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

Promoter: Professor Vincent Kakembo

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.

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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

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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

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

4.3.2 Sensor calibration 48

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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

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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

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

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

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

boolean images with training sets from PVI images 82-83

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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

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

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

deviations 98

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

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SMA - Spectral Mixture Analysis

SR - Simple Ratio

TDR - Time Domain Reflectometry

VIs - Vegetation Indices

WI - Wetness Index

VWC - Volumetric Water Content

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

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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.

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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

P. incana thrives across a diverse range of gradients in the Eastern Cape.

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:

( ) 12 ++−= abaRNIRPVI (1)

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

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 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:

∑=

+=n

k

iikki RfR1

ε , (2)

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:

18

∑=

=n

k

kf1

1 and 0 1≤≤ kf . (3)

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;

Γx22

1 1

1iij

n

j

p

i

jqp

βσ∑∑= =

= (4)

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

+=(X

TX)

-1X

T, and σ

2ij 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).

26

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

27

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.

28

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

29

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

30

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.

31

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.

32

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

Invaded site Geological Lithology/ Vegetation MAR1 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

MASL2 – Metres above Sea Level

33

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).

34

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

35

(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 north-

west 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.

36

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.

37

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).

LEGEND

Alluvial Vegetation

Albany Broken Veld

Albany Coastal Belt

Albany Dune Strandveld

Algoa Dune Strandveld

Algoa Sandstone Fynbos

Mistbelt Grassland

Montane Grassland

Dry Grassland

Bhisho Thornveld

Buffels Thicket

Coastal Lagoons

Salt Marshes

Inland Salt Pans

Lowland Wetlands

Seashore Vegetation

Coega Bontveld

Escarpment Thicket

Freshwater Wetlands

Freshwater Lakes

Great Fish Noorsveld

Great Fish Thicket

Groot Thicket

Escarpment Grassland

Kowie Thicket

Southern Coastal Forest

Southern Karoo Riviere

Mistbelt Forest

Sundays Thicket

Quartzite Fynbos

Suurberg Shale Fynbos

Montane Shrubland

Tsomo Grassland

GPS point in a transect

38

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.

39

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

sites.

Site Sand Silt Clay pH pH OM OM

<2µm 2-50µm 50-2000µm 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

40

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 20 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

41

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-

42

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

43

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.

44

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

1This chapter is based on a paper in preparation for submission to the Ecohydrology Journal - Authors,

Odindi, J. O. and Kakembo, V.

45

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 1st

November 2007 and 1st 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.

46

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 C4 and C3 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 16o-30

oC while winter

temperatures are between 5o-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.

47

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) ECH2O EC-20 soil moisture probes (Decagon

Devices Inc., Pullman, WA) were used. These probes require 10ms of 10Ma at 2.5V

excitation and can be used to measure between 0 – 100% θv within -40o to 60

o C. The

probes were connected to a 5–channel Em5 data logger and ECH2O utility software,

which allowed for automated soil moisture logs and readings.

48

4.3.2 Sensor calibration

The manufacturer’s calibration for ECH2O sensors can be used in soil types with low

to moderate sand and salinity content with an accuracy of ± 0.03m3/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 80 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 1050C. 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 ECH2O 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 594cm3 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

samples were oven dried at 105oC for 24 hours, left to cool and reweighed. The θv in

cm3/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

49

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 (8o) 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 1st 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.

50

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 = a1 + b1*X+ b2*(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 = a1+b1x (7)

Dry moisture loss: Y = {a1+break-point (b1-b2)}+b2x (8)

where: a1 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

51

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

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

0

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Episode 3

Episode 1

Episode 2

52

(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

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53

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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

54

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

0.05

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Grass P. incana Bare

Surfaces

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Highest VWC

Break pointLow est VWC

a) Episode 1.

55

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Surfaces

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b) Episode 2.

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Highest VWCBreak pointLow est VWC

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

56

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 run-

off. 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).

57

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

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.

Rain Surface Highest θv Lowest θv θv at Break-point Actual

episode (cm3/cm3) (cm3/cm3) Break-point threshold break-point

(cm3/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

58

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

*Slope before break-point - Wet slope

*Slope after break-point – Dry slope

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 (cm3/cm3) break-point (cm3/cm3)

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

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

3 Grass -0.00208 0.466 -0.0001 0.1985

P. incana -0.00106 0.269 -0.00007 0.1463

Bare -0.00079 0.192 -0.00004 0.0835

59

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

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61

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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

62

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 (80) 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

63

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.

64

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.

65

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 et 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;

66

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 5oC and 31

0C

respectively.

67

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

68

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 infra- red 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.

69

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®

proMark2TM

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

70

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.

71

a: 2001 green, red and NIR band composite.

b: 2004 green, red and NIR band composite.

72

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:

( ) 12 ++−= abaRNIRPVI (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

73

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)

b)

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).

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).

74

c)

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:

∑=

+=n

k

iikki RfR1

ε (10)

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 (Lu et al., 2003a).

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

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).

75

(Lu, et al., 2003a). To reflect true abundance fractions of endmembers, constrained

unmixing solution was applied where fk is restricted and expressed as:

∑=

=n

k

kf1

1 and 0 1≤≤ kf . (11)

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.

76

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

P. incana Boolean images and fractions comparisons

Classes

-P. incana

-Grass

-Bare surfaces

Accuracy assessments

Mask grass and

bare surfaces

P. incana

boolean images

-GPS co-

ordinates, field

surface data and

colour composites

Image fractions

-P. incana

-Grass

-Bare surfaces

P. incana fractions

Discard grass and bare

surface fractions

Data acquisition

(HRI) and surface

cover validation

Image pre-processing

-Band separation

-Image georectification

-Image enhancement

Signature development Endmember extractions

PVI generation

Su

per

vis

ed c

lass

ific

atio

ns

Pix

el u

nm

ixin

g

77

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

78

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

79

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.

80

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.

81

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.

0

50

100

150

200

250

Green Red NIR

Image band

DN

va

lue

s

Bare surfaces

GrassP. incana

Figure 5.8 a: 2001 endmembers.

82

0

50

100

150

200

Green Red NIR

Image band

DN

va

lue

s

Bare surfaces

Grass

P. incana

Figure 5.8 b: 2004 endmembers.

0

50

100

150

200

Green Red NIR

Image band

DN

va

lue

s

Bare surfaces

Grass

P. incana

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.

83

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.

84

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

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0.45 0.55 0.65 0.75 0.85Wavelength (µm)

Reflecta

nce

Bare surfacesGreen vegetationP. incana

a: Spectral measurements 28/10/ 07.

85

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85

Wavelength (µm)

Re

flec

tan

ce

Bare surfaces

Green vegetation

P. incana

b: Spectral measurement 11/12/2007.

c: Spectral measurements 19/01/2008.

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

January 2008.

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85

Wavelength (µm)

Refle

cta

nce

Bare surfaces

Green vegetationP. incana

86

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

87

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.

88

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.

89

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 un-

mixing 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.

90

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 – 0.88µm 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

91

neighbouring Shamwari Game Reserve, ranges from 7.10C to 19.5

0C in winter and

18.60C 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.

92

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.

93

Table 6.1: Leaf to branch weights and proportions.

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 – 0.88µm 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 – 0.88µm

and readings taken from visible blue (0.45 – 0.5µm), visible green (0.5 – 0.6µm),

visible red (0.6 – 0.7µm) and near infrared (0.7 – 0.88µm). 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 (Rλ) were calculated as

ratios between the reflected radiant flux from the standard white panel and the

reflected radiant flux from the samples using formula:

λ

λ

λλ Rp

LP

LR

= (12)

Where Lλ is the flux from the surface, LPλ is the flux from the panel, Rpλ is the bi-

conical reflectance of the panel under constant view geometry and illumination

(Schaepman-Strub et al., 2004).

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

94

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 0.0005µm 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 – 0.55µm and 0.68µm respectively – (Lillesand et

al., 2004) were obtained. Due to wavelength limitation of the spectrometer used, its

upper limit value of 0.88µm instead of the 1.0µm 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 0.55µm, 0.68µm and 0.88µm. 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 0.55µm, 0.68µm and 0.88µm.

To determine green vegetation, bare surface and P. incana spectral trends between

0.45 to 0.88µm, a 0.015µm 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 0.45µm to 0.88µm

wavelength range was used to show points of inflection and to determine spectral

trends of the surface spectrum at 0.015µm interval using the formula:

( ) ( )nnnn λλρρ −−= ++ 11

1std (13)

Where d1st

is 1st derivative, ρ is reflectance, n is band number and λ is wavelength in

µm (Becker et al., 2005).

95

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 0.65µm (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

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.45 0.55 0.65 0.75 0.85

Wavelength (µm)

Re

fle

cta

nce

1 : 0 3 : 1P. incana canopy 1 : 1 1 : 3 0 : 1

Figure 6.3: Sample ratios and canopy surfaces reflectance values

There was a general increase in reflectance at 0.55µm and 0.65µm with an increase in

the proportion of leaves. However, a decline in reflectance at 0.65µm in comparison

to 0.55µm 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 0.55µm and 0.65µm wavelengths (Table 6.2). The ratio reflectance

SDs were also generally higher at 0.88µm for each wavelength in comparison to

0.55µm and 0.65µm (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 0.55µm was higher than 1:1 branch to leaf ratio (Table 6.2).

96

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.

A rise in the red edge was noted around 0.7µm with more than 1:1 P. incana leaf to

branch ratios. The near infrared plateau was also visible after 0.75µm. 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 (0.68-0.75µm) 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 (0.55µm), red (0.65µm) and NIR

(0.88µm) 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 0.88µm data set is much lower than the >0.4 reflectance units

Wavelength

Ratio 0.55µm 0.65µm 0.88µm .

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

97

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

Gillespie, 2006; Jensen, 2006).

Figure 6.4: The influence of increasing proportion of leaves on reflectance at 0.55µm,

0.65µm and 0.88µm 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 (0.50µm – 0.60µm), 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 (0.65µm) and near

infrared (0.88µm) 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.

R2 = 0.8996

R2 = 0.9927

R2 = 0.9648

0

0.03

0.06

0.09

0.12

0 25 50 75 100

Percentage of leaves in sample

Re

fle

cta

nce

0.55µm

0.65µm

0.88µm

98

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0.45 0.55 0.65 0.75 0.85

Wavelength (µm)

Re

fle

cta

nce

Green Vegetation

Bare soil

P. incana

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 0.55µm and 0.88µm (see Table

6.3). Whereas mean reflectance differences between P. incana vs. bare soil, and bare

soil vs. green vegetation were statistically significant at 0.65µm, the opposite is true

of P. incana vs. green vegetation. By implication, separability between the latter pair

of surfaces cannot be achieved at 0.65µm.

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

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

Alpha level = .05.

Wave-

length

t-test and p-value

Mean reflectance

Reflectance Std. Dev.

PI vs. BS

BS vs. GV

PI vs. GV

PI

BS

GV

PI

BS

GV

0.55µm

12.14; <.001

8.74; <.001

16.15; <.001

0.042

0.074

0.125

0.022

0.034

0.015

0.65µm

24.44; <.001

35.58; <.001

1.03; <.304

0.048

0.183

0.042

0.027

0.061

0.041

0.88µm

27.76; <.001

44.84; <.001

37.61; <.001

0.071

0.290

0.571

0.072

0.091

0.071

99

Green vegetation, bare soil and P. incana reflectance differences were used to

determine separability in the green (0.55µm), red (0.65µm) and NIR (0.88µm) 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 0.65µm

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 0.88µm, 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).

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 0.55µm, 0.65µm and 0.88µm respectively (Figure 6.7). The reflectance

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.5

5

0.6

5

0.8

8

0.5

5

0.6

5

0.8

8

0.5

5

0.6

5

0.8

8

0.5

5

0.6

5

0.8

8

0.5

5

0.6

5

0.8

8

0.5

5

0.6

5

0.8

8

20/10/2007 20/11/2007 20/12/2007 20/1/2008 20/2/2008 20/3/2008

Wavelengths and Date

Re

fle

cta

nce

Bare surface & P. incana G. vegetation & Bare surfaceGreen vegetation & P. incana

Reflecta

nce d

iffe

rence

100

values for P. incana were generally low at all the wavelengths, with the lowest values

at 0.65µm (Figure 6.7).

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 0.5-0.55µm were achieved (Figure 6.8). The

steepest slope tangents in this range were at 0.52µm and the root at 0.55µm. Although

the average derivative values for bare soil were above P. incana between 0.45 –

0.54µm, 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 0.55µm

and 0.68µm. 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 - 0.68µm wavelength, the separability between P.

incana and bare soil was limited to 0.55 – 0.60µm (Figure 6.8).

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.55 0.65 0.88

Re

flec

tan

ce

Wavelength (µm)

P. incana

Bare soilGreen vegetation

101

0.5 0.6 0.7 0.8

-5

0

5

10

15

1st d

eriv

ativ

e of

ref

lect

ance

Wavelength (µm)

Green vegetation

Bare surfaces

P. incana

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

There was no clear separability between P. incana and bare soil between 0.67µm and

0.775µm. 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

0.76µm that can be attributed to the influence of branches on P. incana reflectance.

Beyond 0.77µm 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.

102

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 0.88µm 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.

103

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

104

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

105

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.

106

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

107

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.

108

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142

APPENDIX A

P. INCANA CANOPYAND MIXTURES WITH RESPECTIVE LEAVES TO

BRANCH RATIOS

a) b)

c) d)

e) f)

e) f)

P. incana canopy

143

APPENDIX B

GREEN VEGETATION, BARE SOIL AND P. INCANA MONTHLY

SAMPLES REFLECTANCE SPECTRA

a) 20/10/2007

b) 20/11/2007

Bare surface

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0.45 0.55 0.65 0.75 0.85

Re

fle

cta

nce

Wavelength (µm)

Green VegetationBare soilP. incana

Green vegetation

P. incana

Bare surface

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.45 0.55 0.65 0.75 0.85

Re

fle

cta

nce

Wavelength (µm)

Green VegetationBare soilP. incana

P. incana

Bare surface

Green vegetation

144

c) 20/12/2007

d) 20/1/2008

0

0.1

0.2

0.3

0.4

0.5

0.6

0.45 0.55 0.65 0.75 0.85

Reflecta

nce

Wavelength (µm)

Green VegetationBare soilP. incana

Bare surface

Green vegetation

P. incana

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.45 0.55 0.65 0.75 0.85

Reflecta

nce

Wavelength (µm)

Green VegetationBare soilP. incana Green vegetation

Bare surface

P. incana

145

e) 20/2/2008

f) 20/3/2008

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85

Re

fle

cta

nc

e

Wavelength (µm)

Green Vegetation

Bare soilP. incana

P. incana

Green vegetation

Bare surface

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0.45 0.55 0.65 0.75 0.85

Re

flecta

nce

Wavelength (µm)

Green VegetationBare soilP. incana

P. incana

Bare surface

Green vegetation

146

0.5 0.6 0.7 0.8

-5

0

5

1st d

eriv

ativ

e of

refl

ecta

nce

Wavelength (µm)

January

Green vegetation

Bare soil

P. incana

0.5 0.6 0.7 0.8

-5

0

5

10

15

1st d

eriv

ativ

e of

refl

ecta

nce

Wavelength (µm)

Green vegetation

Bare soil

P. incana

APPENDIX C

FIRST ORDER DERIVATIVES OF THE MONTHLY REFLECTANCE

SPECTRA

a) 20/10/2007

b) 20/11/2007

147

0.5 0.6 0.7 0.8

-5

0

5

10

15

1st d

eriv

ativ

e of

r

efle

ctan

ce

Wavelength (µm)

Green vegetation

Bare soil

P. incana

0.5 0.6 0.7 0.8

-5

0

5

1st d

eriv

ativ

e of

refl

ecta

nce

Wavelength (µm)

Green vegetation

Bare soil

P. incana

c) 20/12/2007

d) 20/01/2008

148

0.5 0.6 0.7 0.8

-5

0

5

10

15

1st d

eriv

ativ

e of

refl

ecta

nce

Wavelength (µm)

Green vegetation

Bare soil

P. incana

0.5 0.6 0.7 0.8

-5

0

5

10

15

1st d

eriv

ativ

e of

refl

ecta

nce

Wavelength (µm)

Green vegetation

Bare soil

P. incana

e) 20/2/2008

f) 20/3/2008

149

Grass

P.incana Bare

P.incana Bare Grass

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

APPENDIX D

PORTION OF CALIBRATED SENSOR MOISTURE LOGS

FOR THE THREE EPISODES AT 1HR INTERVAL

Episode 1 Episode 2 Episode 3

P.incana Grass

150

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

151

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

152

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

153

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

154

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