Improving sub-canopy snow depth mapping with unmanned aerial vehicles: lidar versus structure-from-motion techniques - Harder, Philip; Pomeroy, John; Helgason, Warren D. (The Cryosphere, 2020)
© Author(s) 2020. This work is distributed under the Creative Commons Attribution 4.0 License. Natural Sciences and Engineering Research Council of Canada (Discovery Grants Program – Snow Hydrology), the Canada Research Chairs Program (Canada Research Chair in Water Resources and Climate Change grant), the Canada First Research Excellence Fund (Global Water Futures grant), and the Western Economic Diversification Canada (Smart Water Systems Laboratory grant). Peer Reviewed Vegetation has a tremendous influence on snow processes and snowpack dynamics, yet remote sensing techniques to resolve the spatial variability of sub-canopy snow depth are not always available and are difficult from space-based platforms. Unmanned aerial vehicles (UAVs) have had recent widespread application to capture high-resolution information on snow processes and are herein applied to the sub-canopy snow depth challenge. Previous demonstrations of snow depth mapping with UAV structure from motion (SfM) and airborne lidar have focussed on non-vegetated surfaces or reported large errors in the presence of vegetation. In contrast, UAV-lidar systems have high-density point clouds and measure returns from a wide range of scan angles, increasing the likelihood of successfully sensing the sub-canopy snow depth. The effectiveness of UAV lidar and UAV SfM in mapping snow depth in both open and forested terrain was tested in a 2019 field campaign at the Canadian Rockies Hydrological Observatory, Alberta, and at Canadian prairie sites near Saskatoon, Saskatchewan, Canada. Only UAV lidar could successfully measure the sub-canopy snow surface with reliable sub-canopy point coverage and consistent error metrics (root mean square error (RMSE) <0.17 m and bias −0.03 to −0.13 m). Relative to UAV lidar, UAV SfM did not consistently sense the sub-canopy snow surface, the interpolation needed to account for point cloud gaps introduced interpolation artefacts, and error metrics demonstrated relatively large variability (RMSE<0.33 m and bias 0.08 ...