A Spatial-Temporal Analysis of Wetland Loss and Section 404 Permitting on the Delmarva Peninsula from 1980 to to 2010

Thumbnail Image


Publication or External Link





Geospatial approaches for wetland change analyses have emphasized the evaluation of landscape change on a local level, but have often neglected to examine and integrate regional trends and patterns of land use and land cover change as well as the impacts of wetland management policies. This study attempts to bridge the gaps by integrating a geospatial assessment of land cover change and a geostatistical analysis of the physical and anthropogenic drivers of wetland change. The aim is to demonstrate how urban development, conservation, and climate change policy decisions influenced wetland change trends and patterns on the Delmarva Peninsula from 1980 to 2010.

Historical data on the nine counties on the Delmarva Peninsula illustrated the dynamism of population growth, sprawl, and different wetland management strategies. Data sets from the National Oceanic and Atmospheric Administration, the Chesapeake Bay Program, the U.S. Army Corps of Engineers, the U.S. Fish and Wildlife Service, and the U.S. Census Bureau, and other sources were gathered and assessed.

A land cover database was developed and analyzed using geospatial techniques, such as cross tabulation matrices and hot spot density analyses, in order to quantify and locate land cover change between 1984 and 2010. The results highlighted that anthropogenic drivers such as urbanization and agriculture were associated with the loss of wetlands in coastal areas as well as in upland, forested, suburban areas that were at low risk to flooding, but required deforestation in order to expand residential and commercial development. The greatest quantity and percentage of loss occurred between 1992 and 2001, and it was likely the result of increases in tourism and suburban sprawl (e.g., the Housing Boom and roadway expansion). The majority of wetland loss tapered off in 2000, except on coastal areas suffering from sea level rise and shoreline erosion. The results also reinforced the need to address the negative impacts from certain activities related to agriculture and silviculture, which are exempt from Section 404 of the Clean Water Act, have on wetlands. Physical drivers and processes like inundation from sea level rise and soil erosion from surface runoff force communities to simultaneously adapt to multiple drivers of wetland loss and alteration. This study supports the hypothesis that an increase in development and wetland permitting indicates an increased a risk of wetland loss. In the end, the study demonstrates that geostatistical modelling techniques can be used to predict wetland loss, and that model performance and accuracy can be improved by reducing the multicollinearity of independent variables. Planners and policymakers can use these models to better understand the wetland locations that are at greatest risk to loss, as well as the drivers and landscape conditions that have the greatest influence on the probability of wetland loss.