沿岸上升流和河流沖淡水都會嚴重影響近岸水體的物理、化學性質及其生態系統；南海北部的廣東沿岸同時存在著這兩種現象，因此對於沿岸上升流和沖淡水的運動規律和動力機制的研究對於認識廣東沿岸水體性質和生態環境的變化具有重要的理論意義。本文首先利用多種衛星遙感觀測資料和實地調查資料研究珠江沖淡水和紅海灣內的上升流進而揭示其空間分佈特徵和時間變化規律。而後利用三維的高解析度區域海洋模式（ROMS）建立一個適用於南海北部的海洋動力模式，從而成功反演珠江沖淡水和紅海灣內上升流的發展變化規律，進而揭示對其產生影響的動力原因。 / 通過分析MODIS在2003年7月14日至16日的海表面溫度影像以及同一時期的QuikSCAT風場資料，本文發現在西南風盛行時紅海灣記憶體在一個明顯的上升流區域，而且湧升的冷水首先在紅海灣的西岸出現並呈尖形向東南輸送。通過MODIS衛星觀測的海表面28oC等溫度線和模式類比結果的相互印證，證明本文利用ROMS建立的上升流模式能夠成功再現上升流的發展變化過程。基於模式類比結果的動量平衡分析揭示沿岸方向的壓強梯度和非線性項導致湧升的冷水首先出現在紅海灣西岸，而冷水的楔形離岸輸運則是由東南風引起的Ekman輸運和水準的非線性共同作用的結果。本文還進一步討論了季風、潮汐和海底地形對上升流的產生和擴散過程的影響。模式類比結果表明：在紅海灣觀測到的海表冷水是由西南風驅動的上升流造成的；由海底地形引起的內潮和上升流之間的相互作用會增強底層水的上升運動；湧升冷水的離岸輸運距離受海底地形在垂直於岸線方向的影響：坡度較緩的海底有助於冷水在距離海岸較寬的區域湧升至水面，而坡度陡的海底會將上升流限制在距離海岸較近的區域。 / 珠江沖淡水是本文的另外一個研究重點。通過對2012年6月4日-14日在珠江口和鄰近海域的調查資料的分析，可以看出調查期間珠江沖淡水呈現兩種截然不同的分佈特徵。利用ROMS的嵌套技術，本文建立了一個覆蓋南海北部陸架區的小區域模式。通過與實測資料的對比，驗證了本文建立的區域模式可以很好的反演珠江沖淡水在不同風場作用下的空間分佈特徵。基於模式類比結果的動量平衡分析表明，除了由風引起的海表面Ekman輸運外，非線性对流项項是另外一個影響沖淡水離岸輸運距離的重要因素。 / 通過計算不同風場作用下不同潮時的Froude數發現，在東南風和大潮共同作用下，珠江口鋒面處於超臨界狀態。這與實際調查結果相符合。進一步對潮汐羽流的分析表明，在東南風盛行時，珠江沖淡水由潮汐羽流、再迴圈羽流和羽流沿岸流三部分組成。當盛行風向轉為西南風時，再迴圈羽流部分消失。此時，珠江沖淡水羽流只由潮汐羽流、羽流遠場兩部分組成。計算海表面水準方向上的鹽量輸運發現，鹽量輸運受風場和潮汐的共同作用影響，潮汐導致的表層負鹽度通量可達到風生平均流引起表層負鹽度通量的12.5%。風場影響著沖淡水區域的垂向層化強度和離岸輸運距離。東南風和大潮的共同作用下，水體垂向混合加強，沖淡水離岸輸運距離變短。而盛行西南風時，強密度躍層將表層風引起的混合與底層混合隔開，有助於層化的建立，此時表層沖淡水離岸輸運距離增加。 / The upwelling event that occurred in Guangdong coastal water during 14-16 July, 2003 is observed by using satellite multi-sensor data including the Moderate Resolution Imaging Spectroradiometer (MODIS) sea surface temperature (SST) and QuikSCAT ocean surface winds. Successive MODIS SST images reveal a jet-like upwelling cold water body in surface layer under the forcing of southwesterly winds. The ROMS is used to simulate the upwelling process and explore its dynamics. The model successfully reproduces the jet-like shape of the surface upwelling water as well as the upwelling-developing process by comparisons of 28°C isotherms between the modeling and MODIS SSTs. Analyses of modeled momentums reveal that the large offshore transport appeared on the west side of Honghai Bay as results of high alongshore pressure gradient and nonlinear advections, and in addition to the offshore-ward Ekman transport generated by the southwesterly winds, the enhanced horizontal advection also played an important role in developing the prominent upwelling in Honghai Bay. / As testified by a numerical experiment, it is the wind-driven upwelling not the wind-induced vertical turbulent mixing that induced the surface cold water. Further numerical analyses reveal strong internal tides occurring in Honghai Bay caused by the local bottom topography. The interaction between the upwelling and internal tides enhances the bottom water uplifting. The offshore expansion of the upwelling water is controlled by the cross-shore topography slope: a gentle and offshore-extended slope helps the bottom water to climb up to the surface in a wide range in cross-shore direction, whereas a steep and narrow slope restricts the expansion of the upwelling water and confines the cold water in a narrow band along the shore. / A sea cruise was carried out to capture Pearl River plume structure in the Pearl River Estuary (PRE) and its adjacent coastal waters from 4 June to 14 June, 2012. The cruise data are analyzed to unveil the plume dynamics. A nested model is used to simulate the plume expansion process as well. Model results are compared with cruise observations and tidal gauge sea level data. Modeling results suggest that there is a sub-tidal, anti-cyclonic bulge on the west side out of the river mouth under southeasterly winds, which constitutes a plume re-circulating. When the wind changes to the southwesterly, however, the plume re-circulating vanishes and a plume far-field appears. / The distinct, supercritical plume front occurs with southeasterly winds prevailing in spring tide. The tidal salt deficit flux can reach as high as 12.5% of the mean current flux, and indicates an interaction between the wind forcing and tides. The variation of plume stratification is studied by a scalar parameter. It is found that the stratification of the plume is sensitive to the wind forcing: The southeasterly winds can enhance vertical mixing in the whole water column and restrict seaward expansion of the plume water. Under the southwesterly winds, the strong stratification acts as a barrier separating wind-induced surface vertical mixing and bottom mixing. The plume water in the surface layer maintains its stratification and spreads horizontally. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Gu, Yanzhen. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2013. / Includes bibliographical references (leaves 137-148). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts also in Chinese. / ABSTRACT --- p.I / 摘要 --- p.IV / CONTENTS --- p.VI / List of Tables --- p.VIII / List of Figures --- p.VIII / Acknowledgments --- p.XII / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- General Circulation in the Northern SCS --- p.6 / Chapter 1.1.1 --- Dongsha Current --- p.6 / Chapter 1.1.2 --- SCS Warm Current --- p.7 / Chapter 1.1.3 --- Other Features --- p.8 / Chapter 1.2 --- Coastal Upwelling --- p.8 / Chapter 1.2.1 --- Wind-induced Coastal Upwelling --- p.8 / Chapter 1.2.2 --- Overview of Coastal Upwelling Studies --- p.9 / Chapter 1.3 --- The Pearl River Plume --- p.12 / Chapter 1.3.1 --- Physical Setting --- p.12 / Chapter 1.3.2 --- Overview of River Plume Studies --- p.15 / Chapter 2 --- Regional Ocean Model System --- p.18 / Chapter 2.1 --- Equation of Motion --- p.19 / Chapter 2.2 --- Model Time-Stepping Scheme --- p.20 / Chapter 2.3 --- Boundary Conditions --- p.21 / Chapter 2.4 --- Coordinate System Transformation --- p.22 / Chapter 2.5 --- Vertical Viscosity and Diffusion --- p.26 / Chapter 3 --- Dynamical Study of Coastal Upwelling --- p.27 / Chapter 3.1 --- Satellite Data --- p.27 / Chapter 3.2 --- Data Interpretation --- p.28 / Chapter 3.2.1 --- Upwelling and Wind Fields --- p.28 / Chapter 3.2.2 --- Horizontal Structure and Expansion of the Cold Water Area --- p.33 / Chapter 3.3 --- Model Configuration --- p.34 / Chapter 3.4 --- Model Results --- p.38 / Chapter 3.4.1 --- Sea Surface Temperature and Horizontal Currents --- p.38 / Chapter 3.4.2 --- Cross-shelf Structure --- p.44 / Chapter 3.4.3 --- Momentum Balance --- p.46 / Chapter 3.5 --- Discussions --- p.50 / Chapter 3.5.1 --- Winds --- p.50 / Chapter 3.5.2 --- Internal Tides --- p.53 / Chapter 3.5.3 --- Topography --- p.56 / Chapter 4 --- Dynamical Study of the Pearl River Plume --- p.63 / Chapter 4.1 --- Cruise Observations --- p.63 / Chapter 4.2 --- Data Interpretation --- p.66 / Chapter 4.2.1 --- Observed Surface Salinity Distribution --- p.66 / Chapter 4.2.2 --- Salinity Vertical Distribution --- p.68 / Chapter 4.2.3 --- River Plume Front --- p.74 / Chapter 4.3 --- Model Configuration --- p.76 / Chapter 4.3.1 --- Northern South China Sea Model --- p.78 / Chapter 4.3.2 --- Pearl River Estuary Model --- p.79 / Chapter 4.4 --- Model Results and Verification --- p.80 / Chapter 4.4.1 --- Validation of Surface Salinity --- p.80 / Chapter 4.4.2 --- Validation of Salinity Profile --- p.82 / Chapter 4.4.3 --- Validation of Tidal Elevations --- p.83 / Chapter 4.4.4 --- Plume Horizontal Structure --- p.87 / Chapter 4.4.5 --- Plume Cross-shelf Structure --- p.91 / Chapter 4.4.6 --- Momentum Balance --- p.95 / Chapter 4.5 --- Stratification --- p.101 / Chapter 4.6 --- Plume frontal Froude number --- p.106 / Chapter 4.7 --- Tidal Plume --- p.111 / Chapter 4.8 --- Horizontal salt deficit flux --- p.114 / Chapter 4.9 --- Turbulence Mixing --- p.118 / Chapter 5 --- Conclusions --- p.124 / Chapter Appendix I: --- List of Publications during Ph. D. Study --- p.128 / Chapter Appendix II: --- MODIS SST Image --- p.129 / Reference --- p.137

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