https://doi.org/10.5194/tc-14-1919-2020

Harder, P., Pomeroy, J., & Helgason, W. (2020). Improving sub-canopy snow depth mapping with unmanned aerial vehicles: lidar versus structure-from-motion techniques. The Cryosphere, 14(6): 1919-1935. https://doi.org/10.5194/tc-14-1919-2020

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 to −0.14 m). With the demonstration of sub-canopy snow depth mapping capabilities, a number of early applications are presented to showcase the ability of UAV lidar to effectively quantify the many multiscale snow processes defining snowpack dynamics in mountain and prairie environments.

Highlights
• A novel wastewater-based risk index simplified understanding of viral load
• Significant correlation relationship between wastewater RNA loads and clinical data
• Thresholds derived from daily per capita viral loads and clinic Rt estimates
• Wastewater-based risk index valuable for decision-making of COVID-19 risk

TTSW

https://doi.org/10.5194/tc-14-1919-2020
The data are available at https://doi.org/10.20383/101.0193
The LAStools workflows and R code used to complete the analysis are available from https://github.com/phillip-harder/UAV-snowdepth (https://doi.org/10.5281/zenodo.3804691)