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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
1
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
2
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
3
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
4
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
5
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:
6
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
7
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.
8
9
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
10
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.
11
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
12
(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
13
matter may also affect species distributions, e.g. one species ‘attracting’ or
‘repelling’ another that does not simply reflect similar reactions to
environment.
a
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.
b
Based on the concept of plant community illustrated above, classification of
vegetation communities is more consistent with a Clementsian view of
14
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
15
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
16
(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
17
(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;
18
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
19
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
20
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.
21
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.
1998
AVIRIS data were analyzed using a maximum likelihood
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.
2003
High spatial resolution Compact Airborne Spectrographic
Imager (CASI) reflectance data were examined and
compared to plant community data for a peatland complex
in northern Manitoba, Canada.
Mundt et al.
2006
Evaluating the applicability of high spatial resolution
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.
2009
Mapping of aggregated floodplain plant communities using
image fusion of CASI and LiDAR data along the river Waal in
the Netherlands.
White et al.
2010
Forest structure was explored using Hyperion data over a
625 km2 coastal temperate forest landscape on Vancouver
Island, British Columbia, Canada.
Jones et al.
2010
Assessing the utility of hyperspectral Airborne Imaging
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.
22
Citation Summary of topic
Colgan et al.
2012
Mapping individual tree crowns (classified to species) of
savannah forest communities in Queensland, Australia,
using CASI data through automated classification.
Dalponte et
al. 2012
This paper describes how hyperspectral data allowed the
authors to distinguish similar tree species and the addition
of LiDAR data increase the performance with higher
classification accuracy.
Cho et al.
2012
Mapping seven common savanna tree species or genera in
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.
2012
Classification of eight common savanna tree species in the
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.
2014
This paper describes tree species mapping in Santa Barbara,
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.
2014
This paper describes tree species mapping in European
forest using HyMap, Hyperion and LIDAR data.
23
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.
24
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
25
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.
26
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
27
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
28
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).
29
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
30
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.
31
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
32
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
33
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.
34
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):
35
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
36
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.
37
38
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
39
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
40
(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
41
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.
42
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.
43
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.
44
Figure 3.1. Geographic position of Rottnest Island in relation to the Western Australia coast (Data source: Google Earth).
45
Figure 3.2. Flowchart of the approach in the data analysis using remotely
sensed and field data.
Sampling design
Field survey
Spectral signature Grouping sites
Ecological analysis
(CH 4)
HyMap image
Validation
(Accuracy assessment)
SAM classification
Red Edge NDVI
mask
Classified images
Review Results
46
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
47
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
48
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.
49
Figure 3.3. Flowchart outlining procedures used to create the land cover
classification and to create random sampling.
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
50
a
b
Figure 3.4. (a)QuickBird composite image of Rottnest Island, (b) Land cover
classification of Rottnest Island derived from QuickBird imagery.
.
51
Table 3.1 Total area (ha) and number of points used for sampling in each vegetation cover class.
Vegetation Cover Category Total area (ha)
Cover area (%)
No. of field sites
Percentage of 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
52
Figure 3.5. Stratified random placements of 210, 10x10 m quadrats on Rottnest Island used for field vegetation sampling.
53
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
54
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.
55
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).
56
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.
57
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).
58
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.
59
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
60
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.
61
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 sampling
210 plots
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: GIS-derived environmental data
Distance from beach Distance from sealed roads 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)
62
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
63
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
Bare Percentage of bare area(ground exposure/ground area 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
64
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.
65
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
66
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.
67
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 COMMUNITY/
CLASS TYPE
NUMBER OF SAMPLE PLOTS
Total number of
spectra extracted for each class
type
Training set of each class type employed in SAM analysis
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
69
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).
70
71
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
72
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.
73
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-
74
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).
75
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
77
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.
210
202
15
11 (C)
6 5
4 (B)
187
149
97
23
2 (J) 21 (K)
74 (F)
49 25
52
42 (G)
27 15
10
8 (H) 2 (I)
38
25 (E)
16
10 6
9
6 3
13 (D)
8
5 3
5
3 2
8 (A)
4 4
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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.
79
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
80
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.
81
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).
82
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.
83
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.
84
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.
85
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
86
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.
87
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.
89
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).
90
Plate 4.3 Example of Melaleuca lanceolata woodland community (group C).
Plate 4.4 Example of Acacia rostellifera-Acanthocarpus preissii coastal
community (group D).
91
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).
93
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.
94
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
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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.
96
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.
97
Ave
rage
nu
mb
er o
f sp
ecie
s
Community A
Community C
Community D
Ave
rage
dis
tan
ce
Community E
Community F
Community G
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.
98
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).
VARIABLES
PLANT COMMUNITIES (groups A-K)
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 per plot
2-5 1-5 2-10 6-12 7-13 4-12 4-11 6-8 6 2-6 6-14
Total number of species observed in the community
13.0 9.0 17.0 22.0 25.0 32.0 34.0 20.0 8.0 7.0 21.0
Species estimated: first order Jackknife
17.4 12.7 22.5 26.6 26.9 40.9 44.7 28.7 9.0 9.0 22.9
Species estimated: second order Jackknife
18.6 14.9 23.7 29.3 24.4 46.8 50.6 34.3 9.0 9.0 23.9
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 Richness (S)*
3.88 (±0.40)
- 4.36 (±0.80)
8.62 (±0.60)
9.00 (±0.32)
7.72 (±0.19)
7.48 (±0.30)
6.88 (±0.35)
- - 9.05 (±0.43)
Mean (±SE) of Shannon-Wiener Diversity Index (H’)*
1.03 (±0.09)
- 0.74 (±0.15)
1.90 (±0.09)
1.84 (±0.05)
1.59 (±0.04)
1.56 (±0.05)
1.44 (±0.60)
- - 1.74 (±0.08)
Mean (±SE) of Evenness (J’)* 0.80 (±0.05)
- 0.50 (±0.08)
0.89 (±0.03)
0.84 (±0.02)
0.79 (±0.01)
0.79 (±0.02)
0.75 (±0.02)
- - 0.79 (±0.03)
*Woodlands communities (groups B, I, J) were excluded from the analyses (S, H’, and J’) because their sample sizes were too small.
99
a
b
Figure 4.9 Distribution of species richness and diversity for quadrats on
Rottnest Island, Western Australia: a) Richness; b) Shannon Index.
100
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
101
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
102
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.
103
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
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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.
105
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.
106
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.
107
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.
108
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.
109
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
110
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
111
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).
Figure 4.15 Number of native and invasive species within each plant family
observed on Rottnest Island, Western Australia. Two plant species remained
unidentified.
0
1
2
3
4
5
6
7
Ap
iace
ae
Asp
arag
acea
e
Asp
ho
de
lace
ae
Ast
erac
eae
Ch
en
op
od
iace
ae
Cu
pre
ssac
eae
Cyp
erac
eae
Das
ypo
gon
ace
ae
Epac
rid
acea
e
Eup
ho
rbia
ceae
Go
od
en
iace
ae
Hae
mo
do
race
ae
Lam
iace
ae
Mim
osa
ceae
Myr
tace
ae
Oe
no
the
race
ae
Pap
illio
nac
eae
Pit
tosp
ora
ceae
Po
ace
ae
Ru
tace
ae
Stac
kho
usi
acea
e
Ste
rcu
liace
ae
Native
Invasive
112
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.
113
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).
114
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.
115
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
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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
117
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
118
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
119
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
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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
121
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).
122
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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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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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.
A
B
C
D
E
F
G
H
I
J
K
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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
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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.
144
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
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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).
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).
0 5 10 15 20
A
B
C
D
E
F
G
H
I
J
K
Total area occupied (%)
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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
G. trifida – H. indica A 6 0 1 0 0 0 0 0 0 0 0 1 7 75
C. preissii B 0 3 0 0 0 0 0 0 0 0 1 0 4 75
M. lanceolata – A.rostellifera C 0 0 11 0 0 0 0 0 0 0 0 0 11 100
A. rostellifera – A.preissii D 0 0 0 10 3 0 0 0 0 0 0 0 13 76.92
A. preissii – O.axillaris E 0 0 0 0 25 0 0 0 0 0 0 0 25 100
A.preissii – A. flavescens F 0 0 0 0 0 64 2 0 0 1 7 1 73 86.49
A.preissii – T.divaricata G 0 0 1 0 0 1 34 0 1 2 2 1 41 80.95
A.preissii – L. gladiatum H 0 0 0 0 0 0 0 6 0 0 1 1 7 75
E. platypus woodland I 0 0 0 0 0 0 0 1 1 0 0 0 2 50
P. phylliraeoides shrubs J 0 0 1 0 0 0 0 0 0 1 0 0 2 50
A. preissii-C.candicans K 0 0 1 0 0 0 0 0 0 0 20 0 21 95.24
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
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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
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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
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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
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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
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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
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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
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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
165
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
166
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.
167
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.
168
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
169
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,
170
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
171
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.
172
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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
205
Appendix 2. TWINSPAN (TWO-WAY ORDERED TABLE)
206
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
207
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
208
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
209
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
210
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
211
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
212
Appendix 4. Wind Rose for Rottnest Island showing wind direction and wind
speed in km/h
213
Appendix 5. Hyperspectral band numbers and imaging mode wavelengths (nm).
Band number
Wavelength (nm)
Band number
Wavelength (nm)
Band number
Wavelength (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.
214
Appendix 6. Example of sparse vegetation with NDVI less than 0.05
215
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).
216
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.
217
ANNEX