Multi-Sensor Approach to Assessing the Wildfire Severity-Induced Landslide Risk: A Case of Ischia Island, Italy

This study presents an assessment of wildfire severity and its associated landslide risk on Ischia Island, Italy, integrating meteorological data, including precipitation and maximum temperature, with remote sensing datasets to assess the wildfire that occurred on 28
^th
August 2023. Key findings include mapping wildfire extent using NASA FIRMS data and assessing severity through indices like Normalized Burn Ratio (NBR), differential NBR, Normalized Difference Vegetation Index (NDVI), and differential NDVI using Sentinel-2 images. High-resolution PlanetScope imagery enabled detailed change detection, while historical ground deformation data from Sentinel-1 (2018-2022) revealed significant susceptibility to landslides. The analysis indicated that the areas affected by the wildfire are also prone to landslides, with a mean deformation rate of -8 to -10 mm/year. Post-fire, the reduction in vegetation coverage and subsequent rainfall increased soil erosion and deformation, heightening the landslide risk. Strong correlations were observed between wildfire severity, land surface temperature (LST), and precipitation patterns. This integrated approach highlights the critical need for precise risk assessment and underscores the importance of multi-sensor data in post-fire landscape management and hazard mitigation.