Federated Research Data Repository / dépôt fédéré de données de recherche
Harder, Phillip;
Pomeroy, John;
Helgason, Warren
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2024-03-27
Unmanned Aerial Vehicles (UAV) have had recent widespread application to capture high resolution information on snow processes and the data herein was collected to address the sub-canopy snow depth challenge. Previous demonstrations of snow depth mapping with UAV Structure from Motion (SfM) and airborne-lidar have focused on non-vegetated surfaces or reported large errors in the presence of vegetation. In contrast, UAV-lidar systems have high-density point clouds, measure returns from a wide range of scan angles, and so have a greater 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 with data collected in a 2019 field campaign in the Canadian Rockies Hydrological Observatory, Alberta and at Canadian Prairie sites near Saskatoon, Saskatchewan, Canada. The data archived here comprises the raw point clouds from the UAV-SfM and UAV-lidar platforms, generated digital surface models, and survey data used for accuracy assessment for the field campaign in question as reported in Harder et al., 2019.
This dataset was generated by the work of the Smart Water Systems Laboratory within the Centre for Hydrology at the University of Saskatchewan. This contributes to the objectives of a number of Pillar 3 Global Water Futures projects including Mountain Water Futures and the Transformative Technology and Smart Watersheds.
https://creativecommons.org/licenses/by/4.0/