Global climatic change as well as its consequences such as extreme weather events and sea-level rising has become a focusing issue in the contemporary world. Alpine snow cover is increasingly regarded as a good and sensitive indicator of climatic change due to the less direct interference by human. In western aridzone of China, majority of mountainous areas are covered by snow in winter seasons. This region is one of the most important seasonal snow cover regions in China and also a typical alpine snow cover region in the mid-high latitudes of the Northern Hemisphere. Being less affected by economic development and human activities in the history, the change of permanent and seasonal snow cover in this region echoes the global climatic and environmental change. In addition, snow melt water, which provides the major water supply in the region, is vital for living beings in the arid and harsh environment. It is therefore necessary to understand the snow cover change during the past decades. This study aims to investigate the spatio-temporal pattern of snow cover in the western aridzone of China in the past 30 years by using remote sensing technology and to analyze the relationship between the change of snow cover and global climate. The reliability of remote sensing-derived global snow data is firstly examined. Data consistency and accuracy are assessed against the ground measurements. In order to undertake a down-scale snow depth analysis with other high-resolution environmental data, a method that fuses the low-resolution passive microwave and high-resolution optical snow cover images is proposed. A linear mixture model is adopted in spectral unmixing for modifying snow depth estimates. Time series analysis method is utilized to describe the long-term trend and periodic features. The analysis is applied not only to the whole region but also to the local scale represented by a pixel so that the spatial pattern of the change can be illustrated. Using the result and climatic data, the relationship between snow cover and global/regional climatic change is established. The results make contribute to the understanding of the impacts of climatic change, at regional level, on the spatio-temporal pattern of snow cover in the western aridzone of China. Keywords: Snow and ice, alpine snow cover, remote sensing, spatio-temporal pattern, long-term trend, climatic change, western aridzone of China

Drought is a water related natural hazard. It is difficult to characterize drought because of its diffused nature and spatiotemporal variability. However, understanding the variability of drought characteristics such as severity, frequency, duration, and spatial extent is critical in drought mitigation and planning. Impact of drought on agriculture, water supply, and energy sectors has been long-recognized. The current understanding of drought and its impact is limited due to its complex characteristics and ways in which it impacts various sectors. This study focuses on two important aspects of drought: variability of drought characteristics across different spatial scales, and impact of droughts on crop yield and groundwater. Two drought indices, one integrating severity and spatial coverage, and also taking into account the type of specific crops, were investigated for county level use. The developed indices were used in studying drought at the county level, and its impact on crop yields. These indices can be used for resource allocation at the county level for drought management. Drought is reported in the United States (U.S.) for different administrative units at different spatial scales. The variation of drought characteristics across different spatial scales and scale dependence was investigated, demonstrating the importance of considering spatial scales in drought management. A methodology is proposed to quantify the uncertainty in reported values of drought indices using geostatistical tools. The uncertainty was found to increase with increasing spatial scale size. Artificial Neural Network and regression methods were used to model the impact of drought on crop yield and groundwater resources. Relationships of crop yields and groundwater levels with drought indices were obtained. Overall, this study contributes towards understanding of the spatial variation of drought characteristics across different spatial scales, and the impact of drought on crop yields and groundwater levels. / North Dakota Water Recourses Research Institute (ND WRRI) Fellowship Program / North Dakota State University Graduate School Doctoral Dissertation Award

We present the concept of a frame and the related notion of spatiality. We consider the classical separation axioms in the frame setting and relate these to frame covering properties. Finally, a determination of which covering properties and separation axioms imply spatiality of a frame is made. / Master of Science

Despite their ecological and economic importance, the majority of plant species and their conservation status are poorly known. Only 4% of plants have been assessed globally and listed on the IUCN Red List of Threatened Species; and without plant conservation assessments, many plant species will not feature in conservation planning. Herbarium collection information can significantly increase the number of plant conservation assessments. Thus, the aims of this thesis were: (1) to investigate how the quality of herbarium-based conservation assessments can be optimised; (2) to assess the extent to which herbarium-based conservation assessments reflect the reality on the ground; and (3) to scientifically validate genetic and spatial underpinning of IUCN criteria. Preliminary range-based assessments of the Leguminosae of Madagascar achieved a result consistent with the final conservation rating for over 95% of species when using up to fifteen herbarium specimens. Bioclimatic modelling of range shifts based on future climate change predicted that, in the worst case scenario, up to one third of endemic Leguminosae in Madagascar will be threatened with extinction over the next 100 years. An analysis of the population structure of species of Delonix s.l. (Leguminosae) showed that combining spatial analysis with population genetic data provides a more complete picture of landscape-level population dynamics and the impacts on conservation status. Moreover, range-wide genetic analysis of AFLP markers for four species of Delonix demonstrated a genetic basis for IUCN categories distinguishing between threatened and non-threatened species. Although genetic data are currently not often incorporated in conservation assessments, they are crucial in making accurate management decisions and creating effective action plans for conservation. Only by using all available scientific resources can informed conservation decisions be made and the survival of plants and their associated ecosystems be ensured.

The Modifiable Areal Unit Phenomenon (MAUP) has traditionally been regarded as a problem in the analysis of spatial data organised in areal units. However, the approach adopted here is that the MAUP provides an opportunity to gain information about the data under investigation. Crucially, attempts to remove the MAUP from spatial data are regarded as an attempt to remove the geography. Therefore, the work seeks to provide an insight to the causes of, and information behind, the MAUP. The data used is from the 1991 Census of Great Britain. This was chosen over 2001 data due to the availability of individual level data. These data are of key importance to the methods employed. The methods seek to provide evidence of the magnitude of the MAUP, and more specifically the scale effect in the GB Census. This evidence is built on using correlation analysis to demonstrate the statistical significance of the MAUP. Having established the relevance of the MAUP in the context of current geographical research, the factors that contribute to the incidence of the MAUP are considered, and it is noted that a wide range of influences are important. These include the population size and density of an area, along with proportion of a variable. This discussion also recognises the importance of homogeneity as an influential factor, something that is referenced throughout the work. Finally, a search is made for spatial processes. This uses spatial autocorrelation and multilevel modelling to investigate the impact spatial processes have in a range of SAR Districts, like Glasgow, Reigate and Huntingdonshire, on the scale effect. The research is brought together, not to solve the MAUP but to provide an insight into the factors that cause the MAUP, and demonstrate the usefulness of the MAUP as a concept rather than a problem.

Landscape is about the relationship between people and place and in 2000 was defined by the European Landscape Commission (ELC) as "an area as perceived by people whose character is the result of natural and human actions and interactions”. In the 70s the reason for studying the landscape was because of the necessity of attributing a value to it. Nowadays the motivations behind managing, conserving and enhancing the landscape is because the landscape is the place where people belong to and, consciously or not, recognise themselves. In addition, people identify different landscapes on the basis of the particular combinations of the elements in the landscape. As a consequence a landscape can be distinguished from another on the basis of its character which, according to the Landscape Character Assessment (LCA) guidance for England and Scotland (C. Swanwick and Land Use Consultant, 2002), is defined as “a distinct, recognisable and consistent pattern of elements in the landscape that makes one landscape different from the other rather than better or worse”. This definition was the starting point of a PhD research project aimed at developing and implementing a methodology able to identify and quantify the character of the Scottish landscape through the application of GIS and statistics. The reason for doing this research was to provide the landscape architects and practitioners with a tool that could help them to define the landscape character types in a more consistent, objective, and scientifically robust way. One of the objectives of the research was to identify the spatial patterns formed by the landscape elements by taking into account the influence of the spatial location. The first law of geography, which states that "everything is related to everything else but near things are more related than distant ones" (W Tobler, 1970), was transposed in the assumption of the presence of spatial autocorrelation amongst the data which contributes to form spatial patterns within the data. Since landscape comprises of many elements, data were also multivariate, thus the analysis required a method of calculation able to deal simultaneously with multivariate and spatial autocorrelation issues. MULTISPATI-PCA, a spatially constrained Principal Component Analysis, was the statistical technique applied for the analysis of the data whose results showed that it was possible to detect the spatial structure of the data and that each spatial pattern corresponded to a distinct landscape. Despite their importance in forming the character of the landscape, aesthetic and perceptual aspects were not inlcuded in MULTISPATI-PCA analysis. It was preferred to test the technique only on data that were quantifiable in a more objective way. Perhaps taking into account the human perception of the landscape can be the starting point for future investigation.

Deterministic and stochastic weighting methods are commonly used methods for estimating missing precipitation rain gauge data based on values recorded at neighboring gauges. However, these spatial interpolation methods seldom check for their ability to preserve site and regional statistics. Such statistics and primarily defined by spatial correlations and other site-to-site statistics in a region. Preservation of site and regional statistics represents a means of assessing the validity of missing precipitation estimates at a site. This study evaluates the efficacy of traditional interpolation methods for estimation of missing data in preserving site and regional statistics. New optimal spatial interpolation methods intended to preserve these statistics are also proposed and evaluated in this study. Rain gauge sites in the state of Kentucky are used as a case study, and several error and performance measures are used to evaluate the trade-offs in accuracy of estimation and preservation of site and regional statistics. / by Husayn El Sharif. / Thesis (M.S.C.S.)--Florida Atlantic University, 2012. / Includes bibliography. / Mode of access: World Wide Web. / System requirements: Adobe Reader.

Spatial and temporal interpolation methods are commonly used methods for estimating missing precipitation rain gauge data based on values recorded at neighboring gauges. However, these interpolation methods have not been comprehensively checked for their ability to preserve time series characteristics. Assessing the preservation of time series characteristics helps achieving a threshold criteria of length of gaps in a data set that is acceptable to be filled. This study evaluates the efficacy of optimal weighting interpolation for estimation of missing data in preserving time series characteristics. Rain gauges in the state of Kentucky are used as a case study. Several model performance measures are also evaluated to validate the filling model; followed by time series characteristics to evaluate the accuracy of estimation and preservation of precipitation data characteristics. This study resulted in a definition of region-specific threshold of the maximum length of gaps allowed in a data set at five percent. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2016. / FAU Electronic Theses and Dissertations Collection

Avian influenza H5N1 is one kind of important bird flu. Unfortunately, this virus has swiftly evolved and become highly pathogenic to humans and poultry, resulting in 100% of death in infected poultry and over 60% of mortality among infected human population. Moreover, the virus tends to reassort with other influenza viruses, such as the current swine flu H1N1, to establish themselves in environments and further this epidemic all over the world. The World Health Organization (WHO) has in fact warned that highly pathogenic avian influenza H5N1 poses a graver risk of a global human pandemic than at any time since the Hong Kong outbreak (H3N2) in the 1960s. / Finally, avian influenza is an inter-disciplinary issue across virology, medical geography, and spatial epidemiology. How to quantify and integrate knowledge from different disciplines remains a challenge in fully understanding the disease. We propose a method to formally integrate genetic analysis that identifies the evolution of the H5N1 virus in space and time, epidemiological analysis that determines socio-environmental factors associated with H5N1 occurrence and statistical analysis that identifies outbreak dusters. Our integrated results show a significant advance in findings over reports in, for instance, Gilbert et al. (2008) and we believe our findings are more precise and informative in representing the occurrence and the space-time dynamics of H5N1 spread. Overall, unlike traditional influenza studies, our work sets up a solid foundation for the inter-disciplinary study of this and other spatial infectious diseases. / First, we apply multifractal detrended fluctuation analysis to determine the temporal scaling behavior of outbreaks in Asia, Europe, Africa, and the whole of the world between December 2003 to March 2009. Long-range correlation and multifractality, two important properties characterizing the scaling behavior of complex dynamics, are first detected in the outbreak time series. In addition, this study identifies different temporal scaling behaviors of outbreaks of these continents 8,nd specific seasonal patterns in Asia. These findings confirm our perspective that avian-influenza outbreak behaviors are self-similar over time and are spatially heterogeneous. / One key to preventing such a calamity is to obtain a thorough understanding of the mechanisms of avian influenza transmission and its spatio-temporal patterns of dispersal. The issues at stake are outbreaks' spatial and temporal patterns, the interrelationship of these with the evolution of influenza viruses in such a way that geography is understood as a dimension of the disease's virology, and the human and avian behaviors and socio-ecological environments associated with H5Nl spread. This thesis sets out to study these problems in detail and propose solutions. / Second, we conduct a spatial analysis for global trends and local clusters of H5N1 outbreaks at multiple geographical scales. Currently, the local K function used in a point pattern analysis searches outbreak clusters, assuming the disease is spatially homogeneous. The thesis proposes a much more efficient method to measure the degree of clusters accurately. The modified function works by weighting outbreaks through distances, counting the number of the weighted outbreaks for each lattice point no matter whether the disease emerges in a grid. This weighted local K function extends cluster analysis from a point pattern to lattice data. Spatial representation in these terms then seeks to explore local patterns of H5N1 over a continuous space. / Third, we study a set of socio-environmental factors, which are plausibly associated with the occurrence of H5N1. Spatial epidemiological models are built for predicting the disease at both continental and national levels, covering Indonesia, China, and the whole of East-Southeast Asia. We evaluate the statistical models using 1,000 bootstrap replicates, showing a consistently high rate of prediction, assessed by statistics: AUC, Kappa Index, and pseudo R square. / Ge, Erjia. / Advisers: Yee Leung; Tung Fung. / Source: Dissertation Abstracts International, Volume: 73-06, Section: A, page: . / Thesis (Ph.D.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (leaves 169-197). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [201-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.

[Truncated abstract] The malleefowl (Leipoa ocellata) is a large, ground-dwelling bird that is listed as threatened in all states of Australia in which it occurs. Its range encompasses much of southern Australia; however, much of it has been cleared for agriculture. Malleefowl are thought to have suffered substantial decline owing to multiple threats that include habitat loss, predation from exotic predators, grazing of habitat by introduced herbivores and fire - common threats in the decline of many Australian vertebrate species. The malleefowl has an unmistakeable appearance, unique biology, and widespread distribution across Australia. Consequently, it has been the focus of much scientific and community interest. In the Western Australian wheatbelt, community groups are working to conserve the species and have been actively collecting data on its distribution for over 15 years. The vast majority of these data are presence-only and have been collected in an opportunistic manner but, combined with long-term data from government agencies and museums spanning over 150 years, they present a significant opportunity to inform ecological questions relevant to the conservation of the species. The purpose of this study was to answer key ecological questions regarding the distribution, status and habitat preferences of malleefowl using unstructured occurrence records supplemented by reliable absences derived from Bird Atlas data sets and targeted surveys. Malleefowl in the Western Australian wheatbelt were used as a case study to illustrate: 1) how the decline of a species can be quantified and causes of that decline identified; and 2) how threats can be identified and responses to threats explored. I used bioclimatic modelling to define and explore variation within the climatic niche of the Malleefowl across Australia. '...' This thesis provides substantial additional knowledge about the ecology, distribution and status of malleefowl in Western Australia. It also illustrates how opportunistic and unstructured data can be augmented to investigate key aspects of a species' ecology. Despite the limitations of these data, which primarily relate to variation in observer effort across time and space, they can provide important outcomes that may not be achieved using standard survey and data collection techniques. The utility of opportunistic data is greatest in situations where the species: is recognisable and easily observed; is relatively sedentary; and occurs within a landscape containing consistent land use and habitat types. The approaches used in this study could be applied by researchers to situations where community interest exists for species with these attributes. At a national scale, the malleefowl is predicted to decline by at least 20% over the next three generations. The findings of this thesis suggest that the future for the species in the Western Australian wheatbelt may not be as dire as predicted elsewhere within its range, owing largely to the easing and cessation of threatening processes (e.g. land clearing, grazing of habitat by livestock) and the ability of the species to occupy a variety of habitat types. Despite this perceived security, some caution must be exercised until there is a more complete knowledge of the impact of fox predation and reduced rainfall due to climate change on malleefowl populations. Furthermore, the status of the species beyond the agricultural landscapes in Western Australia requires closer examination.