These elevation data (in meters) in Apalachee Bay, Florida, have been systematically and variably lowered, mitigating the bias in the lidar DEM and improving its spot elevation accuracy by approximately 69% in Apalachee Bay, Florida. These data span the big bend region of Florida’s gulf coast consisting of Gulf, Franklin, Wakulla, Jefferson, and Taylor counties. The data are in GIS raster format. These adjusted data are now suitable for modeling salt marsh evolution and flood inundation under sea-level rise (SLR) scenarios. Lidar data used in this adjustment were collected on March 01, 2018.

High-resolution elevation data provide a foundational layer needed to understand regional hydrology and ecology under contemporary and future-predicted conditions with accelerated sea-level rise. While the development of digital elevation models (DEMs) from light detection and ranging data has enhanced the ability to observe elevation in coastal zones, the elevation error can be substantial in densely vegetated coastal wetlands. In response, we developed a machine learning model to reduce vertical error in coastal wetlands for a 1-m DEM from 2018 that covered Nassau and Duval Counties, Florida. Error was reduced by using a random forest regression model within situ observations and predictor variables from optical and radar-based satellite data and elevation derivatives. Vegetation and elevation data were collected using a real-time kinematic global positioning system (RTK GPS) in coastal wetlands at the National Park Service’s Timucuan Ecological and Historic Preserve in summer 2021 and winter 2022 (n = 344). Predictor variables included information on vegetation greenness, wetness, elevation, and vegetation structure. In the extent of coastal wetlands in Nassau and Duval Counties, the original DEM had a mean absolute error of 0.17-m and a 95th percentile error of 0.48 m. Leave-one-out cross-validation was used to assess the accuracy of the corrected DEM. In coastal wetlands, the corrected DEM had a mean absolute error of 0.08 cm and a 95th percentile error of 0.25 m. The random forest model led to a decrease in the mean absolute error by about 50% and a decrease in 95th percentile by 49%.

Vertical accuracy of elevation data in coastal environments is critical because small variations in elevation can affect an area’s exposure to waves, tides, and storm-related flooding. Elevation data contractors typically quantify the vertical accuracy of digital elevation models (DEMs) developed using light detection and ranging data acquisition on a per-project basis to gauge whether the datasets meet quality and accuracy standards. To better understand the vertical accuracy of DEMs along the Gulf of Mexico and Atlantic coast, we collated over 5200 contractor points for this region that were collected for per-project-level analyses produced for assessing DEMs acquired for the U.S. Geological Survey’s 3D Elevation Program. Upon pooling these data, we integrated attributes related to land cover from the National Oceanic and Atmospheric Administration's (NOAA) Coastal Change Assessment Program (C-CAP) 10 m BETA land cover product. Vegetative state, a derivative of land cover, was described as non-vegetated or vegetated. USGS Lidar Base Specification Quality Level (NGP, 2022), a derivative of point spacing, was obtained via the metadata of the elevation data, and regional classifiers were assigned based on dividing the coastal region using Everglades National Park and the North Carolina-Virginia border.