Multi-Technique Data Fusion for Obtaining High-Resolution 3D Models of Narrow Gorges and Canyons to Determine Water Level in Flooding Events

Precise modeling of narrow gorges is challenging due to extreme confinement, hindering visibility and accessibility. These environments often render Global Navigation Satellite Systems (GNSS)-based positioning unfeasible, a difficulty compounded by water and dense vegetation. Consequently, multi-technique data fusion is required. This study proposes a robust methodology to generate high-resolution 3D models of such complex environments by integrating multiple aerial (e.g., Unmanned Aerial Vehicles, UAVs) and terrestrial techniques. A multi-sensor approach combined UAV-Light Detection and Ranging (LiDAR) and UAV-photogrammetry for external areas with Terrestrial laser scanning (TLS), Mobile Mapping System (MMS), and Spherical Photogrammetry (SP) for the canyon floor. Furthermore, the representativeness of these 3D models was analyzed against standard Digital Terrain Models (DTMs) for determining water height levels during flood events. A one-dimensional hydraulic (1DH) model compared the 3D mesh approach with the traditional 2.5D perspective in a challenging, narrow canyon prone to flooding. Our results show that traditional 2.5D DTMs significantly over- or underestimate water levels in narrow sections—failing to account for overhangs and vertical wall irregularities—whereas high-resolution 3D meshes provide a more realistic representation of hydraulic behavior. This work demonstrates that multi-sensor data fusion is essential for accurate flood risk management and infrastructure planning in complex fluvial environments.