Applying Remote Sensing to Assess Post-Fire Vegetation Recovery: A Case Study of Serra do Açor (Portugal)

Wildfires in the Mediterranean basin, particularly in Portugal, pose significant ecological challenges by altering landscapes and ecosystems. This study examines vegetation recovery in Serra do Açor seven years after the 2017 wildfires, using remote sensing and field data to analyze post-fire dynamics. The primary goal was to assess whether fire severity, measured via the dNBR index from Sentinel-2 imagery, impacts vegetation recovery or if site-specific factors and pre-fire floristic composition are more influential. Randomly assigned plots based on previous land use and fire severity were analyzed for floristic attributes. To quantify and classify cover changes, a supervised classification methodology based on the random forest algorithm was applied to Sentinel-2 data. The results showed no clear link between fire severity and recovery; instead, local factors like soil and topography, along with dominant pre-fire species, influenced recovery. Acacia and eucalyptus communities grew faster and increased the occupied area but exhibited lower diversity than native vegetation communities. Supervised classifications achieved high accuracy (Kappa > 0.90), showing increased shrubland areas and expansion of eucalyptus and acacia. The study highlights the methodology’s effectiveness and potential for broader applications in future research.