Thesis (M.A.)--University of Toledo, 2004. / Typescript. "Submitted as partial fulfillments of the requirements for the Master of Arts degree [in Geography]." Includes bibliographical references (leaves 80-90).

Thesis (M. A.)--University of North Carolina at Greensboro, 2006. / Title from PDF title page screen. Advisor: Roy S. Stine; submitted to the Dept. of Geography. Includes bibliographical references (p. 60-65)

The objective of this study was to deploy a conceptual framework developed by M. Forbes using a geographic information system (GIS) approach to assess the functionality of wetlands in the Galveston Bay Area of Texas. This study utilized geospatial datasets which included National Wetland Inventory maps (NWI), LiDAR data, National Agriculture Imagery Program (NAIP) imagery and USGS National Land Cover data to assess the capacity of wetlands to store surface water and remove pollutants, including nitrogen, phosphorus, heavy metals, and organic compounds. The use of LiDAR to characterize the hydrogeomorphic characteristics of wetlands is a key contribution of this study to the science of wetland functional assessment. LiDAR data was used to estimate volumes for the 7,370 wetlands and delineate catchments for over 4,000 wetlands, located outside the 100-yr floodplain, within a 2,075 square mile area around Galveston Bay. Results from this study suggest that coastal prairie freshwater wetlands typically have a moderate capacity to store surface water from precipitation events, remove ammonium, and retain phosphorus and heavy metals and tend to have a high capacity for removing nitrate and retainremove organic compounds. The results serve as a valuable survey instrument for increasing the understanding of coastal prairie freshwater wetlands and support a cumulative estimate of the water quality and water storage functions on a regional scale.

This study is a test of a methodology to predict changes in elevation and shoreline position of coastal wetlands in Trinity Bay, Texas, in response to projected sea level rise. The study combines numerical modeling and a geographic information system. A smoothing technique is used on a United States Geographical Survey (USGS) digital elevation model to obtain elevation profiles that more accurately represent the gently sloping wetlands surface. The numerical model estimates the expected elevation change by raster cell based on input parameters of predicted sea level rise, mineral and organic sedimentation rates, and sediment autocompaction rates. A GIS is used to display predicted elevation changes and changes in shoreline position as a result of four projected sea level rise scenarios over the next 100 years. Results demonstrate that this numerical model and methodology are promising as a technique of modeling predicted elevation change and shoreline migration in wetlands. The approach has potential utility in coastal management applications.