RapidFEM4D: aboveground biomass density maps for post-Hurricane Ian forest monitoring in Florida

This dataset provides spatially aboveground biomass density (AGBD) maps from 2022 to 2024, created to assess the impacts of Hurricane Ian on forest ecosystems in Florida. The Hurricane Ian made landfall in Florida on September 28, 2022, as a powerful Category 4 and was the second major storm of the 2022 Atlantic hurricane season.Field data were collected in situ during the spring of 2023 and 2024 across 27 plots, each measuring 25 × 25 meters. These plots were strategically distributed to represent a full gradient of AGBD. Within each plot, tree species were identified, and diameter at breast height (DBH) and height measurements were recorded for all individuals with DBH greater than 10 cm. AGBD was estimated for each tree based on species-specific allometric equations, incorporating species identity, DBH, and tree height.To generate the AGBD maps, we integrated multiple remote sensing datasets, including GEDI L4A AGBD estimates, Harmonized Landsat-Sentinel (HLS) optical imagery, and Sentinel-1C radar data, along with other ancillary layers, totaling 245 predictor variables. A random forest regression model was trained using field data and predictor layers to estimate AGBD, which was then upscaled to create a continuous AGBD map for the study area.The final AGBD prediction for each year was obtained by averaging results from multiple bootstrap iterations.. Additionally, uncertainty maps were derived by calculating the standard deviation of predictions across these iterations, providing insight into spatial variability and model confidence.Resources in this dataset:AGBD predictions for consecutive years: RapidFEM4D_AGBD_prediction_2022.tif; RapidFEM4D_AGBD_prediction_2023.tif, and RapidFEM4D_AGBD_prediction_2024.tifMap uncertainties for the respective AGBD predictions: RapidFEM4D_AGBD_uncertainty_2022.tif; RapidFEM4D_AGBD_uncertainty_2023.tif, and RapidFEM4D_AGBD_uncertainty_2024.tifField data used for technical validation: RapidFEM4D_field_data.xlsx