Evaluation of Post-Fire Treatments (Erosion Barriers) on Vegetation Recovery Using RPAS and Sentinel-2 Time-Series Imagery
Resumen: Post-fire soil and vegetation changes can intensify erosion and sediment yield by altering the factors controlling the runoff–infiltration balance. Erosion barriers (EBs) are widely used in hydrological and forest restoration to mitigate erosion, reduce sediment transport, and promote vegetation recovery. However, precise spatial assessments of their effectiveness remain scarce, requiring validation through operational methodologies. This study evaluates the impact of EB on post-fire vegetation recovery at two temporal and spatial scales: (1) Remotely Piloted Aircraft System (RPAS) imagery, acquired at high spatial resolution but limited to a single acquisition date coinciding with the field flight. These data were captured using a MicaSense RedEdge-MX multispectral camera and an RGB optical sensor (SODA), from which NDVI and vegetation height were derived through aerial photogrammetry and digital surface models (DSMs). (2) Sentinel-2 satellite imagery, offering coarser spatial resolution but enabling multi-temporal analysis, through NDVI time series spanning four consecutive years. The study was conducted in the area of the Luna Fire (northern Spain), which burned in July 2015. A paired sampling design compared upstream and downstream areas of burned wood stacks and control sites using NDVI values and vegetation height. Results showed slightly higher NDVI values (0.45) upstream of the EB (p < 0.05), while vegetation height was, on average, ~8 cm lower than in control sites (p > 0.05). Sentinel-2 analysis revealed significant differences in NDVI distributions between treatments (p < 0.05), although mean values were similar (~0.32), both showing positive trends over four years. This study offers indirect insight into the functioning and effectiveness of EB in post-fire recovery. The findings highlight the need for continued monitoring of treated areas to better understand environmental responses over time and to inform more effective land management strategies.
Idioma: Inglés
DOI: 10.3390/rs17203422
Año: 2025
Publicado en: Remote Sensing 17, 20 (2025), 3422 [23 pp.]
ISSN: 2072-4292

Financiación: info:eu-repo/grantAgreement/ES/MCIN-AEI/PID2020-118886RB-I00-AEI-10.13039-501100011033
Financiación: info:eu-repo/grantAgreement/ES/MCIN-AEI/PID2024-160889OA-I00-AEI-10.13039-501100011033
Tipo y forma: Article (Published version)
Área (Departamento): Área Análisis Geográfico Regi. (Dpto. Geograf. Ordenac.Territ.)

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