2025/11/21 by Giuseppe Esposito, Massimo Melillo, Davide Notti +5 · 1 voice
Engineering · Environmental Science · #Fire effects on ecosystems #Landslides and related hazards #Synthetic Aperture Radar (SAR) Applications and Techniques
paper · doi:10.1016/j.srs.2025.100339
openalex publication_date 2025/11/21 · openalex created_date 2025/11/23 · openalex updated_date 2026/07/15
Extreme wildfire events affecting rugged landscapes may pose necessary conditions for the initiation of cascading rainfall-triggered hazards, such as soil erosion, landslides, debris flows and floods. In particular, severe vegetation damage and modification of soil hydraulic properties are among the primary effects of wildfires influencing the post-fire hazards, as documented in many settings worldwide. In this work, we have analyzed the Sentinel-1 SAR backscattering changes before and after an extreme wildfire occurred in Italy, in areas covered by low-lying vegetation, to investigate how VH and VV coefficients relate with vegetation and surface soil moisture behaviors. For this purpose, SAR data have been coupled with NDVI and NDWI indices retrieved from Sentinel-2 imagery, as well as with time series of rainfall and surface soil moisture estimated at plot scale. The most significant findings reveal that the selected burned areas exhibit higher VV backscattering values after the first post-fire intense rainfall, compared to pre-fire conditions. This anomaly depends on the exceptional availability of water in the topsoil, suggesting a reduced vegetation interception together with a likely reduction of the soil infiltration capacity. The lowest VH backscattering values in the period immediately after the fire highlight the vegetation consumption. Nearly one year after the analyzed wildfire, both vegetation and soil conditions appear to have recovered to pre-fire levels. This study demonstrates the potentiality of integrating SAR and optical data to effectively monitor landscapes affected by wildfires. Future research should aim to combine this kind of data with hydrological models to improve post-fire risk mitigation strategies.