 0.65 µm in comparison to 0.55 µm is noticeable for all the ratios (Table 6.2). The 0.88µm wavelength had the highest maximum and minimum reflectance values for all ratios in comparison to similar ratios at 0.55 µm and 0.65 µm wavelengths (Table 6.2). The ratio reflectance SDs were also generally higher at 0.88 µm for each wavelength in comparison to 0.55 µm and 0.65 µm (Table 6.2). There was a distinctly low mean reflectance difference values between 1:0 and 3:1 ratios (0.001) at the three wavelengths, while the 1:3 and 0:1 ratios had a distinctly high (>0.003) mean reflectance difference values (Table 6.2). There was a similar increase in reflectance values with an increase in the proportion of leaves at intermediate ratios (Table 6.2). The P. incana shrub canopy reflectance at 0.55 µm was higher than 1:1 branch to leaf ratio (Table 6.2).

This can be attributed to the higher proportion of canopy leaves, which is approximately 2:1 under field conditions during the wet season. When dormant and photosynthetically inactive during dry spells, the aboveground biomass for P. incana appears as dead material. Its unpalatability obviates the influence of grazing pressure on its leaf to branch ratio.

Table 6.2: Branch to leaf proportions and P. incana canopy reflectance at different wavelengths.

Wavelength

Ratio 0.55 µm 0.65 µm 0.88 µm .

M SD M SD M SD .

1:0 0.028 0.0006 0.036 0.0017 0.053 0.0061 3:1 0.029 0.0030 0.035 0.0031 0.054 0.0048 1:1 0.043 0.0015 0.042 0.0020 0.068 0.0052

P. incana canopy 0.051 0.0011 0.047 0.0010 0.080 0.0061 1:3 0.056 0.0005 0.052 0.0011 0.092 0.0072 0:1 0.063 0.0011 0.055 0.0023 0.123 0.0098

A rise in the red edge was noted around 0.7 µm with more than 1:1 P. incana leaf to branch ratios. The near infrared plateau was also visible after 0.75 µm. This could be attributed to light scattering, lack of absorption pigmentation and decreasing absorption by water (Elvidge, 1990; Kokaly et al ., 2003; Thorhaug et al. , 2006). The presence of the green peak and red edge curve (0.68-0.75 µm) were visible with leaf to branch ratios of greater than 1:1 (see Figure 6.3). As noted earlier, there was a strong positive correlation between the proportion of P. incana leaves and reflectance. Whereas the highest average reflectance was recorded from samples with highest proportion of leaves, there was a general reduction in reflectance of the samples with an increase in the proportion of branches in the sample (see Figure 6.4). Two major inferences can be drawn from comparing the reflectance of different branch to leaf ratios. Firstly P. incana reflectance in the green (0.55 µm), red (0.65 µm) and NIR (0.88 µm) follow the conventional green vegetation reflectance patterns with a peak at the green band, and a higher reflectance difference between the red and the near infrared bands. Secondly, a sample with 100% leaf proportion yields the highest reflectance in all the wavelengths. However, this highest reflectance (M = 0.123, SD = 0.0098) attained at the 0.88 µm data set is much lower than the >0.4 reflectance units

reported in literature for green vegetation (Lillesand et al ., 2004; Adams and Gillespie, 2006; Jensen, 2006).

0.12 0.55 µm 0.65 µm 96 .89 2 = 0 0.88 µm R 0.09

27 2 0.99 R = 0.06 Reflectance 2 0.9648 R = 0.03

0 25 50 75 100

Percentage of leaves in sample

Figure 6.4: The influence of increasing proportion of leaves on reflectance at 0.55 µm, 0.65 µm and 0.88 µm wavelengths.

Laboratory derived spectral reflectance for P. incana canopy monthly samples were compared with corresponding bare soil and E. undulata . Different wavelengths offered different levels of separability. In the green band (0.50 µm – 0.60 µm), the green peak for green vegetation reflectance was visible in all the reflectance measurements. Its unique characteristics at this wavelength range, as described by Jensen (2005), provided a clear separability from P. incana and bare surface (Figure 6.5). Clearer reflectance distinctions were achieved in the red (0.65 µm) and near infrared (0.88 µm) bands. The typical lower green vegetation’s reflectance than bare soil in the red band and higher green vegetation reflectance than bare soil in the near infrared surfaces were discernible (Figure 6.5). Consistently low reflectance values for P. incana were noted in all the bands.

0.9 Green Vegetation 0.8 Bare soil 0.7 P. incana 0.6 0.5 0.4

Reflectance 0.3 0.2 0.1 0 0.45 0.55 0.65 0.75 0.85 Wavelength ( µm) Figure 6.5: Green vegetation, bare soil and P. incana monthly interval samples reflectance (each spectrum is an average from fifty spectral measurements).

The mean reflectance measurements for all samples were generally low and differences between them statistically significant at 0.55 µm and 0.88 µm (see Table 6.3). Whereas mean reflectance differences between P. incana vs. bare soil, and bare soil vs. green vegetation were statistically significant at 0.65 µm, the opposite is true of P. incana vs. green vegetation. By implication, separability between the latter pair of surfaces cannot be achieved at 0.65 µm.

Table 6.3: Sample reflectance t-test and p-values, means and standard deviations.

t-test and p-value Mean reflectance Reflectance Std. Dev.

Wave- PI vs. BS BS vs. GV PI vs. GV PI BS GV PI BS GV length

0.55 µm 12.14; <.001 8.74; <.001 16.15; <.001 0.042 0.074 0.125 0.022 0.034 0.015

0.65 µm 24.44; <.001 35.58; <.001 1.03; <.304 0.048 0.183 0.042 0.027 0.061 0.041

0.88 µm 27.76; <.001 44.84; <.001 37.61; <.001 0.071 0.290 0.571 0.072 0.091 0.071

PI - P. incana BS - Bare surface GV - Green vegetation n = 50 Alpha level = .05.

Green vegetation, bare soil and P. incana reflectance differences were used to determine separability in the green (0.55 µm), red (0.65 µm) and NIR (0.88 µm) band mid point wavelengths. Reflectance differences between the surfaces were low in the green band (Figure 6.6). In the red band, reflectance differences between bare soil and P. incana were high, but low between P. incana and green vegetation (Figure 6.6). Consistently low reflectance differences between green vegetation and P. incana were seen in the red band for the six month reflectance dataset. Consequently, the low reflectance difference makes separating P. incana from green vegetation at 0.65 µm difficult. The NIR band provided the highest reflectance difference values in the six monthly reflectance measurements (Figure 6.6). The clearest separability could therefore be achieved at the 0.88 µm, where the reflectance difference increased gradually from bare soil and P. incana , green vegetation and bare soil to green vegetation and bare surfaces (Figure 6.6).

0.7 Bare surface & P. incana G. vegetation & Bare surface 0.6 Green vegetation & P. incana

0.5

0.4

0.3

Reflectance 0.2

Reflectance Reflectance difference 0.1

0.55 0.65 0.88 0.55 0.65 0.88 0.55 0.65 0.88 0.55 0.65 0.88 0.55 0.65 0.88 0.55 0.65 0.88 20/10/2007 20/11/2007 20/12/2007 20/1/2008 20/2/2008 20/3/2008

Wavelengths and Date

Figure 6.6: Reflectance differences between the respective surfaces.

Clear trends for green vegetation, bare soil and P. incana were also established using six months spectral means. The mean for green vegetation was high, very low and very high at 0.55 µm, 0.65 µm and 0.88 µm respectively (Figure 6.7). The reflectance

values for P. incana were generally low at all the wavelengths, with the lowest values at 0.65 µm (Figure 6.7).

0.8 P. incana 0.7 Bare soil 0.6 Green vegetation 0.5 0.4 0.3 Reflectance 0.2 0.1 0 0.55 0.65 0.88 Wavelength ( µm)

Figure 6.7: Surface reflectance means for the six months data set.

Using the first order derivative on the six months surface spectra, consistent spectral trends showing a clear distinction between green vegetation and bare soil, and green vegetation and P. incana peaks at 0.5-0.55 µm were achieved (Figure 6.8). The steepest slope tangents in this range were at 0.52 µm and the root at 0.55 µm. Although the average derivative values for bare soil were above P. incana between 0.45 – 0.54 µm, it was difficult to separate the two due to similar spectral trends (see Figure 6.8). The best separability between the two surfaces was achieved between 0.55 µm and 0.68 µm. Reliable spectral separability could also be achieved between the two surfaces and green vegetation. Whereas bare soil and green vegetation could be separated within the entire 0.55 - 0.68 µm wavelength, the separability between P. incana and bare soil was limited to 0.55 – 0.60 µm (Figure 6.8).

100

15 Green vegetation Bare surfaces P. incana

5 reflectance derivative of of derivative st 1

-5 0.5 0.6 0.7 0.8 Wavelength ( µm)

Figure 6.8: Spectra for the six months reflectance 1 st order derivative.

There was no clear separability between P. incana and bare soil between 0.67 µm and 0.775 µm. However, this range provided the best separability between the two surfaces and green vegetation, with typical spectral characteristics of the latter clearly exhibited (see Figure 6.8). There was a consistent first order derivative trough at 0.76 µm that can be attributed to the influence of branches on P. incana reflectance. Beyond 0.77 µm there was no clear first order derivative differences established (Figure 6.8).

From the above spectral trends, a general increase in reflectance with an increase in the proportion of P. incana leaves is noticeable. Distinct separability between P. incana , E. undulata and bare surfaces is achievable in the NIR region (0.75-0.88 µm). Apart from P. incana vs. green vegetation that could not be separated at 0.65µm, all the surface combinations could be separated at 0.55µm, 0.65µm and 0.88µm band mid-points. Using first order derivative, the best separability could be achieved at 0.55-0.68µm and 0.55-0.60µm ranges for P. incana and green vegetation and P. incana and bare soil respectively.

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Conclusion

This chapter examined the influence of background materials on P. incana reflectance and compared its reflectance with bare soil and E. undulata . Different branch to leaf ratios gave different P. incana reflectance values. The proportion of leaves in the samples determined ratio sample reflectances, with higher proportions giving higher reflectance. P. incana samples with over 50% leaves showed typical vegetation reflectance trends; however, the highest reflectance from 100% leaf samples was much lower than the conventional green vegetation reflectance. Canopy reflectance for P. incana was higher than 1:1 branch to leaf proportions, indicating the overarching influence of the leaf canopy on an individual P. incana shrub. Whereas branches and background soil may influence P. incana reflectance under field conditions, results of this study demonstrate that P. incana ’s typically low reflectance between 0.45 to 0.88 µm is a function of its leaf canopy. By implication, the hypothesis that ‘the low P. incana reflectance identified in earlier studies using HRI and ASTER is a consequence of high P. incana branch to leaf ratios’ is rejected. An investigation into other factors that contribute to P. incana’s low reflectance, for instance its internal leaf structure is imperative. The best separability between all the surfaces can be achieved in the near infrared band, while reasonable separability is also achievable in the red band. A consistent spectral trend showing a clear distinction between the respective surfaces was achieved using the first order derivative on the six months surface spectra. P. incana’s distinct inter-seasonal spectral characteristics as confirmed by laboratory spectroscopy can be used to augment the existing remedial protocols for the invader shrub using remote sensing techniques.

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Chapter 7: Synthesis

7.1 Introduction

This chapter brings the different strands of the respective chapters together and provides conclusions based on the findings of the study. The chapter starts by relating major climatic and physical variables to P. incana invaded surfaces. The section is followed by a review of moisture flux trends in a P. incana invaded area, bare and grass surfaces and its implication for landscape function. The chapter concludes by reviewing P. incana spectral trends in relation to most commonly associated cover types and the reliability of PVI and SMA applications on HRI within the context of P. incana invasion. Recommendations regarding future research directions are also made.

7.2 P. incana invasion across a range of gradients

Whereas P. incana invasion may be influenced by a diverse range of other interacting variables not covered in this study, landuse and mean annual precipitation seem to be the most important factors influencing P. incana invasion in the Eastern Cape. A clear trend on the effect of geology on P. incana invasion could however not be established, as the invasion was not unique to any geological formation. Land disturbance was however, noted as an outstanding factor in the invasion. As was noted during transect surveys, the invaded nodes lie on disturbed surfaces used for livestock and previously cultivated land. The endemic nature of the invasion in disturbed communal rangelands suggests that land disturbance through overgrazing and land abandonment has a greater influence on P. incana invasion than the invaders attributes.

A distinct isohyet boundary of 619mm beyond which P. incana invasion does not occur was identified by means of the transect survey. By implication, wetness is a P. incana invasion impeding factor. This observation mirrors catchment scale findings by Kakembo et al., (2006 and 2007) that showed higher invasion prevalence on drier hillslopes than gentle and flat surfaces. The combined effect of low precipitation and

103

disturbance is noteworthy, as the areas where P. incana invasion is endemic lie in the low precipitation zone where disturbance in the form of land abandonment and overgrazing are widespread.

There was a consistently lower organic matter content in P. incana invaded areas than un-invaded surfaces. OM depletion in invaded areas can be attributed to the removal of the top soil layer from bare inter-patch areas, which is exercerbated by the dominance of sandy loam soils as identified from soil particle analyses.

7.3 P. incana invasion and soil moisture flux

On the basis of soil moisture flux and retention trends identified on P. incana invaded surfaces, it can be concluded that the conversion of landscapes to dysfunctional systems could be the ultimate result of the alteration of vegetation cover to P. incana - bare surface mosaics, as it leads to reduced infiltration and increased runoff. This confirms the observation by Tongway and Hindley (1995) that the loss of perennial grasses from a landscape alters the output of the environmental envelope so that the availability of water and nutrients over time is insufficient for some vegetation species to persist. Despite their greater moisture retention, grass surfaces were also noted to lose soil moisture more rapidly than P. incana and bare surfaces after rainfall events, due to greater evapo-transpiration. On the other hand, longer moisture retention on bare areas could be attributed to soil crusting, which locks up moisture for longer periods. The greater retention of soil moisture under P. incana invaded surfaces than bare areas should be a major consideration in an effort to restore degraded invaded areas.

The duration of moisture retention seen on grass has strong implications for appropriate strategies for the restoration of P. incana invaded and degraded surfaces. It demonstrates differences in moisture dependencies for the two surfaces such that, moisture elevation in invaded areas would create a suitable environment for grasses to re-establish themselves. Experiments that entailed the use of moisture elevation and retention as a P. incana management strategy have been successful (Kakembo, 2007), as the technique gives early sprouting grass a competitive advantage over P. incana. Improvement of moisture conditions for P. incana will have to be accompanied by 104

best practice land management options, which include keeping grazing out of areas under rehabilitation and repeat clearances in areas being re-colonised.

7.4 P. incana spectral characteristics

A general rise in reflectance with an increase in P. incana leaf ratios was noted. Apart from P. incana vs. green vegetation that could not be separated at 0.65µm, all the surface combinations could be separated at 0.55µm, 0.65µm and 0.88µm band mid- points. Using the first order derivative, the best separability could be achieved at 0.55-0.68µm and 0.55-0.60µm ranges for P. incana and green vegetation and P. incana and bare areas respectively. Consequently, this study confirmed previous studies by Kakembo (2003) and Palmer et al. (2005) that P. incana has unique spectral characteristics from conventional green vegetation reflectance. P. incana’s vertical leaf orientation in relation to the spectral acquisition system and high branch to leaf ratio earlier thought to be the major causes of low P. incana reflectance are therefore discounted.

7.5 Application of pixel and sub-pixel based classifications to separate P. incana

The output in pixel-based methods is often a composition of materials within a pixel (Adam and Gillespie, 2006). In scenarios that may not require local detail, reliable P. incana invasion mapping can be achieved using aggregation of pixel components in HRI. Results in this study show that consistent separability can be achieved when the pixel based PVI is applied to HRI. The biggest advantage of PVI application in P. incana separation is its ability to minimise the effect of background soil reflectance in P. incana invaded environments. This is particularly important in P. incana invaded environments often characterised by inter-patch bare surfaces.

Whereas pixel based techniques like the PVI may be an option in land cover mapping, such techniques may not provide accurate mapping of P. incana invaded surfaces depending on, the spatial resolution of the imagery used. However, this study showed that sub-pixel techniques that de-convolve surface types within a pixel based on selected end-members can be used to account for major cover types within P. incana invaded environments. 105

In keeping with other SMA applications, the reliability of P. incana fractions is dependent on the quality of endmembers selected. Due to the spatial coverage and limited cover types that characterise P. incana invaded surfaces, image based endmember selection is a more suited technique for extracting P. incana fractions. Depending on the number of unique spectra in an image, these characteristics enable fraction extraction from both high and low resolution imagery. In this study, the identification of green vegetation endmembers in P. incana invaded environments was relatively straightforward. However, care should be taken when identifying P. incana and bare surfaces endmembers as their spectral differences were generally small.

Whereas the SMA has commonly been used in low spatial resolution imagery, (see; Souza and Barreto, 2000; Sobal et al ., 2002; Uenishi et al ., 2005), it has also been successfully used in medium (see Robichaud et al ., 2007) and low (see Zhu, 2005; Miao et al ., 2006) spatial resolution situations. This study further confirms that an application of spectral mixture models should not be limited to medium and coarse spatial resolution imagery. In a similar study using a 1x1m spatial resolution Compact Airbone Spectrographic Imager (CASI), Miao et al . (2006) showed reliable mapping of Centaurea solstitialis (Yellow starthistle) invasion in California’s Central Valley grassland using spectral un-mixing.

A combination of pixel based techniques like PVI and sub-pixel techniques like SMA in P. incana mapping can be used to enhance the reliability of invasion interpretation. Whereas it is acknowledged SMA applications may not produce reliable results with a large number of components within a pixel (Adam and Gillespie, 2006), its application within P. incana invasion environments which are often characterised by two other major constituents (green vegetation and bare surfaces) increases its potential as a tool to P. incana mapping.

In summary, this study managed to identify relationship between P. incana invasion and a range of variables. The importance of isohyetic gradients as determinants of invasion boundaries was identified. The study also demonstrated the implications of P. incana invasion for surface moisture flux, particularly the potential of conversion 106

of invaded areas to dysfunctional landscapes. Spectral analyses confirmed that P. incana has unique spectral characteristics from other vegetation types and showed the potential of complimenting pixel and sub-pixel based analyses in P. incana mapping. P. incana spectral investigation was limited to its difference from green vegetation and bare areas. Consequently, to provide further understanding of remote sensing applications in P. incana invasion and its interaction with invaded environments, the following directions for future research are recommended:

i) A comparison between P. incana and typical green vegetation internal leaf structures as potential causes of spectral differences.

ii) Collection of spectra for P incana and other invader vegetation types, some of which have similar characteristics, with a view to assembling a spectral library for delineating invaded environments using imagery.

The main research questions raised in this study namely: • What is the pattern of P. incana occurrence across a range of gradients?

• What is the hydrological response of P. incana invaded surfaces as compared to grass and bare surfaces?

• What is the ideal wavelength for separating P. incana from bare surfaces and green vegetation cover?

• Can consistency be achieved in separating P. incana invaded areas using multi-temporal HRI? Are sub-pixel techniques more effective than pixel ones in P. incana separation using HRI? have all been addressed.

The study has inter alia confirmed the reliability and consistency of HRI in the delineation of P. incana using both pixel and sub-pixel techniques. The imagery is therefore a useful tool in the rehabilitation of areas invaded by undesirable vegetation species.

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APPENDIX A P. INCANA CANOPYAND MIXTURES WITH RESPECTIVE LEAVES TO BRANCH RATIOS

a) b)

P. incana canopy c) d)

e) f)

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APPENDIX B GREEN VEGETATION, BARE SOIL AND P. INCANA MONTHLY SAMPLES REFLECTANCE SPECTRA

0.8 Green Vegetation Green vegetation 0.7 Bare soil P. incana 0.6

0.5

0.4 Bare surface

0.3 Reflectance 0.2 P. incana 0.1

0 0.45 0.55 0.65 0.75 0.85

Wavelength ( µm) a) 20/10/2007

0.9 Green Vegetation 0.8 Bare soil P. incana 0.7 Green vegetation 0.6 0.5

Bare Baresurface surface 0.4

Reflectance 0.3 0.2 P. incana 0.1 0 0.45 0.55 0.65 0.75 0.85 Wavelength ( µm) b) 20/11/2007

143

0.8 Green Vegetation 0.7 Bare soil P. incana Green vegetation

0.6

0.5 Bare surface 0.4

0.3 Reflectance 0.2 P. incana 0.1

0 0.45 0.55 0.65 0.75 0.85 Wavelength ( µm)

c) 20/12/2007

0.6 Green Vegetation Bare soil Green vegetation 0.5 P. incana

0.4

0.3 Bare surface Reflectance

0.2

0.1 P. incana

0.45 0.55 0.65 0.75 0.85

Wavelength ( µm)

d) 20/1/2008

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0.9 Green Vegetation Green vegetation 0.8 Bare soil P. incana 0.7 0.6 0.5 0.4 P. incana

0.3

Reflectance 0.2 Bare surface

0.1 0 0.45 0.55 0.65 0.75 0.85 Wavelength ( µm) e) 20/2/2008

0.9 Green Vegetation Green vegetation 0.8 Bare soil P. incana 0.7

0.6

0.5

0.4

0.3 Bare surface Reflectance 0.2 P. incana 0.1 f) 20/3/2008 0 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 Wavelength ( µm)

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APPENDIX C FIRST ORDER DERIVATIVES OF THE MONTHLY REFLECTANCE SPECTRA

January Green vegetation Bare soil P. incana 5

reflectance derivative of of derivative

st 1 0

-5 0.5 0.6 0.7 0.8 Wavelength ( µm) a) 20/10/2007

15 Green vegetation Bare soil P. incana

5 reflectance derivative of of derivative st 1

-5 0.5 0.6 0.7 0.8 Wavelength ( µm)

b) 20/11/2007

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15 Green vegetation Bare soil P. incana 10

5 derivative of of derivative st reflectance 1

-5 0.5 0.6 0.7 0.8 Wavelength ( µm) c) 20/12/2007

Green vegetation Bare soil P. incana

reflectance

of derivative st 1 0

-5 0.5 0.6 0.7 0.8 Wavelength ( µm)

d) 20/01/2008

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15 Green vegetation Bare soil P. incana

reflectance

of derivative st 1 0

-5 0.5 0.6 0.7 0.8 Wavelength ( µm)

e) 20/2/2008

15 Green vegetation Bare soil P. incana

reflectance derivative of of derivative st

1 0

-5 0.5 0.6 0.7 0.8 Wavelength ( µm) f) 20/3/2008

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APPENDIX D PORTION OF CALIBRATED SENSOR MOISTURE LOGS FOR THE THREE EPISODES AT 1HR INTERVAL

Episode 1 Episode 2 Episode 3

Grass P.incana Bare Grass P.incana Bare Grass P.incana Bare 1 0.3815 0.2315 0.1753 1 0.2015 0.1541 0.1116 1 0.4667 0.2699 0.192 2 0.3809 0.2315 0.1753 2 0.2015 0.1547 0.1116 2 0.4619 0.2705 0.1915 3 0.3809 0.2321 0.1744 3 0.2003 0.1541 0.1107 3 0.4577 0.2711 0.1888 4 0.3809 0.2327 0.1744 4 0.1991 0.1535 0.1098 4 0.4529 0.2705 0.1861 5 0.3803 0.2327 0.1735 5 0.1973 0.1523 0.1098 5 0.4487 0.2705 0.1843 6 0.3797 0.2327 0.1735 6 0.1949 0.1511 0.108 6 0.4451 0.2705 0.1825 7 0.3797 0.2333 0.1726 7 0.1931 0.1499 0.1071 7 0.4427 0.2711 0.1807 8 0.3785 0.2333 0.1726 8 0.1907 0.1481 0.1062 8 0.4397 0.2711 0.1789 9 0.3779 0.2333 0.1726 9 0.1883 0.1463 0.1053 9 0.4367 0.2705 0.1771 10 0.3773 0.2345 0.1726 10 0.1853 0.1445 0.1044 10 0.4331 0.2693 0.1753 11 0.3779 0.2357 0.1726 11 0.1835 0.1433 0.1035 11 0.4289 0.2675 0.1726 12 0.3797 0.2381 0.1726 12 0.1817 0.1421 0.1026 12 0.4247 0.2657 0.169 13 0.3815 0.2405 0.1726 13 0.1793 0.1403 0.1017 13 0.4199 0.2633 0.1663 14 0.3821 0.2417 0.1717 14 0.1775 0.1391 0.1017 14 0.4145 0.2603 0.1636 15 0.3815 0.2417 0.1699 15 0.1757 0.1379 0.1008 15 0.4091 0.2573 0.1609 16 0.3779 0.2399 0.1681 16 0.1739 0.1367 0.0999 16 0.4043 0.2549 0.1591 17 0.3755 0.2387 0.1663 17 0.1727 0.1361 0.0999 17 0.3995 0.2519 0.1564 18 0.3713 0.2363 0.1645 18 0.1715 0.1355 0.099 18 0.3947 0.2495 0.1546 19 0.3683 0.2345 0.1627 19 0.1715 0.1355 0.099 19 0.3905 0.2471 0.1528 20 0.3653 0.2327 0.1609 20 0.1709 0.1355 0.099 20 0.3851 0.2441 0.151

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21 0.3623 0.2309 0.16 21 0.1721 0.1373 0.099 21 0.3815 0.2423 0.1501 22 0.3605 0.2297 0.1591 22 0.1751 0.1397 0.0999 22 0.3773 0.2399 0.1483 23 0.3575 0.2279 0.1582 23 0.1787 0.1427 0.1017 23 0.3743 0.2381 0.1474 24 0.3557 0.2267 0.1573 24 0.1823 0.1457 0.1044 24 0.3707 0.2363 0.1456 25 0.3533 0.2249 0.1555 25 0.1847 0.1475 0.1053 25 0.3677 0.2345 0.1447 26 0.3515 0.2237 0.1555 26 0.1847 0.1475 0.1053 26 0.3647 0.2327 0.1438 27 0.3491 0.2225 0.1546 27 0.1841 0.1469 0.1044 27 0.3617 0.2309 0.1429 28 0.3479 0.2219 0.1537 28 0.1823 0.1457 0.1026 28 0.3587 0.2297 0.142 29 0.3479 0.2225 0.1537 29 0.1805 0.1439 0.1008 29 0.3557 0.2279 0.1411 30 0.3461 0.2213 0.1528 30 0.1781 0.1421 0.0999 30 0.3527 0.2267 0.1402 31 0.3437 0.2201 0.1519 31 0.1763 0.1403 0.0981 31 0.3509 0.2261 0.1393 32 0.3413 0.2189 0.1519 32 0.1739 0.1385 0.0972 32 0.3491 0.2255 0.1393 33 0.3395 0.2183 0.151 33 0.1715 0.1367 0.0963 33 0.3473 0.2249 0.1393 34 0.3383 0.2183 0.1501 34 0.1703 0.1355 0.0954 34 0.3449 0.2237 0.1393 35 0.3377 0.2189 0.1501 35 0.1685 0.1343 0.0945 35 0.3425 0.2225 0.1384 36 0.3389 0.2207 0.151 36 0.1679 0.1337 0.0945 36 0.3389 0.2207 0.1375 37 0.3389 0.2219 0.151 37 0.1661 0.1325 0.0936 37 0.3347 0.2183 0.1357 38 0.3383 0.2225 0.151 38 0.1649 0.1313 0.0927 38 0.3311 0.2159 0.1339 39 0.3365 0.2213 0.1501 39 0.1643 0.1307 0.0927 39 0.3281 0.2141 0.1321 40 0.3335 0.2201 0.1492 40 0.1631 0.1301 0.0918 40 0.3239 0.2117 0.1303 41 0.3311 0.2189 0.1474 41 0.1625 0.1295 0.0918 41 0.3209 0.2099 0.1294 42 0.3275 0.2165 0.1456 42 0.1613 0.1289 0.0909 42 0.3173 0.2075 0.1276 43 0.3239 0.2141 0.1438 43 0.1607 0.1283 0.0909 43 0.3143 0.2057 0.1267 44 0.3209 0.2123 0.1429 44 0.1613 0.1289 0.0909 44 0.3119 0.2039 0.1258 45 0.3179 0.2099 0.142 45 0.1637 0.1313 0.0909 45 0.3095 0.2027 0.1249 46 0.3155 0.2081 0.1411 46 0.1667 0.1343 0.0927 46 0.3071 0.2009 0.124 47 0.3131 0.2063 0.1402 47 0.1709 0.1373 0.0954 47 0.3047 0.1997 0.1231

150

48 0.3107 0.2045 0.1393 48 0.1745 0.1403 0.0972 48 0.3029 0.1985 0.1222 49 0.3089 0.2033 0.1384 49 0.1763 0.1415 0.0981 49 0.3011 0.1973 0.1213 50 0.3059 0.2015 0.1384 50 0.1763 0.1415 0.0981 50 0.2993 0.1961 0.1213 51 0.3047 0.2003 0.1375 51 0.1757 0.1409 0.0972 51 0.2975 0.1955 0.1204 52 0.3029 0.1991 0.1366 52 0.1751 0.1403 0.0963 52 0.2957 0.1943 0.1195 53 0.3011 0.1979 0.1357 53 0.1739 0.1391 0.0945 53 0.2939 0.1931 0.1195 54 0.2993 0.1967 0.1357 54 0.1727 0.1379 0.0936 54 0.2915 0.1919 0.1186 55 0.2969 0.1955 0.1348 55 0.1709 0.1361 0.0927 55 0.2903 0.1919 0.1186 56 0.2957 0.1949 0.1348 56 0.1691 0.1349 0.0918 56 0.2897 0.1919 0.1186 57 0.2939 0.1943 0.1339 57 0.1673 0.1331 0.0909 57 0.2885 0.1919 0.1186 58 0.2939 0.1949 0.1339 58 0.1655 0.1319 0.09 58 0.2873 0.1913 0.1186 59 0.2939 0.1961 0.1339 59 0.1649 0.1313 0.0891 59 0.2849 0.1901 0.1177 60 0.2957 0.1985 0.1348 60 0.1643 0.1307 0.0891 60 0.2825 0.1889 0.1168 61 0.2981 0.2009 0.1357 61 0.1625 0.1295 0.0891 61 0.2789 0.1871 0.115 62 0.2987 0.2021 0.1366 62 0.1619 0.1289 0.0882 62 0.2759 0.1853 0.1141 63 0.2969 0.2015 0.1357 63 0.1613 0.1283 0.0882 63 0.2735 0.1835 0.1132 64 0.2945 0.2003 0.1348 64 0.1601 0.1277 0.0873 64 0.2711 0.1823 0.1114 65 0.2915 0.1985 0.133 65 0.1595 0.1271 0.0873 65 0.2681 0.1805 0.1105 66 0.2885 0.1967 0.1312 66 0.1589 0.1265 0.0873 66 0.2657 0.1787 0.1096 67 0.2843 0.1937 0.1294 67 0.1577 0.1259 0.0873 67 0.2639 0.1775 0.1087 68 0.2813 0.1913 0.1285 68 0.1571 0.1259 0.0873 68 0.2615 0.1757 0.1087 69 0.2777 0.1889 0.1276 69 0.1571 0.1259 0.0873 69 0.2591 0.1745 0.1078 70 0.2753 0.1871 0.1267 70 0.1565 0.1253 0.0873 70 0.2573 0.1733 0.1069 71 0.2729 0.1853 0.1258 71 0.1559 0.1253 0.0873 71 0.2555 0.1721 0.1069 72 0.2705 0.1835 0.1249 72 0.1559 0.1253 0.0873 72 0.2537 0.1709 0.106 73 0.2687 0.1823 0.1249 73 0.1553 0.1253 0.0873 73 0.2519 0.1697 0.1051 74 0.2663 0.1805 0.124 74 0.1547 0.1247 0.0873 74 0.2507 0.1691 0.1051

151

75 0.2645 0.1793 0.1231 75 0.1541 0.1241 0.0873 75 0.2489 0.1679 0.1042 76 0.2633 0.1787 0.1222 76 0.1541 0.1241 0.0864 76 0.2477 0.1673 0.1033 77 0.2621 0.1775 0.1222 77 0.1535 0.1235 0.0864 77 0.2465 0.1667 0.1033 78 0.2609 0.1769 0.1213 78 0.1523 0.1229 0.0864 78 0.2459 0.1673 0.1024 79 0.2597 0.1763 0.1213 79 0.1517 0.1223 0.0855 79 0.2459 0.1685 0.1024 80 0.2591 0.1763 0.1204 80 0.1511 0.1217 0.0855 80 0.2477 0.1703 0.1024 81 0.2579 0.1757 0.1204 81 0.1505 0.1211 0.0855 81 0.2477 0.1709 0.1033 82 0.2567 0.1757 0.1195 82 0.1493 0.1205 0.0846 82 0.2471 0.1715 0.1033 83 0.2567 0.1769 0.1204 83 0.1487 0.1199 0.0846 83 0.2465 0.1715 0.1033 84 0.2573 0.1781 0.1204 84 0.1487 0.1199 0.0846 84 0.2453 0.1709 0.1024 85 0.2573 0.1787 0.1195 85 0.1481 0.1193 0.0846 85 0.2429 0.1697 0.1015 86 0.2561 0.1781 0.1186 86 0.1475 0.1193 0.0846 86 0.2405 0.1685 0.0997 87 0.2531 0.1763 0.1177 87 0.1469 0.1187 0.0846 87 0.2387 0.1673 0.0988 88 0.2501 0.1745 0.1159 88 0.1469 0.1187 0.0846 88 0.2363 0.1661 0.0979 89 0.2477 0.1733 0.115 89 0.1463 0.1181 0.0846 89 0.2345 0.1649 0.097 90 0.2453 0.1721 0.1132 90 0.1457 0.1181 0.0837 90 0.2327 0.1637 0.097 91 0.2429 0.1709 0.1123 91 0.1457 0.1181 0.0846 91 0.2309 0.1631 0.0961 92 0.2399 0.1691 0.1114 92 0.1451 0.1181 0.0846 92 0.2303 0.1625 0.0952 93 0.2381 0.1679 0.1105 93 0.1451 0.1181 0.0846 93 0.2291 0.1619 0.0952 94 0.2363 0.1667 0.1096 94 0.1457 0.1187 0.0855 94 0.2273 0.1607 0.0943 95 0.2345 0.1655 0.1087 95 0.1463 0.1193 0.0855 95 0.2261 0.1601 0.0943 96 0.2321 0.1643 0.1078 96 0.1457 0.1193 0.0855 96 0.2249 0.1595 0.0934 97 0.2303 0.1631 0.1069 97 0.1463 0.1199 0.0864 97 0.2237 0.1589 0.0934 98 0.2285 0.1619 0.106 98 0.1463 0.1199 0.0864 98 0.2231 0.1583 0.0925 99 0.2267 0.1607 0.106 99 0.1469 0.1199 0.0864 99 0.2219 0.1577 0.0925 100 0.2255 0.1601 0.1051 100 0.1463 0.1193 0.0855 100 0.2207 0.1571 0.0916 101 0.2237 0.1589 0.1042 101 0.1457 0.1187 0.0855 101 0.2195 0.1565 0.0916

152

102 0.2225 0.1583 0.1042 102 0.1457 0.1187 0.0846 102 0.2189 0.1571 0.0907 103 0.2213 0.1577 0.1033 103 0.1451 0.1181 0.0846 103 0.2195 0.1577 0.0898 104 0.2207 0.1577 0.1033 104 0.1439 0.1175 0.0846 104 0.2195 0.1577 0.0898 105 0.2189 0.1571 0.1024 105 0.1433 0.1169 0.0837 105 0.2189 0.1577 0.0907 106 0.2171 0.1571 0.1015 106 0.1427 0.1163 0.0837 106 0.2177 0.1571 0.0907 107 0.2177 0.1583 0.1015 107 0.1427 0.1163 0.0837 107 0.2171 0.1571 0.0898 108 0.2177 0.1595 0.1015 108 0.1427 0.1163 0.0837 108 0.2159 0.1565 0.0889 109 0.2177 0.1601 0.1015 109 0.1415 0.1157 0.0837 109 0.2147 0.1559 0.0889 110 0.2177 0.1607 0.1015 110 0.1415 0.1157 0.0837 110 0.2123 0.1547 0.088 111 0.2159 0.1601 0.1006 111 0.1409 0.1151 0.0837 111 0.2111 0.1541 0.0871 112 0.2141 0.1595 0.0997 112 0.1409 0.1151 0.0837 112 0.2099 0.1535 0.0871 113 0.2117 0.1583 0.0979 113 0.1409 0.1151 0.0828 113 0.2081 0.1523 0.0862 114 0.2093 0.1571 0.097 114 0.1409 0.1151 0.0837 114 0.2069 0.1517 0.0862 115 0.2075 0.1559 0.0961 115 0.1403 0.1151 0.0837 115 0.2063 0.1511 0.0862 116 0.2051 0.1541 0.0952 116 0.1403 0.1151 0.0837 116 0.2045 0.1499 0.0853 117 0.2033 0.1529 0.0934 117 0.1403 0.1151 0.0837 117 0.2033 0.1493 0.0853 118 0.2015 0.1517 0.0934 118 0.1409 0.1157 0.0837 118 0.2015 0.1481 0.0844 119 0.1997 0.1505 0.0925 119 0.1409 0.1157 0.0846 119 0.2009 0.1475 0.0844 120 0.1979 0.1493 0.0916 120 0.1415 0.1163 0.0846 120 0.2003 0.1475 0.0844 121 0.1961 0.1481 0.0907 121 0.1421 0.1169 0.0855 121 0.1997 0.1469 0.0844 122 0.1949 0.1475 0.0898 122 0.1427 0.1175 0.0855 122 0.1985 0.1463 0.0835 123 0.1931 0.1463 0.0889 123 0.1433 0.1181 0.0855 123 0.1985 0.1463 0.0835 124 0.1919 0.1457 0.0889 124 0.1433 0.1181 0.0855 124 0.1985 0.1463 0.0835 125 0.1913 0.1451 0.088 125 0.1439 0.1181 0.0855 125 0.1979 0.1463 0.0835 126 0.1901 0.1445 0.088 126 0.1433 0.1175 0.0855 126 0.1985 0.1469 0.0835 127 0.1895 0.1439 0.0871 127 0.1433 0.1175 0.0846 127 0.1979 0.1469 0.0835 128 0.1877 0.1433 0.0871 128 0.1427 0.1169 0.0846 128 0.1979 0.1475 0.0835

153

129 0.1871 0.1433 0.0862 129 0.1421 0.1163 0.0846 129 0.1979 0.1481 0.0835 130 0.1859 0.1433 0.0862 130 0.1415 0.1163 0.0837 130 0.1979 0.1487 0.0835 131 0.1859 0.1439 0.0871 131 0.1409 0.1157 0.0837 131 0.1979 0.1493 0.0835 132 0.1877 0.1457 0.0871 132 0.1403 0.1151 0.0837 132 0.1973 0.1493 0.0826 133 0.1883 0.1469 0.0871 133 0.1403 0.1151 0.0837 133 0.1961 0.1487 0.0826 134 0.1889 0.1475 0.0871 134 0.1397 0.1145 0.0837 134 0.1949 0.1481 0.0817 135 0.1883 0.1475 0.0871 135 0.1397 0.1145 0.0837 135 0.1937 0.1475 0.0808 136 0.1877 0.1475 0.0871 136 0.1391 0.1145 0.0828 136 0.1919 0.1463 0.0808 137 0.1871 0.1475 0.0862 137 0.1385 0.1139 0.0828 137 0.1913 0.1457 0.0808 138 0.1865 0.1469 0.0853 138 0.1385 0.1139 0.0828 138 0.1901 0.1451 0.0799 139 0.1853 0.1457 0.0844 139 0.1385 0.1139 0.0837 139 0.1895 0.1445 0.0799 140 0.1847 0.1451 0.0835 140 0.1385 0.1145 0.0837 140 0.1877 0.1433 0.0799 141 0.1835 0.1439 0.0826 141 0.1397 0.1157 0.0846 141 0.1877 0.1433 0.079 142 0.1823 0.1433 0.0817 142 0.1421 0.1181 0.0855 142 0.1865 0.1427 0.079 143 0.1811 0.1421 0.0808 143 0.1451 0.1205 0.0882 143 0.1859 0.1421 0.079

154