Vegetation recovery after forest fires is a vital indicator of ecosystem resilience. However, the specific differences between structural and functional recovery after fire have remained unclear. In this study, we quantified and compared post-fire recovery using two distinct vegetation indicators: the Enhanced Vegetation Index (EVI) for structural recovery and Solar-Induced Chlorophyll Fluorescence (SIF) for functional recovery. We analyzed the spatiotemporal dynamics and drivers of post-fire recovery. A Transformer model was used to simulate pre- and post-fire variations in EVI and SIF, while a Random Forest model was employed to identify the key drivers of recovery. We analyze the spatiotemporal dynamics and drivers of post-fire recovery. A Transformer model simulates pre- and post-fire variations in EVI and SIF, while a Random Forest model identifies key drivers of recovery. Our results show a steep decline in both indicators after fires, with SIF recovering more slowly than EVI. Three years after the fire, about 78% of burned areas regain at least 80% of their pre-fire EVI levels, but SIF recovery reaches only 70%. Bivariate dependency analysis indicates that precipitation and temperature promote recovery, whereas topography and the Differenced Normalized Burn Ratio (dNBR) have the opposite effect. This study advances a phased, analytical approach to post-fire forest vegetation recovery, offering a dual-perspective framework for understanding forest resilience and providing actionable insights for sustainable restoration and management.
Wang et al. (Fri,) studied this question.