Aboveground biomass density maps for post-hurricane Ian forest monitoring in Florida - Inacio T. Bueno; Carlos A. Silva; Caio Hamamura; Victoria M. Donovan; Ajay Sharma; Jiangxiao Qiu; Jinyi Xia; Kody M. Brock; Monique B. Schlickmann; Jeff W. Atkins; Denis R. Valle; Jason Vogel; et al. (Scientific Data)
Abstract Hurricane Ian caused aboveground biomass density (AGBD) losses across Florida’s forests in the United States, highlighting the need for accurate, large-scale monitoring tools. We combined Global Ecosystem Dynamics Investigation (GEDI) LiDAR data with synthetic aperture radar (SAR) and passive optical satellite imagery to model GEDI AGBD as a function of image-derived data, enabling predictions across the study area and producing continuous AGBD maps. Validation using in situ field data demonstrated high model performance, with an R2 of 0.93 and a root mean square difference (RMSD) of 39.3%. Spatial uncertainty reflecting bootstrap-derived variance remained consistent, with relative standard errors around 90% across the years analyzed. The data are accessible through a web application, RapidFEM4D, enabling researchers and stakeholders to assess AGBD maps for areas of interest. These datasets support monitoring forest recovery, assessing carbon dynamics, and guiding post-hurricane management and restoration. The RapidFEM4D platform facilitates access and analysis of Hurricane Ian’s impact on Florida’s forests, empowering stakeholders with actionable insights and offering a model for similar efforts in other hurricane-prone regions.