Authors: Anthony Campbell*, University of Rhode Island, Yeqiao Wang, University of Rhode Island
Topics: Remote Sensing, Coastal and Marine, Biogeography
Keywords: Salt marsh, land cover change, Google earth engine, time series, mid-Atlantic
Session Type: Paper
Start / End Time: 1:10 PM / 2:50 PM
Room: Balcony B, Marriott, Mezzanine Level
Presentation File: No File Uploaded
Salt marshes provide significant ecosystem services including nursery grounds for fish, a buffer against extreme storms, denitrification, and vast blue carbon repositories. However, salt marshes are at risk of loss from a variety of stressors such as sea level rise (SLR). Determining the dynamics of salt marsh change with remote sensing benefits from high temporal resolution due to spectral variability from disturbance, tides, and seasonality. Time series analysis of salt marshes can broaden our understanding of these changing environments. In this study, Google Earth Engine (GEE) enabled time series of the Landsat archive were used to determine salt marsh change from 1999 to 2018 along the mid-Atlantic coast of the United States. The study analyzed aboveground green biomass in seven mid-Atlantic Hydrological Unit Code 8 watersheds including portions of Maryland, New York, New Jersey, Delaware, Virginia, and North Carolina. The study revealed that the Chincoteague watershed had the highest average change (-120.67 g m-2), and the Eastern Lower Delmarva watershed had the largest net reduction in salt marsh aboveground green biomass from 1999-2018. A comparison of a Worldview-2 derived salt marsh classification, and Landsat derived aboveground green biomass estimates found a positive relationship between biomass estimates and the area of mudflat within the Landsat pixel area (F(1165,1)=1326, p < 0.001).