EGU25-4410, updated on 14 Mar 2025
https://doi.org/10.5194/egusphere-egu25-4410
EGU General Assembly 2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Poster | Monday, 28 Apr, 16:15–18:00 (CEST), Display time Monday, 28 Apr, 14:00–18:00
 
Hall X2, X2.91
Seamless quantification of wet and dry riverscape topography using UAV topo-bathymetric LiDAR
Craig MacDonell1, Richard Williams1, Jon White2, and Kenny Roberts1
Craig MacDonell et al.
  • 1School of Geographical and Earth Sciences, University of Glasgow, Glasgow, United Kingdom (craig.macdonell@glasgow.ac.uk)
  • 2Aetha Ltd., Harpdenden, Hertfordshire, United Kingdom

Quantifying riverscape topography is challenging because riverscapes comprise of both wet and dry surfaces. Over the last decade, considerable advances have been made in demonstrating the capability of mounting topo-bathymetric LiDAR sensors on crewed, occupied aircraft to quantify riverscape topography. However, only recently has miniaturisation of electronic components enabled topo-bathymetric LiDAR to be mounted on consumer-grade Unoccupied Aerial Vehicles (UAVs). Here we evaluate the capability of YellowScan Navigator topo-bathymetric LiDAR sensor, mounted on a DJI Matrice 600 UAV, to survey a 1 km long braided riverscape. This sensor uses full waveform returns, to ensure continuity between underwater points and the surrounding terrain, and has a 44° field of view. In August 2024, a point cloud was collected across a 1 km long, 200 m wide reach of the braided River Feshie, Scotland. Ground-truth data were collected across wet areas using a Sontek M9 Acoustic Doppler Current Profiler (ADCP) as an echo-sounder, and also survey-grade RTK-GNSS (Global Navigation Satellite System) in both wet and dry areas. The processed LiDAR point cloud featured over 10 million points, with a density of approximately 62 points / m2. These points were compared to the ground truth data to assess the vertical accuracy of the survey. Ground-truth mean errors (and standard deviation) across dry gravel bars surveyed with RTK-GNSS were 0.06 ± 0.04 m (n=237). Mean errors along the bed of the shallow channels surveyed with RTK-GNSS were -0.04 ± 0.23 m (n=562). Additionally, mean errors for deeper channels measured with the ADCP’s echo sounder were -0.08m ± 0.23 m (n = 2673). Overall, this case study demonstrates the potential of using a new generation of topo-bathymetric LiDAR sensors that can be mounted on UAVs. This has the potential to further enhance field surveys of wet-dry environments by reducing logistical workloads and increasing efficiency of these surveys. Compared to the various techniques for correcting bathymetric data from Structure-from-Motion imagery or point clouds this direct measurement needs less processing to achieve to continuous topo-bathymetric surface.

How to cite: MacDonell, C., Williams, R., White, J., and Roberts, K.: Seamless quantification of wet and dry riverscape topography using UAV topo-bathymetric LiDAR, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4410, https://doi.org/10.5194/egusphere-egu25-4410, 2025.