Mapping Florida’s forest aboveground biomass density impacted by hurricane Ian through the synergism of spaceborne lidar, SAR, and passive optical data

Understanding the ecological impacts of catastrophic disturbances on forest ecosystems is critical to effective forest management and rapid disaster response. This study investigated changes in aboveground biomass density (AGBD) in Florida's forests following the Hurricane Ian in 2022 using remote sensing techniques. Spaceborne lidar, synthetic aperture radar, and passive optical data were used as input for a Random Forest model to quantify AGBD post-hurricane. Our model results returned a bias of 1.76±3.30 Mg/ha, root mean square error of 46.95±2.39 Mg/ha, and coefficient of determination (R²) of 0.58 (±0.04). Mapping results provided a detailed portrayal of AGBD across the study area and map uncertainties displayed the confidence level in the AGBD predictions. These insights can contribute to the scientific comprehension of forest ecosystems post-disturbance and hold practical implications for fast and timely disaster preparedness and response.