Date of Award
Master of Science (MS)
College of Science and Mathematics
Earth and Environmental Studies
Thesis Sponsor/Dissertation Chair/Project Chair
Lake Mattamuskeet, a large, shallow lake on the coast of North Carolina, has undergone water quality degradation and submerged aquatic vegetation (SAV) decline over recent years. Water depth and water clarity have been established as key drivers of SAV loss. To target locations for the restoration of SAV in the lake, an analysis that focuses on water clarity, water depth, and current SAV presence was developed. Two separate methodologies were conducted and compared to analyze water clarity in the lake. The first applied four years (2013-2016) of Landsat 8 imagery to a previously developed model that predicts water clarity from the imagery. The second applied multiple interpolation techniques to data from surveys performed by the US Fish and Wildlife Service (FWS) during the years 2013-2016. The remote sensing model output was corrected for low model predictions and sun glint may have impacted results, so despite the better seasonal and temporal resolution of the remote sensing methodology, the interpolation methodology was deemed the better approach. The Empirical Bayesian Kriging interpolation technique was named the best overall approach to interpolate SAV presence and water clarity for Lake Mattamuskeet. A bathymetric map of the lake and water level data was used to estimate average water level during the SAV growing season (April-September). SAV habitat is located along the southern and eastern edges of the lake per both methodological approaches. Varying the model’s initial conditions produced similar results in both cases. Increasing water depth decreased available SAV habitat, and decreasing water depth increased available SAV habitat. These results suggest that sea level rise may drive future SAV decline. Managing lake levels may be necessary to retain suitable SAV habitat and promote clear water conditions in the future.
Shanks, Lindsey, "Targeting SAV Restoration at Lake Mattamuskeet Using GIS and Landsat 8 Data" (2017). Theses, Dissertations and Culminating Projects. 607.