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Master of Science / Department of Computing and Information Sciences / Scott A. DeLoach / A geographic information system (GIS) allows an individual to map, model, query, and analyze large quantities of data from a database according to their spatial locations. This project uses the ArcGis Java software Development Kit (SDK) to visualize, manipulate, and comprehend large amounts of publicly available information relevant to a spatial location. The application developed uses a graphical user interface to examine the public data of Riley County, Kansas. The user is able to load shapefiles through the interface and then examine the many spatial locations. By examining a spatial location the user is able to view the associated attribute information, manipulate it, and add additional attributes. Beyond viewing information at selected geometric locations, a user can also query the layer(s) to return the spatial locations that fit the query. These abilities can allow a user to understand and visualize patterns that they would not have been able to easily see from looking at the raw data. Increasing users' understanding of the environment they are working with improves their likelihood of success in their desired objectives.

Vegetation in the San Cristobal Valley of Barry M. Goldwater Range-East was mapped using a combination of field surveys and aerial imagery interpretation to contribute to ongoing inventory of natural resources for the BMGR-East as well as assist in resource management decisions. Eighteen vegetation associations were identified and mapped through collection of 186 samples to characterize vegetation associations. The most common vegetation association was Larrea tridentata monotype, covering 29% of the area mapped. Larrea tridentata is a widely spread shrub throughout the Sonoran, Chihuahuan and Mojave deserts; therefore understanding germination and seedling survival patterns is crucial. Ambrosia dumosa and A. deltoidea exhibit nurse plant-protégé interactions with L. tridentata. Seed density of L. tridentata was studied under Ambrosia species to determine factors controlling germination and seedling density. As expected seed density was greater under Ambrosia canopy than areas with no canopy. Ambrosia species and canopy type did not affect seed density.

Master of Arts / Department of Geography / Richard Marston / Slope failures play a significant role as a mass movement hazard in the deglaciated mountain canyons in Grand Teton National Park. The park’s geologic and glacial histories are unique in comparison to other areas in the Rocky Mountain range. However, few detailed maps and statistical analyses of slope failures as hazards exist for park officials and visitors. The purpose of this study is to produce a comprehensive map of slope failures in five of the most accessible and commonly used canyons of the park: Cascade, Death, Garnet, Granite, and Paintbrush. This project combined fieldwork, LiDAR imagery, and GIS mapping to document five main categories of slope failures—rock slides, rock/debris flows, rock falls, and snow avalanches, as well as complex slope failures involving a combination of these categories. Summary statistics, maps, and histograms of average slope gradient, aspect, and curvature conditions as well as precipitation conditions at the “source” area of slope failures were generated for individual canyons as well as the entire study area. Snow avalanche source areas where debris flows were not readily present occurred most commonly on north and northeast facing slopes, slopes averaging a 40% gradient, and slightly convex slopes. Debris flow source areas occurred most commonly on south and southeast facing slopes, slopes with an average 42% gradient, and on slightly convex slopes. Rock fall source areas were most common on north facing slopes, slopes of an average 55% gradient, and a mostly flat curvature. Rock slide source points were most common on north facing slopes, slopes of an average 54% gradient, and flat to slightly concave slopes. Rock Mass Strength (RMS) values were sampled at a rate of every 0.5 kilometers on the hiking trail of each canyon to provide an introductory insight into rock stability conditions in each canyon. Slope failures not only impact the physical landscape of canyons in Grand Teton National Park but can affect human structures as well. Physical attributes and locations of slope failures were compared to locations of camping zones and hiking trails in the Park to determine areas of common human usage that were most susceptible to past movement events.

Este trabalho apresenta o desenvolvimento de um instrumento de apoio à gestão territorial, denominado Banco de Dados Geográficos – BDG, constituído de uma base de dados georreferenciadas, de um sistema de gerenciamento de banco de dados, de um sistema de informação geográfica – SIG e de um simulador de redes neurais artificiais – SRNA. O roteiro metodológico adotado permitiu a transposição do Detalhamento da Metodologia para Execução do Zoneamento Ecológico-Econômico pelos Estados da Amazônia Legal para um modelo conceitual materializado no BDG, que serviu de suporte para a criação de uma base de dados geográficos, na qual utilizou-se os conceitos de geo-campos e geo-objetos para modelagem das entidades geográficas definidas. Através deste ambiente computacional foram realizados procedimentos de correção e refinamento dos dados do meio físico e sócio-econômicos, de interpretação de imagens de satélite e análises e combinações dos dados, que permitiram definir unidades básicas de informação do território, a partir das quais foram geradas as sínteses referentes à potencialidade social e econômica, à sustentabilidade do ambiente, aos subsídios para ordenação do território, incluindo orientações à gestão do território na área de estudo localizada no sudoeste do estado de Rondônia. Sobre os dados do meio físico, foram utilizadas duas técnicas de análise geográfica: álgebra de mapas e rede neural artificial, que produziram cenários relativos à vulnerabilidade natural à erosão. A análise das matrizes de erros obtidas da tabulação cruzada entre os cenários, revelou uma boa exatidão global (acima de 90%) entre os cenários obtidos através da modelagem via álgebra de mapas e via rede neural artificial e, uma exatidão global regular (em torno de 60%), quando foram comparados os cenários obtidos via álgebra de mapas e via rede neural artificial com o cenário obtido através de procedimentos manuais. / This work presents the development of a tool to support the land management called Geographical Data Base (GDB) formed by a georrefered data base, a data base management system (DBMS), a geographic information system (GIS) and an artificial neural net simulator (ANNS). The methodological approach allowed the conceptual modelling of the methodology of the ZEE (Ecological-Economic Zoning) institutional program within GDB, using both field and object concepts, in which the geographic entities were modelled. Using this computacional framework both natural and socio-economic data were corrected and improved, and also procedures of satellite image interpretation using image processing techniques, of analysis and data manipulation using GIS tools, were accomplished. These procedures allowed to define basic units of mapping and to get the following synthesis for the study area located in State of Rondonia: social potenciality, environmental vulnerability, environmental sustentability, land management maps, and guidelines about land management. With the abiotic and biotic data, two different geographical inference methods were used to produce the environmental vulnerability map: a) the common Map Algebra approach and b) an Artificial Neural Network approach – as a technique to deal with the non-linearities involved in inferencial processes. Error matrices were computed from cross tabulation among different scenaries obtained from those inference methods. A good global accuracy (over 90%) was obtained when ANN and Map Algebra scenaries were compared. Medium global accuracies (around 60%) were obtained when ANN and Map Algebra were compared with scenaries obtained by manual procedures.

abstract: The Western Continental United States has a rapidly changing and complex ecosystem that provides valuable resources to a large portion of the nation. Changes in social and environmental factors have been observed to be significantly correlated to usable ground and surface water levels. The assessment of water level changes and their influences on a semi-national level is needed to support planning and decision making for water resource management at local levels. Although many studies have been done in Ground and Surface Water (GSW) trend analysis, very few have attempted determine correlations with other factors. The number of studies done on correlation factors at a semi-national scale and near decadal temporal scale is even fewer. In this study, freshwater resources in GSW changes from 2004 to 2017 were quantified and used to determine if and how environmental and social variables are related to GSW changes using publicly available remotely sensed and census data. Results indicate that mean annual changes of GSW of the study period are significantly correlated with LULC changes related to deforestation, urbanization, environmental trends, as well as social variables. Further analysis indicates a strong correlation in the rate of change of GSW to LULC changes related to deforestation, environmental trends, as well as social variables. GSW slope trend analysis also reveals a negative trend in California, New Mexico, Arizona, and Nevada. Whereas a positive GSW trend is evident in the northeast part of the study area. GSW trends were found to be somewhat consistent in the states of Utah, Idaho, and Colorado, implying that there was no GSW changes over time in these states. / Dissertation/Thesis / Masters Thesis Geography 2018

abstract: Big data that contain geo-referenced attributes have significantly reformed the way that I process and analyze geospatial data. Compared with the expected benefits received in the data-rich environment, more data have not always contributed to more accurate analysis. “Big but valueless” has becoming a critical concern to the community of GIScience and data-driven geography. As a highly-utilized function of GeoAI technique, deep learning models designed for processing geospatial data integrate powerful computing hardware and deep neural networks into various dimensions of geography to effectively discover the representation of data. However, limitations of these deep learning models have also been reported when People may have to spend much time on preparing training data for implementing a deep learning model. The objective of this dissertation research is to promote state-of-the-art deep learning models in discovering the representation, value and hidden knowledge of GIS and remote sensing data, through three research approaches. The first methodological framework aims to unify varied shadow into limited number of patterns, with the convolutional neural network (CNNs)-powered shape classification, multifarious shadow shapes with a limited number of representative shadow patterns for efficient shadow-based building height estimation. The second research focus integrates semantic analysis into a framework of various state-of-the-art CNNs to support human-level understanding of map content. The final research approach of this dissertation focuses on normalizing geospatial domain knowledge to promote the transferability of a CNN’s model to land-use/land-cover classification. This research reports a method designed to discover detailed land-use/land-cover types that might be challenging for a state-of-the-art CNN’s model that previously performed well on land-cover classification only. / Dissertation/Thesis / Doctoral Dissertation Geography 2019

This research explores the ways in which communities can connect their experiential knowledge of space and place with observed spatial patterns of disease to increase our abilities to both understand underlying processes and implement effective interventions. We develop and test new methods for integrating observed patterns of disease with community knowledge, validate these methods through generation of new knowledge and hypotheses about processes that have produced cancer patterns, begin to translate this new knowledge into potential interventions, generate much needed recommendations for best practices in research that integrates Geographic Information Science (GISc) and community engagement, and generate new hypotheses for future research. Methods include the creation of continuous surface representation maps of cancer burdens and selected behaviors related to health risks, using adaptive spatial filtering, and a community-based project in which community members generate hypotheses regarding high rates of cancer in their community and explore and annotate cancer burden map layers in a GIS environment. We partner with community and public health practice partners in order to increase the likelihood of translation of research results into evidence-based intervention. Methods of spatial data analysis, community mapping and concept mapping are used.

<p>Tropical Cyclones (TCs) intensity and frequency are expected to be impacted by climate change. Despite their destructive potential, these phenomena, which can produce heavy precipitation, are also an important source of freshwater. Therefore any change in frequency, seasonal timing and intensity of TCs is expected to strongly impact the regional water cycle and consequently the freshwater availability and distribution. This is critical, due to the fact that freshwater resources in the US are under stress due to the population growth and economic development that increasingly create more demands from agricultural, municipal and industrial uses, resulting in frequent over-allocation of water resources. </p><p>In this study we concentrate on monitoring the impact of hurricanes and tropical storms on vegetation activity along their terrestrial tracks and investigate the underlying physical processes. To characterize and monitor the spatial organization and time of recovery of vegetation disturbance in the aftermath of major hurricanes over the entire southeastern US, a remote sensed framework based on MODIS enhanced vegetation index (EVI) was developed. At the SE scale, this framework was complemented by a water balance approach to estimate the variability in hurricane groundwater recharge capacity spatially and between events. Then we investigate the contribution of TCs (season totals and event by event) to the SE US annual precipitation totals from 2002 to 2011. A water budget approach applied at the drainage basins scale is used to investigate the partitioning of TCs' precipitation into surface runoff and groundwater system in the direct aftermath of major TCs. This framework allows exploring the contribution of TCs to annual precipitation totals and the consequent recharge of groundwater reservoirs across different physiographic regions (mountains, coastal and alluvial plains) versus the fraction that is quickly evacuated through the river network and surface runoff. </p><p>Then a Land surface Eco-Hydrological Model (LEHM), combining water and energy budgets with photosynthesis activity, is used to estimate Gross Primary Production (GPP) over the SE US The obtained data is compared to AmeriFlux and MODIS GPP data over the SE United States in order to establish the model's ability to capture vegetation dynamics for the different biomes of the SE US. Then, a suite of numerical experiments is conducted to evaluate the impact of Tropical Cyclones (TCs) precipitation over the SE US. The numerical experiments consist of with and without TC precipitation simulations by replacing the signature of TC forcing by NARR-derived climatology of atmospheric forcing ahead of landfall during the TC terrestrial path. The comparison of these GPP estimates with those obtained with the normal forcing result in areas of discrepancies where the GPP was significantly modulated by TC activity. These areas show up to 10% variability over the last decade.</p> / Dissertation

Recent research projects in North America that have advanced the integration of formal mathematical georeferencing and informal placename georeferencing in knowledge organization systems are described and related to visualization applications.