Thesis (M.S.) in Resource Economics and Policy--University of Maine, 2005. / Includes vita. Includes bibliographical references (leaves 97-102).

Thesis (Ph. D.)--Texas State University-San Marcos, 2006. / Vita. Appendices: leaves 101-106. Includes bibliographical references (leaves 107-112).

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<p> Understanding the causes of specific fire regimes is critical for determining the long term impacts of fire on vegetation cover. Numerous studies using 30 m Landsat data find a relationship between fire timing and vegetation type, but this relationship has not been observed at broader scales. In West Africa land-cover patterns are heterogeneous and patchy at the landscape scale and annual fires often burn mosaic patterns. It is well documented that where fires are known to be small and fragmented, the commonly used coarse-resolution MODIS data cannot give accurate estimates of burned area. Moreover, their inability to capture the spatial pattern of land-cover types burned presents a mixed pixel problem, because vegetation and agricultural fields vary on a scale less than 500 m². To overcome these issues, this study uses medium-resolution Landsat data to map land-cover. Landscape ecological indices are used to observe spatial patterns at 500 m scale.</p>

<p> FEMA&rsquo;s <i>Hazus Earthquake Model</i> (FEMA, 2018) is used frequently in the United States for scenario driven catastrophic planning, prioritizing mitigation funding, and disaster response situational awareness. The Hazus Earthquake Model requires a significant array of data for conducting earthquake loss estimations in terms of population demographics, building construction proxies, built environment estimates, critical facility locations, building infrastructure, and a variety of engineering and analysis parameters. A basic baseline of these required inputs is provided with Hazus for all 50 U.S. States, the District of Columbia, and the 5 U.S. territories. These default baseline datasets and engineering/analysis parameters however are somewhat rudimentary, and the incorporation of local data greatly increases results. Hazus enables the United States natural hazards risk assessment community to quantify risk using a robust suite of analysis options. By adapting the <i>Hazus Earthquake Model</i> for a country outside of the United States, risk management professionals across the globe can leverage the significant financial investment and over 20 years of software and scientific methodology investments undertaken by the U.S. government for their communities. While the methods and models are transferable internationally, the limited available data upon which the model is reliant minimizes its application outside of the U.S. Many global population exposure, building, and construction practice datasets do exist that could be leveraged to enable regional Hazus Earthquake modeling capability globally and a methodology for integrating these datasets into the software can facilitate technology transfer for supporting risk reduction. By conducting a quantitative comparison between modeled and actual losses from the 2015 Gorkha (Nepal) Mw 7.8 earthquake, this study aims to answer whether FEMA&rsquo;s U.S.-centric Hazus Earthquake Model can be adapted for Nepal to estimate building damages, injuries, and fatalities using global exposure datasets.</p><p>

Farming has a unique role to play in shaping the landscape and enhancing our environment. In recent years, the industry has declined and no longer makes a significant contribution to the national economy. The impact of animal diseases such as BSE and Foot and Mouth has reduced consumer confidence in the quality of food produced. The UK Government, through the introduction of funding schemes, is aiding the recovery of the industry by encouraging farmers to diversify their farming enterprise. One option is the conversion from intensive to organic farming practices, a decision that involves a high level of risk and uncertainty. This research proposes a role for GIS as a decision support tool for a farm manager exploring the options for organic conversion. Where data is captured and held in multiple applications, the GIS-based Spatial Decision Support System (SDSS) must integrate data and models. The use of the GIS must be intuitive, allowing the farm manager to explore different scenarios for land allocation effectively. The interface must allow the amendment of input parameters and present the results from each scenario in a clear, understandable format. This functionality raises important data handling issues that are investigated through the development of a prototype GIS. The identification and assessment of relevant datasets and the seamless integration of data are fundamental to the design of the GIS. Metadata, adhering to international guidelines, are identified as the chief means for discovering, exploring and acquiring spatial datasets from diverse sources. An assessment of the quality and accuracy of the data is essential if they are to be the basis for decision support. Interoperability issues are discussed and suggestions are proposed for the successful integration of data and models for the SDSS through the GIS interface. By providing a visual medium in which alternative strategies can be evaluated, the GIS will enhance the quality of the final decision made by the farm manager.

Network forecasting has traditionally been conducted using survey data collected by field-based surveys of the road network to model the microclimate surrounding the road network. A review of the literature in the subject area identifies that the current methods rely heavily on a field based survey approach. This research examines the premise that these field surveys replaced by modern GIS modelling methods. The research goes on to describe the development of a variety of GIS based methodologies and software for the calculation of geographical parameters for input into the Geographical Road Ice Prediction model and the dissemination of the resulting ice forecast through a web based GIS system. The methods developed are then discussed in relation to a trial conducted in Hampshire in the winter of 2006/7, which test the methods in a real world scenario. The final element to this research is the exploration of a variety of different GIS datasets to test the methodologies developed and investigate the impact of varying the methodologies and data sources on the network forecast for a small locality in the north of Hampshire. Results suggest that a number of the methodological parameters developed could be adjusted without affecting the resulting forecast and therefore improve the efficiency of the modelling process. It also identifies which of the GIS datasets available in the UK produce the best forecasting results. The research concludes that the methodologies developed have successfully helped predict ice formation on road networks without the use of any manual survey data. Moreover, the methods developed can be used in other research fields. In particular, this research has found a more accurate method for modelling building heights from medium resolution IFSAR data, which has resulted in an 11% improvement in building height estimations. Finally, this research goes onto discuss further research and how a more detailed assessment of the methodologies under different climatic and geographical conditions the methods developed, have the potential to replace parts or all of the survey methods currently used in ice prediction surveys.

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