There is no clear consensus on the impact of local communities on the resources they manage, primarily due to a shortage of studies with large sample sizes that incorporate multiple causal factors. As governments decentralize resource management to local communities, it is important to identify factors that prevent resource degradation, to inform more effective decentralization, and help the development of institutional characteristics that prevent resource degradation. This study used remote sensing techniques to quantify forest biomass in tropical deciduous forests in Kanha Pench landscape of Central India, and used these metrics to identify factors associated with changes in forest biomass. Kanha Pench landscape was chosen because of its variation in forest use, and because forests were transferred over a period where satellite imagery was available to track changes. To verify that remote- sensing measured changes indeed constitute degradation, I conducted ecological studies in six villages, to understand changes in biomass, understory, canopy, species diversity and long-term forest composition in intensively used forests. To understand the impact of institutional variables on changes in forest, I interviewed members of forest management committees in fifty villages in the landscape, and tested which institutional variables were associated with changes in forest canopy since 2002, when the forests were decentralized to local communities. The empirical results are of particular conservation significance in India, where further decentralization of forests to local communities in scheduled under the Forest (Dwellers) Rights Act, 2006. Results indicate that local forest use is associated with decreases in forest biomass, understory, canopy cover, and changes in vegetation structure, species richness and diversity. Most importantly, I found that human use has the potential to alter long- term forest composition as transition of some species to higher size classes is altered where humans use forest more intensively. Particularly, species that are fire and trampling resistant are more likely to become mature trees in intensely used forests. Thus, local forest use is associated with forest degradation as the long-term trajectory of the forest is altered, and forests may not be able to provide ecosystem services including livelihood needs such as fuelwood, construction, and non-timber forest products in the future. At a broader scale, remote sensing techniques (optical imagery Landsat and RADAR imagery ALOS-PALSAR FBD) were able to quantify forest biomass at an acceptable accuracy (67 percent), while more easily operatable MODIS based EVI was not. Landscape analysis showed that changes in forest biomass from 2007 to 2010 were associated with high population density, high fire radiative power and greater distance to towns. Since people only travel approximately 2 kilometers for subsistence forest use, the significance of greater changes further from towns suggests that, at a broader landscape scale, forest degradation is not primarily due to local use, but may be a result of other factors. Action taken to exclude outsiders and lower meeting frequency of committees (never) were identified as institutional variables associated with remotely-sensed positive change in canopy over the period when forest management was transferred (2002 to 2010). Villages with no meetings were also associated with higher incumbency of committee Chairpersons and lower incumbency of other committee members. Simultaneously, while economic payments increased awareness and participation in forest management committees, economic payments were not associated with any action to exclude outsiders from forest use. This suggests that managers need to focus on factors besides economic payments to incentivize committees to exclude outsiders, especially as it is associated with positive changes in the forest. Further, while elite capture of resources (as indicated by incumbency and lack of inclusiveness in decision-making) is not helpful for social equity, it does not appear to be detrimental for forests. Overall, this study suggests a number of management strategies to reduce forest degradation. Managers could focus on forests at a distance from towns and roads, as this is where most negative change in forests appears to occur. They could also work with local communities so that their use of forests does not prevent regeneration of species important for ecosystem services. Managers could also work with committees to find strategies other than economic payments for incentivizing community protection of forests.

O presente trabalho avaliou diferentes parâmetros espaciais e temporais da evapotranspiração real (ETr) para três coberturas vegetais diferentes (cana-de-açúcar, floresta plantada e mata nativa) utilizando o Modelo de Duas Fontes (TSM) em ambiente SIG. Para a utilização do referido modelo, foi realizada uma adaptação do TSM aos padrões brasileiros e uma validação da estimativa de ETr com dados de balanços de chuva-vazão obtidos à campo em três bacias representativas na região de Corumbataí/SP, para dois anos hidrológicos completos. Desenvolveu-se também uma nova metodologia de integralização dos dados de ETr horários em mensais e anuais. Posteriormente, obteve-se a ETr para dois anos de estudo para áreas com cana-de-açúcar, floresta plantada e mata nativa. Avaliou-se como a evapotranspiração variou temporalmente e espacialmente, com a utilização dos parâmetros: altitude, declividade, faces de exposição do terreno, tipos de solo e biomas. Temporalmente, analisando os resultados mensais, sazonais e anuais, a cana-de-açúcar consumiu menos água do que a floresta plantada e a mata nativa. Para a altitude, concluiu-se que, quanto maior a altitude, maior a ETr anual. Quanto à declividade do terreno, concluiu-se que, quanto maior a declividade, maior foi a ETr anual. Quanto às faces de exposição do terreno, a face plana foi a que apresentou menor ETr. Para a mata nativa, as faces não apresentaram diferença; já para a floresta plantada, as faces norte e oeste consumiram mais água, enquanto para a cana-de-açúcar os maiores valores de ETr ocorreram na face oeste. Para os solos, as três coberturas vegetais apresentaram maior ETr em solos diferentes; porém o Neossolo Litólico apresentou altos consumos de água comumente entre todas as coberturas. Por fim, para a cultura da cana-de-açúcar, não foram obtidas diferenças significativas na estimativa de ET real anual quando plantada em áreas de Cerrado ou Mata Atlântica. Já para floresta plantada e mata nativa, houve variações significativas no consumo de água dessas coberturas, em áreas com esses biomas. / This study evaluated different spatial and temporal parameters of actual evapotranspiration (ETr) for three different vegetation cover (sugarcane, planted forest and native forest) using the Two Source Model (TSM) in a GIS environment. For the use of this model, was made an adaptation of TSM to Brazilian standards patterns and was made a validation of the estimated ETr with a rain-flow balance data obtained on the field in three representative watersheds in the region of Corumbataí/SP, Brazil, for two full years hydrological. It was developed a new methodology to convert hourly ETr to monthly and annually data. Later, was obtained the ETr for two years of study to areas with sugarcane, planted and natural forests and was evaluated how this evapotranspiration behaved temporally and spatially, using the parameters: altitude, slope, exposure faces of the terrain, soil types and biomes. Temporally, analyzing the monthly, seasonal and annual results, the sugarcane consumed less water than the planted and natural forests. For altitude, it was found that the higher the altitude, the greater the annual ETr. For the slope aspect, it was concluded that the higher the slope, the greater the annual ETr. As for the land exposure faces, the flat face showed the lowest ETr. For the native forest, the faces showed no difference. For the planted forest, north and west sides consumed more water, while for sugarcane higher ETr values were on the west face. For soils, the three vegetation covers showed higher ETr in different soils, but the Neossolo Litólico showed high water consumption common for all. Finally, for the sugarcane, were not obtained significant differences for the annual actual ET when planted in areas of Cerrado and Atlantic Forest. As for planted and natural forests, there were significant variations in water consumption by these covers on the area of the studied biomes.

Orientador: Rodrigo Lilla Manzione / Banca: Mikael Timóteo Rodrigues / Banca: Daniela Fernanda da Silva Fuzzo / Banca: Edson Luís Piroli / Banca: Sérgio Campos / Resumo: Este trabalho foi desenvolvido para ampliar o conhecimento sobre a área da Estação Ecológica de Avaré (EEcAv), especialmente sobre a presença de água subterrânea, mostrando a importância das áreas de conservação do Estado de São Paulo. O entorno da EEcAv tem ocupação do solo por atividades agrícolas e florestais e tem no uso da água por irrigação uma contribuição para a produção dependente do regime de chuvas sazonais. A área de estudo, localizada no município de Avaré / SP, ocupa uma extensão territorial de aproximadamente 720 hectares. Para este trabalho, foram utilizadas informações dos satélites TRMM, TERRA/AQUA e LANDSAT 8; e os seguintes softwares para subsidiar a análise: QGIS, SAGA e MapWindow (extensão TauDEM). Com a ajuda dessas ferramentas, foi possível: construir os mapas hipsométrico e clinográfico; avaliar índices de vegetação (NDVI e NDWI); estimar e modelagem o fluxo de água subterrânea por meio da Direção do Fluxo, Fluxo Cumulativo e Índice de Umidade Topográfica (ITU); e estimar o volume explorável das águas subterrâneas da área. Com os resultados obtidos, verificou-se que os dados climatológicos do radar de precipitação do TRMM apresentaram melhor correlação com os dados de estação meteorológica de superfície do que os dados de evapotranspiração do produto MODIS 16 do TERRA/AQUA; os índices vegetativos indicam que durante a estação chuvosa há maior atividade fotossinteticamente ativa e, confirma uma maior concentração de umidade na superfície das folhas; a ... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: This work was developed to broaden the knowledge about the Ecological Area of Birds (EEcAv), especially on the presence of groundwater, showing the importance of the conservation areas of the State of São Paulo. The EEcAv environment is occupied by agricultural and forestry activities, and irrigation uses a contribution to production dependent on the seasonal glove regime. A study area, located in the municipality of Avaré / SP, has a territorial extension of approximately 720 hectares. For this work, the information of the satellites TRMM, TERRA/AQUA and LANDSAT 8 were used; The following software for analysis were: QGIS, SAGA and MapWindow (TauDEM extension). With the help of tools, it was possible to: construct hypsometric and clinical concepts; vegetation antecedent (NDVI and NDWI); estimation and modeling of water flow through the Direction of Flow, Cumulative Flow and Topographic Moisture Index (ITU); and estimation of the area's exploitable volume of groundwater. With the results obtained, it was verified that the climatological data of the TRMM precipitation radar were improved with the data of the surface meteorological station than the evapotranspiration data of the TERRA/AQUA MODIS 16 product; the vegetative indexes indicate that, during the season, most photosensitivities are active and confirm a higher concentration of leaf surface capacity; The flow modeling and well data indicate the infiltrated volume flowing to the Rio Novo and the Canela Water Stream, at the same time marking the existence of preferred water paths formed by the presence of the streams, at the same time, to the south and north of the area. Finally, with the explorable volume, the potential of water production in the region under a study area, it was possible to irrigate more than 3,000 hectares of soybean, soybeans and citrus with water without nutrients. / Doutor

Pang Ying Wai. / Thesis submitted in: August 2003. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 134-144). / Abstracts in English and Chinese. / ABSTRACT --- p.i / ABSTRACT (CHINESE) --- p.ii / ACKNOWLEDGEMENTS --- p.iii / TABLE OF CONTENT --- p.iv / LIST OF TABLES --- p.viii / LIST OF FIGURES --- p.x / Chapter CHAPTER ONE --- INTRODUCTION / Chapter 1.1 --- Conceptual Framework --- p.1 / Chapter 1.2 --- Objectives and Significance of the study --- p.3 / Chapter 1.3 --- Study Area --- p.4 / Chapter 1.4 --- Organization of Thesis --- p.9 / Chapter CHAPTER TWO --- LITERATURE REVIEW / Chapter 2.1 --- Landscape Ecology --- p.10 / Chapter 2.1.1 --- Definition of Landscape Ecology --- p.10 / Chapter 2.1.2 --- Dimension of Landscape Ecology --- p.13 / Chapter 2.2 --- Landscape metrics --- p.17 / Chapter 2.3 --- Application of Remote Sensing in Landscape Ecology --- p.25 / Chapter 2.4 --- Wetland Habitats and Situations in Hong Kong --- p.29 / Chapter 2.5 --- Summary --- p.39 / Chapter CHAPTER THREE --- METHODOLOGY / Chapter 3.1 --- Data Description --- p.40 / Chapter 3.1.1 --- Satellite Data --- p.40 / Chapter 3.1.2 --- Topographic Map Sheets and Digital Maps --- p.45 / Chapter 3.2 --- Satellite Data Preprocessing --- p.45 / Chapter 3.3 --- Landuse and Land Cover Classification --- p.47 / Chapter 3.4 --- Landscape Units Extraction --- p.51 / Chapter 3.5 --- Landscape Metrics Identification and Extraction --- p.54 / Chapter 3.6 --- Disturbance Level Identification and Extraction --- p.60 / Chapter 3.7 --- Inter-Classes and Temporal Comparison of Class-based and Landscape-based metrics --- p.61 / Chapter 3.8 --- Summary --- p.62 / Chapter CHAPTER FOUR --- LAND COVER CLASSIFICATION RESULT / Chapter 4.1 --- Introduction --- p.63 / Chapter 4.2 --- Clustering Result --- p.63 / Chapter 4.3 --- Land cover classification results --- p.66 / Chapter 4.4 --- Accuracy Assessment --- p.83 / Chapter 4.5 --- Implication from land covers change --- p.84 / Chapter 4.5.1 --- Mangrove Changes --- p.84 / Chapter 4.5.2 --- Encroachment of Built-up area --- p.89 / Chapter 4.6 --- Summary --- p.91 / Chapter CHAPTER FIVE --- LANDSCAPE METRIC ANALYSIS / Chapter 5.1 --- Introduction --- p.94 / Chapter 5.2 --- Landscape perspective analysis --- p.94 / Chapter 5.2.1 --- "Area, Density and Edge" --- p.95 / Chapter 5.2.2 --- Fractal Dimension and Shape Indices --- p.98 / Chapter 5.2.3 --- "Contagion, Interspersion and Diversity" --- p.99 / Chapter 5.2.4 --- Disturbance information --- p.101 / Chapter 5.3 --- Class perspective analysis --- p.105 / Chapter 5.3.1 --- Mangrove --- p.105 / Chapter 5.3.1.1 --- "Area, Density and Edge" --- p.105 / Chapter 5.3.1.2 --- Fractal Dimension and Shape Indices --- p.107 / Chapter 5.3.1.3 --- Contagion and connectivity --- p.107 / Chapter 5.3.1.4 --- Disturbance information --- p.109 / Chapter 5.3.2 --- Reed bed --- p.111 / Chapter 5.3.2.1 --- "Area, Density and Edge" --- p.111 / Chapter 5.3.2.2 --- Fractal Dimension and Shape Indices --- p.112 / Chapter 5.3.2.3 --- Contagion and connectivity --- p.113 / Chapter 5.3.2.4 --- Disturbance information --- p.114 / Chapter 5.3.3 --- Fishponds --- p.116 / Chapter 5.3.3.1 --- "Area, Density and Edge" --- p.116 / Chapter 5.3.3.2 --- Fractal Dimension and Shape Indices --- p.117 / Chapter 5.3.3.3 --- Contagion and connectivity --- p.118 / Chapter 5.3.3.4 --- Disturbance information --- p.119 / Chapter 5.4 --- Discussion --- p.121 / Chapter 5.4.1 --- Landscape degradation from landscape perspective --- p.121 / Chapter 5.4.2 --- Implication of landscape metrics on land use planning --- p.123 / Chapter 5.4.3 --- Factors affecting the usage of landscape metrics --- p.124 / Chapter 5.5 --- Summary --- p.126 / Chapter CHAPTER SIX --- CONCLUSION / Chapter 6.1 --- Summary of Findings --- p.129 / Chapter 6.1.1 --- Summary of landscape composition --- p.129 / Chapter 6.1.2 --- Summary of landscape configuration --- p.130 / Chapter 6.2 --- Limitations of the Study --- p.132 / Chapter 6.3 --- Recommendations for Further Studies --- p.133 / REFERENCES --- p.134 / APPENDIX --- p.145

GNSS Radio Occultation (GNSS-RO) is an opportunistic Earth sensing technique where GNSS signals passing through the atmosphere are received in low Earth orbit and processed to extract meteorological parameters. As signals are received along an orbit, the measured Doppler shift is transformed to a bending angle profile (commonly referred to as bending angle retrieval), which, in turn, is inverted to a refractivity profile. Thanks to its high vertical resolution and SI traceability, GNSS-RO is an important complement to other Earth sensing endeavors. In the lower troposphere, GNSS-RO measurements often get degraded and biased due to sharp refractive gradients and other complex structures. The main objective of this thesis is to explore contemporary retrieval methods such as phase matching and full spectrum inversion to improve their performance in these conditions. To avoid the bias caused by the standard inversion, we attempt to derive additional information from the amplitude output of the examined retrieval operators. While simulations indicate that such information could be found, it is not immediately straightforward how to achieve this with real measurements. The approach chosen is to examine reflected signal components and their effect on the amplitude output.

改革开放以来，快速的城市化给社会、经济和环境带来了巨大的影响。为了研究城市化的成因和影响，许多学者提出了不同的指标来量化城市化过程。一方面，这些指标大多是从统计年鉴和普查数据中获取的，而这些数据存在连续性差、精确度低、空间信息少等问题，无法准确地描述城市化过程。另一方面，遥感图像包含丰富的关于城市形态、土地组成和社会经济的信息，可以很好地弥补传统数据的不足。因此，研究如何从遥感图像中提取城市化信息，并有效地用于城市化研究具有重要意义。 / 基于中国的城市化特点，本文分别从不同方面深入研究了从遥感图像中提取城市化指标的方法，并且通过这些指标分析了中国在过去二十年间的城市化发展特点。 / 首先，从社会经济发展的角度，本文分析了从夜晚灯光影像提取的遥感指标和社会经济学指标之间的时空关系。分析表明，城市内夜晚灯光的总量可以反映城市化的整体水平，而灯光覆盖区域的平均亮度可以较好反映城市化的总体强度，同时，灯光对城市化的响应会随着区域内亮度的增加而减弱。根据此灯光指标，我们发现在过去的二十年中，具有政治优势和地理优势的城市发展得更快。此外，与内陆其他地区相比，沿海地区的城市发展更加紧凑。 / 其次，从城市建成区面积的角度，本文提出了一种新的城市建成区提取方法，能够较准确地获取城市级的建成区序列。研究表明，从夜晚灯光影像中提取建成区时所选取的阈值会随年份和城市而变化，并且该阈值和城市的经济水平有显著的相关性。根据获取的建成区序列，我们研究了中国城市建成区的扩张特征，结果显示所有城市都表现出明显的扩张特征，该特征在沿海城市、省会城市和经济特区内尤其显著。 / 第三，从城市的空间结构变化角度，本文探讨了如何通过遥感影像量化城市的空间结构，以及如何利用这些量化信息来研究城市圈的发展过程。针对中国三大城市圈，本文比较了不同的遥感图像提取空间结构的特点，并且结合了景观生态学指数、帕雷托分布和梯度分析来研究城市圈内的城市之间的联系、分布和相互影响，发现不同城市圈的发展模式和驱动因素各有差异，具有很强的区域发展特点。 / 第四，从城市扩张形态的角度，本文从夜晚灯光影像中提取了两组指标来量化城市扩张程度。这两组指标分别从某一时间点的城市发展空间形态，以及一段时间内的城市增长模式来测量这一现象。结果显示，中国不同城市圈之间的城市扩张具有不同的特点。 / China has been undergoing rapid urbanization since the "open door" policy in 1978. The fast process of urbanization has significantly influenced its society, economy, and environment. To quantify and describe this process, various indicators of urbanization, which are usually extracted from statistical yearbook and census data, have been adopted. However, these data sets are usually inconsistent, problematic, and cannot depict the spatial information of urbanization. Therefore, remote sensing images are usually employed as complementary datasets. / However, the existing studies remain insufficient for understanding how the information of urbanization can be extracted from remote sensing imagery and used properly, especially for China, where urbanization has unique characteristics. Therefore, this thesis aims to explore the usage of remote sensing techniques to further observe the process of urbanization and from four different perspectives. / From the perspective of socioeconomic development, we analyze the spatial-temporal relationship between indicators derived from NTL images and the socio-economic indicators of urbanization. The results of the analysis indicate that the summed lights in a city can represent the overall level of urbanization and that the average light of lit-up areas can reflect the density of urbanization. Meanwhile, when the amount of NTL approaches saturation, it becomes a less sensitive reflection of the level of urbanization. The proper NTL indicator has then been utilized on the analysis of urbanization in China’s cities during the last 20 years, and the results reveal that the cities with political and geographical advantages have higher levels of urban development. Meanwhile, the cities in metropolitan areas and the Shandong province have undergone a more compact urbanization process than some inland cities. / Second, from the perspective of urban expansion, we extract the time series of urban built-up areas at the city level via a newly-proposed thresholding technique on the calibrated time series of NTL images. The threshold for extracting built-up areas has been found to vary across different cities and years, and it has high correlations with the level of economic development. We then analyze the urban expansion in Chinese cities based on prefecture cities in three provinces of south China. The results indicate that urban expansion occurred in all cities from 1992 to 2010, especially in coastal cities, capital cities, and cities in special economic zones. / Third, from the perspective of spatial pattern evolution, we explored how to quantify the urban spatial pattern, and use it to study the evolution of metropolitan areas. We compare the discrepancies of various remote sensing images in describing spatial patterns and combine the landscape metrics, Pareto distribution, and gradient analysis to explore growth type, distribution, and reaction of cities in metropolitan areas. Moreover, based on the comparison of the spatial patterns among three of the largest metropolitan areas of China during the last twenty years, we find that the driving force and growth type vary over metropolitan areas and that each area has its own regional characteristics. / Fourth, from the perspective of urban sprawl, we introduced two sets of indicators, which can measure urban sprawl both as a certain spatial pattern of urban development and as a type of urban growth, that quantify urban sprawl based on NTL imagery. The results present the degree of urban sprawl in various metropolitan areas in China. / Overall, this thesis extends our understanding on how to use information derived from remote sensing as a proxy for studies on urbanization. Moreover, urbanization in China is scrutinized by remote sensing indicators. / 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. / Liu, Lu. / Thesis (Ph.D.) Chinese University of Hong Kong, 2014. / Includes bibliographical references (leaves 172-190). / Abstracts also in Chinese.

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

生長於潮間帶的紅樹林是熱帶和亞熱帶地區最具生產力的生態系統之一。香港擁有十個紅樹品種，其覆蓋面積約共三百五十公頃。位於香港西北面的米埔是現時香港最大的紅樹林區。這片紅樹林及其鄰近濕地於一九九五年被列為拉姆薩爾重要的濕地。隨著經濟的迅速發展、污染及一些不可持續的開發，全球紅樹林的面積不斷地萎縮。而香港的紅樹也正面對城市發展及基建的直接威脅。因此，了解及監測紅樹林的生長狀況、覆蓋面積的轉變是紅樹林保育的基礎。遙感是具有成本效益和能提供及時數據的技術，在紅樹林的生態保育及監測上發揮著重要功能。 / 是次研究選擇位於米埔的紅樹林區。通過結合高光譜和雷達數據以及實地磡測，以達到三個目的。第一，利用模式辨認分析找出可提高品種辨識度的光譜帶及雷達數據。第二，把挑選出來的光譜帶及雷達數據組合，利用不同的分類法包括最大概似法、决策樹 C5.0演算法、類神經網路及支持向量機進行紅樹林的品種分類，並籍此測試各分類法的精度。第三，利用植被指數及雷達數據中取得的參數為獨立變量，而在野外點測的葉面積指數 (LAI) 為因變量，通過迴歸分析以估算整片紅樹林的葉面積指數，籍此了解紅樹林現時的生物物理狀況。 / 根據特徵選擇的結果，位於高光譜數據中的綠波段 (570nm, 580nm, 591nm及601nm)、紅波段 (702nm)、紅邊位 (713nm)、近紅外波段 (764nm及774nm)、 短波紅外波段 (1276nm, 1316nm及1629nm) 以及在不同季節取得的過濾後向散射數據是最能辨識品種差異。 / 據品種分類的結果顯示，單用多時後向散射特徵數據存在很大誤差。而在大多的情況下，單用光譜數據比起混合光譜及後向散射數據的分類表現為佳。但對於某些品種來說，後向散射數據能給予比較準確的預測。另外，在同數據組合下，分類法在訓練精度上沒有多大的分別。除了類神經網路分類法以外，其他分類法的測試精度總比其訓練精度低。這說明類神經網路模型比起其他分類法的模型要為穩定，而决策樹模型則被過度訓練。根據生產者及使用者精度分析，因為缺乏足夠的訓練樣本，桐花樹及海桑屬的精度較其他品種為低。 / 據不同植被指數的簡單線性迴歸模型顯示，利用三角植被指數 (TVI)及修正葉綠素吸納比例指數一 (MCARI 1) 對於葉面積指數的估算是最準確。相反地，葉面積指數與從雷達數據中取得的參數關係則比較弱。這表示單用雷達參數不能對葉面積指數進行準確的估算。在結合植被指數及雷達參數的多元逐步迴歸分析下，三角植被指數及在灰度共生矩陣下得出的角二階矩參數能減低葉面積指數估算的誤差。總結以上兩項分析，光譜及雷達數據在紅樹林的品種分類及葉面積指數估算上有互補的作用。 / Mangrove is one of the most productive ecosystems flourished in the intertidal zone of tropical and subtropical regions. Hong Kong has ten true mangrove species covering an approximate area of 350 hectares. Mai Po locating in the northwestern part of Hong Kong nourishes the largest mangrove stand and it was listed as a Wetland of Importance under the Ramsar Convention in 1995. Over the years, areas of mangrove have been shrinking globally due to development, pollution, and other unsustainable exploitation and Hong Kong was no exception. In Hong Kong, mangroves are usually sacrificed for urban development and infrastructure construction. Therefore, it is crucial to monitor their growth conditions, change of extent and possible unsustainable practices threatening their existence. Remote sensing being a cost-effective and timely tool for vegetation conservation is most suitable for such purpose. / Taking Mai Po as study area, this study acquired satellite-borne hyperspectral and radar data supplemented with in situ field survey to achieve three purposes. First, features from the remotely-sensed data that are significant to species discrimination were identified through pattern recognition. Second, selected features grouped into different subsets were used to delineate the boundary of mangrove species through supervised classification. In the meantime, classifiers including maximum likelihood (ML), decision tree C5.0 (DT), artificial neural network (ANN) and support vector machines (SVM) were tested for their accuracy performance. The third purpose is to understand the current biophysical condition of mangrove through leaf area index (LAI) modeling by regressing field-measured LAI against vegetation indices, backscatter and textural measures. / Results from feature selection revealed that hyperspectral narrowbands locating in green at 570nm, 580nm, 591nm, 601nm; red at 702nm; red-edge at 713nm; near infrared at 764nm and 774nm and shortwave infrared at 1276nm, 1316nm and 1629nm as well as the multi-temporal filtered backscatter captured in different seasons have high sensitivity to species difference. / Species-based classification using multi-temporal backscatter features alone do not provide a satisfactory accuracy. Comparatively, results from pure spectral bands have better overall accuracy than that from combining spectral and radar features. However, radar backscatter does improve accuracy of some species. Besides, all classifiers had similar variations of training accuracy under the same feature subset. However, the testing accuracy is much lower with the exception of ANN. Performance of ANN was more stable and robust than other classifiers while serious overtraining occurs for the DT classifier. Moreover, most species were mapped accurately as revealed by the producer’s and user’s accuracy with the exception of A. corniculatum and Sonneratia spp. due to deficiency of training samples. / Simple linear regression model with VIs revealed that triangular vegetation index (TVI) and modified chlorophyll absorption ratio index 1 (MCARI1) had the best relationship with LAI. However, weak relationship was found between field- measured LAI and radar parameters suggesting that radar parameters cannot be used as single predictor for LAI. Results from stepwise multiple regression suggested that TVI combined with GLCM-derived angular second moment (ASM) can reduce the estimation error of LAI. To conclude, the study has demonstrated spectral and radar data are complementarity for accurate species discrimination and LAI mapping. / 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. / Wong, Kwan Kit. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2012. / Includes bibliographical references (leaves 434-472). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese. / ACKNOWLEDGEMENTS --- p.II / ABSTRACT --- p.IV / 論文摘要 --- p.VI / TABLE OF CONTENTS --- p.VIII / LIST OF ABBREVIATIONS --- p.XIII / LIST OF TABLES --- p.XV / LIST OF FIGURES --- p.XVIII / Chapter CHAPTER 1 --- INTRODUCTION --- p.1 / Chapter 1.1 --- BACKGROUND TO THE STUDY --- p.1 / Chapter 1.1.1 --- Mangrove Mapping and Monitoring --- p.1 / Chapter 1.1.2 --- Mangrove Mapping and Monitoring --- p.3 / Chapter 1.1.3 --- Role of Remote Sensing in Mangrove Study --- p.4 / Chapter 1.2 --- OBJECTIVES OF THE STUDY --- p.6 / Chapter 1.3 --- SIGNIFICANCE OF THE STUDY --- p.7 / Chapter 1.4 --- ORGANIZATION OF THE THESIS --- p.8 / Chapter CHAPTER 2 --- LITERATURE REVIEW --- p.10 / Chapter 2.1 --- INTRODUCTION --- p.10 / Chapter 2.2 --- FACTORS AFFECTING VEGETATION REFLECTANCE --- p.11 / Chapter 2.2.1 --- Foliar structure and principal constituents --- p.12 / Chapter 2.2.2 --- Foliar optical properties --- p.14 / Chapter 2.2.2.1 --- The visible region (400-700nm) --- p.14 / Chapter 2.2.2.2 --- The red edge (690-740nm) --- p.15 / Chapter 2.2.2.3 --- The near-infrared region (700-1300nm) --- p.16 / Chapter 2.2.2.4 --- The short-wave infrared region (1300-2500nm) --- p.17 / Chapter 2.2.3 --- Canopy architecture --- p.18 / Chapter 2.2.4 --- Background reflectance --- p.19 / Chapter 2.2.5 --- Atmospheric perturbation --- p.20 / Chapter 2.2.6 --- Sun-sensor relationship --- p.22 / Chapter 2.3 --- HYPERSPECTRAL IMAGING AND VEGETATION CLASSIFICATION --- p.23 / Chapter 2.4 --- RADAR IMAGING AND VEGETATION CLASSIFICATION --- p.31 / Chapter 2.5 --- PATTERN RECOGNITION FOR VEGETATION CLASSIFICATION --- p.39 / Chapter 2.5.1 --- The Hughes Phenomenon and Dimensionality Reduction --- p.39 / Chapter 2.5.2 --- Statistical Pattern Recognition and Feature Selection --- p.44 / Chapter 2.5.2.1 --- Search Method --- p.47 / Chapter 2.5.2.1.1 --- Exhaustive search --- p.48 / Chapter 2.5.2.1.2 --- Branch and bound --- p.49 / Chapter 2.5.2.1.3 --- Sequential forward/ backward selection --- p.55 / Chapter 2.5.2.1.4 --- Sequential Floating search --- p.57 / Chapter 2.5.2.1.5 --- Oscillating Search --- p.61 / Chapter 2.5.2.1.6 --- Genetic algorithm --- p.64 / Chapter 2.5.2.2 --- Evaluation criteria --- p.66 / Chapter 2.5.2.2.1 --- Distance measure --- p.67 / Chapter 2.5.2.2.2 --- Information measure --- p.68 / Chapter 2.5.2.2.3 --- Classification error --- p.71 / Chapter 2.5.2.3 --- Feature Selection Stability --- p.72 / Chapter 2.5.3 --- Feature extraction --- p.75 / Chapter 2.6 --- BIOPHYSICAL PARAMETERS MEASUREMENT AND ESTIMATION --- p.77 / Chapter 2.6.1 --- Leaf Area Index (LAI) --- p.78 / Chapter 2.6.2 --- Fraction of Absorbed Photosynthetically Active Radiation (fAPAR) --- p.79 / Chapter 2.6.3 --- In-situ Leaf Area Index Measurement --- p.81 / Chapter 2.6.3.1 --- Direct and Indirect Methods --- p.81 / Chapter 2.6.3.2 --- LAI Estimation through Gap Fraction Inversion --- p.85 / Chapter 2.6.3.3 --- Gap Fraction Ground Measurement --- p.89 / Chapter 2.6.3.3.1 --- LAI-2000 Plant Canopy Analyzer --- p.89 / Chapter 2.6.3.3.2 --- Hemispherical Photography --- p.92 / Chapter 2.6.3.4 --- Correction of Indirect LAI Measurement --- p.99 / Chapter 2.6.3.4.1 --- Clumping --- p.100 / Chapter 2.6.3.4.2 --- Mixture of Green and Non-green Elements --- p.101 / Chapter 2.6.4 --- Empirical Relationship with Spectral Vegetation Indices --- p.102 / Chapter 2.6.4.1 --- Traditional Vegetation Indices --- p.103 / Chapter 2.6.4.2 --- Leaf Area Index Estimation from Hyperspectral and Radar Images --- p.106 / Chapter 2.6.5 --- Physically-based Canopy Reflectance Model Inversion --- p.111 / Chapter 2.6.5.1 --- Canopy Reflectance Model --- p.111 / Chapter 2.6.5.2 --- Model Inversion and Biophysical Parameters Extraction --- p.115 / Chapter 2.7 --- SUMMARY --- p.118 / Chapter CHAPTER 3 --- METHODOLOGY --- p.120 / Chapter 3.1 --- INTRODUCTION --- p.120 / Chapter 3.2 --- STUDY AREA DESCRIPTION --- p.120 / Chapter 3.3 --- METHODOLOGICAL FLOW --- p.124 / Chapter 3.4 --- REMOTE SENSING DATA ACQUISITION AND PROCESSING --- p.127 / Chapter 3.4.1 --- Hyperion - EO-1 --- p.127 / Chapter 3.4.1.1 --- Radiometric correction --- p.127 / Chapter 3.4.1.1.1 --- Vertical strips removal --- p.128 / Chapter 3.4.1.1.2 --- Atmospheric correction --- p.129 / Chapter 3.4.1.1.3 --- Wavelength recalibration --- p.135 / Chapter 3.4.1.1.4 --- SNR enhancement through MNF --- p.137 / Chapter 3.4.1.2 --- Geometric correction --- p.139 / Chapter 3.4.1.3 --- Atmospheric correction algorithms comparison --- p.140 / Chapter 3.4.2 --- ASAR - ENVISAT --- p.141 / Chapter 3.4.2.1 --- Data Acquisition --- p.141 / Chapter 3.4.2.2 --- Data Processing --- p.143 / Chapter 3.4.2.2.1 --- Radiometric and Geometric Correction --- p.145 / Chapter 3.4.2.2.2 --- Speckle Filtering --- p.146 / Chapter 3.5 --- FIELD MEASUREMENTS AND DATA PROCESSING --- p.149 / Chapter 3.5.1 --- Species Distribution --- p.149 / Chapter 3.5.2 --- Leaf Spectra Measurement --- p.151 / Chapter 3.5.2.1 --- Leaf Collection and Handling --- p.152 / Chapter 3.5.2.2 --- ASD FieldSpec 3 Setup --- p.154 / Chapter 3.5.2.3 --- Laboratory setup --- p.156 / Chapter 3.5.2.4 --- Spectra Measurement --- p.158 / Chapter 3.5.2.5 --- Spectral similarity and variability --- p.159 / Chapter 3.5.3 --- In situ Leaf Area Index Measurement --- p.161 / Chapter 3.5.3.1 --- The optical instrument --- p.161 / Chapter 3.5.3.2 --- The LAI survey campaign p163 / Chapter 3.5.3.3 --- Data processing and canopy analysis --- p.166 / Chapter 3.5.3.4 --- Canopy parameter computation gap fraction, LAI, clumping index, mean inclination angle --- p.170 / Chapter 3.5.3.5 --- Field LAI and Their Correlation with Reflectance and Backscattering Coefficient Data Exploration --- p.175 / Chapter 3.6 --- FEATURE SELECTION --- p.175 / Chapter 3.6.1 --- Data Preprocessing and Preparation --- p.178 / Chapter 3.6.2 --- Data Format and Split --- p.183 / Chapter 3.6.3 --- Wrapper-based Approach --- p.185 / Chapter 3.6.4 --- Search Algorithm --- p.187 / Chapter 3.6.5 --- Stability Evaluation --- p.187 / Chapter 3.6.6 --- Feature Frequency analysis --- p.188 / Chapter 3.7 --- MANGROVE SPECIES CLASSIFICATION --- p.189 / Chapter 3.7.1 --- Species Separability --- p.193 / Chapter 3.7.2 --- Gaussian Maximum Likelihood Classifier --- p.193 / Chapter 3.7.3 --- Decision Tree Classifier --- p.194 / Chapter 3.7.4 --- Artificial Neural Network Classifier --- p.197 / Chapter 3.7.5 --- Support Vector Machines Classifier --- p.199 / Chapter 3.7.6 --- Accuracy Assessment --- p.204 / Chapter 3.8 --- LEAF AREA INDEX MODELING --- p.206 / Chapter 3.8.1 --- Preliminary Exploration of Relationship between Hyperspectral bands and LAI --- p.206 / Chapter 3.8.2 --- Vegetation Index Derived from Hyperspectral Data. --- p.206 / Chapter 3.8.3 --- Radar Backscatter and Derived Textural Parameters --- p.208 / Chapter 3.8.4 --- Regression Analysis --- p.211 / Chapter 3.8.5 --- Error Estimation --- p.217 / Chapter 3.9 --- SUMMARY --- p.218 / Chapter CHAPTER 4 --- RESULTS AND DISCUSSION (I) FEATURE SELECTION AND MANGROVE SPECIES CLASSIFICATION --- p.221 / Chapter 4.1 --- INTRODUCTION --- p.221 / Chapter 4.2 --- DATA PROCESSING AND EXPLORATION --- p.221 / Chapter 4.2.1 --- Atmospheric correction algorithms comparison --- p.222 / Chapter 4.2.2 --- Radar Data Speckle Reduction --- p.227 / Chapter 4.2.3 --- Statistical Discrimination of Mangrove Spectral Class --- p.230 / Chapter 4.3 --- FEATURE SELECTION --- p.249 / Chapter 4.3.1 --- Sequential Forward Selection (SFS) --- p.250 / Chapter 4.3.2 --- Sequential Floating Forward Selection (SFFS). --- p.256 / Chapter 4.3.3 --- Oscillating Search (OS) --- p.262 / Chapter 4.3.4 --- Search Algorithms comparison --- p.268 / Chapter 4.3.5 --- Final Subset Selection --- p.270 / Chapter 4.3.6 --- Correlation Analysis --- p.280 / Chapter 4.4 --- IMAGE CLASSIFICATION --- p.283 / Chapter 4.4.1 --- Mangrove Spectral Class Separability --- p.284 / Chapter 4.4.2 --- Gaussian Maximum Likelihood (ML) --- p.288 / Chapter 4.4.3 --- Decision Tree (DT) --- p.297 / Chapter 4.4.4 --- Artificial Neural Network (ANN) --- p.304 / Chapter 4.4.5 --- Support Vector Machines (SVM) --- p.312 / Chapter 4.4.6 --- Algorithm Comparison --- p.321 / Chapter 4.5 --- DISCUSSION AND IMPLICATION --- p.325 / Chapter 4.5.1 --- Feature Selection --- p.325 / Chapter 4.5.2 --- Mangrove Classification --- p.342 / Chapter 4.6 --- SUMMARY --- p.351 / Chapter CHAPTER 5 --- RESULTS AND DISCUSSION (II) - LEAF AREA INDEX MODELING --- p.353 / Chapter 5.1 --- INTRODUCTION --- p.353 / Chapter 5.2 --- DATA EXPLORATION --- p.353 / Chapter 5.2.1 --- Dependent Variable: Field measured LAI --- p.353 / Chapter 5.2.2 --- Independent Variables: Vegetation Index and texture measure --- p.355 / Chapter 5.2.3 --- Hyperspectral bands and LAI --- p.356 / Chapter 5.2.4 --- Normality testing --- p.359 / Chapter 5.2.5 --- Linearity testing --- p.363 / Chapter 5.2.6 --- Outliner detection --- p.365 / Chapter 5.3 --- SIMPLE LINEAR REGRESSION ANALYSIS --- p.366 / Chapter 5.3.1 --- LAI2000 Generalized method --- p.369 / Chapter 5.4 --- STEPWISE MULTIPLE REGRESSION ANALYSIS --- p.381 / Chapter 5.4.1 --- LAI2000 Generalized method --- p.384 / Chapter 5.5 --- DISCUSSION AND IMPLICATION --- p.391 / Chapter 5.5.1 --- LAI model comparison --- p.391 / Chapter 5.5.2 --- Species composition and LAI --- p.393 / Chapter 5.5.3 --- Hyperspectral Bands, Vegetation Indices and LAI --- p.397 / Chapter 5.5.4 --- Backscatter, texture measures and LAI --- p.407 / Chapter 5.5.5 --- Complementarity of Vegetation Index and Radar Parameters --- p.414 / Chapter 5.6 --- SUMMARY --- p.421 / Chapter CHAPTER 6 --- CONCLUSION --- p.423 / Chapter 6.1 --- SUMMARY OF THE STUDY --- p.423 / Chapter 6.2 --- LIMITATION OF THE STUDY --- p.427 / Chapter 6.3 --- RECOMMENDATION --- p.431 / Chapter REFERENCE --- p.434 / Chapter APPENDIX A --- GEOMETRIC CORRECTION OF HYPERSPECTRAL DATA --- p.473 / Chapter APPENDIX B --- SCRIPTS DERIVED FROM FEATURE SELECTION TOOLBOX (FST) FOR FEATURE SELECTION --- p.475 / Chapter APPENDIX C --- PREDICTED LAI(BON) AND LAI(2000) FROM SIMPLE LINEAR REGRESSION MODELS --- p.513 / Chapter APPENDIX D --- PREDICTED LAI(BON) AND LAI(2000) FROM MULTIPLE STEPWISE REGRESSION MODELS --- p.524

Over the last several decades, the majority of the Greenland outlet glaciers have accelerated due to the increased warming in both the atmosphere and the oceans around the polar latitudes. While there is a clear overall acceleration trend over this period, there is significant variability in the glacier responses to climate on seasonal and year-to-year timescales. This variability observed around Greenland is very likely tied to the differences in internal dynamics of individual glaciers and the complex interaction with its local environment. Here I investigate the interaction between ice and water along the ice base as an important mechanism contributing to the observed variability among glaciers in Greenland. I use a range of modeling and radar sounding approaches to study the subglacial hydrology for three types of outlet glaciers, including slow moving, marine terminating glaciers in the west, a land-terminating system in the southwest, and a fast moving, marine-terminating glacier in northern Greenland. These case studies allow me to characterize the basal water distribution, its variability throughout the year and how this drainage behavior varies across different regions of Greenland. To start, I use a hydrological routing model to characterize the subglacial hydrology for three neighboring slow moving (< 100myr−1), marine terminating glaciers in western Greenland. The hydrologic model allows me to examine the sensitivity of basal water routing to subtle changes in basal water pressures. My results reveal that Greenland subglacial drainage can be rerouted across 100’s of km in response to changes in basal water pressures as small as 10%. I conclude that water piracy and subsequent dramatic changes in ice velocity, similar to that observed around the Siple Coast in West Antarctica, can occur in Greenland. Next, I move to a more data-orientated approach and use airborne radar sounding to examine the seasonal variability of basal water distribution. To robustly characterize basal water from radar bed power, I use a novel radar analysis approach that integrates a thermomechanical ice-sheet model to predict the spatial variations of radar attenuation. I improve this approach by including a least-squares minimization to correct for power offsets due to the different radar systems deployed in multiple field seasons. This improved method is first applied to two land-terminating glaciers in the southwest, Russell Glacier, and Isunnguata Sermia. Using two seasons of radar sounding data, I find that the basal water distribution can change between the wintertime and the summertime. My results reveal that during the winter, water resides primarily in small pockets on top of bedrock ridges. In the summer, these pockets of water on the ridges connect and drain into the nearby basal troughs. This seasonal shift in the basal water distribution is actively controlled by the material properties of the bed. Therefore, in addition to the bed topography, the permeability of the bed and the presence of basal sediments could also exert a critical influence on the seasonal development of subglacial drainage. Finally, I apply the radar analysis approach to a fast-flowing marine terminating glacier for Petermann Glacier in Northern Greenland. Here I incorporate an additional step to address the spatial variation in ice chemistry and its effect on radar attenuation. I use this approach to examine the relationship between basal water, ice deformation and the onset of glacier flow. In addition to finding basal water in the fastest flowing region near the ice margin, I identify substantial basal water in the ice sheet interior where meltwater must either be related to the advection of water from upstream or be generated by internal heating due to ice deformation. My results show there are three basal water networks beneath Petermann that connect the ice sheet interior to the margin. Together, the interaction between these basal water networks and the ice deformation enhances and sustains fast flow in the interior of the Petermann catchment. Overall, the research presented in this dissertation suggests that subglacial hydrology is high variable in both space and time. This variation in the hydrologic system can influence the fundamental structure of the ice sheet through changing the transport and storage of basal water and through interacting with ice deformation and the thermal properties of the bed.

Orientador: Eduardo Machado Perea Martins / Banca: Rodrigo Maximo Sanchez Roman / Banca: João Carlos Cury Saad / Banca: Marcelo Girotto / Banca: Andrea Carla Gonçalves Vianna / Resumo: Este é um trabalho de tecnologia computacional aplicada na área agrícola, cujo objetivo principal do trabalho é desenvolver uma rede de sensores sem fio, que envolve aspectos de software e hardware, para o monitoramento térmico no interior de uma casa de vegetação. Além da rede propriamente dita, o trabalho também inclui a sua aplicação no levantamento quantitativo da variabilidade térmica na casa de vegetação o que pode ser aplicado em diversas atividades agrícolas a serem desenvolvidas no interior da estrutura. A proposta é que a rede desenvolvida seja de fácil implementação e manuseio, com baixo custo, e que use técnicas computacionais eficientes a fim de permitir a sua fácil adaptação a diferentes necessidades das pesquisas. As redes de sensores sem fios dependem fundamentalmente de estruturas chamadas de nós de sensoriamento, que são responsáveis pelas medições de um parâmetro físico e também pela troca de informações via rádio. No presente e trabalho, o nó de sensoriamento desenvolvido é constituído pelo módulo de rádio XBee Pro S2B; por um módulo de processamento Arduino Nano e por sensores de temperatura modelo LM35. O processador de cada nó de sensoriamento da rede desenvolvida executa um programa que faz a captura dos dados dos sensores de temperatura e os enviam por rádio à um computador onde existe um software que também foi desenvolvido no presente trabalho com o objetivo de receber, separar e gravar os dados de uma rede de sensores sem fio para o monitoramento térmico em casas de vegetação. Cada nó de sensoriamento possui capacidade de ser conectado a até 8 sensores LM35. Na análise térmica, foram distribuídos dois nós de sensoriamento e um total de 15 sensores distribuídos sistematicamente no interior de uma casa de vegetação do tipo teto em arco com dimensões de 8 X 16 metros localizada em Garça - SP. Os sensores foram ... / Abstract: This work presents the use of computer technology applied in agricultural systems, which involves aspects of software and hardware for thermal monitoring inside a greenhouse. It includes detailed analyses of the thermal variability in the greenhouse, which can be used in various agricultural activities implemented within the structure. The work also includes de design of a wireless sensor network with main features are easy implementation, low cost, and efficient use of computational techniques to allow easy adaptation to new different needs of research. Wireless sensor networks depend of a structure called sensor node, which are responsible for measuring a physical parameter and by wireless information exchange. In this work, the sensing node was designed with a radio module XBee Pro S2B; an Arduino Nano by a processing module; and the LM35 temperature sensor. The sensor nodes processor runs a program that makes the capture of data from temperature sensors and sends them by radio to a computer where a software which was also developed in this work and aim to receive, separate and record data from a wireless sensor network for monitoring the air temperature inside greenhouses. Each sensor node has the ability to be connected to up to eight LM35 sensors. In greenhouse thermal analysis, they were distributed in two sensing nodes connected to 15 sensors systematically distributed into the greenhouse with dimensions of 8 x 16 meters located in Garça - SP, Brazil. The sensors were... / Doutor