A TRANSITION FROM TRADITIONAL MANGROVE REMOTE …60432/Thesis_CDU_60432_Heenkenda_M.pdfA TRANSITION FROM TRADITIONAL MANGROVE REMOTE SENSING TO RECENT ADVANCES: MAPPING AND MONITORING

A TRANSITION FROM TRADITIONAL

MANGROVE REMOTE SENSING TO

RECENT ADVANCES: MAPPING AND

MONITORING

by

Muditha Kumari Heenkenda

(Bachelor of Science (Hons), Master of Geo-information Science)

A thesis submitted in fulfilment of the requirements for the degree of

DOCTOR OF PHILOSOPHY

Research Institute for the Environment and Livelihoods,

Faculty of Engineering, Health, Science and Environment,

Charles Darwin University, Darwin, Australia,

August, 2016

ii

Thesis declaration

I hereby declare that the work herein, now submitted as a thesis for the degree

of Doctor of Philosophy at the Charles Darwin University, is the result of my own

investigations, and all references to ideas and work of other researchers have been

specifically acknowledged.

I hereby certify that the work included in this thesis has not already been

accepted in substance for any degree, and is not being currently submitted in

candidature for any other degree.

I give consent to this copy of my thesis, when deposited in the University

Library, being made available for loan and photocopying online via the University’s

Open Access repository eSpace.

Muditha Kumari Heenkenda

August, 2016

iii

Acknowledgements

First and foremost I would like to thank my supervisors: Dr. Karen Joyce,

Associate Professor Stefan Maier, and Dr. Renee Bartolo for their continuous advice,

time and expertise they contributed to my PhD research. I highly appreciate their

patience, insightful comments and encouragement that contributed substantially to

complete this thesis. Special thanks go to Karen who was always available for advice

and prompt with responses.

My field campaign would not have been successful without the tireless

support of my field assistants: Miguel Tovar Valencia, Olukemi Ronke Alaba, Evi

Warintan Saragih, Silvia Gabrina Tonyes and Dinesh Gunawardena. Sampling inside

mangrove forests is never fun, but these supporters did it consistently without any

complaint. They simply neglected the influence of insects, and a fear of crocodiles.

Lab work could have not been completed without the support and generosity

of Anna Skeleton. I highly appreciate her help throughout the period. I am grateful to

the Director of the Research Institute for the Environment and Livelihoods, Professor

Andrew Campbell for approving funds for field work and publications. Associate

Professor Lindsay Hutley and Dr. Keith McGuinnes; your knowledge and expertise

were greatly appreciated at certain stages of my research. Thanks to Dr. Ian Leiper

and Mrs. Sandra Grant for reviewing parts of my thesis, and being involved in PhD

related discussions.

The Remote Sensing group, Charles Darwin University and my friends

provided advice, assistance and humour. In no particular order they are: Olukemi

iv

Ronke Alaba, Grigorijs Goldbergs, Frederika Rambu Ngana, Miguel Tovar Valencia,

Ian Leiper, David Blondeau-Patissier, and Sofia Oliveira. Thank you so much for

your support. I would like to thank Evi Warintan Saragih and Silvia Gabrina Tonyes

for their encouragement and friendship I received during all ups and downs.

Financial support for this project was provided by Charles Darwin University,

Darwin, Australia. My thanks also go to Ms. Julia Fortune and the Northern Territory

Government for providing data for this study. I am very grateful to the Office of

Research and Innovation staff and the International Office staff for their support

throughout this journey. I highly appreciate the support and generosity of Kristen

Barnes, Katherine Suvvas, and Brooke Williamson who were always there for me.

Thanks to my supervisors, I won the Three Minutes Thesis award at Charles

Darwin University in 2014, and the funds I received from the completion was used to

travel to the University of Calgary, Canada as a visiting student for three months. I

appreciate the generous time, support and encouragement offered by Associate

Professor Greg McDermid and his team at the Department of Geography, University

of Calgary, Canada during the final stages of my thesis write-up.

Last but definitely not least, I would like to thank my dearest husband;

Dinesh for his understanding, patience and support over past four years. It has been

many long hours in front of the computer, days of stress, and days in the field, but

you have made it fly by.

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Table of Contents

Thesis declaration .................................................................................................................... ii

Acknowledgements ................................................................................................................. iii

Table of Contents ..................................................................................................................... v

List of Figures ........................................................................................................................ xii

List of Tables ......................................................................................................................... xx

Abstract ............................................................................................................................... xxiii

Publications contributing to this thesis ................................................................................ xxv

Statement of contribution of each author ........................................................................... xxvii

Chapter 1 – Introduction .......................................................................................................... 1

1.1 Introduction ........................................................................................................................ 2

1.1.1 Remote sensing for mangrove species mapping ........................................................ 7

1.1.2 Quantifying mangrove biophysical variables .......................................................... 14

1.1.2.1 Individual tree crown delineation ................................................................. 16

1.1.2.2 Estimating Above Ground Biomass ............................................................. 18

1.1.2.3 Estimating Leaf Area Index .......................................................................... 21

1.1.2.4 Estimating canopy chlorophyll content ........................................................ 22

1.1.3 Impacts of development pressure on mangroves ..................................................... 23

1.2 Research Problem ............................................................................................................ 24

1.3 Research aim, objectives and research questions ............................................................. 25

1.4 Outline of the thesis ......................................................................................................... 27

1.5 Chapter synopsis .............................................................................................................. 27

1.5.1 Chapter 1 - Introduction ........................................................................................... 27

vi

1.5.2 Chapter 2 - Mangrove species identification: Comparing WorldView-2 with Aerial

Photographs ...................................................................................................................... 28

1.5.3 Chapter 3 - Mangrove tree crown delineation from high resolution imagery .......... 28

1.5.4 Chapter 4 - Quantifying mangrove chlorophyll from high spatial resolution imagery

.......................................................................................................................................... 28

1.5.5 Chapter 5 - Estimating mangrove biophysical variables using WorldView-2 satellite

data .................................................................................................................................... 29

1.5.6 Chapter 6 - Ecological risk assessment using a Relative Risk Model: A case study of

the Darwin Harbour, Darwin, Australia ............................................................................ 29

1.5.7 Chapter 7 - Conclusions and recommendations ....................................................... 30

1.6 References ........................................................................................................................ 31

Chapter 2 - Mangrove Species Identification: Comparing WorldView-2 with Aerial

Photographs ........................................................................................................................... 46

2.0 Abstract ............................................................................................................................ 47

2.1 Introduction ...................................................................................................................... 48

2.2 Data and Methods ............................................................................................................ 52

2.2.1. Study Area .............................................................................................................. 52

2.2.2. Field Survey ............................................................................................................ 53

2.2.3. Remote Sensing Data and Pre-Processing .............................................................. 54

2.2.4. Image Classification ........................................................................................... 57

2.2.4.1. Separating Mangroves and Non-Mangroves ............................................... 59

2.2.4.2. Mangrove Species Classification ................................................................. 62

2.2.5. Accuracy Assessment ............................................................................................. 65

2.3. Results ............................................................................................................................. 65

2.3.1 Field Survey ............................................................................................................. 65

vii

2.3.2Separating Mangroves and Non-Mangroves ............................................................ 66

2.3.3 Mangrove Species Classification ............................................................................. 68

2.3.4 Accuracy Assessment .............................................................................................. 75

2.4 Discussion ........................................................................................................................ 77

2.4.1 Separating Mangroves and Non-Mangroves ........................................................... 77

2.4.2 Comparison of Mangrove Species Classifications................................................... 80

2.4.3 Accuracy Assessment .............................................................................................. 83

2.5 Conclusions and Recommendations ................................................................................ 86

2.6 Acknowledgments ............................................................................................................ 87

2.7 Author Contributions ....................................................................................................... 88

2.8 Conflicts of Interest .......................................................................................................... 88

2.9 References ........................................................................................................................ 88

Chapter 3 - Mangrove tree crown delineation from high resolution imagery ........................ 96

3.0 Abstract ............................................................................................................................ 97

3.1 Introduction ...................................................................................................................... 98

3.2 Data and methods ........................................................................................................... 102

3.2.1 Study area, remote sensing data, and pre-processing ............................................. 102

3.2.2 Extracting mangrove coverage from images ......................................................... 106

3.2.3 Digital Surface Model (DSM) generation .............................................................. 107

3.2.4 Mangrove tree crown delineation .......................................................................... 108

3.2.4.1 WV2 panchromatic, red, and NIR1 bands (PAN-R-NIR1 method) ...... 108

3.2.4.2 WV2 panchromatic, red, and NIR1 bands and DSM

(PAN-R-NIR1-DSM method) ................................................................................ 109

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3.2.4.3 The inverse watershed segmentation (IWS) method with the DSM ...... 110

3.2.5 Accuracy assessment ............................................................................................. 110

3.3 Results ............................................................................................................................ 111

3.3.1 The digital surface model ...................................................................................... 112

3.3.2 Mangrove tree crown delineation .......................................................................... 113

3.3.2.1 WV2 panchromatic, red, and NIR1 bands (PAN-R-NIR1 method) ...... 113

3.3.2.2 WV2 panchromatic, red, and NIR1 bands and DSM (PAN-R-NIR1-

DSM method) ......................................................................................................... 114

3.3.2.3 The inverse watershed segmentation (IWS) method with the DSM ...... 114


3.4. Discussion ..................................................................................................................... 120

3.5 Conclusions and recommendations ................................................................................ 124

3.6 References ...................................................................................................................... 126

Chapter 4 - Quantifying mangrove chlorophyll from high spatial resolution imagery ........ 135

4.0 Abstract .......................................................................................................................... 136

4.1 Introduction .................................................................................................................... 137

4.2 Materials and methods ................................................................................................... 140

4.2.1 Study area and satellite data ................................................................................... 140

4.2.2 Field sampling........................................................................................................ 146

4.2.2.1 Estimating Leaf Area Index ........................................................................ 147

4.2.2.2 Estimating leaf chlorophyll concentration .................................................. 150

4.2.3 Estimating mangrove canopy chlorophyll content from satellite imagery ............ 151

4.2.3.1 Canopy chlorophyll variation with respect to mangrove species ............... 154

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4.3 Results ............................................................................................................................ 155

4.3.1 Field sampling........................................................................................................ 155


4.3.2.1 Canopy chlorophyll variation with respect to mangrove species ............... 160


4.4 Discussion ...................................................................................................................... 162




Appendix 4.A ....................................................................................................................... 170

4.6 References ...................................................................................................................... 171

Chapter 5 - Estimating mangrove biophysical variables using WorldView-2 satellite data:

Rapid Creek, Northern Territory, Australia ......................................................................... 181

5.0 Abstract .......................................................................................................................... 182

5.1 Introduction .................................................................................................................... 183

5.2 Data and methods ........................................................................................................... 186

5.2.1 Study area .............................................................................................................. 186

5.2.2 Field sampling, satellite data and predictor variables ............................................ 187

5. 2.2.1. Remotely-Sensed Data and Predictor Variables ....................................... 188

5.2.3.1 Estimating Leaf Area Index ........................................................................ 193

5.2.3.2 Estimating Above Ground Biomass ........................................................... 196

5.2.3 Predicting LAI and AGB ....................................................................................... 197

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5.3 Results ............................................................................................................................ 199

5.3.1 Predicting Leaf Area Index and Above Ground Biomass ...................................... 202

5.4 Discussion ...................................................................................................................... 207

5.4.1 Predicting LAI ....................................................................................................... 208

5.4.2 Predicating AGB .................................................................................................... 211


5.6 References ...................................................................................................................... 217

Chapter 6 - Regional Ecological Risk assessment using a Relative Risk Model: A case study

of the Darwin Harbor, Darwin, Australia ............................................................................ 224

6.0 Abstract .......................................................................................................................... 225

6. 1 Introduction ................................................................................................................... 226

6.2 Data and method ............................................................................................................ 229

6.2.1 Study area .............................................................................................................. 229

6.2.2 Data input ............................................................................................................... 231

6.2.3 Problem formulation .............................................................................................. 233

6.2.3.1 Ecological Assessment endpoints ............................................................... 235

6.2.4 Risk analysis .......................................................................................................... 238

6.2.4.1 Relative risk calculation ............................................................................. 240

6.2.5 Risk characterisation .............................................................................................. 241

6.2.6 Uncertainty and sensitivity analysis ....................................................................... 242

6.3 Results ............................................................................................................................ 244

6.3.1 Problem formulation .............................................................................................. 244

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6.3.1.1 Habitats and ecological assessment endpoints ........................................... 248

6.3.2 Risk analysis .......................................................................................................... 250

6.3.3 Risk characterisation .............................................................................................. 250


6.4 Discussion ...................................................................................................................... 256



6.6 Acknowledgement ......................................................................................................... 265

6.7 References ...................................................................................................................... 265

Chapter 7 – Conclusions and Recommendations ................................................................. 270

Highlights ............................................................................................................................. 271

Summary .............................................................................................................................. 272

7.1 Research question 1: How can high spatial resolution remote sensing data be used to

discriminate mangroves at species level? ............................................................................ 275

7.2 Research question 2: How can 3D canopy dynamics combined with spectral properties of

mangroves be used to map mangrove biophysical variability? ........................................... 276

7.3 Research question 3: How can spatial information be combined to analyse the

environmental stresses on different mangrove sites? ........................................................... 279

7.4 Limitations and recommendations for future research ................................................... 280

7.4.1 Mapping with high spatial resolution image data .................................................. 281

7.4.2 Relative Risk Modelling ........................................................................................ 283

7.5 Contribution to Knowledge ............................................................................................ 284

Appendices ........................................................................................................................... 287

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List of Figures

Figure 1.1. The boundary of the Darwin Harbour catchment and its mangrove

coverage; Satellite images © ESRI, DigitalGlobe, and GeoEye. .............. 3

Figure 1.2. Urban and rural activities in the Darwin Harbour estuarine and their

effect on the Darwin Harbour (Symbols courtesy of the Integration and

Application Network, University of Maryland Centre for Environmental

Science; http://ian.umces.edu/symbols/). .................................................. 6

Figure 2.1. The study area located in coastal mangrove forest of Rapid Creek in

Darwin, Northern Territory, Australia; WV2 data © Digital Globe.

(Coordinate system: Universal Transverse Mercator Zone 52 L, WGS84).

................................................................................................................. 53

Figure 2.2. Mangrove species mapping using remotely sensed data. Image

segmentation and initial classification was completed in eCognition

Developer 8.7 software. The support vector machine algorithm was

implemented in the ENVI-IDL environment for species classification. . 59

Figure 2.3. The schematic explanation of image objects at different hierarchical

levels. The first level was at pixel level. At the second level: vegetation

and non-vegetation areas were identified. Mangrove areas were extracted

at the final level. ...................................................................................... 62

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Figure 2.4. Spectral profiles of: (a) all features except mangroves extracted from

WV2 image; (b) mangrove species only extracted from WV2 image; (c)

mangrove species extracted from aerial photographs of spatial resolution

0.14 m; and (d) mangrove species extracted from aerial photographs of

spatial resolution 0.5 m. (AM-Avicennia marina, CT-Ceriops tagal,

BE-Bruguiera exaristata, LR-Lumnitzera racemosa, RS-Rhizophora

stylosa, SA-Sonneratiaalba). .................................................................. 66

Figure 2.5. The locations of field samples available for calibration and validation

purposes, and mangrove coverage extracted from the WV2 image. ....... 68

Figure 2.6. (a) Classification of pan-sharpened WV2 image using blue to red-edge

bands; (b) Classification of pan-sharpened WV2 images using red to

NIR1 bands; (c) Classification of WV2 image using blue to red-edge

bands; (d) Classification of WV2 images using red to NIR1 bands; (e)

Classification of aerial photographs with 0.14 m spatial resolution; and

(f) Classification of aerial photographs with 0.5 m spatial resolution. ... 69

Figure 2.7. The comparison of the WorldView2 image and different classification

approaches: (a) Pan-sharpened WV2 image; (b) Classification results of

PS-WV2-VIS; (c) Classification results of PS-WV2-R/NIR1; (d) Aerial

photograph; (e) Classification results of AP0.14M; (f) Classification

results of AP0.5M; (g) WV2 image; (h) Classification results of WV2-

VIS; (i) Classification results of WV2-R/NIR1. ..................................... 71

Figure 2.8. Extents (%) of mangrove species obtained using six different input

datasets: (a) pan-sharpened WV2 image with bands blue to red-edge; (b)

pan-sharpened WV2 image with bands red to NIR; (c) aerial photographs

xiv

with 0.14 m spatial resolution; (d) aerial photographs with 0.5 m spatial

resolution; (e) WV2 image with bands blue to red-edge; and (f) WV2

image with bands red to NIR. ................................................................. 73

Figure 3.1. The study area is located in Rapid Creek in Darwin, Northern Territory,

Australia. The map shows the validation dataset including both field

surveyed and on screen digitised (three dimensionally) trees. Inset shows

the appearance of mangrove tree crowns on the aerial photograph;

Coordinate system: Universal Transverse Mercator Zone 52 L, WGS84;

Aerial photographs © Northern Territory Government (NTG), Australia,

Copyright 2012 NTG. ........................................................................... 103

Figure 3.2. The digital surface model of the Rapid Creek mangrove forest created

from aerial photographs. Inset “A” shows localized circular patterns due

to the image enhancement using focal statistics function in ArcGIS. ... 113

Figure 3.3. (A) Mangrove tree crowns delineated with the PAN-R-NIR1 method

overlaid with the panchromatic image; (B) Overview of the Rapid Creek

mangrove forest; (C) Mangrove tree crowns delineated with the PAN-R-

NIR1-DSM method overlaid with the panchromatic image; (D)

Mangrove tree crowns delineated with the PAN-R-NIR1-DSM method

overlaid with the DSM; (E) Mangrove tree crowns delineated with the

IWS method overlaid with the panchromatic image and tree tops (black

dots); (F) Mangrove tree crowns (black outlines) delineated with the IWS

method overlaid with the DSM and tree tops (black dots). ................... 115

xv

Figure 3.4. (A) Centroid positions obtained from the PAN-R-NIR1 and the

PAN-R-NIR1-DSM methods overlaid with the panchromatic WV2 image

with low pass filtering applied; (B) Overview of the Rapid Creek

mangrove forest; (C) Centroid positions obtained from the IWS method

overlaid with the DSM. ......................................................................... 117

Figure 4.1. The study area is located in the coastal mangrove forest of Rapid Creek in

Darwin, Northern Territory, Australia; (A) WorldView-2 satellite image

and locations of 29 field sampling plots (5 m x 5 m); WorldView-2

images © DigitalGlobe; (B) Australia, the boundary of the Northern

Territory, and the study area; (C) The Rapid Creek mangrove forest

(study area); Coordinate system: Universal Transverse Mercator

Zone 52 L, WGS84. ............................................................................... 141

Figure 4.2. Workflow for mapping chlorophyll content from satellite data and field

samples. This procedure was repeated for the satellite data with 5 m and

10 m spatial resolution. NDVI = Normalized Difference Vegetation

Index from NIR1 and red bands; NDVI2 = Normalized Difference

Vegetation Index from NIR2 and red bands; NDRE = Normalized

Difference Index from NIR1 and rededge bands; NDRE2 = Normalized

Difference Index from NIR2 and rededge bands; GNDVI: Normalized

Difference Index from NIR1 and green bands; CVI: Chlorophyll

Vegetation Index from NIR1, red and green bands. ............................. 145

xvi

Figure 4.3. Linear correlation coefficients against predictor variables with different

spatial resolutions; Green, Yellow, Red, RedEdge, NIR1 and NIR2:

surface reflectance of green, yellow, red, red-edge and near infrared

bands of WorldView-2 image; NDVI = Normalized Difference

Vegetation Index from NIR1 and red bands; NDVI2 = Normalized

Difference Vegetation Index from NIR2 and red bands; NDRE =

Normalized Difference Index from NIR1 and rededge bands; NDRE2 =

Normalized Difference Index from NIR2 and rededge bands; GNDVI:

Normalized Difference Index from NIR1 and green bands; CVI:

Chlorophyll Vegetation Index from NIR1, red and green bands. ......... 156

Figure 4.4. Ranking the importance of predictor variables according to the mean

squared error (%IncMSE); Green, Yellow, Red, RedEdge, NIR1 and

NIR2: surface reflectance of green, yellow, red, red-edge, NIR1 and

NIR2 bands of the WorldView-2 image; NDVI = Normalized Difference

Vegetation Index from NIR1 and red bands; NDVI2 = Normalized

Difference Vegetation Index from NIR2 and red bands; NDRE =

Normalized Difference Index from NIR1 and rededge bands; NDRE2 =

Normalized Difference Index from NIR2 and rededge bands; GNDVI:

Normalized Difference Index from NIR1 and green bands; CVI:

Chlorophyll Vegetation Index from NIR1, red and green bands. ......... 158

Figure 4.5.Canopy chlorophyll variation (g/m2) in the Rapid Creek mangrove forest;

(A) 2 m spatial resolution; (B) 5 m spatial resolution; and (C) 10 m

spatial resolution. .................................................................................. 159

xvii

Figure 4.6. The canopy chlorophyll distribution with mangrove species;

AM- Avicennia marina; BE - Bruguiera exaristata; CT - Ceriops tagal;

LR - Lumnitzera racemosa; RS - Rhizophora stylosa. ......................... 161

Figure 5.1. Rapid Creek coastal mangrove forest, Darwin, Northern Territory,

Australia. The distribution of field sampling plots (5m x 5m) is shown in

the map (the sizes of magenta squares are not to the scale). Aerial

photographs © Northern Territory Government. .................................. 187

Figure 5. 2. Mangrove trees inside the Rapid Creek forest; (A–C) small,

newly-regenerated mangrove trees; (D,E) large, multi-stemmed

mangrove trees; (F,G) densely-clustered areas. .................................... 200

Figure 5.3. Normal score plots of field estimates of (A) Leaf area index; and (B) Log

transformed above ground biomass. ..................................................... 201

Figure 5.4. Cross-validated predictions for the normalized leaf area index (LAI) and

above ground biomass (AGB): (A) predicted versus field measured

normalized LAI with 2-m spatial resolution predictor variables; (B)

predicted versus field measured normalized LAI with 5-m spatial

resolution predictor variables; (C) predicted versus field measured

normalized LogAGB with 2-m spatial resolution predictor variables; (D)

predicted versus field measured normalized LogAGB with 5-m spatial

resolution predictor variables. (RMSEP, root mean square error of

prediction). ............................................................................................ 203

xviii

Figure 5.5. Predicted maps: (A) leaf area index with a 2-m spatial resolution; (B) leaf

area index with a 5-m spatial resolution; (C) above ground biomass with

a 2-m spatial resolution; and (D) above ground biomass with a 5-m

spatial resolution; using the partial least squares regression algorithm.

The distribution of field sampling plots is shown in the maps (the sizes of

the black squares are not to scale). ....................................................... 206

Figure 6.1. The Darwin Harbor catchment and its mangrove distribution .............. 230

Figure 6.2. The workflow diagram for conducting an ecological risk assessment

using the Relative Risk Model following the guidelines published by the

U.S. Environmental Protection Agency ................................................ 233

Figure 6.3. The conceptual diagram showing estuarine activities and their effect on

mangroves, water bodies and then to the Darwin Harbour (Courtesy of

the Integration and Application Network, University of Maryland Centre

for Environmental Science (ian.umces.edu/symbols/)). ....................... 244

Figure 6.4. The conceptual model for the relative risk modelling in the Darwin

Harbor. They were prepared for all risk regions. Exposure and effect

filters were modified with respect to each risk region. ......................... 249

Figure 6.5. The total relative risk to ecological assets in risk regions of the Darwin

Harbor. They were classified as low, medium or high based on the

Jenks’s optimization (Jenks and Caspall 1971). ................................... 252

xix

Figure 6.6. Total risk scores for the ecological endpoints; (A) Maintenance of

mangrove health; (B) Maintenance of water quality ............................. 253

Figure 6.7. The frequency of all possible outcomes of total risk of ecological assets

in the risk regions: (a) Ludmilla Creek, Mitchell Creek, Mitcket Creek,

Palmerston South, and Pioneer Creek; (b) Blackmore River, Elizabeth

River, Howard River, Hudson Creek, and Kings Creek; (c) Rapid Creek,

Reichardt Creek, Sadgroves Creek, Sandy Creek, West Arm, and Woods

Inlet; (d) Bleesers Creek, Buffalo Creek, Charles Point, and Creek A. 254

Figure 6.8. The rank correlations for sub-regions having the highest total risk scores:

(A) Blackmore River; (B) Elizabeth River; (C) Myrmidon Creek; (D)

Bleesers Creek. ...................................................................................... 256

xx

List of Tables

Table 1.1. The most common remote sensing image processing techniques used for

mapping mangroves ................................................................................ 10

Table 1.2. Four main methods for delineating individual tree crowns ...................... 17

Table 1.3. Four main methods and their advantages and disadvantages for estimating

Above Ground Biomass (AGB) of forests (Komiyama et al. 2008;

Metcalfe et al. 2011; Roy and Ravan 1996). ........................................... 18

Table 2.1. Spectral band information of WV2 image and aerial photographs obtained

from UltraCamD camera [29, 30]. .......................................................... 55

Table 2.2. Number of samples (4 m2 each) used for training and number of sample

points generated for validation for each mangrove species. ................... 68

Table 2.3. Overall accuracy and Kappa statistics obtained for each classification. ... 75

Table 2.4. Comparison of producer’s accuracy and user’s accuracy for each species,

obtained from different remote sensing data sources. (AM-Avicennia

marina, CT-Ceriops tagal, BE-Bruguiera exaristata, LR-Lumnitzera

racemosa, RS-Rhizophora stylosa). ........................................................ 76

Table 3.1. Spectral resolution of WV2 image and aerial photographs (DigitalGlobe,

2011; Northern Territory Government, 2010)....................................... 105

xxi

Table 3.2.The number objects extracted as tree tops using three different methods.

............................................................................................................... 116

Table 3.3. A comparison of accuracies of extracted tree crowns: the possible range of

both OverSegmentation and UnderSegmentation values is [0,1] where

zero defines the perfect segmentation. The values closer to zero for

“closeness index” illustrate the closeness of mangrove tree crowns in two

dimensional space which is defined by OverSegmentation and

UnderSegmentation to validation crowns (Clinton et al., 2010). .......... 119

Table 4.1. Vegetation indices used to establish the relationship between mangrove

field samples and remotely sensed data; green, red, red edge, NIR1 and

NIR2 surface reflectance of WorldView-2 satellite data for

corresponding wavelength regions. ....................................................... 143

Table 4.2. Root mean squared errors (RMSE), correlation coefficients, and

coefficients of determinations calculated with respect to predicted

chlorophyll values and independent validation samples ....................... 162

Table 5. 1 Spectral band information of WorldView-2 images ............................... 189

Table 5. 2. Predictor variables generated from the WorldView-2 multispectral image

for estimating above ground biomass and leaf area index. NIR1, NIR2,

red edge, red and green: surface reflectance of Near Infrared 1, Near

Infrared 2, red-edge, red and green wavelength regions, respectively. . 191

xxii

Table 5. 3. Log10-transformed allometric relationships used for different mangrove

species. The equations are in the form: 𝑙𝑙𝑙𝑙𝑙𝑙10𝐵𝐵𝐵𝐵𝑙𝑙𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵 = 𝐵𝐵0 + 𝐵𝐵1 ∗

𝑙𝑙𝑙𝑙𝑙𝑙10𝐷𝐷𝐵𝐵𝐷𝐷; where DBH is the diameter at breast height; B0 and B1 are

regression coefficients. These equations are specific to the Northern

Australia [37,38], North-eastern Queensland, Australia [39] and Sri

Lanka [19] (biomass in kg and DBH in cm). ........................................ 197

.Table 5. 4. Root mean square errors (RMSE’s) and correlation coefficients (r) for

above ground biomass and leaf area index maps with respect to

validation samples ................................................................................. 207

xxiii

Abstract

Mangroves are dense, spatially heterogeneous forests, and provide a wide

range of goods and services. They protect the shore line from powerful waves, and

provide habitat and nursery grounds for variety of animals. Mangroves are one of the

most threatened habitats in the world, and are disappearing rapidly due to urban,

industrial and commercial activities. These threats lead to an increasing demand for

quantitative assessment for mangrove monitoring. Historically, mapping has been

completed with aerial photos over small areas. Recent advances in remote sensing

technology for data acquisition and processing have opened up a wealth of new

options. The aim of this work was to push the boundary of data processing to extract

metrics relevant to mangrove health mapping, monitoring, and management. This

included mangrove species classification, delineating individual tree crowns, and

continuous variable mapping of plant biomarkers. The first ever study classified

mangrove species at Rapid Creek mangrove forest, Australia was achieved 89%

overall accuracy compared to in-situ observations with a WorldView-2 satellite

image and a support vector machine algorithm. The first successful method was

developed to delineate individual tree crowns using object based image analysis

techniques with 92% overall accuracy. One of the innovative prospects of this study

was development of methods to continuous variable mapping of plant biomarkers:

canopy chlorophyll, above ground biomass, and leaf area index using WorldView-2

images, field measurements, random forest and partial least squares regression

algorithms. A relative risk model was developed to assess ecological risk associated

with the Darwin Harbour mangroves. This model identified localized changes:

cleared, stressed and dead mangroves due to natural or anthropogenic influences. In

summary, this research presents unique applications of advanced remote sensing

xxiv

methods for analysing up-to-date spatial information on the current status of Rapid

Creek mangroves, and they are a baseline for mangrove monitoring and

management.

xxv

Publications contributing to this thesis

Peer reviewed papers;

Chapter 2

Heenkenda, M. K., Joyce, K. E., Maier, S. W., & Bartolo, R., 2014. Mangrove

Species Identification: Comparing WorldView-2 with Aerial Photographs, Remote

Sensing, 6 (7): 6064-6088.

Chapter 3

Heenkenda M. K., Joyce K. E., & Maier S. W., 2015a, Mangrove tree crown

delineation from high resolution imagery, Photogrammetric Engineering and Remote

Sensing, Vol. 81, No. 6, pages: 21-30

Chapter 4

Heenkenda, M. K., Joyce, K. E., Maier, S. W. & de Bruin, S. 2015b, Quantifying

mangrove chlorophyll from high spatial resolution imagery, ISPRS Journal of

Photogrammetry and Remote Sensing, vol. 108, 234-244

http://dx.doi.org/10.1016/j.isprsjprs.2015.08.003.

Chapter 5

Heenkenda M. K., Maier S.W & Joyce K. E., 2016, Estimating mangrove

biophysical variables using WorldView-2 satellite data, Journal of Imaging, 2, 24;

doi:10.3390/jimaging2030024.

xxvi

Chapter 6

Heenkenda, M. K. & Bartolo, R. 2015, Regional Ecological Risk Assessment Using

a Relative Risk Model: A Case Study of the Darwin Harbor, Darwin, Australia,

Human and Ecological Risk Assessment: An International Journal, vol. 22, 401 - 423

doi: 10.1080/10807039.2015.1078225.

Formal permission from the publishers was granted to include this material as

attached in Appendices 1 to 4.

Conference Proceedings;

Heenkenda, M. K., Joyce, K. E. & Maier, S. W. 2014, Comparing digital object

based approaches for mangrove tree crown delineation using WorldView-2 satellite

imagery, South-Eastern European Journal of Earth Observation and Geomatics, vol.

3, 169-172.

xxvii

Statement of contribution of each author

The following table detailing relative contribution by me and my co-authors

refers to the five manuscripts taken directly from this thesis. Four manuscripts were

published in peer reviewed international journals prior to thesis submission. One

manuscript has been submitted to a peer reviewed international journal and accepted

subject to minor revisions.

Manuscript 1: Mangrove Species Identification: Comparing WorldView-2 with

Aerial Photographs - Chapter 2

Contributing author Contribution Tasks Performed

M.K. Heenkenda 90% Study design, field data collection, image and

field data analysis, and manuscript writing

K.E. Joyce

10%

Manuscript review and editing

S.W. Maier Manuscript review and editing

R. Bartolo Manuscript review and editing

Manuscript 2: Mangrove tree crown delineation from high resolution imagery -

Chapter 3

Received 2016 ESRI Award for the Second Best Scientific Paper in Geographic

Information Systems from the American Society of Photogrammetry and Remote

Sensing: The imaging and geospatial information society (ASPRS).

xxviii




K.E. Joyce 10%


S.W. Maier Manuscript review and editing

Manuscript 3: Quantifying mangrove chlorophyll from high spatial resolution

imagery - Chapter 4




K.E. Joyce

15%


S.W. Maier Field survey design, manuscript review and

editing

S. de Bruin Advice on field data processing, manuscript

review and editing

Manuscript 4: Estimating mangrove biophysical variables using WorldView-2

satellite data - Chapter 5




K.E. Joyce

15%


S.W. Maier Field survey design, advice on field data

processing, manuscript review, and editing

xxix

Manuscript 5: Regional Ecological Risk Assessment Using a Relative Risk Model:

A Case Study of the Darwin Harbor, Darwin, Australia - Chapter 6


M.K. Heenkenda 90% Relative risk model designing, data collection,

meeting stakeholders, data processing, and

manuscript writing

R. Bartolo 10% Relative risk model design, manuscript review

and editing

1

Chapter 1 – Introduction

2

1.1 Introduction

Mangroves are coastal, salt tolerant woody plants found in sheltered estuaries,

along river banks, deltas and shallow-water lagoons in 124 tropical and subtropical

countries (Food and Agriculture Organisation 2007). Mangroves grow best where

there is low wave energy and sediment deposition of fine particles (Alongi 2002).

There are some exceptional situations where they grow on rocky shores, rootling in

silt-filled depressions (Food and Agriculture Organisation 2007). However, in both

situations, they represent an interface between land and ocean communities. Hence,

mangrove ecosystems are significant for sustaining biodiversity, and providing

various benefits to human activities.

Mangrove ecosystems are significant for a wide range of marine and

terrestrial organisms. Mangroves provide habitats, spawning grounds, nurseries and

nutrients for animals ranging from reptiles to mammals and birds (McKee 1996).

They promote coastal stabilisation, and provide a natural barrier against storms,

cyclones, and tsunamis (Bouillon et al. 2009; Stephen Crooks et al. 2011). Further,

they are known to be highly productive and biochemically active ecosystems

(Suratman 2008). They contribute to the carbon budget as a large store of blue

carbon (Bouillon et al. 2009). Unfortunately, urbanisation, population growth, water

diversion, agriculture, aquaculture, and salt pond construction has led to high rates of

mangrove destruction on a global scale.

Given the range of destructive practices around the world, it has become

critical to create baseline datasets for mangroves. The most recent global data

compilation suggests a current areal mangrove extent of about 152,000 km2

3

(Bouillon et al. 2009; Food and Agriculture Organisation 2007), with Indonesia and

Australia together covering about 30% of this extent. Approximately 35% of the

Australian extent lies along the Northern Territory (NT) coastline.

Figure 1.1. The boundary of the Darwin Harbour catchment and its mangrove

coverage; Satellite images © ESRI, DigitalGlobe, and GeoEye.

4

The largest single expanse of mangroves in the NT is found on the Darwin

Harbour shoreline (Harrison et al. 2009). Although the Darwin harbour mangroves

represent 2% of the national coverage, they comprise 35% of this ecosystem within

the NT (Figure 1.1). On a global scale, Darwin Harbour mangroves contribute

approximately 0.1% of the world’s remaining mangrove extent (Brocklehurst and

Edmeades 1994-1995).

While the Harbour and its catchment encompass a range of distinctive and

special environments that require protection the region is subject to development

pressures and conflicts of use. For example, to accommodate the projected

population growth over next 20 years in the NT, there are many proposed and

ongoing commercial, industrial and residential developments. Recently, the

Australian Government released a white paper on developing the north’s water

resources, infrastructure and workforce for a strong and prosperous economy

(Australian Government 2015). Hence, the growing pressure of rural and urban

development will directly or indirectly impact on Darwin Harbor’s vegetation

species. The growing pressure of development may also increase flooding, velocity

and volume of soil runoff with nutrients, chemicals, sedimentation and pollution. A

key challenge to developing northern Australia is to balance environmental values

with the potential economic and social benefits. In order to do this effectively,

understanding those environmental values and their ecology is critical.

A substantial amount of research has been done in the mangrove forests of

Darwin Harbour to understand the ecology of these ecosystems, and providing

information for effective management. Mangrove monitoring programs have been

conducted since 1997 to obtain baseline data on mangrove productivity and ecology

5

(Water Monitoring Branch, 2005). Further, numerous studies have been undertaken

aiming to understand the ecosystem’s economic and ecological values to set the

balance between mangrove conservation and development (Burford et al. 2008;

Lewis 2002; Metcalfe et al. 2011; Moritz-Zimmermann 2002; NRETAS 2010a,

2010b). However, understanding the enormous complexity of interacting processes

that maintain biodiversity and productivity of mangroves remains a major challenge,

and significant gaps still exist in understanding the likely effects of human

disturbances.

There is no clear approach for integrating the risks due to number of stressors

or likely effects of human disturbances with a numerous possible interactions. Figure

1.2 illustrates multiple stressors due to urban and rural development and their

influence on the coastal environment. Land clearing for urban and rural activities,

agriculture and horticulture, aquaculture, and point sources discharges are major

stressors for estuarine health. For example, the Central Business District of Darwin

was developed after clearing 400 ha of mangroves in 2002 (1.5% of the total

mangrove area) (Northern Territory Government 2002b). The exposure to such

stressors creates a biological degradation of the receiving habitats. At the moment,

impacts from the development pressure of past activities are localised and not well

documented. There is no a proposed method to assess impacts from future

development pressure as well.

6

Figure 1.2. Urban and rural activities in the Darwin Harbour estuarine and their

effect on the Darwin Harbour (Symbols courtesy of the Integration and Application

Network, University of Maryland Centre for Environmental Science;

http://ian.umces.edu/symbols/).

Planners and resource managers have a critical need to create accurate

inventories of mangrove communities and their biophysical characteristics especially

their areal extent, species distribution, and biophysical variability. A detailed survey

of mangroves of Darwin Harbour was completed in 1994 - 1995 based on field

measurements and data obtained from aerial photo interpretations. Yet these data are

limited to a scale of 1: 25000 map scale, and 0.25 hectares spatial resolution

(Brocklehurst and Edmeades 1994-1995). To understand the current situation,

regular map updates are essential. Further, there are no detailed studies explaining

7

biophysical variability (for instance variation of above ground biomass or leaf area

index) of mangroves in this area.

Field survey is essential to identify and locate new mangrove communities

present in a study area and to update existing mangrove maps. The survey should be

planned to represent ranges of mangrove communities, their characteristics and

changes over time within the study area to capture the species and biophysical

variability. Therefore, the process involves a significant time and financial

investment. Mangrove forests in the region are often difficult to access due to the

danger of salt water crocodiles. Further, due to the densely clustered nature of

mangroves and stilt roots, moving within the forest is challenging. Remotely sensed

data provides an alternative approach.

1.1.1 Remote sensing for mangrove species mapping

Aerial photographs and high spatial resolution satellite images are the most

common primary remotely sensed data source for mangrove mapping. Satellite data

with low or medium spatial resolution and laser scanning data are other remote

sensing data sources that can be used for mangrove ecosystem assessment. In the

scientific literature, there are a significant number of studies that involve mangrove

forests, remote sensing data and various image processing algorithms. Table 1.1

shows the most common image processing techniques that have been used for

detecting and delineating mangroves. In summary, these methods are not able to

fully discriminate mangroves species. The majority of mangrove remote sensing

studies use high spatial resolution imagery, mostly with a 5 to 100 m pixel size.

Further, it was clear that the type of data and image processing algorithms used have

8

significant impacts on the accuracy of the resultant mangrove maps. Hence,

determining a suitable data source and robust processing method for mapping

mangroves at species level is in demand.

Image processing logic

and classifiers Mangrove studies Advantages Disadvantages

Manual classifiers

Visual image

interpretation, stereo

digitizing, or manual

boundary tracing

Brocklehurst and Edmeades

(1994-1995); Blasco et al.

(2001); Dahdouh-Guebas et

al. (2002); Sulong et al.

(2002); Verheyden et al.

(2002); Dahdouh-Guebas et

al. (2006); Habshi et al.

(2007)

These methods provide overall

accuracy greater than 90% in

separating mangrove coverages

only.

Methods do not require powerful

computers for image processing.

These methods are based on

image interpretation; hence, the

results are purely dependent on

the analyst, spatial and spectral

resolutions of images.

Methods are time consuming and

costly owing to manual

operations.

Methods unable to differentiate

mangroves at species level with

higher accuracy.

9



Classification logics

Unsupervised

classification,

Supervised

classification,

hybrid-supervised and

unsupervised

classification

Gao (1998); Green et al.

(1998); Lucas et al. (2002);

Nandy and Kushwasha

(2010); Held et al. (2003);

Wang et al. (2004); Ji et al.

(2008); Satyanarayana et al.

(2011); Giri et al. (2011);

Giri et al. (2008); Hossain et

al. (2009)

Depending on the spatial resolution

of the input data, these methods

provide higher accuracies for

mangrove extent mapping as they

allow for computational

classification rather than

interpretation.

Some studies classified individual

species up to about 70% of accuracy

level. Among them, aerial

photographs provided the best

results for mapping individual

species. However, aerial

photographs can be more cost

effective over small areas than

satellite images.

These methods still have a level

of subjectivity depending on the

algorithm used – i.e. can’t

routinely apply and get the same

result each time.

Most common computational

classification errors with these

methods are the omission of

fringe mangroves that are less

than the pixel size resulting in

mixed pixels, and confusion

between mangroves and other

vegetation.

10

Table 1.1. The most common remote sensing image processing techniques used for mapping mangroves



Advanced classifiers

Spectral

transformation,

principle component

analysis (PCA),

vegetation indices,

decision tree algorithm

Green et al. (1998); Held et

al. (2003); Heumann

(2011); Wang et al. (2004);

Vaiphasa et al. (2005);

Vaiphasa et al. (2006);

Wang and Sousa (2009);

Satyanarayana et al. (2011);

Liu et al. (2008)

These methods provided higher

accuracy for separating mangroves

and non-mangroves. For instance,

Liu et al. (2008) achieved greater

than 80% of accuracy for mapping

mangrove coverage. Decision tree

algorithm allows incorporating

digital elevation models and

proximity data for the computational

classification. In a comparison of

classification techniques and data

types, Green et al. (1998) found that

the classification of PCA data

provided significantly better

accuracy than raw images.

These methods also have a level

of subjectivity depending on the

algorithms and data used.

11

12

When applying pixel based classification algorithms there remain several

limitations. The confusion between mangroves, non-mangrove vegetation, urban

areas and even mudflats affects classification accuracies (Gao 1998; Green et al.

1998; Held et al. 2003; Ji et al. 2008; Nandy and Kushwasha 2010). Pixel based

approaches resulted in some confusion between saltmarshes and coastal vegetation

(Kamal and Phinn 2011). Fringe mangrove canopies are commonly less than the

pixel size, resulting in mixed pixels and a consequent over or underestimate of true

mangrove coverage depending on the classification decision (Heumann 2011a).

Although assessing mangrove coverage or mangrove density is possible with high

spatial resolution multispectral imagery (Green et al. 1998; Habshi et al. 2007;

Mitchell et al. 2007), detecting individual mangrove species and estimating canopy

structure remains a challenge.

In recent years new techniques have been developed or adapted to improve

the accuracy of detecting individual species. Some studies integrated a relationship

between mangroves and surrounding environmental gradient. Vaiphasa et al. (2006)

incorporated soil pH data and a Bayesian probabilistic model, and improved the

species mapping accuracy by 12%. However, incorporating soil pH data into the

mapping process was not able to resolve confusion between some mangrove species.

Later, attention was given to test multiple data sources within a rule-based

classification scheme or object based image analysis.

Object based image analysis algorithms typically have a decision-tree that

refines a separation of classes at different levels. Multiresolution segmentation and

class-specific rules having a relationship between image objects at different

13

hierarchical levels improved overall mangrove cover classification accuracy in many

studies (Conchedda et al. 2008; Heumann 2011; Quoc et al. 2013). Hence, an object

based image analysis method was identified as a straightforward, repeatable and

reliable method for classifying mangrove coverage.

Kamal and Phinn (2011) found that multiresolution segmentation

outperformed pixel based approaches (spectral angle mapper and linear spectral

unmixing) in mangrove species mapping. The study obtained more than 75%

classification accuracy for mangrove extent and certain species. A species

classification of SPOT5 satellite data using a decision tree approach and local

knowledge from the region of interest showed accuracies higher than 75% for certain

classes at an object level (Quoc et al. 2013). However, in this study, the mangrove

forest cover fraction per object is affected by image segmentation, and is influenced

by physically visible natural boundaries such as shrimp pond dikes. As tested by

different studies, multiresolution segmentation is a powerful technique; however,

selecting an appropriate scale parameter is crucial for creating meaningful objects.

Similar challenges are faced in other terrestrial environments such as

deciduous forests, tropical rainforests, savannah and grasslands. As a result, more

contextual and probabilistic methods have recently been introduced to terrestrial

vegetation species mapping. For example, sub-pixel classification methods are often

preferred, for example, Markov-random-field based, Artificial Neural Network,

Support Vector Machine, and Random Forests algorithms (Ardila et al. 2011;

Mitchard et al. 2012; Mountrakis et al. 2011; Muad and Foody 2010; Pajares et al.

2010). Knowledge gained in these environments may be applicable for mangroves.

14

1.1.2 Quantifying mangrove biophysical variables

An understanding of mangrove biophysical characteristics is useful for

mangrove management planning, conservation, and change monitoring (Wannasiri et

al. 2013). Hence, estimating increasing or decreasing nutrient levels of mangroves,

and biophysical characteristics would be a significant step towards assessing

variations in mangrove plant status or health. Despite the large number of studies

addressing various aspects of organic matter cycling in mangrove forests in Darwin

Harbour (Alongi 1994; Burford et al. 2008; Larsen and Rudemo 1998; Lu 2006;

McHugh 2004; Metcalfe 2007; Metcalfe et al. 2011; Woodroffe et al. 1988), there is

still a demand for estimating biophysical characteristic variations.

The most important biophysical characteristics that deserve attention are Leaf

Area Index (LAI), Above Ground Biomass (AGB), and canopy nutrient levels. This

is because these characteristics reflect the status of physiological processes of plants

such as photosynthesis, evapotranspiration, carbon cycling, net primary production,

habitat condition and plant health (Flores-de-Santiago et al., 2013; Heumann 2011a).

Tree height, diameter at breast height and crown diameter are considered as the key

physical characteristics for estimating mangrove AGB and LAI (Wannasiri et al.

2013). Nitrogen stress restricts plant productivity and it is highly correlated with

chlorophyll content. Chlorophyll content of any tree reflects the nutritional stresses,

photosynthetic capacity and developmental stages of trees (Felella and Penuelas

1994).

Mangrove forests are generally challenging environments for mapping as a

consequence of architectural heterogeneity such as clustered trees, clumping leaves,

15

and underlying soil, water, and even understory vegetation. Producing precise and

up-to-date spatial information on the current status of mangrove forests is almost

impossible by using traditional field surveys because mangrove swamps are

extremely difficult to access. Estimates of LAI, AGB and canopy chlorophyll are

difficult to obtain in forests owing to the labour required to destructively sample

trees, and the time and money that need to be invested.

As an alternative method for estimating biophysical variations and canopy

nutrients, remotely sensed images are an effective tool. Specifically, these images

record the amount of reflected and absorbed light from different surfaces. As plant

pigments, structure, and water content are all known to affect light interactions in

different wavelengths, it is possible to estimate these properties from the recorded

light signal in an image. Analysis of the images then permits an evaluation of

changes in the environment over time, and preparing decision-making programs for

different time frames. Owing to the availability and low cost of imagery compared to

field measurements, remotely sensed image based measurements are increasingly

used in environmental modelling and ecology for delineating individual tree crowns,

as well as estimating AGB and LAI. The increasing usage of these measurements is

supported by new advances in algorithm development, their high accuracy, reliability

and automation.

16

1.1.2.1 Individual tree crown delineation

Individual tree attributes can be used for forest inventories, assessing forest

regeneration and vegetation changes, and quantifying above ground biomass (AGB)

(Chen et al. 2006). In addition, crown foliage area is related to carbon gain and

water loss (Analuddin et al. 2009), and thus productivity. Therefore, being able to

delineate individual crowns from a crowded canopy is significant for long term

monitoring practises. A review of individual tree or group of trees identification

using remote sensing images identifies four main approaches (Table 1.2).

As described above (Table 1.2), all methods are influenced by image

intensities, sensor characteristics, imaging time, angle, elevation, and homogeneity of

vegetation with reasonable height variations between neighbouring trees. There is no

single method that perfoms equally well for different forest types. They cannot

directly be applied to mangroves due to mangrove forests’ structural complexity or

heterogeneity. Therefore, selecting suitable source data, and appropriately

parameterising an algorithm for mangrove tree crown delineation remain a challenge.

17

Table 1.2. Four main methods for delineating individual tree crowns

Local maxima detection Valley following method

Assumes that tree apexes generate

intensity peaks in images. Assuming a

local maximum is located near or at the

centre of the crown, a local maximum

filter with specific kernel is applied to

the image to detect tree locations

(Pouliot et al. 2002). This is the most

intensively investigated image analysis

approach. However, this method is most

successful with coniferous trees rather

than other forest types (Ardila et al.

2011; Leckie et al. 2005; Lopez 2012).

False detections are observed with

spatial heterogeneous areas or images

with large spectral variations.

Considers the image intensities as

elevation of topographic surface, and

creates a hill pattern aiming to extract

the highest part of the ‘hill’ as a tree

top. Boundaries of tree crowns show

dark values, shady rings or valleys

depending on sun illumination angle

(Gougeon 1995; Leckie et al. 2005).

This method provides best results

when identifying individual trees in

homogeneous landscapes with

reasonable height variations between

neighbouring trees.

Tree modelling and image templates Segmentation method or region

grouping

Constructs geometric attributes of the

trees, and matches to images

considering different viewing

conditions and illuminations (Leckie et

al. 2005). Crown diameter, tree height

and convexity are investigated and often

modelled by means of a generalised

ellipsoid.

Involves identification of groups of

similar neighbouring pixels which

corresponds to objects or part of

objects (Leckie et al. 2005). The

construction of regions of the image is

based on homogeneity of images

characteristics; image brightness,

texture, shape, pixel size etc. This

method is most successful when

identifying individual trees in

homogeneous landscapes (Erikson

2004).

18

1.1.2.2 Estimating Above Ground Biomass

There are four main field survey methods for estimating forest AGB

(Table 1.3) (Bouillon et al. 2008; Komiyama et al. 2008; Metcalfe et al. 2011). They

are labour intensive, and require site and species specific field measurements. To

have a good representation of forest coverage, extensive field sampling is essential

though field sampling is difficult due to muddy soil conditions and heavy weight of

the wood (Komiyama et al. 2008). Because of these methodological problems and

associated uncertainties when extending these conventional methods over a large

area, net primary productivity of mangroves is known to be underestimated (Bouillon

et al. 2008).

Table 1.3. Four main methods and their advantages and disadvantages for estimating

Above Ground Biomass (AGB) of forests (Komiyama et al. 2008; Metcalfe et al.

2011; Roy and Ravan 1996).

Method Description Advantages Disadvantages

Harvest

method

Harvests all trees in a

unit area, and

measure weight of all

woody parts.

Accurate Destructive, unable to

replicate as all trees

must be harvested,

unable to produce in

mature forests due to

heavy woody weight,

time, cost, and labour

intensive

19

Method Description Advantages Disadvantages

Mean-tree

Algorithm

Considers harvest of

average size trees

either for the stand or

within given area

(mean tree

dimensions) and

measure weight of all

woody parts.

Accurate, suitable

for forest

plantations

Only suitable for

forests with

homogeneous tree

sizes,


intensive

Allometry Involves harvesting

individual trees over

a wide range in size

and establishing a

relationship between

measureable tree

dimensions (height of

trees and diameter at

breast height) as

biomass in forest

stands.

Accurate, well

formulated

allometric relations,

once relations are

established they are

repeatable, and the

method becomes

non-destructive

Allometric relations are

site and species

specific,


intensive for generating

relations

Litter fall

estimation

Involves stratified

sampling of litter fall

using litter traps.

Cost effective,

simple calculations

Challenges of sampling

and extrapolating litter

fall rates due to within-

stand heterogeneity,

litter traps must be

elevated above the

highest tide level to

avoid tidal flushing of

litter

20

Remotely sensed data has started addressing the major challenges identified

with these field survey methods by avoiding destructive sampling and reducing time

and cost for field sampling (Table 1.3). Several studies have found strong correlation

between biomass and spectral reflectance values at different wavelength regions

within remotely sensed data (Anaya et al. 2009; Roy and Ravan 1996). Later, the

attention was given to spectral vegetation indices generating from remotely sensed

data rather than spectral reflectance values for estimating AGB (Adam et al. 2010;

Mutanga et al. 2012).

Spectral vegetation indices are mathematical combinations of different

spectral bands mostly in the visible and near infrared regions of the electromagnetic

spectrum. The main purpose of these numerical transformations is to enhance the

information contained in spectral reflectance data for detecting within-field spatial

variability, and to minimize soil, atmospheric and sensor geometry effects (Viña et

al. 2011). These indices provide a simple and convenient approach to extract

information from remotely sensed data. For example, one of the most commonly

used vegetation indices is the Normalised Vegetation Difference Index (NDVI)

derived from red and near infrared wavelength regions. Normalised vegetation

indices derived from different combinations of spectral bands, ratio indices, modified

or soil adjusted vegetation indices are also used for biomass estimation of different

forest types (Huete 1988; Mutanga et al. 2012; Roy and Ravan 1996; Serrano et al.

2000). Further, spectral vegetation indices based approaches are capable of

estimating AGB by reducing major problems associated with field sampling.

21

1.1.2.3 Estimating Leaf Area Index

The ratio of leaf surface area to unit ground surface (Leaf Area Index or LAI)

describes the potential surface area available for leaf gas exchange (Breda 2003).

LAI is an important factor controlling many biological and physical processes of

vegetation such as photosynthesis and respiration. Two main approaches have been

developed for estimating LAI remotely: (1) inversion of canopy radiative transfer

models; and (2) empirical relationships between LAI and spectral vegetation indices

(Viña et al. 2011). Vegetation indices have been more widely used due to their ease

of computation in contrast to the use of radiative transfer models.

Ramsey and Jensen (1996), Green et al. (1997) and Kovacs et al. (2004)

explained the relationship between NDVI and LAI for mangroves in southwest

Florida, British West Indies and Mexico, respectively. Ramsey and Jensen (1996)

described complexity and difficulties in assessing the spectral and structural aspects

of mangroves by analysing canopy reflectance spectra and vegetation indices. The

results were limited by the number and types of sites sampled, and the techniques

used. Green et al. (1997) identified remote sensing as a powerful tool for estimating

LAI achieving 88% of overall accuracy. However, their study area was covered by a

mixture of sparse mangroves and open canopies. These areas received reflectance

values from understorey vegetation as well, resulting in low LAI values. LAI of

degraded mangroves were successfully estimated by Kovacs et al. (2004). Although

the study suggested using its methods for both densely and sparsely distributed

mangroves, it also emphasized using very precise and accurate positioning methods

for sampling and ground controlling. All three studies identified correlating field

observations with remotely sensed information, and determining the regression

22

algorithm as limiting factors to the final results. However, they can be used as guides

when attempting to map mangroves using remote sensing. Non-destructive, remote

determination of LAI replaces time consuming and expensive field measurements.

Further, a large number of spectral vegetation indices have been developed for

estimating LAI. In most cases, these indices have been tested for specific species and

geographic locations only. It is not clear whether they can be applied for different

species with varying structural characteristics and across various geographic

locations.

1.1.2.4 Estimating canopy chlorophyll content

Canopy chlorophyll content is another important biomarker of mangrove

plant status. Leaf pigments, mainly chlorophyll a and chlorophyll b are significant

components responsible for photosynthesis, physiology, and other biological

functions (Zhang et al. 2012). Canopy chlorophyll is also an indicator of crop

nitrogen requirements (Hansen and Schjoerring 2003). Since measuring chlorophyll

in the field is time and cost intensive, remote sensing of chlorophyll content is a

valuable tool (Felella and Penuelas 1994).

Empirical modelling of canopy chlorophyll is based on the statistical

regression between the canopy chlorophyll content and the spectral reflectance

within remotely sensed images. Detecting canopy chlorophyll by remote sensing

techniques has been tested for many terrestrial vegetation types, however, there are a

limited number of studies available for mangrove ecosystems. One study

investigated the spectral responses of different mangrove species under various

conditions such as healthy and degraded (Zhang et al. 2012). They observed

23

significant differences in chlorophyll a and chlorophyll b in different mangrove

species at different wavelength regions. These established models are site, species

and time specific, and cannot readily be applied to other types of vegetation or under

different conditions.

1.1.3 Impacts of development pressure on mangroves

Currently the Darwin Harbour catchment is subject to multiple development

pressures and use conflicts, indicating a major challenge for managing the local

environment (The Darwin Harbour Advisory Committee 2003). The north eastern

part of the Darwin Harbour catchment is already highly developed and native

vegetation and tidal flats have been cleared and drained (Northern Territory

Government 2010). By 2005, approximately 18% of the Harbour catchment had been

cleared of native vegetation for mainly urban and rural residential living,

horticulture, agriculture, infrastructure, defence facilities and manufacturing (Skinner

et al. 2009). A major liquefied natural and petroleum gas project is currently running

in this area. Subsea pipelines that transport liquid hydrocarbons run between onshore

and the Darwin Harbour catchment. For the project duration (2012 to 2017), the

proposed land clearing extent is about 300 ha including 11 ha of mangroves (Carter

2013; INPEX 2013). Further, up to 42 ha of mangroves could experience reduced

health and growth (INPEX 2013). Therefore, a regional plan of management has

been developed for Darwin Harbour and its catchment area, and priority actions are

being implemented to protect its biodiversity.

The first management goal identified by the Darwin Harbour regional plan of

management is to maintain a healthy environment. Hence, the plan encourages

24

ecologically sustainable development through planning and management of future

developments (The Darwin Harbour Advisory Committee 2003). Although, the plan

offers an adaptive and coordinated approach managing and planning within the

harbour and catchment, one of the major challenges is to combine knowledge from

different sources into a consistent framework in order to make practical and sound

management decisions.

To ensure long term sustainability of the Darwin Harbour ecosystem, a

statistically sound, and transparent ecological risk assessment model is essential.

Such a model should enable decision makers to evaluate the likelihood that adverse

ecological effects may occur as a result of exposure to certain stressors in the

catchment. An efficient spatial model will strike the balance between the conflicting

uses and values, and addresses the complexities of planning in the region.

1.2 Research Problem

Maintaining mangrove ecosystem services and a healthy environment is one

of the priority goals of the NT government. This must be in line with many

infrastructure developments along the mangrove dominated shoreline of Darwin

Harbour (Harrison et al. 2009; McHugh 2004; Northern Territory Government

2002a). Although many studies have been done in the mangrove forests of Darwin

Harbour to understand these valuable ecosystems, some knowledge gaps still exist.

In particular, baseline mangrove data need to be updated, in addition to providing an

indication of the species that are vulnerable to clearing, death, or changes to drainage

due to urban and rural developments.

25

Although mangrove species around Darwin Harbour were manually mapped

using field survey and some traditional remote sensing techniques in 1994-1995, the

challenge remains to develop and implement image processing techniques for

mapping, and updating available inventories in an automated manner. Hence, there is

a significant demand for developing an accurate, robust, and repeatable method for

mapping individual mangrove species using remote sensing data. Since there are

major urban and industrial developments projects and associated activities around

Darwin Harbour, ensuring the Darwin harbour ecosystem remains healthy at present

and future levels of development is important. One of the key requirements for

environmental monitoring programs is to combine knowledge from different sources

into a consistent framework that enables stakeholders to make practical and sound

management decisions. Therefore, developing an environmental risk assessment tool

that incorporates information from different sources is essential.

1.3 Research aim, objectives and research questions

This research aims to push the boundary of data processing to extract metrics

relevant to mangrove health mapping, monitoring and management in Darwin

Harbour. The study will also develop a statistically sound ecological risk assessment

model that harmonizes development and conservation around Darwin Harbour to

overcome the challenges associated with mangrove monitoring. To achieve this aim,

the following research questions have been identified, each with a series of

objectives.

26

Question 1: How can high spatial resolution remote sensing data be used to

discriminate mangroves at species level?

Objectives:

I. To identify and map mangroves within a case study site of Darwin Harbour

II. To develop a remote sensing method to distinguish individual mangrove

species

Question 2: How can 3D canopy details combined with spectral properties of

mangroves be used to map mangrove biophysical variability?

Objectives:

I. To extract individual tree crowns or stands within mangrove forest

II. To estimate spatial variability of mangrove canopy chlorophyll

III. To estimate spatial variability of mangrove leaf area index and above ground

biomass

Question 3: How can spatial information be combined to analyse the environmental

stresses on different mangrove sites?

Objectives:

I. To identify human induced environmental stressors on different mangrove

sites

II. To develop a conceptual model of stressors on mangroves, and to identify

risk regions

27

III. To qualitatively analyse the relationships between mangrove health and

human induced environmental stressors using a relative risk model

IV. To quantitatively analyse the uncertainties associated with the relative risk

modelling

1.4 Outline of the thesis

This thesis is composed of five journal manuscripts. Four manuscripts have

already been published in international peer-reviewed journals, and the last one is in

review. The manuscripts are organized into five separate chapters (2 to 6), each of

which stand alone and address one or more research objectives. These chapters are

accompanied by a general introduction (this chapter) and conclusions and

recommendations (chapter 7).

1.5 Chapter synopsis

1.5.1 Chapter 1 - Introduction

The introduction provides the conceptual and theoretical background of the

research reported in the dissertation followed by the research significance. It

discusses the importance for mapping and monitoring mangrove ecosystems, and the

current challenges faced when using traditional survey methods in this environment.

A significant gap in the current state of knowledge regarding mapping mangrove

environments is defined, and the potential for using high resolution remotely sensed

data is demonstrated.

28

1.5.2 Chapter 2 - Mangrove species identification: Comparing WorldView-2

with Aerial Photographs

Mangrove species maps derived from two different layer combinations of

WorldView-2 satellite images are compared with those generated using high

resolution aerial photographs. The successful application of high spatial resolution

remotely sensed data and the support vector machine algorithm for mangrove species

mapping is demonstrated with an accuracy assessment related to field survey data.

With the exception of some formatting to maintain the continuity in the thesis, the

content of this chapter is the same as that of the paper by Heenkenda et al. (2014).

1.5.3 Chapter 3 - Mangrove tree crown delineation from high resolution

imagery

Various tree crown delineation methods are tested with respect to different

remotely sensed data sources. An application of object based image analysis method

for this difficult environment is demonstrated. The accuracy of the proposed methods

is compared with the ground validation measurements. The content of this chapter is

similar to that of the paper by Heenkenda et al. (2015a).

1.5.4 Chapter 4 - Quantifying mangrove chlorophyll from high spatial

resolution imagery

The spatial distribution of mangrove chlorophyll content is mapped with a

combination of remotely sensed data, advanced statistical algorithms and laboratory

analysed field samples. Further, the chapter identifies most important predictor

variables that can be derived from high spatial resolution satellite data for estimating

29

mangrove chlorophyll content in the study area. The accuracy of the proposed

method is analysed based on laboratory measured field samples. This chapter is

based on the paper by Heenkenda et al. (2015b) with some page formatting to

maintain the continuity in the thesis.

1.5.5 Chapter 5 - Estimating mangrove biophysical variables using

WorldView-2 satellite data

The spatial distributions of leaf area index (LAI) and above ground biomass

(AGB) of mangroves are estimated using a combination of field samples and remote

sensing data. A digital cover photography method is implemented to estimate LAI of

mangroves. Site and species specific allometric equations are used for calculating

validation data for AGB. Advanced remotely sensed data processing methods are

then demonstrated for estimating biophysical variations. The content of this chapter

is the same as submitted to the Journal of Ecological Indicators for consideration.

1.5.6 Chapter 6 - Ecological risk assessment using a Relative Risk Model: A case

study of the Darwin Harbour, Darwin, Australia

The ecological risk to the Darwin Harbour mangroves in the context of urban

and rural developments was evaluated using a relative risk model. The model

evaluates the interaction of multiple stressors, habitat and assessment endpoints, and

proves the suitability of this spatial modelling approach for large geographic areas

where multiple human induced stressors are involved. With the exception of some

formatting to maintain the continuity in the thesis, the content of this chapter is exactly

the same that of the paper by Heenkenda and Bartolo (2015).

30

1.5.7 Chapter 7 - Conclusions and recommendations

The concluding chapter specifically details the contribution of this thesis to

the field of mangrove remote sensing. It revisits the main findings and techniques

used throughout each preceding chapter, describing how the approach and results are

significant, new and innovative. Finally, some suggestions to improve results and

ideas for future applications are presented.

31

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resource-management/water/aquatic/ausrivas/darwinharbour>

45

Woodroffe, C. D., Bardsley, K. N., Ward, P. J. & Hanley, J. R. 1988, Production of

Mangrove Litter in a Macrotidal Embayment, Darwin Harbour, N.T., Australia,

Estuarine, Coastal and Shelf Science, vol. 26, 581-598.

Zhang, C., Liu, Y., Kovacs, J. M., Flores-Verdugo, F., Flores-de-Santiago, F. &

Chen, K. 2012, Spectral response to varying levels of leaf pigments collected from a

degraded mangrove forest, Journal of Applied Remote Sensing, vol. 6.

46

Chapter 2 - Mangrove Species Identification:

Comparing WorldView-2 with Aerial

Photographs

This work has been published in the following paper:

Heenkenda, M., Joyce, K., Maier, S. & Bartolo, R. 2014, Mangrove

Species Identification: ComparingWorldView-2 with Aerial Photographs,

Remote Sensing, vol. 6, 6064-6088

Publisher confirmed reproducing the journal article without written

permission since “Remote Sensing” journal is an Open Access journal

(Appendix 1).

47

2.0 Abstract

Remote sensing plays a critical role in mapping and monitoring mangroves.

Aerial photographs and visual image interpretation techniques have historically been

known to be the most common approach for mapping mangroves and species

discrimination. However, with the availability of increased spectral resolution

satellite imagery, and advances in digital image classification algorithms, there is

now a potential to digitally classify mangroves to the species level. This study

compares the accuracy of mangrove species maps derived from two different layer

combinations of WorldView-2 images with those generated using high resolution

aerial photographs captured by an UltraCamD camera over Rapid Creek coastal

mangrove forest, Darwin, Australia. Mangrove and non-mangrove areas were

discriminated using object-based image classification. Mangrove areas were then

further classified into species using a support vector machine algorithm with best-fit

parameters. Overall classification accuracy for the WorldView-2 data within the

visible range was 89%. Kappa statistics provided a strong correlation between the

classification and validation data. In contrast to this accuracy, the error matrix for the

automated classification of aerial photographs indicated less promising results. In

summary, it can be concluded that mangrove species mapping using a support vector

machine algorithm is more successful with WorldView-2 data than with aerial

photographs.

Keywords: mangrove species mapping; object-based image analysis; support vector

machine; WorldView-2; aerial photograph

48

2.1 Introduction

Mangroves are salt-tolerant evergreen forests that create land-ocean interface

ecosystems. They are found in the intertidal zones of marine, coastal or estuarine

ecosystems of 124 tropical and sub-tropical countries and areas [1]. Mangroves are a

significant habitat for sustaining biodiversity and also provide direct and indirect

benefits to human activities.

Despite the increased recognition of their socio-economic benefits to coastal

communities, mangroves are identified as among the most threatened habitats in the

world [2]. Degradation and clearing of mangrove habitats is occurring on a global

scale due to urbanization, population growth, water diversion, aquaculture, and salt-

pond construction [3].

In recent years, numerous studies have been undertaken to further understand

the economic and ecological values of mangrove ecosystems and to provide a means

for effective management of these resources [1, 2, 4–6]. Mangrove forests are often

very difficult to access for the purposes of extensive field sampling, therefore

remotely sensed data have been widely used in mapping, assessing, and monitoring

mangroves [4, 7–10].

According to Adam et al. [11], when using remote sensing techniques for

mapping wetland vegetation, there are two major challenges to be overcome. Firstly,

the accurate demarcation of vegetation community boundaries is difficult, due to the

high spectral and spatial variability of the communities. Secondly, spectral

reflectance values of wetland vegetation are often mixed with that of underlying wet

49

soil and water. That is, underlying wet soil and water will attenuate the signal of the

near-infrared to mid-infrared bands. As a result, the confusion among mangroves,

other vegetation, urban areas and mudflats will decrease map classification accuracy

[8, 12–14]. Consequently, remote sensing data and methods that have been

successfully used for classifying terrestrial vegetation communities cannot be applied

to mangrove studies with the same success.

Using remote sensing to map mangroves to a species level within a study area

presents further challenges. For instance, Green et al. [8] reviewed different

traditional approaches for satellite remote sensing of mangrove forests. After testing

them on different data sources, the study confirmed that the type of data can

influence the final outcome. Heumann [10] further demonstrated the limitations of

mapping mangrove species compositions using high resolution remotely sensed data.

The potential for using hyperspectral remote sensing data for wetland vegetation has

been discussed in numerous studies, however the results are still inconclusive when

considering mangrove species discrimination [10, 11, 13, 15]. Therefore, the

selection of data sources for mangrove mapping should include consideration of ideal

spectral and spatial resolution for the species.

Some laboratory studies using field spectrometers have suggested the ideal

spectral range for mangrove species discrimination [8, 16–18]. In one such study,

Vaiphasa et al. [16] investigated 16 mangrove species and concluded that they are

mostly separable at only a few spectral locations within the 350–2500 nm region.

The study didn’t specifically explore the use of airborne or spaceborne hyperspectral

sensors for mangrove species mapping (such as the best spectral range, number of

bands within that range, and optimal spatial resolution). It has, however, encouraged

50

further full-scale investigations on mangrove species discrimination, which could

involve extensive field and laboratory investigations, necessitating high financial and

time investments. A viable alternative may be to use satellite data with narrow

spectral bands lying within the ideal spectral range for species composition

identification.

The WorldView-2 (WV2) satellite imaging sensor provides such data, and

therefore may have increased potential for accurately mapping the distribution of

mangrove species. Its combination of narrow spectral bands and high spatial

resolution provides benefits over the other freely or commercially available satellite

remote sensing systems albeit at a cost. Therefore, the combination of WV2 data

with advanced image processing techniques will be an added value to wetland

remote sensing.

After determining ideal or optimal image data requirements, the selection of

an appropriate method of processing those data for mapping with maximum

achievable accuracy is critical. Kuenzer et al. [19] provided a detailed review of

mangrove ecosystem remote sensing over the last 20 years, and emphasized the need

for exploitation of new sensor systems and advanced image processing approaches

for mangrove mapping. The most promising results for mangrove mapping can be

found in the study by Heumann [20], who demonstrated high accuracy when

discriminating mangroves from other vegetation using a combination of WV2, and

QuickBird satellite images. However, the accuracy was poor for mangrove mapping

to the species level. Though the advanced technological nature of remotely sensed

images demands solutions for different image-based applications, it can be concluded

51

that little has been adapted to mangrove environments compared to other terrestrial

ecosystems.

An approach that may prove fruitful for mapping mangrove environments is

the Support Vector Machine (SVM) algorithm, a useful tool that minimizes structural

risk or classification errors [20]. SVM is a supervised, non-linear, machine learning

algorithm that produces good classification outcomes for complex and noisy data

with fewer training samples. SVM can be used with high dimensional data as well

[21]. Though this is a relatively new technique for mangrove mapping, it has widely

been applied in other remote sensing application domains with different sensors [22].

For example, Huang et al. [23] compared SVM with three different traditional image

classifiers, and obtained significantly increased land cover classification accuracy

with an SVM classifier.

This study aims to investigate the potential of using high spatial resolution

remote sensing data for discriminating mangroves at a species level. In order to

achieve this aim, three objectives were identified: (a) to identify and extract

mangrove coverage from other vegetation; (b) to apply the SVM algorithm to

distinguish individual mangrove species; and (c) to compare the accuracies of these

techniques when using WV2 and aerial photographs in order to identify the most

accurate combination of data input and image processing technique.

52

2.2 Data and Methods

2.2.1. Study Area

This study focused on a mangrove forest within a small coastal creek system:

Rapid Creek in urban Darwin, Australia (Figure 2.1). It is situated on the north

western coast line of the Northern Territory centered at latitude 12°22′S, and

longitude 130°51′E. This area represents relatively diverse, spatially complex, and

common mangrove communities of the Northern Territory, Australia, and is one of

the more accessible areas in the region for field survey. The aerial extent of the study

area shown in the Figure 1 is approximately 400 hectares.

According to the mangrove classification of Brocklehurst and Edmeades [24],

Avicennia marina (Grey mangroves) and Ceriops tagal (Yellow mangroves) are the

most dominant species for this area, though that study was completed nearly 20 years

ago. Based on the more recent study of Ferwerda et al. [25], there are some other

species such as Bruguiera exaristata (Orange mangroves), Lumnitzera racemosa

(Black mangroves), Rhizophora stylosa (Stilt mangroves), and Aegialitis annulata

(Club mangroves) in the Rapid Creek mangrove forest. However, clear boundaries of

individual mangrove species in this area have not been demarcated by any previous

study.

53

Figure 2.1. The study area located in coastal mangrove forest of Rapid Creek in

Darwin, Northern Territory, Australia; WV2 data © Digital Globe. (Coordinate

system: Universal Transverse Mercator Zone 52 L, WGS84).

2.2.2. Field Survey

Field data were collected during January to April 2013, in the Rapid Creek

mangrove forest. Unfortunately, this did not coincide with the overpass of either

sensor utilized (June 2010). However, the field data can still be considered valid for

calibration and validation purposes, as it is unlikely that any large shifts in species

composition of the site would have been experienced since that time [25]. Further,

54

there was no record of any natural or human disturbances that could have had a

devastating effect on the mangroves during this period. In any case, field sample sites

were located away from edges and transition zones to avoid any errors in

classification due to growth, regeneration, or vegetation decline. After observing the

mangrove zonation patterns visible in the WV2 image, a random sampling pattern

within zones was adopted to identify species.

A sample was defined as a homogenous area of at least 4 m2, which is at least

16 pixels in the WV2 image. Coordinates of these sample polygons and available

species were recorded using a non-differentially corrected Global Positioning System

(GPS). Special attention was given to orient the sample plots in north-south and east-

west directions in order to easier locate them in the images. To overcome GPS

inaccuracies, the plots were located with respect to natural features on the ground as

far as possible. For instance: the distance to water features, roads or edges of mudflats

were recorded. Further, 10 readings were averaged for the final location. Mangrove

species in the field were identified using the Mangrove Plant Identification Tool Kit

(published by Greening Australia Northern Territory) and The Authoritative Guide to

Australia’s Mangroves (published by University of Queensland) [26,27].

2.2.3. Remote Sensing Data and Pre-Processing

A WV2 satellite image was selected as the main image data source for this

study. As the sensor produces images with high spectral and spatial resolution, WV2

is an ideal solution for vegetation and plant studies [28]. The image was acquired on

5 June 2010, with 8 multispectral bands at 2.0m spatial resolution and a

panchromatic band with 0.5m spatial resolution. To compare this work with higher

55

spatial resolution remote sensing data (Table 2.1), true color aerial photographs were

used, which were acquired on 7June 2010 using an UltraCamD large format digital

camera [29].

Table 2.1. Spectral band information of WV2 image and aerial photographs obtained

from UltraCamD camera [29, 30].

Band Spectral Range (nm) Spatial Resolution (m)

WorldView-2

Panchromatic 447–808 0.5

Coastal 396–458 2.0

Blue 442–515 2.0

Green 506–586 2.0

Yellow 584–632 2.0

Red 624–694 2.0

Red-Edge 699–749 2.0

NIR1 765–901 2.0

NIR2 856–1043 2.0

Aerial photographs

Blue 380–600 0.14

Green 480–700 0.14

Red 580–720 0.14

WV2 images were radiometrically corrected according to the sensor

specifications published by DigitalGlobe® [31]. Digital numbers were converted to

at-sensor radiance values, and then to top-of-atmosphere reflectance values. The

56

additive path radiance was removed using the dark pixel subtraction technique in

ENVI 5.0 software. Images were geo-referenced using rational polynomial

coefficients provided with the images, and ground control points extracted from

digital topographic maps of Darwin, Australia.

In order to utilize both the high spatial and spectral resolution options

provided with WV2 panchromatic and multispectral layers, pan-sharpening options

were investigated. Pan-sharpening is defined as a pixel fusion method that increases

the spatial resolution of multi-spectral images [32]. Although there are several

methods available for pan-sharpening, the high pass filter method was selected

because it is known to be one of the best which produces a fused image without

distorting the spectral balance of the original image [33, 34]. Once applied, the

statistical values of the spectral information of the input and output multispectral

products are similar. Palubinskas [35]’s study also proposed the high pass filter

method as a fast, simple and good pan-sharpening method for WV2 images, by

analyzing performances of several image fusion methods. In addition, this method is

known to be one of the best choices when the pixel resolution ratio of higher to lower

is greater than 6:1 [36]. However, special attention must be given when selecting the

filter kernel size as it should reflect the radiometric normalization between the two

images. Chavez et al. [33] stated that twice the pixel resolution ratio is an ideal

solution for the kernel size. This means that a kernel size: 15 × 15 is an optimal

solution for WV2 data. All image radiometric, atmospheric and geometric

corrections must be done prior to pan-sharpening in order to minimize geometric and

radiometric errors.

57

Accordingly, the WV2 multispectral image was then pan-sharpened to 0.5 m

spatial resolution to incorporate the edge information from the high spatial resolution

panchromatic band into the lower spatial resolution multispectral bands using the

high pass filter pan-sharpening method. The Coastal band and the NIR2 band were

not used for further processing because of the limited spectral range of the

panchromatic band (Table 2.1).

The aerial photographs were oriented to ground coordinates following digital

photogrammetric image orientation steps in the Leica Photogrammetric Suite (LPS)

software using image orientation parameters extracted from the camera calibration

report. The coordinates of ground control points were extracted from Australian

geographic reference stations near Darwin, Australia and digital topographic maps of

Darwin [37]. The ortho-photograph of the area was then generated to achieve a

geometrically and topographically corrected image with a resultant resolution of 14 cm

for further studies. Radiometric calibration information was not available.

In order to directly compare with WV2 imagery, another ortho-photo was

created with a pixel size of 0.5m. In order to remove the artifacts that can be created

from resampling, a low pass filter was applied to the raw aerial photographs first.

Ortho-rectification was then completed using the cubic convolution interpolation.

2.2.4. Image Classification

Image classification of WV2 and aerial data was undertaken in two steps.

Firstly, mangroves and non-mangroves were separated using eCognition Developer

8.7 software. Then the classification was refined to discriminate mangroves at a

58

species level using ENVI 5.0 software. The process was applied to two sets of WV2

images: the first WV2 image with a spatial resolution of 2m (without pan-

sharpening) and the other one with a spatial resolution of 0.5 m (with pan-

sharpening), and two sets of ortho-photographs: the first with a spatial resolution of

0.14 m (AP0.14M) and the other one with a spatial resolution of 0.5 m (AP0.5M).

This was done in order to investigate the influence of spatial resolution and the pan-

sharpening effect on classification accuracy. Figure 2.2 shows the overall workflow

diagram for this study, which is described in greater detail below.

59

Figure 2.2. Mangrove species mapping using remotely sensed data. Image

segmentation and initial classification was completed in eCognition Developer 8.7

software. The support vector machine algorithm was implemented in the ENVI-IDL

environment for species classification.

2.2.4.1. Separating Mangroves and Non-Mangroves

To separate mangroves from other features, object-based image analysis (OBIA)

was used. OBIA is based on segmentation, which partitions the image into

meaningful, spatially continuous and spectrally homogeneous objects or pixel groups

[15, 20]. The major challenge is in determining appropriate similarity measures

which discriminate objects from each other. Therefore, the spectral profiles of

identifiable features in the satellite image were analyzed.

Class-specific rules were developed incorporating contextual information from the

WV2 image and relationships between image objects at different hierarchical levels,

to separate mangroves and non-mangroves. The segmentation at level 1 identified

objects that can be grouped to coarse classification structures. All spectral bands

ranging from Blue to NIR1 were used, and weights were assigned as 1 for the

segmentation at each level. The segmentation parameters were selected based on the

pixel size and the expected compactness of resulting objects. Buildings, soil, roads

and mudflats were classified as “Buildings-Roads-Mudflats”, and as the spectral

reflectance ratio of Yellow to NIR1 of this class was less than the mean ratio of

Yellow to NIR1 of objects, the ratio was introduced as a threshold value for the

classification to extract “Buildings-Roads-Mudflats”. The brightness calculated from

reflectance values from Blue to NIR1 bands were analyzed, and the low values (less

than the mean brightness of objects) were used to extract water features. The

60

remaining objects at this level were classified as “Candidate-Mangrove-1” claiming

this class as the parent objects for the next level.

At level 2, specific details (home gardens and other vegetation) within parent

objects were identified (Figure 2.3). Home gardens and other vegetation were

removed considering the objects enclosed by “Buildings-Roads-Mudflats” class.

Further, remaining home gardens and other vegetation were identified using a red

edge normalized difference index (NDVIre) obtained from NIR1 and Red-Edge

bands:

𝑁𝑁𝐷𝐷𝑁𝑁𝑁𝑁𝑟𝑟𝑟𝑟 = �𝑅𝑅𝑁𝑁𝑁𝑁𝑁𝑁1−𝑅𝑅𝑁𝑁𝑅𝑅𝑅𝑅−𝐸𝐸𝑅𝑅𝐸𝐸𝑅𝑅��𝑅𝑅𝑁𝑁𝑁𝑁𝑁𝑁1−𝑅𝑅𝑁𝑁𝑅𝑅𝑅𝑅−𝐸𝐸𝑅𝑅𝐸𝐸𝑅𝑅�

(1)

With 𝑅𝑅𝑁𝑁𝑁𝑁𝑅𝑅1 and 𝑅𝑅𝑅𝑅𝑟𝑟𝑅𝑅−𝐸𝐸𝑅𝑅𝐸𝐸𝑟𝑟 being the reflectance in the NIR1 and Red-Edge

bands respectively [38, 39]. The index was used assuming that it would provide a

good measure of biophysical properties of plants: chlorophyll content and water-

filled cellular structures to separate these classes from others due to the rapid change

in reflectance of vegetation in the Red-Edge region [39].

As the NDVIre of home gardens and other vegetation classes were higher than

the mean NDVIre of objects, the value was introduced as a threshold value for the

classification. The remaining objects from this level were classified as

“Candidate-Mangrove-2”.

At the final level, the normalized difference vegetation index (NDVI)

obtained from NIR1 and Red bands:

61

𝑁𝑁𝐷𝐷𝑁𝑁𝑁𝑁 = (𝑅𝑅𝑁𝑁𝑁𝑁𝑁𝑁1−𝑅𝑅𝑁𝑁𝑅𝑅𝑅𝑅)(𝑅𝑅𝑁𝑁𝑁𝑁𝑁𝑁1−𝑅𝑅𝑁𝑁𝑅𝑅𝑅𝑅) (2)

Where 𝑅𝑅𝑁𝑁𝑁𝑁𝑅𝑅1 and 𝑅𝑅𝑅𝑅𝑟𝑟𝑅𝑅 are the reflectance in the NIR1 and Red bands

respectively, was used to separate “Final-Mangroves”. This vegetation index was

also introduced to the classification as a threshold value since “Final-Mangroves”

class have higher NDVI values than the mean NDVI values of the objects. The

classified objects were closely analyzed for final refinements. Refinements were

done to classify objects by incorporating object geometry and neighborhood

information to the process. For example: the relation to the neighboring borders was

analyzed. The transferability of the rule set was maintained using variables instead of

specific values for class separation. Finally, the outline of the mangrove area was

extracted for further analysis. This method was tested on both sets of WV2 images.

62

Figure 2.3. The schematic explanation of image objects at different hierarchical

levels. The first level was at pixel level. At the second level: vegetation and

non-vegetation areas were identified. Mangrove areas were extracted at the final

level.

The same process was then applied to the two sets of aerial photographs

(AP0.14M and AP0.5M). However, when segmenting AP0.14M, different

segmentation parameters were used at each level due to its higher spatial resolution.

When applying OBIA to aerial photographs, the possibility of developing a

successful rule set for the segmentation is limited due to the broad band width and

limited number of spectral bands of the aerial camera. Further, limitations associated

with radiometric resolution of aerial photographs could affect the accuracies. As a

result, the final mangrove area was manually edited to remove objects of known

home gardens, other vegetation, and grasslands.

2.2.4.2. Mangrove Species Classification

With the increase in mangrove studies and mapping using remote sensing

comes a growing implementation of advanced processing techniques. Although

traditional remote sensing can provide important information for monitoring the

ecosystem, changes, and extent of mangroves, more contextual and probabilistic

methods can be utilized to improve the accuracy of classification, and for

discriminating individual species.

Traditional land cover hard classification is based on the assumption that each

pixel corresponds to a single class [40]. This is not always true. When the

63

instantaneous field of view of the sensor covers more than one class of land cover or

objects, the pixel may have reflectance values from more than one class, and is

defined as a mixed pixel [41]. Mangroves are closed forests which can become very

dense due to their limited habitat range. Mixed pixels therefore have to be expected

in the image. Hence, traditional pixel based image analysis techniques do not fully

exploit the contextual variations in species distribution [10, 11, 20]. A better

alternative is soft classification, which predicts the proportion of each land cover

class within each pixel, resulting in more informative representation of land cover

[40, 42, 43].

One of the soft classification algorithms is the SVM, which locates a best

non-linear boundary in higher dimensional feature space. It works with pixels that

are in the vicinity of classes, while minimizing over-fitting errors of training data

[21]. Hence, a small training set is sufficient to achieve accurate classifications. The

mathematical formation and detailed description of the SVM architecture can be

found in Tso and Mather [21] and Mountrakis et al. [22].

When designing the SVM architecture, careful selection of a kernel function

is important to increase the accuracy of the classification. The position of the

decision boundary always varies with the kernel function and its parameters [21, 22,

44]. Descriptive information about the kernel function and its parameter selection

can be found in Tso and Mather [21].

The extracted mangrove areas were classified using the SVM algorithm. Field

data was collected to represent the five main mangrove species occurring in the study

area: Avicennia marina, Ceriops tagal, Bruguiera exaristata, Lumnitzera racemosa,

64

and Rhizophora stylosa. Sonneratia alba, Excoecaria ovalis, and Aegialitis annulata

have not been used for the classification mainly due to low coverage. In addition,

Sonneratia alba does not exhibit distinctive spectral variation compared to other

species (Figure 2.4b). Field data were then divided into two groups, for training (69

samples) and for validation (47 samples) of the classifiers. The multiclass SVM

classifier developed by Canty [45] was modified and implemented in ENVI

extensions in the IDL environment in order to define the case specific parameters

with an iterative process. This helped to determine the best fitting parameters for this

study. The Radial Basis Function (RBF) was found to be the best kernel function

with a gamma value of 0.09, and penalty parameter of 10.

The SVM algorithm was applied to two sets of band combinations of the

WV2 image. One set uses the visible spectral range, i.e., blue, green, yellow, red and

red-edge bands (WV2-VIS) in order to directly compare it with aerial photographs.

The other set consisted of the red, red-edge and NIR1 bands (WV2-R/NIR1). These

bands were selected based on the research by Wang and Sousa [18]. The study

indicated that the majority of mangrove species which occur in the Rapid Creek area

can be discriminated using the influential wavelengths: 630, 780, 790, 800, 1480,

1530 and 1550 nm spectral bands (which correspond to the red and NIR bands of

WV2). The pan-sharpened sets of images were named as PS-WV2-VIS and PS-

WV2-R/NIR1 for easy reference. The same process was applied to AP0.14M and

AP0.5M. The same training data were used for all four datasets to maintain

consistency between methods.

65

2.2.5. Accuracy Assessment

The accuracy of the mangrove species classification was assessed at the pixel

level using descriptive and analytical statistical techniques. The 560 random

validation points were generated inside the field samples (2 m × 2 m plots).

Therefore, there were a large number of validation points for accuracy assessment for

each species (Table 2.2). The generated maps were visually inspected against field

observations, satellite images and aerial photographs according to Congalton [46]. A

confusion matrix was generated, and users’ and producers’ accuracies, together with

kappa statistics, were investigated for each identified mangrove species.

2.3. Results

2.3.1 Field Survey

There are five mangrove species which are most abundant and can easily be

identified along Rapid Creek: Avicennia marina, Ceriops tagal, Bruguiera

exaristata, Lumnitzera racemosa, and Rhizophora stylosa (Stilt mangroves).

Avicennia marina and Ceriops tagal are the most widely spread species in this area,

while Lumnitzera racemosa covers the majority of the hinterland area. Sonneratia

alba (Apple mangroves) are found at two locations within the site, covering

approximately 20 square meters. Excoecaria ovalis (Milky mangroves) and

Aegialitis annulata (Club mangroves) were also recognized during the field

investigation, though they do not represent significant coverage with in the forest.

66

2.3.2Separating Mangroves and Non-Mangroves

The analysis of spectral profiles within the Rapid Creek coastal area was the

key to introducing class specific rules for OBIA (Figure 2.4).

Figure 2.4. Spectral profiles of: (a) all features except mangroves extracted from

WV2 image; (b) mangrove species only extracted from WV2 image; (c) mangrove

species extracted from aerial photographs of spatial resolution 0.14 m; and (d)

mangrove species extracted from aerial photographs of spatial resolution 0.5 m.

(AM-Avicennia marina, CT-Ceriops tagal, BE-Bruguiera exaristata, LR-Lumnitzera

racemosa, RS-Rhizophora stylosa, SA-Sonneratiaalba).

67

Buildings, soil, mudflats and water showed highly distinctive spectral profiles

compared to other features. The mangrove species are most notably separable within

the range of wavelengths of 478.3nm and 832.9nm, with the exception of Sonneratia

alba, while the spectral profile of Avicennia marina generally is more distinctive

from other species across a broader range of the spectrum.

The locations of field samples are shown in Figure 2.5. The mangrove outline

was successfully extracted from the WV2 image using OBIA. The “Final-

Mangroves” class extracted from the WV2 image (without pan-sharpening) and

aerial images did, however, still include both mangroves and the adjacent home

gardens and other vegetation to be edited manually. The WV2 image was more

useful than the aerial image for extracting the overall mangrove coverage, as less

manual editing was required.

68

Figure 2.5. The locations of field samples available for calibration and validation

purposes, and mangrove coverage extracted from the WV2 image.

Table 2.2 shows the number of sample points that were used for validation for

each species together with the number of samples used for training the classification.

Sample points were generated from47 validation samples shown in Figure 2.5.

Table 2.2. Number of samples (4 m2 each) used for training and number of sample

points generated for validation for each mangrove species.

Species

No. of Samples for

Training

(4 m2 or 16 Pixels Each)

No. of Points for Validating

the Classification

Avicennia marina (AM) 22 216

Bruguiera exaristata (BE) 14 106

Ceriops tagal (CT) 10 80

Lumnitzera racemosa (LR) 12 78

Rhizophora stylosa (RS) 11 96

2.3.3 Mangrove Species Classification

Figure 2.6 shows the derived mangrove species maps. Figure 2.6a was

produced from the PS-WV2-VIS image, and the visual appearance is more closely

related to the dominating species of the area than the other five maps

(Figures 2.6 (b) – (f)). Avicennia marina (AM) and Ceriops tagal (CT) dominate

69% of total mangrove area while Bruguiera exaristata (BE) accounts for only 5%.

69

The majority of the hinterland margin is occupied by Lumnitzera racemosa (LR) or

mixed Lumnitzera racemosa, Bruguiera exaristata and Ceriops tagal. Rhizophora

stylosa (RS) dominates only along the creek and its branches (Figure 2.6a). However,

the visual assessment confirmed that some of the Ceriops tagal has been

misclassified as Bruguiera exaristata (especially on the west of the study area

Figure 2.6a).

Figure 2.6. (a) Classification of pan-sharpened WV2 image using blue to red-edge

bands; (b) Classification of pan-sharpened WV2 images using red to NIR1 bands;

(c) Classification of WV2 image using blue to red-edge bands; (d) Classification of

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WV2 images using red to NIR1 bands; (e) Classification of aerial photographs with

0.14 m spatial resolution; and (f) Classification of aerial photographs with 0.5 m

spatial resolution.

When testing the same method with the PS-WV2-R/NIR1 image, there was

no significant difference in visual appearance of the classification except for the

classes Rhizophora stylosa and Bruguiera exaristata (Figure 2.6a and b). The

hinterland margin and areas along the water features were successfully classified

with their dominated species. Furthermore, the visual appearance of classification of

Rhizophora stylosa and Ceriops tagal are better than that of PS-WV2-VIS

classification.

Figure 2.7 shows an example of positive detection of Lumnitzera racemosa

(orange colour) at hinterland from pan-sharpened WV2 image classifications.

However, when considering the classification results of WV2 image with 2 m spatial

resolution, the detection of Lumnitzera racemosa and Bruguiera exaristata was

relatively poor (Figures 2.7 h and i).

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Figure 2.7. The comparison of the WorldView2 image and different classification

approaches: (a) Pan-sharpened WV2 image; (b) Classification results of PS-WV2-

VIS; (c) Classification results of PS-WV2-R/NIR1; (d) Aerial photograph;

(e) Classification results of AP0.14M; (f) Classification results of AP0.5M; (g) WV2

image; (h) Classification results of WV2-VIS; (i) Classification results of WV2-

R/NIR1.

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Figure 2.6c and d show classifications of the WV2 image without pan-

sharpening. In both instances, most of the Avicennia marina was classified correctly,

especially in the classification of WV2-R/NIR1. In addition, there was no significant

detection of Bruguiera exaristata and Ceriops tagal classes in either classification.

The classification results of WV2-R/NIR1 shows misclassification of Ceriops tagal

and Lumnitzera racemose as Rhizophora stylosa. Further, both results were not able

to capture the mangrove zonation pattern observed in the field.

The accuracy assessment of the PS-WV2-R/NIR1 classification revealed

approximately same overall accuracy as the PS-WV2-VIS classification. Although

the total extent of Rhizophora stylosa and Ceriops tagal was approximately

equivalent to the PS-WV2-VIS image classification, the area covered by Ceriops

tagal was smaller than that class in the PS-WV2-VIS image classification

(Figure2.8a and b). Some of the Ceriops tagal areas may be misclassified as

Rhizophora stylosa and Bruguiera exaristata, because the percentage of the extents

of the other species didn’t change significantly (Figure 2.8). The extents of

Lumnitzera racemosa was almost the same in both instances while the extent of

Avicennia marina was reduced by approximately 2.5 hectares compared to the

PS-WV2-VIS classification.

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Figure 2.8. Extents (%) of mangrove species obtained using six different input

datasets: (a) pan-sharpened WV2 image with bands blue to red-edge; (b) pan-

sharpened WV2 image with bands red to NIR; (c) aerial photographs with 0.14 m

spatial resolution; (d) aerial photographs with 0.5 m spatial resolution; (e) WV2

image with bands blue to red-edge; and (f) WV2 image with bands red to NIR.

The WV2 image with a spatial resolution of 2 m demonstrated rather different

results than the pan-sharpened image classifications. In both instances, the classified

extents of Bruguiera exaristata were less than 1% of the total mangrove area.

However, the extent of Rhizophora stylosa obtained from the WV2-R/NIR1

classification was similar to the pan-sharpened image classification though the visual

appearance indicates some misclassifications around the mudflats and at the edges of

the mangrove coverage (Figure 2.6d). The classification of WV2-VIS did not show

the Ceriops tagal class, whereas the classification of WV2-R/NIR1 represented 2%

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of the total area (Figure 2.8e and f). The extent of Avicennia marina class was not

considerably different to other pan-sharpened WV2 and aerial image classifications.

The classification results of using the AP0.14M input had several differences

compared to the WV2 classifications (Figure 2.6). The classes derived from the

aerial photographs were patchier, and the classification did not capture the mangrove

zonation pattern described in previous studies of this area well. Figure2.7 shows one

example of capturing mangrove zonation patterns by different classification

approaches. Lumnitzera racemosa class obtained from the classification of AP0.5M

was patchier than others. The percentage of the extent of Avicennia marina is almost

the same at all classifications. Further, the extent of Rhizophora stylosa from the

classification of aerial images is the same as that of the classification of PS-WV2-

VIS image. The extents of Lumnitzera racemosa was significantly larger using the

technique on aerial photographs, while the extent of Ceriops tagal was half that of

the pan-sharpened WV2 classification (Figure 2.8c and d).

When classifying the AP0.5M aerial photograph, there were no significantly

large differences in the extents of all classes compared to the AP0.14M

classifications (Figure2.8c and d). A slight increase in Rhizophora stylosa and

Avicennia marina extents and a decrease in Bruguiera exaristata extent were noted

in the AP0.5M classification compared to the AP0.14M classification. These classes

exhibited fewer contiguous patterns and highly deviated from reality, thus producing

some misclassification.

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2.3.4 Accuracy Assessment

The map produced from PS-WV2-VIS best visually represents the zonation

pattern of the different species. This classification also has the highest values for

both overall accuracy (89%) and Kappa statistics (0.86, Table 2.3). Despite the

overall accuracy of the PS-WV2-VIS classification being 2% higher than the PS-

WV2-R/NIR1 classification, the results of the Kappa analysis shows these two

classifications were not significantly different whereas the visual appearance of this

classification is better than some of the classes obtained from PS-WV2-VIS.

Table 2.3. Overall accuracy and Kappa statistics obtained for each classification.

PS-WV2-

VIS

PS-WV2-

R/NIR1

WV2-

VIS

WV2-

R/NIR1 AP0.14M AP0.5M

Overall

accuracy 89% 87% 58% 42% 68% 68%

Kappa 0.86 0.84 0.46 0.25 0.60 0.58

Table 2.3 shows the lowest accuracy assessment figures for the maps

generated from non pan-sharpened WV2 images. The Kappa statistics were not

strong enough to represent the good contingency with validation samples. This is

also supported by the visual appearance of these maps (Figure 2.6).

Visual inspection of the maps produced from the aerial photographs

(AP0.14M and AP0.5M) indicated low quality classification results, especially the

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classification results of AP0.5M. They were not able to capture most of the variations

visible in the photographs. Further, it can be seen that Ceriops tagal and Avicennia

marina species have mostly been misclassified as Rhizophora stylosa and Bruguiera

exaristata (Figure 2.8c). The descriptive analysis of the classification of AP0.14M

has shown the relatively low accuracy of 68%, with Kappa statistics limited to 0.60

(Table 2.3). The accuracy of the map produced from the AP0.5M was slightly lower

(Kappa equals to 0.58), with an overall accuracy of 68% (Table 2.3).

Table 2.4. Comparison of producer’s accuracy and user’s accuracy for each species,

obtained from different remote sensing data sources. (AM-Avicennia marina,

CT-Ceriops tagal, BE-Bruguiera exaristata, LR-Lumnitzera racemosa, RS-

Rhizophora stylosa).

Image Accuracy (%) AM BE CT LR RS

PS-WV2-VIS Producer’s acc. 98 73 55 100 95

User’s acc. 98 72 84 87 89

PS-WV2-R/NIR1 Producer’s acc. 95 54 70 100 100

User’s acc. 100 83 68 72 81

WV2-VIS Producer’s acc. 98 ** ** 82 28

User’s acc. 99 ** ** 13 19

WV2-R/NIR1 Producer’s acc. 94 ** 2 44 70

User’s acc. 98 ** 2 12 13

AP0.14M Producer’s acc. 83 27 45 73 77

User’s acc. 94 46 35 60 59

AP0.5M Producer’s acc. 91 20 65 79 44

User’s acc. 77 25 70 72 65

** Did not calculate individual accuracies

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For individual species classifications, Table 2.4 shows low user’s accuracy

for Bruguiera exaristata for the PS-WV2-VIS image classification, compared to

other species. In contrast, Ceriops tagal has a user’s accuracy of 84% with 55%

producer’s accuracy. Lumnitzera racemosa, for example, has a producer’s accuracy

of 100% while the user’s accuracy is 87%. This means that there were no omissions

from this class, but were more inaccurate inclusions providing an over-estimation of

this coverage. In the PS-WV2-R/NIR1 classification the extent of Rhizophora stylosa

was also over-estimated.

The individual classification accuracies of Bruguiera exaristata did not

calculate for WV2 images as there was no sufficient coverage identified by

classification to do so. However, both producer’s and user’s accuracies of Ceriops

tagal was 2% from WV2-R/NIR1 (Table 2.4). Lumnitzera racemosa was highly

over-estimated in the WV2-VIS classification whereas Rhizophora stylosa did in the

WV2-R/NIR1 classification. These inaccurate inclusions that provide over-

estimations were highly evidence in their visual maps (Figure 2.6). The only

successfully classified class was Avicennia marina.

2.4 Discussion

2.4.1 Separating Mangroves and Non-Mangroves

One of the advantages of using WV2 data is the comparatively large number

of spectral bands available within a limited spectral range. This enables more

flexibility in applying a wide range of rules in OBIA. For example, although the

visual appearance of home gardens and other vegetation are the same as mangroves,

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there is a detectable spectral difference in mangroves within NIR1 and red-edge

regions (Figure 2.4 a and b). In a recent study of mangrove mapping using SPOT-5

satellite images, mudflats within mangrove habitat required manual removal [47]. In

this study, the yellow spectral band was very useful for extracting buildings, soil and

mudflats automatically. The normalized differences calculated from NIR1 and Red-

Edge bands successfully isolated home gardens and other vegetation from

mangroves. However, the possibility of repeating this with aerial photographs was

restricted due to spectral band limitations.

Most of the healthy green grassy areas near the mangrove boundary had

similar spectral profiles to the mangrove species at hinterland (Figure 2.4).

Therefore, the main challenge was to separate mangroves from the healthy green

grass. To achieve this, at different hierarchical levels, contextual information,

geometry and neighbourhood characteristics of objects were used. For example: the

analysis of relation to border to Candidate-Mangrove-1 and Candidate-Mangrove-2

classes, and the normalized difference indices extracted from spectral information of

objects related to their parent objects were used successfully. Having increased the

accuracy of the areal extent of mangroves, the extraction procedure was fully

automated for pan-sharpened WV2 images.

Overall, this approach has effectively discriminated different land cover

classes surrounding a mangrove ecosystem using WV2 images. When using pan-

sharpened images, the whole process was automated, and can be repeated in a robust

manner. There was no manual editing involved. However, when applying OBIA to

the WV2 images with a spatial resolution of 2m, there was some manual editing

involved. The next stage will be to test the transferability of the derived rule set to a

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different location. However, since the rule sets consist of image variables rather than

set numerical values for class discrimination, they can be tested on other areas easily.

Aerial photographs were visually appealing, and it was easy to visually

identify mangroves, as well as gaps between mangroves. When applying OBIA to

the aerial photographs, the high spatial resolution helped to create small, compact

objects in the OBIA environment and then to discriminate vegetation and non-

vegetation features successfully. However, isolating mangroves from home gardens

and other vegetation types was difficult. The OBIA rule set was amended

considering the limitations of spectral and radiometric resolutions of the data. Even

though the aerial image dataset required manual editing, the problem of having a

heterogeneous mixture of vegetation, mangroves, soil and water can be overcome by

isolating the mangrove coverage before further classification.

When comparing the above results, regardless of spatial resolution, a

relatively large number of spectral bands within the limited spectral range of visible

and NIR would be an ideal solution for mangrove coverage identification. This is

supported by the exploratory spectral separability analysis of WV2 images by

Heumann [20]. His study demonstrated increasing accuracy when discriminating

mangroves from other vegetation using WV2 data and a decision tree classification

algorithm. However, the quantitative analysis of radiometric resolution differences of

these datasets must be considered, as it may play a significant role on the

classification accuracies.

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2.4.2 Comparison of Mangrove Species Classifications

As described in section 3.2, the differences in classification of species

composition reflects the reliability of using various data sources and advanced

algorithms for classification. The overall accuracy of the PS-WV2-VIS image

classification is very good. The mangrove zonation pattern described by Brocklehurst

and Edmeades [24] for this area has successfully been identified (Figure 2.6a).

Further, these results are supported by the field surveys of Ferwerda et al. [25], who

identified the presence of Rhizophora stylosa closer towards creek banks or tidal

flats, and Lumnitzera racemosa and Ceriops tagal located on the high tidal range.

The classifications in this study detected similar patterns (Figure 2.6).

The Brocklehurst and Edmeades [24] study is the only published mangrove

mapping studying Darwin, Australia, undertaken almost 20 years ago. It does not

demarcate individual species boundaries. Field investigation of the study area

identified many changes that have occurred over this time period. For instance,

Rhizophora stylosa, and Lumnitzera racemosa were not yet documented. However,

since their study was done using manual field survey methods and visual

interpretation of remote sensing images, updating the changes by repeating their data

capturing methods would require significant time and financial investments.

However, the method introduced by this study is repeatable and could be performed

at reasonable time intervals in order to constantly update mangrove coverage maps.

When using SVM classification algorithm, attention must be given to the

parameter selection of SVM architecture. For example: since the performance of

SVM is based on the kernel function used and its parameters, the penalty parameter

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that works with an optimal boundary selection of the training data has to be

considered carefully [21]. In this study, ten was selected as the ideal penalty

parameter to locate training samples on the correct side of the decision boundary. A

larger penalty parameter of the SVM exhibits over-fitting of training data, thus

reducing classification accuracies.

Another consideration is the benefit of image fusion. Even in this study, when

classifying mangroves without pan-sharpening, individual species accuracies were

low. Over the years, with the development of advanced image/signal processing

techniques, image fusion has become a tool that improves the spatial resolution of

images while preserving spectral information. Many recent studies have indicated

that these algorithms are more sophisticated for improved information extraction

rather solely for visualization. For example: Zhang [48] revised recent studies that

extracted information from pan-sharpened data, and concluded that well pan-

sharpened image could improve the information extraction. Although an

appropriately pan-sharpened image could provide more information for feature

extraction, there is room for further development of pan-sharpening techniques [35,

48].

When comparing these results with global mangrove studies, special attention

must be given to the geographic region. Mangrove ecosystems characteristics are

different from region to region due to soil salinity, ocean current, tidal inundation,

and various geomorphic, edaphic, climatic and biotic factors etc. [6, 10, 49]. Given

these considerations, it will be interesting to see if this technique would be a viable

alternative for the tropical arid or sub-arid mangrove environment as it exhibits

greater structural complexity than this study area. Kamal and Phinn [15] compared

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pixel-based and object-based image analysis techniques using hyperspectral data for

mangrove identification. They were not able to achieve reasonably high accuracies

for many species using WV2 images. Since their study area lies in Queensland,

Australia, and the mangrove ecosystems around this area are known to be similar to

Northern Territory, Australia [4,24] the results can directly be compared to this

study.

Although the most dominating factors for spectral reflectance variation are

biochemical and biophysical parameters of the plants, the reflectance spectra of

mangroves are mostly combined with those of underlying soil, water and

atmospheric vapour. Therefore, a degradation of classification can be expected,

especially in the regions where water absorption is stronger [11]. For example,

although the band selection of PS-WV2-R/NIR1 lies within the ideal spectral range

for classified species identified by Wang and Sousa [18], the results of some classes

were lower than that of PS-WV2-VIS classification due to the broad NIR band,

which includes a region highly sensitive to the water absorption. The classification of

WV2-R/NIR1 indicated the lowest accuracy, demonstrating the importance of high

spectral resolution in achieving high accuracies.

Most of the traditional approaches for mangrove remote sensing are based on

interpretation of aerial photographs. Heumann [10] summarized 11 mangrove studies

using aerial photographs. Among them, Dahdouh-Guebas et al. [50] successfully

mapped individual species using image attributes extracted from aerial photographs.

In that sense, they used visual interpretation techniques rather than computational

classification. However, in this application, aerial photographs with broad spectral

bands could not delineate the features available in the mixed pixels due to spectral

83

resolution limitations resulting high omission errors. This is also evident from the

spectral profile analysis of this study (Figure 2.4). Most of the species were not able

to be discriminated from each other. Therefore, although the same classifier has been

used, the classification from higher spatial resolution aerial photography is of lower

accuracy.

When comparing the outcome of these six data sets, apart from the spatial and

spectral resolutions, radiometric resolution must be taken into account. A sensor with

high radiometric resolution is more sensitive to capture small differences in

reflectance values. Further, electromagnetic characteristics and signal-to-noise ratio

of sensors can influence the classification accuracies. In this study, it was not

possible to take these radiometric effects into account when resampling aerial

photographs to simulate the WV2 image.

2.4.3 Accuracy Assessment

The visual appearance and the statistical values of the PS-WV2-VIS

classification showed the strongest agreement between generated maps and reference

data, according to Congalton [46]. By contrast, the classifications of WV2-VIS and

WV2-R/NIR1 have the lowest level of agreement between species maps and

validation data. The maps generated from AP0.14M and AP0.50M have a moderate

level of agreement to the reference data, having Kappa statistics between 0.60 and

0.64 [46]. The results of the error matrix analysis of species classification were lower

than the pan-sharpened WV2 image classifications (Table 2.4). Despite the visual

clarity and higher spatial resolution of the aerial photographs, the resultant

classifications did not generate better accuracy results than thepan-sharpened WV2

84

images undergoing the same treatment and process. It can be clearly seen that the

WV2 image with a spatial resolution of 2 m was not a successful alternative in this

context.

The error matrix was examined to make more analytical observations about

individual species. This is a very effective way to describe both errors of inclusion

and exclusion of each species represented in the classification [46, 51]. As explained

by Congalton [51], the user’s accuracy is an indication of whether the pixels

classified on the map actually represent the same species on the ground. The

probability of the reference pixel being correctly classified is the producer’s

accuracy.

Scrutiny of the error matrix reveals that there is confusion in discriminating

Ceriops tagal from Bruguiera exaristata and Rhizophora stylosa (Table 2.4). This is

because of their spectral separability measures within that specific range and is also

due to the patchy distribution pattern. For example: mostly Ceriops tagal is mixed

with Bruguiera exaristata and Avicennia marina in this study area [24, 25]. The

WV2 images with low spatial resolution were not able to spectrally discriminate

species and to identify the complex structural situation.

The error matrix also demonstrated the successful detection of Avicennia

marina from the WV2 images with a low spatial resolution. This is because of the

visually distinctive, smooth and similar spatial pattern of Avicennia marina on the

images (or its distinctive spectral profile). Although most of the reference pixels of

Lumnitzera racemosa and Rhizophora stylosa were correctly classified as their

respective classes, their actual representations on maps were poor. This is evident

85

from the lower user’s accuracy (Lumnitzera racemosa got 82% and Rhizophora

stylosa got 70%) than the producer’s accuracy (Lumnitzera racemosa and

Rhizophora stylosa equals to 13%).

Overall, when investigating classifications of both pan-sharpened WV2

images, Lumnitzera racemosa and Avicennia marina have the highest values for the

producer’s accuracy, indicating that the probability of this species being classified as

another is low. The smooth and similar spatial pattern helps the classification

techniques to detect them accurately.

This is evidence that the combination of high spatial resolution remote

sensing data using a relatively large number of spectral bands within the visible and

NIR region and the SVM non-linear machine learning classification technique, is a

powerful tool for mixed environments such as mangroves. However, spatial

autocorrelation will reduce the accuracy up to a certain level. For instance, the noise

from non-leaf surfaces such as tree branches and background can degrade the results

of spectral separability of mangroves and thus can reduce classification accuracy at

species level.

In both instances, the aerial photographs showed lower classification

accuracies than the pan-sharpened WV2 images. For example: at both instances, it

was not possible to successfully differentiate Bruguiera exaristata and Lumnitzera

racemosa from other species, both having lower producers’ and users’ accuracies.

The user’s accuracy of Bruguiera exaristata, Ceriops tagaland Rhizophora stylosa

indicates high errors of commission, because other species were highly misclassified

as them.

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2.5 Conclusions and Recommendations

This study compared high spatial resolution aerial photographs with satellite

remote sensing data for the purpose of mangrove species discrimination in two steps.

First, mangroves and non-mangroves were separated using object-based image

analysis method. Then, the mangrove coverage only was classified into species level.

The study demonstrated that a large number of spectral bands with higher spatial

resolution (pan-sharpened WV2 image) were more accurate than broad spectral

bands within the blue, green, and red regions, when discriminating mangroves from

other features in an image. In addition, our findings show that, using a calibrated,

high radiometric resolution sensor such as WV2 allows greater classification

automation with reduced manual editing.

When further classifying down to species level, the highest accuracy (overall

accuracy of 89%) was obtained from the pan-sharpened WV2 image using five

spectral bands within the visible range. The pan-sharpened visible image covered the

same spectral range with the same spatial resolution as the aerial photographs.

Therefore, the higher accuracy of the former compared to the overall accuracy of

68% from resampled aerial photographs is attributed to the increased number of

narrow bands available for analysis, rather than the total wavelength range.

Compared to these results, however, there is no considerable difference between the

mangrove species map obtained from the pan-sharpened Red, Red-Edge and NIR1

bands of WV2 image. Further, this study demonstrated the significant increase in

classification accuracy when using pan-sharpened imagery, on the condition that

spectral and radiometric integrity is maintained using an appropriate algorithm such

as high pass filter pan-sharpening method.

87

This study also demonstrated a unique application of the support vector

machine algorithm for mangrove species mapping. While this advanced image

processing technique has previously been used in other environments, it is

particularly beneficial for mangroves because it efficiently deals with the dense,

heterogeneous nature of mangrove forests.

Although the method used in this study is tested on a mangrove forest with a

small number of species, the obtained results were very impressive. These results

provide a valuable contribution to the mangrove species mapping methodologies. We

would recommend repeating this process on larger study areas with greater species

diversity in order to determine the efficiency and accuracy of the proposed data and

SVM methods. The method could also be tested with blue to NIR1 wavelength bands

of pan-sharpened WV2 on a computationally powerful system. The transferability of

the rule set developed for OBIA can also be tested on a different data set. Further, to

obtain a higher degree of species classification accuracies, a quantitative analysis of

the effects of differences between radiometric resolutions should be investigated.

2.6 Acknowledgments

The support of the Northern Territory Government, Australia in providing

aerial photographs for this study is gratefully acknowledged. Authors also appreciate

Sandra Grant and three anonymous reviewers for constructive advice and editorial

comments.

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2.7 Author Contributions

Muditha K. Heenkenda designed the research, processed the remote sensing data,

and drafted the manuscript with co-authors providing supervision and mentorship

throughout the process.

2.8 Conflicts of Interest

The authors declare no conflict of interest.

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Chapter 3 - Mangrove tree crown delineation

from high resolution imagery

This work has been published in the following papers:

Heenkenda, M. K., Joyce, K. E. & Maier, S. W. 2015, Mangrove Tree

Crown Delineation from High Resolution Imagery, Photogrammetric

Engineering and Remote Sensing, vol. 81, 471-479; doi:

10.14358/PERS.81.6.471.

Formal permission from the publisher is attached in Appendix 2.

(Reproduced with permission from the American Society for

Photogrammetry and Remote Sensing: The imaging and geospatial

information society).

AWARD - 2016 ESRI Award for the Second Best Scientific Paper in Geographic

Information Systems from the American Society of Photogrammetry and Remote

Sensing: The imaging and geospatial information society (ASPRS).

97

3.0 Abstract

Mangroves are very dense, spatially heterogeneous, and have limited height

variations between neighbouring trees. Delineating individual tree crowns is thus

very challenging. This study compared methods for isolating mangrove crowns using

object based image analysis. A combination of WorldView-2 imagery, a digital

surface model, a local maximum filtering technique, and a region growing approach

achieved 92% overall accuracy in extracting tree crowns. The more traditionally used

inverse watershed segmentation method showed low accuracy (35%), demonstrating

that this method is better suited to homogeneous forests with reasonable height

variations between trees. The main challenges with each of the methods tested were

the limited height variation between surrounding trees and multiple upward pointing

branches of trees. In summary, mangrove tree crowns can be delineated from

appropriately parameterized object-based algorithms with a combination of high

resolution satellite images and a digital surface model. We recommend partitioning

the imagery into homogeneous species stands for best results.

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3.1 Introduction

Mangrove forests represent a land-ocean interface ecosystem in tropical and

sub-tropical regions of the world. They provide a wide range of goods and services

that have been acknowledged for decades at local, national, and global levels (Food

and Agriculture Organisation, 2007). Nevertheless, a rapid rate of destruction of

these intertidal forests due to various anthropogenic disturbances over the last few

decades has been recorded (Suratman, 2008). While earlier descriptive or

observational studies focused on community usage of mangroves, more recently

there has been an increase in analytical studies quantifying the diverse value of

mangrove forests (Komiyama et al., 2008; Voa et al., 2012). To quantitatively

evaluate mangrove forests, estimating individual tree growth, biomass, and

productivity has become important.

The expansion of leaf area or number of leaves available for photosynthesis is

an indicator for the biomass production of trees, and thus leaves composing a crown

indicate individual tree growth, senescence, and death (Analuddin et al., 2009). To

determine biophysical parameters of trees (height, diameter at breast height, growth

rate etc.) using remote sensing methods, individual trees must first be isolated and

tree crown boundaries delineated.

Isolating individual trees and extracting tree structure has a significant

contribution to a variety of applications. Individual trees and their structure can be

used for forest inventories, assessing forest regeneration, quantifying above ground

biomass, and assessing vegetation damage (Chen et al., 2006). Therefore, an

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investigation of mangrove tree crown foliage dynamics would be an added value to

estimate the productivity and health of mangrove forests.

Measuring precise crown foliage dynamics manually in the field is a

challenging task as the irregularities of crown shapes are difficult to capture using

standard field survey equipment. The available field surveying methods for

individual tree attribute extraction are labour intensive and time consuming. As an

alternative, very high resolution satellite and aerial images, as well as laser scanners

might provide a viable option for extracting this information. This is supported by

advances in algorithmic developments towards higher precision, reliability, and

automation. A number of studies have already been conducted to extract individual

tree crowns for various vegetation types using remotely sensed data with varying

degree of success (Blaschke et al., 2011; Chen et al., 2006; Erikson, 2004; Erikson

and Olofsson, 2005; Gougeon and Leckie, 2003; Hirata et al., 2010; Kaartinen et al.,

2012; Larsen et al., 2011; Vauhkonen et al., 2012; Wannasiri et al., 2013; Whiteside

et al., 2011). However, very little information is available for using remote sensing

for delineating mangrove tree crowns.

A review of methods for delineating individual trees or groups of trees in

optical images identified four main approaches: local maxima detection algorithm,

segmentation methods, valley following algorithm, and tree modelling and image

template construction (Lopez, 2012). Kaartinen et al. (2012) tested several other tree

crown extraction methods using airborne laser scanning data. However, in both

instances, the most intensively investigated technique is local maxima detection. It

assumes that tree apexes generate intensity peaks in images, thus local maximum

image brightness values relate to tree top locations (Leckie et al., 2005; Pouliot et al.,

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2002). As this method does not delineate crown boundaries, it has to be combined

with a segmentation method to identify crowns. A segmentation method involves

identification of groups of similar neighbouring pixels that corresponds to objects or

part of objects. There are many approaches for this: grouping textures, morphological

operators, and joining convex-shaped edges. Hence, construction of regions of the

image is based on homogeneity of image characteristics such as image brightness,

texture, shape, and size etc. Furthermore, in advanced approaches, semantic

information can also be incorporated during segmentation (Eisank et al., 2010).

As an alternative approach for a combination of local maxima detection

algorithm and a region growing, the valley following method considers image

intensities as an analogy for a topographic surface (Gougeon, 1995). It creates a hill

pattern, where the tree tops are actually seen as hill tops. Boundaries of tree crowns

show as dark values, shady rings or ‘valleys’ depending on sun illumination angle

(Gougeon, 1995). The valley following algorithm defines a set of semantic rules to

enclose the valleys, leading to the separation of individual crowns (Gougeon, 1995).

However, this method is more suitable for sparsely distributed forests or plantations

than densely clustered natural forests. It works best when crowns have clear

boundaries, are approximately symmetrical, similar in size, and do not overlap each

other (Bunting and Lucas, 2006). Similarly, the topographic concept of a watershed

can also be applied to the contour detection and image segmentation according to

Beucher and Lantuejoul (1979). And, the topographic concept of a watershed can

also be extended to delineate individual tree crowns. It is another more commonly

applied method for determining tree crowns (Edson and Wing, 2011), and called

Inverse Watershed Segmentation (IWS).

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The IWS method converts the elevation of the surface model to form the

equivalent of individual hydrologic drainage basins (Edson and Wing, 2011; Swamer

and Houser, 2012; Wannasiri et al., 2013). These individual drainage basins (canopy

basins) represent tree crowns. As the algorithm relies on an inverted surface model,

the accuracy and the resolution of the digital surface model is important. For

example, Edson and Wing (2011) found that the canopy height model with 1 m

resolution of a conifer forest decreased the commission errors caused by upward

facing branches of the same tree when tree tops were 1m apart from each other.

The tree modelling and image template method constructs geometric models

of trees (templates) and matches them to images (Korpela et al., 2007; Wolf and

Heipke, 2007). During this process, shape parameters such as crown diameter, tree

height, and convexity are investigated and often modelled by means of a generalized

ellipsoid. Therefore, the model of the tree which is similar to the visible trees in the

remotely sensed image in terms of viewing and illumination is created as a three-

dimensional projection of a tree crown (Larsen et al., 2011; Larsen and Rudemo,

1998). Hence, the optimal shape and the placement of bounding ellipse in an image

vary with image acquisition geometry (Larsen and Rudemo, 1998).

Although many algorithms have been investigated for individual tree crown

delineation, it is clear that no single algorithm is universally successful in delineating

the crowns of all different vegetation types (Kaartinen et al., 2012; Larsen et al.,

2011; Li et al., 2008). Most methods are based on image intensities, elevation, and

homogeneity of vegetation with reasonable height variations. Further, these methods

are more successful with plantation forests having regular distances between trees

rather than naturally grown forests (Bunting and Lucas, 2006). Unfortunately, such

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variations are rare in mangrove forests. When considering forest types, although

most already established methods have been successful in coniferous forests, often

they do not work well in deciduous and mixed species forests (Jing et al., 2012).

Deciduous tree crowns are also relatively flat (Jing et al., 2012) and thus resemble

mangrove crowns. Therefore, the methods that have already been developed for

identifying individual trees of terrestrial vegetation may not be suitable for mangrove

environments.

This study evaluated the potential of using high spatial resolution optical

remote sensing data and an object based image analysis (GEOBIA) approach for

outlining mangrove tree crowns by: (a) applying GEOBIA to WorldView-2 (WV2)

data; (b) applying GEOBIA to the combined WV2 and a high resolution digital

surface model (DSM) derived from aerial photography; and (c) applying the ISW

method to the DSM.

3.2 Data and methods

3.2.1 Study area, remote sensing data, and pre-processing

This study focused on a small mangrove forest situated in the Rapid Creek

coastal system near Darwin, Australia (Figure 3.1). The Rapid Creek mangrove

forest covers approximately 60 hectares in extent, and is centred at E/12o22’

S/130o51’. The mangrove forest naturally regenerated after a proposed housing

project was abandoned shortly after a clearing in 1969 (Ferwerda et al., 2007). The

area is comprised of diverse, spatially complex, and common mangrove communities

in the Northern Territory of Australia.

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Figure 3.1. The study area is located in Rapid Creek in Darwin, Northern Territory,

Australia. The map shows the validation dataset including both field surveyed and on

screen digitised (three dimensionally) trees. Inset shows the appearance of mangrove

tree crowns on the aerial photograph; Coordinate system: Universal Transverse

Mercator Zone 52 L, WGS84; Aerial photographs © Northern Territory Government

(NTG), Australia, Copyright 2012 NTG.

A WV2 satellite image acquired on 5 June, 2010 was used as the main source

of remotely sensed data. The image has 8 multispectral bands with 2.0 m spatial

resolution, and a panchromatic band with 0.5 m spatial resolution. The wavelength

range of the WV2 satellite images vary from 447 nm to 1043 nm (Table 3.1). To

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generate a digital surface model, true colour aerial photographs were used. The date

of acquisition of these photographs is 7 June, 2010 and the sensor is UltraCamD

large format digital camera (Table 3.1).

The WV2 image was radiometrically, atmospherically, and geometrically

corrected according to the method adopted by Heenkenda et al. (2014). The red and

the NIR1 bands were pan-sharpened to 0.5 m spatial resolution to incorporate edge

information from the high spatial resolution panchromatic band to the lower spatial

resolution multispectral bands. The high pass filter pan-sharpening method was

utilised as it produces a fused image without distorting the spectral balance of the

original WV2 satellite images (Chavez and Bowell, 1988; Chavez et al., 1991;

Heenkenda et al., 2014; Palubinskas, 2013). Since the spectral range of the

panchromatic band does not cover the spectral ranges of both coastal and NIR2

bands, they were not used for further processing.

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Table 3.1. Spectral resolution of WV2 image and aerial photographs (DigitalGlobe,

2011; Northern Territory Government, 2010)

Band Spectral range (nm) Spatial resolution (m)

WorldView-2 satellite image

Panchromatic 447 – 808 0.5

Coastal 396 – 458

2.0

Blue 442 – 515

Green 506 – 586

Yellow 584 – 632

Red 624 – 694

Red-Edge 699 – 749

NIR1 765 – 901

NIR2 856 – 1043

Aerial photographs from UltraCamD camera

Blue 380-600

0.14 Green 480-700

Red 580-720

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The image was smoothed with a low-pass filtering process. Low-pass filtering

minimizes some of the natural variability of the data and the noise related to sensor

deficiency. It also helps to reduce difficulties associated with having too many details

within crowns (Gougeon, 1995). The focal statistics (maximum) algorithm in

ArcGIS was applied to the panchromatic, red and NIR1 bands of the WV2 image to

identify the local maximum reflectance values within the neighbourhood (ArcGIS

Resources – ESRI). The specified neighbourhood was defined as circular in shape,

and three pixels in size. This threshold value was selected based on the field

observations, assuming that the average radius of a mangrove tree crown was

approximately 1.5 m. These pre-processed panchromatic, red, and NIR1 bands were

then used for further processing.

3.2.2 Extracting mangrove coverage from images

According to Bunting and Lucas (2006), the first step of any tree crown

delineation is to separate trees to be examined and non-trees of the area (generate a

forest mask). Hence, the mangrove coverage was extracted from the WV2 image,

creating an outline of the mangrove area to be further analysed. Full description of

the methodology is available in Heenkenda et al. (2014), but in brief summary, class

specific rules that incorporated texture, geometry, location information, and

relationships between image objects at different hierarchical levels from the WV2

image were used. This mangrove coverage outline was used to avoid mixing other

surrounding features such as other vegetation types, mudflats within mangroves,

roads and buildings, when isolating individual tree crowns.

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3.2.3 Digital Surface Model (DSM) generation

Aerial photographs captured by an UltraCamD camera were oriented to

ground coordinates following the digital photogrammetric image orientation steps in

the Pix4DMapper software. The image orientation parameters were extracted from

the UltraCamD camera calibration report. A DSM was then created with 15 cm

resolution. Ground control points obtained from “National Geospatial Reference

System, Australia” (Geoscience Australia) were included to minimize the errors that

can arise from image matching. Once the DSM was created, the mangrove outline

was used to extract the region of interest for further processing.

The DSM was smoothed using a low pass filter to eliminate unexpected,

random height variations (typically noise) and small gaps, and to enhance crown

edges. The focal statistics (maximum) algorithm in ArcGIS was introduced to the

DSM to identify local maximum values within the neighbourhood. This focal

statistics-maximum tool considers each pixel in the raster, and calculates maximum

values with respect to identified neighbourhood (ArcGIS Resources ESRI, 2012).

Therefore, the resulting pixel gets the maximum value of the given neighbourhood.

In this study, a circular neighbourhood (a kernel) was applied to select the maximum

values within a range of 10 pixels (0.15 x 10 m), thus the number of pixels

corresponds the average radius of tree crowns.

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3.2.4 Mangrove tree crown delineation

Mangrove tree crown extraction was tested on several different layer

combinations using local maxima detection and a region growing algorithm. These

are explained in greater detail below.

3.2.4.1 WV2 panchromatic, red, and NIR1 bands (PAN-R-NIR1 method)

Individual trees were first extracted using a combination of panchromatic,

red, and NIR1 bands of the WV2 image. The red and NIR1 multispectral bands were

selected based on the interaction of light at these wavelengths with vegetation. Red

wavelengths are sensitive to chlorophyll absorption, and the NIR region is useful for

analysing vegetation biomass. The image was initially segmented based on shape

and homogeneity of the WV2 bands using the eCognition software package. Tree

tops were detected by investigating local maxima in the panchromatic and NIR1

bands, assuming they appear brighter than surrounding shaded areas. The resultant

tree tops were 1 pixel in size. An iterative procedure of region growing was

implemented to expand the identified tree tops, and to draw ovals of tree crowns. The

extent of an individual oval should therefore represent the extent of each crown. As a

control parameter for the region growing, the ratio of the NIR1 band of the tree top

object to the neighbouring objects was used assuming that the NIR1 band value is

unique for the entire crown. To remove falsely detected crowns, the mean value of

the Normalised Difference Vegetation Index (NDVI) and the mean standard

deviation of the red band were used. For example: if the NDVI value of the selected

tree crown object is less than the mean NDVI value of objects, and the standard

deviation of the red reflectance is less than the mean standard deviation of the red

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values of objects, then the object was classified as the false tree crown. This helped

to avoid extending tree crowns into the gaps between mangrove trees. Finally, the

tree crowns were smoothed (reshaped avoiding jagged edges) using the morphology

algorithm available in eCognition.

3.2.4.2 WV2 panchromatic, red, and NIR1 bands and DSM (PAN-R-NIR1-DSM

method)

Mangroves often appear very dense and spatially heterogeneous without

considerable tree height differences between neighbouring trees. However,

underlying height values and variations can be used to extract individual tree crowns

with object based image analysis (GEOBIA). The aerial photography derived DSM

was incorporated into the PAN-R-NIR1 method to investigate the possibility of

further improvements of the shapes of tree crowns using actual height variations.

Tree tops were detected by searching local maxima in the DSM. Because of the

limited height variations of mangrove trees between neighbouring trees, in addition

to their densely clustered nature, spectral information of the panchromatic band of

the WV2 image was also incorporated for tree top detection. As some mangrove

species appear to have multiple tree tops due to the complex nature of their branches

(multiple upward pointing branches), all tree tops that were less than 3 m apart were

identified as false tree tops. This 3 m control distance was chosen in congruence with

the average distance between two trees according to the field observations. After

that, an iterative process of region growing from these tree tops was introduced to

delineate crown boundaries. Two control parameters were used to limit the crown

boundary growing. One was the ratio of the NIR1 band value of the tree top object to

neighbouring objects, and the other one was the height difference between the tree

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top object and the edge of the crown. It was assumed that the maximum height of

tree crowns was about 2 m. Finally, a morphology analysis was done to re-shape the

isolated crowns.

3.2.4.3 The inverse watershed segmentation (IWS) method with the DSM

To apply the IWS method, the DSM that did not undergo low pass filtering

was used. First the DSM was inverted, which resulted in turning the tree tops upside

down into depressions or ponds. Tree branches and crown boundaries therefore

became watersheds. The local minimum values (pour points) were then detected and

classified as tree tops. Small imperfections (a pixel or set of pixels whose flow

direction is undefined) in any digital surface model are called sinks (ArcGIS

Resources – ESRI, 2012). Once the sinks were identified from the inverted surface

model, they needed to be filled to generate the flow directions. The watersheds were

based on these flow directions and positions of tree tops. Finally, watersheds were

converted to polygons assuming that they represented tree crowns.

3.2.5 Accuracy assessment

The quality of image segmentation is largely based on the quality of the

source data, especially the radiometric, spatial, and spectral resolutions, in addition to

optimal selection of parameter values for the segmentation. A meaningful rule set

then enables adaptation of the segmentation results correctly based on target objects.

However, to identify the best segmentation method and corresponding data source

for mangrove tree crown isolation, a goodness of polygon matching method

described by Clinton et al. (2010) was used. This calculates OverSegmentation,

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UnderSegmentation, and the closeness index (D), which explains the closeness in the

two dimensional space defined by both OverSegmentation and UnderSegmentation.

OverSegmentation (correctness) and UnderSegmentation (completeness) indicate the

inaccuracies associated with too many or too few segments (Moller et al., 2007;

Zhan et al., 2005). And they are in the range of [0, 1] where zero represents the

perfect segmentation. The closeness (distance) index D is in the range of [0, 21/2].

Validation data were collected by manually digitizing 268 tree crown

boundaries using the Stereo Analyst module in ERDAS IMAGINE software. This

included 56 field surveyed tree locations as well. In order to assess the potential of

mangrove tree crown delineation from high resolution remotely sensed data,

OverSegmentation, UnderSegmentation, and the closeness indices were calculated.

3.3 Results

The Rapid Creek mangrove forest has some areas with homogeneous

mangrove species, and some areas with a more heterogeneous mix. There are five

commonly found mangrove species: Avicennia marina, Ceriops tagal, Bruguiera

exaristata, Lumnitzera racemosa, and Rhizophora stylosa (Heenkenda et al., 2014).

Although Excoecaria ovalis and Aegialitis annulata can also be recognized, their

coverage is relatively limited. Field observations determined that an average distance

between two trees is about 3 m and an average radius of tree crowns is about 1.5 m.

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3.3.1 The digital surface model

The digital surface model (DSM) created for the Rapid Creek mangrove

forest is shown in Figure 3.2. The horizontal accuracy of the DSM is approximately

0.15 m compared to the ground control points. The vertical accuracy is about 0.2 m

with respect to field measurements of tree heights. The DSM showed localised

circular patterns throughout (mostly corresponding to mangrove tree crowns – Figure

2.2 inset A) highlighted by the application of the focal statistics function.

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Figure 3.2. The digital surface model of the Rapid Creek mangrove forest created

from aerial photographs. Inset “A” shows localized circular patterns due to the image

enhancement using focal statistics function in ArcGIS.

3.3.2 Mangrove tree crown delineation

3.3.2.1 WV2 panchromatic, red, and NIR1 bands (PAN-R-NIR1 method)

Mangrove tree crowns extracted with the PAN-R-NIR1 method are shown in

Figure 3.3 (A). Some of the polygons represent aggregated trees rather than single

trees. Successful detection of maximum brightness values representing tree tops in

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the image is evident when polygons are overlaid with the panchromatic image

(Figure 3.4 (A)). However, smooth crown boundaries cannot be perceived. Also,

gaps exist between crown boundaries obtained from the PAN-R-NIR1 method,

though the mangrove forest is very dense by nature.

3.3.2.2 WV2 panchromatic, red, and NIR1 bands and DSM (PAN-R-NIR1-DSM

method)

Figures 3.3 (C) and (D) illustrate the tree crowns delineated using the

PAN-R-NIR1-DSM method overlaid with the panchromatic image and the DSM

respectively. Once the DSM was incorporated, shapes of the canopies were improved

dramatically. When overlaid with the DSM, it can be seen that there are some

clusters of mangrove trees detected rather than individuals (Figure 3.3 (D)). Extents

of the gaps between tree canopies are less obvious than the PAN-R-NIR1 method.

3.3.2.3 The inverse watershed segmentation (IWS) method with the DSM

The IWS method mainly detected clusters of tree canopies with similar

heights, rather than individuals. Figures 3.3 (E) and (F) show the delineated

mangrove tree clusters and initially extracted tree tops overlaid with the

panchromatic image and the DSM respectively. Most of the neighbouring tree tops

were merged together to form a single crown. The DSM showed that there was no

significant range in height differences within tree clusters (Figure 3.3 (F)).

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Figure 3.3. (A) Mangrove tree crowns delineated with the PAN-R-NIR1 method

overlaid with the panchromatic image; (B) Overview of the Rapid Creek mangrove

forest; (C) Mangrove tree crowns delineated with the PAN-R-NIR1-DSM method

overlaid with the panchromatic image; (D) Mangrove tree crowns delineated with the

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PAN-R-NIR1-DSM method overlaid with the DSM; (E) Mangrove tree crowns

delineated with the IWS method overlaid with the panchromatic image and tree tops

(black dots); (F) Mangrove tree crowns (black outlines) delineated with the IWS

method overlaid with the DSM and tree tops (black dots).

Table 3.2 shows the number of objects extracted as tree tops using each

method. The number of objects extracted using the PAN-R-NIR1-DSM method

reported the highest number and from the PAN-R-NIR1 method reported the lowest

number. The difference between these two methods was approximately 11,500

objects. The IWS method extracted 8767 more objects than the PAN-R-NIR1

method.

Table 3.2.The number objects extracted as tree tops using three different methods.

The centroid positions of tree crowns obtained for part of the mangrove forest

is shown in Figure 3.4. All the centroid positions of the PAN-R-NIR1 method are

very close (i.e. they occur at the same location) to the corresponding positions from

PAN-R-NIR1-DSM method (Figure 3.4 (A)). This is because the PAN-R-NIR1

method detected the highest brightness values of the images as tree tops, while the

PAN-R-NIR1-DSM method identified both the highest brightness and height values

as tree tops. When creating the watershed from the inverted surface model, most of

Method No. of objects extracted as tree tops

WV2 image only (PAN-R-NIR1) 21,027

WV2 image and DSM (PAN-R-NIR1-DSM) 32,789

Inverse Watershed Segmentation method (IWS) 29,794

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the tree crowns that had small height differences within their neighbourhood were

clustered together resulting a single tree crown (Figure 3.3 (F)).

Figure 3.4. (A) Centroid positions obtained from the PAN-R-NIR1 and the

PAN-R-NIR1-DSM methods overlaid with the panchromatic WV2 image with low

pass filtering applied; (B) Overview of the Rapid Creek mangrove forest; (C)

Centroid positions obtained from the IWS method overlaid with the DSM.

118


The visual appearance of the results obtained with the PAN-R-NIR1-DSM

method is the one closest to reality. Most of the tree crowns that have reasonable

height variations between neighbouring trees have successfully been detected. The

crown boundaries were smoother than two other methods. Although the tree crowns

obtained with the PAN-R-NIR1 method detected tree tops quite successfully, crown

boundaries had a distinctive jagged shape representing the pixel size of the image

bands. However, this was quite different when considering PAN-R-NIR1-DSM

method. The integration of the high spatial resolution DSM significantly improved

shapes of the crown boundaries. Although the IWS method detected a number of

tree tops closer to the one detected from the PAN-R-NIR1-DSM method (Table 3.2),

it was not possible to delineate the same amount of tree crowns after processing the

surface raster.

The distribution of validation dataset: in-situ surveyed and on screen digitized

tree crowns is shown in Figure 3.1. Table 3.3 shows the calculated statistical values

for different GEOBIA approaches. Tree crowns delineated from the PAN-R-NIR1-

DSM method resulted in the most successful closeness index of 0.11 (relative

accuracy of 92%) followed by the PAN-R-NIR1 combination with a closeness index

of 0.15 (relative accuracy of 89%). There is a slight improvement in tree crown

delineation when incorporating the DSM with optical imagery. OverSegmentation

(correctness) represents the ratio of the area of geographic intersection of reference

objects and tree crowns to the total area of reference objects. Hence, the PAN-R-

NIR1 method got the highest error rate (15%). Completeness (UnderSegmentation)

represents the ratio of the area of geographic intersection of reference objects and

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tree crowns to the total area of corresponding objects (Clinton et al., 2010).

Completeness of both methods is very good, showing 0.02 for UnderSegmentation.

Table 3.3. A comparison of accuracies of extracted tree crowns: the possible range of

both OverSegmentation and UnderSegmentation values is [0,1] where zero defines

the perfect segmentation. The values closer to zero for “closeness index” illustrate

the closeness of mangrove tree crowns in two dimensional space which is defined by

OverSegmentation and UnderSegmentation to validation crowns (Clinton et al.,

2010).

The visual appearance of results obtained from the IWS method was poor

(Figures 3.3 (E) and (F)), and the quantitative accuracy assessment confirmed this in

Table 3.3. A closeness index value of 0.94 indicated very poor completeness due to

high UnderSegmentation value. The OverSegmentation value was close to zero

indicating that the most objects delineated using the IWS method coincided with

reference tree crowns.

Method OverSegmentation UnderSegmentation Closeness index

Overall relative

accuracy PAN-R-NIR1 0.15 0.02 0.15 89%

PAN-R-

NIR1-DSM

0.11 0.02 0.11 92%

IWS 0.02 0.94 0.94 35%

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3.4. Discussion

Various approaches have been developed for detecting individual tree crowns

from remotely sensed data. However, most of these were developed with deciduous

or coniferous forests. Trees within these forests have crowns with conical shapes.

These methods are also generally successful in plantation forests where there are

regular distances between trees (Kaartinen et al., 2012; Leckie et al., 2005;

Tiede et al., 2005). According to Vauhkonen et al. (2012), the success of tree crown

delineation algorithms was significantly affected by the forest structure, especially

tree density and clustering. To confirm that, their study investigated six different tree

crown delineation algorithms for different forest types such as boreal forests in

Norway and Sweden, coniferous and broad leaved forests in Germany, and a tropical

pulpwood plantation in Brazil using laser scanning data. Due to the development of

tree crown delineation algorithms for specific forest types and species compositions,

they are less likely to be applied to mangroves. For example: Hirata et al. (2010)

segmented fewer mangrove tree crowns using QuickBird panchromatic satellite

images and a watershed segmentation than the reality. The presence of intermingled

mangrove tree crowns and the structural complexity of mangrove forests make them

unique and densely clustered.

In this study, tree top detection using local maxima, and crown boundaries

identification using a region growing method was tested on different data layer

combinations from high resolution optical images. This approach is more flexible

compared to the inverse watershed method in detecting tree crowns with varying

sizes as it uses the local maxima as the starting point of the growing seed, and the

local minima as the ending point. For example; the PAN-R-NIR1-DSM combination

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used local maximum values of the DSM and NIR1 band as the starting point of the

seeds, grew them until the ratio of the NIR1 spectral value of the seed object and

neighbouring objects reached to 0.9, and the height difference between the seed

object and the neighbouring objects was up to 2 m. Further, instead of only

investigating the spectral variations of images, incorporating height variations of

canopies was fruitful as the generated DSM identified minor height variations within

canopies (Figure 3.2).

The PAN-R-NIR1 method showed a good visual appearance of mangrove

tree crowns when overlaid on the panchromatic image (Figure 3.3 (A)). This is

supported by the detailed study of Kamal et al. (2014) who confirmed the possibility

of identifying a single mangrove tree crown from remotely sensed images with pixels

smaller than 2 m. However, the number of trees detected using the PAN-R-NIR1

method was less than the PAN-R-NIR1-DSM method, and had relatively large gaps

between demarcated crowns. This was due to the similarities in spectral reflectance

causing some trees to be aggregated and demarcated as one. Therefore, it can be

concluded that the homogeneity within each mangrove species, a percentage of

crown overlap and limited height variations of canopies can affect the accuracy of

delineation. For example: if neighbouring trees were as tall as the seed trees,

brightness variations in the image were unlikely to be seen, and they were therefore

aggregated as one crown (Figure 3.3 (A)). However, conically shaped crowns and

crowns having considerable distance between each other was accurately delineated.

Successful tree crown delineation is strongly influenced by the shape of tree

crowns. Gougeon and Leckie (2006) stated that the detection of tree locations using

local maxima provides good results for medium to dense coniferous stands with

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conical shape crowns in conjunction with GEOBIA. However, this is not the case

with mangroves. Most mangrove species tend to be rather flat as opposed to conical

in shape (Duke, 2006; Wightman, 2006), and therefore the local maxima approach is

less suitable at some places. The best approach is to divide the area into homogenous

forest stands before applying crown delineation methods. This finding supports that

of Larsen et al. (2011), who evaluated six different individual tree crown delineation

algorithms in varying forest conditions.

When the mangrove tree crowns obtained from the PAN-R-NIR1 DSM

combination were overlaid with the high resolution panchromatic image, the visual

appearance was better than the PAN-R-NIR1 combination (Figures 3.3 (C) and (D)).

The shapes of the crown boundaries were improved due to incorporation of both

image reflectance and height variations for region growing. The number of trees

identified using the PAN-R-NIR1-DSM method was larger than the other methods

(3.2). Since the DSM is sensitive to height variations (spatial resolution of the DSM

was 15 cm), trees with small height variations compared to neighbouring trees can

also be detected. Figure 3.4 (C) shows some examples of low laying mangrove tree

identification. However, there might be some false tree identifications as well. If the

DSM is sensitive to small height variations within canopies, multiple upward

pointing branches of the same tree are identified as different trees. Hence, more than

one tree crown can be counted per tree. Although there was a tree top filtering

technique applied in terms of average distance between trees, it was not possible to

avoid false tree identification completely. The filtering distance was calculated

according to field observations and generalized to the Rapid Creek area. To achieve

higher accuracy, this could be done on a species specific level although that in itself

is a challenge (Heenkenda et al., 2014). Further, there might be some inaccuracies

123

with respect to the DSM generation. For example; the homogeneous forest coverage

within communities and the repetitive pattern of mangroves can significantly impact

on the image matching accuracy (Baltsavias et al., 2008; Gruen, 2012). Introducing

ground control points and manual editing processes cannot avoid such irregularities

completely. One of the alternatives is to use laser scanning data with high point

density to generate canopy height models.

Although the IWS method detected tree tops as successfully as the other two

methods (Figure 3.4 (C) and Table 3.2), once we implemented the processing steps,

the method clustered neighbouring mangrove trees with similar characteristics

together and delineated them as a single tree crown (Figures 3.3 (E) and (F)). The

key input for the IWS algorithm is a flow direction map that shows direction of flow

out of each pixel of the inverted surface raster. If the height irregularities to the

neighbouring pixels are negligible with respect to the introduced control algorithms,

the system cannot identify corresponding flow direction (ArcGIS Resources - ESRI,

2012). Although the study considered the average height difference between tree top

and the crown edge (crown height) as a control parameter, the investigation of the

flow direction map showed the clear aggregation of neighbouring trees with

approximately similar heights. As we understand, the only solution to avoid such a

situation is to deal with homogeneous forest stands separately. However,

Wannasiri et al. (2013) stated that the inversed watershed method performed well in

mangrove forests having low percentage of crown overlap conditioning with a

canopy height model generated from airborne laser scanning data. The main reasons

would be the accuracy of the canopy height model and relatively large crowns

(average diameter is about 3 15 m). Further, the increasing percentage of crown

overlap decreases the extraction accuracy of individual tree parameters from canopy

124

height models. Another drawback of this method is that it involved creating many

intermediate processing files, thus the processing itself is complicated. This method

is a good solution for sparsely distributed, homogeneous forests with reasonable

height variations between trees (Swamer and Houser, 2012) but less suitable for

mangrove forest applications.

A quantitative accuracy assessment provides the measure of a goodness of

polygon matching with respect to validation samples. However, highest evaluation

results do not necessarily involve a good segmentation (Clinton et al., 2010). The

obtained results were validated against 268 tree crowns manually digitized from the

stereo models (Figure 3.1). The PAN-R-NIR1-DSM method showed the best results

having the lowest closeness index value or 92% overall relative accuracy (Table 3.3).

The completeness error of this method was also very low indicating almost all

reference objects were classified as tree crowns. When comparing the numerical

values of the accuracy assessment, there was no significant difference between the

PAN-R-NIR1 and the PAN-R-NIR1-DSM methods. However, the crown shape and

the number of trees identified were improved when incorporating the height

information rather than only using spectral information.

3.5 Conclusions and recommendations

This study tested different data layer combinations for delineating individual

tree crowns in a mangrove forest environment. WV2 imagery was used as the

primary data source, supplemented by an aerial photo derived DSM. The

combination of a WV2 image, a DSM and a GEOBIA algorithm was found to

successfully delineate mangrove tree crowns. The achieved overall relative accuracy

125

was 92%. Minor errors were detected where neighbouring trees with similar

characteristics were aggregated together and conversely, where multiple crowns were

assigned to a single tree (upward pointing branches of the same tree identified as

different trees). The WV2 image with a GEOBIA algorithm also provided good

results compared to the PAN-R-NIR1-DSM method. The statistical values show only

minor differences in accuracies between these two methods though the visual

appearance was dramatically improved when incorporating the DSM. The IWS

method, which has traditionally been successfully used in other environments with

more sparse canopies, did not provide data representative of mangrove tree crowns.

Therefore, it can be concluded that the combination of a WV2 image, a DSM

generated from image matching, and a GEOBIA is a relatively low cost, accurate,

and robust approach, which can be applied to heterogeneous forests like mangroves.

The study also confirmed that it is important to classify the imagery into

homogeneous species stands before applying individual tree detection algorithms.

The validation of the detected locations of trees or tree tops is recommended before

applying region growing algorithm in order to overcome the structural complexity

problems associated with mangroves. We also recommend using orthorectified WV2

images for detecting tree tops to eliminate topographic errors and spatial mismatches.

Further, we suggest testing a combination of WV2 image and airborne LiDAR

derived canopy height model to delineate mangrove tree crowns.

126

Acknowledgement

The support of the Northern Territory Government, Australia in providing aerial

photographs for this study is gratefully acknowledged. Authors appreciate Miguel

Tovar Valencia, Evi Warintan Saragih, Silvia Gabrina Tonyes, Olukemi Ronke

Alaba, and Dinesh Gunawardena for their assistance with field work. Authors would

also like to thank three anonymous reviewers and Ian Leiper for constructive advice

and editorial comments.

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Chapter 4 - Quantifying mangrove chlorophyll

from high spatial resolution imagery

Graphical abstract


Heenkenda, M. K., Joyce, K. E., Maier, S. W., & de Bruin, S. 2015,

Quantifying mangrove chlorophyll from high spatial resolution imagery,

ISPRS Journal of Photogrammetry and Remote Sensing, vol. 108,

234-244, http://dx.doi.org/10.1016/j.isprsjprs.2015.08.003.


136

4.0 Abstract

Lower than expected chlorophyll concentration of a plant can directly limit

photosynthetic activity, and resultant primary production. Low chlorophyll

concentration may also indicate plant physiological stress. Compared to other

terrestrial vegetation, mangrove chlorophyll variations are poorly understood. This

study quantifies the spatial distribution of mangrove canopy chlorophyll variation

using remotely sensed data and field samples over the Rapid Creek mangrove forest

in Darwin, Australia. Mangrove leaf samples were collected and analysed for

chlorophyll content in the laboratory. Once the leaf area index (LAI) of sampled

trees was estimated using the digital cover photography method, the canopy

chlorophyll contents were calculated. Then, the nonlinear random forests regression

algorithm was used to describe the relationship between canopy chlorophyll content

and remotely sensed data (WorldView-2 satellite image bands and their spectral

transformations), and to estimate the spatial distribution of canopy chlorophyll

variation. The imagery was evaluated at full 2 m spatial resolution, as well as at

decreased resampled resolutions of 5 m and 10 m. The root mean squared errors with

validation samples were 0.82, 0.64 and 0.65 g/m2 for maps at 2 m, 5 m and 10 m

spatial resolution respectively. The correlation coefficient was analysed for the

relationship between measured and predicted chlorophyll values. The highest

correlation: 0.71 was observed at 5 m spatial resolution (R2=0.5). We therefore

concluded that estimating mangrove chlorophyll content from remotely sensed data

is possible using red, red-edge, NIR1 and NIR2 bands and their spectral

transformations as predictors at 5 m spatial resolution.

137

Keywords: Mangrove, chlorophyll, WorldView-2, satellite imagery, remote sensing

random forests

4.1 Introduction

Mangroves are one of the most productive and biochemically active

ecosystems (Suratman, 2008). Their dense root system reduces coastal erosion, and

protects the coastline from flood, waves and storms. These roots also filter and trap

pollutants, thereby decreasing coastal pollution. Mangrove forests serve as a nursery

area for shrimps, fish and crustaceans. On the global scale, high population pressure

in coastal areas has converted many mangrove forests into infrastructure, salt and

rice production, and aquaculture (Food and Agriculture Organisation, 2007). If these

coastal activities are unsustainably planned and managed, the result will be a large

scale mangrove degradation or deforestation. Therefore, the maintenance of

mangrove ecosystems is important.

Maintenance of mangrove ecosystems requires better knowledge of their

physiological processes such as photosynthesis, net primary production and plant

health (Flores-de-Santiago et al., 2013). The status of these physiological processes

may reflect by the nutrients in vegetation foliage (Filella and Penuelas, 1994). For

example, plant stress may affect the plant pigment system, and thus the

photosynthesis. More specifically, the chlorophyll content of any foliage is correlated

with nitrogen levels, and hence photosynthesis, and developmental stages (Filella

and Penuelas, 1994; Haboudane et al., 2002; Wu et al., 2008). Therefore, assessing

mangrove chlorophyll content is an important tool for ecosystem management.

138

Chlorophyll variations can occur at a variety of scales from individual trees to

communities, or even broader regions. Variations are affected by soil type, soil

nutrients, topography, and daily nutrient intake received from other sources

(Williams, 2012). Compared to other ecosystems, understanding mangrove

biochemical variations especially chlorophyll content is relatively limited (Flores-de-

Santiago et al., 2013; Williams, 2012; Zhang et al., 2012).

Quantifying spatial variability of mangrove chlorophyll from field

observations is time consuming and costly. An intensive sampling scheme is needed

throughout the area of interest to capture fine scale spatial variability. As fine scale

sampling is often prohibitive over large areas, predictive models are created to

extrapolate the information to unsampled regions. Remote sensing is integral in this

process.

Remote sensing has been used for decades to measure the chlorophyll content

of various natural plant communities (Atzberger et al., 2010; Boegh et al., 2012;

Clevers and Kooistra, 2012; Filella and Penuelas, 1994; Joyce and Phinn, 2003). For

example, Wu et al. (2008) compared the performances of several vegetation indices

derived from hyperspectral data in estimating chlorophyll content, and introduced

four new vegetation indices that are highly correlated with canopy chlorophyll.

Gitelson et al. (2003) investigated the spectral characteritics of the relationship

between reflectance and chlorophyll content of maple, chestnut, wild vine and beech

leaves, and developed a technique for non-destructive chlorophyll estimation. These

studies commonly related the absorption and reflectance of light in different

wavelengths with the presence or absence of photosynthetic pigments. Further,

developing statistical relationships between the chemical content extracted from leaf

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samples and light reflectance in specific wavelengths using airborne or satellite data

allows modelling the spatial variations of chlorophyll over a large area.

Chlorophyll absorbs light strongly in the blue and red wavelength regions for

the purpose of photosynthesis. (Kokaly et al., 2009) described the broad wavelength

region of 400-700 nm as the most active region for leaf pigments or chlorophyll.

Filella and Penuelas (1994) identified the red edge region (wavelengths of 680-750

nm) as one of the best remote sensing descriptors of chlorophyll concentration. Hunt

et al. (2013) developed a triangular greenness index (TGI) considering spectral

reflectance at wavelengths: 480, 550, and 670 nm, and confirmed TGI as the best

spectral index for low-cost chlorophyll mapping. However, Wu et al. (2008) stated

that the blue region should not be used to estimate chlorophyll content due to its

overlapping absorption features with carotenoids. Therefore, the chlorophyll

prediction indices that provide higher accuracies are mainly based on the reflectance

around the 550 nm or 680 nm to 750 nm regions. In summary, there are numerous

options for revealing canopy pigment concentration from remotely sensed data.

The challenge is to identify the spectral bands of remote sensing data and

their transformations with the highest predictive power to model the spatial variation

of mangrove chlorophyll. Statistical relationships between field samples and

corresponding reflectance values provide numerical estimates for analysing the best

predictor. For example, Flores-de-Santiago et al. (2013) introduced Vog1 index

stating that the ratio of reflectance at 740 nm to that of at 720 nm as the best

predictor for the south end of the Urias system mangrove forest in Mexico. Zhang et

al. (2012) identified the red-edge position as the best predictor for the degraded

mangroves of Mexican Pacific during the dry season.

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The aim of this study was to model the spatial distribution of mangrove

canopy chlorophyll content using remotely sensed data and field samples. Further,

the study analysed the optimal combination of predictor variables and spatial

resolution for chlorophyll mapping with the random forests regression algorithm.

4.2 Materials and methods

4.2.1 Study area and satellite data

This study focused on the Rapid Creek mangrove forest in Darwin, Northern

Territory, Australia (12o 22’43”S, 130o 51’55”E) (Figure 4.1). The extent of the

mangrove forest is about 3.8 hectares. Avicennia marina, Ceriops tagal, Bruguiera

exaristata, Lumnitzera racemosa, and Rhizophora stylosa are the most common

mangrove species in this forest (Heenkenda et al., 2014). There is relatively limited

coverage of the additional species Excoecaria ovalis and Aegialitis annulata.

A WorldView-2 (WV2) 2.0 m spatial resolution, multispectral satellite image

was selected as the remote sensing data source for this study. The image was

acquired on 26th July, 2013, with eight multispectral bands. The image was

radiometrically corrected with the sensor specifications published by DigitalGlobe®

(Updike, 2010). Digital numbers were converted to at-sensor radiance values, and

then to top-of-atmosphere reflectance values. The additive path radiance was

removed using the dark pixel subtraction technique in ENVI 5.0 software. Finally,

the image was geo-referenced using rational polynomial coefficients provided with

the image, and ground control points extracted from digital topographic maps of

Darwin, Australia (Heenkenda et al., 2014). To avoid the confusion between

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mangroves and non mangroves, mangrove areas were extracted using the object-

based image analysis method described in Heenkenda et al. (2014). We used

contextual information, geometry and neighborhood characteristics of objects at

different hierarchical levels to separate mangrove coverage only.

Figure 4.1. The study area is located in the coastal mangrove forest of Rapid Creek in

Darwin, Northern Territory, Australia; (A) WorldView-2 satellite image and

locations of 29 field sampling plots (5 m x 5 m); WorldView-2 images ©

DigitalGlobe; (B) Australia, the boundary of the Northern Territory, and the study

area; (C) The Rapid Creek mangrove forest (study area); Coordinate system:

Universal Transverse Mercator Zone 52 L, WGS84.

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The WV2 multispectral bands were resampled to 5 m and 10 m spatial

resolution using the cubic convolution resampling method. This was done to simulate

remote sensing images from other satellite missions that provide multispectral

images within the same spectral region such as RapidEye and SPOT 5. Green (506

nm - 586 nm), red (624 – 694 nm), red edge (699 nm – 749 nm), NIR1 (765 nm –

901 nm) and NIR2 (856 nm – 1043 nm) bands of WV2 image and number of band

ratios and indices were calculated with the image data to test their linear relationship

with mangrove chlorophyll content at three different spatial resolutions (Table 4.1).

The calculated vegetation indices and selected WV2 bands were stacked together to

form a single image with 12 bands for each spatial resolution.

To identify individual mangrove tree crowns, we used a stereo model of the

study area generated from an overlapping pair of WV2 panchromatic images

acquired on the same date. To prepare the stereo model, the image pair was oriented

to ground coordinates following digital photogrammetric image orientation steps in

the Leica Photogrammetric Suite (LPS) in ERDAS IMAGINE software. The ground

references were obtained from the Rational Polynomial Coefficients (RPC’s)

calculated during the image acquisition, and ground control points extracted from the

digital topographic map of Darwin, Australia. The accuracy of the stereo model was

assessed with respect to the ground control points.

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Table 4.1. Vegetation indices used to establish the relationship between mangrove

field samples and remotely sensed data; green, red, red edge, NIR1 and NIR2

surface reflectance of WorldView-2 satellite data for corresponding wavelength

regions.

Vegetation index Band relationship Source

Normalized Difference

Vegetation Index (NDVI)

(𝑁𝑁𝑁𝑁𝑅𝑅1 − 𝑟𝑟𝑟𝑟𝑟𝑟) (𝑁𝑁𝑁𝑁𝑅𝑅1 + 𝑟𝑟𝑟𝑟𝑟𝑟)⁄ Rouse et al. (1974) and Ahamed et al. (2011)


Vegetation Index (NDVI2)

(𝑁𝑁𝑁𝑁𝑅𝑅2 − 𝑟𝑟𝑟𝑟𝑟𝑟) (𝑁𝑁𝑁𝑁𝑅𝑅2 + 𝑟𝑟𝑟𝑟𝑟𝑟)⁄ Mutanga et al. (2012)

Normalized Difference Red

Edge index (NDRE)

(𝑁𝑁𝑁𝑁𝑅𝑅1 − 𝑟𝑟𝑟𝑟𝑟𝑟 𝑟𝑟𝑟𝑟𝑙𝑙𝑟𝑟) (𝑁𝑁𝑁𝑁𝑅𝑅1 + 𝑟𝑟𝑟𝑟𝑟𝑟 𝑟𝑟𝑟𝑟𝑙𝑙𝑟𝑟)⁄ Barnes et al. (2000) and Ahamed et al. (2011)

Normalized Difference Red

Edge index (NDRE2)

(𝑁𝑁𝑁𝑁𝑅𝑅2 − 𝑟𝑟𝑟𝑟𝑟𝑟 𝑟𝑟𝑟𝑟𝑙𝑙𝑟𝑟) (𝑁𝑁𝑁𝑁𝑅𝑅2 + 𝑟𝑟𝑟𝑟𝑟𝑟 𝑟𝑟𝑟𝑟𝑙𝑙𝑟𝑟)⁄ Mutanga et al. (2012)

Green Normalized Difference

Vegetation Index (GNDVI)

(𝑁𝑁𝑁𝑁𝑅𝑅1 − 𝑙𝑙𝑟𝑟𝑟𝑟𝑟𝑟𝑔𝑔) (𝑁𝑁𝑁𝑁𝑅𝑅1 + 𝑙𝑙𝑟𝑟𝑟𝑟𝑟𝑟𝑔𝑔)⁄ Gitelson et al. (1996) and Ahamed et al. (2011)

Chlorophyll Vegetation Index

(CVI)

(𝑁𝑁𝑁𝑁𝑅𝑅1 ∗ 𝑟𝑟𝑟𝑟𝑟𝑟) (𝑙𝑙𝑟𝑟𝑟𝑟𝑟𝑟𝑔𝑔2)⁄ Vincini et al. (2007) and Vincini et al. (2008)

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Figure 4.2 explains the overall workflow for mapping chlorophyll content

from the satellite data. The chlorophyll content per square unit of leaf area was

calculated using field samples. The leaf area index of each tree was calculated using

vertical photographs. They were used to estimate the canopy chlorophyll content.

Then, with a suspected non-linear relationship between chlorophyll and the spectral

reflectance, the random forests multiple regression algorithm was selected to

establish the relationship between samples and predictor variables. The modelled

relationship was then applied to the satellite imagery to obtain the chlorophyll map.

The method is discussed in greater details below.

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Figure 4.2. Workflow for mapping chlorophyll content from satellite data and field

samples. This procedure was repeated for the satellite data with 5 m and 10 m spatial

resolution. NDVI = Normalized Difference Vegetation Index from NIR1 and red

bands; NDVI2 = Normalized Difference Vegetation Index from NIR2 and red bands;

NDRE = Normalized Difference Index from NIR1 and rededge bands; NDRE2 =

Normalized Difference Index from NIR2 and rededge bands; GNDVI: Normalized

Difference Index from NIR1 and green bands; CVI: Chlorophyll Vegetation Index

from NIR1, red and green bands.

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4.2.2 Field sampling

Field sampling was conducted from 21st to 25th July, 2013 in the Rapid Creek

mangrove forest. This period coincided with the date of a satellite image overpass to

reduce discrepancies between sample data and predictor information. A sampling

pattern was determined considering the accessibility, density of mangrove forest,

mangrove greenness, and species variation. However, when selecting sampling

locations, attention was given to avoid areas close to water features due to the danger

of salt water crocodiles that inhabit the region. A total of 29 plots were selected with

an extent of 5 m x 5 m each. Within each plot, at least five trees that can easily be

identified on the image were selected for leaf collection. However, there were some

very dense mangrove plots where we could not select five trees due to their

overlapping canopies. From each tree (n=84), five leaves were collected from

different branches that were exposed to direct sunlight. The leaves were immediately

wrapped in an aluminium foil and placed in airtight and appropriately labelled plastic

bags in a cooler bag to transport to the laboratory. The position of each tree was

recorded using a course acquisition global positioning system (GPS). Further,

auxiliary details such as distances to roads and water features that can easily be

identified in the image were collected to aid locating these trees on the image.

Upward looking photographs of the canopy (at a zero zenith angle), were

taken using a Panasonic Lumix DMC-FT2 compact digital camera to calculate the

leaf area index (LAI) of each tree. Two spirit levels were used to minimize the

camera tilt. The camera was set to an aperture priority-automatic exposure mode.

Sixteen evenly spaced photographs were taken to cover each plot area. The number

of trees within the plot was counted in the field.

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The digital cover photography method developed by Macfarlane et al. (2007)

was selected to calculate LAI. This is a fast and reliable method for estimating LAI

within densely clustered mangroves. Vertical photographs were used to estimate the

large gaps between mangrove trees, small gaps within mangrove trees, the proportion

of the ground area covered by the vertical projection of foliage and branches, the

crown cover, the crown porosity, and the woody-to-total area ratio of each sampling

plot. A detailed description of the method to estimate LAI using digital photographs

can be found in Macfarlane et al. (2007) and Pekin and Macfarlane (2009).

All digital photographs were manually checked for overlapping mangrove

trees. An object based image analysis (OBIA) method was introduced to classify the

background (sky), mangrove leaves, and branches/stems using eCognition software.

Areas with high blue reflectance values were classified as the sky, considering the

contrast between vegetation and the background of the photographs. The ratio

between the reflectance in the blue and the red bands were further considered to

separate the background and the vegetation. Vegetation was further classified into

leaf area and branches/stems. To identify branches/stems from vegetation, the

greenness calculated from photographs were used as Eq. (1) (Liu and Pattey, 2010).

𝐺𝐺𝑟𝑟𝑟𝑟𝑟𝑟𝑔𝑔𝑔𝑔𝑟𝑟𝐵𝐵𝐵𝐵 = 2 ∗ 𝐺𝐺 − 𝐵𝐵 − 𝑅𝑅 (1)

Where G, B, and R represent the reflectance (intensity levels) recorded with the

green, blue and red bands of the digital camera.

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The background of the vertical photograph was then classified into large gaps

between trees (gL), and small gaps within trees (gT) using size and relations to

neighboring features. For example, if the area of the gap was less than 50 pixels and

surrounded by vegetation, it was classified as a small gap within a tree. The total

number of pixels for large gaps and small gaps were calculated (see Appendix 4 A

for the detailed description of this classification).

The fraction of the foliage cover (ff) as the proportion of the ground area

covered by the vertical projection of foliage and branches (Macfarlane et al., 2007;

Walker and Tunstall, 1981), and the crown cover (fc) were calculated as per Eqs. (2)

and (3).

𝑓𝑓𝑐𝑐 = 1 − 𝑙𝑙𝐿𝐿 ∑𝑃𝑃𝐵𝐵𝑃𝑃𝑟𝑟𝑙𝑙𝐵𝐵⁄ (2)

𝑓𝑓𝑓𝑓 = 1 − 𝑙𝑙𝑇𝑇 ∑𝑃𝑃𝐵𝐵𝑃𝑃𝑟𝑟𝑙𝑙𝐵𝐵⁄ (3)

Where gL is the total number of pixels of large gaps and gT is the total number

of pixels, fc is the crown cover, ff is the fraction of the foliage cover, and ∑𝑃𝑃𝐵𝐵𝑃𝑃𝑟𝑟𝑙𝑙𝐵𝐵is

the total number of pixels of the photograph.

The crown porosity (ɸ) is the proportion of the ground area covered by the

vertical projection of foliage and branches within the perimeter of the crowns of

individual plants. It was calculated from Eq. (4).

ɸ = 1 − 𝑓𝑓𝑓𝑓 𝑓𝑓𝑐𝑐⁄ (4)

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Where ff is the fraction of the foliage coverage and fc is the crown cover.

The effective plant area index (Lt) that includes the contribution from woody

elements to the total plant cover was estimated using the modified version of the

Beer-Lambert law as specified in Eq. (5).

𝐿𝐿𝑡𝑡 = −𝑓𝑓𝑐𝑐 ∗ (𝑙𝑙𝑔𝑔(∅) 𝑘𝑘⁄ ) (5)

Where fc is the crown cove, ɸ is the crown porosity and k is the canopy

extinction coefficient.

The canopy extinction coefficient (k) that defines an angle and spatial

arrangement of leaves was taken as 0.5 for this study. Different vegetation types

inherit different k values. For example, Clough et al. (1997) estimated k value of

mangroves as 0.56 considering direct measurements of mangrove leaf angles and

measurement of beam transmittance (DEMON instrument). The value of 0.525 can

also be considered as an appropriate value for mangrove stands (Green and Clark,

1997; Green et al., 1997). However, according to Perera et al. (2013), 0.5 is a good

average value for mangroves when there is no already estimated one.

Then we calculated the woody-to-total area ratio (α) and the actual leaf area

index (LAI) from Eqs. (6) and (7) (Chen, 1996; Chen et al., 1997).

𝛼𝛼 = ∑(𝐴𝐴𝑟𝑟𝑟𝑟𝐴𝐴𝐵𝐵)∑(𝐴𝐴𝑟𝑟𝑟𝑟𝐴𝐴𝑉𝑉) (6)

𝐿𝐿𝐿𝐿𝑁𝑁 = 𝐿𝐿𝑡𝑡 ∗ (1 − 𝛼𝛼) (7)

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Where ∑(𝐿𝐿𝑟𝑟𝑟𝑟𝐵𝐵𝐵𝐵) is the total area of branches except leaves, ∑(𝐿𝐿𝑟𝑟𝑟𝑟𝐵𝐵𝑉𝑉) is the

total area of vegetation including branches, and Lt is the effective plant area index.

The final results represented an actual leaf area index of individual plots or a

cluster of trees. This was then divided by the number of trees to get a value per tree.

4.2.2.2 Estimating leaf chlorophyll concentration

A 10.4 mm diameter core was extracted from a mangrove leaf using a

sharpened metal tube, and ground with a small amount of 80% acetone (Wellburn,

1994). The slurry was put into a centrifuge tube, and the total amount made to a

volume of 5 ml. The samples were centrifuged for five minutes at 2500 rpm.

Approximately 1-2 ml of the supernatant was pipetted into a quartz cell for

spectrophotometric analysis. The absorbance of the solution was recorded at 663 nm

and 646 nm, and Chlorophyll a and b concentrations (mg/l) were calculated based on

the Eqns. (1) and (2) (Wellburn, 1994). The summation of these two represents the

total chlorophyll concentration (Arnon, 1949; Mackinney, 1938, 1941; Wellburn,

1994). To compare this with spectral reflectance of satellite images as an areal

measure, chlorophyll contents per square unit leaf area were calculated (g/m2). This

method was repeated for five leaves per tree, and the average value was considered

as the chlorophyll content per square unit leaf area of the particular tree. Finally, the

canopy chlorophyll concentration of each canopy was estimated multiplying the

chlorophyll content per square unit leaf area of the particular tree with the

corresponding LAI.

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Chlorophyll a (Ca) = 12.25A663 – 2.79A646 (8)

Chlorophyll b (Cb) = 21.5A646 – 5.1A663 (9)

Where “A” is the absorbance at the wavelength identified by the subscript (in nm)

4.2.3 Estimating mangrove canopy chlorophyll content from satellite imagery

The random forests regression model is a nonparametric algorithm for

nonlinear multiple regression (Breiman and Cutler, 2001). The model calculates a

response variable (chlorophyll) by growing an ensemble of different decision trees,

and putting each pixel to be modelled down each of the decision trees. The response

is determined by evaluating the responses from all of these trees. In regression, a

resulting value for a pixel is the mean of all of the predictions of the random forests

(Breiman and Cutler, 2001). See Breiman (2001), Breiman and Cutler (2001),

Vincenzi et al. (2011) and Horning (2010) for a more detailed description of random

forests.

To identify mangrove trees on the stereo model of the study area, GPS

locations as well as additional information that were collected during field sampling

were used. Crown outlines of these trees were three dimensionally digitized on-

screen, and were overlaid with the predictors to calculate the corresponding predictor

variable values. Once overlaid, the pixels those have more than 70% of their extent

inside the tree crown were selected, and their corresponding predictor variable values

were noted. This identified pixels for calibration (n=57) and validation (n=22)

purposes.

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Variables of interest in an experiment (those that are measured or observed)

are called response or dependent variables. Other variables in the experiment that

affect the response, and can be set or measured by the experimenter are called

predictor or independent variables. In this study, response variables are canopy

chlorophyll, and predictor variables are remotely sensed data derived vegetation

indices and WV2 individual bands.

To generate the relationship between field samples and these selected

predictor variable values (Table 4.1), the random forests regression was completed

using the “randomForest” package in R software (Breiman et al., 2014; Horning,

2011; Liaw and Wiener, 2002). After several iterations of fitting the random forests

model with different parameters, the optimal solutions were identified, such as the

number of random trees to be generated, and the number of variables that should be

used at each split when generating random trees.

When growing individual trees, the random forests algorithm uses

approximately two thirds of samples for fitting trees (model calibration)(Breiman,

2001; Vincenzi et al., 2011). The remaining samples are called “out-of-bag” (OOB)

data, and they are used to internally validate the model or to determine the error

drawn at each step (at each terminal node). In regression, the prediction error in the

OOB data is recorded as a mean of squared residuals (MSE). We recorded the MSE

value after parameter optimization as this indicated the predictive ability of the

model.

The importance of each predictor variable to the regression model was then

evaluated. The “randomForest” package provides two measures for variable

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importance. One is based upon the mean decrease of accuracy in predictions on the

OOB data when the given variable is excluded from the model. First, the prediction

error in the OOB data for each tree is calculated (MSE). Then the same is calculated

after permuting each predictor variable on the OOB data. The difference between

these two instances are then averaged over all trees, and normalized by the standard

deviation of the differences (Liaw and Wiener, 2002).The other variable importance

measure is the total decrease in node impurity that resulted from splits over the

variable, averaged over all trees. Impurity is measured by the residual sum of squares

(Breiman, 2001; Horning, 2010). However, due to impurity bias for selecting split

variables, the variable importance measure can also be biased (Gromping, 2009).

In summary, the importance measure shows how much the mean squared

error (or impurity) changes when the variable is randomly permuted. For example, if

we randomly permute a variable that do not contribute to the prediction, the

prediction may not improve, and a small changes of the mean squared error (and

impurity) can only be noted. Therefore, high mean squared error indicates more

important variable (Breiman, 2001; Breiman and Cutler, 2001).

In this study, the decreasing mean squared error as the measure of variable

importance was used. This measure has also been adapted as the state-of-the-art

approach by various studies (Gromping, 2009). We also used the “varSelRF”

package in R software (Diaz-Uriarte, 2014) to find out the optimal number of

predictor variables that can be used to obtain the predicted map of canopy

chlorophyll at different spatial resolutions. This package uses the OOB error

minimization criteria to eliminate the least important variables.

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Once the random forests model was calibrated and internally validated, it was

applied to the selected predictors to create a map of mangrove canopy chlorophyll

over the study area. For this study, we used a “raster” package from the R software

(Hijmans and van Etten, 2012) to predict the generated random forests model (with

all trees in the forest) using training data.

4.2.3.1 Canopy chlorophyll variation with respect to mangrove species

Once the chlorophyll map with the highest accuracy was identified, predicted

chlorophyll values for each mangrove species were assessed. Random points were

generated (n=500) using ArcGIS software over the mangrove species map produced

by Heenkenda et al. (2014). Corresponding canopy chlorophyll values were assigned

to these points, and the chlorophyll variation with respect to mangrove species was

investigated.


The mean of squared residuals (OOB error) was analyzed to estimate the

predictive performance of the random forests model. The validation dataset was used

to assess the accuracy of the predicted map. The root mean squared error (RMSE)

and a correlation between predicted chlorophyll values and measured chlorophyll

values were recorded. The coefficient of determination (R2) was calculated to check

how close the data to the fitted regression line. The process was repeated for the data

with 5 m and 10 m spatial resolutions.

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4.3 Results

4.3.1 Field sampling

The canopy chlorophyll values retrieved from field samples (n=84) varied

from 0.06 to 3.85 g/m2. The mean chlorophyll value was 1.32 g/m2, and the variance

was 0.78 g/m2. The LAI for individual trees ranged from 0.39 to 15.62. The average

LAI was 4.0.


Correlation coefficients were used to explore the relationship between

chlorophyll content and satellite data. A weak linear correlation between field

samples and satellite data was found (Figure 4.3). There is variability in correlation

not only between different predictor variables, but also between different spatial

resolutions. In some cases linear trend correlations were even positive at one spatial

resolution, while negative at a different resolution. For instance, although CVI index

had positive correlation for 2 m spatial resolution data, it had negative correlation for

both 5 m and 10 m satellite data (Figure 4.3). Hence, a small variation of any point

may change the trend line from positive to negative or negative to positive.

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When considering 2 m spatial resolution, the NIR1 band, CVI and NDRE

indices showed the highest positive linear correlation with field samples (0.12, 0.14

and 0.26 respectively) while NIR2 showed the lowest correlation (0.02). Green,

yellow, red and red-edge bands revealed a negative linear correlation with field

samples (Figure 4.3).

Figure 4.3. Linear correlation coefficients against predictor variables with different

spatial resolutions; Green, Yellow, Red, RedEdge, NIR1 and NIR2: surface

reflectance of green, yellow, red, red-edge and near infrared bands of WorldView-2

image; NDVI = Normalized Difference Vegetation Index from NIR1 and red bands;

NDVI2 = Normalized Difference Vegetation Index from NIR2 and red bands; NDRE

= Normalized Difference Index from NIR1 and rededge bands; NDRE2 =

Normalized Difference Index from NIR2 and rededge bands; GNDVI: Normalized

Difference Index from NIR1 and green bands; CVI: Chlorophyll Vegetation Index

from NIR1, red and green bands.

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From predictor variables with 5 m spatial resolution, NDRE2 index showed

the highest positive correlation while NIR2 band showed the lowest correlation

(0.01). Correlations obtained for green, yellow, red-edge, NIR1 bands and CVI,

NDVI and GNDVI indices of 10 m spatial resolution decreased with increasing

chlorophyll content, displaying a negative linear correlation. The red-edge band and

CVI index obtained the highest negative linear correlation (-0.20 and -0.21

respectively) while NDVI2 and NDRE2 had the highest positive linear correlation

(0.21 and 0.25 respectively). The correlation coefficient with NDRE was lower than

that of 2 m and 5 m resolutions. Therefore, it is difficult to interpret a strong linear

correlation between field samples and predictor variables at these spatial resolutions.

The most suitable parameters for the random forests model were selected

based on the OOB errors. The high number of random trees (n=2000) produced an

optimal solution having the lowest OOB error. OOB errors were 0.42, 0.54 and

0.55 g/m2 for 2 m, 5 m and 10 m resolutions respectively. The optimal number of

variables tried at each split node is four. However, compared to the variance of

canopy chlorophyll samples, these OOB errors are high, indicating relatively low

predictive performances of the models.

It was found that three variables are sufficient for predicting canopy

chlorophyll at 2 m spatial resolution, while four variables are required at 5 m and 10

m spatial resolution. Figure 4.4 shows the importance of each predictor variable

when estimating the response variable (canopy chlorophyll). The NIR1 band,

GNDVI and NDRE2 indices were considered the most important for 2 m spatial

resolution. However, they had relatively low importance at 5 m and 10 m spatial

resolution. The NDRE2, NDVI, and NDVI2 indices and the yellow band were the

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four most important predictor variables when the spatial resolution was 5 m. Yellow

and green bands, NDRE2 and NDVI2 indices were the four most important

predictors for 10 m spatial resolution. In all instances, the NDRE index showed the

lowest importance (Figure 4.4).

Figure 4.4. Ranking the importance of predictor variables according to the mean

squared error (%IncMSE); Green, Yellow, Red, RedEdge, NIR1 and NIR2: surface

reflectance of green, yellow, red, red-edge, NIR1 and NIR2 bands of the

WorldView-2 image; NDVI = Normalized Difference Vegetation Index from NIR1

and red bands; NDVI2 = Normalized Difference Vegetation Index from NIR2 and

red bands; NDRE = Normalized Difference Index from NIR1 and rededge bands;

NDRE2 = Normalized Difference Index from NIR2 and rededge bands; GNDVI:

Normalized Difference Index from NIR1 and green bands; CVI: Chlorophyll

Vegetation Index from NIR1, red and green bands.

159

The canopy chlorophyll variation in Rapid Creek mangrove forest varies from

0.36 g/m2 to 2.79 g/m2 at three spatial resolutions (Figure 4.5). The lowest predicted

value for the map with 2 m spatial resolution was 0.43 g/m2 while more similar

values were estimated at 5 m and 10 m resolutions (0.36 g/m2 and 0.37 g/m2). The

highest chlorophyll values were shown along the creek (and its branches) at all

resolutions. Lowest chlorophyll values were always close to the edge of the

mangrove forest in the landward direction.

Figure 4.5.Canopy chlorophyll variation (g/m2) in the Rapid Creek mangrove forest;

(A) 2 m spatial resolution; (B) 5 m spatial resolution; and (C) 10 m spatial resolution.

160

4.3.2.1 Canopy chlorophyll variation with respect to mangrove species

As shown in Figure 4.6, Avicennia marina and Rhizophora stylosa had the

highest mean canopy chlorophyll value (1.6 g/m2). However, their canopy

chlorophyll distribution ranges are different. Chlorophyll content of Avicennia

marina ranged from 0.76 to 2.74 g/m2 while that of Rhizophora stylosa is 0.82 to

2.60 g/m2. However, the median canopy chlorophyll content of the Avicennia marina

was1.4 g/m2and, 50% of the dataset was within the range of 1.4 g/m2 to 2.7 g/m2.

Lumnitzara racamosa had the lowest mean canopy chlorophyll value (1.2 g/m2).The

median canopy chlorophyll content of the Lumnitzara racamosa was1.2 g/m2and,

50% of the dataset was within the range of 1.1 g/m2 to 1.3 g/m2 (Figure 4.6). The

distribution range of Bruguiera exaristata is 0.66 to 2.72 g/m2. Ceriops tagal and

Bruguiera exaristata had approximately equal mean values (1.24g/m2 and 1.25 g/m2

respectively) and distribution patterns (Figure 4.6). 75% of their canopy chlorophyll

content values were less than 1.3 g/m2.

161

Figure 4.6. The canopy chlorophyll distribution with mangrove species;

AM- Avicennia marina; BE - Bruguiera exaristata; CT - Ceriops tagal;

LR - Lumnitzera racemosa; RS - Rhizophora stylosa.


The RMSE values, correlation coefficients (r) between predicted maps and

individual validation samples, and R2 are shown in Table 4.2. The 5 m spatial

resolution map showed the highest correlation coefficient (0.71) and R2equals to 0.5

indicating a strong linear relationship between predicted chlorophyll values and

measured chlorophyll samples. At 2 m spatial resolution, relatively low

correspondence between predicted values and measured values was observed. The

map with 10 m spatial resolution also showed a strong correlation with the validation

data.

(g/m

2

)

162

Table 4.2. Root mean squared errors (RMSE), correlation coefficients, and

coefficients of determinations calculated with respect to predicted chlorophyll values

and independent validation samples

4.4 Discussion

Mangroves those are prone to natural or anthropogenic disturbances (stressed

mangroves) experience decreases in leaf chlorophyll concentrations, incident light

absorbance, and overall productivity (Filella and Penuelas, 1994; Flores-de-Santiago

et al., 2013). Although a useful measure of these effects is quantifying nitrogen

stress, this is difficult to measure using remote sensing techniques. As leaf nitrogen

content is highly correlated with its chlorophyll content, chlorophyll can be used as a

surrogate measure. As such, we have demonstrated a method that estimates

mangrove canopy chlorophyll from remotely sensed data.

The LAI measurements obtained from the digital cover photography method

are at a successful level of accuracy. Although there is no recorded previous study in

the region to compare with, results are in line with few other available mangrove

studies around the world. For example, leaf area indices of Avicennia marina

Spatial

resolution

Root Mean Squared

Error (g/m2)

Correlation

coefficient (r)

Coefficient of

determination (R2)

2 m 0.82 0.28 0.08

5 m 0.64 0.71 0.50

10 m 0.65 0.66 0.44

163

plantations in Thailand varied from 0.5 to 5.0 (Laongmanee et al., 2013). A remotely

sensed data derived mangrove leaf area indices of British West Indies varied from

0.8 to 7.0 having 3.96 as the mean (Green et al., 1997). It can therefore be concluded

that the LAI values of sampled trees are at an acceptable level.

Leaf samples exhibited low chlorophyll concentration per square meter

compared to few available studies around the world. For instance, Flores-de-Santiago

et al. (2013) estimated mangrove chlorophyll following the same laboratory

procedure, and obtained the variation from 0.5 to 6.0 g/m2 for both dry and rainy

seasons. On the other hand, some studies used a chlorophyll meter to obtain canopy

chlorophyll values. A lack of concurrent laboratory testing for validation or

mangrove based chlorophyll calculation formulae led these studies to obtain low

chlorophyll concentration per square meter. However, this is not the situation with

this study. Since we used a laboratory based leaf chlorophyll analysis method, the

low chlorophyll concentration may represent some other external factors.

The Rapid Creek catchment is part of the larger Darwin Harbor catchment

including the largest urban area in Darwin, Australia, and therefore, carries relatively

higher pollutant load (Skinner et al., 2009). According to the report published by the

Northern Territory government, the Darwin Harbor catchments’ most soils are

relatively infertile with high potential acidity levels in the mangrove soil (Hill and

Edmeades, 2008). As mangrove nutrients or material exchange through tidal

inundation and flood water is a well-known phenomenon (Adame and Lovelock,

2011) the resultant nutrient deficiencies may affect to the Rapid Creek mangrove

chlorophyll concentration.

164


Numerous studies have examined the relationship between spectral

reflectance across the electromagnetic spectrum and leaf/canopy chlorophyll

concentration of various forest types (Hunt et al., 2013). Many studies have shown a

strong linear relationship between spectral reflectance and field samples. However,

this was not observed in this study despite testing various spectral bands and their

spectral transformations at different resolutions (Figure 4.3). A major obstacle to

radiometrically characterize mangroves is the extremely dynamic area where

mangroves grow. The resultant pixels are comprised of vegetation, soil and water

(Blasco et al., 1998; Kuenzer et al., 2011). In this study, special attention was given

to select sampling trees which we could easily identify on the image. Although these

trees had a less percentage of canopy overlapping with neighboring trees, the

resultant image pixels have more tendencies to mix with surrounding soil or mud.

Further, some sampling areas of the Rapid Creek mangrove forest covers densely

clustered, matured trees, and some areas cover with newly regenerated small

mangrove trees. The age factor of trees also affects the chlorophyll concentration of

canopies. Accounting all these reasons, we were not able to see a strong linear

relationship between spectral indices and canopy chlorophyll content as other

terrestrial vegetation exhibits.

We found no correlation or a poor negative linear relationship between

red-edge band and sample data (Figure 4.3), although Filella and Penuelas (1994)

identified the red edge location to evaluate plant chlorophyll. Some mangrove studies

showed a positive linear correlation with red edge position. For instance, during a dry

season, there was a positive correlation between red edge position and the

165

chlorophyll content for degraded mangroves in in Sinaloa, Mexico (Zhang et al.,

2012). Healthy mangroves of the same area exhibited a high positive correlation

between one of the spectral transformations of red edge position (the ratio of the

reflectance at 740 nm to that of 720 nm -Vog1 index) (Flores-de-Santiago et al.,

2013). Instead, in this study, the variable importance measures identified the

red-edge band or its spectral transformations as an important predictor variable at

three spatial resolutions indicating a strong non-linear relationship (Figure 4.4). For

example, NDRE2 derived from NIR2 and red-edge bands was considered as an

important variable at all three instances.

The red, green and yellow bands also got priorities (Figure 4.4). The yellow

band of WV2 image is designed to investigate the yellowness of vegetation. Further,

it is well known that NIR1, NIR2, and green bands are not only important for

extracting information, but they also minimize the spectral contamination of soil,

topography, and viewing angle of the sensor (Hunt et al., 2013). Reflectance in the

near infrared region is strongly influenced by the walls of the spongy mesophyll cells

of leaves, and therefore healthier leaves exhibit stronger reflectance in the near

infrared region (Zhang et al., 2012). Therefore, red, red-edge, NIR1 and NIR2 bands

of the WorldView-2 satellite image, and their derived normalized difference indices

were identified as the best predictor variables.

In this study, since we did not observe a strong linear relationship between

field samples and predictor variables, we used random forests nonlinear, multiple

regression algorithms. In this application, the random forests model provided a

moderate statistical means to quantify mangrove chlorophyll, while having numerous

advantages over other statistical regression methods. The model can support a large

166

number of variables with a relatively small number of training samples. Most

importantly, non-linear random forests regression does not over fit the response

variable (Breiman, 2001; Breiman and Cutler, 2001). The main disadvantage of the

application of this regression model is that it is not possible to predict or extrapolate

the values beyond the range in the training dataset. Therefore, the training dataset

(samples) must be selected carefully to represent the full range of potential

variations.

The canopy chlorophyll content appears to be correlated with proximity to

water features. For example, along the water features of the Rapid Creek mangrove

forest, maps show the highest chlorophyll values. These areas experience high daily

inundation rates, and consequently the trees are healthy and tall. Leaf chlorophyll is

highly dependent on soil nutrient, daily water intake and the exposure to direct

sunlight (Blasco et al., 1998). The landward margin of this mangrove forest receives

a relatively less daily intake of water, and therefore the soil salinity is high. Mostly

these areas are dominated by Lumnitzara racamosa. As shown in Figure 4.6,

Lumnitzara racamosa exhibited the lowest canopy chlorophyll content over the area.

Rhizophora stylosa dominated along water features, and showed relatively high

chlorophyll values. The spatial distribution patterns of all output maps are consistent

(low values closer to the landward margin and high values closer to the water

features) although the accuracy estimates are at low or moderate levels. Our resultant

maps cannot be used to determine chlorophyll values of specific trees but can be

used to investigate the spatial variations throughout the study area at a smaller scale.

167


The map with 5 m spatial resolution showed the lowest RMSE value

(0.64 g/m2) compared to the validation data, and the highest correlation between

predicted and measured values (0.71) and the highest R2 value (0.5). Compared to 10

m spatial resolution map, there is only a slight quantitative difference. However,

pixel values of 10 m might be an integrated measure of individual trees, their

understory, soil and shadows. On the other hand, remotely sensed data with 2 m

spatial resolution had pixel sizes smaller than the size of an individual tree crowns

and a group of pixels characterizes individual trees.

It can be concluded that satellite images with 5 m spatial resolution would be

a suitable solution for chlorophyll mapping in this area. This is supported in part by

the study of Kamal et al. (2014). Although their results are limited to Moreton Bay,

Australia, they confirmed that a pixel size larger than 4 m is suitable for mapping

mangrove vegetation formation, communities and larger mangrove features.

According to their findings, pixel sizes between 0.5 m to 2 m would be an ideal

solution for perceiving structural variations within mangrove canopies (average tree

crowns, foliage clumping, and canopy gaps). However, these results can significantly

vary with characteristics of the mangrove forest and its available species. For

example, the average diameter of the tree crowns and the distance between trees

would make an enormous difference to the end results, increasing or decreasing the

level of information that can be extracted from corresponding pixels. According to

our results, WV2 spectral bands of red, red-edge, NIR1 and NIR2, and their spectral

transformations of 5 m spatial resolution would be the best input data source for

mangrove canopy chlorophyll estimation from the random forests algorithm for the

168

Rapid Creek mangrove forest. As an alternative solution, the RapidEye satellite

images with red-edge and near infrared band (or SPOT5 images) can be used.

Owing to moderate accuracies obtained from this study, a number of errors

were identified throughout the mapping process. Positions of field samples are the

most significant source of uncertainty. The geometric accuracy of the WV2 satellite

image was 0.25 m (half a pixel) compared to the ground control points established in

Darwin, Australia. The positional accuracy of the GPS receiver is about 5-10 m.

Although we used field measured, additional information to identify sampled trees

on images, at the end, field samples may not sufficient representatives of the image

pixels.

For future studies, we would recommend a transect method for field sampling

for more successful results. Two ends of transects should be established outside the

mangrove forest with a highly accurate positioning system. Then, all other

measurements that based on the established transect lines will be with greater

accuracy.


This is the first study that mapped mangrove canopy chlorophyll variation

over the Rapid Creek mangrove forest, combining remotely sensed data, laboratory

measured samples, and random forests regression algorithm. Although we could find

a few studies that analysed the radiometric characteristics of mangrove leaf pigments

and their relation to various vegetation indices derived from satellite data, it is

difficult to find a recorded study for mapping a spatial distribution of mangrove

169

chlorophyll around the world as well. Hence, this study confirmed integrating

remotely sensed data and field samples for mapping mangrove canopy chlorophyll

content over a large area. The random forests regression algorithm showed moderate

results for this application, while there is a weak linear relationship between field

samples and the satellite data.

The best qualitative and quantitative results were obtained from the

chlorophyll maps with 5 m spatial resolution. The red, red-edge, NIR1 and NIR2

bands of the WorldView-2 satellite image, and their derived normalized difference

indices were identified as the best predictor variables. The chlorophyll map with

spatial resolution of 10 m also provided a similar accuracy level. The lowest

accuracy was obtained from the map with 2 m spatial resolution. As an alternative

solution, we would recommend using the RapidEye satellite images, including

red-edge band and near infrared bands.

Although we used field measured additional information to identify sampled

trees on images, it seems that the field samples are not sufficient representatives of

the image pixels. For future studies, we would recommend a transect method for

field sampling with greater positional accuracy. Two ends of these transects should

be established outside the mangrove forest with a highly accurate positioning system,

and the other measurements must be based on these transect lines. Further, the

authors would recommend testing this method on a different mangrove forest to see

the transferability of the method used.

170

Appendix 4.A

171

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181

Chapter 5 - Estimating mangrove biophysical

variables using WorldView-2 satellite data: Rapid

Creek, Northern Territory, Australia

Graphical abstract


Heenkenda M. K., Maier S.W., & Joyce K. E. 2016, Estimating mangrove

biophysical variables using WorldView-2 satellite data: Rapid Creek,

Northern Territory, Australia, Journal of Imaging, 2, 24;

doi:10.3390/jimaging2030024

Publisher confirmed reproducing the journal article without written

permission since “Journal of Imaging” is an Open Access journal.

182

5.0 Abstract

Mangroves are one of the most productive coastal communities in the world.

Although we acknowledge the significance of ecosystems, mangroves are under

natural and anthropogenic pressures at various scales. Therefore, understanding

biophysical variations of mangrove forests is important. An extensive field survey is

impossible within mangroves. WorldView-2 multi-spectral images having a 2-m

spatial resolution were used to quantify above ground biomass (AGB) and leaf area

index (LAI) in the Rapid Creek mangroves, Darwin, Australia. Field measurements,

vegetation indices derived from WorldView-2 images and a partial least squares

regression algorithm were incorporated to produce LAI and AGB maps. LAI maps

with 2-m and 5-m spatial resolutions showed root mean square errors (RMSEs) of

0.75 and 0.78, respectively, compared to validation samples. Correlation coefficients

between field samples and predicted maps were 0.7 and 0.8, respectively. RMSEs

obtained for AGB maps were 2.2 kg/m2 and 2.0 kg/m2 for a 2-m and a 5-m spatial

resolution, and the correlation coefficients were 0.4 and 0.8, respectively. We would

suggest implementing transects method for field sampling and establishing end

points of these transects with a highly accurate positioning system. The study

demonstrated the possibility of assessing biophysical variations of mangroves using

remotely-sensed data.

Keywords: Mangrove, above ground biomass, leaf area index, WorldView-2, partial

least square regression

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5.1 Introduction

Mangrove forests are a dominant feature of many tropical and subtropical

coastlines. They have a variety of growth forms, including intertidal trees, palms and

shrubs that often grow in dense stands [1]. Although mangroves form valuable

ecosystems along sheltered coastal environments, on the global scale, they are

disappearing at an alarming rate [2]. For instance, by 2000, the worldwide mangrove

extent has fallen below 15 million ha, down from 19.8 million ha in 1980 [3]. The

world has thus lost five million ha of mangroves over twenty years, or 25% of the

extent found in 1980. The main reasons for rapid mangrove destruction and land

clearings are urbanization, population growth, water diversion, aquaculture,

agriculture and salt pond construction.

Land clearings throughout catchments and in urbanized areas can cause an

increased volume of water entering watercourses, carrying substances, such as

topsoil, chemicals, rubbish and nutrients. These substances deposit on sediments in

which mangroves grow. The increased amount of water influences the rate of erosion

or deposition of sediments, causing a significant problem for the health of aquatic

habitats. Therefore, when sustainable developments are progressing through land

clearing, it is necessary to ensure effective mangrove conservation.

To set the balance between mangrove conservation and developments, one of

the vital roles of monitoring and ultimately managing mangroves is to create an

accurate and up-to-date quantitative analysis of their baseline health parameters.

However, assessing mangrove health is not a straightforward task. This is due to the

complex structure of forests, their biophysical variations and the interaction between

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mangroves, soil, water and salinity. Therefore, indirect measures that correlate with

the levels of vegetation stresses, such as above ground biomass (AGB), canopy

nutrient levels, particularly canopy nitrogen level, and leaf area index (LAI), can

eventually be considered.

Most of the conventional methods that have been developed for estimating

AGB, LAI and canopy nutrient levels have limitations when extended over space and

time. For example, estimating biomass using the allometric method is based on

measureable canopy dynamics, such as tree height and diameter at breast height

(DBH). Due to the within-stand heterogeneity of canopies, labour-intensive, site- and

species-specific field measurements are crucial [4–6]. However, field sampling

within mangrove forests is challenging. Many mangrove species have complex aerial

root systems, which make sampling difficult. Furthermore, mangroves are dense and

rather difficult to walk through. Remotely-sensed data addresses the major

challenges (especially field sampling) identified with already developed conventional

methods that estimate AGB and LAI. Most remote sensing-based approaches are

capable of estimating plant biophysical characteristics by reducing the shortcomings

of field observations.

There are numerous studies to estimate the biophysical characteristics of

vegetation using remotely-sensed data. The key issue is to correlate the intensity of

electromagnetic energy absorbed or reflected by the plant (spectral reflectance) with

ground measurements of biophysical variables. This spectral reflectance is either

measured in situ (using spectrometers) or via airborne or spaceborne sensors. Several

studies have found strong correlations between forest biomass or LAI and spectral

reflectance values at different wavelengths [7,8]. Eckert [9] and Ahamed et al. [10]

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summarized different studies that analysed the levels of vegetation greenness in

terms of vegetation indices derived from remotely-sensed data for estimating

biomass. For example, Anaya et al. [7] and Satyanarayana et al. [11] used the

normalized difference vegetation index (NDVI) calculated from red and near infrared

wavelengths of remotely-sensed data for estimating biomass. However, compared to

other terrestrial vegetation types, little has been adapted to mangroves.

Compared to other terrestrial ecosystems, a few studies associated with field

samples and remotely-sensed data for producing thematic maps over mangrove

forests can be found [11–16]. Clough et al. [17], Green and Clark [18] and Green et

al. [14] established a relationship between vegetation indices derived from remotely-

sensed data and in situ LAI samples for mangroves.

Kamal et al. [15] studied the spatial resolution of satellite images, spectral

vegetation indices and different mapping approaches for LAI estimation at Moreton

Bay, Australia, and Karimunjawa Island, Indonesia. The study confirmed that the

LAI estimation accuracy using remotely-sensed data was site specific and varied

across pixel sizes and image segmentation scales. Since there is no recorded study

related to the Rapid Creek mangrove forest in Darwin, Australia, the demand still

exists for estimating the LAI of mangroves by integrating in situ samples, remotely-

sensed data and advanced statistical regression algorithms. To the same extent, we

could find only a few studies that mapped AGB of mangroves over a large area. For

example, recently, Zhu et al. [16] retrieved mangrove AGB from field data and

WorldView-2 (WV2) satellite images. This study tested Sonneratia apetala and

Kandelia candel mangrove species and a limited number of vegetation indices for

AGB mapping over the study site. The study confirmed the importance of the red

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edge band of WV2 satellite image and associated vegetation indices for AGB

estimation over other spectral regions.

Komiyama et al. [19], Komiyama et al. [5], Fu and Wu [4] and Perera and

Amarasinghe [20] derived only a relationship between mangrove canopy dynamics,

AGB and vegetation indices. Therefore, testing the possibility of mapping AGB of

mangroves over a large area from remotely-sensed data is still required.

This study aimed to quantify the spatial distribution of LAI and AGB of

mangroves using field measurements and WV2 satellite images. To achieve this aim,

the study compared two different spatial resolutions of WV2 data (original spatial

resolution of multispectral bands (2 m) with resampled multispectral bands (5 m))

and partial least squares regression algorithm for mapping LAI and AGB variations

over a large area.

5.2 Data and methods

5.2.1 Study area

The Rapid Creek mangrove forest in Darwin, Northern Territory, Australia

was selected as the test site for this study (Figure 5.1). This area is dominated by five

different mangrove species - Avicennia marina, Ceriops tagal, Bruguiera exaristata,

Lumnitzera racemosa, and Rhizophora stylosa [20]. Other species do not represent

significant coverage to be considered separately.

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Figure 5.1. Rapid Creek coastal mangrove forest, Darwin, Northern Territory,

Australia. The distribution of field sampling plots (5m x 5m) is shown in the map

(the sizes of magenta squares are not to the scale). Aerial photographs © Northern

Territory Government.

5.2.2 Field sampling, satellite data and predictor variables

A total of 29 plots, 5 m × 5 m in extent, were identified in the field. These

plots were selected with trees having similar characteristics (species, age, height and

DBH) to represent calculated values accurately. Each of them was oriented in a

north-south and east-west direction in order to locate them easily in the satellite

image. Inside each plot, five trees were selected to take measurements based on their

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ability to be identified in satellite images; that is isolated and without clumping to

neighbouring tree crowns. A sampling pattern was determined considering the

accessibility, density of mangrove forest, mangrove greenness and species variation.

Attention was also given to avoid areas close to water features due to the danger of

salt water crocodiles that inhabit the region.

Tree height, diameter at breast height and species were recorded. Species

were identified based on the guidelines provided by Duke [22] and Wightman [23].

The positions of each the tree and the four corners of each plot were recorded using

the non-differentially-corrected Global Positioning System (GPS). Apart from the

GPS measurements, we collected some additional measurements to support

positioning field plots in the image. These measurements especially refer to distances

to closest roads, water features and other features that can easily be identified in the

image.

5. 2.2.1. Remotely-Sensed Data and Predictor Variables

An overlapping pair of WV2 satellite images acquired on 26 July 2013 was

used as the remotely-sensed data for this study. The main reason for having an

overlapping pair of images was to prepare a stereo model of the area to manually

extract individual tree crowns. The satellite image capture coincided with the field

data acquisition. The satellite is in a nearly circular, Sun-synchronous orbit with a

period of 100.2 min. The spatial resolution of multispectral bands was 2.0 m, and the

panchromatic band was 0.5 m. Table 1 shows the detailed spectral band description

of WV2 images.

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Table 5. 1 Spectral band information of WorldView-2 images

Band Spectral Range (nm) Spatial Resolution

(m)

Panchromatic 447–808 0.5

Coastal 396–458

2

Blue 442–515

Green 506–586

Yellow 584–632

Red 624–694

Red-edge 699–749

NIR1 765–901

NIR2 856–1043

WV2 images were radiometrically corrected according to the method

described by Heenkenda et al. [21]. First, digital numbers were converted to

at-sensor-radiance values, and then, they were converted to top-of-atmosphere

reflectance values. The dark pixel subtraction technique in ENVI 5.0 software was

applied to remove the additive path radiance. Finally, images were geo-referenced

using rational polynomial coefficients provided with the images and ground control

points extracted from digital topographic maps of Darwin, Australia. The

georeferenced surface reflectance values were used for further analysis.

Non-mangrove areas of images were masked as per Heenkenda et al. [21].

Class-specific rules were developed based on the contextual information from the

WV2 images, geometry and neighbourhood characteristics of objects at different

hierarchical levels to separate mangrove coverage only (see Heenkenda et al. [21] for

a detailed description).

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Plant growth is correlated with the expansion of leaf canopy and the

increasing weights of woody elements. Hence, the AGB and LAI of mangroves can

relate to vegetation indices derived from remotely-sensed data. To take the maximum

advantage of the relatively narrow spectral bands of WV2 multispectral images, this

study selected nine vegetation indices and the green, yellow, red, red-edge, NIR1 and

NIR2 bands of the WV2 image to predict LAI and AGB over the study area

(Table 5.2). These narrowband greenness indices use spectral reflectance values in

the red, red-edge, NIR1 and NIR2 regions, and they produce a measure of the

photosynthetic characteristics of vegetation. Hence, narrowband greenness indices are

more suitable measures of the biophysical variations and vigour of green vegetation

than broadband greenness vegetation indices. Vegetation indices shown in Table 5.2

were calculated for predicting AGB and LAI from WV2 data. Then, spectral bands

of the WV2 satellite image and vegetation indices were stacked together to form one

image with multiple layers (15 layers) for further processing.

The WV2 multispectral bands were resampled to a 5-m spatial resolution

using the cubic convolution resampling method. This was done to simulate remote

sensing images from other satellite missions that provide multispectral images within

the same spectral region, such as RapidEye. The number of band ratios and

vegetation indices was calculated as explained in Table 2. The green (506 nm–586

nm), red (624–694 nm), red-edge (699 nm–749 nm), NIR1 (765 nm–901 nm) and

NIR2 (856 nm–1043 nm) bands of the WV2 image and the calculated band ratios

and indices were stacked together to form a single image with 15 bands of a 5-m

spatial resolution for further processing.

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Table 5. 2. Predictor variables generated from the WorldView-2 multispectral image

for estimating above ground biomass and leaf area index. NIR1, NIR2, red edge, red

and green: surface reflectance of Near Infrared 1, Near Infrared 2, red-edge, red and

green wavelength regions, respectively.

Vegetation Index Band Relationship Source


Vegetation Index

(NDVI)

(NIR1 − red) (NIR1 + red)⁄ Rouse et al. [24] and

Ahamed et al. [10]


Red Edge index

(NDRE)

(NIR1 − Red Edge) (NIR1 + Red ⁄ Ahamed et al. [10] and

Barnes et al. [25]

Green Normalized

Difference Vegetation

Index (GNDVI)

(NIR1 − green) (NIR1 + green)⁄

Ahamed et al. [10], Li et

al. [26] and Gitelson et al.

[27]

Green Normalized

Difference Vegetation

Index 2 (GNDVI2)

(NIR2 − green) (NIR2 + green)⁄ Mutanga et al. [28]


Vegetation Index 2

(NDVI2)

(NIR2 − red) (NIR2 + red)⁄ Mutanga et al. [28]


Red Edge index 2

(NDRE2)

(NIR2 − Red Edge) (NIR2 + Red ⁄ Mutanga et al. [28]

Renormalized

Vegetation Index

(RDVI)

(NIR1 − red) √NIR1 + red⁄ Li et al. [26]

Ratio Vegetation Index

(RVI) NIR1 red⁄ Li et al. [26]

Modified Soil Adjusted

Vegetation Index

(MSAVI)

(1 + 0.5)(NIR1 − red) (NIR1 + r⁄ Qi et al. [29]

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An overlapping panchromatic image pair was oriented to ground coordinates

following the digital photogrammetric image orientation steps in the Leica

Photogrammetric Suite (LPS)/ERDAS IMAGINE software to obtain a stereo model

of the area. The rational polynomial coefficients (RPC’s) calculated during the image

acquisition and ground control points extracted from the digital topographic map of

Darwin, Australia (scale of 1: 10,000), were used as references. Further, the quality

of the stereo model was assessed by comparison with ground control points. Then,

outlines of the mangrove field plots that were assessed in the field were digitised

using a stereo model. To identify the four corners of field plots in the image, the GPS

locations, as well as the indirect measurements that were collected during field

sampling were used. To calculate the corresponding predictor variable values of each

plot, all pixels within a field plot polygon were considered. However, pixels that

represented less than 70% in extent within a plot were discarded from

further calculation.

The study selected the digital cover photography method developed by

Macfarlane et al. [30] for calculating the LAI of field sampled mangrove trees. Since

mangroves are densely clustered with muddy soil underneath the canopy cover, the

digital cover photography method is more reliable than plant canopy analysers or

area meters (LAI-2200C, LI-3100C, LI-COR 6400, etc.). Vertical photographs of the

canopy, pointing the lens of the camera upwards, were taken using a Panasonic

Lumix DMC-FT2 compact digital camera. Two levelling bubbles were attached to

the camera to ensure that photographs were taken without a tilt. The camera was set

to an aperture priority-automatic exposure mode. Due to the densely-clustered nature

of mangroves, it is impossible to isolate one tree and take photographs that represent

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its canopy area. Therefore, 16 evenly-spaced photographs were taken within each

plot. The number of trees within the plot was counted.


According to Macfarlane et al. [30], vertical photographs were used to

estimate the large gaps between mangrove trees, small gaps within mangrove trees,

the proportion of the ground area covered by the vertical projection of foliage and

branches, the crown cover, the crown porosity and the woody-to-total area ratio of

each sampling plot. A detailed description of the method to estimate LAI using

digital photographs can be found in Macfarlane et al. [30], Pekin and Macfarlane

[31] and Heenkenda et al. [32].

All digital photographs were classified using eCognition software for further

calculations. By considering the contrast between vegetation and the background (the

sky) of the photographs, areas with higher blue reflectance values were classified as

sky. The ratio between the reflectance of blue and red bands was further considered

to separate the background and the vegetation. The background was further classified

into large gaps (gL) between tree crowns and total gaps (gT), considering the relation

to neighbouring features. For example, if the area of the gap is less than 50 pixels

and it is surrounded by vegetation, it is classified as a small gap within a tree.

Finally, a total number of pixels for large gaps and small gaps was summed to obtain

gT (see Heenkenda et al. [32] for the detailed description).

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Vegetation was further classified into leaf area and branches/stems. The

greenness values were used to identify branches/stems from vegetation. Hence, the

greenness was calculated from photographs using Equation (1) [33].

𝐺𝐺𝑟𝑟𝑟𝑟𝑟𝑟𝑔𝑔𝑔𝑔𝑟𝑟𝐵𝐵𝐵𝐵 = 2 ∗ 𝐺𝐺 − 𝐵𝐵 − 𝑅𝑅 (1)

Where G, B, and R represent the reflectance (intensity levels) recorded with the

green, blue and red bands of the digital camera.

The fraction of foliage cover (ff) that is the proportion of the ground area

covered by the vertical projection of foliage and branches [30, 31, 34], and the crown

cover (fc) were calculated as Eqs. (2) and (3).

𝑓𝑓𝑐𝑐 = 1 − 𝑙𝑙𝐿𝐿 ∑𝑃𝑃𝐵𝐵𝑃𝑃𝑟𝑟𝑙𝑙𝐵𝐵⁄ (2)

𝑓𝑓𝑓𝑓 = 1 − 𝑙𝑙𝑇𝑇 ∑𝑃𝑃𝐵𝐵𝑃𝑃𝑟𝑟𝑙𝑙𝐵𝐵⁄ (3)

Where gL is the total number of pixels of large gaps (gaps between tree crowns) and

gT is the total number of pixels of gaps, fc is the crown cover, ff is the fraction of the

foliage cover, and ∑𝑃𝑃𝐵𝐵𝑃𝑃𝑟𝑟𝑙𝑙𝐵𝐵 is the total number of pixels of the photograph.

Considering these results, the crown porosity (ф) which is the proportion of

the ground area covered by the vertical projection of foliage and branches within the

perimeter of the crowns of individual plants was calculated from the Eq. (4)

ɸ = 1 − 𝑓𝑓𝑓𝑓𝑓𝑓𝑐𝑐

(4)

Where ff is the fraction of the foliage coverage and fc is the crown cover.

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The effective plant area index (Lt) includes the contribution from woody

elements to the total plant cover. Hence, it provides an overestimation for leaf area

index [29]. The effective plant area index was estimated using the modified version

of the Beer-Lambert law as specified in Eq. (5).

𝐿𝐿𝑡𝑡 = −𝑓𝑓𝑐𝑐 ∗ln (ɸ)𝑘𝑘

(5)

Where fc is the crown cover; ɸ is the crown porosity; and k is the canopy extinction

coefficient.

As Perera et al. [35] suggested, the canopy extinction coefficient (k) was

taken as 0.5 for this study. This represents the average value of already published k

values for mangroves around the world. The factor k mainly depends on stand

structure and canopy architecture; therefore, different vegetation types have different

values. For example, Breda [36] reviewed k values for forest stands with broad

leaves and found a range of values from 0.42 to 0.58. Macfarlane et al. [30] assumed

k =0.5 for eucalypt forests. Finally, we calculated the woody-to-total area ratio (α)

and the actual leaf area index (LAI) from Equations (6) and (7) [37,38].

𝛼𝛼 = ∑(𝐴𝐴𝑟𝑟𝑟𝑟𝐴𝐴𝐵𝐵)∑(𝐴𝐴𝑟𝑟𝑟𝑟𝐴𝐴𝑉𝑉) (6)

𝐿𝐿𝐿𝐿𝑁𝑁 = 𝐿𝐿𝑡𝑡 ∗ (1 − 𝛼𝛼) (7)

Where ∑(𝐿𝐿𝑟𝑟𝑟𝑟𝐵𝐵𝐵𝐵) is the total area of branches except leaves; ∑(𝐿𝐿𝑟𝑟𝑟𝑟𝐵𝐵𝑉𝑉) is the total

area of vegetation including branches; and Lt is the effective plant area index.

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The final results represented an actual LAI of individual plots or a cluster of

trees. This was then divided by the number of trees to get a value per tree.

5.2.3.2 Estimating Above Ground Biomass

The study selected the allometric method for estimating AGB from field

measurements. The basic theory of the allometric relationship is that the growth rate

of one part of the organism is proportional to that of another [5], and therefore, it

depends on measurable canopy dynamics, such as tree height and DBH. Once the

regression relationship between canopy dynamics is established, the regression

equation estimates the standing biomass of a tree. However, allometric relationships

for mangroves are species and site specific [5]. Therefore, special attention was

given to use allometric equations developed for mangrove species in Northern

Australia or Southeast Asia. Since there was no equation derived for Sonneratia alba

and Excoecaria agallocha var. ovalis, the common equation developed by Bai [39]

for mangroves in the Northern Territory, Australia, was used (Table 5.3).

The logarithmic transformation of DBH values was calculated using

Microsoft Excel software. Equations from Table 5.3 were used to calculate Log10

(Biomass). Finally, the calculated above ground biomass values were converted to

AGB per square meter (unit = kg/m2) considering the extent of each plot.

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Table 5. 3. Log10-transformed allometric relationships used for different mangrove

species. The equations are in the form: 𝑙𝑙𝑙𝑙𝑙𝑙10(𝐵𝐵𝐵𝐵𝑙𝑙𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵) = 𝐵𝐵0 + 𝐵𝐵1 ∗ 𝑙𝑙𝑙𝑙𝑙𝑙10(𝐷𝐷𝐵𝐵𝐷𝐷);

where DBH is the diameter at breast height; B0 and B1 are regression coefficients.

These equations are specific to the Northern Australia [37,38], North-eastern

Queensland, Australia [39] and Sri Lanka [19] (biomass in kg and DBH in cm).

Mangrove Species B0 B1 Study

Avicennia marina −0.511 2.113 Comley and McGuinness [40]

Bruguiera exaristata −0.643 2.141 Comley and McGuinness [40]

Ceriops tagal −0.7247 2.3379 Clough and Scott [41]

Lumnitzera racemosa 1.788 2.529 Perera and Amarasinghe [20]

Rhizophora stylosa −0.696 2.465 Comley and McGuinness [40]

Sonneratia alba −0.634 2.248 Bai [39]

Excoecaria agallocha var.

ovalis −0.634 2.248 Bai [39]

5.2.3 Predicting LAI and AGB

Multivariate regression methods are some of the most-widely used methods

for estimating plant biophysical and biochemical variables, in particular for

developing empirical models of variables of interest based on satellite image data.

Among them, partial least squares regression (PLSR) receives much popularity, as it

has been designed to outperform the problems of collinearity of prediction variables

and over-fitting with relatively few samples. Hence, PLSR is suitable when the

number of predictor variables is large and they are highly collinear [26,42,43]. This

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study selected the PLSR algorithm to model the relationship between field samples

and satellite data. The “pls” package in R software [44] was used for the calculation.

In its simplest form, PLSR specifies a regression model by projecting the

predictor variables and response variables to a new space. That is, data are first

transformed into a different and non-orthogonal basis similar to a principal

component analysis, and the most important partial least square components are

considered for building a regression model. Hence, the PLSR procedure extracts

successive linear combinations of partial least square factors or components. Once

the optimal number of partial least square components is selected, the relationship

can be predicted over a large area. A detailed description of the PLSR algorithm is

available in Mevik and Wehrens [44], Hastie et al. [45] and Wang et al. [43].

The PLSR algorithm performs best with normalized data without outliers

[45]. Therefore, the LAI sample data were normalized with the mean equal to zero

and the variance equal to one and analysed for outliers. The mangrove field plot

polygons were considered to extract predictor variable values from all pixels within

plots, as described previously for a 2-m spatial resolution. Finally, these sampled

pixels were randomly divided into two sets: training (70% of sample data) and

validation (30% of sample data). By using the “pls” package, the optimal number of

components for the prediction or predictive abilities of the model was assessed

considering the prediction root mean square error (RMSEP). To obtain RMSEP, the

model was internally cross-validated using the leave-one-out cross-validation method

with training data. Once the optimal number of components was selected, the model

was used to predict LAI over the study area using the “raster” package in R software

[46]. Finally, we de-normalized the raster maps considering the mean and variance of

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the original field samples to obtain real LAI values. A low pass filter (3 × 3 kernel)

was applied to smooth data by reducing local variation and removing noise. This

process was repeated to analyse the spatial variation of LAI over the Rapid Creek

mangrove forest with predictor variables of a 5-m spatial resolution.

Field samples of log-transformed AGB values (allometric equations directly

calculated log-transformed AGB) were analysed for the normality and outliers. Then,

the above process was repeated to predict AGB over the study area. The sampling

plot polygons were considered to extract predictor variable values from all pixels

within plots.


The accuracies of models were internally assessed using a leave-one-out

cross-validation method with training samples at all instances. However, the

accuracy of the predicted LAI and AGB maps was assessed using validation data.

Approximately one third of field samples of LAI and AGB separately was

considered as the validation data (randomly selecting 30% of the sampling pixels

within field plots). Root mean squared errors (RMSE’s) and correlation coefficients

(r) between predicted values and field measurements were recorded.

5.3 Results

The locations of field plots are shown in Figure 5.1. Three field plots at the

northeast corner of the study area have low biomass values. Their DBH values vary

from 1.5 cm to 3.5 cm, and tree heights vary from 1.4 m to 3.6 m. Figure 5.2A–C

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shows mangrove trees in these areas. They are newly-regenerated, small trees. Some

areas are covered with large, wide-spread, multi-stemmed mangrove trees

(Figure 5.2D,E). Their DBH values vary from 10.0 cm to 19.0 cm, and heights range

from 4.0 m to 8.0 m. There are some areas especially near water features covered by

densely-clustered mangroves with different heights (Figure 5.2F,G).

Figure 5. 2. Mangrove trees inside the Rapid Creek forest; (A–C) small,

newly-regenerated mangrove trees; (D,E) large, multi-stemmed mangrove trees;

(F,G) densely-clustered areas.

(A)

(D) (C)

(B)

(G)

(F) (E)

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The normal score plot of field samples is shown in Figure 5.3. The normal

score represents alternative values to data points within a dataset that would be

expected from a normal distribution. Points on the reference line are closer to

normality, and horizontal departures indicate departures from normality. Hence, few

sampled mangrove trees were removed from further calculations, as they exhibited

non-normality with extremely low or high values (Figure 5.3A,B). The normality

was measured with respect to the coefficient of determination of regression (R2). The

R2 of LAI equals 0.96, and the log-transformed AGB showed 0.9. Once outliers were

removed, the remaining field samples were normally distributed.

Figure 5.3. Normal score plots of field estimates of (A) Leaf area index; and (B) Log

transformed above ground biomass.

(B

(A

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5.3.1 Predicting Leaf Area Index and Above Ground Biomass

A number of studies indicated PLSR as a powerful tool to extract spectral

signatures and to create reliable models of LAI [25]. The performance of the PLSR

model: a “goodness of fit” is represented by a root mean squared error of prediction

(RMSEP). The RMSEP of the PLSR for LAI prediction was 0.69 with four

components when considering a 2-m spatial resolution. This value confirmed a good

relation between predictor variables and LAI field samples. Therefore, four

components with all predictor variables were used for final mapping. The RMSEP of

a 5-m spatial resolution was 0.78 with six components. Six components with all

predictor variables were used for final mapping. If the prediction model is perfect,

the RMSEP and RMSE (the root mean squared error for training and validation data)

should be very similar. However, the RMSE values of LAI map with a 2-m and a

5-m spatial resolution were 0.75 and 0.78, respectively (Table 5.4). Although the

slight difference with respect to a 2-m spatial resolution shows model overfitting, the

model performance for 5-m spatial resolution data was good.

Figure 5.4 A–D shows the cross-validated predictions with selected

components versus measured values (or field samples). Most of the points

(normalized LAI and AGB values) slightly deviate from the aspect ratio = 1 line.

However, there is no indication of a curvature or other anomalies. The validation

results (RMSEP) are shown in Figure 5.4.

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Figure 5.4. Cross-validated predictions for the normalized leaf area index (LAI) and

above ground biomass (AGB): (A) predicted versus field measured normalized LAI

with 2-m spatial resolution predictor variables; (B) predicted versus field measured

normalized LAI with 5-m spatial resolution predictor variables; (C) predicted versus

field measured normalized LogAGB with 2-m spatial resolution predictor variables;

(D) predicted versus field measured normalized LogAGB with 5-m spatial resolution

predictor variables. (RMSEP, root mean square error of prediction).

The predicted LAI map with a 2-m spatial resolution is shown in Figure 5.5A.

The highest LAI value is 13.0, and the lowest one is 0.2. The mean LAI value is 3.9

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with a standard deviation of 1.1. Approximately 70% of data ranged from 0.8 to 4.5.

Although LAI values are normally distributed, the pattern has a high dispersion. The

majority of data values are between 0.8 and 4.5. LAI values are high where closer to

edges of water features. The upper right side of the map (Figure 5.5A) shows low

LAI values, and this area is dominated by relatively small recently re-generated

mangrove plants.

The predicted LAI map with a 5-m spatial resolution is shown in Figure 5.5B.

The highest LAI value is 13.3, and the lowest one is 1.1. The mean LAI value is 4.2

with a standard deviation of 0.8. More than 70% of LAI values are between 3.2 and

4.8 showing a normal distribution. However, LAI values are high where closer to

edges of water features. The upper right side of the map (Figure 5.5B) shows low

LAI values, as shown in the map with a 5-m spatial resolution (Figure 5.5B).

When comparing Figure 5.5A,B at this scale, it can be seen that results

obtained from 2-m spatial resolution images provide finer LAI spatial variations than

results from 5-m spatial resolution images. However, at a larger scale, Figure 5.5A

shows scattered LAI variation patterns. These patterns do not correctly represent the

spatial variations of mangrove trees around those areas and, thus, the spatial

distribution of LAI of the area.

The visual appearances of the AGB maps are in-line with the field

observations. Most of the areas having low AGB values are dominated by relatively

small and young mangrove trees. For instance, three field plots at the northeast

corner of the study area (Figure 5.1) have low biomass values. The average measured

DBH in this area was 1.8 cm, and the average height of mangrove trees was 1.6 m.

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Mangrove trees are dense and tall along the water features, and these areas showed

high AGB values.

When considering the performance of the PLSR model for AGB, the RMSEP

for a 2-m spatial resolution was 0.89 kg/m2; however, once predicted AGB over the

study area, the RMSE was 2.2 kg/m2. The correlation coefficient obtained with

respect to validation samples was 0.4 (Table 5.4). Hence, it can be concluded that the

model was over fitted. The RMSEP for a 5-m spatial resolution was 1.7 kg/m2, and

the RMSE with respect to validation data was 2.0 kg/m2 with strong linear

correlation between predicted and sampled AGB values.

The predicted AGB values with a 2-m spatial resolution ranged from 0.12

kg/m2 to 425.5 kg/m2 with a mean value of 22.5 kg/m2 and a standard deviation of

8.1 kg/m2 (Figure 5.5C). AGB values with a 5-m spatial resolution ranged from 0.12

kg/m2 to 423.2 kg/m2 with mean value of 18.4 kg/m2 and a standard deviation of 7.2

kg/m2 (Figure 5.5D). At both instances, the AGB values showed a skewed

distribution rather than a normal distribution. The majority of the data (more than

70%) were in between 30 kg/m2 and 267 kg/m2. The main reason should be the

extremely large, multi-stemmed Avicenna marina and Rhizophora stylosa mangrove

trees in this forest. They were spread over a large area without secondary forest

underneath. The highest measured, as well as highest predicted AGB values

represent these areas (423.2 kg/m2 and 425.5 kg/m2)

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Figure 5.5. Predicted maps: (A) leaf area index with a 2-m spatial resolution; (B) leaf

area index with a 5-m spatial resolution; (C) above ground biomass with a 2-m

spatial resolution; and (D) above ground biomass with a 5-m spatial resolution; using

the partial least squares regression algorithm. The distribution of field sampling plots

is shown in the maps (the sizes of the black squares are not

to scale).

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The RMSE values with respect to the validation samples and correlation

coefficients between predicted values and validation samples were recorded

(Table 5.4). We used randomly-selected, approximately one third of field samples as

the validation samples for both instances. Although the AGB map with a 2-m

resolution showed low linear correlation between predicted values with validation

samples, the LAI map showed a good correlation. AGB and LAI maps with a 5-m

spatial resolution showed a strong linear correlation between predicted values and

validation samples.

.Table 5. 4. Root mean square errors (RMSE’s) and correlation coefficients (r) for

above ground biomass and leaf area index maps with respect to validation samples

Biophysical Variable RMSE Correlation Coefficient

Spatial resolution 2 m 5 m 2 m 5 m

Above ground biomass

(AGB) 2.2 kg/m2 2.0 kg/m2 0.4 0.8

Leaf area index (LAI) 0.75 0.78 0.7 0.8

5.4 Discussion

The major advantage of assessing AGB and LAI from remotely-sensed data is

the estimation of AGB and LAI over large areas without having extensive field

campaigns. This is also a solution for many logistical and practical problems arising

with field efforts. For example, access to interior extremely-dense mangrove patches

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is extremely difficult. In addition, the methods are non-destructive, relatively fast and

economical with less labour force.

5.4.1 Predicting LAI

Mangrove canopies are densely clustered with overlaps. Extended root

systems and muddy soil underneath the canopy cover make it difficult for sampling.

Although the widely-accepted method for LAI estimation is the simulation of canopy

light profiles using conventional instruments, such as portable plant canopy analysers

or area meters, the digital cover photography method is more reliable than

conventional instruments for mangrove forests. The digital cover photography

approach is independent from the radiation condition of the forest and can be used

for an extensive sampling. This method was later tested for types of cameras, digital

file compression, image size and ISO equivalences, and little or no effect on

estimating LAI was found [31]. Lui and Pattey [33] also recommended using the

digital cover photography method by comparing its results with LAI estimation from

conventional equipment.

The mean value of the predicted LAI map with a 2-m spatial resolution (3.9)

is slightly lower than previously-recorded mangrove studies around the world. For

instance, Clough et al. [17] estimated the LAI of Rhizophora apiculata mangrove

forest in Malaysia using three different methods and obtained mean LAI values: 4.9,

4.4 and 5.1. LAI of Avicennia marina plantations in Thailand varied from 0.5 to 5.0.

For the homogeneous mangrove stands at Moreton Bay, Australia, LAI values

ranged from 0.26 to 3.23 with 1.97 as the mean value [15]. Kamal et al. [15] also

assessed heterogeneous mangrove stands at Karimunjawa Island, Indonesia, and

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obtained LAI values from 0.88–5.33 (mean value = 2.98). A remote sensing study on

LAI in the British West Indies showed a range from 0.8–7.0 with 3.96 as the mean.

Statistics from the predicted LAI map of this study with a 5-m spatial resolution (4.2)

was as close as previous mangrove studies. However, comparing results from this

study with other studies is not a perfect approach to make a conclusion regarding the

spatial distribution of LAI. They are site and species specific. When looking at cross-

validated predictions, most of the predicted LAI values are lower than field sample

values (Figure 5.4A,B). One consideration is the canopy extinction coefficient (k)

used to calculate Lt. Although the used value (0.5) represents the average of already

published k values for mangroves around the world, this might not be the correct

value for this area. Further, LAI values vary with the spatial resolutions of remote

sensing images used for data processing. Kamal et al. [15] recently confirmed that

the accuracy of LAI estimation was site specific and depends on the pixel size of the

remotely-sensed images, and their study even indicated two different LAI

distribution patterns for homogeneous and heterogeneous mangrove forests.

The performance of the PLSR model: a “goodness of fit” is represented by a

root mean squared error of prediction (RMSEP). The accuracy of the predicted maps

was assessed using the root mean squared error (RMSE) compared to the validation

samples. If the prediction model is perfect, RMSEP and RMSE (the root mean

squared error for training and validation data) should be very similar. However, in

this study, values related to a 2-m spatial resolution are slightly different, indicating a

model overfitting. The best model performances were shown when using a 5-m

spatial resolution. According to Zheng and Moskal [47], the accuracy of LAI

estimation depends on two main reasons: overlapping and clumping between leaves

within canopies due to the non-random distribution of foliage and light obstruction

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from canopy branches, trunks and stems. The first reason relates to this study, as

well, due to mangrove canopy overlapping. The latter will not be a problem because

of using vertical photographs rather than simulating light transmission. Another

reason for obtaining low accuracies with a 2-m spatial resolution than a 5-m spatial

resolution would be the spatial mismatch between field samples and predictor

variables due to positional errors.

To establish the relationship between field samples and predictor variables, a

corresponding predictor pixel value for each field sample should correctly be

extracted. Hence, the positional accuracy of field samples and the pixel size is

important. Laongmanee et al. [48] assessed the optimal spatial resolution for

estimating the LAI of Avicennia marina plantations in Thailand. The best results

were produced by satellite images with a 10-m spatial resolution. Although there is

still room to confirm this finding, Green and Clark [18] suggested analysing the

information of satellite data within a 5 m × 5 m block of pixels regardless of the

spatial resolution for LAI estimation. Green and Clark [18] also argued that although

high resolution satellite data provide significantly greater levels of accuracy, they

were not capable of fixing positional errors. Hence, it is clear that the satellite data

with a 2-m spatial resolution are not suitable to establish this match-minimizing

effect from the positioning errors. According to this study, although 2-m or 5-m

spatial resolutions can interchangeably be used for mapping LAI in the Rapid Creek

mangrove forest, we would recommend testing a 5 m × 5 m block of pixels

regardless of the spatial resolution for LAI estimation, as suggested by Green and

Clark [18]. Further, we would recommend using the transect method for field

sampling to minimize positional errors. Two ends of transects can be established

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outside the mangrove forest with DGPS (differential GPS). Then, all other

measurements can be based on the established transect line with greater accuracy.

5.4.2 Predicating AGB

We used allometric equations for AGB calculation. Allometric relationships

are highly species specific and less site specific [5,19]. Although special attention

was given to using allometric relationships that were specifically developed for

Northern Territory, Australia, mangrove species, we used some equations that were

developed for Northern Queensland, Australia, and Sri Lanka. In this study, it was

not possible to use the common equation proposed by Komiyama et al. [19], as it

requires the vegetation density, which we did not measure in the first place.

Therefore, for the mangrove species for which we could not find already developed

allometric equations, we used a generic equation developed by Bai [39] for the

Northern Territory, Australia, mangroves.

Some of the sampled Avicennia marina and Rhizophora stylosa species are

multi-stemmed mangrove trees. As explained by Clough et al. [49], when there are

multi-stemmed mangrove trees, each branch should be treated as a discrete tree, and

the dry weight of the common butt should be included to obtain a robust AGB of the

tree. However, this discrepancy is important if the consideration is to calculate the

AGB of different woody parts of trees only, rather than total AGB [49]. Hence, in

this study, we treated each branch of multi-stemmed mangrove trees as a separate

tree. The dry weight of the common butt was neglected.

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The predicted AGB maps are approximately in-line with visual field

observations and AGB calculations. Along water features, mangroves exhibit high

AGB values. Mangroves are known to have low productivity where closer to the

landward margin [35]. These areas experience relatively less tidal inundation

frequency and shorter duration together with a minimal freshwater influence [50].

The low productivity is also connected with higher salinity and less nutrients, and

this is strongly evidenced in the Rapid Creek mangrove forest. Above ground

biomass varies with the age of the tree, temperature, solar radiation and oxygen, as

well [51]. In this study area, there is no doubt that all trees would experience the

same temperature, solar radiation and oxygen, but the age of trees, salinity and

nutrients levels are different. As some parts of the Rapid Creek mangrove forest are

regenerated forests after clearings and natural disasters, there are trees with different

ages. Especially closer to the northeast corner of the forest, trees are younger than the

rest of the forest and small, and they showed low AGB values.

When considering the performance of the PLSR model for predicting AGB

with a 2-m spatial resolution, it was found that the model was over fitted (Figure 4).

The high spectral variation and shadows caused by canopy may create difficulty in

developing PLSR model. However, the model performance for a 5-m spatial

resolution was at an acceptable level. There was a strong linear correlation between

predicted and field sample AGB values. The correlation coefficient is relatively high

compared to the very limited number of mangrove studies that are available to-date

without indicating any model overfitting. As we suspected earlier when estimating

LAI from satellite data, one reason for obtaining low accuracies with a 2-m spatial

resolution than a 5-m spatial resolution would be the spatial mismatch between field

samples and predictor variables due to positional errors. When estimating AGB from

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satellite data, a 5-m spatial resolution would be the most suitable approach against a

2-m spatial resolution.

Although there are several studies investigating the relationship between the

physical parameters of mangrove trees and AGB and developing allometric

equations, there is a limited number of recorded studies mapping the spatial

distribution of AGB over large areas around the world. For instance, in Qi’ao Island,

Guangdong Province, China, predicted AGB of mature Kandelia candel mangroves

ranged from 15.51 kg/m2 to 40.66 kg/m2 with the average value of 24.77 kg/m2, and

artificially-restored Sonneratia apetala mangroves ranged from 3.4 kg/m2 to 23.42

kg/m2 with the average value of 11.38 kg/m2 [16]. However, in this study, we did not

find Kandelia candel and Sonneratia apetala mangroves, and thus, this limits the

comparison possibilities.

Apart from the statistical analysis of model accuracies, the authors are

confident about the LAI and AGB model performances with respect to a 5-m spatial

resolution at the northeast corner of the study area, southern areas and areas along

water features. For instance, as shown in Figure 5.2A–C, northeast areas are

dominated by regenerated small mangrove trees. Southern areas have relatively tall

and dense mangroves. There is no recorded natural or anthropogenic disasters related

to these areas, and mangrove trees are relatively mature. However, the model

performances along the western edge of the mangrove forest are confusing. These

edges are dominated by Lumnitzera racemosa mangrove trees and are tall and mature

with relatively small DBH values (around 5 cm). On the other hand, these trees

receive less amounts of water, thus nutrients, compared to the other areas due to

ground height variations.

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The canopy height model of the vegetation has a direct and increasingly well-

understood relationship to above ground biomass, and therefore, one of the best

predictors for biomass would be a canopy height model [8]. Simard et al. [12] also

used elevation data derived from the Shuttle Radar Topography Mission (SRTM) for

estimating mangrove heights and above ground biomass. They used airborne laser

scanning data and extensive field samples to calibrate these elevation data. We

created an accurate digital surface model of the area using a pair of WV2

panchromatic images. However, due to the lack of elevation data with reliable

accuracy, we were not able to create a canopy height model of the area. Hence,

although we identified the possibility of integrating the canopy height model as a

predictor variable, we did not use it.

In this study, we did not consider very small gaps (less than 2 m) between

mangrove trees due to processing difficulties with the spatial resolutions with which

we dealt. Rather than masking out large non-mangrove areas from satellite images,

we would suggest identifying gaps between mangrove trees and excluding them from

further analysis. This would remove the noise of predicted images. Additionally, the

complexity of species composition, stand structures and the densely-clustered nature

of mangrove forests might add some errors, forming mixed pixels of remotely-sensed

data.


One of the key issues associated with effective planning and management of

mangrove forests is up-to-date information about the ecosystem. To collect

information, extensive field sampling is difficult and time consuming. Compared to

215

other terrestrial ecosystems, a few studies that are associated with field samples and

remotely-sensed data for producing thematic maps over mangrove forests can be

found. This study investigated mapping above ground biomass (AGB) and leaf area

index (LAI) from WorldView-2 satellite images and field samples. Site- and species-

specific allometric relationships were used for calculating above ground biomass for

sampled trees. The leaf area index was obtained from the digital cover photography

method. Hence, the field sampling process was fast and economical.

The relationships between both biophysical variables, LAI and AGB, with

predictor variables (2-m spatial resolution) were established separately using the

partial least squares regression algorithm. Once these relationships were established,

they were used to predict response variables over the study area. The accuracies of

predicted maps were analysed compared to validation samples. The process was

repeated for the predictor variables with a 5-m spatial resolution. The LAI map with

a 2-m spatial resolution showed a root mean square error of 0.75, and the map with a

5-m spatial resolution showed a root mean square error of 0.78 compared to the

validation samples. The correlation coefficients between field samples and predicted

maps were 0.7 and 0.8, respectively. Root mean square errors obtained for AGB

maps were 2.2 kg/m2 and 2.0 kg/m2 for a 2-m and a 5-m spatial resolution, and the

correlation coefficients were 0.4 and 0.8, respectively.

The satellite data with higher spatial resolution should be associated with

accurately-positioned field samples. Therefore, we would suggest implementing the

transects method for field sampling and establishing end points of these transects

outside the mangrove forest with a highly accurate positioning system, such as a

216

differential GPS. Further, we would recommend analysing a 5 m × 5 m block of

pixels regardless of the spatial resolution for LAI and AGB estimation.

In conclusion, the study demonstrated the possibility of assessing the

biophysical variations of mangroves using WorldView-2 satellite data. This would

lead to better mangrove conservation and management of the Rapid Creek area.

Acknowledgments: The authors thank Miguel Tovar Valencia, Evi Warintan

Saragih, Silvia Gabrina Tonyes, Olukemi Ronke Alaba and Dinesh Gunawardena for

their assistance in the field surveying.

Author Contributions: Muditha K. Heenkenda designed the research, processed the

remote sensing data, and drafted the manuscript with co-authors providing

supervision and mentorship throughout the process.

Conflicts of Interest: The authors declare no conflict of interest.

217

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Chapter 6 - Regional Ecological Risk assessment

using a Relative Risk Model: A case study of the

Darwin Harbor, Darwin, Australia


Heenkenda, M. K. & Bartolo, R. 2015, Regional Ecological Risk

Assessment Using a Relative Risk Model: A Case Study of the Darwin

Harbor, Darwin, Australia, Human and Ecological Risk Assessment: An

International Journal, vol. 22, 401 – 423,

doi: 10.1080/10807039.2015.1078225.


225

6.0 Abstract

There are many proposed and ongoing commercial, industrial and residential

developments within the Darwin Harbor catchment in Northern Australia, to

accommodate the projected population growth over the next 20 years. Hence, it is

necessary to ensure the balance between these developments and ecosystem

conservation. We evaluated ecological risk for the Darwin Harbor using a relative

risk model (RRM). The catchment was divided into 22 risk regions based on small

catchment boundaries and their homogeneity. Through the RRM, we ranked and

summed the stressors and habitats within regions. The interaction between stressors

and habitats were modelled through exposure and effect filters. The ecological

assessment endpoints were maintenance of the mangrove health and the maintenance

of water quality. The risk regions: Myrmidon Creek, Blackmore River, Bleesers

Creek and Elizabeth River showed the highest total relative risk for ecological assets.

These risk regions had a high percentage cover of industrial, commercial, residential

areas, diffuse entry points, and climate change effects. The Creek A, Sandy Creek,

West Arm and the Pioneer Creek were the risk regions with lowest total relative risk

scores. The RRM is a robust application that is suitable for a large geographic area

where multiple stressors are of concern.

Indexing terms: Ecological risk assessment, Relative Risk Model, mangrove, water

quality, Darwin Harbor

226

6. 1 Introduction

Darwin Harbor, located in the Northern Territory, Australia is a large

indented embayment with three main arms: East Arm, Middle Arm and West Arm.

The Harbor is globally significant as a macro tidal environment with a tidal range of

about 7 to 8 meters, and monsoonal climate with uniformly high temperatures and

solar radiation (Metcalfe 2007).The Darwin Harbor shoreline is covered by the most

floristically diverse, and one of the largest mangrove habitats in the Northern

Territory (Lee 2003). These mangroves comprise 36 species out of the 50 species

regarded as mangroves worldwide, and cover approximately 20, 400 hectares

(Metcalfe 2007).

There are major development projects and associated activities around

Darwin Harbor such as significant plans for population growth, industrial expansion,

resource processing infrastructure, and consequent increases in shipping and

dredging. These developments pose risks for the natural, cultural, aesthetic and

recreational values of the Harbor, and need to be managed carefully. Currently the

Darwin Harbor catchment is subject to multiple pressures and conflict of use

resulting in a major challenge for the government, industry and the community in

managing the Harbor environment (The Darwin Harbor Advisory Committee 2003).

The catchment encompasses a range of distinctive and special environments. Hence,

it is important for the Harbor and its catchment to remain biologically productive,

and for the water quality and habitats to be protected.

The Northern Territory government undertakes water quality, and ecosystem

monitoring and testing throughout Darwin Harbor region. One of the key

227

requirements for environmental monitoring programs in Darwin Harbor is the ability

to combine knowledge from different sources into a consistent environmental

management framework that enables stakeholders to make practical and sound

management decisions. Due to the current situation, the natural resources of Darwin

Harbor region are experiencing increasing pressure from the growing human

population, and subsequent rural and industrial developments. For example, although

water resources of Darwin Harbor region are considered to be in good condition,

there are areas of concern where sewage outfalls and urban stormwater can impact on

the quality of the receiving water. Further, the assimilative capacity of the Harbor

and waterways to receive pollutants from point and non-point sources, other

understanding about key ecological processes and their vulnerabilities are poorly

known (Northern Territory Government 2010). Hence, the decision makers are

interested in evaluating the likelihood that adverse ecological impacts may occur as a

result of exposure to multiple stressors in the region.

The Northern Territory government and other agencies that are responsible

for industrial developments around Darwin Harbor have initiated their own

individual environmental risk assessment programs rather than a single combined

approach. One example is the dredging program introduced by a liquefied natural

and petroleum gas project (INPEX). The key part of the project is managing

environmental impacts in order to preserve the health of Darwin harbor. Hence, its

main objectives are to monitor and respond to impact on environmentally sensitive

receptors from offshore dredging, and to understand likely impacts of sedimentation

and dredging activities (INPEX 2013). However, there is no way to combine

sedimentation effects from other industrial developments to this assessment. To our

knowledge, there is no integrated regional ecological risk assessment approach for

228

policy makers, community organisations, managers and investors to support more

robust debate, and more transparent decision making regarding rural and urban

developments for Darwin Harbor. Hence, to maintain the sustainability of the Darwin

Harbor ecosystem, a statistically sound, transparent, repeatable ecological risk

assessment approach is essential. This study is a significant contribution to the

regional ecological risk assessment of Darwin Harbor.

The traditional ecological risk assessment methods comprise of evaluating the

interaction of three components: stressor, receptor and response (Landis and Wiegers

1997). This is fairly simple at a single contaminated site with only one stressor

involved. To expand this to a regional scale, the scale, complexity of the structure,

and the regional spatial components such as sources, habitats and impacts to the

assessment endpoints must be considered (Landis and Wiegers 1997). The relative

risk model (RRM) evaluates multiple, dissimilar stressors simultaneously and

comparatively at a regional scale (Landis and Wiegers 1997; Wiegers et al. 1998).

Although this method was introduced to assess the risk of chemical stressors in the

beginning, it has successfully been introduced to analyse biological or ecological

stressors (Colnar and Landis 2007; Moraes et al. 2002; O'Brien and Wepener 2012;

Yu et al. 2015). For example, recently Bartolo et al. (2012) assessed the ecological

risk of Australia’s tropical rivers, and identified the most significant threats to the

ecological assets of the northern tropical rivers.

The objective of this study was to evaluate the relative risk to ecological

assets of the Darwin Harbor catchment. Therefore, the application is; (a) to identify

the sources and habitats in different locations of the Darwin Harbor catchment; (b) to

rank their importance in each location; (c) to integrate them to predict the relative

229

risk levels; and (d) to assess the uncertainties that may propagate through the

modelling process.

6.2 Data and method

6.2.1 Study area

The Darwin Harbor’s management area encompasses 3227 km2, and extends

from Charles Point in the west to Gunn Point in the east. This includes the estuarine

areas and tributaries of Woods Inlet, West Arm, Middle Arm, East Arm, Howard

River, Elizabeth River and the land that drains into these waterways. Hence, the

catchments include the cities of Darwin, Palmerston, Cox Peninsula, and new cities

of rural areas (Figure 6.1). The population was about 120900 in 2006, however, the

predicted population by the Australian Bureau of Statistics as of 2021 is 184500

(Skinner et al. 2009).

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Figure 6.1. The Darwin Harbor catchment and its mangrove distribution

1 Blackmore River 7 Elizabeth River 13 Micket Creek 19 Sadgroves Creek

2 Bleesers Creek 8 Howard River 14 Myrmidon Creek 20 Sandy Creek

3 Buffalo Creek 9 Hudson Creek 15 Palmerston South 21 West Arm

4 Charles Point 10Kings Creek 16 Pioneer Creek 22 Woods Inlet

5 Creek A (Middle Arm) 11Ludmilla Creek 17 Rapid Creek

6 Darwin Central

Business District (CBD) 12Mitchell Creek 18 Reichardt Creek

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6.2.2 Data input

The digital topographic data layers of land use and land cover (scale 1:20,

000, produced in 2002) were used as a preliminary data source for this study. The

urban and rural residential areas, and transport and communication features were

updated using Google Earth satellite image acquired on 28th March, 2014. The digital

10K topographic data series was used to extract water features, for example streams,

rivers and dams, in the region (Geoscience Australia 2002). Since rivers and streams

are line features, we introduced buffer zones of 5 m and 2.5 m for rivers and streams

respectively to form polygon features. These buffer distances were selected

considering the average width of water features. The “waterbodies” data set was

prepared combining streams, rivers, dams and other water features in the area. The

digital elevation model produced from the Shuttle Radar Topography Mission

(SRTM), with a spatial resolution of 30m, was used to analyse sub-catchment

vulnerability to sea level rise. The recent reports published by the oil company -

INPEX for the Darwin LNG (liquefied natural gas) plant include the statistics and

spatial distribution of areas that are going to be affected by increasing sedimentation

based on the onshore dredging (van Senden et al. 2013). The statistics of nutrient

loads and other suspended solids were obtained from a report published by the

Northern Territory government analysing the pollutant load entering to Darwin

Harbor due to urban and rural land-use (Skinner et al. 2009). Unfortunately, we were

not able to find point source data for the Ludmilla Creek catchment. The Darwin

Harbor sub-catchment boundaries were adopted from the Australian Collaborative

Land Use Mapping program (V5), and were used as initial data for risk regions.

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Regional ecological risk assessment methods evaluate the interaction of three

different regional spatial components, which include: sources that release stressors;

the habitat where the receptors reside; and impacts to the assessment endpoints

(Landis and Wiegers 1997). Stressors can be derived from diverse sources, including

available topographic data, reports, and expert knowledge. Receptors are often linked

to habitats. The interaction between these stressors and habitats is explained in terms

of ranked exposure values. The impact to the assessment endpoint is explained

through the ranked effects. According to Landis and Wiegers (1997), at a regional

level, the ranking of exposures and effects must be done considering the spatial and

temporal heterogeneity of them.

The method used in this study was similar to Landis and Wiegers (1997).

First, the method identified sources and their reflecting stressors, habitats, and

ecological assessment endpoints. Then the area was divided into sub-areas to be used

as risk regions based on their homogeneity. In this study, we considered sub-

catchments and their homogeneity to define risk regions. The stressors and habitats

were ranked for each risk region. Finally, an ecological risk for each risk region was

calculated by quantitatively determining the interaction of stressors and habitats.

According to U.S. Environmental Protection Agency, there are three main

phases for ecological risk assessment: problem formulation; risk analysis; and risk

characterization (USEPA (US Environmental Protection Agency) 1998). Each phase

is divided into sub phases as shown by Figure 6.2. They are discussed in greater

detail in the proceeding sections.

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Figure 6.2. The workflow diagram for conducting an ecological risk assessment

using the Relative Risk Model following the guidelines published by the U.S.

Environmental Protection Agency

6.2.3 Problem formulation

The problem formulation phase is a process that identifies the nature of the

problem through existing data. The foundation for the problem formation is based on

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how well we understand the system we are investigating. We reviewed available

literature, including reports published by the Northern Territory government

analysing different aspects of health of the Darwin Harbor, the Darwin Harbor

regional management plans, and consulted experts in the field (Bartolo et al. 2012;

Brocklehurst and Edmeades 1994-1995; Darwin Harbor Regional Plan of

Management 2003; Metcalfe 2007; NRETAS 2010a; 2010b; van Senden et al. 2013;

Skinner et al. 2009) for preparing a conceptual diagram to understand the current

situation of the catchment. Varieties of anthropogenic factors have resulted in

development pressure on Darwin Harbor catchment. Based on the information

collected and the conceptual diagram, we identified the major sources of this

pressure, and categorized them into different stressors. For example, the source

“Agriculture” was categorized into the following stressors: cropping; horticulture;

and aquaculture. The corresponding ecosystem response variables for stressors were

identified accordingly. For example, one of the ecosystem response variables for

cropping is influx of nutrients and chemicals.

The habitat defines where the receptors reside at a regional scale (Landis and

Wiegers 1997). The habitats that were directly related to the health of the Darwin

Harbor, and that had spatial data available were selected for this study. Further, we

referred to the values identified by various experts in the field, the percentage of land

cover, vulnerability to urban pressure, and significance of ecosystems in the region to

decide on habitats to be considered for this study. Sources and threats were also

defined based on the prepared conceptual diagram. Special attention was given to the

availability of spatial data.

235

At this stage, we examined the integration and usage of available information

on sources, stressors and their characteristics, exposure opportunities, the ecosystem

components potentially at risk, and ecological effects. Then, a conceptual diagram

was prepared combining all information to summarise our understanding of the

Darwin Harbor ecosystem. This aided in finalising the sources, stressors and habitats

and their interactions that were included in the RRM.

6.2.3.1 Ecological Assessment endpoints

According to the USEPA (US Environmental Protection Agency) (1998),

management goals are desired characteristics of ecological values that need to be

protected. There are five key management goals identified by the Darwin Harbour

regional plan of management (The Darwin Harbour Advisory Committee 2003). The

first goal is to maintain a healthy environment. The outcomes to be achieved through

this goal are; (a) improved understanding and knowledge of the region’s

environment; (b) protection and enhancement of freshwater, estuarine and marine

water quality; and (c) protection of the health and functioning of ecosystems and

conservation of biodiversity. The third goal which is linked to this first goal is to

encourage ecologically sustainable development through planning and management

of future developments (The Darwin Harbour Advisory Committee 2003). The other

three management goals are mainly related to the conflicting uses of the Darwin

Harbor region rather than the ecological values. Hence, we considered the above

explained first and third management goals and their expected outcomes only to

decide on ecological assessment endpoints.

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Assessment endpoints are expressions of an environmental value that is to be

protected (USEPA (US Environmental Protection Agency) 1998). Ecological related

endpoints often help to sustain the natural structure, function, and biodiversity of an

ecosystem. Hence, deciding on an assessment endpoint is the process of defining a

goal for a risk assessment through an ecological entity and its attributes. However,

there are two main steps involved when selecting ecological endpoints (USEPA (US

Environmental Protection Agency) 1998). The first step is to select ecological values

that are suitable for assessment endpoints. Therefore, the ecological relevance,

vulnerability to potential stressors, and relevance to management goals were

considered (Obery and Landis 2002; USEPA (US Environmental Protection Agency)

1998). The other step is to identify the specific ecological entity and the

characteristics of these entities that required a protection. In that sense, the ecological

assessment endpoints reflect management goals, and the ecosystem they represent.

The Darwin Harbor is a small estuary covered by one of the largest and most

diverse areas of mangroves in the Northern Territory. The Harbor supports a range of

estuarine, freshwater, and terrestrial environments. Although the catchment mostly

comprises vacant Crown land, it also contains a mix of commercial, industrial and

residential areas including Darwin, Palmerston and Cox Peninsula cities. There has

been some excellent research and mangrove monitoring in Darwin Harbor to balance

the ongoing industrial developments and the conservation. However, a significant

knowledge gaps remain. For example, the report; “The mangrove forest of Darwin

Harbour” by Moritz-Zimmermann (2002) stresses the need to study the likely effects

of human disturbances to mangroves. The recommendations made by McHugh

(2004), McGuinnes (2003), NTG (2005) and the Darwin Harbour Regional Plan of

237

Management (2003) also explicitly explain the need of an integrated approach for

mangrove monitoring.

Most of the catchment, about 80%, is undeveloped and comprises savannah

woodlands and forests. They are not in pristine condition due to high fire frequency

and access tracks (Skinner et al. 2009). Extensive growth of invasive weeds (gamba

and mission grass) during the wet season increases the fire frequency and intensity

during the dry season. Further, freshwater lagoons, grassy swamps, paperbark

woodlands, floodplains and rainforest cover approximately 6% of the catchment

(Skinner et al. 2009). Land clearing and urbanization alter overland flow paths of

runoff, reduce the volume of water that infiltrates to groundwater, and the time

runoff takes to enter the rivers and creeks. There are about 1150 hectares of

nationally significant, mostly dry rainforest in the Harbor catchment. They appear as

small patches around the margin of the tidal flats, and most patches are less than 10

hectares in extent (Northern Territory Government 2010). However, there are some

large areas of rainforests or vine-thicket habitat at Blaydin Point, Wickham Point,

Flagstaff Hill and Kings Table. Fortunately, these forest patches are mostly

surrounded and protected by mangroves. The influences from the development

pressure are minimal, and forest patches are in pristine condition (Northern Territory

Government 2010).

The Northern Territory government initiated numerous water quality studies

in Darwin Harbor (Skinner et al. 2009; Smith and Haese 2009). They reported the

contribution from urban and rural runoff and point sources. It was shown that land

use influences the water quality and flow volume that enter the Darwin Harbor, and

consequently changes the water quality of the Darwin Harbor. The Harbor itself is a

238

home for a diverse range of marine species: salt water crocodiles, dugongs, marine

turtles, and a variety of fish. However, the marine invertebrate fauna of the Harbor

region is poorly understood and is still being described. Hence, there is a potential

for severe impacts on coastal water quality and overall ecological health of the

Harbor due to the increasing population and land developments unless the water

quality of rivers and streams entering the Harbor is not well managed.

The ecological endpoints were selected subsequent to this information and

expert opinion about the study area. Further, the characteristics that need to be

protected for these endpoints were identified.

6.2.4 Risk analysis

Risk analysis is the second phase of the RRM. Here, two primary components

of the risk: exposure and effects, and their relationship between each other, and

ecosystem characteristics were analysed (Figure 6.2). First, the area can be divided

into more homogeneous regions: risk regions for further analysis. Risk regions can

be derived based on the topography, habitat or any other criteria that helps to form

homogeneous regions. For example, Yu et al. (2015) sub-divided their study area

into risk regions based on morphological, ecological, environmental and

administrative characteristics. O'Brien and Wepener (2012) introduced a combination

of management objectives, source information, and habitat data as criteria for

forming risk regions in South Africa. Bartolo et al. (2012) identified sub-regions of

Northern Tropical Rivers, Australia based on river basins (catchments). Colnar and

Landis (2007) and Hayes and Landis (2004) used watershed and bathymetric

boundaries for subdivision of the area, while Moraes et al. (2002) cosidered the

239

watersheds and landuse of the area. Catchment area limits, boundaries of

environmentally protected areas, territory boundaries, habitat and source types, the

flow of tributaries into Mountain river/contours, incremental gradient of human

activities of the lower reaches of the catchment are few other criteria used by

different studies (Moraes et al. 2002; Obery and Landis 2002; Walker et al. 2001;

Wiegers et al. 1998).

Most of Darwin Harbor’s catchment is undeveloped and mainly comprises

mangroves, savanna woodlands, urban and rural land uses. We divided the area into

different regions considering the boundaries of catchments (drainage boundaries),

homogeneity, and data availability for further modelling. Some small catchments

were merged with neighbouring catchments that have similar topographical

characteristics and land use forming homogeneous sub-regions (Figure 6.1).

The relationships and the probabilities of interaction between risk

components are described by numerical weighting factors called filters (Colnar and

Landis 2007; Yu et al. 2015). We identified two filters: a conceptual model pathway

from source to habitat as an exposure filter, and a conceptual model pathway from

habitat to endpoint as an effect filter. As described by Colnar and Landis (2007) and

Wiegers et al. (1998), exposure filters were developed considering two major issues:

(a) Will the source release the stressor; (b) Will the stressor then occur and persist in

the habitat. The effect filters were developed considering whether the assessment

endpoint occur in and utilize the habitat, and whether results from interaction with

the stressor are positive (undesirable) or negative (beneficial). If the pathway is

completed, we assigned a value of “one” to the corresponding filter, otherwise the

240

value is “zero”. We did not apply weights for exposure and effect filters in this

study.

The threats and habitats were ranked based on two-point scale within the

range of 0-6, and 0, 2, 4, and 6 represented no, low, moderate and high respectively

(Bartolo et al. 2012; Yu et al. 2015). The percentage cover of each threat and habitat

within sub-regions were considered for ranking. Then, they were classified according

to the Jenk’s optimization method (Jenks and Caspall 1971) in ArcGIS software.

This method minimizes squared deviations of class means or considers a goodness of

variance fit. The optimization is achieved when the quantity goodness of variance fit

is maximized. Hence, the classification is based on the natural grouping that is

inherited in the data. For example, when there is a greater possibility of occurrence

of one specific threat in one risk region compared to other risk regions, the rank

assigned was as six. The rank was equal to zero, when it is very unlikely to occur.

The point source discharge data were sub-divided into the following four

different stressors: nutrients (nitrogen and phosphorus); metals (aluminium, arsenic,

cadmium, chromium, copper, nickel, lead and zinc); Total Suspended Solids (TSS);

and Volatile Suspended Solids (VSS). They were ranked based on the percentage of

the number of kilograms per risk region (with respect to the areal extent of the

corresponding sub-region).

6.2.4.1 Relative risk calculation

The quantitative analysis of risks of each region depends on the interaction of

all components such as stressors, habitats, exposures and effects. Hence, the

241

magnitudes of the total threats in the risk region, the sum of the potential threat

exposure in the region, and the total risk to the ecological assessment endpoints were

calculated as explained by Landis and Wiegers (1997), Moraes et al. (2002) and

Bartolo et al. (2012). The following formulas were used for the calculation. For

example, to calculate the sum of stressors in each risk region, Eqn. (1) was used.

𝑆𝑆𝑆𝑆𝐵𝐵 𝑙𝑙𝑓𝑓 𝐵𝐵𝑠𝑠𝑟𝑟𝑟𝑟𝐵𝐵𝐵𝐵𝑙𝑙𝑟𝑟𝐵𝐵 𝐵𝐵𝑔𝑔 𝑟𝑟𝐵𝐵𝐵𝐵𝑘𝑘 𝑟𝑟𝑟𝑟𝑙𝑙𝐵𝐵𝑙𝑙𝑔𝑔 = ∑(𝑆𝑆𝑠𝑠𝑟𝑟𝑟𝑟𝐵𝐵𝐵𝐵𝑙𝑙𝑟𝑟𝐵𝐵) (1)

When the stressor has potential to impact habitat, the exposure filter received

a value of “one”, and the sum of potential stressor exposure in the region was

calculated as Eqn. (2).

𝑆𝑆𝑆𝑆𝐵𝐵 𝑙𝑙𝑓𝑓 𝑝𝑝𝑙𝑙𝑠𝑠𝑟𝑟𝑔𝑔𝑠𝑠𝐵𝐵𝐵𝐵𝑙𝑙 𝐵𝐵𝑠𝑠𝑟𝑟𝑟𝑟𝐵𝐵𝐵𝐵𝑙𝑙𝑟𝑟𝐵𝐵 𝑟𝑟𝑃𝑃𝑝𝑝𝑙𝑙𝐵𝐵𝑆𝑆𝑟𝑟𝑟𝑟 𝐵𝐵𝑔𝑔 𝑟𝑟𝐵𝐵𝐵𝐵𝑘𝑘 𝑟𝑟𝑟𝑟𝑙𝑙𝐵𝐵𝑙𝑙𝑔𝑔 = ∑(𝑆𝑆𝑠𝑠𝑟𝑟𝑟𝑟𝐵𝐵𝐵𝐵𝑙𝑙𝑟𝑟 ∗ ℎ𝐵𝐵𝑎𝑎𝐵𝐵𝑠𝑠𝐵𝐵𝑠𝑠)

(2)

The total risk to the ecological assessment endpoints in each risk regions was

calculated as Eqn. (3);

𝑇𝑇ℎ𝑟𝑟 𝑠𝑠𝑙𝑙𝑠𝑠𝐵𝐵𝑙𝑙 𝑟𝑟𝐵𝐵𝐵𝐵𝑘𝑘 𝑠𝑠𝑙𝑙 𝑠𝑠ℎ𝑟𝑟 𝑟𝑟𝑒𝑒𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝐵𝐵𝑒𝑒𝐵𝐵𝑙𝑙 𝐵𝐵𝐵𝐵𝐵𝐵𝑟𝑟𝐵𝐵𝐵𝐵𝐵𝐵𝑟𝑟𝑔𝑔𝑠𝑠 𝑟𝑟𝑔𝑔𝑟𝑟𝑝𝑝𝑙𝑙𝐵𝐵𝑔𝑔𝑠𝑠𝐵𝐵 𝐵𝐵𝑔𝑔 𝑟𝑟𝐵𝐵𝑒𝑒ℎ 𝑟𝑟𝐵𝐵𝐵𝐵𝑘𝑘 𝑟𝑟𝑟𝑟𝑙𝑙𝐵𝐵𝑙𝑙𝑔𝑔 =

∑(𝑇𝑇𝑙𝑙𝑠𝑠𝐵𝐵𝑙𝑙 𝑟𝑟𝐵𝐵𝐵𝐵𝑘𝑘 𝑠𝑠𝑙𝑙 𝑟𝑟𝑒𝑒𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝐵𝐵𝑒𝑒𝐵𝐵𝑙𝑙 𝐵𝐵𝐵𝐵𝐵𝐵𝑟𝑟𝐵𝐵𝐵𝐵𝐵𝐵𝑟𝑟𝑔𝑔𝑠𝑠 𝑟𝑟𝑔𝑔𝑟𝑟𝑝𝑝𝑙𝑙𝐵𝐵𝑔𝑔𝑠𝑠) (3)

6.2.5 Risk characterisation

This is the final phase of the risk assessment (Figure 6.2). The results from

the relative risk calculation indicate the regional perspective of risks in the Harbor

242

catchment. The total risk in each risk region, risk in each habitat, and the total risk to

assessment endpoints were used to derive site specific information.

6.2.6 Uncertainty and sensitivity analysis

As risk predictions in the RRM are point estimates that derive from data and

subjective expert knowledge, they are uncertain. There are three main uncertainty

categories that can propagate through the RRM. Model structure uncertainty is

propagated because key elements are not included in the conceptual model due to a

lack of knowledge, data availability and over simplification. Assigning an input

value to the model is considered as another source of uncertainty. This specifically

related to ranking stressors, habitats with a lack of knowledge of interactions

between model components, simplification and assumptions. The quality of the data,

their availability and currency can also be categorised as data uncertainties. The

subjective judgements of experts contribute some errors.

Three uncertainty levels (ranks) were used (low, moderate and high). For

example, when the data are site specific and high quality, the uncertainty level was

categorised as low. When there were no site specific data (or no data), and the quality

was poor, outdated, and if we made one assumption due to less knowledge about

data, was ranked as medium uncertainty. For example, since residential data of this

study is outdated, we updated them with Google Earth images, however, all new

buildings near residential areas were assumed as houses, and were ranked as medium

uncertainty. If there were no site specific data (or no data), and the quality was poor,

outdated, and more than one assumption made due to less knowledge about data,

they were ranked as high uncertainty. The discrete probability distributions were

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introduced to each component as explained by Yu et al. (2015), and Hayes and

Landis (2004). For example, for the RRM model components with high uncertainty,

a probability of 0.6 was assigned. The adjacent ranks received a probability of 0.2

each. For the model components with medium uncertainty, the probability was 0.8

with two adjacent ranks having the probability of 0.1 each. No distributions were

assigned to the components with low uncertainty.

The RRM produced point estimates for risks based on ranks and filters from

imperfect data. The Monte Carlo simulation was applied to determine the entire

range of possible outcomes and likelihood of achieving each of the model

components using Crystal Ball® software. Here, we introduced already defined

uncertainty levels of each component and ran the simulations for 1000 iterations. We

repeated these simulations different times and noted that there were no differences

after 1000 iterations.

The sensitivity analysis was also undertaken using the Crystal Ball® software.

This analysis explains the influence of uncertainties of sources to the model

sensitivity or parameter uncertainty (Colnar and Landis 2007). The model parameters

were classified according to the correlation coefficients that were generated through

the sensitivity analysis with respect to their contribution to the prediction of

uncertainty. Hence, a parameter that has high impact on uncertainty within the model

should obtain high correlation (Colnar and Landis 2007).

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6.3 Results

6.3.1 Problem formulation

We referred to available literature and expert knowledge to identify sources,

stressors and their characteristics, exposure opportunities, the ecosystem components

potentially at risk, and ecological effects in the Darwin Harbor catchment. The

resultant conceptual diagram shows the interaction between urban or rural activities

and their effect on coastal environment (Figure 6.3).

Figure 6.3. The conceptual diagram showing estuarine activities and their effect on

mangroves, water bodies and then to the Darwin Harbour (Courtesy of the

Integration and Application Network, University of Maryland Centre for

Environmental Science (ian.umces.edu/symbols/)).

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The major sources of threats for estuarine areas are land clearing for urban

and rural developments, land clearing for industrial and communication

developments, agriculture, point source discharges and climate change. They will

directly or indirectly impact on species and habitat destruction, increased flooding,

increasing (velocity and volume) soil runoff with nutrients and chemicals, increasing

sedimentation and pollution. For example, vegetation serves to slow the flow of

water and increase its infiltration into soil and groundwater storages. This enables the

water to flow more gradually into watercourses over a longer period of time.

However, because of extensive land clearing throughout most catchments and,

especially in more urbanized areas, the increased volume and velocity of water can

enter watercourses during flood. When it floods, runoff can carry substances such as

topsoil, chemicals, rubbish, nutrients, and oil and grease from roads. This polluted

water can be a significant problem for the health of mangroves. This may lead to the

degradation of mangroves unless these stressors are not addressed appropriately.

Since runoff from the Winnellie industrial area flows into mangroves of the Darwin

Harbor, heavy metal and nutrient concentration have been compared with other areas

which are not affected by urban runoff (Askwel 2008). Marginally higher

concentrations of heavy metals have been observed in the Winnellie mangrove

sediments compared to other sites. However, the conditions of mangroves are poorly

studied over time compared with such chemical pollutions.

The accumulation of sediment can greatly be increased by upstream

disturbances such as vegetation clearing and urbanisation. Although mangroves

colonise sedimentary environments, excessive sediment deposits can reduce growth

or even kill mangroves (McGuinnes 2003). Complete burial of root structure may

disturb gas exchange, killing root tissue and trees (McHugh 2004). Since mangroves

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are susceptible to pollution, pollutants from urban areas and agrochemicals results in

short term and long term effects on mangroves (Askwel 2008) because mangrove

environments are a trap for rubbish accumulation. A Darwin Harbor mangrove

rubbish survey found the three most common types of rubbish are aluminium cans,

plastic bottles and plastic bags (Lewis 2002). They were thought to be sourced from

storm water drains, tides and direct littering at the sites, and their rate of

decomposition is indefinite or quite high (Lewis 2002).

Currently, there is a major liquefied natural and petroleum gas project around

Darwin Harbor being run by INPEX. Subsea pipelines which transport liquid

hydrocarbons run between the gas fields and onshore at the Middle Arm (Metcalfe

2007). For the entire project, the proposed land clearing extent is about 300 ha

including mangroves. Parallel to this gas project, major industrial developments in

the Middle Arm are being discussed. According to a report published by INPEX at

different stages of the project, there is a high possibility of increasing sedimentation

over certain areas of the Darwin Harbor catchment (van Senden et al. 2013).

The point source discharges can either be hot water outflows (storm water

drainages) or treated sewage discharges. A hot water outflow from storm drainage

reduces mangrove leaf area and increases defoliation (McGuinnes 2003). Increased

water temperature shows mortality of seeds. Also at low levels, nutrients entering

waterways can lead to increasing productivity by promoting the growth of even algae

and plants. One of the consequences of urbanization is discharging pre-treated

sewage to mangrove swamps as efficient, low cost and natural wastewater treatment

(Lewis 2002). Nutrient pollution in the mangroves can have various effects.

Basically, it can enhance tree growth and productivity. On the other hand,

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excessively high nutrient concentration causes excessive algal growth which can

hinder oxygen exchange and mangrove seedling (McGuinnes 2003). Since there are

waste water treatment plants around Darwin Harbor (Darwin Central Business

District, Palmerston South, Buffalo Creek and Ludmilla Creek), increasing

sedimentation and accumulation nutrients as well as pollutants to the mangroves can

be expected.

Numerous water quality studies of the Darwin Harbor determined the

contribution from diffuse sources. The main nutrient load contributor is sewage

effluent and the effect is more significant at a local scale, such as a tidal creek level

where point source nutrients are discharges rather than at a whole-of-Harbor scale

(Skinner et al. 2009).

Climate change predictions indicate that sea level will increase by one metre

during the course of a 20 year period. This will highly affect mangroves with species

and habitat destruction showing a backward movement of zonation pattern.

We categorized each source into different stressors (Figure 6.4). For example,

the stressors related to the land clearing for urban and rural developments were

identified as residential, transportation, utilities and recreational activities. Utilities

are defined as electricity generation and transmission, gas treatment, storage and

transmission. The source, land clearing for industrial and communication

developments, was divided into three different stressors that included manufacturing

and industrial, dredging, and services. Dredging reflects the areas that are going to be

affected once the ongoing gas project has completed the onshore dredging. Services

include the areas that are allocated to the provision of commercial or public services

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substantially influencing the natural environment. The agriculture runoff from

croplands and horticulture lands are increasing levels of nutrients and turbidity in

streams and in mangroves. Available topographic data allowed us to categorise the

source agriculture into three different areas: cropping, horticulture and aquaculture.

6.3.1.1 Habitats and ecological assessment endpoints

Since unlocking the many possibilities for increasing infrastructure

development along the mangrove dominated shoreline of Darwin Harbor, ensuring

the maintenance of ecosystem services and a healthy environment is one of the

priority goals of the Government (Harrison et al. 2009; Lee 2003). Although many

studies have been undertaken in the mangrove forests of Darwin Harbor to

understand this valuable ecosystem, some knowledge gaps still exist. The

maintenance of mangrove health (sustainable ecosystem) and maintenance of water

quality (aquatic biodiversity) were selected as assessment endpoints for this study.

The attention was given to the management goals of the Northern Territory

government (The Darwin Harbor Advisory Committee 2003), the ecological

relevance to the stakeholders of the region, and vulnerability to potential stressors.

Most of the areas of the Darwin Harbor catchment are vacant Crown land

(Harrison et al. 2009). Although there are a number of habitat types present in the

catchment, mangroves, streams, rivers and other water bodies are the most available

terrestrial and aquatic habitats that highly influence to the health of the Darwin

Harbor. Hence, we selected mangroves and water bodies as habitats of the RRM.

Change in species structure, change in habitat for flora and fauna, increase in

sedimentation, and influx of nutrients were considered as effects of stressors in

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mangrove habitat. For water bodies, we accounted for change in waterways, change

in ground water level, influx of nutrients, influx of chemicals, and increase in

sedimentation.

The exposure to the stressor creates a possibility of a biological degradation

on habitats. For example, the land clearing stressor indicates a reduction of mangrove

coverage, change in species structure, reduction of habitat for flora and fauna. Point

source discharges exposures an influx of nutrients. As explained in Figure 6.4,

exposure and effect filters were identified for mangroves and water features.

Figure 6.4. The conceptual model for the relative risk modelling in the Darwin

Harbor. They were prepared for all risk regions. Exposure and effect filters were

modified with respect to each risk region.

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6.3.2 Risk analysis

Based on sub-catchment boundaries, we created 22 risk regions from an

initial 42 sub-catchements of the Harbor. For example: Howard River risk region

includes six sub-catchments; Blackmore River and Elizabeth River risk regions were

formed merging seven and eight of the Darwin Harbor’s sub-catchments

respectively. Howard River, Elizabeth River and Blackmore River risk regions are

the largest sub-regions including major river systems. According to Skinner et al.

(2009), they are classified as rural type land uses. Further, Sadgroves Creek, Hudson

Creek and Reichardt Creek risk regions comprise with the largest area of light

industrial land use. Hudson Creek is subjected to an industrial expansion of the

nearby East Arm Port. Rapid Creek, Buffalo Creek, Ludmilla Creek and the Darwin

Central Business Districtrisk regions include extensive urban areas.

6.3.3 Risk characterisation

The total relative risk was calculated for the 22 risk regions. Myrmidon

Creek, Blackmore River, Bleesers Creek and Elizabeth River risk regions showed the

highest total relative risk of ecological assets in the risk region. The Creek A, Sandy

Creek, West Arm and the Pioneer Creek were the risk regions with the lowest total

relative risk to ecological assets. The model did not calculate the total relative risk to

the Darwin Central Business District risk region as its mangrove habitat rank is zero.

The Bleesers Creek catchment is adjoining to the Winnellie industrial area, and

therefore, the percentage of metal, TSS, and sedimentation are high compared to

other catchments. Myrmidon Creek is closer to Palmerston city, has influence from

residential and industrial areas, and contains a sewage plant and diffuse entry point.

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Blackmore River and Elizabeth River catchments have high impact from agriculture

and point sources.


2 Bleesers Creek 8 Howard River 14 Myrmidon Creek 20Sandy Creek


4 Charles Point 10 Kings Creek 16 Pioneer Creek 22Woods Inlet

5 Creek A (Middle Arm) 11 Ludmilla Creek 17 Rapid Creek

6 Darwin Central

Business District (CBD) 12 Mitchell Creek 18 Reichardt Creek

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Figure 6.5. The total relative risk to ecological assets in risk regions of the Darwin

Harbor. They were classified as low, medium or high based on the Jenks’s

optimization (Jenks and Caspall 1971).

We investigated the total relative risk to each assessment endpoint separately.

The highest risks related to the maintaining mangrove health were found in

Sadgroves Creek, Reichardt Creek, Blackmore River, Myrmidon Creek, Bleesers

Creek and Elizabeth River (Figure 6.6 (A)). Mangroves of Creek A, Sandy Creek,

West Arm and Pioneer Creek are in a pristine condition with low risks. Although we

did not include influence from point source discharges to the Ludmilla Creek risk

region, it showed moderate total risk to the ecological assets. If the point source

discharges were included, the total relative risk of Ludmilla Creek risk region would

be high.

The total risk values obtained for the maintenance of water quality correspond

to the risk values for the maintenance of mangrove health (Figures 6.6 (A) and (B)).

Although the risk scores for both maintenance of mangrove health and maintenance

of water quality of Buffalo Creek catchment are moderate, this will be high in the

near future due to the expanding Lyons and Muirhead residential developments. To

the same extent, the Palmerston South and Mitchell Creek catchments will

experience higher risk scores due to Palmerston’s new urban footprints: Bellamack

and Roseberry suburbs.

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Figure 6.6. Total risk scores for the ecological endpoints; (A) Maintenance of

mangrove health; (B) Maintenance of water quality


The simulations of total risk to each ecological endpoint were modelled. The

frequency of the variation of total risk values was shown in Figure 6.7. As shown in

Figure 6.7 (a), the simulated total risk scores of the Pioneer Creek, Micket’s Creek,

Creek A, Sandy Creek and the West Arm are characterised by distributions with data


2 Bleesers Creek 8 Howard River 14 Myrmidon Creek 20 Sandy Creek


4 Charles Point 10 Kings Creek 16 Pioneer Creek 22 Woods Inlet

5 Creek A (Middle Arm) 11 Ludmilla Creek 17 Rapid Creek

6 Darwin Central Business

District (CBD) 12 Mitchell Creek 18 Reichardt Creek

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spikes. They indicated less uncertainty in source-habitat-endpoint pathways. The

Rapid Creek and the Woods Inlet distributions indicated moderate uncertainty of the

components (Figure 6.7 (b)). However, Reichardt Creek, Bleesers Creek, Myrmidon

Creek, Palmerston South, Elizabeth River and Blackmore River showed the most

widely spread distributions. This indicated higher uncertainties of model

components.

Figure 6.7. The frequency of all possible outcomes of total risk of ecological assets

in the risk regions: (a) Ludmilla Creek, Mitchell Creek, Mitcket Creek, Palmerston

South, and Pioneer Creek; (b) Blackmore River, Elizabeth River, Howard River,

Hudson Creek, and Kings Creek; (c) Rapid Creek, Reichardt Creek, Sadgroves

Creek, Sandy Creek, West Arm, and Woods Inlet; (d) Bleesers Creek, Buffalo Creek,

Charles Point, and Creek A.

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The sensitivity analysis calculated rank correlations for all model components

(i.e. risk regions, sources, habitats, and assessment endpoints). The rank correlation

indicates whether the uncertainty of any model component has an effect on the

uncertainty of the forecast. Figure 6.8 shows the rank correlation coefficients of the

risk regions having the highest total risk scores. These graphs show the relative

magnitude of influence for each variable. Assumptions made for water bodies within

the Elizabeth River, Bleesers Creek, and Blackmore River risk regions have a

dominant effect on the uncertainty of the forecast (Figures 6.8 (A), (B) and (D)). In

two instances (Blackmore River and Elizabeth River), it accounted nearly 80% of the

variation in forecast values, and can consider as the most important assumption in the

model. Treated sewage/nutrients of the Elizabeth River contributed least to the

forecast variance. It means that this assumption has a small effect; it could be

ignored or even could be eliminated from the analysis. The influence from the habitat

mangroves was relatively higher than other components.

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Figure 6.8. The rank correlations for sub-regions having the highest total risk scores:

(A) Blackmore River; (B) Elizabeth River; (C) Myrmidon Creek; (D) Bleesers

Creek.

6.4 Discussion

Ecological risk assessment is an important tool for supporting and improving

environmental decision-making. The RRM was used as such a tool for assessing the

impact of potential sources on habitats within the Darwin Harbor catchment in the

context of ongoing developments. A conceptual model integrating existing

information and expert opinion focused on describing source-habitat-endpoint

pathways was developed as an important foundation in the RRM process. This

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approach to conceptual model development has limitations, and we suggest

organising a series of stakeholder meetings to gather information rather than informal

meetings for future work. This would facilitate stakeholders to share their own

perspectives, come to agreement on what the conceptual model should include, and

to avoid subjective opinions.

Accurate and current spatial information is required for aquatic habitats, point

source discharges and land clearing. For example, we did not obtain point source

data for the Ludmilla Creek risk region which encompasses an abandoned treated

sewage discharge point. The Mitchell Creek risk region does not include the

influence from the recently populated Bellamack suburb. The current stressors and

effects from the LNG project in the Harbor need to be incorporated, especially those

effects due to the increase in sedimentation.

Although the results of the RRM applied here are representative of current

knowledge of stressors and effects on mangroves, we would recommend applying

the model to individual sub-catchments (42) rather than the merged sub-catchments

that resulted in the 22 risk regions. For example, the nutrient and total pollutant loads

of the Howard River, Blackmore River and the Elizabeth River are higher than other

risk regions due to their larger areal extent (Skinner et al. 2009). However, to

minimize such effects in this study, we calculated the percentage of kilograms per

each point source values with respect to the areal extent of each risk region.

It is clear that catchments with a high percentage cover of industrial and

commercial land use, and residential areas received the highest risk to each

ecological endpoint. Mangroves close to development and population centres

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suffered some clearing and localized damages from contaminants. These catchments

received higher ranks for residential, nutrients, metal, and VSS than undeveloped

areas. For example, Elizabeth River risk region has the highest total risk to the

ecological endpoints, and dredging, horticulture, cropping and aquaculture obtained

relatively high ranks. Mangroves and waterbodies also ranked high. The Creek A

obtained the lowest total risk to the ecological endpoints. Currently, although there is

high impact from sea level rise and sedimentation stressors to this risk region, other

stressors have minimal or no impact. Further, mangrove habitat was also ranked high

due to impacts from sedimentation and sea level rise. The prediction of climate

change effects has a major role in risk to mangroves. Due to the elevation variation

of the catchments, some areas will suffer from climate change effects or sea level

rise. Most of the catchments where the total risk of ecological assets is low have less

influence from land clearing for industries, diffuse entries and climate change.

This model provides a robust framework for assessing the regional ecological

risk and the benefit of Monte Carlo analysis in describing uncertainty in a RRM. The

outcome of this study should not be used for decision making; rather it can be used

as a framework for future studies. This is a useful approach for integrating all

components: multiple assessment endpoints, threats and habitats together, and

addressing the knowledge gap. According to the Northern Territory’s long-term

development plan, parts of Mitchel Creek, Palmerston South, Elizabeth River and

Blackmore River will become residential and light industrial areas to accommodate

the increasing population. These catchments already have high or moderate levels of

risks. For example, Palmerston South risk region already accounted for high risks for

various stressors namely: residential, utilities, sedimentation, sea level rise, waste

water/metals, and stormwater drainage/suspended solids. Hence, this is an important

259

issue to be considered when making decisions. The development plan is being

proposed for the Middle Arm of the Darwin Harbor (the City of Weddell) especially

for the Pioneer Creek catchment. Currently, the Pioneer Creek catchment

experiences low total risk of the ecological assets compared to others. For example, it

has low impact from stressors: land clearing for residential and transport and

communication, sedimentation and point sources discharges and surface runoff, and

there is no influence from other sources. The proposed developments will increase

the land clearing for residential and commercial activities, and subsequently the total

relative risk of the region. To the same extent, Skinner et al. (2009) predicted a

significant increase of pollutant load as a result of these developments. Therefore,

this is another important issue to be considered by the decision makers. Instead, the

risk regions with moderate total risks, for example, Sandy Creek and Kings Creek

have more possibilities for developments. Currently, these risk regions have minimal

or no influence from the majority of sources such as agriculture, aquaculture, climate

change, and land clearing for residential or commercial developments. However,

although the RRM can provide useful information about the ecological risk of

corresponding areas, it is difficult to select one risk region over another for locating

developments without a proper methodological approach.

The Integrated Water Resources Management (IWRM) approach promotes

the coordinated development and management of water, land and related resources,

in order to maximize the resultant economic and social welfare in an equitable

manner without compromising the sustainability of vital ecosystems. This has been

introduced by the Global Water Partnership (GWP) (Global Water Partnership 1996).

According to IWRM, stakeholder participation, river basin planning, pollution

control, and monitoring are few activities that constitute water management. This is a

260

similar approach as to what has been undertaken through the “Darwin Harbour

Strategy” by the Northern Territory Government.

The “Darwin Harbour Strategy” is a guide for sustainable development of the

Darwin Harbor region, and it supports the integrated management of the Darwin

Harbor region’s diverse environmental, social, cultural and economic values and

uses. However, the Northern Territory's water resources are considered to be under

relatively little pressure due to a comparatively small population base and low

intensity of land use in the catchment. This situation is changing due to a predicted

increasing population in the near future and therefore the Northern Territory is

increasingly becoming subject to higher demands, so integrated assessment

approaches are recommended. Hence, we would also suggest continuing the already

introduced “Darwin Harbour Strategy” approach to have coordinated development

and management of water, land and related resources in the region.

In summary, increasing population and consequent land development has a

potential for more severe impact on water quality and ecological health of the

Darwin Harbor catchment. For example, the potential increase of pollutant load and

nutrient load from the medium and long-term plans for the Harbor catchment

developments has already been predicted. Land clearing, residential and industrial

activities will increase. Therefore, we demonstrated the successful application of the

RRM to assess these risks. The model is repeatable and can use to predict the

ecological risk in the near future with various scenarios.

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Although the Darwin Harbour catchment is a rapidly developing area,

Blackmore River, Howard River and Elizabeth River catchments are classified as

rural land uses compared with catchments closer to the Darwin Central Business

District which are classified as urban areas. These rural areas are poor in data

availability and data quality. Available topographic data are outdated; more than 10

years old. For example, through the sensitivity analysis, the waterbodies data

contributed to the majority of uncertainty for the Elizabeth River, Blackmore River,

and Bleesers Creek catchments as there is less spatial data available for waterbodies

in these catchments (Figure 6.8). The error contribution from mangroves to the risk

analysis in Blackmore River, Elizabeth River, Myrmidon Creek and Bleesers Creek

regions are also higher than other regions. The mangrove coverage spatial data set is

almost 20 years old, and it does not include already cleared and newly regenerated

areas exhibiting higher data uncertainty. For instance, the mangrove clearing in the

Middle Arm due to the activities of the natural gas project is not included in the

analysis. Therefore, the main limitation related to this application is data input. The

outdated or lack of data impacts the analysis. Although we updated newly formed

residential areas using Google Earth, there may be some errors associated with this

interpretation due to difficulties in determining industrial and residential land use

from the satellite imagery without field verification (that is, both cover types appear

similar in the imagery) .

The introduction of subjective expert opinion may add some gross errors to

the model. The ranks, exposure and effect filters are subjective. This subjectivity can

change the model outputs. A further issue related to the conceptual model is that the

262

model can over-simplify the real world situation. The assigning of ranks in the

uncertainty analysis is subjective. This is also based on expert opinion and existing

information. Hence, we would recommend organising a series of stakeholder

meetings and gathering information from various users, and collecting current

information before implementing the RRM. If uncertainties are minimised, the RRM

can be used by decision-makers and land managers to prepare action plans to reduce

threats from urban development to the coastal environment and mangroves.


There are many ongoing and proposed industrial, commercial and residential

development projects in the Darwin Harbor catchment, and subsequently the natural

resources of Darwin Harbor region are experiencing an increasing development

pressure. As it is necessary to ensure the balance between these developments and

conservation, appropriate tools that combine knowledge from different sources into a

consistent environmental management framework are needed. One of the key

requirements for environmental monitoring programs in Darwin Harbor is an

integrated approach for ecological risk assessment that enables stakeholders to make

practical and sound management decisions. Therefore, in this study, we introduced

the application of a RRM to assess the ecological risk of the Darwin Harbor, and this

application is a significant contribution to the current knowledge in this context.

The application of the RRM involves three major phases: problem formation;

risk analysis; and risk characterization. We investigated existing knowledge and

consulted expert opinion in the problem formation phase. Then, a conceptual model

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was created combining all information. Spatial data from topographic databases and

statistics relating to various Darwin Harbor monitoring projects were collected.

In the second phase of the analysis, we identified the ecological endpoints and

corresponding model components including sources, stressors and habitats. The

management goals of Darwin Harbor were considered in determining the ecological

assessment endpoints which were the maintenance of mangrove health and the

maintenance of water quality. The large Darwin Harbor catchment boundary and

boundaries of sub-catchments were adapted from the Australian Collaborative Land

Use Mapping program (V5). However, homogeneous small catchments were merged

and formed 22 risk regions for further analysis. We created a RRM to rank and sum

each source of stressors, and habitat in each risk region. Once model components

were ranked according to the percentage coverage of sub-regions (with respect to

individual areal extent), the pathway of sources-habitats-endpoints was created.

Risk characterisation is the last phase of the modelling process. The

uncertainty of each model component was investigated and classified into low,

medium and high, and the discrete probability distribution was introduced. The

Monte Carle simulation was used to obtain all possible values of risk scores. We

analysed the total relative risk of the ecological assets in risk regions. They were

classified into three classes according to the Jenk’s optimization.

The sub regions: Myrmidon Creek, Blackmore River, Bleesers Creek and

Elizabeth River showed the highest total relative risk to ecological assets in the risk

regions. Creek A, Sandy Creek, West Arm and Pioneer Creek showed the lowest

total relative risk of ecological assets. The study did not calculate the total relative

264

risk for the Darwin Central Business District region. These results indicated that risk

regions with a high percentage cover of industrial, commercial, residential areas,

diffuse entry points, and climate change effects contributed to the highest risk to each

ecological endpoint.

The relative risk model is a robust application that is suitable for a large

geographic area where multiple stressors are of concern. Data uncertainty is the

major limitation associated with the model. For example, one of the selected habitats

to be analysed is mangroves and spatial data on mangrove coverage is almost 20

years old. It does not include either recently cleared or regenerated mangrove areas.

We would not recommend using these results for decision making due to the various

uncertainties, however, this can be used as a framework for future studies. We

further would recommend organising a stakeholder meeting at the first phase of the

model for data collection. This will reduce the effect of subjective opinions. We

would also suggest continuing the already introduced “Darwin Harbour Strategy”

approach to have coordinated development and management of water, land and

related resources in the region.

265

6.6 Acknowledgement

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Codorus Creek Watershed Applying the Relative Risk Model. Human and Ecological

Risk Assessment 8: 405-28.

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Yu, W., Zhang, L., Ricci, P. F., Chen, B. & Huang, H. 2015, Coastal ecological risk

assessment in regional scale: Application of the relative risk model to Xiamen Bay,

China, Ocean & Coastal Management, vol. 108, 131-139.

270

Chapter 7 – Conclusions and Recommendations

271

Highlights

• Mangrove species within the Rapid Creek mangrove forest were classified

using remotely sensed images. When discriminating mangroves from other

features in the study area, a large number of spectral bands with higher spatial

resolution were more accurate than broad spectral bands within the blue, green,

and red regions. A significant increase in classification accuracy was achieved

when using pan-sharpened imagery of five spectral bands within the visible

range. This approach can be used repeatedly not only to map new mangroves

but also to update existing maps.

• Mangrove tree crowns were successfully delineated using a combination of

WorldView-2 imagery, a digital surface model, and a GEOBIA algorithm.

Minor errors were detected where neighbouring trees with similar

characteristics were aggregated together and when upward pointing branches

of the same tree identified as different trees. However, these results are

invaluable especially to identify dead mangrove trees, to calculate number of

trees newly re-generated and to quantify biomass of each trees.

• The spatial variability of mangrove canopy chlorophyll was mapped using the

red, red-edge, NIR1 and NIR2 bands of the WorldView-2 satellite image, and

their derived normalized difference indices. The field samples were not

sufficient representatives of the image pixels due to inaccuracies of GPS

measurements and exhibited some errors. This is the first step towards

analysing mangrove canopy health and the approach can be improved

introducing more precise calibration such as very precisely measured tree

272

locations and very high resolution remotely sensed images preferably from

Unmanned Aerial Vehicles.

• The spatial variability of Leaf Area Index and Above Ground Biomass of

mangroves were also mapped using high resolution satellite images and digital

photographs. The results identified where the most healthy and unhealthy

mangrove forests were located.

• The ecological risk associated with Darwin Harbour mangroves was assessed

in the context of rural and urban development using a relative risk modelling

approach. The catchment was divided into 22 risk regions based on small

catchment boundaries and their homogeneity. The interaction between stressors

and habitats were modelled through exposure and effect filters. For instance,

the risk regions: Myrmidon Creek, Blackmore River, Bleesers Creek and

Elizabeth River showed the highest total relative risk for ecological assets.

These risk regions had a high percentage cover of industrial, commercial,

residential areas, diffuse entry points, and climate change effects. Therefore,

the tool can be served as an instrument that ensures the balance between rural

and urban developments and the mangrove conservation in the future.

Summary

Mangroves are one of the most globally important, salt tolerant woody plants

that are subjected to numerous natural and anthropogenic disturbances. Fast and

reliable methods to map and monitor mangrove ecosystems are vital for their

protection and management. However, detailed mangrove maps at the community or

273

species level are challenging to produce. Field observations are time consuming,

expensive, and difficult due to densely clustered nature of trees and muddy soil.

Therefore, remote sensing is the only way to obtain spatially extensive information

over the large areas that they cover. At the scale of an image pixel, mangrove

ecosystems are heterogeneous. Commonly used per-pixel analyses cannot adequately

describe the variations in their biophysical properties. Advanced digital image

analysis algorithms suitable for classifying mangrove species and quantifying

biophysical variations at sub-pixel level were tested. The Rapid Creek mangrove

forest, Darwin, Australia was selected as the test site.

The most recent study related to the Rapid Creek mangrove forest was found

in between 1994 and 1995. Since individual mangrove species have varying

tolerances to the period, frequency, and depth of inundation, precise and up-to-date

spatial information on the current status of this area is a prerequisite for sustainably

conserving and monitoring the ecosystem. The study discriminated mangrove and

non-mangrove areas using object based image classification. Integrating mangrove-

environment relationships was exploited in order to improve the mapping accuracy.

Mangrove areas were then further classified into species using a support vector

machine algorithm. Attention was also given to delineate individual tree crowns for

assessing and monitoring mangrove species.

Attributes of individual mangrove trees may help to analyse their biophysical

variations, growth patterns, and regeneration after natural or anthropogenic

disturbances. An object based image analysis method was tested for different

remotely sensed data sources for individual tree crown delineation. The combination

of high resolution satellite image, a digital surface model of the area, and object

274

based image analysis methods gave promising results. Further, the study analysed

biophysical variations of mangrove trees. According to the scientific literature, most

important plant biophysical properties are canopy chlorophyll, above ground biomass

and leaf area indices. Hence, this study developed fast, reliable, and low cost

methods for estimating these biomarkers from remotely sensed data. Developed

methods were validated using in-situ field measurements. Overall the results of this

study reveal the potential of using remotely sensed data for deriving spatial

information on current status of mangroves. However, to build resilience into

mangrove conservation plans, decision makers need to identify and protect

mangroves that are prone to disturbances. The study developed a spatial model that

assesses the ecological risk to the mangroves.

The traditional ecological risk assessment methods evaluate the interaction of

stressor, receptor and response components of the systems. When expanding these

methods to regional scale, the scale, complexity of the structure, and the regional

spatial components must be incorporated. Hence, this study introduced a relative risk

model that evaluates multiple, dissimilar stressors simultaneously and comparatively

at a regional scale.

The aim of this dissertation was to push the boundary of remote sensing data

processing to extract metrics relevant to mangrove health mapping, monitoring and

management in Darwin Harbour. The three main research questions and their

subsequent objectives are the core of the thesis. Their significance is discussed in the

following sections. The dissertation presents not only new outcomes for mapping

mangrove species and their biophysical variations but also an ecological risk

assessment tool to monitor mangrove ecosystems around Darwin Harbour.

275

7.1 Research question 1: How can high spatial resolution remote sensing data be

used to discriminate mangroves at species level?

The question was answered in two steps. First, the study separated mangroves

and non-mangroves using object based image analysis method. Then, the mangrove

coverage only was classified into species level. To my knowledge, Chapter 2 is the

first study of its kind to comprehensively compare the application of high resolution

satellite imagery with aerial photographs for mangrove mapping.

The study demonstrated that a large number of spectral bands with higher

spatial resolution (pan-sharpened WorldView-2 (WV2) image) were more accurate

than broad spectral bands (aerial photographs) within the blue, green, and red

regions, when discriminating mangroves from other features in an image. The spatial

relationship between mangroves and their surrounding environment (contextual

information) help to improve the mapping accuracy. When further classifying down

to species level, the highest accuracy (overall accuracy of 89%) was obtained from

the pan-sharpened WV2 image using five spectral bands within the visible range.

The pan-sharpened visible image covered the same spectral range with the same

spatial resolution as the aerial photographs. Therefore, the higher accuracy of the

former compared to the overall accuracy of 68% from resampled aerial photographs

is attributed to the increased number of narrow bands available for analysis, rather

than the total wavelength range. Compared to these results, however, there is no

considerable difference between the mangrove species map obtained from the pan-

sharpened Red, Red-Edge and NIR1 bands of WV2 image. Further, this study

demonstrated the significant increase in classification accuracy when using pan-

sharpened imagery, under the condition that spectral and radiometric integrity is

276

maintained using an appropriate algorithm such as high pass filter pan-sharpening

method.

This study also demonstrated a unique application of the support vector

machine algorithm for mangrove species mapping. While this advanced image

processing technique has previously been used in other environments, it is

particularly beneficial for mangroves because it efficiently deals with the dense,

heterogeneous nature of mangrove forests. However, spatial autocorrelation will

reduce the classification accuracy up to a certain level. The noise from non-leaf

surfaces such as tree branches and background can degrade the results of spectral

separability of mangroves.

7.2 Research question 2: How can 3D canopy dynamics combined with spectral

properties of mangroves be used to map mangrove biophysical variability?

The research question was answered in three steps. First, Chapter 3 tested

different remote sensing data sources and object based image analysis algorithms for

delineating mangrove individual tree crowns. A number of studies have already been

conducted to extract individual tree crowns for various vegetation types using

remotely sensed data with varying degree of success. Very little information is

available for mangrove forests, and already developed methods are more successful

with plantation forests having regular distances between trees rather than naturally

grown forests. Plantation forests show more similarities in size due to the similar age

of individuals as well. Unfortunately, similar sizes and regular distances between

mangroves trees are rare, and those methods cannot readily be applied to mangroves.

Therefore, Chapter 3 provided a significant contribution to this context. Following on

277

from this work, Chapter 4 mapped spatial variations of mangrove canopy

chlorophyll. There are a few studies that analysed mangrove biophysical variables

and their relation to various vegetation indices derived from satellite data, however,

to my knowledge, it is difficult to find a recorded study for mapping a spatial

distribution of mangrove chlorophyll. Chapter 5 evaluated Above Ground Biomass

(AGB) and Leaf Area Index (LAI) values over the Rapid Creek mangrove forest.

Although the digital cover photography method has already been tested with a few

homogeneous forest types, this is the first study that used this method to estimate

LAI over a heterogeneous forest like mangroves. Calculated LAI values were used as

in-situ calibration and validation data. The random forests regression algorithm and

partial least squares regression algorithm showed promising results when mapping

spatial variation of these biomarkers, especially when there is a weak linear

relationship between field samples and the satellite data.

In this study, to delineate individual tree crowns, WV2 imagery was used as

the primary data source, supplemented by an aerial photo derived Digital Surface

Model (DSM). The combination of a WV2 image, a DSM and an object based image

analysis was found to successfully delineate mangrove tree crowns. The achieved

overall relative accuracy was 92%. Minor errors were detected where neighbouring

trees with similar characteristics were aggregated together and conversely, where

multiple crowns were assigned to multiple trees (upward pointing branches of the

same tree identified as different trees). The visual appearances of tree crowns were

dramatically improved when incorporating the DSM. The inverse watershed method,

which has been successfully used in other forest environments with sparser canopies,

did not provide data representative of mangrove tree crowns. The combination of a

WV2 image, a DSM, and an object based image analysis approach is a relatively low

278

cost, accurate, and robust approach, which can be applied to heterogeneous forests

like mangroves.

To measure mangrove chlorophyll content, mangrove leaf samples were

collected and analysed in the laboratory. Then, canopy chlorophyll content was

calculated using LAI of sampled trees. The nonlinear random forests regression

algorithm was used to describe the relationship between canopy chlorophyll content

and remotely sensed data, and to estimate the spatial distribution of canopy

chlorophyll variation. The imagery was evaluated at full 2 m spatial resolution, as

well as at reduced resolutions of 5 m and 10 m. The root mean squared errors with

validation samples were 0.82, 0.64 and 0.65 g/m2 for maps at 2 m, 5 m and 10 m

spatial resolution respectively. The correlation coefficient was analysed for the

relationship between measured and predicted chlorophyll values. The highest

correlation (0.71) was observed at 5 m spatial resolution. Hence, this study

confirmed integrating remotely sensed data and field samples for mapping mangrove

canopy chlorophyll content over a large area. The random forests regression

algorithm showed moderate results for this application, while there is a weak linear

relationship between field samples and the satellite data.

Chapter 5 describes quantification of spatial variations of AGB and LAI in

the study site. Field measurements, vegetation indices derived from WV2 images,

and a partial least square regression algorithm were used to derive LAI maps and

AGB maps. The highest accuracies for LAI and AGB were obtained at 5 m spatial

resolution. The correlation coefficients between field samples and predicted maps

were 0.8 in both instances. Further, there was a strong linear correlation (0.7)

between LAI and AGB as found for other forest types. Hence, the study

279

demonstrated the possibility of assessing standing characteristics of mangroves using

remotely sensed data.

7.3 Research question 3: How can spatial information be combined to analyse

the environmental stresses on different mangrove sites?

This research question was answered in the Chapter 6. Regional ecological

risk assessment methods evaluate the interaction of three different regional spatial

components, which include: sources that release stressors; the habitat where the

receptors reside; and impacts to the assessment endpoints. This is fairly simple at a

single contaminated site with one stressor. However, the relative risk model (RRM)

evaluates multiple, dissimilar stressors simultaneously and comparatively at a

regional scale. The application of the RRM involves three major phases: problem

formulation, risk analysis, and risk characterization. During the problem formulation

phase, existing knowledge about the region was investigated. Experts in this context

were consulted. Then, a conceptual model was created combining all information.

Spatial data from topographic databases and statistics relating to various Darwin

Harbor monitoring projects were collected. In the second phase of the analysis,

ecological endpoints and corresponding model components including sources,

stressors and habitats were identified. The management goals of Darwin Harbor were

considered in determining the ecological assessment endpoints which were the

maintenance of mangrove health and the maintenance of water quality. The large

Darwin Harbor catchment boundary and boundaries of sub catchments were adapted

from the Australian Collaborative Land Use Mapping program (V5). However,

homogeneous small catchments were merged and formed 22 risk regions for further

analysis. Once model components were ranked according to the percentage coverage

280

of sub-regions (with respect to individual areal extent), the pathway of sources

habitats endpoints was created. Risk characterisation was the last phase of the

modelling process. The uncertainty of each model component was investigated and

classified into low, medium and high, and the discrete probability distribution was

introduced. The Monte Carle simulation was used to obtain all possible values of risk

scores. The total relative risk of the ecological assets in risk regions were analysed

and classified according to the Jenk’s optimization theory. Risk regions with a high

percentage cover of industrial, commercial, residential areas, diffuse entry points,

and climate change effects contributed to the highest risk to each ecological

endpoint.

In conclusion, successful development of advanced remote sensing methods

for mangrove mapping and monitoring is presented in this thesis. More precisely, an

object based image analysis method to discriminate mangrove and non-mangrove

areas, appropriately parameterised support vector machine algorithm for classifying

mangroves at species level, individual tree crown delineation, sub-pixel mapping

algorithms for analysing spatial variations of mangrove biophysical characteristics,

and a relative risk model for assessing ecological risk of Darwin Harbour mangroves

are the main outcomes of this thesis. Overall the study represents a significant

contribution to the current knowledge in the context of mangrove mapping and

monitoring.

7.4 Limitations and recommendations for future research

Chapters 2 – 6 addressed the limitations to methods used and results obtained

separately. This final section briefly addresses key areas that remain critically

281

challenging for mapping any wetland environment with broad band high spatial

resolution data.

7.4.1 Mapping with high spatial resolution image data

The difficulties with integrating field and image data within heterogeneous

environments should not be underestimated. In this study, errors due to mismatch

between image pixels and field samples were addressed by including additional

information collected in the field for mangrove tree identification. However, even

with 2 m pixel resolution, it is still difficult to confidently locate sampled trees in the

imagery. Work should commence to address the challenges of linking field sampled

mangrove tree positions with image data. For instance, for future studies, we would

recommend a transect method for field sampling for more successful results. Two

ends of transects should be established outside the mangrove forest with a highly

accurate positioning system. Then, all other measurements that based on the

established transect lines will be with greater accuracy. This is not an issue that is

specific to mangrove remote sensing, but is a critical area of research that should be

considered when mapping any forest environment.

When separating mangrove and non-mangroves and identifying individual

tree crowns, the spectral resolution of remotely sensed images played a more crucial

role than spatial resolution. Although this conclusion is specific to this study only,

the main reason would be the lack of integration of spatial features such as shape and

texture or colour of remotely sensed images for object based image analysis. Within

the shape criterion, the degree of smoothness of the object border and the

compactness of the objects can be altered. Having said that, one example is to

282

introduce the shape criterion from very high resolution aerial images together with

the digital surface model to improve the accuracy of the individual tree crown

delineation.

Another limitation of this study was the ability to transfer developed methods

to other locations around the world with different mangrove environment settings.

The results from mangrove species mapping using the support vector machine

algorithm were site specific – that is the algorithm was parameterised according to

the test site. As such, the framework for using this classification can be applied in

other areas, however specific parameters will need modifying. Every attempt was

made to create transferable variables within the object based image analysis

environment for separating mangroves from other features, and ultimately

delineating tree crowns. However modifications within the ruleset for other locations

and conditions are inevitable. Therefore, in order to apply these methods to vast and

different species-rich mangrove forests, further studies are required.

The recent advances in remote sensing imaging technology provide a wide

range of image datasets available both commercially and for research purposes.

Hence, the other limitation associated with this study was the investigation of limited

number of remotely sensed images; that is the main images used for this study over

the Rapid Creek mangrove forest were WorldView-2 satellite images and aerial

photographs obtained from the UltraCamD camera. When analysing individual tree

crowns, it was found that the higher spatial resolution such as 1 m or more would

provide higher accuracy in identifying individual shrub crowns, gaps between trees

and foliage clumping. Future work needs to investigate the utility of both high spatial

and spectral resolution datasets towards these aspects. For instance, when

283

considering towards the recent advances in imaging; Unmanned Aerial Vehicles

(UAV) would be an option. They can be used for optical, infrared and hyperspectral

imaging. They are incorporated with on-board high precise gyroscopes and GPS

receivers, and thus the stability of these systems is high; positioning is accurate.

Although UAV can use for rapidly and frequently surveying inaccessible areas and

obtaining high spatial resolution images, the spatial scale of the phenomena under

investigation is important.

The spatial scale of analysis (suitable spatial resolution of remote sensing

data) should match the scale of phenomena under investigation. Remote sensing data

with too many details may result in lower overall accuracy due to an increase in

within-class spectral variability. Although imaging using UAV’s open the path for

acquiring data with very high resolution even up to a spatial resolution of 1 cm,

future studies are necessary to understand the effect of scale variation in mangrove

environment. For instance, these very high spatial resolution images (1 cm to 20 cm)

allow analysing mangrove leaf structures and leaf orientation that enable managers

for sustainable mangrove conservation. However, as discovered by this study, they

may not provide better results in terms of mangrove canopy chlorophyll or above

ground biomass variations.

7.4.2 Relative Risk Modelling

According to this study, a relative risk model is a robust application that is

suitable for a large geographic area where multiple stressors are of concern. In this

application, data uncertainty is the major limitation associated with the model. Some

topographic data that we used for the analysis was more than 20 years old. They do

284

not correctly represent current situation of the Darwin Harbour, for example, there is

no correct information about recently cleared mangroves and recently populated

suburbs. An investigation of up-to-date data for relative risk modelling remains a

challenge. Further, rather than informal discussion, it is necessary to organise a series

of stakeholder meetings at the first phase of the modelling process for problem

formulation. This will reduce the effect of subjective opinions of experts regarding

interaction between stressors and their effect on ecological endpoints.

7.5 Contribution to Knowledge

This study signifies the operational use of advanced remote sensing

techniques such as object based image analysis methods and pixel based image

processing algorithms (support vector machine, random forest and partial least

squares regression) for mangrove mapping. The study also demonstrates the utility of

a regional ecological risk assessment method to assess the ecological risk associated

with mangroves. This model evaluated the interaction of sources that release

stressors, the habitat where the receptors reside and impacts to the assessment

endpoints for mangrove monitoring purposes.

The contributions of this thesis to the body of scientific knowledge are;

• An innovative method developed and tested to integrate satellite data, a

digital surface model derived from aerial photographs and an object based

image analysis techniques for separating mangroves and non-mangroves,

285

• The first ever effort to identify the most suitable remote sensing data source

with the support vector machine algorithm for mapping individual mangrove

species,

• Development of a method to isolate individual mangrove tree crowns or

groups of trees using remote sensing data and object based image analysis

techniques,

• The first assessment of spatial variability of mangrove canopy chlorophyll

over a broad area,

• The first ever application of digital photography method for calculating

mangrove leaf area index for calibration and validation purposes,

• Demonstration of the utility of remote sensing data and image processing

techniques for analysing spatial variability of mangrove above ground

biomass and leaf area index, and

• The first ever effort to develop a relative risk model to assess the ecological

risk associated with mangroves in the context of rural and urban

development.

In summary, this research presents unique applications of advanced remote

sensing methods for analysing up-to-date spatial information of mangrove forests,

and signifies the use of remotely sensed data against field observations. These

286

methods and the risk model can be used as a baseline for mangrove monitoring and

management.

287

Appendices

Appendix 1

Re: Requesting a formal written permission to re-use an aticle in a thesis Remote Sensing Editorial Office [[email protected]] Sent:Thursday, 5 November 2015 6:33 PM To: Muditha Kumari Heenkenda

Dear Mr./Ms. Heenkenda,

Thank you for your email. According to our copyright statement, MDPI

maintains Open Access publishing, which means that the authors hold the

copyright, readers can freely read, download articles published in MDPI

journals. So you can reuse the materials you published with us without

obtaining our permissions. You may find more details at

http://www.mdpi.com/about/openaccess

Wish you good luck with the thesis preparation and getting approval

smoothly.

Best Regards,

Xuanxuan Guan

Senior Assistant Editor

Remote Sensing Editorial Office

(http://www.mdpi.com/journal/remotesesing)

/Remote Sensing/ Impact Factor (2014), 3.180

Top Open Access Journal in Remote Sensing

Remote Sensing at Twitter: https://twitter.com/RemoteSens_MDPI

---

Ms. Xuanxuan Guan, M.Sc.

MDPI Branch Office, Beijing

Room 2207, Jincheng Center, No. 21 Cuijingbeili

Tongzhou District, Beijing 101101, China

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On 2015/11/6 0:34, Muditha Kumari Heenkenda wrote:

> /Remote Sensing/Editorial Office,

> MDPI AG, Klybeckstrasse 64, 4057 Basel,

> Switzerland.

>

>

> Dear Sir or Madam,

>

>> As a main author of the following paper, I would like to request a

> formal written permission from the publisher to incorporate this article

> in my thesis as PhD by publication. I would highly appreciate your

> response.

>

> /Heenkenda, M., Joyce, K., Maier, S. & Bartolo, R. 2014, Mangrove

mailto:[email protected]

http://www.mdpi.com/about/openaccess

http://www.mdpi.com/journal/remotesesing)



http://www.mdpi.com/journal/remotesensing/

> Species Identification: Comparing WorldView 2 with Aerial Photographs,

> Remote Sensing, vol. 6, 6064-6088, doi:10.3390/rs6076064.

> http://www.mdpi.com/2072-4292/6/7/6064/

>

>

>

> Thanks,

>

>

> Muditha K. Heenkenda

>

http://www.mdpi.com/2072-4292/6/7/6064/

Appendix 2

Muditha K. Heenkenda,

Research Institute for the Environment and Livelihoods,

Charles Darwin University, Darwin, Australia

[email protected]

ASPRS grants you permission to reproduce the following ASPRS material in your

PhD dissertation Charles Darwin University, Darwin, Australia:

Heenkenda, M. K., Joyce, K. E. & Maier, S. W. 2015, Mangrove Tree Crown Delineation

from High Resolution Imagery, Photogrammetric Engineering and Remote Sensing, vol. 81,

471-479; doi: 10.14358/PERS.81.6.471. Please acknowledge the reprinted material with the following citation:

“Reproduced with permission from the American Society for Photogrammetry and

Remote Sensing, Bethesda, Maryland, www.asprs.org.”

If you require additional permissions or would like to use the material in a different format or

as part of another work, please let me know.

Sincerely,

Matthew

Austin Publications Production

Assistant (301) 493-0290 ext.

108 (301) 493-0208

(Fax)

[email protected]


Appendix 3

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Appendix 4

Permissions

T & F Reference Number: P111615-02 11/16/2015 Muditha Heenkenda Ellongewen Drive Casuarina Campus NT909 Australia [email protected] Dear Sir or Madame, We are in receipt of your request to reproduce your article Muditha K. Heenkenda & Renee Bartolo Regional Ecological Risk Assessment Using a Relative Risk Model: A Case Study of the Darwin Harbor, Darwin, Australia Human and Ecological Risk Assessment: An International Journal (Online) DOI: 10.1080/10807039.2015.1078225 For use in your dissertation and to post to your institutional repository You retain the right as author to post your Accepted Manuscript on your departmental or personal website with the following acknowledgment: “This is an Accepted Manuscript of an article published in Human and Ecological Risk Assessment: An International Journal online [August 20, 2015], available online: http://www.tandfonline.com/doi/full/10.1080/10807039.2015.1078225 This permission is all for print and electronic editions. For the posting of the full article it must be in a secure, password-protected intranet site only. An embargo period of twelve months applies for the Accepted Manuscript to be posted to an institutional or subject repository. We will be pleased to grant you permission free of charge on the condition that: This permission is for non-exclusive English world rights. This permission does not cover any third party copyrighted work which may appear in the material requested. Full acknowledgment must be included showing article title, author, and full Journal title, reprinted by permission of Taylor & Francis LLC (http://www.tandfonline.com). Thank you very much for your interest in Taylor & Francis publications. Should you have any questions or require further assistance, please feel free to contact me directly.


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A TRANSITION FROM TRADITIONAL MANGROVE REMOTE …60432/Thesis_CDU_60432_Heenkenda_M.pdfA TRANSITION FROM TRADITIONAL MANGROVE REMOTE SENSING TO RECENT ADVANCES: MAPPING AND MONITORING