When an earthquake happens, one of the most important tasks of disaster managers is to conduct damage assessment; this is mostly done from remotely sensed data. This study presents a new method for building detection and damage assessment using high-resolution satellite images and LiDAR data from Port-au-Prince, Haiti. A graph-cut method is used for building detection due to its advantages compared to traditional methods such as the Hough transform. Results of two methods are compared to understand how much our proposed technique is effective. Afterwards, sensitivity analysis is performed to show the effect of image resolution on the efficiency of our method. Results are in four groups. First: based on two criteria for sensitivity analysis, completeness and correctness, the more efficient method is graph-cut, and the final building mask layer is used for damage assessment. Next, building damage assessment is done using change detection technique from two images from period of before and after the earthquake. Third, to integrate LiDAR data and damage assessment, we showed there is a strong relationship between terrain roughness variables that are calculated using digital surface models. Finally, open street map and normalized digital surface model are used to detect possible road blockages. Results of …

This thesis presents a Web-based geographic information system (GIS) application for campus emergency management that allows users to visualize, integrate, and analyze student population, facilities, and hazard data for efficient emergency management of University of North Texas before, during, and after a terrorism event. End-users can locate and search the source area of an event on a digital map from the ArcIMS-based Website. The website displays corresponding population information and attributes of impacted facilities in real time. School officials and first responders including police, firefighters and medical personnel can promptly plan the appropriate rescue and response procedures according to the displayed results. Finally, the thesis outlines the limitations of Web-based GIS in the arena of campus emergency management.

GIS has been utilized in cultural resources management for decades, yet its application has been largely isolated to predicting the occurrence of archaeological sites. Federal and State agencies are required to protect archaeological sites that are discovered on their lands, but their resources and personnel are very limited. A new methodology is evaluated that uses modern light detection and ranging (LiDAR) and historic aerial photogrammetry to create digital terrain models (DTMs) capable of identifying sites that are most at risk of damage from changes in terrain. Results revealed that photogrammetric modeling of historic aerial imagery, with limitations, can be a useful decision making tool for cultural resources managers to prioritize conservation and monitoring efforts. An attempt to identify key environmental factors that would be indicative of future topographic changes did not reveal conclusive results. However, the methodology proposed has the potential to add an affordable temporal dimension to future digital terrain modeling and land management. Furthermore, the methods have global applicability because they can be utilized in any region with an arid environment.

This study analyzes the proposed passenger rail line expansion along US Interstate Highway 35 in Denton County, Texas. A multi-dimensional approach was used to investigate potential environmental justice (EJ) consequences from the expansion of the transportation corridor. This study used empirical and historical evidence to identify and prioritize sites for potential EJ concerns. Citizen participation in the decision making process was also evaluated. The findings of this research suggest that the southeast Denton community has the highest potential for environmental justice concerns. This study concludes by offering suggestions for an effective public participation process. These include the incorporation of a community's local history into an environmental justice assessment, and tailoring the public planning process to the demographics and culture of the residents.

The ground water quality of the Trinity Aquifer for wells sampled between 2000 and 2009 was examined using multivariate and spatial statistical techniques. A Kruskal-Wallis test revealed that all of the water quality parameters with the exception of nitrate vary with land use. A Spearman’s rho analysis illustrates that every water quality parameter with the exception of silica correlated with well depth. Factor analysis identified four factors contributable to hydrochemical processes, electrical conductivity, alkalinity, and the dissolution of parent rock material into the ground water. The cluster analysis generated seven clusters. A chi-squared analysis shows that Clusters 1, 2, 5, and 6 are reflective of the distribution of the entire dataset when looking specifically at land use categories. The nearest neighbor analysis revealed clustered, dispersed, and random patterns depending upon the entity being examined. The spatial autocorrelation technique used on the water quality parameters for the entire dataset identified that all of the parameters are random with the exception of pH which was found to be spatially clustered. The combination of the multivariate and spatial techniques together identified influences on the Trinity Aquifer including hydrochemical processes, agricultural activities, recharge, and land use. In addition, the techniques aided in identifying areas …

In this paper, I will show how applying a spatially explicit context to an existing environmental health surveillance framework is vital for more complete surveillance of disease, and for disease prevention and intervention strategies. As a case study to test the viability of a spatial approach to this existing framework, the risk of human exposure to Lyme disease will be estimated. This spatially explicit framework divides the surveillance process into three components: hazard surveillance, exposure surveillance, and outcome surveillance. The components will be used both collectively and individually, to assess exposure risk to infected ticks. By utilizing all surveillance components, I will identify different areas of risk which would not have been identified otherwise. Hazard surveillance uses maximum entropy modeling and geographically weighted regression analysis to create spatial models that predict the geographic distribution of ticks in Texas. Exposure surveillance uses GIS methods to estimate the risk of human exposures to infected ticks, resulting in a map that predicts the likelihood of human-tick interactions across Texas, using LandScan 2008TM population data. Lastly, outcome surveillance uses kernel density estimation-based methods to describe and analyze the spatial patterns of tick-borne diseases, which results in a continuous map that reflects disease rates based …