Abstract
The analysis of river systems at appropriate spatial and temporal scales is essential to support sustainable river management and to develop solutions to mitigate the impacts of natural hazard. Recently, new possibilities have been opened for river monitoring thanks to emerging remote sensing technologies which are providing an unparalleled amount of data at spatial and temporal resolutions not available in the past. This thesis aims to investigate the potential of Unmanned Aerial Vehicles (UAV) and satellite derived river datasets to semi-automatically monitor river forms and processes. Specifically, it focuses on the semi-automatic extraction of certain hydromorphological indicators, key to understand river processes: sediment grain size analysis, and proxy of channel dynamics (erosion/deposition). UAV derived products are used as ground-truth datasets to calibrate and correlate satellite information from Sentinel-2 multispectral data and Sentinel-1 radar data. This has allowed to explore the accuracy and the limitations of what can be measured depending on different river size and type. The fusion of freely available satellite data (Sentinels) and low-cost UAV river data opens to a new generation of cross-scale river monitoring, where the ability to explore historical trajectories of channel processes is opening the way for a more comprehensive and consistent characterization of river systems.