With funding from the NASA Applied Sciences Program, the USGS partnered with the NOAA National Estuarine Research Reserve System (NERRS) to develop a remote sensing-based forecasting model of tidal marsh response to sea-level rise to address the potential impacts of sea-level rise on coastal ecosystems and dependent wildlife species. The Marsh Equilibrium Model (MEM) is a one-dimensional mechanistic model of marsh elevation change based on feedbacks between field-measured organic (plant biomass) and inorganic (suspended sediment) inputs. Working at Rush Ranch, a San Francisco Bay National Estuarine Research Reserve (NERR) site, scientists tested the feasibility of obtaining two important inputs to MEM from Landsat 8: peak biomass and annual average suspended sediment concentration (SSC). The sensitivity of MEM was tested with respect to remotely sensed inputs compared to field-measured inputs, and to error associated with the remote sensing inputs. Comparison of Landsat 8 and field-based MEM inputs found no significant difference in projections across 95% of the marsh plain area at 100 years, with both projections illustrating a subtle “sinking” of the marsh. Integration of remote sensing data would transform MEM into a spatial model for forecasting coastal marsh vegetation distributions and habitat suitability for special status species to aid regional decision making.
Projected vegetation community maps at year 2100 for Rush Ranch, Suisun Marsh, California, based on Marsh Equilibrium Model (MEM) outputs and a lidar digital elevation model (DEM). The top map was generated with inputs derived from Landsat 8 to MEM, and the bottom map was generated with field data inputs to MEM. Comparison of Landsat 8 and field-based MEM inputs found no significant difference in projections across 95% of the marsh plain area at 100 years, with both projections illustrating a subtle “sinking” of the marsh. North is oriented toward the top of the image.