Fusing remote sensing products from different satellite sensors allows the development of enhanced maps of the distribution of coastal wetland plants and more accurate models of coastal elevations. This critical information about vulnerability to sea-level rise and hurricane flooding is being used by Department of Interior partners and State and local agencies to improve management in a changing climate. In 2022, U.S. Geological Survey (USGS) scientists developed a 10-meter resolution vegetation classification model using wet- and dry-season imagery from Sentinel-1 (synthetic aperture radar data) and Sentinel-2 (multispectral data) acquired from 2015–2017, prior to hurricane Irma in September 2017.
Coastline elevation is an important characteristic for understanding floods in tidal wetlands. Even small changes in elevation can translate to large differences in inundation time, which is a crucial metric for estimating future flooding vulnerability. Elevation data are usually derived from airborne lidar, but dense vegetation can block the lidar signal from reaching the soil surface, causing a positive bias in bare-earth digital elevation models (DEMs). Using a statistical model (LEAN: Lidar Elevation Adjustment using NDVI) and ground calibration datasets, USGS scientists developed a new DEM for southwestern Florida that accounts for the bias from vegetation. The LEAN model assumes that plant density is correlated with the normalized difference vegetation index (NDVI), calculated from National Agriculture Imagery Program imagery.
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