published_or_final_version / Environmental Management / Master / Master of Science in Environmental Management

published_or_final_version / Environmental Management / Master / Master of Science in Environmental Management

published_or_final_version / abstract / toc / China Area Studies / Master / Master of Arts

by Sam Shun-shun Lau. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1997. / Includes bibliographical references (leaves 194-220). / Acknowledgments --- p.i / Abstract --- p.ii / Table of Contents --- p.iv / List of Plates --- p.vii / List of Tables --- p.viii / List of Figures --- p.xi / Chapter CHAPTER 1 --- Introduction / Chapter 1.1 --- Mai Po Marshes Nature Reserve (Hong Kong) --- p.1 / Chapter 1.1.1 --- Site description --- p.1 / Chapter 1.1.2 --- Ecological and conservation importance --- p.4 / Chapter 1.1.2.1 --- Local and international recognition --- p.4 / Chapter 1.1.2.2 --- Birds --- p.7 / Chapter 1.1.2.3 --- Gei wais and its wildlife --- p.10 / Chapter 1.1.2.4 --- Fishpond aquaculture --- p.20 / Chapter 1.1.3 --- Pollution sources and impacts --- p.22 / Chapter 1.2 --- Self-purification Capability of Wetland Ecosystems --- p.32 / Chapter 1.2.1 --- Principles and mechanisms --- p.32 / Chapter 1.2.2 --- Treatment efficiency --- p.38 / Chapter 1.3 --- Objectives and Outlines of the Present Study --- p.42 / Chapter CHAPTER 2 --- Water Quality of the Mai Po Marshes Nature Reserve / Chapter 2.1 --- Introduction --- p.45 / Chapter 2.2 --- Materials and Methods --- p.49 / Chapter 2.2.1 --- Water sampling and analyses --- p.51 / Chapter 2.2.2 --- Statistical analyses --- p.52 / Chapter 2.3 --- Results and Discussion --- p.52 / Chapter 2.3.1 --- Water quality in the Mai Po Marshes --- p.55 / Chapter 2.3.2 --- Spatial pattern of water pollution --- p.74 / Chapter 2.3.3 --- Temporal pattern of water pollution --- p.77 / Chapter 2.3.4 --- Correlation between various parameters --- p.79 / Chapter 2.4 --- Conclusions / Chapter CHAPTER 3 --- Nutrient and Metal Contaminationin Sediments of the Mai Po Marshes Nature Reserve / Chapter 3.1 --- Introduction --- p.81 / Chapter 3.2 --- Materials and Methods --- p.83 / Chapter 3.2.1 --- Sediment collection --- p.83 / Chapter 3.2.2 --- Laboratory analyses --- p.84 / Chapter 3.2.3 --- Statistical analyses --- p.84 / Chapter 3.3 --- Results and Discussion --- p.85 / Chapter 3.3.1 --- Pollution loads in sediments --- p.85 / Chapter 3.3.2 --- Spatial variation of contamination in sediments --- p.88 / Chapter 3.3.3 --- Temporal variation of contamination in sediments --- p.113 / Chapter 3.3.4 --- Vertical variation of contamination in sediments --- p.124 / Chapter 3.3.5 --- Correlation between various parameters --- p.131 / Chapter 3.4 --- Conclusions --- p.133 / Chapter CHAPTER 4 --- Behaviour of Contaminants in Sediments in a Shrimp-growing Gei Wai / Chapter 4.1 --- Introduction --- p.136 / Chapter 4.2 --- Materials and Methods --- p.137 / Chapter 4.2.1 --- Sediment collection --- p.137 / Chapter 4.2.2 --- General physico-chemical analyses --- p.139 / Chapter 4.2.3 --- Effects of salinity and temperature --- p.140 / Chapter 4.2.4 --- Effects of drying --- p.140 / Chapter 4.2.5 --- Toxicity assays --- p.141 / Chapter 4.2.5.1 --- Preparation of sediment extract for toxicity tests --- p.141 / Chapter 4.2.5.2 --- Microtox® toxicity testing --- p.141 / Chapter 4.2.5.3 --- Algal bioassay --- p.142 / Chapter 4.2.5.3.1 --- Algal culture --- p.142 / Chapter 4.2.5.3.2 --- Algal growth inhibition test --- p.142 / Chapter 4.2.5.4 --- Amphipod bioassay --- p.143 / Chapter 4.2.6 --- Statistical analyses --- p.144 / Chapter 4.3 --- Results and Discussion --- p.144 / Chapter 4.3.1 --- General properties --- p.144 / Chapter 4.3.2 --- Effects of temperature and salinity --- p.148 / Chapter 4.3.3 --- Effects of drying --- p.151 / Chapter 4.3.4 --- Toxicity assays --- p.154 / Chapter 4.4 --- Conclusions --- p.156 / Chapter CHAPTER 5 --- Self-purification Capability of Gei Wais at the Mai Po Marshes Nature Reserve / Chapter 5.1 --- Introduction --- p.159 / Chapter 5.2 --- Materials and Methods --- p.161 / Chapter 5.2.1 --- Sample collection --- p.161 / Chapter 5.2.2 --- Laboratory analyses --- p.161 / Chapter 5.2.3 --- Statistical analyses --- p.166 / Chapter 5.3 --- Results and Discussion --- p.167 / Chapter 5.3.1 --- Changes in water quality --- p.178 / Chapter 5.3.1 --- Removal efficiency --- p.185 / Chapter 5.4 --- Conclusions / Chapter CHAPTER 6 --- General Conclusions --- p.188 / References / Appendices

劇烈的城市化過程已經在世界上多個地區發生並產生了許多的城市群，珠江三角洲正是這樣的一個城市群。目前，珠江三角洲上的城市土地利用和土地覆蓋已經發生了巨大的變化。而其中最重要的一個結果就是大量城市不透水層的出現，並已經極大地影響著當地的城市環境，如城市洪水、城市氣候、水污染和大氣污染等。因此，城市不透水層及其分佈的估算對於監測和管理城市化進程及其對環境的影響有著重要的意義。然而，由於城市土地覆蓋類型的多樣性，精確的城市不透水層的估算（ISE）仍然是一個極具挑戰性的課題。本論文旨在通過融合光學遙感和合成孔徑雷達（SAR）遙感技術來提高亞熱帶濕潤區城市不透水層估算的精度。此外，論文還將探索亞熱帶濕潤區土地覆蓋類型分類的季節性變化及其對城市不透水層提取的影響。本論文的研究結果主要包括以下幾個部分。 / 首先，本研究發現亞熱帶濕潤區不透水層提取的季節性效應與中緯度地區截然不同。在亞熱帶濕潤地區，冬季是最適合用遙感影像進行不透水層提取的。原因是由於冬季是旱季，雲量少，許多可變來源區域（VSA）沒有水體覆蓋，而在遙感影像中，水體容易和暗不透水層混淆。另一方面，秋季最不適合不透水層提取，因為存在大量的雲層，並且，大量的降水導致VSA區域充滿水，從而增加了與暗不透水層混淆的區域面積。此外，大量的雲層在影像中也是呈現高反射特徵的，因此極容易和亮不透水層混淆，這是秋季不適合用於提取不透水層的另一重要原因。 / 其次，提出了一種新的基於形狀自我調整鄰域（SAN）的特徵提取演算法。該特徵提取演算法類比人類視覺對圖像感知的強大能力，進行遙感影像低層特徵的提取。實驗結果表明，SAN特徵提取方法對非監督分類有顯著的提高，其中總體分類精度從0.58提高到0.86，而Kappa係數從0.45提高到0.80。SAN特徵對於監督分類的精度也有一定的提高，這些都表明，與傳統的特徵提取方法相比，SAN特徵對遙感影像分類具有重要的作用。 / 再次，通過對比分析光學遙感影像和SAR影像發現，單獨採用光學遙感影像進行不透水層提取比單獨採用SAR影像取得更好的結果。同時，單獨採用光學遙感（Landsat ETM+）時，支援向量機（SVM）比人工神經網路（ANN）取得更好的結果，這是因為ANN對於亮不透水層與幹裸土之間，以及暗不透水層與陰影之間的光譜混淆更加敏感。然而，當單獨採用SAR遙感（ENVISAT ASAR）時，ANN則取得更好的結果，這是由於SVM分析SAR影像時更容易產生雜訊，並具有明顯的邊緣效應。因此，融合光學遙感和SAR遙感具有重要的意義。通過比較不同圖像融合層次發現，像元級融合（Pixel Level Fusion）會降低單獨採用光學遙感提取不透水層的精度，因而不適合光學和SAR影像的融合。而特徵級融合（Feature Level）決策級融合（Decision Level）可以更好的把不透水層從陰影區域和裸土中區分出來，因為更加適合光學與SAR的融合。 / 最後，三組不同的光學遙感和SAR遙感影像被用於評估本論文提出的光學和SAR融合方法，包括Landsat ETM+與ASAR影像，SPOT-5與ASAR影像，以及SPOT-5與TerraSAR-X影像。此外，還比較了不同的融合方法（人工神經網路、支援向量機和隨機森林）對融合結果的影響。結果表明，用光學和SAR遙感影像融合提取不透水層有利於減少在光學遙感影像中容易出現的光譜混淆現象，從而提高不透水層提取的精度。另外，隨機森林在融合光學和SAR影像中效果較其它兩種方法，因為隨機森林對兩種不同的資料來源區別對待，而這正是符合光學遙感與SAR遙感截然不同的工作方式的特點，從而能更好的融合光學遙感和SAR遙感。 / 本論文的研究成果有助於探索亞熱帶濕潤區中物候特點和氣候特點對城市不透水層提取所產生的季節性效應；同時也為融合光學遙感和SAR遙感影像提取城市不透水層提供了一個技術框架。由於珠江三角洲是一個亞熱帶濕潤區一個典型的快速城市化的城市群區域，本文所提出的方法框架和所得到研究結論可為世界上其它亞熱帶濕潤區的城市遙感研究提供一定的參考價值。 / Dramatic urbanization processes have happened in many regions and thus created a number of metropolises in the world. The Pearl River Delta (PRD) is one of such typical areas, where the urban land use/land cover has been significantly changing in the recent past. As one of the most important implications, a large increment of impervious surface (IS) turned out to be one of the features of fast urbanization process and has been influencing the urban environment significantly, including urban flooding, urban climate, water pollutions, and air pollutions. Therefore, the estimation of IS would be very helpful to monitor and manage the urbanization process and its impacts on the environment. However, accurate estimation of urban IS remains challenging due to the diversity of land covers. This dissertation attempts to fuse optical and SAR remote sensing data to improve the accuracy of urban impervious surface estimation (ISE) in humid subtropical regions (HSR). The seasonal characteristics of land covers and its impacts on ISE in HSR are all investigated. Some interesting findings are summarized as follows. / Firstly, the study demonstrates quite a special pattern of the seasonal effects of ISE in humid subtropical areas that is different from that in mid-latitude areas. According to the results, in subtropical monsoon regions, winter is the best season to estimate IS from satellite images. There are little clouds, and most of the Variable Source Areas (VSA) is not filled with water. On the other hand, autumn images obtained the lowest accuracy of IS due to the clouds coverage and the water in VSA. Autumn is a rainy season in a subtropical monsoon region, for which clouds occur very often and VSA areas are always filled with water. Consequently, clouds are confused with bright IS due to their similarly high reflectance, and more water in VSA is confused with dark IS due to their similarly low reflectance. / Secondly, a novel feature extraction technique, based on the shape-adaptive neighborhood (SAN), is proposed to incorporate the advantages of human vision into the process of remote sensing images. Quantitative results showed that improvement of SAN features is particularly significant improvement for the unsupervised classifier, for which the overall accuracy increased from 0.58 to 0.86, and the Kappa coefficient increased from 0.45 to 0.80, indicating promising applications of SAN features in the unsupervised processing of remote sensing images. / Thirdly, a comparison study of ISE between optical and SAR image demonstrates that single optical image provides better results than using single SAR image. In addition, results indicate that support vector machine (SVM) is a better choice for ISE using Landsat ETM+ (optical) images, while artificial neural network (ANN) turns out to be more sensitive to the confusion between dry soils and bright IS, and between shades and dark IS. However, ANN gets a better result using ASAR (SAR) image with higher accuracy, while the SVM classifier produces more noises and has some edge effects. Considering both the merits and demerits of optical and SAR images, synergistically fusing the two data sources should be a promising solution. Comparison of three different levels of fusion shows that pixel level fusion seems not appropriate for optical-SAR fusion, as it reduces the accuracy compared to the single use of optical data. Meanwhile, feature level fusion and decision level fusion obtained better accuracy, since they improves the identification of IS from shaded areas and bare soils. / Fourthly, a methodological framework of fusing the optical and SAR images is proposed. Three different data sets are used to assess the effectiveness of this methodological framework, including the Landsat TM and ASAR images, the SPOT-5 and ASAR images, and the SPOT-5 and TerraSAR-X images. In addition, different methods (e.g. ANN, SVM and Random Forest) are employed and compared to fusion the two data sources at a mixed level fusion of pixel and feature levels. Experimental results showed that the combined use of optical and SAR image is able to effectively improve the accuracy of ISE by reducing the spectral confusions that happen easily in optical images. Moreover, Random Forest (RF) demonstrated a promising performance for fusing optical and SAR images as it treats the two data sources differently through a random selection procedure of variables from different data sources. / The major outcome of this research provides evidence of the seasonal effects on IS assessment due to phenological and climatic characteristics, as well as provides an applicable framework of methodology for the synergistic use of optical and SAR images to improve the ISE. Since the PRD region is highly typical of many fast growing areas, the methodology and conclusions of this research would serve as a useful reference for other subtropical, humid regions of the world. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Zhang, Hongsheng. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2013. / Includes bibliographical references (leaves 169-185). / Abstract also in Chinese. / ACKNOWLEDGEMENTS --- p.i / ABSTRACT --- p.iii / 論文摘要 --- p.vii / Table of Contents --- p.xi / List of Abbreviations --- p.xv / List of Tables --- p.xvii / List of Figures --- p.xviii / Chapter CHAPTER 1 --- INTRODUCTION --- p.1 / Chapter 1.1 --- Research background --- p.1 / Chapter 1.2 --- Research questions and hypotheses --- p.4 / Chapter 1.3 --- Objectives and significance --- p.6 / Chapter 1.4 --- Organization of the thesis --- p.7 / Chapter CHAPTER 2 --- LITERATURE REVIEW --- p.11 / Chapter 2.1 --- Introduction --- p.11 / Chapter 2.2 --- Significance of Impervious Surface --- p.12 / Chapter 2.2.1 --- Environmental significance --- p.12 / Chapter 2.2.2 --- Socio-economic significance --- p.16 / Chapter 2.3 --- Climatology and Phenology in HSR --- p.18 / Chapter 2.3.1 --- Characteristics of the climate and phenology --- p.18 / Chapter 2.3.2 --- Seasonal effects from Climatology and Phenology --- p.20 / Chapter 2.4 --- Land-cover complexity in rapid urbanized region --- p.21 / Chapter 2.5 --- Approaches of ISE --- p.22 / Chapter 2.5.1 --- Sub-pixel approaches --- p.22 / Chapter 2.5.2 --- Per-pixel approaches --- p.23 / Chapter 2.5.3 --- Synergistic use of optical and SAR data for ISE --- p.27 / Chapter 2.6 --- Summary --- p.28 / Chapter CHAPTER 3 --- STUDY AREA AND DATA SETS --- p.31 / Chapter 3.1 --- Study area --- p.31 / Chapter 3.1.1 --- Site A: Guangzhou --- p.32 / Chapter 3.1.2 --- Site B: Shenzhen --- p.33 / Chapter 3.1.3 --- Site C: Hong Kong --- p.34 / Chapter 3.2 --- Satellite data --- p.35 / Chapter 3.2.1 --- Landsat ETM+ --- p.35 / Chapter 3.2.2 --- SPOT-5 --- p.36 / Chapter 3.2.3 --- ENVISAT ASAR --- p.36 / Chapter 3.2.4 --- TerraSAR-X --- p.37 / Chapter 3.3 --- Digital Orthophoto data --- p.38 / Chapter 3.4 --- In-situ data --- p.39 / Chapter 3.5 --- Summary --- p.40 / Chapter CHAPTER 4 --- METHODOLOGY --- p.43 / Chapter 4.1 --- Framework --- p.43 / Chapter 4.2 --- Per-pixel modeling of ISE --- p.45 / Chapter 4.3 --- Investigation of the seasonal effects --- p.46 / Chapter 4.4 --- Feature extraction --- p.47 / Chapter 4.4.1 --- Conventional approach --- p.48 / Chapter 4.4.2 --- Novel approach based on shape-adaptive neighborhood --- p.48 / Chapter 4.4.2.1 --- The concept of shape-adaptive neighborhood --- p.49 / Chapter 4.4.2.2 --- The determination of a shape-adaptive neighborhood --- p.51 / Chapter 4.4.2.3 --- Extracting spatial features --- p.54 / Chapter 4.5 --- Fusing the optical and SAR data --- p.58 / Chapter 4.5.1 --- Multi-source image co-registration --- p.60 / Chapter 4.5.2 --- Compare the single use of optical and SAR image --- p.61 / Chapter 4.5.3 --- Compare different levels of fusion --- p.62 / Chapter 4.5.4 --- Fusion with supervised classifiers --- p.65 / Chapter 4.5.4.1 --- Artificial neural network --- p.66 / Chapter 4.5.4.2 --- Support vector machine --- p.68 / Chapter 4.5.4.3 --- Random Forest --- p.71 / Chapter 4.6 --- Results validation and accuracy assessment --- p.75 / Chapter 4.6.1 --- Training and testing data sampling --- p.75 / Chapter 4.6.2 --- Accuracy assessment --- p.76 / Chapter 4.7 --- Summary --- p.77 / Chapter CHAPTER 5 --- RESULTS AND DISCUSSION (I) - ASSESSMENT OF SAN FEATURES --- p.79 / Chapter 5.1. --- Analysis of threshold to determine the SAN --- p.79 / Chapter 5.2. --- Feature extraction from SAN --- p.80 / Chapter 5.3. --- Assessment of the SAN features with classification --- p.82 / Chapter 5.3.1 --- Training samples and classification --- p.82 / Chapter 5.3.2 --- Testing samples and accuracy --- p.84 / Chapter 5.3.3 --- Assess the effectiveness of the SAN based features --- p.85 / Chapter 5.4 --- Summary --- p.87 / Chapter CHAPTER 6 --- RESULTS AND DISCUSSION (II) - SEASONAL EFFECTS OF ISE --- p.89 / Chapter 6.1 --- Seasonal effects of ISE --- p.89 / Chapter 6.2 --- Analyzing the seasonal changes of typical targets --- p.92 / Chapter 6.3 --- Comparing the seasonal sensitivity of methods --- p.96 / Chapter 6.4 --- Summary --- p.97 / Chapter CHAPTER 7 --- RESULTS AND DISCUSSION (III) - URBAN LAND COVER DIVERSITY --- p.101 / Chapter 7.1 --- Introduction --- p.101 / Chapter 7.2 --- Urban LC classification Using RF --- p.102 / Chapter 7.2.1 --- Optimization of RF --- p.102 / Chapter 7.2.2 --- Land covers classification with optimized RF --- p.104 / Chapter 7.2.3 --- Compare RF with other decision tree-based methods --- p.107 / Chapter 7.3 --- Summary --- p.108 / Chapter CHAPTER 8 --- RESULTS AND DISCUSSION (IV) - FUSING OPTICAL&SAR DATA --- p.111 / Chapter 8.1 --- Introduction --- p.111 / Chapter 8.2 --- Comparison of ISE with single optical and SAR data --- p.111 / Chapter 8.2.1 --- ISE with ETM+ data --- p.112 / Chapter 8.2.1.1 --- Mapping the IS --- p.112 / Chapter 8.2.1.2 --- Effects of the parameter configurations of the methods --- p.114 / Chapter 8.2.2 --- ISE with ASAR data --- p.115 / Chapter 8.2.2.1 --- Mapping the IS --- p.115 / Chapter 8.2.2.2 --- Effects of the parameter configurations of the methods --- p.117 / Chapter 8.2.3 --- Comparisons over the data and methods --- p.119 / Chapter 8.2.4 --- Discussion and implications --- p.121 / Chapter 8.3 --- Comparison of different levels of fusion method --- p.122 / Chapter 8.3.1 --- Fusion strategies at different levels --- p.122 / Chapter 8.3.2 --- Results of feature extractions --- p.124 / Chapter 8.3.3 --- Fusion results on different levels --- p.126 / Chapter 8.3.4 --- Comparisons --- p.128 / Chapter 8.3.5 --- Discussion and implications --- p.129 / Chapter 8.4 --- Synergizing optical and SAR data with RF --- p.130 / Chapter 8.4.1 --- Feature extraction from ASAR data --- p.130 / Chapter 8.4.2 --- Determine the optimal number of features in each decision tree --- p.132 / Chapter 8.4.3 --- Determine the optimal numbers of decision trees in the RF --- p.134 / Chapter 8.4.4 --- ISE with optimized RF --- p.135 / Chapter 8.4.5 --- Discussion and implications --- p.140 / Chapter 8.5 --- A comprehensive study: ISE using SPOT-5 and TerraSAR-X data --- p.142 / Chapter 8.5.1 --- Data set and experiment design --- p.142 / Chapter 8.5.2 --- Feature extraction of SPOT-5 data --- p.145 / Chapter 8.5.3 --- Feature extraction of TerraSAR-X data --- p.148 / Chapter 8.5.4 --- LULC classification with optimized models --- p.149 / Chapter 8.5.5 --- ISE with optimized models --- p.152 / Chapter 8.5.6 --- Discussion and implications --- p.155 / Chapter 8.6 --- Summary --- p.156 / Chapter CHAPTER 9 --- CONCLUSIONS --- p.159 / Chapter 9.1 --- Findings and conclusions --- p.159 / Chapter 9.1.1 --- Seasonal effects of ISE in HSR --- p.159 / Chapter 9.1.2 --- Feature extraction methods --- p.160 / Chapter 9.1.3 --- Comparison between optical and SAR data --- p.161 / Chapter 9.1.4 --- Fusion level and fusion methods --- p.162 / Chapter 9.2 --- Recommendations for future research --- p.163 / Chapter 9.2.1 --- Feature extraction --- p.163 / Chapter 9.2.2 --- Study areas selection and design --- p.163 / Chapter 9.2.3 --- Validation with in-situ data --- p.164 / Chapter 9.2.4 --- Fusion level and strategy --- p.164 / Chapter 9.2.5 --- Fusion methods --- p.165 / References --- p.169 / Chapter Appendix I --- Codes for Determining Shape-adaptive Neighborhood --- p.186 / Chapter Appendix II --- Publication list related to this thesis research --- p.188