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Unmanned aerial vehicle structure from motion and lidar data for sub-canopy snow depth mapping
Description: | 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. |
Authors: | Harder, Phillip; University of Saskatchewan; 0000-0003-2144-2767 Pomeroy, John; University of Saskatchewan Helgason, Warren; University of Saskatchewan |
Keywords: | Unmanned Aerial Vehicle lidar structure from motion snow depth point cloud digital surface model |
Field of Research: | Earth and related environmental sciences > Geomatics and earth systems observations > Geomatics and earth systems observations, not elsewhere classified
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Publication Date: | 2020-01-13 |
Publisher: | Federated Research Data Repository / dépôt fédéré de données de recherche |
Funder: | Natural Sciences and Engineering Research Council of Canada (NSERC) Canada Research Chairs (CRC) Canada First Research Excellence Fund (CFREF); Global Water Futures Program (GWF) Western Economic Diversification Canada (WD) Global Water Futures (GWF) |
URI: | https://doi.org/10.20383/101.0193 |
Related Identifiers: |
This dataset is cited by
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Geographic Coverage: | Place Name Fortress Mountain Snow Laboratory City Kananaskis Province / Province / Territory Alberta Territory Country Canada City Clavet Province / Province / Territory Saskatchewan Territory Country Canada City Rosthern Province / Province / Territory Saskatchewan Territory Country Canada |
Geographic Point: | Latitude 50.833 Longitude -115.220 Latitude 51.941 Longitude -106.379 Latitude 52.694 Longitude -106.461 |
Appears in Collections: | Global Water Futures |
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Creative Commons Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/
Citation
Harder, P., Pomeroy, J., Helgason, W. (2020). Unmanned aerial vehicle structure from motion and lidar data for sub-canopy snow depth mapping. Federated Research Data Repository. https://doi.org/10.20383/101.0193