Evaluation and Analysis of Remote Sensing-Based Approach for Salt Marsh Monitoring

Abstract

In the United States (US), salt marshes are especially vulnerable to the effects of projected sea level rise, increased storm frequency, and climatic changes. Sentinel-2 data offer the opportunity to observe the land surface at high spatial resolutions (10 m). The Sentinel-2 data, encompassing Cumberland Island National Seashore, Fort Pulaski National Monument, and Canaveral National Seashore, were analyzed to identify temporal changes in salt marsh presence from 2016 to 2020. ENVI-derived unsupervised and supervised classification algorithms were applied to determine the most appropriate procedure to measure distant areas of salt marsh increases and decreases. The Normalized Difference Vegetation Index (NDVI) was applied to describe the varied vegetation biomass spatially. The results from this approach indicate that the ENVI-derived maximum likelihood classification provides a statistical distribution and calculation of the probability (>90%) that the given pixels represented both water and salt marsh environments. The salt marshes captured by the maximum likelihood classification indicated an overall decrease in salt marsh area presence. The NDVI results displayed how the varied vegetation biomass was analogous to the occurrence of salt marsh changes. Areas representing the lowest NDVI values (−0.1 to 0.1) corresponded to bare soil areas where a salt marsh decrease was detected.