Quantifying Lateral Fluvial Dynamics Using Sentinel-2: Monitoring Medium-Large Rivers in Italy

Abstract

Understanding river channel evolution is essential for effective management, as lateral erosion shapes riverbeds, floodplains, and riparian habitats. Despite advancements in remote sensing, translating these technologies into operational tools for institutional monitoring remains a challenge. This study introduces a semi-automated framework designed to bridge this gap by quantifying lateral mobility and bank retreat rates in high-energy, gravel-bed rivers. The methodology utilizes a Random Forest classifier applied to Copernicus Sentinel-2 time series (2016–2024) across five rivers in Piedmont (Italy). By detecting pixel-level class shifts between water, vegetation, and sediment, the procedure provides a proxy for lateral mobility. Validated against 120 km of manual delineations, the model achieved high spatial accuracy for both bank retreat rates and eroded bank lengths measurements (Mean Absolute Deviation of 2.64 m/yr and 4.34%, respectively). Integrating discharge data and the hydraulic infrastructure cadaster of the Sesia River, the proposed framework effectively detects reaches prone to significant geomorphic changes, isolates their underlying drivers, simulates 10-year evolutionary trajectories, and demonstrates strong predictive capabilities regarding future channel–infrastructure interactions. This study marks a shift from sporadic, labor-intensive assessments to a dynamic, systematic monitoring framework. Leveraging free satellite data, it provides competent authorities with an objective, cost-effective tool to prioritize interventions, supporting flood risk reduction and river restoration.