Ip Oi-ching. / Thesis submitted in: October 2001. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2002. / Includes bibliographical references (leaves 142-152). / Abstracts in English and Chinese. / LIST OF ABBREVIATIONS --- p.ix / LIST OF TABLES --- p.xiv / LIST OF FIGURES --- p.xviii / LIST OF APPENDICES --- p.xxii / Chapter CHAPTER ONE --- INTRODUCTION / Chapter 1.1 --- Conceptual Framework --- p.1 / Chapter 1.2 --- Objectives --- p.4 / Chapter 1.3 --- Significance --- p.5 / Chapter 1.4 --- Study Area --- p.6 / Chapter 1.5 --- Organization Of Thesis --- p.8 / Chapter CHAPTER TWO --- LITERATURE REVIEW / Chapter 2.1 --- Quality of Life and Indicators --- p.9 / Chapter 2.1.1 --- Scope of study for quality of life --- p.9 / Chapter 2.1.2 --- Development and evolution of quality of life studies --- p.13 / Chapter 2.1.3 --- Quality of life indicators --- p.14 / Chapter 2.2 --- Quality of Life Studies using Remote Sensing data --- p.17 / Chapter 2.2.1 --- Attributes derived from remote sensing --- p.17 / Chapter 2.2.2 --- Environmental changes and landuse change --- p.17 / Chapter 2.2.3 --- Housing quality --- p.18 / Chapter 2.2.4 --- Integration of remote sensing data and census data --- p.19 / Chapter 2.3 --- Quality of Life Study and Application of Remote Sensing in Hong Kong --- p.20 / Chapter 2.4 --- Summary --- p.22 / Chapter CHAPTER THREE --- METHODOLOGY / Chapter 3.1 --- Data Description --- p.24 / Chapter 3.1.1 --- Biophysical data --- p.24 / Chapter 3.1.2 --- Socioeconomic indices --- p.28 / Chapter 3.1.3 --- Data extracted at Tertiary Planning Unit --- p.30 / Chapter 3.2 --- Satellite Data Preprocessing --- p.32 / Chapter 3.2.1 --- Radiometric and atmospheric correction --- p.38 / Chapter 3.2.2 --- Image normalization --- p.43 / Chapter 3.2.3 --- Geometric correction --- p.44 / Chapter 3.3 --- Landuse and Land-cover Classification --- p.45 / Chapter 3.4 --- Spectral Data Extraction and Transformation --- p.47 / Chapter 3.5 --- Integration of Spectral and Census Data for Quality of Life Modeling --- p.49 / Chapter 3.5.1 --- Inter-relationship between biophysical data and socioeconomic data --- p.50 / Chapter 3.5.2 --- Integrated quality of life modeling --- p.50 / Chapter 3.6 --- Summary --- p.51 / Chapter CHAPTER FOUR --- DATA DESCRIPTION / Chapter 4.1 --- Socioeconomic Data --- p.53 / Chapter 4.2 --- Spectral Data --- p.62 / Chapter 4.2.1 --- Raw data --- p.62 / Chapter 4.2.2 --- Landuse and land cover --- p.63 / Chapter 4.2.3 --- Vegetation indices --- p.66 / Chapter 4.2.4 --- Tasseled cap components --- p.67 / Chapter 4.2.5 --- Surface temperature --- p.69 / Chapter 4.2.6 --- Principal components extracted from biophysical variables --- p.70 / Chapter 4.3 --- Summary --- p.79 / Chapter CHAPTER FIVE --- INTERRELATIONSHIP BETWEEN SPECTRAL VARIABLES AND SOCIOECONOMIC VARIABLES / Chapter 5.1 --- Framework of Analysis --- p.82 / Chapter 5.2 --- Correlation among Socioeconomic and Biophysical data --- p.84 / Chapter 5.3 --- Stepwise Multiple Linear Regression Models --- p.91 / Chapter 5.3.1 --- Biophysical data as dependent variable --- p.91 / Chapter 5.3.1.1 --- NDVI as dependent variable --- p.95 / Chapter 5.3.1.2 --- URBANM as dependent variable --- p.98 / Chapter 5.3.2 --- Socioeconomic data as dependent variable --- p.101 / Chapter 5.3.2.1 --- POP´ؤDEN as dependent variable --- p.101 / Chapter 5.4 --- Summary and Discussion --- p.103 / Chapter CHAPTER SIX --- QUALITY OF LIFE ANALYSIS / Chapter 6.1 --- Indictors for Quality of Life Study --- p.105 / Chapter 6.2 --- QOL Indicators --- p.110 / Chapter 6.3 --- Spatial Variation of QOL --- p.114 / Chapter 6.4 --- Temporal Variation of QOL --- p.125 / Chapter 6.5 --- Summary and discussion --- p.131 / Chapter CHAPTER SEVEN --- CONCLUSION / Chapter 7.1 --- Summary of Findings --- p.134 / Chapter 7.1.1 --- Inter-relationship between socioeconomic and biophysical variables --- p.135 / Chapter 7.1.2 --- Quality of life indicators --- p.135 / Chapter 7.2 --- Limitations of the Study --- p.13 8 / Chapter 7.3 --- Recommendations for Further Studies --- p.140 / REFERENCES --- p.142 / APPENDIX A --- p.153 / APPENDIX B --- p.157 / APPENDIX C --- p.167

Ma Fung-yan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (leaves 137-141). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgements --- p.iv / Table of Contents --- p.v / List of Tables --- p.ix / List of Figures --- p.x / Chapter CHAPTER 1 --- INTRODUCTION / Chapter 1.1 --- Introduction and background --- p.1 / Chapter 1.2 --- Objectives --- p.4 / Chapter 1.3 --- Significance --- p.5 / Chapter 1.4 --- Organization of the thesis --- p.5 / Chapter CHAPTER 2 --- LITERATURE REVIEW / Chapter 2.1 --- Introduction --- p.7 / Chapter 2.2 --- Hyperspectral remote sensing --- p.7 / Chapter 2.2.1 --- Current imaging spectrometers available --- p.8 / Chapter 2.2.2 --- Applications of hyperspectral remote sensing --- p.9 / Chapter 2.2.2.1 --- Biochemistry of vegetation --- p.10 / Chapter 2.2.2.2 --- Spatial and temporal patterns of vegetation --- p.12 / Chapter 2.3 --- Tree species recognition --- p.12 / Chapter 2.3.1 --- Factors affecting spectral reflectance of vegetation --- p.14 / Chapter 2.3.1.1 --- Optical properties of leaf --- p.14 / Chapter 2.3.1.2 --- Canopy structure --- p.15 / Chapter 2.3.1.3 --- Canopy cover --- p.16 / Chapter 2.3.1.4 --- Illumination and viewing geometry --- p.16 / Chapter 2.3.1.5 --- Spatial and temporal dynamics of plants --- p.17 / Chapter 2.3.2 --- Classification algorithms for hyperspectral analysis --- p.17 / Chapter 2.3.2.1 --- Use of derivative spectra for tree species recognition --- p.17 / Chapter 2.3.2.2 --- Linear discriminant analysis --- p.18 / Chapter 2.3.2.3 --- Artificial neural network --- p.19 / Chapter 2.3.3 --- Tree species recognition using hyperspectral data --- p.21 / Chapter 2.4 --- Data compression and feature extraction --- p.22 / Chapter 2.4.1 --- Analytical techniques of data compression --- p.23 / Chapter 2.4.2 --- Analytical techniques of feature extraction --- p.25 / Chapter 2.4.2.1 --- Feature selection by correlation with biochemical and biophysical data --- p.25 / Chapter 2.4.2.2 --- Spatial autocorrelation-based feature selection --- p.27 / Chapter 2.4.2.3 --- Spectral autocorrelation-based feature selection --- p.29 / Chapter 2.4.2.3.1 --- Optimization with distance metrics --- p.29 / Chapter 2.4.2.3.2 --- Stepwise linear discriminant analysis --- p.30 / Chapter 2.5 --- Summary --- p.31 / Chapter CHAPTER 3 --- METHODOLOGY / Chapter 3.1 --- Introduction --- p.33 / Chapter 3.2 --- Study site --- p.33 / Chapter 3.3 --- Instrumentation --- p.34 / Chapter 3.4 --- Data collection --- p.35 / Chapter 3.4.1 --- Laboratory measurement --- p.36 / Chapter 3.4.2 --- In situ measurement --- p.39 / Chapter 3.5 --- Methods of data analysis --- p.40 / Chapter 3.5.1 --- Preprocessing of data --- p.40 / Chapter 3.5.2 --- Compilation of hyperspectral database --- p.42 / Chapter 3.5.3 --- Tree species recognition --- p.42 / Chapter 3.5.3.1 --- Linear discriminant analysis --- p.44 / Chapter 3.5.3.2 --- Artificial neural network --- p.44 / Chapter 3.5.3.3 --- Accuracy assessment --- p.45 / Chapter 3.5.3.4 --- Comparison of different data processing strategies and classifiers --- p.45 / Chapter 3.5.3.5 --- Comparison of data among different seasons --- p.46 / Chapter 3.5.3.6 --- Comparison of laboratory and in situ data --- p.46 / Chapter 3.5.4 --- Data compression --- p.47 / Chapter 3.5.5 --- Band selection --- p.47 / Chapter 3.6 --- Summary --- p.48 / Chapter CHAPTER 4 --- RESULTS AND DISCUSSIONS OF TREE SPECIES RECOGNITION / Chapter 4.1 --- Introduction --- p.50 / Chapter 4.2 --- Characteristics of hyperspectral data --- p.50 / Chapter 4.3 --- Tree species recognition --- p.79 / Chapter 4.3.1 --- Comparison of different classifiers --- p.82 / Chapter 4.3.1.1 --- Efficiency of the classifiers --- p.83 / Chapter 4.3.1.2 --- Discussions --- p.83 / Chapter 4.3.2 --- Comparison of different data processing strategies --- p.84 / Chapter 4.3.3 --- Comparison of data among different seasons --- p.86 / Chapter 4.3.4 --- Comparison of laboratory and in situ data --- p.88 / Chapter 4.4 --- Summary --- p.92 / Chapter CHAPTER 5 --- RESULTS AND DISCUSSIONS OF DATA COMPRESSION AND BAND SELECTION / Chapter 5.1 --- Introduction --- p.93 / Chapter 5.2 --- Data compression --- p.93 / Chapter 5.2.1 --- PCA using in situ spectral data --- p.93 / Chapter 5.2.1.1 --- Characteristics of PC loadings --- p.95 / Chapter 5.2.1.2 --- Scatter plots of PC scores --- p.96 / Chapter 5.2.2 --- PCA using laboratory spectral data --- p.99 / Chapter 5.2.2.1 --- Characteristics of PC loadings --- p.102 / Chapter 5.2.2.2 --- Scatter plots of PC scores --- p.103 / Chapter 5.2.2.3 --- Results of tree species recognition using PC scores --- p.107 / Chapter 5.2.3 --- Implications --- p.107 / Chapter 5.3 --- Band selection --- p.108 / Chapter 5.3.1 --- Preliminary band selection using stepwise discriminant analysis --- p.108 / Chapter 5.3.1.1 --- Selection of spectral bands --- p.109 / Chapter 5.3.1.2 --- Classification results of the selected bands --- p.109 / Chapter 5.3.1.3 --- Seasonal comparison using stepwise linear discriminant analysis --- p.114 / Chapter 5.3.1.4 --- Implications --- p.116 / Chapter 5.3.2 --- Band selection using hierarchical clustering technique --- p.116 / Chapter 5.3.2.1 --- Hierarchical clustering procedure --- p.116 / Chapter 5.3.2.2 --- Selection of spectral band sets --- p.119 / Chapter 5.3.2.3 --- Classification results of the selected band sets --- p.124 / Chapter 5.4 --- Summary --- p.127 / Chapter CHAPTER 6 --- SUMMARY AND CONCLUSION / Chapter 6.1 --- Introduction --- p.129 / Chapter 6.2 --- Summary --- p.129 / Chapter 6.2.1 --- Tree species recognition --- p.129 / Chapter 6.2.2 --- Data compression --- p.130 / Chapter 6.2.3 --- Band selection --- p.131 / Chapter 6.3 --- Limitations of this study --- p.132 / Chapter 6.4 --- Recommendations for further studies --- p.133 / Chapter 6.5 --- Conclusion --- p.136 / BIBLIOGRAPHY --- p.137 / APPENDICES / Appendix 1 Reflectance of the 25 tree species in four seasons with three levels of leaf density --- p.142-166 / "Appendix 2 Confusion matrices of tree species recognition using original spectra, first derivatives spectra and second derivatives spectra with 138 bands classified by linear discriminant analysis for each season" --- p.167-178 / "Appendix 3 Confusion matrices of tree species recognition using original spectra, first derivatives spectra and second derivatives spectra with 138 bands classified by neural networks for each season" --- p.179-190 / Appendix 4 Confusion matrices of tree species recognition using 21 tree species with original spectra classified by linear discriminant analysis for seasonal comparison --- p.191-193 / Appendix 5 Confusion matrices of tree species recognition using the first eight PC scores classified by linear discriminant analysis for each season --- p.194-197 / "Appendix 6 Confusion matrices of tree species recognition using original spectra, first derivatives spectra and second derivatives spectra classified by stepwise linear discriminant analysis (Case 2) for each season" --- p.198-209 / "Appendix 7 Confusion matrices of tree species recognition using original spectra, first derivatives spectra and second derivatives spectra classified by stepwise linear discriminant analysis (Case 3) for each season" --- p.210-220 / "Appendix 8 Confusion matrices of tree species recognition using 21 tree species with original spectra, first derivatives spectra and second derivatives spectra classified by stepwise linear discriminant analysis for seasonal comparison" --- p.221-229 / Appendix 9 Confusion matrices of tree species recognition using the spectral bands selected by hierarchical clustering procedures and classified by linear discriminant analysis for each season --- p.230-257