<p> In California, natural vegetation is experiencing an increasing amount of stress due to prolonged droughts, wildfires, insect infestation, and disease. Remote sensing technologies provide a means for monitoring plant species presence and function temporally across landscapes. In this his dissertation, I used hyperspectral visible shortwave infrared (VSWIR), hyperspectral thermal (TIR), and hyperspectral VSWIR + broadband TIR imagery to derive key observations of plant species across a gradient of environmental conditions and time frames. In Chapter 2, I classified plant species using hyperspectral VSWIR imagery from 2013&ndash;2015 spring, summer, and fall. Plant species maps had the highest classification accuracy using spectra from a single date (mean kappa 0.80&ndash;0.86). The inclusion of spectra from other dates decreased accuracy (mean kappa 0.78&ndash;0.83). Leave-one-out analysis emphasized the need to have spectra from the image date in the classification training, otherwise classification accuracy dropped significantly (mean kappa 0.31&ndash;0.73). In Chapter 3, I used hyperspectral TIR imagery to determine the extent that high precision spectral emissivity and canopy temperature can be exploited for vegetation research at the canopy level. I found that plant species show distinct spectral separation at the leaf level, but separability among species is lost at the canopy level. However, species&rsquo; canopy temperatures exhibited different distributions among dates and species. Variability in canopy temperatures was largely explained by LiDAR derived canopy structural attributes (e.g. canopy density) and the surrounding environment (e.g. presence of pavement). In Chapter 4, I used combined hyperspectral VSWIR and broadband TIR imagery to monitor plant stress during California&rsquo;s 2013&ndash;2015 severe drought. The temperature condition index (TCI) was calculated to measure plant stress by using plant species&rsquo; surface minus air temperature distributions across dates. Plant stress was not evenly distributed across the landscape or time with lower elevation open shrub/meadows, showing the largest amount of stress in June 2014, and August 2015 imagery. Plant stress spatial variability across the study area was related to a slope&rsquo;s aspect with highly stressed plants located on south or south-southwest facing slopes. Overall, this dissertation quantifies the ability to temporally study plant species using hyperspectral VSWIR, hyperspectral TIR, and combined VSWIR+TIR imagery. This analysis supports a range of current and planned missions including Surface Biology and Geology (SBG), Environmental Mapping and Analysis Program (EnMAP), National Ecological Observatory Network (NEON), Hyperspectral Thermal Emission Spectrometer (HyTES), and ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS). </p><p>

<p> Climate warming is occurring at an unprecedented rate in the Arctic, seriously impacting sensitive environments, and triggering land cover change. These changes are compounded by localized human influences. This work classifies land cover change for the Lower Yenisei River, identifies those changes that were climate- and anthropogenic- induced, and discusses the implications for the underlying permafrost system. This is accomplished using a modified version of the &ldquo;Landsat dense time stacking&rdquo; methodology for three time periods spanning 29 years that are representative of Russian socio-economic transitions during the mid- to late-1980s (1985-1987), the early 2000s (2000-2002), and the contemporary 2010s (2012-2014). The classified area includes three cities indicative of different post-Soviet socio-economic situations, including continued population and infrastructure decline (Igarka), a relatively stable community (Dudinka), and a community receiving local reinvestment (Norilsk). The land cover classification, in tandem with regional climate reanalysis data, enabled climate- and anthropogenic- induced changes to be identified, characterized, and quantified. Climatic changes within the natural environments have produced a steady greening effect throughout the study area, as well as an increase in large lake abundance, indicative of permafrost degradation. Pollution, in close proximity to heavy industrial activity, caused a secondary plant succession process. The results of this work provide both map products that can be applied to future research in this region, as well as insights into the impacts of the warming climate and human presence on sensitive Arctic environments.</p>

Thesis (Ph.D.)--University of Washington, 2005. / (UnM)AAI3183424. Source: Dissertation Abstracts International, Volume: 66-07, Section: B, page: 3606. Chair: Charles F. Raymond.

Chan, King-Chong. / Thesis (M.Phil.) -- Chinese University of Hong Kong, 1991. / Bibliography: leaves 150-163. / Abstract --- p.i / Acknowledgements --- p.ii / Figures --- p.vii / Tables --- p.x / Chapter Chapter 1 --- Introduction --- p.i / Chapter 1.1 --- Background --- p.2 / Chapter 1.2 --- Objectives --- p.5 / Chapter 1.3 --- Hypotheses --- p.6 / Chapter 1.4 --- Organization of the Thesis --- p.6 / Chapter Chapter 2 --- Literature Review --- p.8 / Chapter 2.1 --- Land Use and Land Cover --- p.10 / Chapter 2.2 --- Informational Classes and Spectra I Classes --- p.11 / Chapter 2.3 --- Simple Per-Pixel Classification Method --- p.12 / Chapter 2.4 --- Scene Noise and Boundary Effect --- p.14 / Chapter 2.5 --- Using Filtered Data --- p.16 / Chapter 2 .6 --- Textura1 Classifier --- p.18 / Chapter 2.7 --- Contextual Classifier --- p.22 / Chapter 2.8 --- Geographic Information System (GIS) --- p.24 / Chapter 2.9 --- Expert System and Artificial Intelligence (AI) --- p.25 / Chapter 2.10 --- Concluding Remarks --- p.27 / Chapter Chapter 3 --- Methodology --- p.30 / Chapter 3.1 --- Spectral Class Composition Method (SCCM) --- p.32 / Chapter 3.1.1 --- The Concept of the Spectral Class Composition Method --- p.32 / Chapter 3.1.2 --- Unsupervised Classification Process --- p.39 / Chapter 3.1.3 --- Training Process --- p.39 / Chapter 3.1.4 --- Proportion Counting --- p.40 / Chapter 3.1.5 --- Number of Spectral Class --- p.41 / Chapter 3.1.6 --- Window Size --- p.42 / Chapter 3.1.7 --- Transect Process --- p.43 / Chapter 3.1.8 --- Classification Task --- p.45 / Chapter 3.1.9 --- Summary --- p.47 / Chapter 3.2 --- Research Design --- p.49 / Chapter 3.2.1 --- Study Area --- p.49 / Chapter 3.2.2 --- Data and Instruments Used --- p.51 / Chapter 3.2.3 --- C1assification Scheme --- p.51 / Chapter 3.2.4 --- Accuracy Assessment --- p.52 / Chapter Chapter 4 --- Results and Discussion I--- Examining the Relationship Between Land Use and Spectral Classes --- p.55 / Chapter 4. 1 --- Unsupervised Classification --- p.57 / Chapter 4.1.1 --- Unsupervised Classification Process --- p.57 / Chapter 4.1.2 --- Unsupervised Classification Results --- p.58 / Chapter 4.1.3 --- Difference Between Spectral Class Maps --- p.65 / Chapter 4.2 --- Training Process --- p.68 / Chapter 4.2.1 --- Definition of Training Process --- p.68 / Chapter 4.2.2 --- Selection of Training Sites --- p.69 / Chapter 4.2.3 --- Spectral Class Composition Data Extracted from the Training Sites --- p.70 / Chapter 4.2.4 --- Spectral Heterogeneous Characteristics of Land Use Types --- p.73 / Chapter 4.2.5 --- Different Number of Spectral Classes --- p.77 / Chapter 4.2.6 --- Similar Composition Results in Some Land Use Types --- p.80 / Chapter 4.2.7 --- Using Spectra1 Class Composition Data as Rules of Classification --- p.81 / Chapter 4.3 --- Proportion Counting --- p.83 / Chapter 4.3.1 --- Window-Based Proportion Counting Process --- p.83 / Chapter 4.3.2 --- Transect Process --- p.85 / Chapter 4.3.3 --- Variation of Spectra I Class Proportion within a Land Use Type --- p.91 / Chapter 4.3.4 --- Variation of Spectral Class Proportion among Land Use Types --- p.95 / Chapter 4.4 --- Summary --- p.103 / Chapter Chapter 5 --- Resu1ts and Discussion II --- Classification and Accuracy Assessment --- p.104 / Chapter 5.1 --- Rule-Based Land Use Classification --- p.106 / Chapter 5.1.1 --- Derivation of Rules for Classification --- p.106 / Chapter 5.1.2 --- Using Rules for Classification --- p.106 / Chapter 5.1.3 --- Modification of the Rules --- p.109 / Chapter 5.1.4 --- C1assification Resu11s --- p.109 / Chapter 5.2 --- Accuracy Assessment --- p.118 / Chapter 5.2.1 --- Accuracy Assessment Process --- p.118 / Chapter 5.2.2 --- Analysis of Error Matrices --- p.123 / Chapter 5.2.3 --- Comparison Between Spectral Class Composition Method and Simple Per-Pixel Method --- p.126 / Chapter 5.2.4 --- Discussion on the Resui.ts of Producer's and User's Accuracy --- p.130 / Chapter 5.2.5 --- Discussion on Number of Spectral Classes --- p.132 / Chapter 5.2.6 --- Discuss i on on Window Size --- p.134 / Chapter 5 .3 --- Summary --- p.136 / Chapter Chapter 6 --- Conclusion --- p.138 / Chapter 6.1 --- Summary --- p.139 / Chapter 6.2 --- Limitations and Problems --- p.142 / Chapter 6.3 --- Contribution --- p.147 / Chapter 6.4 --- Further Research --- p.148 / Bibliography --- p.150

No description available.

Thesis (M.A.) - Carleton University, 2002. / Includes bibliographical references (p. 130-144). Also available in electronic format on the Internet.

Geographic Information System technology today allows for the rapid analysis of vast amounts of spatial and non-spatial data. The power of a GIS can only be effected with the rapid collection of accurate input data. This is particularly true in the case of the South African National Working for Water (WFW) Programme where large volumes of spatial data on alien vegetation infestations are captured throughout the country. Alien vegetation clearing contracts cannot be generated, for WFW, without this data, so that the accurate capture of such data is crucial to the success of the programme. Mapping Invasive Alien Plant (IAP) data within WFW is a perennial problem (Coetzee, pers com, 2002), because not enough mapping is being done to meet the annual requirements of the programme in the various provinces. This is re-iterated by Richardson, 2004, who states that there is a shortage of accurate data on IAP abundance in South Africa. Therefore there is a need to investigate alternate methods of data capture; such as remote sensing, whilst working within the existing WFW data capture standards. The aim of this research was to investigate the use of Landsat ETM imagery as a data capture source for mapping alien vegetation for the WFW Programme in terms of their approved mapping methods, for both automated and manual classification techniques. The automated and manual classification results were compared to control data captured by differential Global Positioning Systems (DGPS). The research tested the various methods of data capture using Landsat ETM images over a range of study sites of varying complexity: a simple grassland area, a medium complexity grassy fynbos site and a complicated indigenous forest site. An important component of the research was to develop a mapping (classification) Ranking System based upon variables identified by WFW as fundamental in data capture decision making: spatial and positional accuracy, time constraints and cost constraints for three typical alien invaded areas. The mapping Ranking System compared the results of the various mapping methods for each factor for the study sites against each other. This provided an indication of which mapping method is the most efficient or suitable for a particular area.

Planners and managers often rely on coarse population distribution data from the census for addressing various social, economic, and environmental problems. In the analysis of physical vulnerabilities to sea-level rise, census units such as blocks or block groups are coarse relative to the required decision-making application. This study explores the benefits offered from integrating image classification and dasymetric mapping at the household level to provide detailed small area population estimates at the scale of residential buildings. In a case study of Boca Raton, FL, a sea-level rise inundation grid based on mapping methods by NOAA is overlaid on the highly detailed population distribution data to identify vulnerable residences and estimate population displacement. The enhanced spatial detail offered through this method has the potential to better guide targeted strategies for future development, mitigation, and adaptation efforts. / Includes bibliography. / Thesis (M.A.)--Florida Atlantic University, 2016. / FAU Electronic Theses and Dissertations Collection