INTEGRATING HYMAP AIRBORNE HYPERSPECTRAL SENSOR DATA AND FIELD BASED ...

399 Pages·2010·13.7 MB·English

INTEGRATING HYMAP AIRBORNE HYPERSPECTRAL
SENSOR DATA AND FIELD BASED SPECTROMETER
DATA TO MAP ARID ZONE VEGETATION

A thesis submitted in fulfilment of the requirements for the degree of
Master of Applied Science (Land Information)

Kerryn P. Robinson
Bachelor of Applied Science (Environmental Analysis)

School of Mathematical and Geospatial Sciences,
College of Science, Engineering and Health,
RMIT University,
September 2008

DECLARATION

I, Kerryn Patricia Robinson, declare that:

a) except where due acknowledgement has been made, the work as presented, has
been undertaken and completed by me;
b) the work has not been submitted previously, in whole or in part, to qualify for any
other academic award;
c) the content of the thesis is the result of work that has been carried out since the
official commencement date of the approved research program;
d) any editorial work, paid or unpaid, carried out by a third party is acknowledged;
e) ethics procedures and guidelines have been followed.

SIGNED _________________________________________________________________
Kerryn Patricia Robinson

ii
iii
ACKNOWLEDGEMENTS
In addition to the usual challenges associated with a research degree, transferring interstate,
and being diagnosed with cancer, partway through the course, added extra dimensions. I
would like to acknowledge the assistance of the many people, named and unnamed, who
have given encouragement and pacified frustrations throughout all the challenges and
setbacks of the last several years thus allowing me to complete this research.
I would also like to acknowledge the kind permission of the Australian Department of
Defence for access to the Woomera Test Area during the High Explosive Trials, and the
information, assistance and courtesy provided by the staff at the site. The Defence Scientific
and Technology Organisation (DSTO) generously granted me access to the source data for
this project. Mr Guy Byrne, CSIRO (Department of Environmental Management) is
acknowledged for his patient explanations, expertise and assistance in the data collection.
Friends and colleagues are a boon to one’s sanity, and I would like to acknowledge my
friends and colleagues, who were willing to listen to my ramblings and act as sounding
boards when I needed to explore ideas. The assistance provided by Mr Marcus Reston who
answered some of the more knotty questions about the ATREM algorithm, which is no
longer supported by CSES, is acknowledged. Dr John Reid, at the Melbourne Herbarium
was very generous with his time and the Herbarium collections for the identification of the
plant species.
RMIT and its staff provided support in the completion of this project. Thesis supervisors
have the unenviable task of guiding candidates through a course of study. I would like to
acknowledge the support and understanding given by my RMIT supervisors. Professor
Simon Jones, who found himself with the daunting task of following Dr Peter Woodgate,
without the background knowledge associated with the original planning of the research. In
addition, I would like to acknowledge Associate Professor Chris Bellman, who regularly
reminded me that I was undertaking a Masters Degree, not changing the world. Both were
very patient and tolerant when illness and frustration jeopardized the study. Their guidance,
comments, and suggestions for the improvement of the manuscript of the thesis were
sometimes greeted with a less than gracious demeanour. Their suggestions and probing
questions were always gratefully received, and I believe, improved the final thesis. Finally, I
would like to acknowledge my husband, Jim, who, as well as sharing the successes, provided
saintly patience and understanding thus supporting me through enumerable frustrations and
disappointments, during the research.
iv
CONTENTS

Acknowledgements iv
Summary 1

Chapter 1
The Use of Hyperspectral Imagery to Map Arid Land Vegetation at the Large Scale
3
Explosives Test Area, Woomera, South Australia
1.1 Background Information 3
1.2 Aims 3
1.3 Research Questions 3
1.4 Experiment Design 4
. 1.5 Layout of the Thesis 4
Chapter 2
The Spectral Signatures of Vegetation Species and Their Significance in the
7
Classification of Hyperspectral Imagery
2.1 Introduction 7
2.2 EMR/Matter Interactions
7
2.2.1 Transmittance 8
2.2.2 Reflection 10
2.2.3 Absorption 12
2.2.4 Emittance 13
2.2.5 Scattering 14
2.3 The Sun as a Black Body, and the Effects of the Atmosphere
16
on the Emitted Solar Radiation
2.3.1 In-Scene Statistics 18
2.3.1.1 Dark Area Subtraction 18
2.3.1.2 Internal Average Reflectance 18
2.3.2 In-scene calibration pixels or well-characterised
18
calibration panels
2.3.2.1 Empirical Line Calibration 19
2.3.3 The Radiative Transfer Model 20
2.3.3.1 Combination of Calibration Areas and
23
Radiative Transfer Model
2.3.4 The Spectral Signature of Vegetation 23
2.4 The Challenge of Resolution 26
2.4.1 Temporal Resolution 26
2.4.2 Radiometric Resolution 27
v
CONTENTS

2.4.3 Spatial Resolution 29
2.4.3.1 Mixed pixels 30
2.4.4 Spectral Resolution 31
2.4.4.1 The smallest detectable wavelength
change necessary to identify absorption 33
features
2.4.4.2 Mixed Pixel Analysis 35
2.4.4.3 Spectral Feature Fitting (SFF) 37
2.4.4.4 Spectral Angle Mapper (SAM) 40
2.4.4.5 Mixture Tuned Matched Filtering
44
(MTMF)
2.5 The Spectral Signature of Vegetation 49
2.6 Classification Methods for Remotely Sensed Data 52
2.6.1 Classification 54
2.6.2 A Means to an End 54
2.7 Classification of Vegetation in Hyperspectral Imagery 55
2.7.1 Can it be done? 55
2.7.2 The Knowledge Gap 56
2.8 Conclusion 57
Chapter 3
Australian Arid and Semi-Arid Environments 58
3.1 Australia’s Environment 58
3.1.1 Geomorphology 58
3.1.1.1 Land area 58
3.1.1.2 Geomorphologic regions 58
3.1.1.3 Deserts 59
3.1.1.4 Drainage 59
3.1.1.5 Vegetation 59
3.1.2 Some Problems Associated with Land Management 60
3.1.2.1 Introduced species 60
3.1.2.2 Regeneration 60
3.1.2.3 Land management practices 64
3.1.3 Problems Associated with Surveys and Monitoring 64
3.1.3.1 Remoteness 64
3.1.3.2 Temporal issues 65
3.1.3.3 Spatial issues 65
vi
CONTENTS

3.1.3.4 Baseline survey issues 66
3.1.4 The Overseas Experience 67
3.1.5 The Australian Experience 71
3.2 The Assessment of the Suitability of a Site for Vegetation
71
Mapping using Hyperspectral Imagery
3.2.1 Department of Defence Invitation 71
3.2.2 Site Description 76
3.2.2.1 Location 76
3.2.2.2 Site history 77
3.2.2.3 Physical environment 78
3.2.2.4 Vegetation 80
3.2.2.5 Fauna 82
3.2.2.6 Climate 82
3.2.2.7 Site support infrastructure 82
3.3 Summary 83
Chapter 4
Selection of Sensors Used for the Collection of Data 86
4.1 The Selection of Sensors Used in the Project 91
4.1.1 Analytical Spectral Device (ASD) FieldSpec Pro
91
Sensor
4.1.1.1 Assessment 93
4.1.2 HyMap® Aircraft-borne Sensor 93
4.1.2.1 Instrument calibration 94
4.1.2.2 Geometric Corrections 95
4.1.2.3 Assessment 96
4.1.3 EO-1 Hyperion 96
4.1.3.1 Instrument Calibration 97
4.1.3.2 Geometric Corrections 98
4.1.3.3 Assessment 98
4.1.4 QuickBird® 99
4.1.4.1 Instrument Calibration 100
4.1.4.2 Geometric Corrections 100
4.1.4.3 Assessment 100
4.2 Summary 100
Chapter 5
Experiment Design and Sample Collection Protocols 102
vii
CONTENTS

5.1 Introduction 102
5.1.1 Experiment Design 102
5.1.2 Collection of Field Spectra 103
5.1.2.1 Target spectra to be collected with the
103
ASD
5.1.3 Collection of Imagery 104
5.1.4 Calibration of Imagery 104
5.1.5 Classification of Imagery 104
5.1.6 Ground-truthing 105
5.1.7 Statistics 105
5.1.8 Software 106
5.2 Data Collection Protocols 106
5.2.1 Previous Work 106
5.2.2 Collection Protocol 108
5.2.2.1 Know Your Instrument 108
5.2.2.2 Data Collection 108
5.2.2.3 Record Keeping 108
5.2.2.4 Imagery Calibration 114
5.2.2.5 Data Reduction 115
5.3 Sample Collection and Preparatory Work 116
5.3.1 Preparatory Work and Research 116
5.3.1.1 Permissions 116
5.3.1.2 Meteorology 117
5.3.1.3 Ground Control and Calibration Panels 118
5.3.1.4 Availability of Satellite Imagery 119
5.3.2 On-site sampling 119
5.3.2.1 Field Sensors 120
5.3.2.2 Airborne Hymap® Sensor 123
5.3.2.3 Marking of Sample Points 124
5.3.2.4 Reference Specimens 125
5.3.3 Post Field Work Sampling 126
5.3.3.1 Mineral Reference Samples 126
5.3.3.2 Plant Reference samples 126
5.3.3.3 Data Selection 127
5.3.3.4 Instrument differences between the two
128
ASDs
viii
CONTENTS

5.3.3.5 Weather 128
5.3.3.6 Atmospheric conditions 128
5.3.3.7 Time of day at moment of capture 128
5.3.3.8 Pixel size 129
5.4 Summary 129
Chapter 6
Imagery Processing and Classification Results 131
6.1 Overview 131
6.2 Pre-processing of Imagery 132
6.2.1 Methods Selected for Use for Imagery Collected over
132
the Woomera Explosives Test (WET)
6.2.1.1 ELC Method 133
6.2.1.2 ATREM combined with ELC Method
133
(ATREM/ELC Cascade)
6.3 Imagery Classification Algorithms 136
6.3.1 Preliminary Analysis 136
6.3.2 Classification Algorithms 140
6.3.3 Soil NDVI 141
6.3.4 Ancillary Imagery Processing 142
6.3.4.1 Imagery Registration 142
6.4.3.2 Pan-sharpening 142
6.4 Classification Overview 143
6.5 Classification Results 148
6.6 Observations 150
6.6.1 North-South Flight Lines 150
6.6.1.1 Atmospheric Calibration:
ATREM/Empirical Line Calibration
(ELC) 150
Classification Algorithm: Mixture Tuned
Matched Filter (MTMF)
6.6.1.2 Atmospheric Calibration:
ATREM/Empirical Line Calibration
(ELC) 152
Classification Algorithm: Spectral
Feature Fitting (SFF)

ix