A Knowledge-based approach of satellite image classification for urban wetland detection

Title from PDF of title page, viewed on July 30, 2014Thesis advisor: Wei JiVitaIncludes bibliographical references (pages 85-93)Thesis (M. S.)--Dept. of Geosciences. University of Missouri--Kansas City, 2014It has been a technical challenge to accurately detect urban wetlands with remotely sensed data by means of pixel-based image classification. This is mainly caused by inadequate spatial resolutions of satellite imagery, spectral similarities between urban wetlands and adjacent land covers, and the spatial complexity of wetlands in human-transformed, heterogeneous urban landscapes. Knowledge-based classification, with great potential to overcome or reduce these technical impediments, has been applied to various image classifications focusing on urban land use/land cover and forest wetlands, but rarely to mapping the wetlands in urban landscapes. This study aims to improve the mapping accuracy of urban wetlands by integrating the pixel-based classification with the knowledge-based approach. The study area is the metropolitan area of Kansas City, USA. SPOT satellite images of 1992, 2008, and 2010 were classified into four classes -- wetland, farmland, built-up land, and forestland -- using the pixel-based supervised maximum likelihood classification method. The products of supervised classification are used as the comparative base maps. For our new classification approach, a knowledge base is developed to improve urban wetland detection, which includes a set of decision rules of identifying wetland cover in relation to its elevation, spatial adjacencies, habitat conditions, hydro-geomorphological characteristics, and relevant geostatistics. Using ERDAS Imagine software's knowledge classifier tool, the decision rules are applied to the base maps in order to identify wetlands that are not able to be detected based on the pixel-based classification. The results suggest that the knowledge-based image classification approach can enhance the urban wetland detection capabilities and classification accuracies with remotely sensed satellite imageryAbstract -- List of illustrations -- List of tables -- Acknowledgements -- Introduction -- Literature review -- Methodology -- Findings and analysis -- Discussion and conclusion -- Reference lis

ASSOCIATIONS BETWEEN INORGANIC ARSENIC EXPOSURE AND THE DEVELOPMENT OF TYPE 2 DIABETES: DIETARY AND GENETIC SUSCEPTIBILITY

Compelling evidence has linked high exposure to inorganic arsenic (iAs) with increased risk of Type 2 diabetes (T2D). There is growing concern that low-to-moderate level of iAs exposure may contribute substantially to the epidemic of T2D. Nevertheless, the results of the current perspective studies are inconsistent, which could be attributable to varied susceptibility due for example to differences in intake of beneficial nutrients and existence of genetic variants of enzymes involved in iAs metabolism. We capitalized on China Health and Nutrition Survey with measured baseline (i.e. 2009) iAs exposure using toenail; Mg and Zn intake at baseline; fasting glucose and insulin at follow-up (i.e. 2015). Using multivariable adjusted regression models, we investigated the associations between baseline toenail arsenic and T2D incidence and indicators of glucose homeostasis at follow-up. We also examined potential effect modification by Mg and Zn intake at baseline on iAs-associated diabetes. In addition, we determined the gene-environment interaction using data from Mexico. We found a positive association between baseline iAs exposure and fasting glucose as well as odds of incident T2D. In addition, our findings suggest that instead of insulin resistance, pancreatic β-cell dysfunction is primarily involved in iAs-associated T2D. Moreover, though the association between baseline iAs exposure and pancreatic β-cell dysfunction at follow-up was stronger among participants with adequate Zn intake, the joint association between iAs exposure and dietary intake of Mg and Zn supports the beneficial effects of adequate Mg and Zn intake. In addition, our findings confirm that several genetic variants of arsenic methyl transferase (AS3MT) are in part responsible for the inter-individal differences in iAs metabolism, and roles of the variants may differ among populations with different levels of iAs exposure. Our study adds to the research on iAs and T2D by determining how, in the population with low-to-moderate iAs exposure, baseline iAs exposure relates to the development of T2D over 6 years. The proposed study also informs efforts to maximize the effectiveness of Mg and Zn intake to combat the diabetogenic effects of iAs and identifies genetically susceptible subgroups due to impaired iAs metabolism.Doctor of Philosoph