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Post-Fire Restauration in Mediterranean Watersheds: Coupling WiMMed Modeling with LiDAR–Landsat Vegetation Recovery

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Autor
Velasco Pereira, Edward A.
Navarro-Cerrillo, Rafael M.
Editor
MDPI
Fecha
2025
Materia
Wildfires
Hydrological modeling
Post-fire restoration
Landsat and ALS integration
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Resumen
Wildfires are among the most severe disturbances in Mediterranean ecosystems, altering vegetation structure, soil properties, and hydrological functioning. Understanding post-fire hydrological dynamics is crucial for predicting flood and erosion risks and vegetation restoration in fire-prone regions. This study investigates the hydrological responses of Mediterranean watersheds following a wildfire event by integrating WiMMed (Watershed Integrated Management in Mediterranean Environments), a distributed, physically based hydrological model, with high-resolution vegetation data derived from LiDAR and Landsat imagery. A Priority Post-Fire Restoration Index (PPRI) was calculated as the weighted sum of the six parameters runoff (mm), flow accumulation (mm), distance to drainage network (m), slope (%), erodibility (K), lithology, and LiDAR index under a sediment reduction and runoff peak reduction scenario. The post-fire hydrological processes modeled with WiMMed described the dynamics of surface runoff and soil moisture redistribution across the upper soil layers after fire, and their gradual attenuation with vegetation regrowth. The spatial distribution of the PPRI identified specific zones within the burned watershed that require urgent restoration measures (10% and 4.55% under sediment reduction and peak reduction scenarios, respectively). The combined use of process-based modeling and remote sensing offers valuable insights into watershed-scale hydrological resilience and supports the design of post-fire restoration strategies in Mediterranean landscapes.
URI
http://hdl.handle.net/10396/35162
Fuente
Pereira, E. A. V., & Cerrillo, R. M. N. (2025). Post-Fire Restauration in Mediterranean Watersheds: Coupling WiMMed Modeling with LiDAR–Landsat Vegetation Recovery. Remote Sensing, 18(1), 26.
Versión del Editor
https://doi.org/10.3390/rs18010026
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