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No description available.

Relationships between the breeding bird populations of the southern Appalachian cove hardwood and mixed oak-pine habitat types were studied during the 1977 and 1978 breeding seasons, in Craig County, Virginia. Relationships between habitat structure and bird utilization for each of the 12 most common breeding species were also investigated. Bird and habitat data were collected within 100 meter x 50 meter transect areas. Eleven transects were located in the mixed oak-pine habitat and 8 in cove hardwood habitat. Relative density and species diversity of the 2 bird communities were essentially the same. Species composition was similar; however, relative dominance structures of the 2 communities were different. The blue-gray gnatcatcher (Polioptila caerulea), red-eyed vireo (Vireo olivaceus) and worm-eating warbler (Helmitheros vermivorus) exhibited a significant preference for the cove hardwood habitat. The ovenbird (Seiurus aurocapillus) exhibited a significant preference for the mixed oak-pine habitat, and the pine warbler (Dendroica pinus) and rufous-sided towhee (Pipilo erythrophthalmus) were observed exclusively in the mixed oak-pine areas. The relative density of the singing males was significantly greater in 1978 than in 1977. Multiple discriminant and regression analyses were used to analyze species/habitat associations. Eighty habitat components were considered for inclusion in these analyses. The "best" models derived for each species were presented and all were significant at the 0.05 level. Each species' association with the surrounding forest was best characterized by different combinations of habitat components, suggesting that resource division was adequately described through vegetative community structure. Research needs and potential uses for this type of data in nongame bird management were discussed. / Master of Science

During 1982-85, population dynamics and denninq ecology of black bears (Ursus americanus) were investigated in Shenandoah National Park, Virginia. Foot snares and culvert traps were used to capture 115 bears a total of 149 times. Radio transmitter collars were fitted to 47 bears. The age structure of the bears captured was indicative of an exploited population. The minimum breeding age of females was 2 years, but 3 years was the modal age. Mean litter size determined by cub counts was 2.0 and females usually bred every second year. Annual mortality rates were estimated at 30% for cubs, 54% for yearlings, 39% for 2-year olds, and 21.5% for older bears. Radio collared adult males had an annual mortality rate of 41.5%, over 5-fold that of adult females (7.5%). Bear density was estimated at 1 bear/0.96-1.49 km'. This high density was explained in part by the difference in male and female mortality rates. The estimated rate of population increase indicated that the population was stable. The most common den types were rock cavities (29 of 61) and above-ground tree cavities (19 of 61). Males did not den in tree cavities. Den sites were not selected for forest type, aspect, or elevation, but ground slope was greater at den sites (P < 0.001) than at random points. Among- and within-year differences in dates of den entry, den emergence, and parturition were unrelated to weather and hard mast production. / Master of Science / incomplete_metadata

Availability of dead fish to bald eagles (Haliaeetus leucocephalus), prey preferences of bald eagles, and the effects of food on their distribution and movements on the northern Chesapeake Bay were examined from April 1988-July 1989. Dead fish surveys were conducted, by boat, to monitor dead fish availability in several eagle-use areas of the northern Bay, and 3 methods were used to describe disappearance rates of dead fish: dead fish cages, anchored dead fish and floating dead fish. Live fish availability was monitored using gillnets. Dead fish were most available to eagles from May through September, with a peak in availability in June (0.75 dead fish/km with fish die-offs not included, and 3.5 dead fish/km with fish die-offs included). Channel catfish (Ictalurus punctatus) comprised the largest portion of dead fish in early summer months (30% and 28% of total seen, excluding fish die-offs). In contrast, live catfish comprised only 0.4% and 2.1% of the fish caught near the surface in gillnets during spring and summer indicating that dead catfish may be more available, relative to other species, than live catfish. Atlantic menhaden (Brevoortia tyranus) comprised 83-98% of the fish seen in 2 fish die-offs (175 total fish). Only 2 dead fish were seen along 147.7 km of dead fish surveys in winter (0.014 dead fish/km). Most (95%) dead menhaden that we anchored near the bottom off Aberdeen Proving Ground (APG) in summer were scavenged before becoming rancid (X̅= 0.4 days). In contrast, 70% of dead menhaden that we put out in winter became rancid before being scavenged (X̅= 9 days). Pairs of prey items were offered on shoreline areas to wild bald eagles and on platforms to 2 captive bald eagles. All pair-wise combinations of channel catfish, gizzard shad (Dorosoma cepedianum), menhaden and white perch (Morone americana) were offered. We also paired gizzard shad with mallards (Anas platyrhynchos) and rabbits (eastern cottontails, Sylvilagus floridanus, or domestic rabbits, Oryctolagus cuniculus) in shoreline trials, and gizzard shad with mallards and eastern gray squirrels (Sciurus carolinensis) in captive eagle trials. Wild and captive eagles preferred catfish (P=0.0072 and P<0.0002, respectively), and showed no preference for gizzard shad, menhaden nor white perch. Wild eagles preferred gizzard shad over mallards in summer and in winter (P= 0.062 and P=0.002, respectively), while captive eagles preferred mallards over gizzard shad (P= 0.039). Wild eagles selected gizzard shad 4 of 4 times over rabbits (P= 0.125), while captive eagles selected squirrels 5 of 5 times over gizzard shad (P=0.062, both eagles combined). Handling time and familiarity with prey seem to be major factors influencing prey preference, though prey availability seems to determine the actual diet of eagles on the northern Bay. The prediction that the autumn decline in fish abundance on the northern Chesapeake Bay causes eagle distribution to shift from APG to 2 autumn/early winter concentration areas on the northern Bay (Susquehanna River and the Eastern Shore) and then to Blackwater National Wildlife Refuge (BWNWR) and vicinity (winter concentration area on the lower Bay) was tested. By supplying fish (mostly gizzard shad) ad libidum each morning at 2 sites from 28 September through 11 December 1988 a situation in which fish availability did not decline on APG was simulated. Eagle use of the sites increased from 4 eagles seen on first morning that we supplied fish to a peak of 63 eagles seen on the morning of 8 December. Based on shoreline surveys and relocations of 39 radio-tagged nonbreeding Chesapeake hatched eagles, eagle distribution shifted to the Susquehanna River, where eagles feed on gizzard shad, as in 1986 and 1987. However, they did not shift to the Eastern Shore to feed on waterfowl as they had done in 1986 and 1987. Supplemental feeding on APG failed to keep eagles from moving to the lower Bay. Although local eagle distribution on the northern Bay in autumn seems to be dependent on food availability, the autumn decline in fish abundance may not be the proximate factor causing movement to BWNWR and vicinity. / Master of Science

Thesis (MSc)--University of Stellenbosch, 2006. / ENGLISH ABSTRACT: An understanding of the phylogeography and evolutionary processes involved in speciation is essential for the conservation and management of any particular species. To investigate the phylogeographic patterns in Agama atra from the Cape Fold Mountains (CFM), 98 individuals from 38 geographically close localities were analysed. In addition, to understand the phylogeographic associations between the CFM populations and the rest of Southern Africa, 18 specimens from 12 localities outside the CFM were also included. A total of 988 characters derived from two mitochondrial DNA fragments (control region and ND2) revealed 59 distinct haplotypes in the CFM. Parsimony, Bayesian and maximum likelihood analyses revealed four distinct clades associated with geography within the CFM. These clades were supported by a haplotype network and were defined as the Cape Peninsula clade, the Limietberg clade, the northern CFM clade and the central CFM clade. Analysis of molecular variance confirmed the high degree of genetic structure within the CFM, with more than 75% of genetic variation found among the geographic areas. SAMOVA and nested clade analysis (NCA) suggest that the central CFM clade may be more diverse than detected by the networks and the phylogenetic analyses. The processes that caused the four distinct genetic groups in the CFM are not yet clear. Using a speculative molecular clock estimate, the main cladogenesis of A. atra within the CFM took place, approximately ~6.5 - 9 MYA. This dating coincides well with the documented Miocene-Pliocene climate fluctuations which might have contributed towards the isolation among lineages. The genetic structure found in A. atra is also markedly congruent with what has been found in other taxa such as Mesamphisopus spesies, Potamonautes brincki, and Pedioplanis burchelli and this would further support vicariance as a main isolating factor here. / AFRIKAANSE OPSOMMING: ‘n Goeie begrip van die filogeografie en die evolusionêre gebeurtenisse wat verband hou met spesiasie is belangrik vir die bewaring en bestuur van enige spesie. Om die filogeografiese patrone in Agama atra van die Kaapse Plooiberge (KPB) te ontleed, was 98 individue van 38 nabygeleë lokaliteite geanaliseer. Tesame met bogenoemde monsters was 18 individue van 12 lokaliteite van buite die KPB ook geanaliseer om die filogeografiese verwantskappe tussen die KPB bevolkings en die res van Suidelike Afrika te ondersoek. Uit ‘n totaal van 988 karakters verkry uit twee mitochondriale DNS fragmente (die kontrole gebied en ND2) is 59 haplotipes gevind. Parsimonie en modelgebaseerde filogenetiese analises dui daarop dat vier groepe geassosieer met geografie binne die KPB voorkom. Die groepe word geondersteun deur ‘n haplotipe netwerk en word soos volg gedefinieer: ‘n Kaapse Peninsula groep, ‘n Limietberg groep, ‘n noordelike KPB groep en ‘n sentrale KPB groep. Analises van molekulêre variansie (AMOVA) bevestig die hoë graad van genetiese struktuur binne die KPB, met meer as 75% genetiese variasie gevind tussen die geografiese areas. SAMOVA en gesetelde groep analises (“NCA”) stel voor dat die sentrale KPB groep dalk meer variasie vertoon as wat die netwerk en filogenetiese analises vertoon. Die prosesse wat die vier genetiese groepe tot stand gebring het is nog nie bekend nie. Volgens ‘n spekulatiewe molekulêre klok berekening het die hoof kladogenese van A. atra binne die KPB ongeveer ~6.5 - 9 miljoen jaar (MJ) gelede plaasgevind. Hierdie datering stem goed ooreen met die gedokumenteerde Mioseen-Plioseen klimaat veranderinge wat isolasie van die groepe kon bewerkstellig het. Die genetiese struktuur van A. atra in the KPB is ook gevind in ander taksa soos Mesamphisopus spesies, Potamonautes brincki, en Pedioplanis burchelli en bevestig dus dat vikariansie hier die hoof faktor vir isolasie is.

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

No description available.

The species richness, abundance and composition of saproxylic Coleoptera was compared between 'old-growth' and mature-managed hemlock-hardwood forests in southeastern Ontario, Canada. Beetles were sampled weekly from 29 April until 3 October 2003 using large-area flight-intercept traps (FITs) and trunk-window traps (TTs). Analyses were conducted using the Fisher's alpha and Simpson's diversity indices, rarefaction, indicator species analysis and cluster analysis. A total of 11,888 fungivorous Coleoptera was collected from 11 families and 73 species (excluding Nitidulidae). Nitidulidae were analysed separately with traps yielding 2,129 sap beetles comprising 30 species. The species richness and abundance of fungivorous Coleoptera did not differ significantly between the two forest types. Conversely, the species abundance of nitidulid beetles was higher in managed forests and the species richness higher in 'old-growth' forests. Several species were strongly associated with either managed or 'old-growth' forest types. Certain species such as Anisotoma inops (Leiodidae) and Glischrochilus sanguinolentus (Nitidulidae) were much more frequently caught in TTs than in FITs.

I examined saproxylic beetle responses in two silvicultural systems of the eastern mixedwood boreal forest of Quebec. I first investigated habitat-use and aspen-host use of saproxylic and bark and wood-boring beetles in remnant forest patches (cut-bock separators and small patches) left after harvest, theorized to resemble natural post-fire residual trees and snags. A second study focused on effects of partial cutting, a method that may serve to imitate natural succession dynamics. / Remnant forest patches had the highest saproxylic and bark and woodboring beetle species richness and relative abundance. Although non-significant, higher larval densities were also collected from remnant forest patches. In the second study, partial cut patches had an intermediate saproxylic beetle assemblage compared to uncut forest and clearcuts. / This research has brought new information on the effects of alternative harvesting practices on saproxylic beetles, supporting the hypothesis that biodiversity is best preserved based on forest management that is diversified and based on natural disturbance dynamics.