Quantifying Forest Structural and Functional Responses to Fire Severity Using Multi-Source Remotely Sensed Data

Wildfires play a pivotal role in shaping and regulating the structural characteristics of forest ecosystems. This study examined post-fire vegetation dynamics following the 2020 Bighorn Fire in the Santa Catalina Mountains, Arizona, USA, by integrating pre- and post-fire airborne LiDAR data with Landsat-derived burn severity indices from 2019 to 2024. We analyzed structural and functional vegetation traits across 12,500 hectares to assess the changes pre- to post-fire, and to evaluate how these changes were influenced by the burn severity. We applied a correlation analysis to explore the relationships among the structural variables across different vegetation cover types. Non-parametric LOESS regression revealed that the dNBR was more strongly associated with changes in the tree density than with vertical structural attributes. The functional recovery, indicated by the NDVI, generally outpaced the structural recovery captured by the NBR. Densely forested areas experienced greater declines in vegetation volumes and slower regeneration, whereas herbaceous and sparsely vegetated areas showed a more rapid, but compositionally distinct, recovery. The divergence between the NDVI and NBR trajectories underscores the importance of integrating structural and functional indicators to comprehensively assess the post-fire ecosystem resilience and inform targeted restoration efforts.