<p> The increasing density of utilities within roadway right-of-way overburdens transportation agencies in two ways. In utility coordination, there is an increased difficulty in the manual identification of utility conflicts, while in utility permitting, there is an increased difficulty in the case-by-case review of utility permits for potential conflicts due to lack of readily available, easily accessible and inter-operable utility location data. This study proposes the development of a utility conflict detection framework within a geo-spatial environment, using non-geospatial utility location data obtained from Indiana Department of Transportation&rsquo;s (INDOT) Electronic Permitting System (EPS), as a step towards addressing these challenges. The automatic detection of utility conflicts is demonstrated using spatial conflict detection models, wherein a conflict report and a visualization of conflict area/volume are generated. A data-entry GUI is developed to streamline data-entry of utility/highway project design parameters into a geodatabase, which is populated with existing highway and utility location information. The framework is designed to be implemented by state transportation agencies for: (1) early detection of utility conflicts in highway projects (before 60% design) and in utility permitting (before a permit is approved/denied), and (2) to automate the utility conflict detection process. Early detection provides opportunities for transportation agencies to make proactive design decisions, avoiding expensive utility relocation and preventing utility breaks/accidents. Automatic detection eases the burden of utility coordinators and utility permitting engineers, from manual identification of utility conflicts. Illustrative examples of a utility and highway project are used to demonstrate the performance of the framework in automatic detection of potential utility conflicts.</p><p>

<p>Quantifying groundwater recharge is of crucial importance for sustainable groundwater management. While many recharge quantification techniques have been devised, few provide spatially and temporally distributed estimates for regional-scale water resource assessments. In this study, a GIS-based and USGS-developed recharge quantification tool ? the Soil Water Balance (SWB) model ? was applied to produce fine-tuned recharge constraints and document spatial and temporal dynamics of recharge. SWB has, as of yet, been tested solely in coastal and continental temperate-humid climate zones. This study expands testing of SWB to a Mediterranean climate zone, focusing on Catalina Island, California. Catalina has experienced significant water supply issues due to a prolonged drought. Using available climate, land use/land cover and hydrology data, the SWB model yields annual recharge values for the time period 2008-2014 of 0.05 mm/year to over 82 mm/year. Results of this thesis provide information on spatial and temporal patterns of groundwater recharge on Catalina Island.

<p> Hydrologic connectivity in the Atchafalaya River Basin has been dominated by anthropogenic structures for the past 50+ years. To better understand how these structures have affected the Basin, two techniques were used to assess the hydrologic connectivity within tracts of land near Bayou Sorrel. The first technique used historical and current aerial imagery to create a canopy change map in ArcMap 10.3. Three classifications&mdash;full canopy, partial canopy, and open water&mdash;were used for the analysis. The canopy change map showed that from 1957 to 1998, &ldquo;full canopy&rdquo; decreased by 18%, &ldquo;open water&rdquo; increased by 4%, and &ldquo;partial canopy&rdquo; increased by 26%. The second technique used tree-ring width and ring-to-ring carbon isotope measurements on seven bald cypress trees (<i>Taxodium distichum </i>) to test whether or not evolution of a regional distributary channel has a measurable effect on tree growth. Neither the carbon isotope data nor the tree-ring widths correlated with channel evolution, but tree-ring width did correlate with monthly temperature during the growing season, suggesting an overarching climate signal on tree-ring growth at this site. Frequent flooding at the site may be responsible for the lack of any correlation between tree-ring carbon isotope measurements and channel evolution or climate; however, additional work to test the effect of flooding on tree stress is needed. I conclude that canopy change maps can be a useful tool for assessing changing hydrology within the Atchafalaya River Basin.</p><p>

<p> Despite a rising interest in intermittent river systems, landscape influences on long-term wetting and drying patterns of streamflow are not well understood. There has been a significant increase in the presence of intermittent rivers worldwide due to climate change and subsequent increases in groundwater abstraction, and these effects are intensified in already arid regions such as the American Southwest. Consequently, the spatial extent of wet and dry reaches of Arizona&rsquo;s Agua Fria River, Cienega Creek, and San Pedro River has been documented by citizen scientists for several years. Citizen science involves the use of trained members of the public for data collection, and the analysis of datasets produced from citizen science projects have become a huge asset to the scientific community. Here, we synthesize the most current data (1999&ndash;2016) to determine what stream and valley characteristics act as drivers for patterns of surface water flow. Geologic, geomorphic, and land cover characteristics of these rivers were analyzed via aerial imagery and Digital Elevation Models within ArcGIS 10.3 in conjunction with the Soil and Water Assessment Tool model. Principal Component Analysis was used in order to assess trends across sites. A set of landscape intermittency metrics was produced and then further analyzed using Multiple Linear Regression. We found that land cover had a significant (p-value &lt; 0.01) positive correlation with reach average (i.e., the proportion of channel wet). Physical watershed and channel characteristics each had a negative correlation with both intermittency metrics (i.e., wet/dry status and reach average). However, their results were not significant to the 0.05 level. This study begins to shed light on the drivers of landscape intermittency patterns of desert streams and demonstrates the utility of citizen science in regard to the study of intermittent river systems.</p><p>

<p> In recent decades, there has been an increased awareness of the concentration of the poor in rural and underdeveloped areas and increased attention to scaled economic and multi-dimensional assessments as tools for targeting rural poverty. While this has led to new forms of development intervention in previously neglected regions across the Global South, in Morocco this system of poverty reduction continues to exclude key sites and stakeholders. This thesis asks how local state offices and non-state actors participate in or disrupt the structural systems of development in Morocco and what potential these local communities have for contributing to standardized knowledge production of poverty and development. I use participatory mapping workshops, interviews, and &ldquo;studying up&rdquo; strategies to answer questions of access &ndash; physical and social &ndash; to development planning and interventions. My findings indicate that the Moroccan rural development complex is structurally exclusionary to remote rural communities. The state and its partners have portrayed rural spaces as quickly rising out of poverty thanks to their decentralized and participatory development schemes, yet incongruently, local recipients in the least accessible areas live in spaces devoid of interventions. With all development practices inherently tied to state standards, any oversight or exclusion by state targeting is magnified by the same oversight of its development partners. The scale of targeting and evaluation in international metrics has contributed to this neglect, and the unfortunate result has been a feedback loop of inaccessibility for remote rural pockets of the country. I explain why one spatial indicator, village accessibility to social services, is an appropriate addition to poverty assessments and development targeting, drawing from my conversations with villagers in rural Tinghir Province and the results of my geospatial analysis.</p><p>

The study of uncertainty in Geographic Information Systems (GIS) and environmental models has received increasing attention in recent years, due in part to the widespread use of GIS for resource management. This study used GIS-based techniques in order to compare several different forest inventory and forest cover datasets. These datasets pertain to an area of the boreal mixedwood forest in northeastern Alberta which covers roughly 73,000 km2, and which has recently been approved for logging. The datasets include two forest inventories based on aerial photographs, and a forest cover classification based on remotely sensed satellite data. Simple logical operations were used to transform the datasets to a form suitable for comparison. Standard GIS overlay techniques were used to compare the agreement among different datasets. Visualization techniques were used to display patterns of agreement in attribute space (contingency tables), and in geographic space (maps of uncertainty). Agreement between the two forest inventories was about 50% (Percent Correctly Classified), with a Kappa value of 0.4, for a classification based on species composition. In general, much of the misclassification was between ecologically similar types, particularly between different combinations of aspen and white spruce. Comparison of the forest inventories with the classified satellite image was done using a simplified land cover classification with five categories. Agreement was about 55% (Percent Correctly Classified), with a Kappa value of 0.3. Possible sources of discrepancy among datasets include change over time, differences in spatial scale, differences in category definitions, positional inaccuracy, boundary effects and misclassification. Analyses were conducted to characterize the effect of each of these sources of disagreement. The agreement was strongly affected by the distance to boundary, indicating a boundary effect extending to more than 100 meters. Differences in spatial scale accounted for a small proportion of discrepancy. None of the other possible sources had a measurable effect on the discrepancy. It was therefore inferred that misclassification accounted for a large proportion of the discrepancy. Estimated levels of uncertainty were propagated through models including simple growth and yield tables and a more complex harvest scheduling model. It was found that uncertainty in model outputs was strongly affected by uncertainty in inventory data, uncertainty in volume yield curves, and perhaps most importantly, by a poor understanding of disturbance and forest dynamics in the region. The results of the analysis show that these uncertainties may have significant economic and ecological implications. / Arts, Faculty of / Geography, Department of / Graduate

<p> Light Detection and Ranging (LiDAR) technology combined with high-resolution differential Global Positioning Systems (dGPS) provide the ability to measure coastal elevation with high precision. This study investigates the use of LiDAR data and GIS to conduct time-series analyses of coastal sediment volume shifts during the 2006-2007 El Ni&ntilde;o winter, Summer of 2007 and following 2007-2008 La Ni&ntilde;a winter in the Oceanside Littoral Cell (OLC). The OLC, located in Southern California, spans from Dana Point to La Jolla and includes over 84 km of coastline. The ability to quantify sediment volume changes contributes to the scientific understanding of the role El Ni&ntilde;o storms play in the OLC sand budget. This study provides a method to analyze LiDAR data to evaluate coastal geomorphologic changes over time. Additionally, identifying specific areas of coastal beach erosion associated with historical El Ni&ntilde;o events can aid beach managers, planners, and scientists in protecting the valuable coastline. LiDAR datasets were prepared and formatted which included ground classifying millions of elevation points. Formatted datasets were inputted into an Empirical Bayesian Kriging (EBK) model, creating high-resolution, 1-meter grid cell, Digital Elevation Models (DEMs). The EBK model also incorporated uncertainty into the workflow by producing prediction error surfaces. LiDAR-derived DEMs were used to calculate sediment volume changes through a technique called DEM differencing. Results were visualized through a series of maps and tables. Overall results show that there was a higher rate of beach sediment erosion during the 2006-2007 El Ni&ntilde;o winter than the 2007-2008 La Ni&ntilde;a winter. Sediment accretion was evident during the intermediary Summer of 2007. Future applications of this study include incorporating bathymetric datasets to understand near-shore sediment transport, evaluating sediment contribution through cliff erosion, and conducting decadal scale studies to evaluate long-term trends with sea level rise scenarios. </p>

Digital Elevation Models have been in use for more than three decades and have become a major component of geographic information processing. The intensive use of DEMs has given rise to many accuracy investigations. The accuracy estimate is usually given in a form of a global measure such as root-mean-square error (RMSE), mostly from a producer's point of view. Seldom are the errors described in terms of their spatial distribution or how the resolution of the DEM interacts with the variability of terrain. There is a wide range of topographic variation present in different terrain surfaces. Thus, in defining the accuracy of a DEM, one needs ultimately to know the global and local characteristics of the terrain and how the resolution interacts with them. In this thesis, DEMs of various resolutions (i.e., 10 arc-minutes, 5 arc-minutes, 2 km, 1 km, and 50 m) in the study area (Prince George, British Columbia) were compared to each other and their mismatches were examined. Based on the preliminary test results, some observations were made regarding the relations among the spatial distribution of DEM errors, DEM resolution and the roughness of terrain. A hypothesis was proposed that knowledge of the landscape characteristics might provide some insights into the nature of the inherent error (or uncertainty) in a DEM. To test this statistically, the global characteristics of the study area surfaces were first examined by measures such as grain and those derived from spectral analysis, nested analysis of variance and fractal analysis of DEMs. Some important scale breaks were identified for each surface and this information on the surface global characteristics was then used to guide the selection of the moving window sizes for the extraction of the local roughness measures. The spatial variation and complexity of various study area surfaces was characterized by means of seven local geomorphometric parameters. The local measures were extracted from DEMs with different resolutions and using different moving window sizes. Then the multivariate cluster analysis was used for automated terrain classification in which relatively homogeneous terrain types at different scale levels were identified. Several different variable groups were used in the cluster analysis and the different classification results were compared to each other and interpreted in relation to each roughness measure. Finally, the correlations between the DEM errors and each of the local roughness measures were examined and the variation of DEM errors within various terrain clusters resulting from multivariate classifications were statistically evaluated. The effectiveness of using different moving window sizes for the extraction of the local measures and the appropriateness of different variable groups for terrain classification were also evaluated. The major conclusion of this study is that knowledge of topographic characteristics does provide some insights into the nature of the inherent error (or uncertainty) in a DEM and can be useful for DEM error modelling. The measures of topographic complexity are related to the observed patterns of discrepancy between DEMs of differing resolution, but there are variations from case to case. Several patterns can be identified in terms of relation between DEM errors and the roughness of terrain. First of all, the DEM errors (or elevation differences) do show certain consistent correlations with each of the various local roughness variables. With most variables, the general pattern is that the higher the roughness measure, the more points with higher absolute elevation differences (i.e., horn-shaped scatter of points indicating heteroscedasticity). Further statistical test results indicate that various DEM errors in the study area do show significant variation between different clusters resulting from terrain classifications based on different variable groups and window sizes. Cluster analysis was considered successful in grouping the areas according to their overall roughness and useful in DEM error modelling. In general, the rougher the cluster, the larger the DEM error (measured with either the standard deviation of the elevation differences or the mean of the absolute elevation differences in each cluster). However, there is still some of the total variation of various DEM errors that could not be accounted for by the cluster structure derived from multivariate classification. This could be attributed to the random errors inherent in any of the DEMs and the errors introduced in the interpolation process. Another conclusion is that the multivariate approach to the classification of topographic surfaces for DEM error modelling is not necessarily more successful than using only a single roughness measure in characterizing the overall roughness of terrain. When comparing the DEM error modelling results for surfaces with different global characteristics, the size of the moving window used in geomorphometric parameter abstraction also has certain impact on the modelling results. It shows that some understanding of the global characteristics of the surface is useful in the selection of appropriate/optimal window sizes for the extraction of local measures for DEM error modelling. Finally, directions for further research are suggested. / Arts, Faculty of / Geography, Department of / Graduate

Ph.D. (Geography) / Please refer to full text to view abstract

The objective of this paper is to develop a well-defined and sound framework for the implementation of a multi-participant GIS program and to illustrate the developed framework by its application to the Departments of the Town of Blacksburg. A multi-participant approach to implementing GIS technology faces greater challenges than a single-participant GIS project, due to the unique culture, structure, policy, decision-making rule and expectations of participants from implementation of GIS technology. So a successful program depends upon aligning different characteristics of the program's structure to those of participants, individually and collectively. Maximizing the system's potentials necessitates a well-defined implementation framework that can help manage changes and integrate the technology in organizations. Therefore, the paper focuses on the implementation aspects of multi-participant GIS programs. The paper achieves its objective by exploring various issues involved in the implementation process of GIS technology and multi-participant GIS programs, develops a framework for implementing a multi-participant GIS program and applies the framework to the Departments of the Town of Blacksburg to achieve successful implementation. The illustration of the Town of Blacksburg identifies various constraints for multi-participant GIS program for which recommendations are provided to achieve successful implementation. / Master of Urban and Regional Planning