Exploring the Potential of Multi-Sensor and Multi-Scale Remotely Sensed Data Integration to Improve Flood Monitoring

This work proposes an integrated near-real time operational system based on satellite and Unmanned Aerial Vehicle (UAV) methodologies. The proposed workflow leverages an algorithm for the computation of multi-temporal probability flood maps, based on a stack of Synthetic Aperture Radar (SAR) images (e.g. Sentinel-1). By obtaining a wide-scale overview of the most flood-prone areas, the system allows to investigate them on-demand and with improved spatio-temporal resolution, exploiting the potential of UAVs and a High-Performance Structure from Motion (SfM) photogrammetry algorithm. UAV-derived high-resolution topographic data are then used to constrain the probabilistic flood hazard assessment through multi-temporal analyses and the extraction of detailed hydro-geomorphological parameters. Our approach is here tested over a reach of the Basento river in the Basilicata region (southern Italy), demonstrating its value in terms of accuracy, efficiency, and timeliness of flood monitoring efforts, enhancing disaster preparedness and response strategies.