Aerial-Drone-Based Tool for Assessing Flood Risk Areas Due to Woody Debris Along River Basins
River morphology is highly dynamic, requiring accurate datasets and models for effective management, especially in flood-prone regions. Climate change and urbanisation have intensified flooding events, increasing risks to populations and infrastructure. Woody debris, a natural element of river ecosystems, poses a dual challenge: while it provides critical habitats, it can obstruct water flow, exacerbate flooding, and threaten infrastructure. Traditional debris detection methods are time-intensive, hazardous, and limited in scope. This study introduces a novel tool integrating artificial intelligence (AI) and computer vision (CV) to detect woody debris in rivers using aerial drone imagery that is fully integrated into a geospatial Web platform (WebGIS). The tool identifies and segments debris, assigning risk levels based on obstruction severity. When using orthoimages as input data, the tool provides georeferenced locations and detailed reports to support flood mitigation and river management. The methodology encompasses drone data acquisition, photogrammetric processing, debris detection, and risk assessment, and it is validated using real-world data. The results show the tool’s capacity to detect large woody debris in a fully automatic manner. This approach automates woody debris detection and risk analysis, making it easier to manage rivers and providing valuable data for assessing flood risk.
Authors
- David Sánchez-Jiménez (ORCID: https://orcid.org/0009-0009-7280-4677)
- Ángel Marqués‐Mateu (ORCID: https://orcid.org/0000-0003-1343-103X)
- Diego González‐Aguilera (ORCID: https://orcid.org/0000-0002-8949-4216)
- Diego Guerrero (ORCID: https://orcid.org/0000-0002-4073-1065)
- Innes Barbero-García
Institutions
- Universidad de Salamanca (ES)
- Catholic University of Ávila (ES)
- Universitat Politècnica de València (ES)
Publication Details
- Journal
- Drones
- Published
- 2025-03-06
- DOI
- https://doi.org/10.3390/drones9030191
- Citations
- 3
- Primary Topic
- Remote Sensing and LiDAR Applications
- Type
- article
- Field-Weighted Citation Impact
- 2.40