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.

No description available.

No description available.

<p> This study performed an assessment of the representational accuracy of the forest class of the GlobeLand30 (GL30) global land cover data sets for the country of Mexico using a robust geographically distributed forest inventory survey of the forests in Mexico. The representational accuracy assessment was carried out for both the 2000 and 2010 GL30 data sets. The detailed attribute data associated with the validation set demonstrates how GL30 classifies specific forest types and how canopy coverage and number for trees per site influence the likelihood of GL30 identifying the sites correctly as forests. The results indicate that producers accuracies range from 72.3% to 97.3%. The tropical forests (89.1%) were better represented by the GL30 forest class than the temperate forest (73.9%). The most poorly represented classes from the temperate (oak: 72.3%) and tropical (low dry deciduous jungle: 74.9%) groups were deciduous. Receiver Operator Curve and Area Under the Curve analyses show that canopy coverage of a site is a better predictor of GL30, correctly identifying the site as forest for temperate forest, and that the number of the trees per site is a better predictor of GL30 correctly identifying a site as forest for tropical forests. The results also indicate a distinct spatial variability in the location of the sample sites that are misidentified as forests by GL30. The results of this thesis will help researchers and professionals better understand the representational accuracy of the GL30 data sets for the forests in Mexico.</p><p>

<p> Archaeologists have been interested in relationship between environmental variability and cultural change for the last six decades. By understanding how, when, and why humans adapt to environmental change, archaeologists and anthropologists can better understand the development and complexity of human cultures. In northwest Alaska, archaeologists hypothesize that environmental variability was a major factor in both growing coastal population density, with large aggregated villages and large houses, between 1000 and 500 years ago (ya), and subsequent decreasing population density between 500 ya and the contact era. After 500 ya people are thought to have dispersed to smaller settlements with smaller house sizes in coastal areas, and perhaps, upriver. This settlement pattern was identified through research at four site locations over 30 years ago. The changing geographic distribution of sites, associated settlement size, and house size has not been examined in detail. A more careful examination of changing northwest Alaskan settlement patterns is needed before larger questions about socio-economic organization can be addressed. I use Geographic Information Systems (GIS) to evaluate the evidence for a geographic redistribution of Arctic peoples during the Late Holocene. </p><p> I constructed a database of settlement location and site attribute information, specifically the number of houses within each settlement and the size (m²). Data were collected from a dataset of Western Arctic National Parklands (WEAR), the Alaska Heritage Resource Survey (AHRS) database of archaeological sites in Alaska, 409 unpublished site reports and field notes curated by the National Park Service (NPS) and Bureau of Indian Affairs (BIA), and the results of recent fieldwork in northwest Alaska. A total of 486 settlements were identified within the northwest Alaska with 128 settlements having temporal and site attribute data. </p><p> I incorporated settlement size data into a GIS database and then carried out global, Moran&rsquo;s I, local Moran&rsquo;s I, and local Getis-Ord spatial analyses to test whether settlement redistribution occurred and if key settlement locations shifted after 500 ya. The site attribute data (number of houses and average size of houses) are used to test the additional aspects of the proposed settlement pattern change after 500 ya. A total of 83 settlements with 465 houses are used to test if the average size of settlements and average house size changed after 500 ya. </p><p> The results of the spatial analyses indicate no statistically significant patterns in the spatial distribution of settlements. Site attribute analysis shows no statistical difference in the average number of houses per village or the average size of houses before or after 500 ya. The results of this work build our understanding of regional settlement patterns during the late Holocene. By testing settlement pattern change, i.e. settlement distribution, settlement size, and house size, future research into settlement pattern change can begin to evaluate likely causes for the observed changes. My method, specifically the use of GIS as a method for testing settlement pattern change, can be applied to other regions and temporal scales.</p><p>

<p> This thesis provides an extensive look at where permitted non-farm uses and dwellings have clustered within Exclusive Farm Use (EFU) zones in the Northern Willamette Valley in Oregon. There is a looming concern that non-farm related uses and dwellings, or non-farm development, are conflicting with agricultural preservation strategies. Specifically, non-farm developments can potentially undermine the critical mass of farmland needed to keep the agricultural economy sustainable, but until now, studies have lacked spatially precise data to systematically track these phenomena. This thesis offers methodological contributions towards analyzing these operations and presents a broad account of what has been occurring in the region. Using permit approval data from the Department of Land Conservation and Development (DLCD) and 2015 county tax lot shapefiles, I geocoded the locations of these uses and dwellings. I used location quotient and spatial autocorrelation coefficients to identify non-farm hotspots in the region and summarized different typologies that have developed. The findings reveal that viticulture operations have amassed near Dundee and Newberg in Yamhill County, while commercial activities and home occupations have clustered near the Salem-Keizer UGB. Concurrently, dwellings have clustered near the Yamhill-Polk County border. Finally, I offer suggestions to improve Oregon&rsquo;s agricultural land use policy and data management process, as well as advocate for more intensive research in the future to generate narratives for our results.</p><p>

Geographic Information System (GIS) technology aids in storage, manipulation, processing, analysis and presentation of spatial data sets. GIS can effectively interrogate large multidisciplinary exploration data sets in the search for new mineral exploitation targets. A spatial database, the AGGeneys Exploration Database (AGGED), has been created, comprising exploration data gathered during two decades of exploration for base-metals in the Aggeneys area, Bushmanland, South Africa. AGGED includes data extracted from analog maps, as well as digital remotely sensed sources, stored in vector and raster data structures, respectively. Vector data includes field based observations such as the extent of outcropping geological units, litho- and chrono-stratigraphic data; structural data; laboratory data based on regional geochemical stream sediment and traverse sampling; cadastral data and known mineral occurrences. Raster data includes Landsat satellite TM imagery and airborne magnetic data. Spatial variation within single data maps are examined. Spatial correlation between three different data maps are facilitated using colour analysis of hue, saturation and value components in a perceptual colour model. Simultaneously combining lead and zinc data with Landsat TM and geophysical magnetic data spatially delineates four new "geoscience" anomalies in the area under investigation. Two distinctive anomalies occur on the farms Aroams and Aggeneys. The Aroams anomaly (GSAl) has not been previously recognised, whereas the Aggeneys anomaly (GSA2) has been located before. The two other "geoscience" anomalies, on the farm Haramoep (GSA3 and GSA4 ), are slightly less distinct. Overlaying fold axial trace patterns and anomalies on the farm Haramoep, indicate that F2 and F3 fold structures are closely associated with these two anomalies. The location of the Aroams anomaly occurs along the same east-west trend of the four known major ore-deposits viz. Big Syncline, Broken Hill, Black Mountain and Gamsberg. Extrapolating F2 and F3 fold patterns using magnetic data locates this Aroams anomaly along the F3 axial trace extending from Big Syncline through to Gamsberg. The elevated Pb-Zn geochemical anomaly and structural data associated with the Aroams anomaly makes it a promising future exploitation target. The AGGED database can be expanded both in geographic extent to include surrounding areas, and to allow for inclusion of future surveys. Analytical processing of data in AGGED can also be continued and expanded. GIS is a burgeoning field and developments in GIS technology will impact on the explorationist. Developments in object-oriented and knowledge-based database technologies, visualisation techniques and artificial intelligence, incorporated in future GIS need to be closely monitored and evaluated by geoscience explorationists.

This thesis concerns the establishment of a Geographic Information System for the Cape Peninsula and the use of SPOT satellite imagery to map land cover classes. The former is seen as a necessary tool to promote judicious conservation management decisions for the fragile "Fynbos" ecosystem, and the latter as a convenient means of acquiring up-to-date information concerning the environment, and to monitor change.

Many regions around the world suffer from a lack of authoritatively-collected data on factors critical to understanding human well-being. This challenges our ability to understand the progress society is making towards reducing poverty, improving lifespans, or otherwise improving livelihoods. A growing body of research is exploring how deep learning algorithms can be used to produce novel estimates of sparse development data, and how access to such data can impact development efforts. This dissertation contributes to this literature in three parts. First, using Landsat 8 satellite imagery and data from the Armed Conflict Location & Event Data Project, convolutional neural networks are trained to predict locations where conflict is likely to result in fatalities for one year. Second, building on the findings in chapter 1, this dissertation explores the potential to extend predictions to a time series using both yearly and six month intervals. Finally, chapter 3 introduces GeoQuery, a dynamic web application which utilizes a High Performance Computing cluster and novel parallel geospatial data processing methods to overcome challenges associated with integrating, and distributing geospatial data within research communities.