Articles | Volume 22, issue 8
https://doi.org/10.5194/hess-22-4165-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-22-4165-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Technical note: Bathymetry observations of inland water bodies using a tethered single-beam sonar controlled by an unmanned aerial vehicle
Department of Environmental Engineering, Technical University of
Denmark, Kgs. Lyngby, Denmark
Daniel Olesen
National Space Institute, Technical University of Denmark, Kgs.
Lyngby, 2800, Denmark
Jakob Jakobsen
National Space Institute, Technical University of Denmark, Kgs.
Lyngby, 2800, Denmark
Cecile Marie Margaretha Kittel
Department of Environmental Engineering, Technical University of
Denmark, Kgs. Lyngby, Denmark
Sheng Wang
Department of Environmental Engineering, Technical University of
Denmark, Kgs. Lyngby, Denmark
Monica Garcia
Department of Environmental Engineering, Technical University of
Denmark, Kgs. Lyngby, Denmark
Peter Bauer-Gottwein
Department of Environmental Engineering, Technical University of
Denmark, Kgs. Lyngby, Denmark
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- Quantification of bedform dynamics and bedload sediment flux in sandy braided rivers from airborne and satellite imagery R. Strick et al. 10.1002/esp.4558
- Modeling lake bathymetry and water storage from DEM data constrained by limited underwater surveys K. Liu & C. Song 10.1016/j.jhydrol.2021.127260
- Modelling of ice jam floods under past and future climates: A review P. Rokaya et al. 10.1016/j.hydroa.2022.100120
- Can we estimate the lake mean depth and volume from the deepest record and auxiliary geospatial parameters? P. Zhan et al. 10.1016/j.jhydrol.2022.128958
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Latest update: 06 Oct 2024
Short summary
Water depth observations are essential data to forecast flood hazard, predict sediment transport, or monitor in-stream habitats. We retrieved bathymetry with a sonar wired to a drone. This system can improve the speed and spatial scale at which water depth observations are retrieved. Observations can be retrieved also in unnavigable or inaccessible rivers. Water depth observations showed an accuracy of ca. 2.1 % of actual depth, without being affected by water turbidity or bed material.
Water depth observations are essential data to forecast flood hazard, predict sediment...