HyMap airborne hyperspectral imagery and field-based ecological analysis for plant community assessment on Rottnest Island, Western Australia
Laily Mukaromah
A THESIS submitted to Murdoch University in completion of the requirements for the degree of Master of Philosophy in Environmental Science
School of Veterinary and Life Sciences Murdoch University September 2017
Statement of original contribution
I hereby declare that this thesis entitled “HyMap airborne hyperspectral imagery and field-based ecological analysis for plant community assessment on Rottnest Island, Western Australia” submitted to the School of Veterinary and Life Sciences, Murdoch University, is my own work and, it contains no materials previously published or written by another person. This thesis has been completed throughout my enrolment at Murdoch University and has not been used previously for a degree at any other institution.
During the course of the degree, data analysis and mapping in this thesis was also used to identify Quokka diet and shelter, and I was a co-author of the following peer-reviewed journal article:
Poole HL, Mukaromah L, Kobryn HT, Fleming PA 2014. Spatial analysis of limiting resources on an island: diet and shelter use reveal sites of conservation importance for the Rottnest Island quokka. Wildlife Research 41: 510-521.
Laily Mukaromah
Perth, Western Australia February 2017
Abstract
Accurately classifying and mapping plant communities is essential for natural resource conservation and management planning. In recent decades, the application of hyperspectral remote sensing to biodiversity assessment and vegetation mapping has become an increasingly effective approach in supporting conservation efforts. The key focus of this work was to classify and map the vegetation on Rottnest Island, Western Australia by conducting a vegetation survey and using hyperspectral imagery. The application of HyMap airborne hyperspectral data to map plant communities seeks to capture unique spectral characteristics of plant communities, thereby allowing more accurate mapping and analysis of the complexity and heterogeneity of vegetation.
Rottnest Island, Western Australia, represents a valuable natural habitat for biodiversity conservation due to its unique features, but is highly vulnerable to disturbance, especially from invasive speceis. The aim of this research was twofold: 1. to classify the distribution patterns of plant communities as well as their floristic composition through a quadrat survey and investigate their relationship to environmental factors 2. to map the communities using HyMap hyperspectral data and compare the remote sensed map with the field data. The study is divided into two major parts. Firstly, analysis of vegetation was based on a vegetation survey for 210 plots derived from a stratified random sample. TWINSPAN classification and Mutidimensional scaling (MDS) ordination were used to elucidate plant communities and the ecological relationships among them. Secondly, HyMap data was used to map the vegetation communities using the Spectral Angle Mapper (SAM) classification.
Eleven vegetation types were identified from the TWINSPAN analysis comprising a variety of heathy communities, woodlands, coastal scrub, and halophytic lake-shore communities. NMDS revealed distance from the coast and disturbance by fire as potential drivers of community distribution. Heath communities were extensive across the island, and were characterised by dominance of Acanthocarpus preissii. In addition, results underlined that disturbance likely has a considerable impact on the distribution of invasive plants, particularly Trachyandra divaricata. The HyMap image classification demonstrated its utility in mapping the vegetation with evaluation performed using accuracy matrices indicated a high accuracy (86.2%) and reliability (Kappa coefficient 0.84).
This study explored the relationship between field vegetation data and the unique spectral signatures extracted and classified for each community type. The map is an important summary of the vegetation patterns for comprehensive cover of the entire landscape of Rottnest Island and will be useful for management strategies and further research, including, monitoring change and potential disturbance impacts on the island. The high accuracy of the classification based on remotely-sensed data highlights the growing efficiency of such data as surrogates for field-based approaches to landscape analysis and interpretation.
Acknowledgements
I would like to express my deepest and sincere gratitude to my supervisors, Prof. Neal Enright and Dr. Halina Kobryn, for giving me the opportunity to work with them, and for their encouragement, advice, and continuous guidance throughout the entire period of study. I am extremely grateful to Dr. Halina Kobryn for teaching me and giving me a good academic atmosphere to learn many new things about GIS and hyperspectral remote sensing.
I also gratefully acknowledge Murdoch University and Rottnest Island Authority (RIA) for the financial assistance and in-kind support provided during my research project. I would also like to extend my gratitude to Rottnest Island Authority for the opportunity to pursue my study in Rottnest Island, and especially RIA’s staff who supported me while I was on the island to do the fieldwork. I have been privileged to have received an Australian Development Scholarship, awarded by the Australian Agency for International Development (AusAID).
My special thanks to Dr. Philip Ladd, Dr Christina Birnbaum and Dr. Niels Brouwers for their kind support and brilliant comments on my thesis. My gratitude goes to Professor Trish Fleming, and Holly Poole, for their encouragement, and for their effort to get our quokka paper published. I would like to thank also to the diligent volunteers for their invaluable help, Prof Ann Hamblin and Lok Wang Fung. We defeated the hot summer of the island climate through long and ascendant paths of the island.
Thanks also to Dr. Helen Allison, Dr. Joe Fontaine, and Dr. Maya Saphira for their beautiful friendship. Finally, I wish to thank my parents and my little son, M. Hafidz Imtiyaz, for their love and endless support during the completion of my study.
Table of contents
Chapter 1 ...... 1
Introduction ...... 1 1.1. Research needs and significance ...... 4 1.2. Research aims and thesis outline ...... 5
Chapter 2 ...... 9
Literature Review ...... 9 2.1. Ecological context for plant community classification: concepts and methods ...... 9 2.1.1. The concept of plant community ...... 9 2.1.2 Methods for analysis and classification of plant communities ..14 2.2. Remote sensing applications in vegetation classification and mapping ...... 16 2.2.1. Perspectives and challenges for plant community mapping ……………………………………………………………………………………..19 2.2.2. A framework for plant community mapping ...... 23 2.2.2.1. Spectral characteristics of vegetation ...... 26 2.2.2.2. Image classification and Spectral Similarity Measures ...... 29 2.2.2.3. Vegetation Mask and Red Edge Normalized Vegetation Index (Red Edge NDVI) ...... 31 2.2.2.4. Accuracy Assessment ...... 33
Chapter 3 ...... 38
Methods ...... 38 3.1. Study site ...... 38 3.1.1. Rottnest Island vegetation ...... 39 3.1.2. Disturbance and exotic plant species on Rottnest Island ...... 40 3.2. Data Collection ...... 42 3.2.1. Sampling Design ...... 46
3.2.1.1. Stratified random sampling design ...... 46 3.2.1.1.1. Preliminary land cover classification ...... 46 3.2.1.1.2. Generating random points ...... 47 3.2.2. Field Vegetation Sampling ...... 53 3.2.3. Ancillary Data ...... 55 3.2.4. Hyperspectral data ...... 56 3.3. Data Analysis ...... 57 3.3.1. Ecological data analysis...... 57 3.3.2. Hyperspectral data analysis and classification ...... 64 3.3.2.1. Spectral signature extraction ...... 64 3.3.2.2. Vegetation Mask ...... 65 3.3.2.3. Image classification ...... 66 3.3.2.4. Accuracy assessment of image classification ...... 68
Chapter 4 ...... 71
Analysis of plant communities on Rottnest Island ...... 71 4.1. Introduction ...... 71 4.2. Methods ...... 72 4.3. Results ...... 73 4.3.1. Classification of plant communities ...... 73 4.3.2. Distribution of plant species and communities ...... 78 4.3.3. Patterns in species richness and diversity of plant communities ...... 94 4.3.4. Floristic variation in community composition ...... 100 4.3.5. Invasive plant species ...... 109 4.4. Discussion ...... 115 4.4.1. Classification of plant communities and relationships to environment ...... 115 4.4.2. Richness and diversity of plant communities ...... 124
Chapter 5 ...... 129
Mapping plant communities on Rottnest Island using HyMap airborne hyperspectral data ...... 129 5.1. Introduction ...... 129
5.2. Methods ...... 131 5.3. Results ...... 132 5.3.1. Spectral signatures of plant communities ...... 132 5.3.2. Vegetation Mask ...... 138 5.3.3. Image Classification ...... 141 5.3.4. Map Accuracy ...... 148 5.4. Discussion ...... 151 5.4.1. Spectral features and grouping of the vegetation ...... 151 5.4.2. Vegetation mask ...... 155 5.4.3 Image classification and map accuracy ...... 156
Chapter 6 ...... 163
General discussion ...... 163 6.1. Analysis and Mapping of Plant Communities ...... 163 6.2. Implications for Conservation ...... 169 6.3. Further research ...... 171
REFERENCES ...... 173
APPENDICES ...... 203
ANNEX ...... 217
List of Tables
Table 2.1. Mapping approach in relation to the use of higher spatial and spectral data for vegetation mapping focussed on floristic composition and vegetation community...... 21 Table 3.1. Total area (ha) and number of points used for sampling in each vegetation cover class...... 51 Table 3.2. Braun-Blanquet cover-abundance scale used as a field estimate of abundance for each plant species...... 55 Table 3.3. Summary of variables and other spatial data measured/ calculated/analysed for field data analyses...... 61 Table 3.4. Three letter abbreviations of species name used as first matrix in MDS ordination diagrams...... 62 Table 3.5. Description of environmental variables used as the second matrix in MDS ordination diagrams...... 63 Table 3.6. Spectra signature collected within the extraction of (3x3) pixel window and the spectra subset selected for each class label (community types) generated in Spectral Angle Mapper classification ...... 68 Table 4.1. Plant species observed in quadrats on Rottnest Island. For each species, the following information is given: scientific name and family, and percentage frequency of occurrence. Asterisks indicate invasive species...... 75 Table 4.2. The eleven plant communities identified from the 51 species by 210 plots Rottnest Island data set analysed using TwoWay Indicator Species Analysis (TWINSPAN)...... 78 Table 4.3. Species numbers observed, species numbers estimated to occur (Jackknife 1 and 2), means (±SE) for species richness, diversity, and evenness, and numbers of singletons and doubletons, for the eleven classes of vegetation communities (group A-K)...... 98
Table 5.1. Confusion matrix generated for the accuracy assessment of final vegetation map...... 150
List of Figures
Figure 2.1. The concept of plant community illustrated as species response curves along environmental gradient (Kent et al. 1997); a). Clementsian view, b). Gleasonian view...... 13 Figure 2.2. Spectral reflectance curve of vegetation showing energy wavelength, absorption features and vegetation components controlling vegetation reflectance characteristics ...... 28 Figure 2.3. Schematic diagram of spectral angle between reference spectrum (spectra of vegetation class in the spectral library) and unknown spectrum (image pixel spectra) in a multi-dimensional image of band 1 and 2 (after Jensen 1993)...... 31 Figure 3.1. Geographic position of Rottnest Island in relation to Western Australia coast...... 44 Figure 3.2. Flowchart of the approach in the data analysis using remote sensed and field data...... 45 Figure 3.3. Flowchart outlining procedures used to the create land cover classification...... 49 Figure 3.4. (a).QuickBird composite image of Rottnest Island, (b). Land cover classification of Rottnest Island derived from QuickBird imagery...... 50 Figure. 3.5. Stratified random placement of 210, 10x10 m quadrats on Rottnest Island used for field vegetation sampling...... 52 Figure 3.6. Three georeferenced flight lines of Hymap reflectance data for Rottnest Island with band combinations 85-26-3 as near-normal colour display...... 57 Figure 3.7. Illustration of pixel extraction from the Hymap data (5 out of 9 pixels per plots). Five pixels selected and extracted within a 3x3 pixel window were highlighted in grey colour...... 65 Figure 4.1. Dendrogram resulting from Two-Way Indicator Species Analysis (TWINSPAN) to level 6 for the 51 species by 210
quadrats vegetation data set for Rottnest Island. Numbers presented in the diagram are the total number of quadrats associated with each group; the letters A-K show the final eleven community types identified...... 77 Figure 4.2. Geographic distribution of plant communities on Rottnest Island showing the eleven plant communities referred to Table in 4.5...... 80 Figure 4.3. Geographic distribution of habitat generalist, A. preisii on Rottnest Island based on proportion of species cover abundance (Braun-Blanquet scale) ...... 81 Figure 4.4. Distribution of L. gladiatum (a habitat specialist associated with dunes and creek lines) on Rottnest Island scaled by species cover abundance (Braun-Blanquet scale) ...... 81 Figure 4.5. Geographic distribution of lake-edge species based on species cover abundance (Braun-Blanquet scale): (a). G. trifida, (b) H. indica, (c) H. halocnemoides ...... 82 Figure 4.6. Geographic distribution of coastal species based on species cover abundance (Braun-Blanquet scale): (a). O.axillaris, (b) W. dampieri, (c) S. crassifolia...... 83 Figure 4.7. Species Area and mean ecological distance curve for vegetation data (51 species in 210 quadrats) sampled on Rottnest Island. Distance measure: Sorensen (Bray-Curtis). Observed richness = 51, First-order jackknife estimated richness = 56.0, Second- order jackknife estimated richness = 53.0. Number of singletons = 5. Number of doubletons = 8...... 96 Figure 4.8. Species Area Curves for each of the identified plant community communities (A-K) on Rottnest Island based on TWINSPAN analysis of data for 51 plant species from 210 quadrats. Distance measure: Sorensen (Bray-Curtis)...... 97 Figure 4.9. Distribution of species richness and diversity for quadrats on Rottnest Island: a). Richness; b). Shannon Index...... 99
Figure 4.10. Distribution maps of Evenness for quadrats on Rottnest Island...... 100 Figure 4.11. Ordination diagrams showing MDS axis 1 and axis 2 of sites scores, for 51 species in 210 plots with environmental variable vectors (Monte Carlo test p<0.001). The description of the abbreviation of environmental attributes overlayed by the joint plot is presented in Table 3.5. Distribution of eleven plant communities produced by TWINSPAN groups is overlayed; community types (community A-K) presented in this diagram refers to community types presented in Table 4.2. Communities highlighted in pink (community A) and green (community B and C) represent outliers...... 105 Figure 4.12. Ordination diagram showing MDS axis 1 and axis 2 species scores, for 51 species in 210 plots with environmental variable vectors. (Monte Carlo test p<0.001). Species are labeled with acronyms consisting of the first three letters of the genus and the first three letters of the species name. A full species list is shown in Table 3.4...... 106 Figure 4.13. MDS ordination showing axis 1 and axis 2 sites scores, for 31 species in 186 plots with environmental variable vectors (Monte Carlo test p<0.001). Species occurring in less than 2% of plots were excluded from this analysis. Distribution of eight plant communities produced by TWINSPAN groups is overlayed. .. 107 Figure 4.14. MDS ordination showing axis 1 and axis 2 species scores, for 31 species in 186 plots with environmental variable vectors. Species occurring in less than 3% of plots were excluded from this analysis (Monte Carlo test p< 0.001). Species are labeled with acronyms consisting of the first three letters of the genus and the first three letters of the species name. A full species list is given in Table 3.4...... 108
Figure 4.15. Cumulative number of native and invasive species within each plant family observed in Rottnest Island. Two plant species remained unidentified...... 111 Figure 4.16. Relative distribution of native and invasive plants per community type (eleven classes of communities= group A-K): a). richness of native and invasive plants, b). diversity of native and invasive plants ...... 112 Figure 4.17. Distribution maps of number of invasive species on Rottnest Island overlaid with the extent of fire disturbance (fire occurrence since 1955)...... 113 Figure 4.18. Geographic distribution of invasive species T. divaricata on Rottnest Island based on species cover abundance (Braun- Blanquet scale)...... 113 Figure 4.19. Several invasive species occurring in high cover abundance on disturbed ground: a. Trachyandra divaricata, b. Lagurus ovatus, c. Rostraria cristata interspersed with T. divaricata, d. Avena barbata...... 114 Figure 5.1. The average of the reflectance spectra of plant communities extracted from Hymap imagery...... …136 Figure 5.2. Spectral signatures of each class of plant community used in Spectral Angle Mapper. For each class, spectra signatures were displayed in different colours to demonstrate variability within one community. For legend codes (A-K) refer to community classes...... …137 Figure 5.3. Images portraying Red Edge Normalized Difference Vegetation Index (RENDVI) on each flightline. Darker tone pixels signify dense vegetation, while brighter tone pixels signify sparse vegetation: Black, blue and green coloured pixels represent trees and dense shrubs; pink coloured pixels represent scrubs/heath/grasses...... 139
Figure 5.4. Red Edge Normalized Difference Vegetation Index (RENDVI) derived mask of each flightline used for SAM classification (threshold value=0.05)...... 140 Figure 5.5. Final map of vegetation communities on Rottnest Island...... 143 Figure 5.6. Distribution patterns of lake community (G.trifida – H.indica community) on Rottnest Island. Insert illustrates of the biggest patches of this community found on the west side of Lake Baghdad...... 144 Figure 5.7. Distribution patterns of woodlands and shrubs communities on Rottnest Island (C.preissii woodland, M. lanceolata – A.rostellifera, P.phylliraeoides, E.platypus and A.rostellifera – A. preissii) ...... 145 Figure 5.8. Distribution patterns of heath communities on Rottnest Island: A. preissii – A. flavescens, (community F), A. preissii –T. divaricata (community G), A. preissii – L. gladiatum (community H) and A. preissii - C. candicans (community K) ...... 146 Figure 5.9. Distribution patterns of A.preissii –O.axillaris coastal scrub community (community E) on Rottnest Island. Inserts illustrate of the detail of distribution over inland areas near salt-lake. . 147 Figure 5.10. Percentage areas per class for the classification HyMap images extracted from total pixels occupied by each class of plant community on Rottnest Island. For community codes (A-K) refer to community classes (Table 4.2)...... 149
List of Plates
Plate 4.1 Example of G. trifida- H. indica halophyte community (group A) showing the gradient from the outer zone with G. trifida to the inner zone with H. indica and H. halocnemoides ...... 89 Plate 4.2 Example of Callitris preissii woodland community (group B) ... 89 Plate 4.3 Example of Melaleuca lanceolata woodland community (group C)...... 90 Plate 4.4 Example of Acacia rostellifera-Acanthocarpus preissii coastal community (group D) ...... 90 Plate 4.5 Example of coastal scrub Acanthocarpus preissii - Olearia axillaris (group E) characterized by coastal plants Olearia axillaris, Westringia dampieri, and Scaevola crassifolia and with high abundance of Acanthocarpus preissii ...... 91 Plate 4.6 Example of Acanthocarpus preissii-Austrostipa flavescens heath community (group F) with high abundance of Lagurus ovatus ...... 91 Plate 4.7 Example of Acanthocarpus preissii-Trachyandra divaricata heath community (group G) with presence of Austrostipa flavescens and Lagurus ovatus ...... 92 Plate 4.8 Example of Acanthocarpus preissii-Lepidosperma gladiatum heath community (group H)...... 92 Plate 4.9 Example of Pittosporum phylliraeoides scrub community (group I) found on limestone ridges ...... 93 Plate 4.10 Example of A. preissii - Conostylis candicans scrub community (group K) found in revegetation sites of C. preissii and M. lanceolata with high occurrence also of Avena barbata ...... 93
Chapter 1 Introduction
Several Mediterranean-climate ecosystems are included in the 25 Global
Biodiversity Hotspots known for globally significant biodiversity and high degree of endemism but also under considerable threat due to human activities (Vogiatzakis et al. 2006; Myers et al. 2000). South-western Australia is recognized as one of the world’s biodiversity hotspots owing to its tremendous richness of plants and the high level of anthropogenic threat to their on-going persistence (Hobbs and Mooney 1998; Saunders et al. 1991).
This region, also botanically known as the South-Western Australian Floristic
Region, houses more than 7000 vascular plant taxa, of which approximately
50% are endemic (Coates and Atkins 2001; Hopper and Gioia 2004). However, human disturbances have markedly altered ecosystems in south-western
Australia since European settlement in the early 1800s and have led to considerable habitat loss and fragmentation, including changes to fire regimes, and accidental and deliberate introductions of alien plant and animal species
(Hobbs and Mooney 1993; Mittermeier et al. 2004; Saunders et al. 1991).
Furthermore, in south-western Australia, climate change is predicted to be a large threat to the region's species and ecosystem integrity. Many aspects of projected climate change will likely have significant consequences with changes in ecosystem structure and function, together with a significant loss
1 of biodiversity and increasing wildfire risks (Working Group II Contribution to the Intergovernmental Panel on Climate Change 2007).
Islands represent a special class of ecosystems with typical features of high endemism, highly range-restricted species and lower species richness compared to mainland areas (Kier et al. 2009; Rosenzweig 1995). Unique conjunctions of their biogeographic boundaries (oceanic barriers and relative distances to mainland), history, and climate have led to remarkable evolutionary processes often resulting in distinctive morphological and ecological characteristics (Cody 2006; Montmollin and Strahm 2005). Due to their relatively small size and greater vulnerability to natural catastrophes than mainland areas, and especially given the continuing threats to island biodiversity from human disturbance, island ecosystems are therefore highly significant for global prioritization of conservation efforts (Buckley and Jetz
2007; Mortimer et al. 1996; Hill 2009; Pysek and Richardson 2006; Rietbergen
2008).
Rottnest Island offers a natural field laboratory for research due to its insularity and unique wildlife. Rottnest Island, which also supports high tourist amenity use, is an A class Reserve managed by the Rottnest Island
Authority under the Land Administration Act 1997. However, this island has in the past, and continues to, experience high levels of human disturbance from frequent fires, clearing, weed infestation and increased grazing (including by the native quokka) – all of which have affected the native vegetation ( Rippey
2 and Hobbs 2003; Rippey and Rowland 1995). Consequently, an appropriate conservation and sustainable development strategy needs to be designed to form an integral part to any approach to preserving biodiversity and maintaining ecosystem function on the island.
In order to inform conservation strategy, especially in the establishment of more efficient planning and monitoring of natural resources, an accurate knowledge of the distribution of plant species and communities is required
(Chambers et al. 2007; Margules and Pressey 2000). Classifying and mapping vegetation is a suitable approach to summarise information on the distribution and diversity of plant species and communities at a particular site or area.
However, mapping vegetation of Mediterranean-climate ecosystems is challenging due to high spatial heterogeneity of the landscape, especially where there are complex vegetation patterns related to varying topography, soils and other geomorphological features (Blondel and Aronson 1995; Lewis
2002).
Recent advances in remote sensing studies have raised a novel prospect in investigating and interpolating vegetation properties across large spatial extents (Bobbe et al. 2001; Goodwin et al. 2005). Compared to multispectral sensors, hyperspectral sensors offer greater potential discrimination of plant species and communities and their biophysical environments than previously possible. Hyperspectral sensors, which have become available to provide both relatively higher spectral and spatial resolution, allow extraction of more
3 detailed information on spectral variability of the landscape features (Xie et al.
2008). Accordingly, the application of hyperspectral remote sensing data for vegetation assessment may provide new benefits in natural resource management and planning (Govender et al. 2008; Rocchini et al. 2007; Roff et al. 2006). Hyperspectral remote sensing may therefore support more advanced research on the mapping of vegetation and communities, especially to gain more understanding of the vegetation patterns and characteristics of
Mediterranean-climate island landscapes.
This thesis provides quantitative assessment of plant community variability and its association with selected environmental factors. It investigates the utility of hyperspectral data for mapping the plant communities of Rottnest
Island, and evaluates the results of hyperspectral mapping alongside the ecological analysis. The ecological management values of this new analysis are then considered.
1.1. Research needs and significance
Information on spatial patterns of plant species and community distributions as well as invasion intensity provides a baseline for vegetation monitoring, and also for determining pest control and restoration efforts. Remote sensing studies provide a sound basis from which to pursue more detailed analyses of plant communities and vegetation condition to better inform management approaches for biodiversity conservation. Hyperspectral image analysis, when
4 combined with on-ground observation, can provide a comprehensive understanding of the current patterns of native vegetation distribution and their environmental determinants, as well as threats from invasive species and capacity for native species recovery (Falkowski et al. 2009; Jones et al. 2011;
Lawrence et al. 2006; Mitri and Gitas 2012).
Through a combination of hyperspectral remote sensing and field-based analysis of vegetation cover and condition, this study provides a detailed vegetation map for the whole of Rottnest Island. This will provide a solid foundation to assist conservation goals and monitoring programs.
1.2. Research aims and thesis outline
This study aims to classify the vegetation of Rottnest Island into communities based on field-vegetation data, and to explore the utility of HyMap hyperspectral imagery for mapping these community types. Based on the analysis of the results of these investigations, this study seeks to understand how the vegetation patterns of Rottnest Island are influenced by natural and human factors, with particular emphasis on invasive plant species. This project will recommend possible approaches to conserve plant species and communities, and provide a base-line for measurement of change in the future.
To achieve these goals, the research was conducted with the following objectives:
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1. Determine the patterns of native plant communities and weedy
terrestrial plant species across Rottnest Island based on field-
vegetation data, as well as exploration of the vegetation-environment
relationship.
2. Classify and map the distribution patterns of these plant communities
across Rottnest Island using HyMap hyperspectral imagery.
3. Evaluate the utility of plant community classification and the remote
sensing approach as a method for plant community delineation and
mapping.
4. Provide conservation managers with accurate and reliable data for
the application of conservation activities and monitoring schemes.
This thesis comprises of six chapters. Chapter 1 provides a contextual overview of the thesis and the aims of this study. Chapter 2 is a literature review of the plant community and vegetation mapping, and covers the use of hyperspectral remotely sensed data for mapping vegetation. In chapter 3, the regional context of the Island and the study sites are briefly described, including vegetation types and environmental conditions described in previous studies. The methodology developed for this project is also presented in this chapter, including sampling design, vegetation survey, ecological analysis, hyperspectral image processing and classification. The next two chapters are the results chapters for the major topics that are covered by this thesis. Chapter 4 focuses on ecological analysis of the field-based data and includes the classification and pattern of plant communities. This chapter also
6 explores and quantifies the distribution of native vegetation and floristic richness in relation to environmental attributes, including level of invadedness, fire history and other human impact factors. Chapter 5 describes
HyMap hyperspectral analysis for classification and mapping the plant communities of Rottnest Island, and an accuracy assessment of the classified images. Chapter 6 presents a general discussion and synthesis of the results, including conclusions and recommendations.
7
8
Chapter 2 Literature Review
This chapter presents a literature review of the two different areas of study to provide the theoretical foundation for the mapping of plant communities using hyperspectral imagery for Rottnest Island. Firstly, it considers the conceptualisation and theories of plant communities, which has theoretical links to approaches for mapping vegetation based on classification. This section covers the community-versus continuum debate and species responses to environmental factors based on both views of vegetation. The second section addresses the application of remote sensing for vegetation mapping, and in particular, the application of hyperspectral imagery to vegetation mapping, in relation to plant spectral characteristics and hyperspectral image classification.
2.1. Ecological context for plant community classification: concepts and methods
2.1.1. The concept of plant community
“Community ecology” is one of the most important concepts in the area of vegetation science. This concept has had a significant influence on the development of ecology as a science as well as conservation programmes for biological resources (Keith 2009). Community is described as the associations
9 of plants and animals which occur in defined areas and dominated by one or more prominent species or by some physical characters (Daubenmire 1968;
Rickleffs and Miller 2000). A different view of the community concept is proposed by Whittaker (1975) and Begon et al. (2006) who define community as an assemblage of species that occur together and are linked to each other by their interactions as well as their responses to the environment. The early definitions stressed the dominance concept, whereas the later emphasized species assemblages and interactions.
Through time, the concept of community has been defined in various ways by different authors. However, several attributes such as species composition, occurrence in a specific environment or habitat and an interaction between species is common to almost all ideas about community (MacNally et al. 2002;
Begon et al. 2006). Laughlin and Abella (2007) and Keith (2009) also emphasised that the concept focused on understanding the patterns and main drivers of community composition.
While various definitions exist about the community concept, particular studies or authors have questioned the existence of a natural vegetation unit.
Two major issues in the conceptualization of the nature of plant communities concern the continuity of vegetation and the difficulty in identifying clear boundaries separating different types. These issues are summarised by the most prominent debate in the history of ecology: Clementsian (community or association approach) versus Gleasonian (continuum approach) paradigms.
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The Clements’ view (also recognized as the superorganismic view) identifies three ideas: communities are bounded, ruled by laws of succession, and these laws are due to internal and structural features of the community type. In the view of a “community-unit”, vegetation occurs in discreet recognizable units called associations (also known as the concept of “association-unit”), and multiple species co-occur along an environmental gradient. This concept, which was formalized by Clements (1916), proposed that the associations are formed by the repeated assemblages of plants found in similar habitats. These assemblages are classified according to the total floristic composition of the communities and environmental variation which have similar values within a distinct spatial patch and boundary.
In contrast, Henry Gleason (1926) proposed the ‘individualistic’ concept which emphasizes differences rather than similarities. In his view, the associations of organisms particularly adapted to one or other environment are not distinct functional groups. He argued that there are no consistent associations of species even within the same climate condition, because species respond individualistically along environmental gradients in both space and time. In the Gleasonian view, the concept of plant associations is illustrated as
“fluctuating and fortuitous immigration of plants and an equally fluctuating environment” (Gleason 1926).
The concept of plant co-occurrence illustrated as species response curves along an environmental gradient is presented in Figure 2.1. The Clementsian concept shows the rise and decline of species groups with similar distribution
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(species assemblage or community). Species reach peak abundance and disappear in correlated fashion in relation to environmental gradients where the finishing point of one species group coincides with the establishment of another along the gradient (Figure 2.1a). In the Gleasonian view, each species has its upper and lower boundaries in different places along any given environmental gradient, reflecting scattered and overlapped patterns of the centres and boundaries of species distribution along the gradient (Figure
2.1b). In this concept, a group of species might be identified at one point in space, but no distinct groups of species are predicted to occur repeating over space and time (Gleason 1939; Keddy 2007; O’Neill et al. 1986; van der Maarel
2005). Plant species are distributed as a continuum where the composition of communities changes continuously along abstract gradients/complex gradients in environmental or ecological factors (also termed as the concept of
“vegetational continuum”).
Both Clementsian vs Gleasonian views have their own application for vegetation analysis. The Gleasonian concept focuses on the species and the transitions in real vegetation, particularly across long gradients. In contrast, the Clementsian concept stresses the recurring species composition
(community unit), integrated system and consistency (Keddy 2007; O’Neill et al. 1986), so that boundaries can be defined (i.e., by narrow ecotones). Since both Clementsian and Gleasonian approaches are based on the relationship between species and environment, biotic interactions (i.e. interspecific interactions among species themselves) are not essentially measured. This
12 matter may also affect species distributions, e.g. one species ‘attracting’ or
‘repelling’ another that does not simply reflect similar reactions to environment.
a
b
Figure 2.1. The concept of plant community illustrated as species response curves along environmental gradient (Kent et al. 1997); a). Clementsian view, b). Gleasonian view.
Based on the concept of plant community illustrated above, classification of vegetation communities is more consistent with a Clementsian view of
13 vegetation. While it is clear species have individual environmental requirements it can be usful to recognise groups of species that co-occur in a particular association in an area as a community for the purposes of summarising information about the vegetation and communicating this to other workers who may be interested in the vegetation for purposes such as conservation planning.
The concept of plant community provides a practical foundation to distinguish plant associations and derive vegetation units from the natural plant cover.
The analysis of plant community is further presented in below.
2.1.2 Methods for analysis and classification of plant communities
Numerous methods are available to describe vegetation on the basis of a classification into vegetation units and quantitative analysis of vegetation has mostly employed a variety of multivariate approaches for examining community data (Bakus 2006; Daubenmire 1968; Mueller-Dombois and
Ellenberg 1974). Multivariate analyses include exploratory techniques that allow the plant assemblages to be distinguished based on their similarities and dissimilarities in terms of species composition. Classification and ordination are the most widely used techniques (Belbin and McDonal 1993).
Classification is applied to group floristic abundance data into classes or groups, using plot samples of species by maximizing within-group similarity
14 and minimizing between-group similarity. A variety of different methods are available for identifying relatively homogeneous groupings in data: agglomerative vs. divisive, polythetic vs. monothetic, and hierarchical vs. non- hierarchical. One of the most widely employed techniques is TWINSPAN classification. TWINSPAN is a polythetic, divisive, hierarchial classification that generates a two-way table from a sites-by-species matrix by reciprocal averaging (Gauch and Whittaker 1981). In this technique, the data for species and sample units are classified simultaneously along a dominant gradient resulting in a two-way ordered table that summarizes the variation in floristic composition (McCune and Grace 2002; Kent and Coker 1992; van Tongeren
1995).
Ordination is a method that arranges plots in relation to each other in terms of species composition and/or environmental attributes (Kent and Coker 1992).
Ordination summarizes data ordered in a high-dimensional space by exploring and presenting the strongest low dimensional structure that can be displayed in two or three dimensions without a substantial loss of information (McCune and Grace 2002). The arrangement of data is based on spatial display of similarity, which summarises the relationship among samples (Wratten and
Fry 1980). Various methods of ordination exist and the decision as to which method should be used depends on the objectives of the study and the nature of the data. The most frequently used methods include principal components analysis (PCA), detrended correspondence analysis (DCA), canonical correspondence analysis (CCA), and non metric multidimensional scaling
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(NMDS). Non-metric Multidimensional scaling (NMDS) is now considered as one of the most reliable techniques of indirect ordination, and the use of this method now dominates multivariate approaches to analysis in community ecology (McCune and Grace 2002). NMDS maximizes the rank-order correlation of calculated distance measures between pairs of samples in ordination space. As this technique only requires the ordinal relationship, it is not constrained by linearity requirements, data transformations, and measurement error (Gauch 1981; Tong 1989). Furthermore, it can encompass gradients of high beta diversity (Prentice 1980), so differing fundamentally from other ordination techniques (Clarke 1993).
2.2. Remote sensing applications in vegetation classification and mapping
Vegetation maps summarise phytosociological information in a visual display that allows map users to view the vegetation in relation to other landscape features. Vegetation maps have been widely applied as a basis for other studies or purposes such as conservation and management applications (Küchler
1967; Küchler and Zonneveld 1998). Maps consisting of the information about vegetation properties in a spatial framework for a specific area are very important for two major reasons. Firstly, vegetation maps can be used as a visual tool to communicate with user/audience, simplifying a complex set of information in a spatially referenced form. Secondly, it is an essential tool for analytical purposes because it provides a spatially referenced vegetation database, which can be analysed with other spatially referenced data
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(Millington and Alexander 2000). Hence, vegetation maps play a significant role in conservation, especially for representing the ecological aspects of landscape heterogeneity (Menakis et al. 2000; Millington and Alexander 2000;
Narumalani et al. 2004).
Remote sensing has played an essential role for several decades in mapping applications. Remote sensing data sets consist of simple panchromatic images of multispectral and more recently hyperspectral data. They provide spatially continuous information with appropriate spatial accuracy and coverage over large areas (Saatchi et al. 2008; Turner et al. 2003; Xie et al. 2008; Zagajewski et al. 2005). Aerial photography and multispectral remote sensing have been used as tools to map vegetation, particularly forest vegetation at a broad level over large areas (Hoffer 1984; Mullerova 2004). The aerial photography commonly used is limited to ultraviolet, visible and reflective infra-red wavelengths up to 0.9 µm, while multispectral remote sensing systems detect radiation over several separate wavelength band ranges (generally between three and six spectral bands) at various spectral resolutions within the visible to middle infrared region of the electromagnetic spectrum. On the basis of multispectral remote sensing systems, several Earth Observatory satellites have also been used extensively for global scale vegetation mapping. These satellite missions include LANDSAT series (Landsat Thematic Mapper and
Landsat Multispectral Scanner), SPOT, IKONOS, Advanced Very High
Resolution Radiometer (AVHRR), and QuickBird (Carpenter et al. 1999;
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Helmer et al. 2000; Knorn et al. 2009; Millington and Alexander 2000; Rogan et al. 2002; Sedano et al. 2005; Wang et al. 2004; Xie et al. 2008).
Over the past two decades, advances in sensor technology have enabled the development of a new technology known as hyperspectral remote sensing.
Hyperspectral imagery, also known as imaging spectroscopy, is a new system of remote sensing that provides measurements of spectral properties of surface features using a large number of narrow and contiguous spectral bands. These sensors typically collect hundreds of spectral bands, ranging from the visible, near-infrared, mid-infrared, and short-wave infrared portions of the electromagnetic spectrum (Jensen 2000; Kruse et al. 1993) (Figure 2.2).
Remotely sensed data with high spectral resolution provide extensive analyses of earth surface features, allowing the extraction of higher levels of spectral information. Since the hyperspectral data provide an almost continuous spectral reflectance signature, these sensors are more sensitive to subtle variations in reflected energy and therefore the more likely unique characteristics are to be defined. Accordingly, the hyperspectral data provides a potentially substantial enhancement of spectral measurement capabilities over conventional multispectral remote sensor systems, and can improve distinction between different image elements ( Jensen 2007; Kumar et al.
2001; Ustin et al. 2004; Zomer et al. 2009). In addition, spatial resolution is critical because it determines the level of accuracy of classification. For example, spatial issue is relevant because fine-scale differences within and between plant communities may be difficult to resolve (Asner 1998; Ustin et
18 al. 2004). While spatial resolution of hyperspectral data is not always higher than those of multispectral, hyperspectral data that is available with finer spatial resolution may be adequate for detecting the smaller, local differences in species distributions; thus it increases classification accuracy.
Further detail of the application of hyperspectral remote sensing for vegetation mapping and the mapping approach/procedure is covered in section 2.2.1.
2.2.1. Perspectives and challenges for plant community mapping
Mapping and characterization of natural vegetation remains challenging, particularly due to the complexity of the spectral response of vegetation associated with diverse landscape elements (Janssen 2004; Küchler 1984;
Küchler and Zonneveld 1988; Roelofsen et al. 2014; Sanders et al. 2004;
Verrelst et al. 2009). Natural landscapes are generally complex, in which the spatial heterogeneity of environmental factors regulate complex natural patterns of plant distribution across the landscape. Many studies have highlighted the difficulty in using image spectra to discriminate vegetation types such as the difference between heathland and forest in a fine-grained mosaic of vegetation and exposed rock or bare soil (e.g. Beeri et al. 2007;
Boschetti et al. 2007; He et al. 2006; Lewis 2002).
Owing to the spatially heterogeneous nature of natural landscapes, the concept of ecological communities can help to simplify and generalize the complex
19 natural patterns of vegetation (Verrelst et al. 2009). While physical features of the vegetation such as structure of the canopy, density of plants and possibly distribution of dominant species may be relatively easily quantified remotely, floristic and compositional changes of native ecosystems may be more difficult to determine and these attributes are closely associated with the landscape features over space and time, including climate, soil, topography, and human disturbance (Chapin III et al. 2002; O’Neill et al. 1986). When structural features can be associated with the plant community then the remotely sensed data can be used to map the plant communities.
Hyperspectral data has been increasingly used to analyze, characterize, model, classify and map natural vegetation (Ustin et al. 2004; Ustin et al. 2009; Xie et al. 2008) and some successful examples of this are listed in Table 2.1. The mapping approach for plant communities, including image classification and accuracy assessment, will be addressed further in the next section.
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Table 2.1. Selected examples in relation to the use of higher spatial and spectral data for vegetation mapping focussed on forest characteristics, tree species, and vegetation community.
Citation Summary of topic
Martin et al. AVIRIS data were analyzed using a maximum likelihood 1998 algorithm for the classification of 11 forest cover types in
natural forest in Massachusetts, including pure and mixed stands of deciduous and conifer species. Thomas et al. High spatial resolution Compact Airborne Spectrographic 2003 Imager (CASI) reflectance data were examined and compared to plant community data for a peatland complex in northern Manitoba, Canada. Mundt et al. Evaluating the applicability of high spatial resolution 2006 hyperspectral data and small-footprint Light Detection and Ranging (LiDAR) data to map and describe sagebrush in a semi-arid shrub steppe rangeland. Verrelst et al. Mapping of aggregated floodplain plant communities using 2009 image fusion of CASI and LiDAR data along the river Waal in the Netherlands. White et al. Forest structure was explored using Hyperion data over a 2010 625 km2 coastal temperate forest landscape on Vancouver Island, British Columbia, Canada. Jones et al. Assessing the utility of hyperspectral Airborne Imaging 2010 Spectrometer for Applications (AISA) imagery and small footprint discrete return Light Detection and Ranging (LiDAR) data for mapping 11 tree species in and around the Gulf Islands National Park Reserve, in coastal South-western Canada.
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Citation Summary of topic
Colgan et al. Mapping individual tree crowns (classified to species) of 2012 savannah forest communities in Queensland, Australia, using CASI data through automated classification. Dalponte et This paper describes how hyperspectral data allowed the al. 2012 authors to distinguish similar tree species and the addition of LiDAR data increase the performance with higher classification accuracy. Cho et al. Mapping seven common savanna tree species or genera in 2012 the Sabi Sands Reserve and communal lands adjacent to Kruger National Park, South Africa using Carnegie Airborne Observatory (CAO) hyperspectral data, WorldView-2, QuickBird and LIDAR data. Naidoo et al. Classification of eight common savanna tree species in the 2012 Greater Kruger National Park region, South Africa, using a combination of hyperspectral and Light Detection and Ranging (LiDAR)-derived structural parameters, in the form of seven predictor datasets, in an automated Random Forest modelling approach. Alonzo et al. This paper describes tree species mapping in Santa Barbara, 2014 California, USA, using hyperspectral and LiDAR data fusion. A total of 29 plant species were mapped based on the crown object delineation. Ghosh et al. This paper describes tree species mapping in European 2014 forest using HyMap, Hyperion and LIDAR data.
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2.2.2. A framework for plant community mapping
In the study of the characteristics and extent of natural plant communities, the detection of plant assemblages through remote sensing is commonly based on spectral differentiation of “species group”. Since it is generally assumed that every surface object has its own unique pattern of reflected, emitted, and absorbed radiation across the spectral bands, the same types of surface objects should therefore have similar spectral response patterns (Campbell 1996).
The multispecies assemblages governed by different numbers and combinations of plant species (species composition and abundance) would reveal distinct spectral characteristics that would be different from the specific response of one species. Accordingly, each community is assumed to be spectrally unique and separable. However, spectral differences between plant communities are often subtle and can be difficult to discern. Cochrane (2000) has indicated that the spectral response curves of different plant species are generally very similar, and this is particularly due to the major reflecting elements being similar across species. The similarity of spectral response is often linked to biochemical and biophysical parameters of the plants’ leaves and canopy such as chlorophyll a and b, carotene, and xanthophylls (Asner
1998; Kumar et al. 2001). Consequently, the spectral responses of vegetation provide one of the greatest challenges for the classification of vegetation remotely.
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Numerous approaches have been developed to improve vegetation mapping.
For instance, an integrated approach combining vegetation analysis and remote sensing techniques has been advancing the field of vegetation mapping, and this approach may enhance classification accuracy (Cingolani et al. 2004; Schmidtlein et al. 2007; Verrelst et al. 2009). Several studies have emphasized that a classification approach is considered to be the most appropriate in vegetation mapping; especially in stratifying vegetation into lower class hierarchical levels (i.e. plant community level) as well as capturing more detailed ecological variability (Malik and Husain 2006; Malik and Husain
2008; Schmidtlein et al. 2007). Multivariate analysis is generally applied to field-ecological data to define a level of generalization of vegetation and identify units that are discernible by remote sensing techniques (Belluco et al.
2006; Oldeland et al. 2010; Thomas et al. 2003; Verrelst et al. 2009). Cingolani et al. (2004) and Verrelst et al. (2009) also highlighted the important factors that need to be considered, i.e. defining discrete units of vegetation classes distinguishable by selected remote-sensing data and determining representative training sites. In this way, stratifying and classifying vegetation into relatively homogenous classes using field-ecological data can be used to provide the basis for determination of spectral reflectance characteristic of the vegetation classes (Cihlar 2000; Thomas et al. 2003; Verrelst et al. 2009).
In the model of remote sensing analysis, relating ground cover information with image spectra is important (Hill and Thomson 2005; Martin et al. 1998;
Schmidtlein and Sassin 2004; Verrelst et al. 2009; Thomas et al. 2003). The
24 field survey collects reference data, providing basic information on floristic attributes to enable description and classification of the plant communities
(Cihlar 2000; Brook and Kenkel 2002; Greenberg and Dobrowski 2006).
Vegetation analysis and detail ground survey pursued in this study will be presented in Chapter 3. In addition to aiding in understanding the controls over plant community distribution, knowledge of the characteristics of plant communities will be used as a pre-defined group to extract spectral information from the hyperspectral dataset.
Image classification is one of the most important parts of digital image analysis; hence selecting an appropriate classification procedure is essential.
The methods of image classification can be generally grouped into manual, automated, and hybrid approaches (Lilliesand and Kiefer 2000; Horning et al.
2010). Among all these techniques, the automated image classification algorithms are the most common. These methods can be broken down into: supervised classification and unsupervised classification (Jensen 2005;
Richards and Xiuping 2006). In recent decades, the development and use of advanced classification algorithms have been evolving, particularly in addressing the issues of choosing the most appropriate method and improving the classification accuracy. Among classification algorithms, Spectral Angle
Mapper (SAM) is one of the more widely used classifiers in hyperspectral data processing (Chang 2003; Clark et al. 2005; Govender et al. 2008; Hue et al.
2008; Schmidt et al. 2004). The SAM algorithm pursued in this study will be discussed below.
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2.2.2.1. Spectral characteristics of vegetation
Spectral characteristics of vegetation represent the information about the absorption feature at a particular wavelength. These spectral signatures are illustrated in the electromagnetic spectrum by lower reflectance of visible and high reflectance of Near-infrared (NIR) wavelengths (Smith 2012) (Figure
2.2.). In general, the absorption features of vegetation are determined by the same few chemical components (Ustin et al. 2009; Wessman 1994). Yet, they are also influenced by a number of factors, including amount, vigour, productivity, structure and floristic composition of vegetation, as well as soil moisture, substrate, topography and atmospheric effects (Richardson and
Wiegand 1977; Vogelmann and Moss 1993).
Spectral reflectance of vegetation in visible wavelengths (0.4-0.7 µm) is influenced by the composition and concentration of leaf pigments, such as chlorophyll a, b and β-carotenoids (Tucker and Garrett 1977; McCoy 2005).
Healthy and photosynthetically active vegetation absorbs energy in the red and blue wavelengths, while energy in the green wavelength is reflected
(Bannari et al. 1995; McCoy 2005). The electromagnetic energy is absorbed as particular wavelengths range from 0.43-0.45 µm and from 0.65-0.66 µm
(Jensen 2000), and therefore, vegetation presents low reflectance in the red and blue visible regions. Distinct absorption features found at discrete locations on the electromagnetic spectrum are useful to estimate plant component or chemical contents. For example, chlorophyll a and b which absorb at 0.64 and 0.66 µm can provide an indication of health and vigour of
26 plants (Curran 1989; Ustin et al. 2009). At the longer wavelength, electromagnetic energy absorbed at the Short-wave Infrared (SWIR) region
(0.13-3.0 µm) is highly sensitive to cellulose and lignin (Shoshany 2000) and leaf water content (Dickson et al. 1999; Nagler et al. 2001), where the plant moisture content directly influences the absorption of water at these wavelengths (Nagendra 2001; McCoy 2005).
In the near infrared (NIR) (0.7-0.13 µm) region, spectral reflectance of vegetation is influenced by the internal leaf structure and leaf cell component
(Bannari et al. 1995; McCoy 2005). Reflectance spectra in this region can be used for estimating the chemical content of leaves, for example, for estimating lignin, cellulose and nitrogen contents of riparian fallen leaves, in bamboos, herbaceous plants, deciduous and evergreen trees (Takahashi et al. 2002). NIR reflectance is also very useful to discriminate vegetation from other land cover, such as soil and water (Figure 2.2). In addition, the variability of plant species reflectance across NIR region offers the potential to discriminate vegetation at the species level (Paterson et al. 2001; Holmgren and Persson
2004; McCoy 2005; McCloy 2006). Furthermore, as NIR reflectance is related to green biomass, species discrimination can also be achieved based on biomass or foliage differences (Nagendra 2001).
Spectral reflectance curves show a large increase in spectral reflectance across
NIR region, between the red visible and NIR wavelengths (Figure 2.2), and this sharp increase is known as the ‘red edge’. Spectral characteristics of plants in
27 the `red edge' position can be related to biophysical and biochemical properties of vegetation, such as plant vigour and chlorophyll content, LAI, and biomass (Almeida and Filho 2004; Bracher and Grant 1994; Cho et al. 2008;
Cho and Skidmore 2006). The ‘red-edge’ feature can also be applied to derive spectral vegetation indices, such as Normalized Difference Vegetation Index
(NDVI). NDVI has been widely applied for vegetation/species mapping and other biodiversity surrogates such as plant species richness in several environments (Carter et al. 2005; Levin et al. 2007; Lucas et al. 2008;
Krishnaswamy et al. 2004).
Figure 2.2. Spectral reflectance curve of vegetation showing energy wavelength, absorption features and vegetation components controlling vegetation reflectance characteristics (Smith 2012).
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2.2.2.2. Image classification and Spectral Similarity Measures
Spectral Angle Mapper (SAM) is one of the most important algorithms in term of spectral distance metrics applied for material identification and spectral characterization, including spectral variability, similarity, discrimination, and divergence (Belluco et al. 2006; Chang 2003; Christian and Krishnayya 2009;
Clark et al. 2005; Govender et al. 2008; Hue et al. 2008; Schmidt et al. 2004;
Zomer et al. 2009). SAM is different from conventional classifiers (i.e.
ISODATA, minimum distance, maximum likelihood, Mahalanobis distance, artificial neural network, decision trees, and fuzzy) as it is computed based on the "angular distances", while conventional classifiers rest on the spectral distance concept. In this way, spectral angle classifier measures the shape of the spectral pattern, so image pixels that have similar shape patterns are classified together into the same cluster or information class, whereas the latter relies on the statistical distribution pattern in feature space (Kruse et al.
1993; Sohn and Rebello 2002). Spectral angle classifier generally provides consistent results in different ecoregions, generating better classification results than those of conventional classifiers such as Mahalanobis distance and maximum-likelihood classifiers (Sohn and Rebello 2002).
SAM is a physically-based spectral distance metric that compares the spectra of image pixels to the spectra of reference endmembers (Schowengerdt 2007;
Kruse et al. 1993). By measuring the angle between two vectors of spectral signature on the basis of spectral-angle distance, i.e. the unknown spectrum
(vegetation spectrum) and a reference spectrum (the “endmember”, “training
29 classes”, or “pure types”), the spectral similarity between them can be determined (Kruse et al. 1993; Jensen 2005). The schematic diagram of the spectral similarity is shown in Figure 2.3. In this algorithm, the spectral similarity (α) for each selected pixel in the hyperspectral image is measured by comparing two spectra (spectrum s1 and spectrum s2) as a vector in an n- dimensional space (Kruse et al. 1993; van der Meer 2006).
This algorithm is considered as relatively insensitive to illumination and albedo effects when applied to measure spectral similarity on the calibrated reflectance data (Kruse et al. 1993). It is also insensitive to data variances and to the size of the training data set, and does not require the data to be normally distributed (Sohn and Rebello 2002). In addition, SAM algorithm uses only the vector direction and not the vector length, and the level of illumination only affects its magnitude, not the direction of the vector.
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Figure 2.3. Schematic diagram of spectral angle between reference spectrum (spectra of vegetation class in the spectral library) and unknown spectrum (image pixel spectra) in a multi-dimensional image of band 1 and 2 (after Jensen 1993).
2.2.2.3. Vegetation Mask and Red Edge Normalized Vegetation Index (Red Edge NDVI)
Several studies have applied vegetation indices (VIs) to mask out vegetated areas from remote sensed imagery and used the masked images in the classification process (Underwood et al. 2003; Xiao et al. 2005; Yamano et al.
2003). The Normalized Difference Vegetation Index (NDVI) is commonly applied in masking vegetation to select the pixels representing cover with vegetation present and exclude pixels with soil/bare areas or water. When this index is applied on the vegetated areas, it will yield positive values because vegetation has high near-infrared and low red or visible reflectance. NDVI is valuable because it effectively accounts for illumination (which is often an
31 issue for supervised and unsupervised classification), and it is also effective for filtering out atmospheric scattering. However, NDVI produces low and similar values for both non-photosynthetic vegetation and soil, which can lead to confusion between them. NDVI is very sensitive to the variability of background conditions, which is most apparent in the areas with sparse vegetation and high topographic relief (Lillesand and Kiefer 2000).
There are several existing studies that use Red Edge parameter to account for chlorophyll concentration and other vegetation characteristics such as biomass, nitrogen content and leaf area index (LAI) (Darvishzadeh et al. 2009;
Filella and Penuelas 1994; Gupta et al. 2003; Lamb et al. 2002). Red edge is the point of maximum slope between red and near-infrared regions of the reflectance spectrum, where this spectrum region is strongly correlated with foliar chlorophyll content (Horler et al. 1983; Miller et al. 1990). Red edge hyperspectral indices include several indices (Gupta et al. 2003):
Vogelmann (VOG) R740/R720, (R734-R747)/(R715+R726), and
(R734-R747)/(R715+R720)
RESP (Red Edge Spectral Parameter) R750/ R710
GMI (Gitelson and Merzylak Index) R750/ R700
The Red Edge Normalized Vegetation Index (Red Edge NDVI), also known as
NDVI705, is a narrowband greenness modified from NDVI. The Red Edge NDVI, which is applied by using bands along the red edge instead of the main absorption and reflectance peaks, is measured by the ratio of within-leaf
32 scattering and the effect of leaves' chlorophyll content. This index is defined by an equation below (ENVI 2005):
The Red Edge NDVI is very useful for vegetation assessment as it provides a sensitive environmental indicator that can detect stress in leaves, for instance caused by drought and disease (ENVI 2005; Jung et al. 2006). This hyperspectral index is very sensitive to canopy foliage and senescence phenological stages, and has the advantage of not being affected by leaf surface reflectance (Gupta et al. 2003; Jung et al. 2006).
2.2.2.4. Accuracy Assessment
Cihlar et al. (1998) proposed six criteria for the performance evaluation of a classification method: accuracy, reproducibility, robustness, ability to fully use the information content of the data, uniform applicability, and objectiveness. Different approaches to this evaluation can be implemented, ranging from a qualitative evaluation based on expert knowledge to a quantitative accuracy assessment based on sampling strategies (Congalton
1991; Lu and Weng 2007). Accuracy assessment provides an evaluation procedure of a classified image. Since a thematic map derived from remotely sensed data always has inherent errors, accuracy is very important to create a more reliable and accurate map.
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A common method for accuracy assessment is through the use of a confusion matrix (Foody 2002; Congalton and Green 2008). The confusion matrix, also known as an error matrix, is an arrangement of rows and columns that identifies the number of sample units assigned to a certain category in one classification (e.g. based on vegetation data) and the number of sample units assigned to a certain category in another classification (e.g. based on hyperspectral data) (Congalton and Green 2009).
In general, a report for the confusion matrix includes overall accuracy (OA), error of commission, error of omission, producer’s accuracy (PA), user’s accuracy (UA) and Kappa coefficient (Congalton 1991). Overall accuracy is the sum of the number of pixels classified correctly divided by the total number of pixels classified. Producer accuracy shows the percentage of a particular category correctly classified which is calculated by dividing the number of correct pixels of the reference pixels in image classification by the actual number of ground truth pixels for that specific category, whereas the user accuracy is calculated by dividing the number of correctly mapped pixels for a class category by the total pixels assigned to that category. Commission error
(errors of inclusion) is the percentage of extra pixels in a class and omission errors (errors of exclusion) refers to the percentage of pixels left out of a class.
Commission error (Ce) and omission errors (Oe) are calculated by an equation below (Congalton and Green 1999):
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Kappa analysis is recognized as a powerful technique in the analysis of a single error matrix and in comparing the difference between different error matrices
(Congalton 1991; Foody 2004). The Kappa coefficient represents the measurement of the overall agreement of a matrix which takes non-diagonal elements into account. The result of performing a KAPPA analysis is a KHAT statistic and is computed as:
where r is the number of rows in the matrix, xii is the number of observations in row i and column i, x i+ and x +i are the marginal totals of row i and column i, respectively, and N is the total number of observations (Bishop et al., 1975;
Cohen, 1960).
This literature review supports the research themes on the various aspects of plant community and hyperspectral mapping. Various hypotheses, viewpoints and published results of other researchers are included in this chapter. The concepts plant community provide theoretical foundation for classifying and
35 analysing the distribution patterns of plant assemblage over the landscape, while the hyperspectral mapping is based on the ecological variability of identified plant communities and the variation in the absorption features of plant assemblage. Methods and important findings will be addressed in the next chapters.
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Chapter 3 Methods
This chapter describes the study site and methods used to address the aims of this project. The methods described include field sampling of vegetation and environmental variables, analysis of the field and remote sensing data, classification of the hyperspectral imagery, and evaluation (accuracy assessment) of the classification images.
3.1. Study site
The study site was Rottnest Island, Western Australia. Rottnest Island lies approximately 18 km from the western coast of Western Australia near
Fremantle, between 31°59’14.90” - 32°1’35.18” S and 115°26’58.71” -
115°33’31.65” E (Figure 3.1.). The island has a Mediterranean-type climate characterized by cool, wet winters and warm, dry summers, with average annual precipitation of 577 mm, average daily maximum temperature of
22.1°C and average minimum temperature of 15.6°C (Bureau of Meteorology
2011).
Rottnest Island is part of a sequence of coastal limestone islands and reefs formed by nearshore marine and eolian sedimentation during the late
Quaternary (Playford 1983). It covers an area of approximately 1900 ha and
38 measures 10.5 km from east to west and up to 4.5 km north-south widths, with
10% of the area occupied by large salt lakes which separate the eastern and western parts of the island (Playford 1983). The maximum elevation is 47 metres (Glenister et al. 1959). Through the course of the Pleistocene, Rottnest
Island has been part of the mainland during glacials (i.e. associated with periods of low sea-level), and an island during interglacials, most recently separating from mainland Western Australia at least 6500 years ago as sea- levels rose following the end of the last glacial (Glenister et al. 1959). Rottnest
Island is composed of late Pleistocene to middle Holocene aeolianite (Tamala
Limestone), with Pleistocene coral-reef (Rottnest Limestone) thinly intercalated (Glenister et al. 1959; Playford 1983). These formations are overlaid with younger sediments consisting of thin middle Holocene to modern deposits: shell beds (Herschell limestone), dune sand, beach sand, swamp deposits, and lake deposits (Playford 1983).
3.1.1. Rottnest Island vegetation
Six major habitat types cover the island terrestrial environment, namely: coastal sand and dune vegetation, salt lakes, brackish swamps and freshwater pools, woodlands, heaths and shrublands, and planted vegetation in settlement areas. Melaleuca lanceolata, Callitris preissii, and Acacia rostellifera are the dominant native woody species on the island (Pen and Green 1983;
Rippey and Hobbs 2003). Three plant communities have dominated the vegetation of Rottnest Island over recent centuries; low closed woodlands
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(Callitris preissii subsp. Preissii - Melaleuca lanceolata subsp. occidentalis woodland community), Acacia rostellifera scrub, and Acanthocarpus preissii -
Austrostipa flavescens sclerophyllous heath (Rippey and Hobbs 2003). Large areas of C. preissii – M. lanceolata woodland, with C. preissii abundance generally exceeding that of M. lanceolata, occurred in the eastern part of
Rottnest Island until European settlement commenced around 1831 (Pen and
Green 1983). While dense woodland and forest stands (with sparse understorey) were formed by these species, the western part of the island was covered by more open vegetation (Pen and Green 1983). Currently, the vegetation across the Island is dominated by A. preissii which forms a low diversity community, most likely due to increased fire associated with human settlement (Rippey et al. 2003). On the coastal dunes, vegetation is dominated by Olearia axillaris, Westringia dampieri, Scaevola crassifolia, Rhagodia baccata, and Guichenotia ledifolia. In the areas nearest to salt lakes, the vegetation is characterized by Chenopodiaceae, such as Salicornia australis,
Halosarcia halocnemoides, and Halosarcia indica (Rippey and Roland 1995).
3.1.2. Disturbance and exotic plant species on Rottnest Island
The frequent use of fire by early settlers for land clearing, and hunting of quokkas (Setonix brachyurus) by Aboriginal prisoners contributed to the decrease in the woodland community (Rippey and Hobbs 2003). Degradation of the C. preissii – M. lanceolata woodland community following European settlement (1831-present) occurred due to loss and fragmentation as a
40 response to large-scale clearing and harvesting of the woodland for agriculture and settlement development (Pen and Green 1983). The frequency of fire has declined significantly since the mid 1900’s as island management has increasingly focussed on the needs of tourism and remnant vegetation conservation (Rippey and Hobbs 2003). The last major fire was in 1955, when around two thirds of the Island was burnt, while a smaller fire burnt about 90 ha in the area between the centre of the island and the north coast in 1997
(Rippey and Hobbs 2003).
In terms of disturbance by animals, legislation to protect the quokka (still in operation today) has contributed to a markedly increased quokka population on the island, with grazing by them becoming a major factor in further loss and fragmentation of the C. preissii – M. lanceolata woodland community, primarily through its negative impact on seedling recruitment (Rippey and Hobbs 2003).
Regeneration of C. preissii and M. lanceolata rarely occurs in areas with large quokka populations, such as near the main settlement (Storr et al. 1959). A number of fenced ‘plantation’ areas have been planted with a range of woody native species in an effort to address this problem.
Degradation of C. preissii – M. lanceolata woodland on Rottnest Island has resulted in the expansion of the native, but less palatable (to herbivores), A. preissi - A. flavescens heath community, which now covers a large portion of the island. Before the 1950s there was no record of the domination of A. preissi.
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Today this spiney, grass-like shrub species forms a low diversity community dominating the vegetation across the Island (Rippey et al. 2003).
The floristic composition of plant communities in and around the developed areas on the island is dominated by introduced species. However, some introduced species also have wide distribution across the island in degraded natural communities (Rippey et al. 2003). Eighty-three exotic species have been recorded on Rottnest Island in 1998-2001 (Rippey et al. 2003). This comprises 42% of the total of 196 species recorded for the island. Some exotic species, due to their strong competitiveness, are considered a threat to the
Island’s vegetation and are now targeted for suppression. These species include Zantedeschia aethiopica, Euphorbia paralias, Ricinus communis,
Rhamnus alaternus, and Nicotiana glauca.
3.2. Data Collection
This study used remote sensing, geographic information systems, and field data as primary sources of data and as analysis tools. Two datasets of remotely sensed imagery were acquired for the study area:
(1) QuickBird satellite imagery, used to design the field vegetation sampling
(pre-fieldwork stage), and
(2) Hyperspectral airborne imagery (Hymap), used for the detailed
description and analysis for vegetation classification and vegetation
mapping.
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The overview of the methods pursued in this study is shown in Figure 3.2. It covers the main part of the research procedure starting from development of ground data sampling methodology to the final results.
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Figure 3.1. Geographic position of Rottnest Island in relation to the Western Australia coast (Data source: Google Earth).
44
Sampling design
Field survey
Ecological analysis HyMap image Red Edge NDVI (CH 4) mask
Grouping sites Spectral signature
SAM classification
Classified images Review Results
Validation
(Accuracy assessment)
Figure 3.2. Flowchart of the approach in the data analysis using remotely sensed and field data.
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3.2.1. Sampling Design
3.2.1.1. Stratified random sampling design
To assess the vegetation on Rottnest Island, a stratified random sampling method was used. This method is routinely used for vegetation mapping studies, as it is efficient and minimizes the effects of spatial autocorrelation
(Fortin et al. 1989; Mc Coy 2005; Lu and Weng 2007). Land cover classification was used as the basis for site selection, and the research area was then stratified based on the cover classes. Further detail for sampling design is presented below and the overview of sampling design with the development of land cover classification is presented in Figure 3.3.
3.2.1.1.1. Preliminary land cover classification
QuickBird imagery was chosen to create a broad classification of vegetation cover. This satellite image has medium spectral and spatial resolution; thus it is possible to distinguish vegetation heterogeneity to represent potentially different plant communities. The QuickBird satellite image used in this project was acquired on 17 June 2005. The QuickBird data had four multispectral bands (B1: 0.45–0.52 μm, B2: 0.52–0.60 μm, B3: 0.63–0.69 μm, B4: 0.76–0.90
μm) with 2.4 meter spatial resolution and was corrected for atmospheric effects. The overview of the sampling design, including land cover classification and random point sampling generation, is shown in Figure 3.3.
Figure 3.4a shows the QuickBird composite image of Rottnest Island used in
46 this study. IDRISI Taiga was used for image processing and classification
(Eastman 2009).
An unsupervised classification was performed using the cluster analysis method. Prior to unsupervised classification, water bodies (areas with water; i.e. lakes and ocean) were masked from the image, while the intertidal areas were then converted to individual bitmap masks in order to remove them from further image classification. Cluster analysis was performed on the masked
QuickBird image to classify the image into the several desired classes. This classification produced 12 classes of land cover, of which ten were identified as classes with vegetation and two were unvegetated (i.e. bare/bright sand, dunes and blowouts) (Table 3.1; Figure 3.4.b.). The ten classes of vegetation cover were used in the next step to develop a stratified field sampling scheme for locating vegetation quadrats according to the vegetation spectral groupings.
3.2.1.1.2. Generating random points
The number of sites per each cover class was selected proportional to its extent covered by each cover class. Table 3.1 shows the proportional stratified random sampling scheme displaying the proportion (total area in hectares) and number of sampling points assigned in each cover class proportional to the study area. Total 210 sites constitute the reasonable size sample that can be measured in the time available. Before generating the random points, the
47 highly cleared and disturbed settlement areas located in the north-east corner of the island were excluded from this sampling regime (Figure 3.5).
The random point generation tool, in the Hawth’s Analysis Tools for ArcGIS software package (Beyer 2004), was used to generate the random stratified sampling point locations of 210 sites within the ten vegetation cover classes.
Sample points were assigned within each cover category by applying random numbers. A minimum distance of 100 m was applied between sites within each cover class in an attempt to improve the statistical independence of the sites
(Guisan & Zimmermann 2000). The sample points were assigned within homogenous areas in order to reveal relationships between ground and remotely sensed data (Mehner et al. 2004; McCoy 2005; Reinke and Jones
2006). Figure 3.5 shows the random placement of the 210 sites across the island.
To ensure the spatial compatibility between ground data and pixel resolution, a quadrat size of 10x10 m (100 m2) was applied for field sampling. This quadrat size was chosen as being large enough to provide an adequate sample of both woodland and shrubland vegetation types (Mueller-Dombois and
Ellenberg. 1974) and because the pixel size of HyMap images is 3.5 meters so that a 3x3 pixel window (equivalent of 10.5x10.5 m) would be roughly equivalent to the quadrat size.
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QuickBird Imagery
Water (sea and lakes) masked, intertidal areas digitized
CLUSTER analysis
Land cover classification Parameter found: 12 clusters Cluster representing vegetation: 10 Clusters
Hawth's Analysis Tools
Random point sample generation
Figure 3.3. Flowchart outlining procedures used to create the land cover classification and to create random sampling.
49
a
b
Figure 3.4. (a)QuickBird composite image of Rottnest Island, (b) Land cover classification of Rottnest Island derived from QuickBird imagery. .
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Table 3.1 Total area (ha) and number of points used for sampling in each vegetation cover class.
Vegetation Cover Category Total area Cover area No. of Percentage of (ha) (%) field sites field sites (%) grasses/low vegetation 1 260.95 17.37 35 16.67 grasses/low vegetation 2 144.31 9.61 19 9.05 grasses/low vegetation 3 143.00 9.52 19 9.05 grasses/low vegetation 4 142.33 9.47 19 9.05 bare/ sparse grasses 202.22 13.46 27 12.86 sparse low vegetation 158.40 10.54 21 10.00 low vegetation/sparse shrubs 255.72 17.02 34 16.19 low vegetation/low shrubs associated with slopes 89.84 5.98 12 5.71 dense and medium height vegetation 60.65 4.04 12 5.71 dense vegetation 44.88 2.99 12 5.71 Total area mapped 1502.3 100 210 100
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Figure 3.5. Stratified random placements of 210, 10x10 m quadrats on Rottnest Island used for field vegetation sampling.
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3.2.2. Field Vegetation Sampling
HyMap airborne hyperspectral imagery of Rottnest Island was flown in April
2004 for a mapping project on marine habitats of Rottnest Island. Vegetation on the island was exposed to normal dry season conditions over the summer
– autumn period to April 2004 when the HyMap data were acquired.
Vegetation plot sampling for the present study began in November 2009 and continued until April 2010. Ground-based vegetation sampling began in
November to ensure observation of the maximum number of vascular plant species (i.e. including winter-spring annuals and geophytes). However, since the HyMap was flown in April (autumn), subsequent analyses were done after removing all the annuals/geophytes. This ensured collection of plant species data comparable with that revealed by the hyperspectral imagery to be used in subsequent analyses.
The vegetation on this island is considered inherently very slow growing and there were no major events affecting vegetation loss (e.g. fire, major weed invasion, clearing) during 2004-2009 (the time between the image acquisition and ground vegetation sampling). On this basis, it was assumed that there was no significant change in plant species composition
53 on Rottnest Island during those years, so that the HyMap image is adequately representative for vegetation mapping at this time.
The stratified random points (latitude, longitude) generated were imported to a GPS (Garmin GPSmap 76Cx) for use in establishing field location of each point sample. Some quadrats were moved slightly due to disturbance factors such as edge of the road, with the exact location of each quadrat recorded with latitude-longitude coordinates (degrees, minutes, seconds) using a GPS and marked with a stake in the centre of the quadrat. The species names and abundance (cover) of all plants were recorded in each plot using the Braun-
Blanquet cover-abundance scale (Mueller-Dombois and Ellenberg 1974) to visually estimate the cover of each species (Table 3.2). The taxon names used in this study conform to those of Rippey and Rowland (1995) and Florabase
(Western Australian Herbarium 1998). Additional ecological data collected within each plot included: disturbance types and cover, average plant height, maximum plant height, and maximum DBH (diameter at breast height) in the case of trees (if present), aspect and slope (using compass and clinometer, respectively), and estimated percentage cover of rocks and bare ground.
Samples of unknown plant species were collected for identification purposes.
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Table 3.2. Braun-Blanquet cover-abundance scale used as a field estimate of abundance for each plant species.
Field estimate (%) Braun-Blanquet scale
<1% + (very rare) 1-5 1 ( rare) 6-25 2 (occasional) 26-50 3 (frequent) 51-75 4 (common) 76-100 5 (abundant)
3.2.3. Ancillary Data
Additional environmental attributes were calculated for each site (Table 3.5).
For instance, proximity of settlement/roads/coastline was measured from the plots using ArcGIS distance measurement tools to examine the effect of human-caused disturbances on the distribution of plant species and communities. This included distance from building/settlement areas, distance from roads (sealed road, unsealed road, and track/firebreak road) and distance from the coast. Topographic variables such as slope, aspect, and elevation were extracted from a Digital Elevation Model (DEM) of Rottnest
Island using ArcGIS software. To examine the effect of major fires (known to have occurred in 1955 and 1997) on vegetation, frequency of fire in each plot was also extracted from the polygons depicting areas burnt at different times in the past. The fire data were obtained from the map of fire occurrence on
Rottnest Island (Rippey and Hobb 2003).
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Wind rose data, derived from wind speed and direction, were calculated from
Bureau of Meteorology data (Bureau of Meteorology 2010) so that the potential effects of wind could be interpolated for each quadrat location based on its aspect, slope and distance from the coast. The wind rose data are presented in appendix 4. A heat index (HI) was calculated for each site using aspect and slope data to examine the potential effect of this microclimatic index on species composition and structure as a result of the combination of incoming radiation with high temperatures (Enright 1994; Mc Cune and Keon
2002):
HI = cos (slope aspect-315) x tan (slope angle)
All data for environmental variables were used as the second matrix in Non
Metric Multidimensional Scaling (NMDS) analyses (Section 3.3.1.). ArcGIS version 9.3 (ESRI 2005) was used to conduct the majority of GIS operations.
3.2.4. Hyperspectral data
Hyperspectral imagery of Rottnest Island was acquired on 26th April 2004 by
HyVista Corporation, Australia. Three flight lines (Figure 3.6) were collected from the same platform at the altitude of 1600 m to capture images with a spatial resolution of 3.5x3.5 m pixels. These data were provided as atmospherically corrected, and consisted of 125 spectral bands (15 nm band widths) in the range from 450 to 2480 nm. Data were further georeferenced using GLT files supplied by HyVista Corporation.
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Figure 3.6. Three georeferenced flight lines of HyMap reflectance data for Rottnest Island with band combinations 85-26-3 as near-normal colour display.
3.3. Data Analysis
3.3.1. Ecological data analysis
Data were analysed using both simple summary measures and more complex, multivariate methods. Species richness (Margalef), diversity (Shannon-
Wiener, Simpson), and evenness, and gamma diversity were computed as per standard methodologies (Simpson 1949; Magurran 1988; Margalef 1958;
Shannon and Weiner 1963; Whittaker 1972). Multivariate methods of classification and ordination, including Two-way Indicator Species Analysis
(TWINSPAN), Non Metric Multidimensional Scaling (NMDS), as well as species area curve and Multiple Response Permutation Procedure (MRPP) were conducted using PC-ORD for Windows, version 4.0 (McCune and Grace 2002).
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Plant species recorded in this vegetation survey were classified into plant functional groups based on their life-form: trees, shrubs, lilies and sedges, grasses, and herbs. Invasive plants were all classified as non-native plants.
Environmental data sets used in this study are summarized in Table 3.3.
Classification and ordination were applied to the quadrat data to obtain an independent (from environmental factors and spectral attributes) floristic and ecological characterization of the vegetation, as well as to explore vegetation relationships to environmental factors. Vegetation communities were identified in this project using TWINSPAN (Hill 1979). TWINSPAN classification was applied on the species cover dataset of 51 species in 210 quadrats using the default cut levels. This analysis was carried out until the sixth division, producing eleven groups (communities) and sixteen sub-groups
(sub communities). Vegetation ‘groups’ derived from the analysis within this thesis are referred to as “communities”.
To test group differences between vegetation communities, MRPP was applied on the species cover data for the 210 quadrats dataset with null hypothesis of no relationship between derived vegetation groups. MRPP is a nonparametric technique that tests the hypothesis of no difference/distinctness between two or more groups based on ecological similarity. The difference of each group is evaluated by the A-statistic, the statistical significance is evaluated by asymptotic approximation (p-value), and the test statistic T describes the separation between the groups. The smaller T is, the stronger the separation.
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The highest possible value for A is 1, indicating perfectly homogenous groups
(all entities must be identical within groups), while A=0 indicates within-group heterogeneity equal to chance expectation (McCune and Grace 2002).
Species area curves were applied for all datasets (210 quadrats) and for each community to determine an adequacy of sampling effort. A Kruskal-Wallis test was used to compare richness, diversity, and evenness among communities. In this analysis, communities with very low samples were excluded in the subsequent analyses. Variations in species richness and diversity, both native and invasive, were also summarized using boxplots that depict the mean and quantiles.
Distribution of community types was plotted over Rottnest Island using ArcGIS software. Species richness, Shannon-Wiener index (H), Simpson's index of diversity (D), and Evenness (E) were also plotted based on their value in each quadrat. Several abundant species, habitat-specialist, and invasive species were also plotted based on their presence (or absence) in each quadrat.
To visualize patterns in vegetation type and environmental data among quadrats, NMDS was applied using the Sorensen (Bray-Curtis) distance measure. NMDS was applied on the full dataset consisting of 51 species in 210 quadrats. Joint plots (Peck 2010) were then applied to explore the relationship between variation in species composition among plots and the environmental attributes measured. Joint plots, the vectors that represent environmental
59 factor gradients, can examine the relationship of multiple responses simultaneously. The relative relationships of the variables to the ordination axes are explained in a second matrix, and a simple scatterplot of the ordination axes is generated and overlain by a number of vectors. The direction of vector shows its relative association with the two axes and the vector length is proportional to the magnitude of the association. Groupings derived from the TWINSPAN classification were also imposed on the ordination to visualize clustering of sites into groups, providing further insight into the organization of plant assemblage related to environmental controls.
With attention to plot outliers, identification and removal of 24 plot samples was undertaken based on the TWINSPAN and first NMDS analyses prior to a second analysis. NMDS was then applied on a reduced dataset consisting of 31 species in 186 quadrats. During the extraction from the full dataset, species with less than 3 occurrences were removed. Deleting rare species is very useful to avoid distortion and to reduce noise in the data set without losing critical ecological information, and often improving identification of the relationships between community composition and environmental factors in multivariate analyses (Marchant 2002; Mc Cune et al. 2002).
Descriptions of the data sets for plant species names and environmental attributes used in MDS ordination as first (vegetation) and second
(environmental) matrix are presented in Table 3.4. and Table 3.5., respectively.
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Table 3.3 Summary of variables and other spatial data measured/ calculated/analysed for field data analyses.
DATA/CATEGORY VARIABLES
Sampling Design Stratified Random Sampling Number of field 210 plots sampling Identified species All vascular plant species Field measurement Plant species name Percentage cover of plant species Slope Aspect Elevation Percentage of bare areas Percentage of litter Percentage of rock Average height Maximum height Field variables Cover abundance Species richness Shannon diversity index Simpson diversity index Evenness index Number of native species Number of invasive species Number of trees Number of shrubs Number of lilies and sedges Number of grasses Number of herbs Ancillary data Wind rose Heat Index Ancillary data: Distance from beach GIS-derived Distance from sealed roads environmental data Distance from unsealed roads Distance from firebreaks Distance from buildings/settlements Frequency of fire since 1955 Analysis of field data Two-way Indicator Species Analysis (TWINSPAN) classification Multidimensional Scaling (MDS) ordination Multiple Response Permutation Procedure (MRPP) Vegetation mapping Spectral signature of plant communities Masking by applying RENDVI (Red Edge Normalized Vegetation Index) Spectral Angle mapper (SAM) Map validation (Accuracy assessment/ confusion matrix)
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Table 3.4 Six letter abbreviations of species names used as first matrix in MDS ordination diagrams.
ABBREVIATION DESCRIPTION OF SPECIES SCIENTIFIC NAME
ACALIT Acacia littorea ACAROS Acacia rostellifera ACASAL Acacia saligna ACAPRE Acanthocarpus preissii AIRCUP Aira cupaniana ASPFIS Asphodelus fistulosus ATRCIN Atriplex cinerea ATRSP Atriplex sp AUSFLA Austrostipa flavescens AVEBAR Avena barbata BRADIS Brachypodium distachyon BRODIA Bromus diandrus BULSEM Bulbine semibarbata CALPRE Callitris preissii CARSP Carex sp CENSP Centaurea sp CONCAN Conostylis candicans DIPDAM Diplolaena dampieri EUCGOM Eucalyptus gomphocephala EUCPLA Eucalyptus platypus EUPPEP Euphorbia peplus GAHTRI Gahnia trifida GUILED Guichenotia ledifolia HALHAL Halosarcia halocnemoides HALIND Halosarcia indica INUGRA Inula graveolens ISONOD Isolepis nodosa LAGOVA Lagurus ovatus LEPGLA Lepidosperma gladiatum LEUINS Leucopogon insularis LEUPAR Leucopogon parviflorus LOMMAR Lomandra maritima MELLAN Melaleuca lanceolata OENSP Oenanthera sp OLEAXI Olearia axillaris PITPHY Pittosporum phylliraeoides RHABAC Rhagodia baccata ROSCRI Rostraria cristata SCACRA Scaevola crassifolia SENLAU Senecio lautus SP13 sp13 SP14 sp14 SPILON Spinifex longifolius SPOVIR Sporobolus virginicus STASP Stackhousia sp SUAMAR Suaeda maritima
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ABBREVIATION DESCRIPTION OF SPECIES SCIENTIFIC NAME
TEMSP Templetonia sp THOCOG Thomasia cognata TRADIV Trachyandra divaricata TRACOE Trachymene coerulea WESDAM Westringia dampieri
Table 3.5 Description of environmental and response variables used as the second matrix in MDS ordination diagrams.
ABBREVIATION DESCRIPTION OF ENVIRONMENTAL VARIABLES
Veg type Vegetation type/community Mean Mean abundance of species S Species richness E Evenness index H Shannon diversity index Elev Elevation Slope Slope Aspect Aspect HI Heat Index WR Wind rose Max Ht Maximum height of plants Ave Ht Average height of plants DISSEA Distance of field plot to sealed road DISUNS Distance of field plot to unsealed road DISFIR Distance of field plot to firebreak CLODIS Closest distance of field plot to the three type of distance DISBUI Distance of field plot to building/settlement DISBEA Distance of field plot to beach Fire Fire frequency since year 1955 Rock Rock cover Percentage of bare area(ground exposure/ground area Bare without vegetation) Litter Percentage of litter Native Number of native species Invasive Number of invasive species Grass Number of grass species LILSED Number of lily and sedge species Herb Number of herb species Shrub Number of shrub species Tree Number of tree species
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Classification analysis applied in this step is essential to identify discrete patterns with distinctly clustered appearance of vegetation. The MDS ordination methods applied in this ecological section were used to observe and clarify the vegetation patterns and floristic variation of plant communities, and their associated environmental factors. Georeferenced sites of the classified vegetation community were used then as a basis to develop the spectral signatures from the HyMap image. Based on the ordination plots, several overlapped sites that occurred in transitional communities were excluded from further spectral signature analysis. HyMap vegetation mapping is presented below.
3.3.2. Hyperspectral data analysis and classification
3.3.2.1. Spectral signature extraction
Classification of HyMap images is a multistep procedure. The classification was performed using SAM by subsequently applying RENDVI-mask into SAM routine. As a reference of spectral signatures extraction, 210 georeferenced field sites were used for the training set. As the pixel size of the HyMap image was 3.5x3.5 m, a 3x3 pixel window that was seen as an equivalent to 10x10 m of field quadrat plot, was used to extract the spectral signature from each sample plot. The use of a 3x3 pixel cluster, rather than individual image pixels, is also recommended by McCoy (2005) as a minimum size for ground data collection in remote sensing projects.
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Spectral signatures were extracted based on the reference of the georeferenced vegetation quadrat sites. To extract spectral signature from the imagery, the georeferenced 210 sample plot locations was overlaid on the
HyMap imagery. For each field sample plot, signatures within a window of 3x3 pixels were then extracted from five pixels neighbouring and surrounding the centre of each plot (Figure 3.7.). Training sets of pixels were created for the eleven classes of community type as belonging to the 210 sample plots and the total spectra signatures of 1050 were extracted (Table 3.6.).
Figure 3.7. Illustration of pixel extraction from the HyMap data (five out of nine pixels per plots). Five pixels selected and extracted within a 3x3 pixel window were highlighted in grey colour.
3.3.2.2. Vegetation Mask
Several vegetation indices were explored in ENVI software to find the most suitable index in the autumn image (April) that was characterized by dry vegetation lacking in chlorophyll absorption. The Red Edge Normalized
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Vegetation Index (Red Edge NDVI) was chosen as it was able to differentiate vegetation from other land cover types and show the best indicator of low sparse vegetation.
A RENDVI-mask was created on the HyMap data to include only the vegetation pixels in the image processing analysis. By applying this mask to image classification processing, The RENDVI-mask images were created for each flightline with threshold value 0.1.
3.3.2.3. Image classification
During the image classification stage, the overlapped spectral signatures were identified within each class of plant communities, and the spectral signatures classes were then revised. The detail procedures are described below:
1. The spectra signatures were grouped for each individual community type
respectively, and then saved as spectral library for each community type,
representing one class feature of community types.
2. The reflectance spectrum of each individual community type was plotted
on the histogram to visualize the position of the main features of a typical
reflectance curve. The signatures were then evaluated for their ability to
optimise vegetation maps, with an assessment of feature similarity of each
signature within a group. Signatures appearing as prominent outliers
were examined, e.g. distinct signatures with excessive amplitude in the
feature plot space were identified and removed.
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3. Spectral Angle Mapper classification (SAM) was executed for each
signature by exploring several thresholds of radians (from the lowest
radian of 0.1 to the highest of 1) to accomplish best spectral matches to
the spectral library. SAM classification results were reviewed based on the
prior knowledge of field sites and were compared with the distributions
shown by the color composite image. The maximum angle (radians) 0.07
was chosen by using this threshold classification image showing the best
SAM match at each site, representing the minimum spectral angular
distance rule (smallest spectral angular distance).
4. SAM was performed for each individual community class/type, and then
spectral library of each class was reviewed based on the proportion of
matched pixels. In this way, spectra of the classified pixels were directly
compared with spectral library. To calculate spectral matches for each
pixel, basic statistic in ENVI was applied, and the proportion of matched
pixels was calculated for each specific class. Spectral signature data least
significant (represent least proportion) were removed from the spectral
library, and subsets were created for spectral library of each class.
5. Finally, a total of 140 spectral signatures were used as the input for the
eleven classes to be included in SAM processing (Table 3.6.). Each flight
line was processed individually and RENDVI-mask was applied during
SAM processing.
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Table 3.6 Spectra signature collected within the extraction of (3x3) pixel window and the spectra subset selected for each class label (community types) generated in Spectral Angle Mapper classification.
PLANT NUMBER OF Total number of Training set of each COMMUNITY/ SAMPLE spectra extracted class type employed CLASS TYPE PLOTS for each class in SAM analysis type A 8 40 8 B 4 20 10 C 11 55 20 D 25 125 12 E 13 65 24 F 74 370 15 G 42 210 15 H 8 40 11 I 2 10 5 J 2 10 5 K 21 105 15 Total 210 1050 140
3.3.2.4. Accuracy assessment of image classification
The evaluation of a classified image was determined through the process of accuracy assessment using a confusion matrix (Foody 2002; Congalton and
Green 2008). Sample units are assigned to a certain category in one classification based on vegetation data and to a certain category based on
HyMap hyperspectral data. In this project, 210 sample plots were checked and examined using field data and the classified image. One specified class on the
68 classified image was classified as correct category if any five pixels out of nine within a 3x3 pixel window were mapped as correct pixels. An error matrix was created, and then user’s accuracy, producer’s accuracy, overall accuracy, and kappa coefficient were calculated. Total area of each class was also calculated from the total number of pixels belonging to each specified class, and then the percentage area was calculated for each class.
The above-mentioned methods are used for the plant community analysis outlined in Chapter 4 and HyMap vegetation mapping outlined in Chapter 5.
Vegetation sampling was conducted based on the stratified random sampling, and the field floristic dataset was used for plant community analysis, spectral delineation of plant community classes, and testing the map-accuracy of plant communities. In Chapter 4, I present the analysis of plant communities based on the floristic and environmental datasets using multivariate methods, including Two-way Indicator Species Analysis (TWINSPAN) classification,
Multiple Response Permutation Procedure (MRPP), species area curve, and
Non Metric Multidimensional Scaling (NMDS) ordination. In Chapter 5, the grouping approach of TWINSPAN classification was used to characterize and discriminate the HyMap spectral signature of plant community classes.
Mapping plant communities was achieved by relating the field floristic data and HyMap spectral data, and then image classification was accomplished using Spectral Angle Mapper (SAM).
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Chapter 4 Analysis of plant communities on Rottnest Island
4.1. Introduction
This chapter presents an analysis of plant community and associated environmental data for Rottnest Island. Rottnest Island contains diverse habitats that are characterized by plant communities ranging from salty lakes, coastal areas to inland environments (Pen and Green 1983); however, human disturbance has led to the degradation of the natural vegetation (Storr 1963).
A number of previous vegetation surveys have documented the flora of
Rottnest Island (McArthur 1957; Marchant and Abbott 1981; Rippey and
Hobbs 2003; Storr 1962); yet, quantitative analysis of vegetation on Rottnest
Island has been limited. Analysis of plant communities can assist in understanding the pattern and distribution of species that assemble and maintain ecological diversity, thus it can assist management conservation of natural vegetation and ecosystems.
The aims of this Chapter were to analyse and classify the vegetation of Rottnest
Island. The Chapter will further address the distribution patterns of plant communities and species, community floristic composition and community and species relationships to environmental factors, and to analyse whether
71 species functional groups differed in their distribution along these environmental gradients. Also, to investigate the distribution and abundance of alien plant species and how they relate to environmental factors and disturbance, i.e. human disturbance/infrastructure (settlements and roadside gradients) and disturbances associated with fires.
The detailed research questions addressed in this Chapter were:
How does floristic composition vary between plant communities and
how are they distributed spatially across the island?
What is the relationship between plant community composition and
distribution, plant functional groups and environmental factors?
Which plant communities have the highest degree of invasion by alien
plant species?
4.2. Methods
The methods carried out for this study are described in detail in Chapter 3. The sampling design is outlined in section 3.2.1 (3.2.1.1 and 3.2.1.2), the field methods are described in section 3.2.2., while the statistical analyses for this section are described in section 3.3.1. The full vegetation survey dataset of 51 species and 210 plots is used for most of the data analyses described in this chapter unless otherwise stated, e.g. a second MDS analysis is also described, based on a reduced dataset of 31 species and 186 plots after removal of rare species and outlier plots.
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4.3. Results
Fifty one plant species were recorded within the 210 plots enumerated across
Rottnest Island (Table 4.1). However, nearly half (43%) of all species were rare, occurring in less than 3 % of plots. A further fourteen species were also recorded in the immediate vicinity of quadrats, so that 65 species in total were recorded during the fieldwork (Appendix 1). The flora was dominated by perennial shrubs, which comprised 41.2% of the total species. Other life forms were grasses 17.6%, herbs 11.7%, lilies 11.7%, sedges 9.8% and trees 7.8%.
These observed species belonged to 22 families and 42 genera. The families with most species were; Poaceae (9), Chenopodiaceae (6), Asteraceae (4),
Cyperaceae (4), Asphodelaceae (3), Mimosaceae (3), and Myrtaceae (3).
4.3.1. Classification of plant communities
Two-Way Indicator Species Analysis (TWINSPAN) classification, which was performed on the floristic data consisting of 51 species in 210 quadrats, recognized eleven classes of vegetation. At the first division level, TWINSPAN separated lakeshore/salt lake-terrestrial transition zone quadrats (8 plots) from all other terrestrial vegetation (202 plots). The second split divided the terrestrial vegetation (202 plots) into two structurally different groups: woodlands (15 plots) and shrublands/heathlands (187 plots). The classification was continued to the 6th level, with a final grouping into 11 classes determined based on the nature and size of groupings at each level
(Figure 4.1). Subsequent analyses were based on these classes, which here-
73 after are referred to as community types. Using the indicator and preferential species, and Two-way ordered table, characteristic species were identified for each community and communities were named using the dominant species in each (Table 4.2). A Multi-response Permutation Procedure (MRPP) analysis based on Sorensen’s index of similarity (SI) showed significant differences among vegetation groups (A-statistic=0.47; T= -51.73; p<0.001).
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Table 4.1 Plant species observed in quadrats on Rottnest Island. For each species, the following information is given: scientific name and family, and percentage frequency of occurrence. Asterisks indicate invasive species.
SPECIES NAME FAMILY Occurrence
Acacia littorea Mimosaceae 14 Acacia rostellifera Mimosaceae 37 Acacia saligna Mimosaceae 2 Acanthocarpus preissii Dasypogonaceae 194 Aira cupaniana* Poaceae 4 Asphodelus fistulosus* Asphodelaceae 22 Atriplex cinerea Chenopodiaceae 2 Atriplex sp. Chenopodiaceae 1 Austrostipa flavescens Poaceae 171 Avena barbata* Poaceae 62 Brachypodium distachyon* Poaceae 1 Bromus diandrus* Poaceae 12 Bulbine semibarbata Asphodelaceae 1 Callitris preissii Cupressaceae 18 Carex sp* Cyperaceae 6 Centaurea sp. Asteraceae 2 Conostylis candicans Haemodoraceae 80 Diplolaena dampieri Rutaceae 3 Dittrichia viscosa* Asteraceae 2 Eucalyptus gomphocephala* Myrtaceae 4 Eucalyptus platypus* Myrtaceae 97 Euphorbia peplus* Euphorbiaceae 6 Gahnia trifida Cyperaceae 75 Guichenotia ledifolia Sterculiaceae 4 Halosarcia halocnemoides Chenopodiaceae 7 Halosarcia indica Chenopodiaceae 2 Isolepis nodosa Cyperaceae 5
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SPECIES NAME FAMILY Occurrence
Lagurus ovatus* Poaceae 113 Lepidosperma gladiatum Cyperaceae 35 Leucopogon insularis Epacridaceae 15 Leucopogon parviflorus Epacridaceae 9 Lomandra maritima Asparagaceae 19 Melaleuca lanceolata Myrtaceae 28 Oenothera sp.* Oenotheraceae 6 Olearia axillaris Asteraceae 57 Pittosporum phylliraeoides Pittosporaceae 1 Rhagodia baccata Chenopodiaceae 46 Rostraria cristata* Poaceae 71 Scaevola crassifolia Goodeniaceae 20 Senecio lautus Asteraceae 29 Spinifex longifolius Poaceae 1 Sporobolus virginicus Poaceae 2 Stackhousia sp. Stackhousiaceae 7 Suaeda australis Chenopodiaceae 2 Templetonia sp. Papillionaceae 2 Thomasia cognata Sterculiaceae 68 Trachyandra divaricata* Asphodelaceae 145 Trachymene coerulea Apiaceae 8 Westringia dampieri Lamiaceae 46 unidentified (sp13)# 4 unidentified (sp14)# 12 # two non-flowering herbaceous species could not be identified
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210
202 8 (A)
15 187 4 4
11 (C) 4 (B) 149 38
6 5 97 52 25 (E) 13 (D)
23 74 (F) 42 (G) 10 16 9 8 5
2 (J) 21 (K) 49 25 27 15 8 (H) 2 (I) 10 6 6 3 5 3 3 2
Figure 4.1. Dendrogram resulting from Two-Way Indicator Species Analysis (TWINSPAN) to level 6 for the 51 species by 210 quadrats vegetation data set for Rottnest Island. Numbers presented in the diagram are the total number of quadrats associated with each group. The letters A-K show the final eleven community types identified.
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Table 4.2. The eleven plant communities (groups A-K) identified from the 51 species by 210 plots Rottnest Island data set analysed using TwoWay Indicator Species Analysis (TWINSPAN).
Group PLANT COMMUNITY
A Gahnia trifida - Halosarcia indica salt lake shore edge
community
B Callitris preissii woodland
C Melaleuca lanceolata - Acacia rostellifera woodland
D Acacia rostellifera – Acanthocarpus preissii coastal scrub
E Acanthocarpus preissii - Olearia axillaris coastal scrub
F Acanthocarpus preissii - Austrostipa flavescens heath
G Acanthocarpus preissii - Trachyandra divaricata heath
H Acanthocarpus preissii - Lepidosperma gladiatum heath
I Pittosporum phylliraeoides scrub
J Eucalyptus platypus woodland
K Acanthocarpus preissii-Conostylis candicans heath
4.3.2. Distribution of plant species and communities
Geographic distribution of plant communities across Rottnest Island is shown in Figure 4.2. Spatial pattern of plant species distribution associated with the defined vegetation types (from TWINSPAN analysis) across the 210 sample quadrats (Figures 4.3 – 4.6) confirmed the results of Figure 4.2.
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A. preissii was the most frequent (92.38%) and widely distributed encountered native species, occurring in all community types other than those associated with the margins of salt lakes (Figure 4.3). A. preissii was the dominant species in heath communities (i.e. A. preissii - A. flavescens, A. preissii
- T. divaricata, A. preissii - C. candicans, and A. preissii - Lepidosperma gladiatum; groups F, G, H, and K) and was abundant in coastal scrub (i.e., A. preissii – O. axillaris, and A. rostellifera - A. preissii communities). It was also found in the outermost zone in halophytes communities (i.e., the transition zone between halophytes and heath communities) and woodland communities (i.e. C. preissii and M. lanceolata - A. rostellifera) and as an understorey species with P. phylliraeoides, E. gomphocepala, and E. platypus.
Geographic distribution maps showed that several species were restricted to specific habitats associated with particular vegetation types. For example, L. gladiatum was restricted to salt lake, coastal, and backswamp habitats (Figure
4.4). Similarly, Gahnia trifida and species from the Chenopodiaceae family (e.g.,
H. indica and H. halocnemoides) occurred predominantly on the shores of salt- lakes (Figure 4.5).
O. axillaris, W. dampieri, and S. crassifolia were found primarily in coastal habitat (e.g., on the south-west coast, and a few places on the north-west coast)
(Figure 4.6). These species had high observed frequencies of 57, 46, and 20, respectively, across the 210 sample plots. O. axillaris was also sporadically
79 found further inland (Figure 4.6a), while W. dampieri was found occasionally in the south-east (Figure 4.6b).
Figure 4.2. Geographic distributions of plant communities on Rottnest Island showing the eleven plant communities referred to Table in 4.5.
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Figure 4.3. Geographic distribution of habitat generalist, A. preisii on Rottnest Island based on proportion of species cover abundance (Braun-Blanquet scale).
Figure 4.4. Distribution of L. gladiatum (a habitat specialist associated with dunes and creek lines) on Rottnest Island scaled by species cover abundance (Braun-Blanquet scale).
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a
b
c
Figure 4.5. Distribution of lake-edge species based on species cover abundance (Braun-Blanquet scale): (a) G. trifida, (b) H. halocnemoides, (c) H. indica.
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a
b
c
Figure 4.6. Distribution of coastal species based on species cover abundance (Braun-Blanquet scale): (a) O. axillaris, (b) W. dampieri, (c) S. crassifolia.
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A brief description of the eleven plant communities derived from TWINSPAN analysis follows.
The halophyta (group A)
The Gahnia trifida - Halosarcia indica community (group A; n=8 quadrats) was characterized by salt-tolerant plants, including G. trifida, H. indica and H. halocnemoides, and has a high dominance of halophytes (Chenopodiaceae).
This halophyte community was restricted to the shores of salt lakes (Plate 4.1).
In the zone nearest to the water, the dominant species were H. indica and H. halocnemoides along with Atriplex cinerea, whereas G. trifida and Isolepis nodosa dominated the outer (drier, but still saline) zone of the salt lakes habitat along with Lepidosperma gladiatum. Marginal encroachment of A. preissii in this zone (the outer-zone of salt lakes) reflected a saline habitat gradient with distance from the shoreline. Other species, including Ragodia baccata occurred in both these zones.
Woodlands (groups B and C)
The Callitris preissii woodland community (group B; n=4) was characterized by strong dominance of C. preissii with very little or no understorey (Plate 4.2).
The Melaleuca lanceolata – Acacia rostellifera community (group C; n=11) occurred as both monotypic stands of M. lanceolata, and open stands with A. rostellifera in a lower layer (Plate 4.3). Several variants found in the woodland communities represent different composition of woodland plants, which were mainly comprised of woody tree species (C. preissii and M. lanceolata). A.
84 preissii occasionally occurred in these woodland communities as an understorey layer with varying cover. Other species that formed a sparse understorey were heathy plants, including Thomasia cognata and Guichenotia ledifolia and monocots such as Lepidospermum gladiatum, Conostylis candicans, Austrostipa flavescens, and Lomandra maritima.
Woodland communities mostly occurred in the eastern part of the island, commonly being fragments of remnant vegetation and as
‘restoration/revegetation’ plantings in plantation sites. Several plantation sites with these woodland species were also found on the western part of the island, reflecting discontinuous patterns of these communities over the island.
Several stands of Melaleuca lanceolata were also planted in the south coastal zone for wind-breaks and shading purposes. In the areas near salt lake habitats, Melaleuca woodlands were found associated with Gahnia trifida.
These woodlands sometimes occur with high litter levels, while understorey plants were scarce likely due to dense canopy cover.
Coastal communities (groups D and E)
The Acacia rostellifera - Acanthocarpus preissii community (group D; n=13) identified in this study occurs on coastal dunes close to the southern coast.
This community was predominantly found on primary dunes, from behind the foredunes to varying distances inland. The Acacia community sometimes formed a dense thicket with no or very little understorey layer (Plate 4.4), but also occurred in mixture with an understorey layer dominated by
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Acanthocarpus preissii. A. rostellifera also occurred in association with Acacia littorea, and Acacia saligna was also sometimes present in this community.
Other characteristic species of this plant community were Olearia axillaris,
Scaevola crassifolia and Westringia dampieri which are generally not found further inland to the north. The Acacia community also occurred as scattered patches on exposed limestone and sandplain soils.
The Acanthocarpus preissii - Olearia axillaris coastal scrub community (group
E; n= 25) occurred on coastal dunes, mainly distributed in the south-west of the island and showed limited occurrence elsewhere. O. axillaris was considered a significant indicator of this coastal community, but A. preissii was more abundant than Olearia in several sites. Other coastal species included
Westringia dampieri, Scaevola crassifolia and Rhagodia baccata (Plate 4.5). S. crassifolia was dominant on the foredunes and rocky shores, and there was a high abundance of this species also on disturbed sites, such as roadside/firebreaks. The coastal perennial grass, Spinifex longifolius, was encountered at only one site, on the sandy seashore. This species is restricted to near-coastal sites but its infrequent recording in the quadrats reflects the low frequency of quadrats located in this habitat type. In the north and eastern coastal zones, however, the community composition was more similar to the inland heathy communities dominated by A. preissii and Austrostipa flavescens.
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Heath communities (groups F, G, H, and K)
Heath communities were the most extensive vegetation type on the island.
These communities were dominated by Acanthocarpus preissii. A. preissii -
Austrostipa flavescens (group F; n=74) was the most widespread heath type, followed by A. preissii - Trachyandra divaricata (group G; n=42) and A. preissii
- Conostylis candicans (group K; n=21). Other abundant species were Thomasia cognata, Lomandra maritima, Conostylis candicans, and the weedy species
Lagurus ovatus and Euphorbia peplus.
Groups F and G have wide distributions across the island, but are mostly found in the western part. The area which was burned by a wildfire in 1955 was characterized by low cover of these heath communities with trees infrequent.
These communities had similar composition, with the latter community similar to the first except for the high degree of encroachment by the invasive
T. divaricata, especially in more open and disturbed areas.
The A. preissii - Conostylis candicans community (group K) was associated with revegetation/plantation sites. Compared to the previous heath communities,
C. candicans was more abundant, while Austrostipa flavescens and Trachyandra divaricata were less common. Two tree species were planted in these sites, M. lanceolata and C. preissii, which were still in the juvenile stage, so that the community is expected to develop into woodland over time in the absence of major disturbance.
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Finally, the A. preissii - Lepidosperma gladiatum community (group H, n=8) occurred with A. preissii frequently having the highest cover. Floristically, this group is closely allied with the other heath communities but it was strongly characterized by the coastal sedge species L. gladiatum. This community characteristically occurred as small isolated patches found occasionally near the coast, on salt lake shores and in damp micro-habitats.
Another woodland community, dominated by the introduced amenity species,
Eucalyptus platypus, (group J; n=2) occurred on the eastern part of the island.
The last community recognized is the Pittosporum phylliraeoides scrub community (group I; n=2) which is patchily distributed on limestone ridges in the eastern part of the island (Plate 4.8). This community only occurred in two of the 210 plots, suggesting that it is highly restricted. Sampling design and representativeness of the quadrat samples will be further addressed in
Chapter 6 after considering the evidence for vegetation types based on remotely-sensed data.
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Plate 4.1 Example of G. trifida- H. indica halophyte community (group A) showing the gradient from the outer zone with G. trifida to the inner zone with H. indica and H. halocnemoides.
Plate 4.2 Example of Callitris preissii woodland community (group B).
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Plate 4.3 Example of Melaleuca lanceolata woodland community (group C).
Plate 4.4 Example of Acacia rostellifera-Acanthocarpus preissii coastal community (group D).
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Plate 4.5 Example of coastal scrub Acanthocarpus preissii - Olearia axillaris (group E) characterized by coastal plants Olearia axillaris, Westringia dampieri, and Scaevola crassifolia and with high abundance of Acanthocarpus preissii.
Plate 4.6 Example of Acanthocarpus preissii-Austrostipa flavescens heath community (group F) with high abundance of Lagurus ovatus.
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Plate 4.7 Example of Acanthocarpus preissii-Trachyandra divaricata heath community (group G) with presence of Austrostipa flavescens and Lagurus ovatus.
Plate 4.8 Example of Acanthocarpus preissii-Lepidosperma gladiatum heath community (group H).
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Plate 4.9 Example of Pittosporum phylliraeoides scrub community (group I) found on limestone ridges.
Plate 4.10 Example of A. preissii - Conostylis candicans scrub community (group K) found in revegetation sites of C. preissii and M. lanceolata with high occurrence also of Avena barbata.
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4.3.3. Patterns in species richness and diversity of plant communities
The species area curve for the 210 vegetation quadrats showed that the species accumulation and sample distance estimates stabilized and show little change with increasing sample size beyond 100 quadrats (Figure 4.7). The species-area curves for each separate community type showed that sample sizes vary from being more than adequate to inadequate in terms of characterizing the species compositional attributes of those communities.
The samples for A. preissii - O. axillaris coastal low shrub community (group E; n=25) and A. preissii - C. candicans heath community (group K; n=21) reach an asymptote (Figure 4.8). The species area curves for coastal community (group
D, n=13) and heath communities (groups F and G; n= 74, 42), respectively, also show flattening towards an asymptote reflecting adequacy of sample size
(Figure 4.8). However, high numbers of singletons occurred in these heath communities suggesting that more species would be found if more quadrats had been measured. This is reflected in the high number of expected species compared with observed species based on the jackknife 1 and 2 estimators
(Table 4.3).
Woodlands (groups B, C, I and J), halophytes (group A), and the A. preissii - L. gladiatum heath community (group H) showed continuously increasing species-area curves, and continuously decreasing distance values, as well as substantially higher estimated richness (jackknife estimates) than observed species richness, reflecting insufficient sampling to fully describe these
94 community types. These community types were based upon samples of only 4,
11, 2, 2, 8, and 8 quadrats, respectively (Figure 4.8; Table 4.3.).
Average richness and diversity were lower in the woodlands (groups B and C), intermediate in the heathy communities (groups F, G, and H), and highest in the coastal communities (groups C and D) and A. preissii - C. candicans heath community (group K) (Table 4.4). Evenness had similar patterns to richness and diversity. Moderate values of evenness were identified in heath and coastal communities, a high value was identified in the halophytes G. trifida –
H. indica community (group A) and the highest was in the Pittosporum phylliraeoides scrub community (group I).
The distribution pattern of species richness, diversity, and evenness across the island (Figure 4.9 and Figure 4.10) showed that coastal communities and several heath communities had higher richness and diversity than other communities. Species richness (S) per quadrat ranged from 1 to 14, while diversity (H’) ranged from near zero to 2.34 and evenness (J’) ranged from near zero to 0.99.
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Figure 4.7. Species area and mean ecological distance curve for vegetation data (51 species in 210 quadrats) sampled on Rottnest Island, Western Australia. Distance measure: Sorensen (Bray-Curtis). Observed richness = 51, First-order jackknife estimated richness = 56.0, Second-order jackknife estimated richness = 53.0. Number of singletons = 5. Number of doubletons = 8. The dotted lines are 95th percentile confidence intervals.
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Community A Community C Community D
Community E Community F Community G distanceAverage
Average number of species number of Average
Community H Community K
Number of subplots
Figure 4.8. Species area curves for each of the identified plant communities on Rottnest Island, Western Australia. Distance measure: Sorensen (Bray- Curtis). The dotted lines are 95th percentile confidence intervals. Woodlands communities (groups B, I, J) were excluded from the analyses because their sample sizes were too small.
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Table 4.3. Species numbers observed, species numbers estimated to occur (Jackknife 1 and 2), means (±SE) for species richness, diversity, and evenness, and numbers of singletons and doubletons, for the eleven TWINSPAN plant communities (group A-K).
PLANT COMMUNITIES (groups A-K) VARIABLES A B C D E F G H I J K
Number of sites 8 4 11 13 25 74 42 8 2 2 21 Range of species recorded 2-5 1-5 2-10 6-12 7-13 4-12 4-11 6-8 6 2-6 6-14 per plot Total number of species 13.0 9.0 17.0 22.0 25.0 32.0 34.0 20.0 8.0 7.0 21.0 observed in the community Species estimated: 17.4 12.7 22.5 26.6 26.9 40.9 44.7 28.7 9.0 9.0 22.9 first order Jackknife Species estimated: 18.6 14.9 23.7 29.3 24.4 46.8 50.6 34.3 9.0 9.0 23.9 second order Jackknife Number of singletons 5 5 6 5 2 9 11 10 2 4 2 Number of doubletons 4 1 5 2 5 3 5 3 6 3 1 Mean (±SE) of Species 3.88 - 4.36 8.62 9.00 7.72 7.48 6.88 - - 9.05 Richness (S)* (±0.40) (±0.80) (±0.60) (±0.32) (±0.19) (±0.30) (±0.35) (±0.43) Mean (±SE) of Shannon- 1.03 - 0.74 1.90 1.84 1.59 1.56 1.44 - - 1.74 Wiener Diversity Index (H’)* (±0.09) (±0.15) (±0.09) (±0.05) (±0.04) (±0.05) (±0.60) (±0.08) Mean (±SE) of Evenness (J’)* 0.80 - 0.50 0.89 0.84 0.79 0.79 0.75 - - 0.79 (±0.05) (±0.08) (±0.03) (±0.02) (±0.01) (±0.02) (±0.02) (±0.03) *Woodlands communities (groups B, I, J) were excluded from the analyses (S, H’, and J’) because their sample sizes were too small.
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a
b
Figure 4.9 Distribution of species richness and diversity for quadrats on Rottnest Island, Western Australia: a) Richness; b) Shannon Index.
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Figure 4.10 Distribution of evenness for quadrats on Rottnest Island, Western Australia.
4.3.4. Floristic variation in community composition
The first Non Metric Multidimensional Scaling (NMDS) analysis, run for all 210 quadrats (51 species x 210 quadrats), resulted in an optimal 2 dimensional solution (final stress of 0.23 with ordination axes 1 and 2 explaining 43.0 and
18.4 percent of the variation in the rank distance matrix respectively for a total r2 of 61.4%) (Figures 4.11; 4.12). Results of NMDS analysis were overlayed with the classified vegetation groups generated from TWINSPAN, confirming the distributional trends of the species groups defined from TWINSPAN classification.
Distinct groupings were identified for woodland and halophyte associations, while a high degree of overlap was identified in other assemblages (Figures
100
4.11; 4.12). The number of tree species, maximum and average height of vegetation, and the distance to the nearest shoreline correlated positively with the first axis; related to the influence of woodland communities, i.e. Callitris preissii and Melaleuca lanceolata - A. rostellifera communities (groups B and C)
(r2 = 0. 61, p<0.001). On the other hand, the gradients of bare-ground, distance from firebreaks, evenness, the abundance of invasive species, abundance of herbaceous species/lilies and sedges, and abundance of grasses, correlated negatively with the first axis; reflecting the distribution of heath communities, primarily A. preissii – A. flavescens, A. preissii – T. divaricata, A. preissii -
Lepidosperma gladiatum and A. preissii - C. candicans communities (groups F,
G, H and K). Slope angle, cover abundance, species richness, diversity, and the abundance of native and shrub species all correlated positively with the second axis, reflecting the distribution of coastal plant communities; i.e. A. rostellifera – A. preissii and A. preissii – O. axillaris communities (groups D and
E).
Species occurrence patterns in relation to the first two axes of the NMDS ordination showed that most plant species were associated with the heath and coastal communities and aggregated closely together with low scores on the first axis and high scores on the second axis (Figure 4.12). These plant assemblages consisted of shrubs, herbaceous species/lilies, grasses, and sedges. On the other hand, tree species (e.g. Melaleuca lanceolata and Callitris preissii) occurred as outliers with high scores on both axes, while the halophytes of salt lake habitat (e.g. Halosarcia halocnemoides, Halosarcia
101 indica, Atriplex cinerea and Suaeda maritime) and Sporobolus virginicus (found in some coastal quadrats) had high scores on the first axis and low scores on the second. Species of the other outlier community type, the perennial herbs/sedges Isolepis nodosa and Gahnia trifida were located with intermediate scores on both axes, reflecting occurrence in quadrats ecotonal between salt lake and heathy community types. Bulbine semibarbata and
Spinifex longifolius occurred with low scores on both axes and corresponded to quadrats with a high degree of bare-ground/disturbed area.
The second NMDS analysis which was performed on the reduced dataset of 31 species in 186 plots identified a 3 dimensional solution (final stress of 0.21 with successive axes explaining 25.4, 24.6 and 23.4 percent of the variation in the rank distance matrix, respectively, for a total r2 of 73.4% (Figures 4.13;
4.14). In this reduced dataset, 20 species identified as rare (species with occurrence in less than 3 plots) were removed, while 24 outlier plots (based on the TWINSPAN and NMDS analyses) were also removed; i.e. the halophyte
(G. trifida – H. indica community) and woodland communities (C. preissii and
M. lanceolata – A. rostellifera). Two other plots were also removed (plots numbers 127 and 107 which contained Bulbine semibarbata and Spinifex longifolius, respectively,) which despite belonging to heath and coastal scrub communities, were outliers due to a high level of bare-ground. This provided the opportunity to explore variation more closely for the heathy vegetation which dominates the island.
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The first NMDS axis was negatively correlated with number of tree species (r2
= 0. 73, p<0.001) (Figures 4.13; 4.14). Plots with low scores on axis 1 included those in the A. preissii – C. candicans community, and those including the tree species Callitris preissii and Melaleuca lanceolata (Figure 4.14). These tree species were found in young plantation stands (juvenile stage) classified within the heath community type, which are likely to develop into woodlands over time in the absence of disturbance.
Distinct patterns were observed for heath communities (groups F, G, H, and K) and coastal communities (groups D and E) along axis 2 (Figures 4.13; 4.14).
NMDS plots showed the gradient of coastal-heath communities with some degree of community overlap, likely because the transition states between both assemblages were dominated by A. preissii. A. preissii and several herbaceous species (lilies and herbs) and grasses were positioned in the centre of the NMDS space, and were shared by many heath communities. The results of the TWINSPAN analysis superimposed on the ordinated plots revealed that the floristic variation was clearly discernible for those heath communities (i.e. A. preissii - A. flavescens, A. preissii - T. divaricata, A. preissii -
C. candicans, and A. preissii - Lepidosperma gladiatum; groups F, G, H, and K, respectively). Several heath assemblages overlapped considerably in ordination space reflecting floristic similarity of those assemblages.
The second NMDS axis correlated positively with gradients of bare-ground, the abundance of herbaceous species/lilies and sedges, and the abundance of
103 invasive species (r2 = 0.73, p<0.001). Several overlapping heath types were identified, particularly the A. preissii – A. flavescens (group F) and A. preissii –
T. divaricata (group G) communities, reflecting the degree of similarity between those types. This second axis also strongly correlated with the area affected by fire, distance to nearest coast/shoreline, distance to nearest road
(sealed road), and the abundance of grasses; characterised by the distribution of the A. preissii – A. flavescens community. The species with high scores on the second axis were those with affinities for disturbance including invasive grasses (i.e. Avena barbata, Rostraria cristata, and Lagurus ovatus), and invasive lilies/herbs (i.e. T. divaricata, Asphodelus fistulosus, Oenothera sp)
(Figure 4.14).
The second NMDS axis was negatively correlated with distance to nearest building, cover-abundance (plant-cover), the abundance of shrub species, native species, and maximum height of vegetation, reflecting the distribution of coastal communities (r2 = 0. 73, p<0.001) (Figure 4.13). Low-scoring species on this second axis included coastal plant species; Scaevola crassifolia,
Westringia dampieri, Olearia axillaris, Rhagodia baccata, Leucopogon insularis,
Leucopogon parviflorus, Trachymene coerula, Acacia rostellifera, Acacia littorea and the two plant species that could not be identified (Figure 4.14). Plant species associated with the A. preissii - Lepidosperma gladiatum community, indicated by high scores on the first axis and relatively low scores on the second, were mostly associated with coastal vegetation.
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Figure 4.11. Ordination diagram showing NMDS axis 1 and axis 2 of sites scores, for 51 species in 210 plots with environmental variable vectors (Monte Carlo test p<0.001). The description of the abbreviation of environmental attributes overlayed by the joint plot is presented in Table 3.5. Distribution of the eleven plant communities produced by TWINSPAN groups is shown by colour-coding; community types presented in this diagram (community A-K) refer to the community types (group A-K) presented in Table 4.2. Communities highlighted in pink (community A) and green (communities B and C) represent outliers.
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Figure 4.12. Ordination diagram showing NMDS axis 1 and axis 2 species scores, for 51 species in 210 plots with environmental variable vectors also shown (Monte Carlo test p<0.001). Species are labeled with acronyms consisting of the first three letters of the genus and the first three letters of the species name. A full species list is provided in Table 3.4.
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Figure 4.13. NMDS ordination showing axis 1 and axis 2 sites scores, for 31 species in 186 plots with environmental variable vectors also shown (Monte Carlo test p<0.001). Species occurring in less than 2% of plots were excluded from this analysis. Distribution of the eight claases of plant communities produced by TWINSPAN is shown by colour-coding; community types (community D-K) presented in this diagram refers to community types presented in Table 4.2.
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Figure 4.14. NMDS ordination showing axis 1 and axis 2 species scores, for 31 species in 186 plots with environmental variable vectors. Species occurring in less than 3% of plots were excluded from this analysis (Monte Carlo test p< 0.001). Species are labeled with acronyms consisting of the first three letters of the genus and the first three letters of the species name. A full species list is given in Table 3.4.
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4.3.5. Invasive plant species
From the total of 51 plant species recorded in the 210 quadrats, 38 were native species (representing 20 families), and 13 were invasive species (representing
6 families) (Figure 4.15). Of the native species, Chenopodiaceae was the most species-rich family, but this largely reflected the presence of halophytes which were restricted to highly saline habitats (salt lakes and coastal dunes). Other native species recorded belonged predominantly to Cyperaceae, Mimosaceae, and Poaceae families. The two most common native species were
Acanthocarpus preissii (Dasypogonaceae) and Austrostipa flavescens
(Poaceae), which occurred in 194 and 171 quadrats, respectively.
Most invasive plants encountered on Rottnest Island were annuals (70%) with a low number of perennials (30%). The two most common invasive plants were perennial herbs, and lilies from the family Asphodelaceae, i.e.
Trachyandra divaricata and Asphodelus fistulosus. Species from the family
Poaceae contributed the largest number of invasive species, comprising six annual grasses with three species (i.e. Lagurus ovatus, Rostraria cristata, and
Avena barbata) being the most common across all plots.
Heath communities had higher richness of invasive plants compared to other communities (Figure 4.16). Highest richness of invasive species occurred in the A. preissii – A. flavescens community (group F), while this community also had lowest native richness, supporting the NMDS ordination results.
Compared to heath communities, coastal communities (groups C and D) had
109 higher native richness and diversity, whilst they had markedly lower invasive species richness and diversity. In woodland communities (groups B, C, I and J), invasive plants were of very low occurrence; however, there were few sites in these communities. The halophyte community (group A) had the lowest richness and diversity of invasive plants, indicating the limiting occurrence of invasive (non-salt tolerant) plants which only occurred on the edge (ecotones) of the high salinity environment.
Figure 4.17 shows the total number of invasive plants per plot across the island. High numbers of invasive plants are found in the area burned in 1955, with this area dominated by heath communities. High number of invasive plants also occurred on the eastern part of the island around the main settlement area, whereas fewer invasives occur in the western zone furthest away from the main areas of human habitation. From the figure it can be seen that the area affected by fire in both 1955 and 1997 had fewer invasive species, perhaps due to the low number of sampling plots located in this area which is relatively small in size (i.e., 6 plots in 90 ha). This area was planted with C. preissii and M. lanceolata as part of woodland restoration following the 1997 fire. The restoration efforts may have reduced the establishment and spread of invasive plants.
T. divaricata was the most common invasive across the study region and was commonly found with high percentage cover per site (Figure 4.18). High cover of this invasive plant was common in the 1955 fire-affected area. This invasive
110 also occurred at a few sites in the east, which corresponds with the main settlement/urban area. During the fieldwork, this invasive plant was also frequently observed in disturbed areas such as cleared sites and firebreaks
(Figure 4.19).
7
6
5
4
3 Native 2 Invasive
1
0
Poaceae
Apiaceae
Rutaceae
Lamiaceae
Myrtaceae
Asteraceae
Cyperaceae
Mimosaceae
Epacridaceae
Sterculiaceae
Cupressaceae
Goodeniaceae
Papillionaceae
Asparagaceae
Euphorbiaceae
Pittosporaceae
Asphodelaceae
Oenotheraceae
Haemodoraceae
Stackhousiaceae Chenopodiaceae Dasypogonaceae
Figure 4.15 Number of native and invasive species within each plant family observed on Rottnest Island, Western Australia. Two plant species remained unidentified.
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a
b
Figure 4.16 Box-whisker plots (central horizontal line, mean; boxes, standard error; bars, 95% confidence interval) showing the distribution of native and invasive plants in each community type (groups A-K): a) richness of native and invasive plants, b) diversity of native and invasive plants.
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Figure 4.17 Distribution maps of number of invasive species per plot sampled on Rottnest Island, Western Australia, overlaid with the extent of fire disturbance (fire occurrence since 1955).
Figure 4.18 Distribution of the invasive species T. divaricata on Rottnest Island, Western Australia, based on species cover abundance (Braun-Blanquet scale).
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a b
c d
Figure 4.19 Several invasive species occurring in high cover abundance on disturbed ground: a. Trachyandra divaricata, b. Lagurus ovatus, c. Rostraria cristata interspersed with T. divaricata, d. Avena barbata.
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4.4. Discussion
4.4.1. Classification of plant communities and relationships to environment
This study provided an overview of classification, characterization and distribution patterns of plant communities associated with habitat- environmental characteristics on Rottnest Island. Analysis of the vegetation plot data recognized eleven distinctive plant communities which belonged to woodlands, scrubs, heathlands, coastal scrubs, and halophyte community types. The TWINSPAN analysis helped understand the variability of woodlands and heathlands, and this was also confirmed by the results of NMDS ordination. However, nearly half (43%) of the total species found in this study were rarely recorded, suggesting that plant communities on Rottnest Island are assembled as various combinations of relatively few, abundant species.
The species being rarely recorded in this study explains the idea of communities demonstrating a significantly flawed model for the way vegetation occurs in the real world.
The total number of vascular plant species observed in this study was lower than previously reported. For example, Rippey and Hobbs (2003) stated that
Rottnest Island has a total of 196 plant species; however, 42% of those plant species observed on the island were invasive plants that mostly occurred near the main settlement. In this study, settlement areas were excluded from sampling with focus on the natural and semi-natural vegetated landscapes of
115 the island, so plant species dominating the settlement area were likely not all recorded in the field floristic surveys. Likewise, areas dominated by introduced tree species that have been deliberately planted for aesthetic purposes were also excluded from the floristic survey, including Ficus macrophylla, Araucaria heterophylla, Eucalyptus utilis and Casuarina glauca. In addition, the lower number of plant species encountered in this study may likely be caused by the sampling design and season of sampling applied in this study; sampling in Autumn meant that winter ephemerals and geophytes were unlikely to be found, and the stratified random sampling may have missed some small, specialized habitat species. For example, two community types in this study, Pittosporum phillyreoides (group I) and Eucalyptus platypus (group
J), occurred in only 2 plots each out of a total sample of 210 plots. Additionally, a long history of human disturbance (e.g., tree harvesting) may have resulted in certain species being under-represented in vegetation types where they historically may have been prominent.
Woodland communities were distinct from shrub and heath communities.
These woodland communities currently occur as small remnant patches predominantly found in the eastern part of the island. Several studies have showed that fire disturbance regimes significantly influence woodland vegetation (Greenlee and Langenheim 1990; Keith et al. 2010; Russell-Smith et al. 1998), while plant recruitment can also be constrained by edaphic factors such as nutrients and water availability (Donovan and Ehleringer 1992;
Jacobsen et al. 2008; Jacobsen et al. 2009; Wevill and Read 2010). On Rottnest
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Island, native woodlands have degraded since European settlement and a strong limitation for woodland recovery is due to fire (Rippey and Hobbs
2003). Callitris species are fire-sensitive due to their limited capacity for vegetative regeneration after fire (Bradstock and Cohn 2002; Rippey and
Hobbs 2003). In addition, grazing has been shown to contribute substantially to the poor establishment of Callitris seedlings (Cheal 1993; Rippey and Hobbs
2003).
The native shrub A. rostellifera grows most commonly in coastal areas. Dense thickets of A. rostellifera frequently occurred on the slopes ofsouthern coastal dunes. Previous reports have suggested that A. rostellifera is an important pioneer of active dunes (Sauer 1965). The community dominated by this species was much more extensive than woodland communities. On the nearby
Garden Island, which has similar environmental conditions to Rottnest, C. preissii and M. lanceolata are extensive as major community dominants, while
A. rostellifera is also widespread. Considerable abundance of A. rostellifera on
Rottnest Island may have been caused by lack of fire (Rippey and Hobbs 2003), and indeed, historic evidence available showed that these coastal dunes on this island were not affected by the last major fire in 1955. Further research would be useful to reveal more information about the relationship of historical disturbance regimes to current vegetation composition on the island.
The salt-pan halophyte community (group A) was associated with the shores of salt lakes. This plant assemblage was designated by the first separation in
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TWINSPAN analysis and was also discrete in the NMDS analysis. The severity of the salt-pan environment means the community that it supports has very few species that overlap with other communities on the island. Salt lakes commonly found in arid and semi-arid Australia generally support a diverse range of salt-tolerant plants (De Deckker 1983), and several factors have been shown to be important drivers for determining the floristic composition on the shores of salt-lakes, including various degrees of inundation, soil/sediment salinity, ground-water salinity, and soil texture (Barret 2006; Boer and
Sargeant 1998; Kruger and Peinemann 1996). Likewise, the A. preissii – L. gladiatum community (group H) also has a narrow habitat requirement restricted to duneslack habitats (Mc Arthur and Bartle 1981).
Heathlands were the dominant communities on Rottnest Island. The A. preissii
- A. flavescens community was the most widespread, encompassing in total 74 plots, while A. preissii - T. divaricata was also widespread and characterized by high dominance of T. divaricata. The communities have compositional similarities but the dominance of Trachyandra in group G leads to its separation from group F related to the degree of disturbance indicated by the negative correlation with distance from firebreaks and bare ground. The other heath community, A. preissii - C. candicans (group K), was less widespread than both heath communities mentioned above. This community had similar floristic features to the A. preissii - A. flavescens community; however the abundance of C. candicans, and juveniles of C. preissii and M. lanceolata provide
118 distinct characteristics indicating the areas are likely to develop into natural woodland or woody heathland communities in the future.
The dominance of A. preissii within most recognized floristic groups across the island, with a frequency of 92.38% and having few restrictions on its distribution except being excluded from the salt lake habitat, is concordant with previous studies. Hesp et al. (1983) noted that A. preissii had become the most widespread species on the island. On the mainland, A. preissii grows along the west coast of Western Australia from Exmouth to Pemberton (Wheeler et al. 2002; Barrett and Tay 2005). Its rhizomatous, tufted habit confers a considerable resilence to burning, allowing it to resprout strongly after fire and its sclerophyllous morphology makes it very unpalatable to grazers. This combination of attributes allows it to dominate sites that have been repeatedly burnt and are subject to grazing pressure after a fire.
The coastal scrub community (A. preissii - O. axillaris community; group E) was characterized by high relative frequencies of O. axillaris and other coastal plants, such as S. crassifolia and R. baccata. This coastal assemblage has a strong floristic resemblance to the coastal plant community identified along the western coastline of Western Australia. For example, O. axillaris and S. crassifolia commonly occur from Shark bay to Eyre (Dixon 2011). In coastal ecosystems, terrestrial plant communities generally evolved under a regime of coastal influences, including strong winds, salt spray and soils with a low waterholding capacity (Barbour 1978). While the Wind Rose gradient (wind
119 direction, speed and frequency) was not significant in explaining the distribution of coastal plant communities in the NMDS, the predominant distribution of the coastal community in the south-western coastal zone of the island coincides with the prevailing wind direction. Strong prevailing winds from the south-west pick up salt spray from the breaking waves as they approach the beach. The north and east of the island are more protected with lower wind and wave energy, so the coastal vegetation zone is narrower with heath communities (primarily the A. preissii - A. flavescens and A. preissii - T. divaricata community) approaching the coast.
Distances from the coastline indicate a shift from the coastal community to heathlands along NMDS axis 2. This may reflect the gradient in levels of environmental exposure affecting floristic composition of coastal dune communities occurring at various distances from the coast (Acosta et al. 2009;
Cori 1999). This will influence nutrients in the soil derived from wind-blown sea spray as well as pH (Kim and Yu 2008) and also levels of salt load on plant foliage. The more extensive distribution of the coastal community on the southern and western sides of the island than on other aspects suggests that level of exposure to prevailing wind and salt spray are primary controls on the coastal community’s extent.
A strong relationship between invasive plants and disturbance (the gradients of fire, bare ground, and distance to roads) was an expected outcome. The disturbance gradient was strongly correlated with the distribution of heath
120 communities (i.e. the A. preissii - A. flavescens and A. preissii - T. divaricata communities). High proportions of invasive plants occurred in these heath communities with the more open nature of disturbed habitats allowing easier invasion by alien species than for woodland sites. The grassier A. preissii - A. flavescens community tended to have a more frequent occurrence of the invasive grasses such as L. ovatus, Rostraria crostata, and A. barbata; while the
A. preissii - T. divaricata community tended to have more of the introduced lilies such as T. divaricata and Asphodelus fistulosus.
The pattern and relative abundance of the invasive T. divaricata is particularly concerning as it is becoming a more significant and threatening invasive plant on the island, as also identified by Rippey and Hobbs (2003). This exotic perennial geophyte, originally from South Africa, was first reported on the
Western Australian coastline near Perth in the 1930s and has been spreading on Rottnest island since the 1950s (Storr 1962). T. divaricata was the most frequent and abundant invasive species recorded in this study and is ubiquitous in plant community group G. This invasive plant was associated with disturbances such as fire and bare ground, and capitalised on the high level of disturbance found in association with roads and tracks. Its unpalatability to quokkas is likely to be an important element in its invasive ability. It also occurs on Garden Island where it is sprayed with herbicides along tracks in an attempt to control its spread (P. Ladd, personal communication 2016).
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Disturbance is and has been a particularly important influence on the vegetation of Rottnest Island and has a temporal as well a physical aspect. As habitat becomes more disturbed the native species become less dense and this reduces their competitive influence allowing invasive plants to spread and become established (Le Maitre et al. 2004; Vosse et al. 2008). The physical distribution of species such as A. fistulosus and T. divaricata is particularly associated with road-sides and firebreaks where disturbance is repetitious. A combination of their unpalatability, prolific seed production and ability to resprout after defoliation (Queensland Government 2016) enables them to sequester space by vegetative means and to find new disturbed sites by seed dispersal. Road development and utility corridors can cause both soil disturbance and habitat fragmentation that promotes the existence of disturbance-tolerant species (Honnay et al. 2002; Mortensen et al. 2009), which leads to a prevalence of invasive plants, both growing and in the soil seedbank (King and Buckney 2001; Lin et al. 2006).
The historical fire regime on the island is part of the temporal aspect of disturbance. The last relatively extensive fire on the island was in 1997.
However, the island has had a long history of fire since European settlement
(Rippey and Hobbs 2003) and the contrast with Garden Island where forests are much more extensive than on Rottnest is significant. In addition, other disturbances such as grazing can significantly affect vegetation in
Mediterranean ecosystems (Dorrough et al. 2004a; 2004b). Rippey and Hobbs
(2003) summarised historical accounts of the Rottnest Island vegetation and
122 concluded that a combination of frequent (usually small) anthropogenic fires and quokka grazing have been instrumental in converting much of the vegetation on the island to the Acanthocarpus – dominated heathland. The contrast with Garden Island where anthropogenic influences have been much less intensive and where Callitris and Melaleuca stands and A. rostellifera scrub are extensive is important. On Garden Island fires are infrequent but may burn large areas. In 1956 a fire burnt a large part of the island (McArthur 1996) much of which is now occupied by Callitris – Melaleuca forest and some areas of A. rostellifera scrub. Tammar wallabies have similar grazing effects to quokkas and focus on seedlings of the trees and resprouting acacias after fire.
However, large fires remove large numbers of the marsupials so grazing pressure is low in the aftermath of large fires, and then forest and Acacia scrub can regenerate.
The current distribution of communities on Rottnest Island is unlikely to change in the immediate future. Rippey and Hobbs (2003) identified that the
Acanthocarpus heathland is one of several stable-state plant communities on the island and change from the heathland to other communities would require considerable management intervention. Seedling regeneration of Callitris and
Melaleuca is infrequent without fire which releases a pulse of seeds from the serotinous capsules. Survival of recruits is furthermore not possible unless quokkas are excluded from the area where seeds have been shed, to prevent grazing of the seedlings. A. rostellifera will reproduce vegetatively by suckering as well as seed but again quokkas need to be excluded until the suckers or
123 seedlings have reached a height greater than a quokka can reach. Over most of the heathland areas there are no source plants of Callitris, Melaleuca or A. rostellifera to provide seed or suckers so a change from the heathland stable state is not possible without planting seedlings/saplings and fencing to prevent quokka grazing.
Based on the Australian federal government SPRAT profile (Department of the
Environment and Energy 2016) the density of quokkas on Rottnest is the highest of anywhere in Western Australia. In comparison with the only other insular population on Bald Island on the Western Australian south coast the
Rottnest population is estimated as 5 times (approx 526/ sq km vs 100/sqkm) that on Bald Island. While a carrying capacity for the species has not been calculated there is likely to be an unacceptably high grazing pressure on the vegetation of Rottnest and it has been identified that although most of the dominants of the heath vegetation are unpalatable, regeneration of unfenced heath is still slower than in fenced areas (Rippey and Hobbs 2003). The combination of grazing and exposure to the strong winds and salt spray over the low-lying landscape of Rottnest will contribute to the continued dominance of Acanthocarpus heathland on the island.
4.4.2. Richness and diversity of plant communities
The plant communities on Rottnest Island are rather species poor compared with most other communities in Western Australia. In a study of a
124 chronosequence from coastal communities to upland heath communities near
Jurien about 200km north of Rottnest the coastal communities had a mean of approximately 18 species in 28 m2 while the plots at the end of the chronosequence had a mean of over 40 species (Zemunik et al. 2016). In contrast the much larger 100 m2 plots on Rottnest only had means from 4 to 9 species. The coastal communities in Western Australia are naturally rather species poor and all the vegetation on Rottnest is in the coastal zone. However, the heathland communities had the highest species richness and diversity of communities recognized on Rottnest. The heathland communities tended to have higher species richness than other communities due to the number of invasive species. Strong relationship between invasive plants and disturbance gradients (indicated by the gradients of fire, bare ground, and distance to roads) reflects that disturbances may facilitate the spread and establishment of invasive plants. Highest invasibility in heath communities was related to openness (bare ground) suggesting that high levels of bare ground represent numerous patches which are likely suitable for invasives to establish
(Bradford and Lauenrot 2006; Setterfield 2005; Kulmatiski et al. 2006;
Raghubanshi and Tripathi 2009; Tighe et al. 2009).
The lowest diversity was for the communities of specific habitats such as the halophyte (group A) and A. preissii - Lepidosperma gladiatum (group H) communities that occupy restricted habitats and in the case of the halophytes, extreme environments (Barrett 2006). Many studies have shown that habitats with the highest regional species richness are generally correlated with
125 diverse topography (Bennie et al. 2006; Klimek et al. 2007; Marini et al. 2007).
The topography of Rottnest is subdued and in many areas the vegetation is exposed to severe wind and salt spray influences, restricting the suitability of the sites for many species. The woodland communities occur in some of the more sheltered areas of the island but still had low diversity and richness that is likely to be related to the competitive influence of the tree canopies due to shading of the understorey, restricting the species that can survive in the shaded conditions and reducing water availability during the summer for the lower growing species with restricted root mass. In addition, decades of woodland exploitation for timber and firewood on Rottnest Island has led to considerable decline of Callitris preissii and Melaleuca woodlands and thus resulting lower richness (Rippey and Hobbs 2003). This supports the general view that degraded woodland associated with long history of human disturbances and fire regimes are often characterized by lower plant richness and diversity (Brudvig and Damschen 2011).
In conclusion, the analysis of the vegetation of Rottnest Island based on a vegetation survey using stratified random sampling of quadrats identified eleven distinctive plant communities, largely attributable to floristic differences between woodlands, a variety of heath vegetation types, the halophyte vegetation around salt-lakes, and coastal communities. This floristic variation is explained by a combination of environmental - habitat characteristics and disturbance regimes. The disturbance regimes especially fire and grazing, over many years have led to the extensive coverage of the
126 island by heathland. Currently the exposure and low lying topography of most of the island and the restricted distribution of the tree species prevent re- establishment of tree dominated vegetation in areas now supporting heathland. The heathland vegetation will continue to dominate the island unless interventions are made to enable recruitment of trees so that succession could proceed to reinstate the dominance of woodland vegetation.
The plant communities identified in this Chapter are used in the next Chapter as a basis for comparison with the spatial thematic representation of vegetation on Rottnest Island.
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Chapter 5 Mapping plant communities on Rottnest Island using HyMap airborne hyperspectral data
5.1. Introduction
Vegetation mapping is essential for assisting natural resource and conservation management (Horning et al. 2010; Shoshany 2000; Verrelst et al.
2009). In ecological studies, mapping of plant communities has been widely applied to represent the distribution of different types of vegetation at various landscape scales (Dirnböck et al. 2003; Franklin 1995; Gould 2000; Hearn et al. 2011). By mapping the type and distribution of plant communities on a landscape, it can provide a spatial representation of natural resource variability and ecological condition (Akasheh et al. 2008; Cingolani et al. 2004;
Elmahboub 2009; Ernst et al. 2003; Mollot et al. 2007).
In Mediterranean-type ecosystems, vegetation mapping may be challenging due to the high spatial heterogeneity of landscapes. These ecosystems experience disturbance and are seasonally-stressed environments, resulting in distinctive floristic composition with high seasonal variability (Blondel and
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Aronson 1995). Several studies have indicated that vegetation mapping remains a challenge due to number of factors, such as spectral similarity between various vegetation types, seasonal effects and environmental or landscape heterogeneity (Lewis 1998; Thomas et al. 2003; Ustin 2004).
Remote sensing has been widely applied to produce thematic maps of vegetation patterns and distribution (Foody 2002; Nagendra 2001; Turner et al. 2003). In recent years, studies have demonstrated that hyperspectral remote sensing is highly promising for characterizing and discriminating vegetation with more appropriate spatial information over its geographical extent and distribution (Clark et al. 2005; Lucas et al. 2008; Roelofsen et al.
2014; Xie et al. 2008). Owing to high spatial and spectral resolution, hyperspectral imagery has been successfully used to produce high-detail vegetation maps with much better accuracy than multispectral remote sensing. In comparison to multispectral remote sensing, hyperspectral imagery allows capturing more detailed information on spectral variability; thus it can improve the characterization and differentiation of vegetation
(Clark et al. 2005; Costa et al. 2007; Lucas et al. 2008; Schmidt et al. 2004).
The objective of this work was to generate a vegetation map of Rottnest Island using HyMap hyperspectral image data. Using the Spectral Angle Mapper algorithm (SAM), image classification was conducted based on the measurement of the spectral differences among the pixels in imagery and relating it to the field data. Spectral Angle Mapper (SAM), a non-parametric
130 classifier that uses a solid angle and vector as the comparators, measures the angle between the high dimensionality hyperspectral dataset and each individual vegetation spectrum in multi-dimensional space (Kruse et al. 1993;
Jensen 2005).
The objective of this study was achieved by combining vegetation clustering and HyMap spectral data of Rottnest Island. Multivariate clustering as described in Chapter 4 identified eleven distinctive classes of plant community
(classification was performed on the field floristic data of Rottnest Island), and these classes were used in this Chapter as a basis of spatial thematic representation of the plant communities. These classes are proposed in this study as the optimal number which can be mapped and discriminated using dry season HyMap hyperspectral imagery. By integrating vegetation clustering and HyMap data, this study may provide a way of using plot data from a limited number of areas to complete a map of the plant communities over the whole of Rottnest Island.
5.2. Methods
The methods carried out for this study are described in detail in Chapter 3. The sampling design is described in section 3.2.1 (3.2.1.1 and 3.2.1.2), the vegetation analysis (field methods) are described in section 3.2.2., while the
HyMap data analyses and classification are described in sections 3.3.2. and
3.2.4. Analysis of plant communities using TWINSPAN classification identified
131 eleven communities (see Chapter 4), then spectral grouping of vegetation classes was performed based on the classes of the communities. A total of 140 spectra representing the eleven classes of community types were used for training samples employed in Spectral Angle Mapper (SAM) classification.
5.3. Results
5.3.1. Spectral signatures of plant communities
Spectral profiles of plant communities appear very similar in shape, but subtle variation existed, as dictated by their spectral patterns having different magnitude from visible to Short-wave Infrared (SWIR) region. Figure 5.1 shows the average value of spectral signature of each community types. Figure
5.2 shows the representative spectral profile of each plant community created for SAM classification. For each single class, the spectral signatures were represented in different colours to show the variability of reflectance value within one class. Considerable variation in the magnitude along the electromagnetic spectrum of the spectral features pronounced the spectral differences expressed within each single class.
The most distinctive spectral feature was shown by Gahnia trifida - Halosarcia indica halophytes community (group A; the salty sedges of lakes-shore community), expressed as very different amplitude of its spectral signature in the visible and near infrared (NIR) region. Woodland and scrub communities were particularly discernible from all other communities, based on the
132 absorption feature of green vegetation. In contrast, heath communities had much more resemblance to dry grass vegetation. Variation in reflectance were observed for A. rostellifera – A. preissii shrubs community (group D) and coastal scrubs A. preissii – O. axillaris (group E), enabling their distribution to be distinguished from those of other communities.
The reflectance spectra of woodland communities (i.e. Callitris preissii,
Melaleuca lanceolata – Acacia rostellifera, Pittosporum phylliraeoides, and
Eucalyptus platypus; groups B, C, I, and J respectively) was expressed as the
“most green” vegetation spectrum with a pronounced green peak, a red absorption feature and large increase in reflective response in red-edge, also relatively high NIR reflectance. Although woodland communities (groups B, C,
I, and J) were spectrally very similar to each other, the spectral signatures of each woodland type could be discriminated based on the distinct magnitude and tonal variations present over the same wavelength range. Further visual examination of spectral features indicated that variability in the spectral reflectance between each individual woodland community was apparent, particularly the spectral features observed within green, red and red-edge regions. For example, the differences in green reflectance peaks were identified in the range of the green spectral region. Callitris preissii community
(group B) had a lower but sharper green peak (centred at 730 nm) as compared to the other woodland communities. In the spectral region between red and NIR (680-730 nm), a sharper increase of the curve shape of red-edge wavelength was obvious for this Callitris preissii woodland. Interestingly, the
133 spectra profile of C. preissii woodland (group B) showed relatively low reflectance in NIR region compared to other woodland communities. Similar features of spectral signatures with different magnitude in the green peak and red-edge bands existed in other woodland communities, but the M. lanceolata
– A. rostellifera (group C) has green reflectance peak which is more similar to those of coastal shrub community (group D).
Heath communities (groups F, G, H, and K) showed primarily higher reflectance in the visible spectral region, lower curve in the NIR spectral region
(1.1-1.8 µm), and higher reflectance in the SWIR region than woodland and other communities. Most importantly, a particular feature observed for heath communities was having higher reflectance in red band than woodlands. At most wavelengths, the reflectance spectra of heath communities were almost identical to each other, yet subtle differences existed as expressed by slight differences in the visible and SWIR region. High spectral variability was observed within a single-class, while overlapping spectra signatures were apparent between classes; particularly for groups F, G, and K. Compared to other classes, the spectral features of the heath communities showed greater variability than those of the other communities, mainly in the SWIR spectral region (1500 – 1800 nm).
Reflectance spectra of A. rostellifera – A. preissii shrubs community (group D) showed similar patterns with woodland communities, but it had higher reflectance in the shortwave infrared (SWIR) region than those of woodland
134 communities. Differences in the spectral response were also observed for coastal scrubs (group E), as compared to those of woodlands, shrub, and heath communities. This coastal scrubs community had higher reflectance in visible spectral region than those of the woodland communities (groups B, C, I, and J), lower reflectance in the NIR spectral region than those of woodland (groups B,
C, I, and J) and shrub (group D) communities accompanied with marginal increase in the red-edge wavelength, and slightly lower reflectance in the
SWIR spectral region than heath communities (groups F, G, H, and K).
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Figure 5.1 The average of the reflectance spectra of the eleven classes of plant communities extracted from HyMap imagery. Note that vertical scale values have been scaled up.
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A B
C D
E F
G H
I J
K
Figure 5.2 Spectral signatures of each class of plant community used in Spectral Angle Mapper. For each class, spectra signatures were displayed in different colours to demonstrate variability within one community. For legend codes (A- K) refer to the eleven community types (Table 4.2.). Note that vertical scale values have been scaled up.
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5.3.2. Vegetation Mask
The image masking applied with Red Edge Normalized Difference Vegetation
Index (Red Edge NDVI) demonstrated its ability to include only vegetated areas.
Red Edge NDVI images calculated from HyMap imagery are shown in Figure 5.3 and the vegetation mask derived from Red Edge NDVI are shown in Figure 5.4.
The application of the Red Edge NDVI-derived mask in the classification routines prevented some non-vegetated areas with mixed pixels still containing some portion of tree or shrub canopies (i.e. roads, bare ground, and buildings, also salt lake boundaries) being classified as vegetation; so those areas were not misclassified. Some examples of bare ground areas in the classified images (i.e. blowout identified in south-east and north-west coastal zones) are presented in
Appendix 7. These clearly demonstrated that after subsequently applying the procedure of vegetation masking, the classified image showed that bare ground and other non-vegetated areas could be excluded.
This mask also allowed minimizing the background material, such as background exposure from very sparse heath communities. The result of image masking applied with Red Edge NDVI was much better than other broadband indices such as Normalized Vegetation Index (NDVI). Assessment and exploration studies performed on Normalized Vegetation Index (NDVI) demonstrated that several sites of heath communities (which were characterized by sparse cover and dry condition) had NDVI values below zero (see Appendix 6).
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Flightline 1 Flightline 2 Flightline 3
Figure 5.3 Red Edge Normalized Difference Vegetation Index (RENDVI) on each flightline. Darker tone pixels signify dense vegetation, while brighter tone pixels signify sparse vegetation: Black, blue and green coloured areas represent trees and dense shrubs; pink coloured pixels represent scrubs/heath/grasses on Rottnest Island, Western Australia.
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Flightline 1 Flightline 2 Flightline 3
Figure 5.4 Red Edge Normalized Difference Vegetation Index (RENDVI) derived mask of each flightline used for SAM classification (threshold value=0.05) on Rottnest Island, Western Australia.
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5.3.3. Image Classification
The classified image generated from Spectral Angle Mapper (SAM) classification is shown in Figure 5.5. In this final map, each plant community was displayed as composite on a red-blue-green (RGB) color map. The classified image showed that the spatial patterns of plant communities corresponded to the patterns of plant community presented in Chapter 4. Individual display of each community showed a more detailed pattern of that community across the landscape (Figure
5.6., 5.7., 5.8., and 5.9.).
The G. trifida – H. indica lake-shores community (Figure 5.6) aligned closely to the shores of several lakes, such as Lake Baghdad, Lake Herschell, and
Government House Lake. The largest patches occurred along the west side of
Lake Baghdad and reached about 30 meters in width, whereas several small patches occurred in the northern shores of Lake Herschell and eastern shores of
Government House Lake. A number of small scattered distributions were also identified in the eastern area of the island.
C. preissii woodland (group B) was mostly distributed in the eastern area and in plantation sites (Figure 5.7). M. lanceolata – A. rostellifera community (group C) was also mainly concentrated in the eastern part of the island (associated with the settlement area) and plantation sites, but it had a wider distribution than that of the other woodland communities, including the area near to salt lakes. The other woodland/ shrub communities, i.e. E. platypus woodland (group I) and P. phylliraeoides shrubs (group J) occurred within a small area in the eastern part of the island. A. rostellifera – A. preissii coastal community (group D) had a wide 141 distribution in coastal dune, particularly in the western, southern, and eastern part of the island. The biggest patch of this community was identified on south- west coastal dunes comprising an area of 0.25 ha.
Heath communities, primarily A. preissii – A. flavescens, A. preissii – T. divaricata, and A. preissii - C. candicans (groups F, G, and K) were among the most widespread communities identified and mapped in this study (Figure 5. 8). A. preissii – A. flavescens (group F) and A. preissii – T. divaricata (group G) heath communities were spread through the island, notably in the western area of the island (spatially identified in the area affected by fire in 1955). A. preissii - C. candicans (group K) was mostly identified in the western part of the island, corresponding with plantation of C. preissii and M. lanceolata (which was identified in a juvenile stage). A. preissii – L. gladiatum community (group H) comprised very small areas in the southern and northern part of the island associated with coastal, swamp and salt lake habitats.
The A. preissii – O. axillaris coastal scrub (group E) community mostly occurred in the western and southern part of the island (Figure 5.9), however this community was also scattered further inland (in the center area of the island) suggesting an over population of the class. The vegetation map also shows that at the western end of the island, the coastal habitats were mostly occupied by coastal scrub community (group E), but vegetated dune areas in the north-facing coast were more similar to A. preissii – A. flavescens heath community (group F).
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Figure 5.5 Final map of plant communities on Rottnest Island, Western Australia The extent of the classified vegetation map is shown as the whole of the study area.
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Figure 5.6 Distribution patterns of the halophyte community (G. trifida – H. indica community) on Rottnest Island. Insert illustrates the biggest patches of this community found on the west side of Lake Baghdad.
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Figure 5.7 Distribution patterns of woodland and shrub communities on Rottnest Island: C. preissii community (group B), M. lanceolata – A. rostellifera community (group C), P. phylliraeoides community (group I), E. platypus community (group J), and A. rostellifera – A. preissii community (group D).
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Figure 5.8 Distribution patterns of heath communities on Rottnest Island: A. preissii – A. flavescens community (group F), A. preissii –T. divaricata community (group G), A. preissii – L. gladiatum community (group H) and A. preissii - C. candicans community (group K).
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Figure 5.9 Distribution patterns of A. preissii –O. axillaris coastal scrub community (group E) on Rottnest Island. Inserts illustrate the detail of distribution over inland areas near salt-lake.
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5.3.4. Map Accuracy
Accuracy assessment of the vegetation classification was done using all 210 plot samples and the result of the accuracy is presented as the confusion matrix. An overall accuracy of 86.19% was achieved with the Kappa coefficient 0.84 (Table
5.1).
The producer accuracy retrieved for each class of plant community, varied greatly from 25% to 100% (Table 5.2). The highest producer accuracy was achieved for the G. trifida – H. indica halophyte community (group A), the C. preissii woodland community (group B), and the A. rostellifera – A. preisii coastal scrub community (group D). The user accuracy varied from 50% to 100%.
Highest user accuracy was achieved at 100% for M. lanceolata – A. rostellifera woodland (group C) and A. preissii – O. axillaris coastal scrub community (group
E) (Table 5.1). The other woodland community, the E. platypus woodland (group
J) had lowest producer accuracy. This community, along with P. phylliraeoides shrubs community (groups J), had the lowest user accuracy. These communities, being less common, only had two plot samples, respectively. Consequently, the very low numbers of sample plots are reflected in the accuracy results.
Total mapped area is presented as percentage area occupied by each class of plant community (Figure 5.10). This figure represents the abundance fraction of each community, confirming the results of spatial distribution depicted in Figure
5.5. In general, heath communities (A. preissii - C. candicans community, A. preissii
–A. flavescens, and A. preissii – T. divaricata) occurred in high abundance on the
148 island. The coastal scrub A. preissii – O. axillaris community (group E) was the second most abundant after A. preissii - C. candicans community (group K); however, this is likely an over population of this coastal community, where it spreads further inland (see Figure 5.9) and was explained as an error of commission (see Table 5.1).
K J I H G F Total area occupied (%) E D C B A
0 5 10 15 20
Figure 5.10 Percentage areas per class for the classification HyMap images extracted from total pixels occupied by each class of plant community on Rottnest Island. For community codes (A-K) refer to community classes (Table 4.2).
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Table 5.1. Confusion matrix generated for the accuracy assessment of the final vegetation map. For legend codes (A-K) refer to community classes (Table 4.2)
Plant Classified image Comission User communities A B C D E F G H I J K Unclassified error accuracy A 6 0 1 0 0 0 0 0 0 0 0 1 7 75 G. trifida – H. indica B 0 3 0 0 0 0 0 0 0 0 1 0 4 75 C. preissii C 0 0 11 0 0 0 0 0 0 0 0 0 11 100 M. lanceolata – A.rostellifera D 0 0 0 10 3 0 0 0 0 0 0 0 13 76.92 A. rostellifera – A.preissii E 0 0 0 0 25 0 0 0 0 0 0 0 25 100 A. preissii – O.axillaris F 0 0 0 0 0 64 2 0 0 1 7 1 73 86.49 A.preissii – A. flavescens G 0 0 1 0 0 1 34 0 1 2 2 1 41 80.95 A.preissii – T.divaricata H 0 0 0 0 0 0 0 6 0 0 1 1 7 75 A.preissii – L. gladiatum I 0 0 0 0 0 0 0 1 1 0 0 0 2 50 E. platypus woodland J 0 0 1 0 0 0 0 0 0 1 0 0 2 50 P. phylliraeoides shrubs K 0 0 1 0 0 0 0 0 0 0 20 0 21 95.24 A. preissii-C.candicans Omission error 6 3 15 10 28 65 36 7 2 4 30 0 Producer accuracy 100 100 73.3 100 89.3 98.5 94.4 85.7 50 25 66.7 Overal accuracy (OA) = 86.19%, Kappa coefficient = 0.84
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5.4. Discussion
In this study, I investigated whether HyMap data can be used to map the plant communities in great detail and high accuracy for the whole of Rottnest Island through Spectral Angle Mapper algorithm. On the basis of extensive field reference data, the results showed the potential of vegetation clustering to identify eleven vegetation classes within the study area. This study showed possibility of classifying and mapping different plant communities using the spectral grouping determined by floristic TWINSPAN classification. In doing so, this study has demonstrated the reliability of vegetation mapping both in terms of field information and confusion matrix.
5.4.1. Spectral features and grouping of the vegetation
Distinct spectral patterns emerged for the differentiation between woodland, scrub, coastal scrub, halophyte, and heath vegetation. The spectra distinction most notably occurred in the visible, red edge, NIR, and SWIR regions. Grouping of spectral classes allowed for appropriate use as the training set in the image classification.
In the visible region (0.5-0.8 µm), woodland generally had low reflectance with a pronounced green peak in the green wavelength region. Along the visible region, where the energy in the red and blue wavelengths are strongly absorbed and
151 green wavelength is effectively reflected, specific vegetation reflectance and absorption features are associated with plant pigment absorption maxima of chlorophyll b, α-carotene and β-carotene (Tucker and Garrett 1977; McCoy 2005).
Studies have highlighted that the absorption in the visible blue and red spectral region can be related to a large amount of green leaves and biomass (Carter 1994;
Carter et al. 1996; Gitelson et al. 1996). A pronounced green peak in the green and
NIR of these woodland spectra indicates that this vegetation is photosynthetically active during the dry autumn season when the HyMap data were collected.
Similar trends were also observed for coastal communities. The coastal shrub community (group D) exhibited green peak in the visible green wavelength region, and coastal scrub community (group E) also had a slight increased reflectance in the green spectral region.
In contrast, the spectral signatures of heath communities (groups F, G, H, and K) exhibited high reflectance in the visible bands (green and red spectral regions).
Reflectance in the red part of the spectrum is related to the absorption of photosynthetically active radiation (PAR) and is influenced by the soil underneath
(Eitel et al. 2007; Xie et al. 2008). Consistent with the characteristics of plant communities described in Chapter 4, high reflectance in green and red spectral regions can be explained by the communities that have relatively sparse cover
(e.g. groups F and H), and the spectral response can also be mixed with the abundance of bare soil substrate. In addition, the distinctive spectral feature
152 observed for the Gahnia trifida - Halosarcia indica halophyte community (group
A) also contained significant reflectance peaks in red wavebands. The spectra of this saltpan vegetation showed a much higher curve along the visible and NIR regions.
In red edge region, substantial difference in the reflective response observed for woodland and heath communities suggests that woodlands are likely more photosynthetically active. The woodlands had a considerable increase in reflective response in red-edge region, while heath exhibited a gentle red-edge slope. The spectral reflectance of vegetation corresponding to red-edge wavelength has been reported to be a function of chlorophyll (Horler et al.1983;
Miller et al. 1991), and a feature noted by Blackburn (1998) that the pixel values of the red edge bands are not influenced by soil background, atmospheric effects, or variability in canopy covers. Several studies have also demonstrated that the reflectance in red edge region is important to differentiate between healthy and stressed vegetation (Carter 1994; Carter et al. 1996; Gitelson et al. 1996).
In the near-infrared (NIR) region, woodlands have significantly higher reflectance than heath (groups F, G, H, and K) and other communities (i.e. the halophyte and coastal vegetation). NIR region is mostly associated with photosynthetic activity
(Xie et al. 2008). Several variables are also reported to influence the spectral response in this region, including vegetation cover and biomass (Eitel et al. 2007;
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Xiao et al. 2004). However, it is worth noting that within the woodland communities, the spectral profiles of C. preissii woodland (group B) showed relatively low reflectance in NIR region, likely attributed to characteristics of this woodland having relatively sparse canopy cover. Sparse canopy cover, soil, ground litter and non-photosynthetic parts of the trees (bark, branches) likely contribute to the overall reflectance of this Callitris woodland.
In the shortwave infrared (SWIR) region, heath (groups F, G, H, and K) had higher reflectance than the woodlands and other communities. Additionally, most important features were apparent in the SWIR region for heath communities, revealing greater variability within a single class of the community, and this was particularly apparent for the A. preissii – A. flavescens (groups F) and the A. preissii
– T. divaricata (group G) communities. Relatively high variability in the SWIR region was also apparent for other low vegetation, i.e. the coastal scrub community (group E). Variability in SWIR spectral region could be attributed to water present in plant leaves (Dennison and Roberts 200; Dickson et al. 1999;
Nagler et al. 2001). Since the plant communities were exposed to dry conditions when the HyMap imagery was collected in April (autumn season), low levels of moisture content most likely influenced plants’ water absorption in this region.
Similar features were observed by Asner et al. (2000) for the spectral signatures at grassland site, where the differences in soil reflectance caused the greatest landscape variation in the SWIR regions.
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5.4.2. Vegetation mask
The narrow-band Red Edge Normalized Vegetation Index (Red Edge NDVI) used for masking in this study demonstrated its applicability to discriminate vegetation
(both green and dry vegetation) from the soil background, particularly in very sparse heath communities with high background material (e.g. soil, ground litter, and dry leaves). Red Edge NDVI, being narrow-band, is very sensitive to slight changes in canopy chlorophyll content (Sims and Gamon 2002). While Red Edge
NDVI is very effective in detecting stress in leaves (for instance caused by drought), it has the advantage of not being affected by leaf surface reflectance
(Gupta et al. 2003; Jung et al. 2006). Gupta et al. (2003) and Jung et al. (2006) also reported that Red Edge NDVI is not only especially sensitive to canopy foliage but also senescence phenological stages.
Vegetation indices (VIs) have been used extensively for analyzing, mapping, and monitoring spatial distributions of physiological and biophysical characteristics of vegetation. Most VIs have been developed on the red-edge region, which is the region between 680 and 800 nm (Baret et al. 1992), enables the enhanced characterization of vegetation biophysical properties and land surface condition while attempting to minimalizing the effects of backgrounds caused by variations in soil reflectance (Dorigo et al. 2007; Glenn et al. 2008). Normalized Difference
Vegetation Index (NDVI), for example, has been extensively applied to explore vegetation’s spectral signature characteristics. NDVI is a broadband greenness
155 index which is generally applied as a robust indicator of chlorophyll absorption; however, NDVI does have some limitations related to soil background brightness.
Testing on Normalized Vegetation Index (NDVI) demonstrated that several sites have NDVI values below zero (data were not shown in this thesis; see Appendix
6), reflecting the low biomass and vegetation cover of the most sparse and disturbed heath communities. It is important to emphasize that NDVI can successfully separate vegetation from non-vegetated areas, but this index does not successfully differentiate the densely vegetated grass from the sparsely vegetated area (Adams and Gillespie 2006). While the aim of this study was not designed to compare Red Edge NDVI and other indices, this study demonstrated that the use of Red Edge NDVI was especially valuable to exclude non-vegetated areas. By excluding the areas known to have no vegetation, masking the image using Red Edge NDVI can therefore prevent those areas from being misclassified and will effectively improve the classification accuracy.
5.4.3 Image classification and map accuracy
The results of Spectral Angle Mapper (SAM) classification demonstrated the derived classification has strong agreement with the field data, confirming the current extent and distribution of plant communities throughout the island. The
HyMap data can be used to map the vegetation in this study with the overall accuracy achieved at 86.19% and a Kappa agreement of 0.84.
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There was a better classification accuracy of woodland than for other communities. The classification map showed that the spatial distribution of woodland was congruent with the field data of the community distribution on the island. This was expected, since the woodland communities generally have the characteristics of relatively homogeneous structure and composition. Jones et al.
(2010) stated that unique characteristics of tree species may be very useful for differentiating certain species, as different tree species can have distinct canopy architecture. However, the variability of spectral signatures observed within the single-class of woodland community likely encompassed a wide range of spectral variability of that specific woodland, which can be related to differences in tree condition, such as tree health and growth stages (Sampson et al. 2001). The spectral variability observed within one specific class can also be explained by the differences in light absorption and scattering across the wavelength regions
(Lucas et al. 2008). This study demonstrated that the discrimination and selection of each vegetation community from the image spectra can assist in extracting the pixel of those specific classes, thus it is appropriate for the training step in image classification. In addition, the spatial compatibility between the canopy size of woodland communities and the pixel size of Hymap image may have contributed to the high accuracy of mapping by SAM algorithm.
At the class level, C. preissii woodland was mapped with highest accuracy compared to other communities having both greater producer’s and user’s
157 accuracy, alongside lesser omission and commission error. This woodland consisted predominately of one common tree species (i.e. C. preissii). Other plants in the lower strata in these woodlands were scarce. Thus, the spectra of the woodland community were not much influenced by understorey plants. Although
C. preissii tends to have a sparse canopy the underlying soil surface and non- photosynthetic components (e.g. bark and bare branches) seemed not to influence the reflectance of this vegetation.
Low accuracies of other woodland communities (i.e. groups C, I, and J) can be partly explained by a lower number of validation points compared to the other communities. The M. lanceolata – A. rostellifera woodland (group C) had a low level of producer accuracy, but this woodland community had very low number of samples (n=4), which in turn influenced the accuracy results. The omission error observed in this community came from the site identified as heath community (the A. preissii – A. flavescens community). In this case, the occurrence of understorey layer (e.g. A. preissii) in this woodland likely contributed to the misclassification. Similar results were also found for other woodland communities (i.e. groups I and J) where low number of validation points lead to lower accuracy. More field data are likely required to increase the sample size of woodland communities which could provide better classification accuracy (Foody
2003).
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Heath communities exhibited both high user and producer accuracies with considerable confusion between heath communities, particularly groups F, G, and
K. Highest omission and commission errors were observed for the A. preissii – A. flavescens community (group F), with the errors of assignment corresponded to other heath communities (i.e. groups G and K). Lowest producer accuracy was observed for the A. preissii - C. candicans community (group K), yet the omission error also corresponded to other heath communities (i.e. groups F and G). Heath communities encompass a range of heterogeneous cover which consists of overlapping herbs and grasses. In particular, the dominance of A. preissii observed in these heath communities contributed to the difficulties in image classification.
The A. preissii – L. gladiatum community (group H) had relatively high user and producer accuracy along with the lowest omission and commission errors compared to other communities. This community exhibited a unique spectral signature of L. gladiatum but also spanned high variability. L. gladiatum was mostly associated with a high abundance of A. preissii and was surrounded by other heath communities and exhibited patchy distribution from the coast to further inland.
The A. preissii - O. axillaris coastal scrub community (group E), as represented by the unique spectral signatures of the coastal community (with high dominance of
O. axillaris), was mapped with highest user accuracy compared to other
159 communities but its extent was overestimated (Figure 5.11). The omission error observed in this coastal community corresponded to the heath communities (e.g. the A. preissii – A. flavescens community). Overlapping of common species, notably
A. preissii, was observed in the coastal and heath communities. Indeed, this coastal community was easily confused with heath communities, most likely because the spectra response was strongly influenced by the presence of A. preissii.
The overall classification results have shown that remote sensing based vegetation mapping using HyMap image have achieved a relatively high accuracy and reliability (with the classification accuracy achieved at 86.19% and
Kappa coefficient 0.84). Several studies have demonstrated that vegetation mapping using hyperspectral images results in a more accurate distribution of plant communities. For instance, Christian and Khirsnayya (2009) reported that classifying Hyperion data in the tropics yielded the overall accuracy 51-59.57% and Vyas et al. (2011) classified tropical vegetation using Hyperion data and achieved the overall accuracy of 66%.
This study also emphasized the importance of understanding the unique spectral features of the particular community type being imaged and classified using
Spectral Angle Mapper (SAM) classification (Kamal and Phinn 2011). Vegetation clustering by applying TWINSPAN classification, as presented in Chapter 4, revealed a useful approach for stratifying and classifying vegetation into
160 communities. The classified vegetation could then be used to inform the process of selecting training areas and reduce spectral confusion between each community class. Furthermore, this study also highlighted that Red Edge NDVI was efficient for vegetation masking, particularly when applied to sparse and dry heath vegetation. Finally, this study demonstrated the potential for using HyMap imagery to discriminate and map the plant communities on Rottnest Island.
Evaluation of the utility of vegetation survey and hyperspectral approach for vegetation mapping are considered further in Chapter 6.
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Chapter 6 General discussion
This project presents a detailed analysis of plant communities and vegetation map for Rottnest Island, Western Australia based on both field and remotely sensed data analyses. The plant community analysis presented in Chapter 4 included the richness and diversity of plant species and their assemblages and the corresponding environmental characteristics that contribute to the distribution of these communities. Also, the current state of plant invasions was determined.
In Chapter 5, the vegetation was mapped using HyMap hyperspectral image using
Spectral Angle Mapper (SAM) classification, and related to the classification based on field data. In this chapter the findings of this study are discussed in the contexts both of remote sensing data utility for vegetation analysis, and nature reserve management in context of Rottnest Island.
6.1. Analysis and Mapping of Plant Communities
TWINSPAN classification and NMDS ordination results presented in this study
(Chapter 4) provided general insights into understanding the patterns of plant communities on Rottnest Island. Eleven plant communities were identified and
163 described for Rottnest Island vegetation. The NMDS analysis provided an indication of floristic variation and the general environmental factors that influenced the distribution of plant communities. These distribution patterns were associated with habitat properties that reflected both local environmental factors and current and past disturbances.
In Mediterranean-type landscapes, vegetation is prominently characterized by sclerophyllous shrublands and dry woodlands (Cowling et al. 1996). The maritime location of Rottnest and the limited substrate of sand and limestone restrict the vegetation to a narrow subset of the plant communities of the south west of Western Australia. Heath communities are the most widespread across the island, consisting of herbaceous plants/lilies and grasses, and are dominated by the most widespread heath-like lily species - A. preissii. Heathlands occur as dominant communities on this island, the result of long-term changes after woodland clearing and increased disturbance by fire (Motzkin and Foster 2002;
Rippey and Hobbs 2003). In Western Australia, historical disturbance events are one of the primary factors explaining the vegetation patterns (Wardell-Johnson et al. 2004). Woodland communities (i.e. Callitris preissii and Melaleuca lanceolata -
A. rostellifera communities) were likely much more widespread on the island before major human interventions since the early 1800s (Rippey and Hobbs
2006). They now occur as scattered patches on the island, due to these significant disturbance impacts. The degradation of woodland communities has been
164 predominantly related to the history of human settlements, and limited seedling recruitment of native woodland species has driven the succession to open heath communities (Linderman et al. 2006; Mitchell 1990; Oyugi 2008).
The patterns of plant communities are generally influenced directly or indirectly by several interacting environmental variables (Coughenour and Ellis 1993;
Fernandez-Gimenez and Allen-Diaz 2001; Sankaran et al. 2008; Van Der Waal et al. 2009). In this study, the distribution patterns of some plant communities corresponded to the distance from the coastline. The degree of marine influence in terms of salt spray and strong prevailing winds create different environmental regimes from the south-west to the north-east of the island, influencing the extent of the coastal community (group E) corresponding with the south-west coastal dunes. In addition, the distribution of other communities associated with the coastal habitat, i.e. Acacia rostellifera (group D) and A. preissii - Lepidosperma gladiatum (group H), also showed strong association between the plant community and habitat features related to local site features (Feagin and Ben Wu
2007; Scarano 2001).
The heath communities are the most severely invaded by introduced species, with
Trachyandra divaricata particularly widespread and so common that it is a signature species in community G. The species is also very common along roadsides and its invasion is facilitated by disturbance. The open structure of the
165 heathland communities leads to their high invasability and many of the invasive species are annuals that survive through the dry summer in the soil seedbank.
This plant functional group can thus escape the low water availability of the summer and the grazing pressure from quokkas as the herbage biomass decreases into the dry season.
The HyMap image classification using Spectral Angle Mapper (SAM) showed significant potential for classifying and mapping plant communities on Rottnest
Island. Based on the vegetation specific reflectance of each community type, SAM classifier was able to differentiate and map the eleven classes of vegetation derived from the field data analyses. This was expected since our method had been developed to reduce spectral confusion, by stratifying vegetation into community levels (the lower hierarchical level) (Alexander and Millington 2000).
Community clustering is considered as most appropriate in the framework of vegetation mapping, allowing the distinction between different coverages and compositions of heterogeneous formations, resulting in a significantly superior accuracy of the classified map (Christian and Khirsnayya 2009; Schmidtlein et al.
2007). Lillesand and Kiefer (2000) also emphasized that the quality of the training sample set is an important element for vegetation mapping. Floristic homogeneity within a community is critical and variation in training sample selection would influence the classification results.
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There were two challenging issues identified in the application of HyMap remotely sensed data for vegetation mapping on Rottnest Island. Firstly, Rottnest
Island is characterized by a large area (more than two third of the island) of heath communities, particularly dominated by A. preissii – A. flavescens and A. preissii –
T. divaricata (groups F and G). These heath communities are “open canopy” low vegetation, where bare ground surface is significant; indicating high reflection of soil background. Secondly, the HyMap image was acquired in April (autumn season), when there are low levels of effective photosynthetically active radiation.
In addition, heath communities (groups F, G, H, and K) showed higher reflectance in the SWIR spectral region with great variability, likely indicating that low water content significantly affected leaf reflectance. This may have led to confounding effects of spectral characteristics on the spectral data, allowing significant mixing of the soil and vegetation responses (with little photosynthetic activity). This study demonstrated that Red Edge NDVI masking improved the ability to discriminate green and dry vegetation from the soil in the sparse heath communities. The sensitivity of vegetation red edge affects significantly changes with canopy foliage moisture/chlorophyll content and senescence (Gupta et al.
2003; Jung et al. 2006). Results of vegetation masking indicate that this narrow band hyperspectral greenness index ensures significantly high sensitivity to soil reflectance and soil brightness, thus it can effectively enhance the exclusion of the non-vegetated areas and improve accuracy.
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The results also suggest that heathlands belonging to several classes can be spectrally distinguished and classified. Plant communities are assumed to have homogenous species composition within a single class, yet this study illustrates that they exhibit variability in spectral response. Vegetation reflectance is considered to be primarily a function of biophysical and biochemical characteristics (Asner 1998), while species composition and other environmental conditions are likely to influence the spectral signature of heathland vegetation.
Environmental heterogeneity at local scales resulting from disturbance- environmental condition has also been identified as an important factor for fine- scale floristic composition and diversity in heathlands (Bradstock et al. 1997). In arid ecosystems, Asner et al. (2000) indicated that soil reflectance and fractional vegetation cover are the dominant factors contributing to reflectance variation, while other factors such as litter reflectance and leaf transmittance also contribute to the spectral variation of sparse vegetation. High levels of misclassification between similar plant communities can sometimes be attributed to the abundance of other shrub species in each community, and this is particularly true for heath communities because heath plants are often associated with abundant other species (grasses and herbaceous species) in this study so there is floristic variation on a small scale. These results are in line with previous studies, which found that spectral variability can be caused by several factors, such as the dominant species and the similarity of any adjacent plant communities, or heterogeneity of cover and physical environment (Baatuuwie
168 and van Leeuwen 2011; Lewis 1994). Overall, the findings of the thesis demonstrate that HyMap data are capable to capture fine-scale information that discriminates between heath communities representing different species composition and environmental conditions with significant presence of bare soil.
This may have implications for HyMap analysis and mapping methods for plant community or other ecological parameters in the future.
This study showed that HyMap data can be used to produce a vegetation map with a relatively high accuracy (86.2%) and reliability (Kappa coefficient 0.84) of a large area with variable vegetation cover. The technique was able to distinguish between communities that appeared structurally very similar, thus allowing a detailed view of the distribution of compositionally different plant communities.
While field data will always be necessary to produce training areas for the classification of the remote sensing data, a huge saving in time and hence resources can be made by removing the need for a large number of field plots to produce an accurate and useful map.
6.2. Implications for Conservation
Analysis of vegetation and mapping of plant assemblages are essential to provide high-quality reference information for conservation goals. This study provides baseline data, both floristic and spatial, which can aid in conservation programs,
169 monitoring change over time, and further research of ecosystem studies such as succession, restoration, human impact and climate change on Rottnest Island.
Additionally, maps can be used to focus attention on areas that may need remediation or where facilities can be sited to protect vegetation. Rottnest is a popular tourist destination and coastal areas in particular can be under high visitor pressure, resulting in trampling that is detrimental to vegetation cover.
The quokka is a strong tourist draw card and detailed vegetation maps, together with observational studies, can be used to identify the location of resting sites where the animals shelter from the heat of summer or where the animal’s main food plants occur (Poole et al. 2014). In terms of re-establishing woodland and scrub on the island, maps are useful to identify sites where outliers of the main species (C. preissii, M. lanceolata, A. rostellifera) occur that could be used as nuclei for encouraging tree reinvasion of heathland to convert it to woodland. Poole et al. (2014) noted that the diet of quokkas varied in relation to the area where the animal was feeding and additionally that the diet seems to have changed since an earlier study 50 years ago. The tree and shrub species are favoured food items but their availability is limited by their narrow current distribution on the island.
Increasing the coverage of thickets of A. rostellifera, in particular, could provide better forage for the animals than they currently can access. The diet as assessed by Poole et al. (2014) contained considerable amounts of Trachyandra and
Asphodelus both of which are considered to be unpalatable or poisonous to animals based on other studies. The quokkas may be forced to eat such taxa due
170 to a shortage of preferred food plants, especially in summer. Accurate detailed maps could be used to identify the best areas for re-establishment of preferred food species.
6.3. Further research
This study provided a detailed vegetation map of Rottnest Island. The detailed field data and HyMap spectral data may provide baseline data for other applications, especially for vegetation, habitat and ecosystem monitoring. For example, predictive mapping at the species level is critical to predict future distributions of native species. A similar modelling approach can also be applied to map the distribution of invasive species, such as T. divaricata, that is abundant in open and disturbed areas, and where the distribution and cover abundance of invasive species appears to be increasing with time.
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APPENDICES
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Appendix 1. Additional species recorded outside the quadrat
Plant Species Family Acrotriche cordata Ericaceae Alyxia buxifolia Apocynaceae Anagallis arvensis Primulaceae Atriplex isatidea Chenopodiaceae Austrostipa elegantissima Poaceae Beyeria viscosa Euphorbiaceae Calocephalus browniii Asteraceae Clematis macrophylla Ranunculaceae Conyza sp. Asteraceae Crassula sp. Crassulaceae Eremophila glabra Myoporaceae Hypochaeris glabra Asteraceae Inula graveolens Asteraceae Isolepis sp. Cyperaceae Juncus sp. Juncaceae Moraea flaccida Iridaceae Poa sp. Poaceae Polypogon maritimus Poaceae Sonchus oleraceus Asteraceae Tetragonia sp. Aizoaceae Threkeldia diffusa Chenopodiaceae Thysanotus patersonia Asparagaceae Urtica urens Urticaceae Vulpia sp. Poaceae
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Appendix 2. TWINSPAN (TWO-WAY ORDERED TABLE)
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Appendix 3. The latitude and longitude location and elevation of field sampling
Plot number Latitude Longitude Elevation (m dpl)
1 -32.0076 115.52822 7.296 2 -32.0196 115.45921 8.262 3 -32.0198 115.47002 8.397 4 -32.0189 115.46291 9.191 5 -32.005 115.50691 8.437 6 -32.0207 115.46922 8.96 7 -32.0146 115.47981 7.836 8 -32.0198 115.46339 9.383 9 -31.9959 115.50271 8.961 10 -32.0143 115.49734 7.893 11 -32.0212 115.46747 1.014 12 -32.0063 115.49607 1.067 13 -31.9971 115.49548 1.091 14 -32.0152 115.48226 8.72 15 -32.016 115.49573 7.561 16 -32.0077 115.52823 7.299 17 -31.9975 115.51453 6.821 18 -32.0102 115.51362 9.347 19 -32.0126 115.49527 7.6 20 -32.0185 115.46906 8.483 21 -31.9906 115.51603 8.005 22 -32.0229 115.45392 1.015 23 -32.0118 115.5313 7.411 24 -31.9906 115.50828 1.034 25 -32.0132 115.5022 9.001 26 -32.016 115.48881 7.852 27 -32.0099 115.4936 9.29 28 -32.019 115.46132 8.837 29 -31.9897 115.50957 8.511 30 -32.0196 115.46245 8.85 31 -32.0063 115.51691 1.167 32 -32.0045 115.53255 7.622 33 -32.0175 115.45674 8.406 34 -32.0185 115.49441 9.307 35 -32.0167 115.48752 8.196 36 -32.0102 115.53647 7.703 37 -32.0124 115.52844 7.522
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Plot number Latitude Longitude Elevation (m dpl)
38 -32.003 115.50165 8.628 39 -32.0217 115.52573 8.347 40 -32.0166 115.46639 8.618 41 -32.0059 115.50788 1.076 42 -32.0069 115.51335 9.861 43 -32.0098 115.50963 7.526 44 -31.9968 115.50668 7.922 45 -32.0007 115.50617 7.874 46 -32.0083 115.52751 7.808 47 -32.0163 115.47198 8.026 48 -32.0082 115.54557 7.954 49 -32.0188 115.52543 9.862 50 -32.0076 115.5067 9.61 51 -32.0074 115.50032 1.14 52 -32.0092 115.53732 7.892 53 -31.9922 115.51405 7.207 54 -32.0028 115.48799 9.318 55 -32.0096 115.51 7.482 56 -31.9908 115.50842 8.905 57 -31.9953 115.51119 7.238 58 -32.0145 115.48591 8.421 59 -32.0025 115.50531 7.947 60 -32.0175 115.49318 1.001 61 -32.0131 115.5226 7.979 62 -31.9935 115.5223 7.94 63 -32.0038 115.53158 7.451 64 -32.0154 115.5308 8.475 65 -32.0159 115.48681 8.879 66 -32.0111 115.48621 9.307 67 -32.0044 115.54826 6.917 68 -31.9894 115.51268 7.566 69 -32.017 115.49502 8.58 70 -32.0064 115.49924 1.053 71 -32.0043 115.5007 1.217 72 -32.0137 115.51584 7.445 73 -32.0114 115.54255 8.794 74 -31.9989 115.49316 8.915 75 -32.0219 115.46732 1.006 76 -32.0216 115.46079 1.008
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Plot number Latitude Longitude Elevation (m dpl)
77 -32.0188 115.46032 1.017 78 -32.0203 115.46963 8.575 79 -31.9972 115.51404 8.123 80 -32.0023 115.51295 8.528 81 -31.9938 115.51945 7.025 82 -31.9938 115.51326 6.792 83 -32.0092 115.51079 8.815 84 -31.9984 115.50378 9.475 85 -32.0212 115.47024 8.878 86 -32.0138 115.52814 7.615 87 -32.0237 115.45359 1.123 88 -31.9926 115.51517 6.776 89 -31.9919 115.50363 8.775 90 -32.0038 115.51067 8.943 91 -31.9949 115.50661 9.719 92 -32.0128 115.52134 1.048 93 -32.012 115.50967 8.708 94 -32.011 115.5063 1.079 95 -32.0021 115.51286 7.793 96 -32.0001 115.50506 1.074 97 -32.001 115.5238 7.306 98 -32.0003 115.54033 6.591 99 -32.0051 115.49048 1.147 100 -32.007 115.54694 7.154 101 -31.9975 115.50087 1.091 102 -31.9965 115.49925 1.347 103 -31.9963 115.50035 1.132 104 -32.0109 115.54381 9.14 105 -32.0036 115.51016 8.786 106 -32.0073 115.50362 1.375 107 -32.0157 115.5309 7.102 108 -32.017 115.48821 9.336 110 -32.0137 115.49323 7.332 111 -32.0085 115.50979 8.065 112 -32.0025 115.51169 8.812 113 -32.0087 115.50352 1.167 114 -32.0058 115.55059 7.07 115 -32.0068 115.50015 1.249 115 -31.9949 115.52682 7.061
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Plot number Latitude Longitude Elevation (m dpl)
116 -32.0122 115.48821 1.116 117 -32.0026 115.51074 8.369 118 -32.0079 115.55047 1.044 119 -32.0064 115.49311 1.173 120 -32.0087 115.49838 9.654 121 -31.9998 115.49969 8.706 122 -32.004 115.51316 8.368 123 -32.0003 115.52513 7.536 124 -32.0092 115.49912 1.016 125 -32.0101 115.51854 8.924 126 -32.0064 115.50601 8.413 127 -32.0066 115.55021 7.069 128 -32.0041 115.5459 7.007 129 -32.0164 115.46347 9.325 130 -31.9996 115.51819 6.78 131 -31.9902 115.51184 7.468 132 -32.0069 115.52659 7.559 133 -31.9991 115.52783 6.743 134 -32.0075 115.54783 7.225 135 -32.0048 115.50355 8.368 136 -32.0034 115.51562 7.218 137 -32.0123 115.49555 7.639 138 -32.0108 115.49758 8.528 139 -32.0187 115.52481 9.412 140 -32.0105 115.49827 9.203 141 -32.0116 115.49446 7.506 142 -32.003 115.50447 7.638 143 -32.0135 115.49676 7.579 144 -32.0097 115.48778 9.9 145 -32.0139 115.4962 7.311 146 -32.0087 115.49995 1.154 147 -31.993 115.5204 8.675 148 -32.0047 115.54716 7.409 149 -32.0139 115.49822 7.972 150 -32.0158 115.4973 7.12 151 -32.0087 115.53418 9.234 152 -32.0111 115.53844 9.057 153 -32.0233 115.45818 9.486 154 -32.0163 115.48846 7.587
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Plot number Latitude Longitude Elevation (m dpl)
155 -31.9924 115.52591 8.428 156 -32.002 115.50117 9.193 157 -32.0034 115.50396 8.73 158 -32.0081 115.48962 1.305 159 -32.0027 115.49431 1.067 160 -32.0219 115.4563 1.044 161 -32.0165 115.45988 8.426 162 -32.0157 115.4619 8.696 163 -32.0046 115.49613 1.018 164 -32.0043 115.49875 1.28 165 -32.008 115.4932 1.054 166 -32.0018 115.50108 8.739 167 -32.0216 115.45507 1.078 168 -32.0041 115.50096 1.205 169 -31.9983 115.50101 9.439 170 -31.9999 115.49799 9.836 171 -32.0034 115.49851 1.18 172 -32.0137 115.49048 8.828 173 -32.0218 115.45558 1.067 174 -32.0016 115.49926 9.412 175 -32.0186 115.52624 9.093 176 -32.0194 115.52907 1.184 177 -32.0117 115.53682 9.897 178 -32.0139 115.52017 1.032 179 -31.9981 115.49362 9.53 180 -32.0074 115.48838 1.069 181 -31.9913 115.51757 6.979 182 -31.9948 115.52432 6.724 183 -32.0103 115.50365 1.027 184 -32.0136 115.5179 9.691 185 -32.0193 115.52875 1.123 186 -32.0017 115.4904 9.693 187 -31.9917 115.52615 9.493 188 -32.022 115.52577 8.779 189 -32.0044 115.51506 8.975 190 -31.9945 115.52767 8.824 191 -32.0066 115.51926 8.473 192 -31.995 115.51076 7.416 193 -32.0017 115.51668 7.274
210
Plot number Latitude Longitude Elevation (m dpl)
194 -32.0074 115.51852 9.215 195 -31.9965 115.50743 8.517 196 -32.0004 115.50225 9.794 197 -32.0015 115.50419 8.334 198 -31.9957 115.51116 8.659 199 -32.0134 115.49585 7.278 200 -32.0112 115.49431 8.433 201 -32.0092 115.52564 7.266 202 -32.0039 115.52112 9.363 203 -32.0033 115.5234 9.098 204 -31.9981 115.50605 9.357 205 -31.9992 115.50593 1.18 206 -32.0044 115.51807 8.728 207 -32.0088 115.50399 1.087 208 -32.012 115.50194 8.786 209 -32.0075 115.53691 8.011 210 -32.0096 115.5375 7.614
211
Appendix 4. Wind Rose for Rottnest Island showing wind direction and wind speed in km/h
212
Appendix 5. Hyperspectral band numbers and imaging mode wavelengths (nm).
Band Wavelength Band Wavelength Band Wavelength number (nm) number (nm) number (nm) 1 454.8 43 1062.2 85 1703 2 468.9 44 1076.8 86 1715.4 3 483.8 45 1091.5 87 1727.6 4 498.6 46 1106.1 88 1740 5 512.9 47 1120.6 89 1752.3 6 528.1 48 1135.2 90 1764.3 7 543.3 49 1149.4 91 1776.3 8 557.9 50 1163.6 92 1788.3 9 572.7 51 1177.9 93 1800.1 10 587.6 52 1192.2 94 1950.1 11 602.4 53 1206.1 95 1969.1 12 616.9 54 1220 96 1988.2 13 631.2 55 1234.2 97 2007.5 14 645.8 56 1248.3 98 2026.6 15 660.4 57 1262.2 99 2045.9 16 674.9 58 1275.9 100 2065.1 17 689.3 59 1289.8 101 2083.6 18 704 60 1303.4 102 2101.9 19 718.5 61 1317.2 103 2120.2 20 732.6 62 1391.2* 104 2138.6 21 746.8 63 1406.3* 105 2156.9 22 761.1 64 1420.9 106 2174.7 23 775.3 65 1435.2 107 2191.7 24 789.4 66 1449.7 108 2209.9 25 803.7 67 1464 109 2228 26 818 68 1478.2 110 2245.4 27 832.3 69 1492.3 111 2263.5 28 846.4 70 1506.2 112 2280.6 29 860.5 71 1519.8 113 2297.7 30 874.2 72 1533.5 114 2314.4 31 879.7 73 1547.3 115 2331 32 895.1 74 1560.8 116 2347.9 33 910 75 1574 117 2364.8 34 925.4 76 1587.2 118 2381.5 35 941 77 1600.5 119 2397.8 36 956.4 78 1613.7 120 2413.8 37 971.6 79 1626.6 121 2429.9 38 986.9 80 1639.6 122 2446.1 39 1002.2 81 1652.5 123 2462.2 40 1017.2 82 1665.2 124 2477.9 41 1032.4 83 1677.7 125 2493.2* 42 1047.3 84 1690.4 * wavelengths from 395–429 and 2401–2500 nm were removed due to high levels of noise.
213
Appendix 6. Example of sparse vegetation with NDVI less than 0.05
214
Appendix 7. Examples of final classified images which represent successfully masked bare ground areas (i.e. blowouts). RGB HyMap image (a and c) and classified images after applying non-vegetated mask (b and d).
215
Appendix 8. Example of vegetation map of coastal area along the southern coast of Rottnest Island. Inserts illustrate the detail over smaller areas which were characterized by mixed vegetation communities in a relatively small area.
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ANNEX
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