Assessment of Forest Fire Impact and Vegetation Recovery in the Ghalahmah Mountains, Saudi Arabia

Forest fires are a critical ecological disturbance that significantly impact vegetation dynamics, biodiversity, and ecosystem services. This study investigates the impacts of forest fires in the Ghalahmah Mountains, Saudi Arabia, using remote sensing data and fire impact models to assess fire severity, environmental drivers, and post-fire vegetation recovery. The research integrates Landsat 8, Sentinel-2, and DEM data to analyze the spatial extent and severity of a 2020 fire event using the Relativized Burn Ratio (RBR). Results reveal that high-severity burns covered 49.9% of the affected area, with pre-fire vegetation density (NDVI) and moisture (NDWI) identified as key drivers of fire severity through correlation analysis and Random Forest regression. Post-fire vegetation recovery, assessed using NDVI trends from 2021 to 2024, demonstrated varying recovery rates across vegetation types. Medium NDVI areas (0.2–0.3) recovered fastest, with 134.46 hectares exceeding pre-fire conditions by 2024, while high NDVI areas (>0.3) exhibited slower recovery, with 26.55 hectares still recovering. These findings underscore the resilience of grasslands and shrubs compared to dense woody vegetation, which remains vulnerable to high-severity fires. The study advances fire ecology research by combining multi-source remote sensing data and machine learning techniques to provide a comprehensive understanding of fire impacts and recovery processes in semi-arid mountainous regions. The results suggest valuable insights for sustainable land management and conservation, emphasizing the need for targeted fuel management and protection of ecologically sensitive areas. This research contributes to the broader understanding of fire ecology and supports efforts to post-fire management.