The invasion of pteronia incana (blue bush) along a range of gradients in the Eastern Cape Province : it's spectral chacteristics and implications for soil moisture

i

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

i 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.

ii 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.

iii 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.

iv 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

v 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

vi

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

vii 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

viii 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

ix 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

x 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

xi 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 0.55µm, 0.65µm and 0.88µm 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

xii 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

xiii 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

xiv SMA - Spectral Mixture Analysis

SR - Simple Ratio

TDR - Time Domain Reflectometry VIs - Vegetation Indices

WI - Wetness Index

1

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.

2 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

3 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

4 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.

5 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

6 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.

7

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;

8 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

9 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

10 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

11 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 run-off 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.

12 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

13 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.

14 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.

15 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 infra-red 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 =

(

)

16 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).

17 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 multi-spectral (Lu et al., 2004), hyper multi-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:

∑

+

=

f

R

R

ε

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 fkis restricted and expressed as:

18

∑

=

f

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;

Γx 2 2 1 1 1 i ij n j p i j q p

∑ ∑

σ

β

where Γx is the mean square error (MSE), n is the number of spectral bands, p is the number of endmembers, qj are the main diagonal elements in the symmetric matrix

(X+)T, X+=(XTX)-1XT, and σ2ij 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

19 (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

20 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

21 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

22 areas that react to different leaf components within the 0-2.5µm 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).

23 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 – 0.52µm portion is highly sensitive to both carotenoids and chlorophyll while 0.63 – 0.69 µm portion is highly sensitive to

24 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 0.7µm) – 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 1.3µm 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 1.3µm, 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 1.4µm and 1.9µm 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

25 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 1.9µm (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 0.6-0.7µm and a slight dip between 0.85 and 0.9µm in comparison to soil types without iron oxide are often visible (Jensen, 2007).