Integrated Geospatial Analysis of Burn Severity and Vegetation Recovery of the California August Complex Fire in 2020

Large wildfires increasingly alter vegetation structure and ecosystem recovery trajectories at landscape scales, requiring reliable geospatial methods for post-fire assessment. This study evaluates burn severity and vegetation recovery following the 2020 California August Complex Fire using an integrated framework combining multispectral satellite imagery, spatial statistics, and airborne LiDAR data. Burn severity was quantified using differenced Normalized Burn Ratio (dNBR), and vegetation recovery was assessed through a multi-temporal NBR time series spanning pre-fire (2015), fire-year (2020), and post-fire (2025) conditions. Optimized Hotspot Analysis (Gi*) was applied to isolate statistically significant clusters of high burn severity and reduce bias in recovery estimates. LiDAR-derived vegetation height metrics were used to evaluate structural impacts across severity classes. Results indicate substantial vegetation loss during the fire year, followed by recovery to approximately 85% of pre-fire NBR values by 2025. High-severity areas exhibited slower recovery compared to lower-severity zones. The integration of spectral indices, spatial clustering, and 3D structural analysis provides a multidimensional assessment of wildfire impacts and supports improved understanding of landscape-scale ecosystem response and post-fire management planning.