Change detection is amongst the most effective critical examination methods used in remote sensing technology. In this research, new methods are proposed for building and vegetation change detection using only LiDAR data without using any other remotely sensed data. Two LiDAR datasets from 2009 and 2013 will be used in this research. These datasets are provided by the City of Surrey. A Parcel map which shows parcels in the study area will be also used in this research because the objective of this research is detecting changes based on parcels. Different methods are applied to detect changes in buildings and vegetation respectively. Three attributes of object –slope, building volume, and building height are derived and used in this study. Changes in buildings are not only detected but also categorized based on their attributes. In addition, vegetation change detection is performed based on parcels. The output shows parcels with a change of vegetation. Accuracy assessment is done by using measures of completeness, correctness, and quality of extracted regions. Accuracy assessments suggest that building change detection is performed with better results.

Over 9 million new cases of tuberculosis (TB) were reported worldwide in 2013. While the TB rate is much lower in the US, its uneven distribution and associated explanatory variables require interrogation in order to determine effective strategies for intervention and control. However, paucity of case data at fine geographic scales precludes such research. This research, using zip code level data from 837 confirmed TB cases in Tarrant County obtained from Texas Department of State Health Services, explores and attempts to explain the spatial patterns of TB and related risk markers within a framework of place vulnerability. Readily available census data is then used to characterize the spatial variations in TB risk. The resulting model will enable estimations of the geographic differences in TB case variables using this readily available census data instead of time-consuming and expensive individual data collection.

Population estimation in inter-censual years has many important applications. In this research, high-resolution pan-sharpened IKONOS image, LiDAR data, and parcel data are used to estimate small-area population in the eastern part of the city of Denton, Texas. Residential buildings are extracted through object-based classification techniques supported by shape indices and spectral signatures. Three population indicators -building count, building volume and building area at block level are derived using spatial joining and zonal statistics in GIS. Linear regression and geographically weighted regression (GWR) models generated using the three variables and the census data are used to estimate population at the census block level. The maximum total estimation accuracy that can be attained by the models is 94.21%. Accuracy assessments suggest that the GWR models outperformed linear regression models due to their better handling of spatial heterogeneity. Models generated from building volume and area gave better results. The models have lower accuracy in both densely populated census blocks and sparsely populated census blocks, which could be partly attributed to the lower accuracy of the LiDAR data used.

Buruli ulcer (BU) is an environmental bacterium caused by Mycobacterium ulcerans. Modes of transmission and hosts of the disease remain unknown. The purposes of this study are to explore the environmental factors that are possibly explain the spatial distribution of BU, to predict BU cases by using the environmental factors, and to investigate the impact of land use and land cover change on the BU distribution. The study area covers the southwest portion of Ghana, 74 districts in 6 regions. The results show that the highest endemic areas occur in the center and expand to the southern portion of the study area. Statistically, the incidence rates of BU are positively correlated to the percentage of forest cover and inversely correlated to the percentages of grassland, soil, and urban areas in the study area. That is, forest is the most important environmental risk factor in this study. Model from zero-inflated Poisson regression is used in this paper to explain the impact of each land use and land cover type on the spatial distribution of BU. The results confirm that the changes of land use and land cover affect the spatial distribution of BU in the study area.