Fire, Journal Year: 2025, Volume and Issue: 8(5), P. 172 - 172
Published: April 30, 2025
Forest fires are a critical ecological disturbance that significantly impact vegetation dynamics, biodiversity, and ecosystem services. This study investigates the impacts of forest in Ghalahmah Mountains, Saudi Arabia, using remote sensing data fire models to assess severity, environmental drivers, post-fire recovery. The research integrates Landsat 8, Sentinel-2, DEM analyze spatial extent severity 2020 event Relativized Burn Ratio (RBR). Results reveal high-severity burns covered 49.9% affected area, with pre-fire density (NDVI) moisture (NDWI) identified as key drivers through correlation analysis Random regression. Post-fire recovery, assessed NDVI trends from 2021 2024, demonstrated varying recovery rates across types. Medium areas (0.2–0.3) recovered fastest, 134.46 hectares exceeding conditions by while high (>0.3) exhibited slower 26.55 still recovering. These findings underscore resilience grasslands shrubs compared dense woody vegetation, which remains vulnerable fires. advances ecology combining multi-source machine learning techniques provide comprehensive understanding processes semi-arid mountainous regions. results suggest valuable insights for sustainable land management conservation, emphasizing need targeted fuel protection ecologically sensitive areas. contributes broader supports efforts management.
Language: Английский