This study utilized geographic information systems, remote sensing, and infrared-triggered cameras to assess white-tailed deer habitat in Denton County, Texas. Denton County is experiencing tremendous growth in both population and development. Despite their presence here historically, white-tailed deer were all but extirpated by the beginning of the 20th century, and there are no data available which support their presence in Denton County again until the 1980's. This study attempts to equate the increase in white-tailed deer population to Denton County's transformation from an agricultural to an urban economy and lifestyle. Eighteen sites were chosen throughout the county to research the following metrics: geology, soils, landcover, landscape ecology, streams, shorelines, land use, population, roads, structures, and census methods.

An accurate fire model is a useful tool in predicting the behavior of a prescribed fire. Simulation of fire requires an extensive amount of data and can be accomplished best using GIS applications. This paper demonstrates integrative procedures of using of ArcGIS™, ERDAS Imagine™, GPS, and FARSITE© to predict prescribed fire behavior on the Kaibab-Paiute Reservation. ArcGIS was used to create a database incorporating all variables into a common spatial reference system and format for the FARSITE model. ArcGIS Spatial Analyst was then used to select optimal burn sites for simulation. Our predictions will be implemented in future interagency efforts towards vegetative restoration on the reservation.

Le nerprun bourdaine (Rhamnus frangula L.) est une espèce exotique qui envahit plusieurs régions du sud du Québec, et plus particulièrement la région administrative de l'Estrie. Actuellement, on connaît encore peu l'écologie de l'espèce dans le contexte québécois et il n’existe pas de portrait d’ensemble de sa distribution dans les forêts tempérées de cette région. Dans ce contexte, le premier objectif du projet était de cartographier par télédétection la distribution du nerprun bourdaine dans deux secteurs de l'Estrie. Un second objectif était d'évaluer les variables environnementales déterminantes pour expliquer le recouvrement de nerprun bourdaine. La phénologie du nerprun bourdaine diffère de celle de la plupart des espèces indigènes arborescentes puisque ses feuilles tombent plus tard en automne. Cette caractéristique a permis de cartographier, par démixage spectral, la probabilité d'occurrence du nerprun bourdaine grâce à une série temporelle d'images du capteur OLI de Landsat 8. Le recouvrement du nerprun bourdaine a été calculé dans 119 placettes sur le terrain. La cartographie résultante a montré un accord de 69% avec les données terrain. Une image SPOT-7, dont la résolution spatiale est plus fine, a ensuite été utilisée, mais n’a pas permis d'améliorer la cartographie, puisque la date d’acquisition de l’image n’était pas optimale dû à un manque de disponibilité. Concernant le second objectif de la recherche, la variable la plus significative pour expliquer la présence de nerprun bourdaine était la densité du peuplement, ce qui suggère que l’ouverture de la couverture forestière pourrait favoriser l’envahissement. Néanmoins, les résultats tendent à démontrer que le nerprun bourdaine est une espèce «généraliste» qui s’adapte bien à plusieurs conditions environnementales. / Glossy buckthorn (Rhamnus frangula L.) is an exotic species invading many areas in southern of Quebec, particularly in the Eastern Townships. Currently, we do not know very much about the species ecology and no thorough study of its distribution in temperate forest has been performed. Therefore, the first objective of the project was to map the spatial distribution of glossy buckthorn in two areas of the Eastern Townships, using remote sensing techniques. The second objective was to evaluate the environmental variables, or predictors, best explaining the presence of glossy buckthorn. The phenology of glossy buckthorn differs from most of the indigenous tree species found in this area because its leaves fall later in autumn. This characteristic allowed to map, using spectral unmixing, the probability of occurrence of glossy buckthorn, with temporal Landsat 8 (OLI) imagery data series. Glossy buckthorn coverage was calculated on 119 plots on the field. The resulting maps showed an agreement of 69% with field data. A SPOT-7 image, which has a finer resolution than Landsat 8 (OLI), was then used but it did not improve the quality of the map, since its acquisition date was not optimal, due to a lack of availability. Concerning the second objective of the research, the best variable explaining the presence of glossy buckthorn was stand density, which leads to believe that forest cover openings could ease the establishment of buckthorn. However, the results tend to show that glossy buckthorn is a generalist species, easily adapting to various environmental conditions.

Doctor of Philosophy / Department of Agronomy / P.V. Vara Prasad / Agriculture is the largest (90%) consumer of all fresh water in the world. The consumptive use of water by vegetation represented by the process evapotranspiration (ET) has a vital role in the dynamics of water, carbon and energy fluxes of the biosphere. Consequently, mapping ET is essential for making water a sustainable resource and also for monitoring ecosystem response to water stress and changing climate. Over the past three decades, numerous thermal remote sensing based ET mapping algorithms were developed and these have brought a significant theoretical and technical advancement in the spatial modeling of ET. Though these algorithms provided a robust, economical, and efficient tool for ET estimations at field and regional scales, yet the uncertainties in flux estimations were large, making evaluation a difficult task. The main objective of this study was to evaluate and improve the performance of widely used remote sensing based energy balance models, namely: the Surface Energy Balance Algorithm for Land (SEBAL), Mapping Evapotranspiration at high Resolution and with Internalized Calibration (METRIC), and Surface Energy Balance System (SEBS). Data used in this study was collected as part of a multi-disciplinary and multi-institutional field campaign BEAREX (Bushland Evapotranspiration and Agricultural Remote Sensing Experiment) that was conducted during 2007 and 2008 summer cropping seasons at the USDA-ARS Conservation and Production Research Laboratory (CPRL) in Bushland, Texas. Seventeen high resolution remote sensing images taken from multispectral sensors onboard aircraft and field measurements of the agro-meteorological variables from the campaign were used for model evaluation and improvement. Overall relative error measured in terms of mean absolute percent difference (MAPD) for instantaneous ET (mm h[superscript]-[superscript]1) were 22.7%, 23.2%, and 12.6% for SEBAL, METRIC, and SEBS, respectively. SEBAL and METRIC performances for irrigated fields representing higher ET with limited or no water stress and complete ground cover surfaces were markedly better than that for dryland fields representing lesser ET and greater soil water deficits with sparser vegetation cover. SEBS algorithm performed equally well for both irrigated and dryland conditions but required accurate air temperature data. Overall, this study provides new insights into the performance of three widely used thermal remote sensing based algorithms for estimating ET and proposed modifications to improve the accuracy of estimated ET for efficient management of water resources.

In 2005, Hurricane Katrina’s diverse impacts on the Greater New Orleans area included damaged and destroyed trees, and other despoiled vegetation, which also increased the exposure of artificial and bare surfaces, known factors that contribute to the climatic phenomenon known as the urban heat island (UHI). This is an investigation of UHI in the aftermath of Hurricane Katrina, which entails the analysis of pre and post-hurricane Katrina thermal imagery of the study area, including changes to surface heat patterns and vegetative cover. Imagery from Landsat TM was used to show changes to the pattern and intensity of the UHI effect, caused by an extreme weather event. Using remote sensing visualization methods, field data, and local knowledge, the author found there was a measurable change in the pattern and intensity of the New Orleans UHI effect, as well as concomitant changes to vegetative land cover. This finding may be relevant for urban planners and citizens, especially in the context of recovery from a large-scale disaster of a coastal city, regarding future weather events, and other natural and human impacts.

L'augmentation importante de la population mondiale, et par conséquent de ses besoins, exerce une pression de plus en plus importante sur les surfaces forestières. L'outil le mieux adapté au suivi des forêts, à l'échelle du globe, est la télédétection. C'est dans ce contexte que se situe ce travail de thèse, qui vise à améliorer l'estimation des paramètres biophysiques des arbres à partir de données de télédétection. L'originalité de ce travail a été d'étudier cette estimation des paramètres biophysiques en menant plusieurs études de sensibilité avec une démarche expérimentale sur des données expérimentales et sur des données simulées. Tout d'abord, l'étude s'est portée sur des séries temporelles de mesures de diffusiométrie radar obtenues sur deux sites : l'un constitué d'un cèdre en zone tempérée et l'autre d'une parcelle de forêt tropicale. Puis, cette étude de sensibilité a été poursuivie en imageant, avec une résolution élevée, plusieurs parcelles aux configurations différentes à l'intérieur d'une forêt de pin. Enfin, des données optiques et radars simulées ont été fusionnés afin d'évaluer l'apport de la fusion de données optique et radar dans l'inversion des paramètres biophysiques. / The significant increase of the world population, and therefore its needs, pushes increasingly high in forest areas. The best tool for monitoring forest across the globe is remote sensing. It is in this context that this thesis, which aims to improve the retrieval of biophysical parameters of trees from remote sensing data, takes place. The originality of this work was to study the estimation of biophysical parameters across multiple sensitivity studies on experimental data and simulated data. First, the study focused on the time series of radar scatterometry measurements obtained on two sites: one characterized by a cedar in the temperate zone and the other by a forest plot of rainforest. Then, the sensitivity analysis was continued by imaging with high resolution, several forest plots with different configurations within a pine forest. Finally, simulated radar and optical data were combined to evaluate the contribution of optical and radar data fusion in the inversion of biophysical parameters.

<p>Forest canopies are a critical component of forest ecosystems as they influence many important functions. Specifically, the structure of forest canopies is a driver of the magnitude and rate of these functions. Therefore, being able to accurately measure canopy structure is crucial to ensure ecological models and forest management plans are as robust and efficient as possible. However, canopies are complex and dynamic entities and thus their structure can be challenging to accurately measure. Here we study the feasibility of using lidar to measure forest canopy structure across large spatial extents by investigating the compatibility of aerial and terrestrial lidar systems. Building on known structure-function relationships measured with terrestrial lidar, we establish grounds for scaling these relationships to the aerial scale. This would enable accurate measures of canopy structural complexity to be acquired at landscape and regional scales without the time and labor requirements of terrestrial data collection. Our results illustrate the potential for measures of canopy height, vegetation area, horizontal cover, and canopy roughness to be upscaled. Furthermore, we highlight the benefit of utilizing multivariate measures of canopy structure, and the capacity of lidar to identify forest structural types. Moving forward, lidar is a tool to be utilized in tandem with other technologies to best understand the spatial and temporal dynamics of forests and the influence of physical ecosystem structure. </p>

<div> <div> <p>Global Navigation Satellite System Reflectometry (GNSS-R) has presented a great potential as an important approach for ocean remote sensing. Numerous studies have demonstrated that the shape of a code-correlation waveform of forward-scattered Global Positioning System (GPS) signals may be used to measure ocean surface roughness and related geophysical parameters such as wind speed. Recent experiments have extended the reflectometry technique to transmissions from communication satellites. Due to the high power and frequencies of these signals, they are more sensitive to smaller scale ocean surface features, which makes communication satellites a promising signal of opportunity (SoOp) for ocean remote sensing. Recent advancements in fundamental physics are represented by the new scattering model and bistatic radar function developed by Voronovich and Zavorotny based on the SSA (Small Slope Approximation). This new model allows the partially coherent scattering in low wind conditions to be correctly described, which overcomes the limitations of diffuse scattering inherited in the conventional KA-GO (Kirchhoff Approximation-Geometric Optics) model. Furthermore, exploration and practice using spaceborne platforms have become a primary research focus, which is highlighted by the launch of CYGNSS (Cyclone Global Navigation Satellite System) in 2016. CYGNSS is a NASA (National Aeronautics and Space Administration) Earth Venture Mission consisting of an 8 micro-satellite constellation of GNSS-R instruments designed to observe tropical cyclones.</p><p>However, in spite of the significant achievements made in the past 10 years, there are still a variety of challenges to be addressed currently in the ocean reflectometry field. To begin with, the airborne demonstration experiments conducted previously for S-band reflectometry provided neither sufficient amount of data nor the desired scenarios to assess high wind retrieval performance of S-band signals. The current L-band empirical model function theoretically does not also apply to S-band reflectometry. With respect to scattering models, there have been no results of actual data processing so far to verify the performance of the SSA model, especially on low wind retrievals. Lastly, the conventional model fitting methods for ocean wind retrievals were proposed for airborne missions, and new approaches will need to be developed to satisfy the requirement of spaceborne systems.<br></p><p>The research described in this thesis is mainly focused on the development, application and evaluation of new models and algorithms for ocean wind remote sensing. The first part of the thesis studies the extension of reflectometry methods to the general class of SoOps. The airborne reception of commercial satellite S-band transmissions is demonstrated under both low and high wind speed conditions. As part of this effort, a new S-band geophysical model function (GMF) is developed for ocean wind remote sensing using S-band data collected in the 2014 NOAA (National Oceanic and Atmospheric Administration) hurricane campaign. The second part introduces a dual polarization L- and S-band reflectometry experiment, performed in collaboration with Naval Research Lab (NRL), to retrieve and analyze surface winds and compare the results with CYGNSS satellite retrievals and NOAA data buoy measurements. The problems associated with low wind speed retrieval arising from near specular surface reflections are studied. Results have shown improved wind speed retrieval accuracy using bistatic radar cross section (BRCS) modeled by the SSA when compared with KA-GO, in the cases of low to medium diffuse scattering. The last part focuses on the contributions to the NASA-funded spaceborne CYGNSS project. It shows that the accuracy of CYGNSS ocean wind retrieval is improved by an Extended Kalman Filter (EKF) algorithm. Compared with the baseline observable methods, preliminary results showed promising accuracy improvement when the EKF was applied to actual CYGNSS data.<br><br></p></div></div>

by Chan, Cheung-Wai. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1994. / Includes bibliographical references (leaves 184-189). / ACKNOWLEDGMENTS --- p.ii / LIST OF ILLUSTRATIONS --- p.iii / LIST OF TABLES --- p.iv / Chapter Chapter One --- Introduction --- p.1 / Chapter 1.1 --- Introduction / Chapter 1.2 --- Background / Chapter 1.3 --- Objectives of this study / Chapter 1.4 --- The Structure of the thesis / Chapter Chapter Two --- Literature Review --- p.9 / Chapter 2.1 --- Introduction / Chapter 2.2 --- Rural-urban fringe / Chapter 2.3 --- Characteristics of land use changes in the fringes / Chapter 2.4 --- Factors affecting the mechanism of land use changes in the urban fringe / Chapter 2.5 --- Parties involved in land use change decision / Chapter 2.6 --- Land use in the urban fringe of Zhujiang Delta / Chapter 2.6.1 --- Cities' in China / Chapter 2.6.2 --- Urban fringes in China ---the case of Zhujiang Delta / Chapter 2.6.3 --- Land use pattern in the Zhujiang Delta / Chapter Chapter Three --- Methodology --- p.29 / Chapter 3.1 --- Introduction --- Change detection techniques of land use changes / Chapter 3.1.1 --- A concept of change detection / Chapter 3.1.2 --- Change detection techniques / Chapter 3.2 --- Method employed to detect land use change in Zhujiang Delta / Chapter 3.3 --- Procedures / Chapter 3.3.1 --- Data description / Chapter 3.3.2 --- Preprocessing / Chapter a. --- Atmospheric correction / Chapter b. --- Image Registration / Chapter i. --- Spatial interpolation / Chapter ii. --- Intensity interpolation / Chapter 3.3.3 --- Image differencing / Chapter 3.3.4 --- Post-classification comparison / Chapter a. --- Land Use / Land Cover classification scheme / Chapter b. --- Definitions and image characteristics of land / Land cover classes / Chapter c. --- Supervised classification / Chapter d. --- Training sites / Chapter e. --- Maximum likelihood classifier / Chapter f. --- Accuracy assessment / Chapter g. --- post-classification comparison / Chapter Chapter Four --- Study Area --- p.64 / Chapter 4.1 --- Physical and agricultural landscape of Zhujiang Delta ---a general description / Chapter 4.1.1 --- Physical landscape / Chapter 4.1.2 --- Urban develoment / Chapter 4.1.3 --- Agricultural landscape / Chapter 4.2 --- Shunde / Chapter 4.2.1 --- Agricultural land use / Chapter 4.2.2 --- The image of Shunde / Chapter 4.3 --- Dongguan / Chapter 4.3.1 --- Agricultural land use / Chapter 4.3.2 --- The image of Dongguan / Chapter 4.4 --- Guangzhou / Chapter 4.4.1 --- Agricultural land use / Chapter 4.4.2 --- The image of Guangzhou / Chapter 4.5 --- Land use changes expected within the study area / Chapter Chapter Five --- Results and discussions --- p.83 / Chapter 5.1 --- Image differencing / Chapter 5.2 --- Results of classifications / Chapter 5.2.1 --- Shunde / Chapter 5.2.2 --- Dongguan / Chapter 5.2.3 --- Guangzhou / Chapter 5.3 --- Post-classification comparison change detection / Chapter Chapter Six --- Land use chancre analysis of the urban fringesin Zhujiang Delta --- p.107 / Chapter 6.1 --- Framework for discussion / Chapter 6 .2 --- Land use / land cover changes in Shunde / Chapter 6.2.1 --- Rural-to-urban changes / Chapter 6.2.2 --- Rural-to-rural changes / Chapter 6. 3 --- Land use / land cover changes in Dongguan / Chapter 6.3.1 --- Rural-to-urban changes / Chapter 6.3.2 --- Rural-to-rural changes / Chapter 6.4 --- Land use / land cover change in Guangzhou / Chapter 6.4.1 --- Rural-to-urban changes / Chapter 6.4.2 --- Rural-to-rural changes / Chapter 6 .5 --- Comparson of land use changes of the study area / Chapter 6.5.1 --- Land-use change comaprison / Chapter 6.5.2 --- Rural-to-urban changes / Chapter 6.5.3 --- Rural-to-rural changes / Chapter a. --- Origins of wet cropland and market gardening / Chapter b. --- Origins of woodland / Chapter c. --- Origins of fish ponds / Chapter 6.5.4 --- Conclusions / Chapter Chapter Seven --- Conclusion --- p.175 / Chapter 7.1 --- Land use change in the urban fringes in the Zhujiang Delta ---a comparison / Chapter 7.2 --- the applicability of western theories on land use change to the Zhujiang Delta / Chapter 7.3 --- Remote sensing method as an application for land use change monitoring in China

Ip Oi-ching. / Thesis submitted in: October 2001. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2002. / Includes bibliographical references (leaves 142-152). / Abstracts in English and Chinese. / LIST OF ABBREVIATIONS --- p.ix / LIST OF TABLES --- p.xiv / LIST OF FIGURES --- p.xviii / LIST OF APPENDICES --- p.xxii / Chapter CHAPTER ONE --- INTRODUCTION / Chapter 1.1 --- Conceptual Framework --- p.1 / Chapter 1.2 --- Objectives --- p.4 / Chapter 1.3 --- Significance --- p.5 / Chapter 1.4 --- Study Area --- p.6 / Chapter 1.5 --- Organization Of Thesis --- p.8 / Chapter CHAPTER TWO --- LITERATURE REVIEW / Chapter 2.1 --- Quality of Life and Indicators --- p.9 / Chapter 2.1.1 --- Scope of study for quality of life --- p.9 / Chapter 2.1.2 --- Development and evolution of quality of life studies --- p.13 / Chapter 2.1.3 --- Quality of life indicators --- p.14 / Chapter 2.2 --- Quality of Life Studies using Remote Sensing data --- p.17 / Chapter 2.2.1 --- Attributes derived from remote sensing --- p.17 / Chapter 2.2.2 --- Environmental changes and landuse change --- p.17 / Chapter 2.2.3 --- Housing quality --- p.18 / Chapter 2.2.4 --- Integration of remote sensing data and census data --- p.19 / Chapter 2.3 --- Quality of Life Study and Application of Remote Sensing in Hong Kong --- p.20 / Chapter 2.4 --- Summary --- p.22 / Chapter CHAPTER THREE --- METHODOLOGY / Chapter 3.1 --- Data Description --- p.24 / Chapter 3.1.1 --- Biophysical data --- p.24 / Chapter 3.1.2 --- Socioeconomic indices --- p.28 / Chapter 3.1.3 --- Data extracted at Tertiary Planning Unit --- p.30 / Chapter 3.2 --- Satellite Data Preprocessing --- p.32 / Chapter 3.2.1 --- Radiometric and atmospheric correction --- p.38 / Chapter 3.2.2 --- Image normalization --- p.43 / Chapter 3.2.3 --- Geometric correction --- p.44 / Chapter 3.3 --- Landuse and Land-cover Classification --- p.45 / Chapter 3.4 --- Spectral Data Extraction and Transformation --- p.47 / Chapter 3.5 --- Integration of Spectral and Census Data for Quality of Life Modeling --- p.49 / Chapter 3.5.1 --- Inter-relationship between biophysical data and socioeconomic data --- p.50 / Chapter 3.5.2 --- Integrated quality of life modeling --- p.50 / Chapter 3.6 --- Summary --- p.51 / Chapter CHAPTER FOUR --- DATA DESCRIPTION / Chapter 4.1 --- Socioeconomic Data --- p.53 / Chapter 4.2 --- Spectral Data --- p.62 / Chapter 4.2.1 --- Raw data --- p.62 / Chapter 4.2.2 --- Landuse and land cover --- p.63 / Chapter 4.2.3 --- Vegetation indices --- p.66 / Chapter 4.2.4 --- Tasseled cap components --- p.67 / Chapter 4.2.5 --- Surface temperature --- p.69 / Chapter 4.2.6 --- Principal components extracted from biophysical variables --- p.70 / Chapter 4.3 --- Summary --- p.79 / Chapter CHAPTER FIVE --- INTERRELATIONSHIP BETWEEN SPECTRAL VARIABLES AND SOCIOECONOMIC VARIABLES / Chapter 5.1 --- Framework of Analysis --- p.82 / Chapter 5.2 --- Correlation among Socioeconomic and Biophysical data --- p.84 / Chapter 5.3 --- Stepwise Multiple Linear Regression Models --- p.91 / Chapter 5.3.1 --- Biophysical data as dependent variable --- p.91 / Chapter 5.3.1.1 --- NDVI as dependent variable --- p.95 / Chapter 5.3.1.2 --- URBANM as dependent variable --- p.98 / Chapter 5.3.2 --- Socioeconomic data as dependent variable --- p.101 / Chapter 5.3.2.1 --- POP´ؤDEN as dependent variable --- p.101 / Chapter 5.4 --- Summary and Discussion --- p.103 / Chapter CHAPTER SIX --- QUALITY OF LIFE ANALYSIS / Chapter 6.1 --- Indictors for Quality of Life Study --- p.105 / Chapter 6.2 --- QOL Indicators --- p.110 / Chapter 6.3 --- Spatial Variation of QOL --- p.114 / Chapter 6.4 --- Temporal Variation of QOL --- p.125 / Chapter 6.5 --- Summary and discussion --- p.131 / Chapter CHAPTER SEVEN --- CONCLUSION / Chapter 7.1 --- Summary of Findings --- p.134 / Chapter 7.1.1 --- Inter-relationship between socioeconomic and biophysical variables --- p.135 / Chapter 7.1.2 --- Quality of life indicators --- p.135 / Chapter 7.2 --- Limitations of the Study --- p.13 8 / Chapter 7.3 --- Recommendations for Further Studies --- p.140 / REFERENCES --- p.142 / APPENDIX A --- p.153 / APPENDIX B --- p.157 / APPENDIX C --- p.167