<p> This study investigates the potential for hydrothermal alteration and circulation in lava domes using combined analytical, remote sensing and numerical modeling approaches. This has been accomplished in three parts: <i>1) </i> A comprehensive field, geochemical and remote sensing investigation was undertaken of the hydrothermal system in the Santiaguito lava dome complex in Guatemala. The Santiaguito domes were found to contain mainly hydrous silica alteration, which is unlikely to weaken dome rock, but the summit of Santa Maria was found to contain pervasive argillic alteration (clay minerals), which do pose more of a collapse-related hazard. These results were confirmed by hot spring geochemistry which indicated that water in the domes was responsible for some rock dissolution but had a residence time too short to allow for secondary mineralization. <i>2)</i> A finite element numerical modeling approach was developed which was designed to simulate the percolation of meteoric water in two dome geometries (crater-confined and 'perched'), and the results were compared to the surface expression of hydrothermal systems on existing lava domes. In both cases, we concluded that simulated domes which lacked a high-temperature (magmatic) heat source could not develop a convecting hydrothermal system and were dominated by gravitational water flow. In these low-temperature simulations, warm springs (warmer high fluid fluxes) were produced at the base of the dome talus and cool springs were dispersed lower down the slope/substrate; fumaroles (high vapor fluxes) were confined to the dome summits. Comparison with existing dome cross sections indicates that the simulations were accurate in predicting fumarole locations and somewhat accurate at predicting spring locations, suggesting that springs may be subject to permeability contrasts created by more complicated structural features than were simulated in this study. <i>3)</i> The results of the numerical modeling were used to calculate alteration potential in the simulated domes, indicating the most likely areas where alteration processes might either reduce the strength of a dome or reduce permeability that could contribute to internal pressurization. Rock alteration potential in low-temperature lava domes was found to be controlled by material permeability and the presence or absence of a sustained heat source driving hydrothermal circulation. High RAI values were preserved longer in low-permeability domes, but were more strongly developed in domes with higher permeabilities. Potential for mineral dissolution was highest at the base of the dome core, while the potential for mineral precipitation is highest at the dome core-talus interface. If precipitated minerals are impermeable, the dome core/talus interface would be a likely location for accumulation of gases and initiation of gas-pressurization-related collapse; if alteration is depositing weak (i.e. clay) minerals in this area, the dome core/talus interface might be a candidate for collapses occurring as the result of alteration processes. </p><p> The results of this study are all geared toward answering two broad questions: <i> Where are hydrothermal alteration processes likely to occur or be focused within lava domes?</i> and <i>What effect could these processes have on dome stability?</i> In the specific case of the Santiaguito dome complex, the combination of a quickly-recharged, low-temperature hydrothermal system in the inactive domes actually indicated a low possibility of collapse related to alteration minerals. This result was reinforced by the results of the numerical modeling, which indicated that domes are unlikely to develop sustained hydrothermal convection without the presence of a significant (magmatic) heat source and&mdash;in the case of Santiaguito&mdash;are likely to produce more hydrous silica alteration minerals when they also lack a source of acidic gases. Models of alteration potential do detail, however, that both shallow and deep dome collapses are still a possibility with a low-temperature hydrothermal system, given either a) a source of acidic gases to drive the formation of clay minerals (which are most likely to be deposited at the core/talus interface of a dome, or b) enough deposition of silica minerals in pore spaces to lower permeability in dome rock and promote internal gas pressurization. The results of this study are not limited to lava domes, as the volcanic edifices on which they rest are composed of the same materials that comprise lava domes and are therefore susceptible to the same hydrothermal processes. Further simulations of both lava domes and their associated edifices, including mineral species models, could help constrain under what conditions a lava dome or volcano is likely to develop areas of weak mineral precipitates (such as clay minerals) which could provide sites for collapse, or develop an impermeable cap of silicate minerals which could trap rising vapor and contribute to the pressurization of the edifice in question (which can in turn lead to collapse).</p>

<p> Permanent Global Positioning System (GPS) networks have been established around the globe for a variety of uses, most notably to monitor the activity of fault lines and tectonic plate motion. A model for utilizing GPS as a tool for hydrologic monitoring is also developed. </p><p> First, observations of the recent movement of the land surface throughout California by the Scripps Orbit and Permanent Array Center (SOPAC) GPS network are explored. Significant seasonal cycles and long term trends are related to historical observations of land subsidence. The pattern of deformation throughout the state appears to be caused by the occurrence of poroelastic deformation of the aquifer in the Central Valley, and elastic crustal loading by surface water and the winter snowpack in the Sierra Nevada Mountains. The result is a sort of teeter-totter motion between the Valley and the mountains where the Valley sinks in the dry season while the mountains lift, and the mountains sink in the wet season while the Valley lifts. </p><p> Next, the elastic crustal deformation caused by the winter snowpack is explored more thoroughly at 6 high elevations throughout the Western United States. Expected annual deformation as a result of thermoelastic and snow water equivalent are calculated using SNOTEL observations and an elastic half-space model. The results demonstrate the dominance of snow loading on the seasonal vertical land surface deformation at all 6 GPS stations. The model is then reversed and applied to the GPS vertical site-position observations in order to predict snow water equivalent. The results are compared to SNOTEL observations of snow water equivalent and soil moisture. The study concludes that GPS site-position observations are able to predict variations in snow water equivalent and soil moisture with good accuracy. </p><p> Then a model which incorporates both elastic crustal loading and poroelastic deformation was used to predict groundwater storage variations at 54 GPS stations throughout the Central Valley, CA. The results are compared to USGS water table observations from 43 wells. The predictions and observations show a similar magnitude and spatial pattern of groundwater depletion on both a seasonal and long term timescales. Depletion is focused on the southernmost part of the Valley where GPS reveals seasonal fluctuation of the water table around 2 m and 8 m/yr of water table decline during the study period. GPS also appears to respond to deformation from peat soils and changing reservoir storage in the northern parts of the Valley. </p><p> Finally, preliminary work exploring the potential for using GPS as a tool for monitoring snowmelt runoff and infiltration is explored at one station in Eastern Idaho. Taking the difference between the change in GPS water storage estimates with time and the change in SNOTEL observed snow water equivalent with time produces a time series of infiltration, or the amount of water added to storage in the geologic profile. Then subtracting the estimated infiltration and snow water equivalent from the total precipitation observed by SNOTEL produces a time series of runoff. The estimated runoff at the GPS site was compared to observations from a nearby stream gauge and the foundation for a more extensive comparison is laid out. </p><p> The overall impact of this work is to introduce the unique hydrologic information and monitoring capabilities which can be accessed through monitoring of the land surface position using GPS. As GPS networks grow and expand worldwide, the available data should be harnessed by the hydrologic community for the benefit of local water management as well as improvements to data assimilated models. The work presented here represents only a small fraction of the wealth of knowledge that could result from a budding field of GPS hydrologic remote sensing. (Abstract shortened by UMI.)</p>

Thèse (M.Sc.)--Université de Sherbrooke (Canada), 2007. / Titre de l'écran-titre (visionné le 1 février 2007). In ProQuest dissertations and theses. Publié aussi en version papier.

The Microwave Remote Sensing Lab (MIRSL) at the University of Massachusetts has developed a second-generation L-band synthetic aperture microwave radiometer referred to as the Electronically Steered Thinned Array Radiometer, or ESTAR, which measures soil moisture or ocean salinity from an airborne platform. This dissertation reviews the basics of synthetic aperture microwave radiometry, then details recent modifications to the ESTAR instrument, including the change to a horizontally polarized antenna, and improvements to the instrument's thermal control. The dissertation discusses calibration methods, including corrections to the null feedback radiometer (NFR) data used to form the system response matrix, or G-matrix. It also describes image calibration, noting steps taken to reduce image ripple. Results obtained during the Southern Great Plains 1997 (SGP'97) hydrology experiment in Oklahoma are discussed and compared to rainfall data obtained from the Oklahoma Mesonet system of weather stations. This data set is the largest one of its type obtained by ESTAR to date, in terms of area of geographical and temporal coverage.

Satellite-based remotely sensed data were used to estimate regional-scale surface energy fluxes and a water deficit index of a semi-arid heterogeneous region in southeast Arizona. Spectral reflectance and radiometric temperature of the surface, derived from the digital counts of TM bands of LANDSAT-5 satellite, were used for this purpose. These reflectance and temperature, along with conventional meteorological information of the region, were used as inputs to numerical models which estimate surface energy fluxes. Point-based meteorological data of the region were spatially extrapolated over a grid of 120 m X 120 m so that it could be used with the spatially continuous remotely sensed data. The water deficit index (WDI) was estimated using surface temperature and a spectral vegetation index, "soil adjusted vegetation index" (SAVI). The surface fluxes were net radiation flux, sensible heat flux, soil heat flux and latent heat flux. Measured values obtained from the meteorological flux measurement (METFLUX) stations in the study area were compared with the modeled fluxes. Latent heat flux (LE) was the most important one to estimate in the scope of this study. The method of spatially extrapolating the point-based meteorological information and combining with the remotely sensed data produced good estimation of LE for the region, with a mean absolute difference (MAD) of 65 W/m² over a range of 67 to 196 W/m² . Also it was found that the numerical models that were previously used to estimate daily LE values from a region using mid-day remotely sensed data (mostly from NOAAAVHRR) can also be used with the mid-morning remotely sensed data (from LANDSAT). Out of the two models tested for this purpose (`Seguin-Itier' and 'Jackson' models), one was found to need some modification so that it could use mid-morning remotely sensed data as inputs. The other was found to be useable as it is, without any modification. Outputs from both models compared well with the measured fluxes from the METFLUX stations. In an effort of estimating the water deficit of the different biomes of the region, WDI of the biomes were estimated. The main goal of this effort was to be able to monitor the surface hydrologic conditions of the region using remotely sensed vegetation and surface information, and minimum ground data. Good estimation of the water deficit condition of the area were obtained by this method. This method was found to be sensitive to a few of the ground information such as wind speed and leaf area index (LAI). It was also found that if the required ground data were correctly estimated, this method could be used as an operational procedure for monitoring the vegetation water stress of the biomes and hence for better management of the region.

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