Thesis--University of Florida. / Description based on print version record. Typescript. Vita. Includes bibliographical references (leaves 382-389).

Made available in DSpace on 2014-06-11T19:26:12Z (GMT). No. of bitstreams: 0 Previous issue date: 2004-06-28Bitstream added on 2014-06-13T18:54:18Z : No. of bitstreams: 1 santacruz_j_me_rcla.pdf: 4925185 bytes, checksum: b063597b446c3e62f0445834b177fc05 (MD5) / Um manguezal da costa central do Estado de São Paulo, afetado por um derramamento de petróleo há vinte anos, mostrou, atualmente, nos 3 centímetros superficiais de sedimento, uma fauna de foraminíferos normal e abundante. Esta comunidade foi representada por 22 espécies pertencentes às subordens Trochamminina, Textulariina e Allogromiina. Os altos valores de dominância e os baixos valores de diversidade, eqüitatividade e riqueza de espécies são decorrentes da permanente instabilidade físico-química no substrato. As biofácies Miliammina fusca (1), Trochammina inflata - Arenoparrella mexicana - Miliammina fusca (2) e Arenoparrella mexicana - Haplophragmoides wilberti (3), constituem três faixas distintas deste ecossistema com águas intersticiais hipohalinas, ácidas e sub-óxicas. A primeira, no interior do manguezal, caracteriza-se por ser a mais estressante delas, apresentando os mais baixos valores de diversidade e eqüitatividade e os mais altos valores de dominância. As duas últimas, respectivamente nas franjas sub-externa e externa do manguezal, têm propriedades bióticas similares, havendo na terceira maior estresse. Embora não se tenha observado anormalidades nas tecas ou na estrutura da comunidade de foraminíferos, a presença de manchas de petróleo biodegradado situadas a aproximadamente 12 cm da superfície, ainda estão afetando o desenvolvimento de um bosque de Laguncularia racemosa presente nessa área. / A mangrove in the central coast of São Paulo state, which was effected by oil spill twenty years ago, shows, at the present, an abundant and normal foraminifera fauna in its upper mud layer (1-3 cm depth). It is represented by 22 species pertaining to suborders Trochamminina, Textulariina and Allogromiina. High values of species dominance and low values of diversity, equitability and species richness, are due to the constant physical-chemical instability of the substrate. The biofacies Miliammina fusca (1), Trochammina inflata - Arenoparrella mexicana - Miliammina fusca (2) and Arenoparrella mexicana - Haplophragmoides wilberti (3) constitute three distinct belts, of this ecossistem with hipohaline, acidic and oxygen-poor interstitial water. The first one, inner mangrove, is characterized by the higher stress level, which the lowest values of diversity and equitability and the highest values of species dominance. The second and third ones, respectively sub-external and external fringe, are constituted by similar biotic structure, with higher stress at the third one. Although it is not observed abnormalities in tests or in the structure of foraminifera comunity, spots of biodegraded oil trapped approximately 12 cm downward the substrate are still affecting the growth of Laguncularia racemosa trees in the area.

Orientador: Dimas Dias Brito / Banca: Beatriz Beck Eichler / Banca: Yara Schaeffer-Novelli / Banca: Antônio Fernando M. Camargo / Banca: Eduardo A. M. Koutsoukos / Resumo: Um manguezal da costa central do Estado de São Paulo, afetado por um derramamento de petróleo há vinte anos, mostrou, atualmente, nos 3 centímetros superficiais de sedimento, uma fauna de foraminíferos normal e abundante. Esta comunidade foi representada por 22 espécies pertencentes às subordens Trochamminina, Textulariina e Allogromiina. Os altos valores de dominância e os baixos valores de diversidade, eqüitatividade e riqueza de espécies são decorrentes da permanente instabilidade físico-química no substrato. As biofácies Miliammina fusca (1), Trochammina inflata - Arenoparrella mexicana - Miliammina fusca (2) e Arenoparrella mexicana - Haplophragmoides wilberti (3), constituem três faixas distintas deste ecossistema com águas intersticiais hipohalinas, ácidas e sub-óxicas. A primeira, no interior do manguezal, caracteriza-se por ser a mais estressante delas, apresentando os mais baixos valores de diversidade e eqüitatividade e os mais altos valores de dominância. As duas últimas, respectivamente nas franjas sub-externa e externa do manguezal, têm propriedades bióticas similares, havendo na terceira maior estresse. Embora não se tenha observado anormalidades nas tecas ou na estrutura da comunidade de foraminíferos, a presença de manchas de petróleo biodegradado situadas a aproximadamente 12 cm da superfície, ainda estão afetando o desenvolvimento de um bosque de Laguncularia racemosa presente nessa área. / Abstract: A mangrove in the central coast of São Paulo state, which was effected by oil spill twenty years ago, shows, at the present, an abundant and normal foraminifera fauna in its upper mud layer (1-3 cm depth). It is represented by 22 species pertaining to suborders Trochamminina, Textulariina and Allogromiina. High values of species dominance and low values of diversity, equitability and species richness, are due to the constant physical-chemical instability of the substrate. The biofacies Miliammina fusca (1), Trochammina inflata - Arenoparrella mexicana - Miliammina fusca (2) and Arenoparrella mexicana - Haplophragmoides wilberti (3) constitute three distinct belts, of this ecossistem with hipohaline, acidic and oxygen-poor interstitial water. The first one, inner mangrove, is characterized by the higher stress level, which the lowest values of diversity and equitability and the highest values of species dominance. The second and third ones, respectively sub-external and external fringe, are constituted by similar biotic structure, with higher stress at the third one. Although it is not observed abnormalities in tests or in the structure of foraminifera comunity, spots of biodegraded oil trapped approximately 12 cm downward the substrate are still affecting the growth of Laguncularia racemosa trees in the area. / Mestre

In South Africa mangrove forests are located in estuaries from Kosi Bay in KwaZulu-Natal (KZN) to Nahoon Estuary in the Eastern Cape. The aims of this study were to determine the present state of mangroves in KwaZulu-Natal, by assessing the current population structure, the changes in cover over time and associated anthropogenic pressures. A second objective of this study was to determine the effect of harvesting on the population structure and sediment characteristics in the Mngazana mangrove forest. To determine if harvesting was sustainable at Mngazana Estuary; the growth and mortality rates and associated growth conditions were measured. Finally by using population modelling sustainable harvesting limits were determined by predicting the change in population structure over time. The study focussed on the KwaZulu-Natal province as a fairly recent study addressed mangrove distribution and status in the Eastern Cape Province. A historical assessment of all mangroves forests in KwaZulu-Natal (KZN) revealed that the potential threats to mangroves in South Africa include; wood harvesting, altered water flow patterns coupled with salinity changes, prolonged closed-mouth conditions and subsequent changes to the intertidal habitat. As a result mangroves were completely lost from eleven estuaries in KZN between 1982 and 1999 and a further two estuaries by 2006. Mangroves only occurred in those estuaries where the mouth was open for more than 56 percent of the time with the exception of St Lucia, where the mouth has been closed for longer but the mangrove communities have persisted because the roots of the trees were not submerged. All mangrove forests in KZN were regenerating in terms of population structure as they had reverse J-shaped population curves as well as high adult: seedling ratios. Kosi Bay and Mhlathuze Estuary were two of the larger forests that showed signs of harvesting (presence of tree or branch stumps), but the greatest threat to smaller estuaries seems to be altered water flow patterns due to freshwater abstraction in the catchments and the change of land use from natural vegetation to sugar-cane plantations. These threats affect the hydrology of estuaries and the sediment characteristics (particle size, redox, pH, salinity, temperature) of the mangrove forests. The environmental conditions under which the mangrove forests currently exist were determined for five species. Lumnitzera racemosa and Ceriops tagal exhibited a narrow range of conditions as these species are only found at Kosi Bay, while Avicennia marina, Bruguiera gymnorrhiza and Rhizophora mucronata were found to exist under a wider range of conditions. The growth rate and response to environmental conditions of the three dominant species were important to determine as these species are impacted by harvesting. Mangrove growth rates were measured at Mngazana Estuary in the Eastern Cape, the third largest mangrove forest in South Africa. Areas of this estuary where mangroves harvesting has occurred, show significant differences in sediment characteristics as well as changes in population structure in harvested compared to non harvested sites. The growth rate (in terms of height) of Avicennia marina individuals increased from seedlings (0.31 cm month-1) to adults (1.2 cm month-1), while the growth of Bruguiera gymnorrhiza stabilised from a height of 150 cm at 0.65 cm month-1. The growth of Rhizophora mucronata peaked at 0.72 cm month-1 (height 151-250 cm) and then decreased to 0.4 cm month-1 for taller individuals. Increases in diameter at breast height (DBH) ranged between 0.7 and 2.3 mm month-1 for all species. Some environmental variables were found to be important drivers of growth and mortality of individuals less then 150 cm. A decrease in sediment pH significantly increased the mortality of Avicennia marina seedlings (0-50 cm) (r = - 0.71, p<0.05) and significantly decreased the growth of Rhizophora mucronata and Bruguiera gymnorrhiza seedlings (r = -0.8, r = 0.52 – p < 0.05 respectively). At Mngazana Estuary, mortality of this species showed a positive correlation with sediment moisture content indicating that this species prefers drier conditions. The density of Rhizophora mucronata was significantly correlated to porewater temperature in Northern KZN as was the growth of adult (>300 cm) Rhizophora trees at Mngazana Estuary. Mortality of Avicennia marina individuals (51-150 cm) was related to tree density indicating intraspecific competition and self thinning. Selective harvesting of particular size classes of Rhizophora mucronata was recorded when comparing length of harvested poles (~301 cm) and the size class distribution of individuals. Taking into account the differences in growth rate for each size class for this species it will take approximately 13 years to attain a height of 390 cm which is the height at which trees are selected for harvesting at this estuary. This is 2.6 times slower than those individuals growing in Kenya. The feasibility of harvesting is dependent on the growth rate of younger size classes to replace harvested trees as well as the rate of natural recruitment feeding into the population. Different harvesting intensity scenarios tested within a matrix model framework showed that limits should be set at 5 percent trees ha-1 year-1 to maintain seedling density at > 5 000 ha-1 for R. mucronata. However harvesting of Bruguiera gymnorrhiza should be stopped due to the low density of this species at Mngazana Estuary. Harvesting of the tallest trees of Avicennia marina can be maintained at levels less than 10 percent ha-1 year-1. Effective management of mangrove forests in South African is important to maintain the current state, function and diversity of these ecosystems. Management recommendations should begin with determining the freshwater requirements of the estuaries to maintain the mouth dynamics and biotic communities and deter the harvesting of (whole) adult trees particularly those species that do not coppice. Further management is needed to ensure that forests are cleared of pollutants (plastic and industrial), and any further developments near the mangroves should be minimized.

Mangrove forests have one of the highest capacities of any ecosystem to sequester carbon. Mangroves in the Red Sea exist in a uniquely saline, high temperature, nutrient limited environment and the effects on carbon storage and greenhouse gas (GHG) emissions from arid mangrove soils is understudied. The flux of carbon dioxide (CO2) and methane (CH4) has the potential to enhance or reduce the carbon storage capacity of mangroves, which is an important nature-based solution for carbon drawdown to limit global warming. To determine the magnitude of CO2 and CH4 flux from mangrove soil in the Red Sea, soil cores were incubated on a monthly basis for over a year in light and dark conditions. Soil properties such as salinity, organic carbon, water content, bulk density, and stable isotopes, along with environmental variables such as inundation frequency and temperature were measured to resolve the drivers and variation of GHG flux over time. Additionally, 16S and 18S rRNA metabarcoding was conducted to determine the relative influence of prokaryotes and eukaryotes in the microbial mat within this mangrove ecosystem, and the microbial contribution to GHG flux. Oxygen microsensors were used for fine-scale resolution of the microbial mat, to determine photosynthetic rates and oxygen profiles. Fluxes were found to be highly variable, with the highest correlation between GHG flux and soil water content (p<0.05). Both prokaryotic and eukaryotic components of the microbial mat had a significant relationship with GHG flux, with mixed impacts depending on the taxa. These findings show that Red Sea mangroves, despite their lower carbon storage capacity, are a negligible source of GHG to the atmosphere unlike other regions where GHG emissions offset a greater proportion of carbon storage potential. Additionally, the importance of the microbial mat in this ecosystem is demonstrated, and an important consideration for future studies on mangroves and their potential as a nature-based solution against global warming.

No / Peoples¿ Republic of Bangladesh and the Asian Development Bank (ADB)

Understanding the mechanisms that control sedimentation in the anchialine caves of the Yucatan Peninsula, Mexico is vital for interpreting the sedimentary deposits therein. External forcing mechanisms of varying scales, such as eustatic sea-level rise and large storm events, can have a significant influence on the rate and composition of sediment transported and deposited in the cave. Using sediment cores, high resolution radiocarbon dating, cave mapping and continuous aquifer attribute data, it was shown that sedimentation patterns in the cave were not controlled by sea-level rise/fall alone. Overlying vegetation and cave physiography were controlling factors which resulted in sedimentation in the cave being transient in time and space. The coastal aquifer responded to seasonal variations in precipitation but also showed a broad regional response to intense rainfall associated with Hurricane Ingrid in 2013. Due to the extensive hydraulic conductivity of the aquifer, the hydrologic response to Hurricane Ingrid was shorted lived (weeks) while its effect on sedimentation in the cave lasted for months. Sedimentation rates in the cave did not respond to elevated precipitation alone but showed a link with overlying vegetation. In regions of the cave with overlying mangrove forest, sedimentation was significantly higher than areas with tropical forest coverage. Mangrove forests baffled sediment creating an aquitard which resulted in the ponding of meteoric waters and subsequent enrichment in nutrients. Nutrient rich meteoric waters were funneled into cenotes increasing primary productivity for organic matter sediment production. Sedimentary deposits in anchialine caves are subject to punctuated sedimentation as a result of external forcing mechanisms or triggers. In the case of Yax Chen the trigger for sedimentation was not contemporaneous with Holocene sea-level rise. This has important implications for the use of cave sediments as proxies for sea-level research and paleo hurricane studies. / Thesis / Candidate in Philosophy

Thesis (Ph. D.)--University of Hawaii at Manoa, 2004. / Includes bibliographical references. / Also available by subscription via World Wide Web / xv, 252 leaves, bound ill., map 29 cm

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

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