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The Cryosphere An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/tc-2019-284
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-2019-284
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 16 Dec 2019

Submitted as: research article | 16 Dec 2019

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This preprint is currently under review for the journal TC.

Advances in mapping sub-canopy snow depth with unmanned aerial vehicles using structure from motion and lidar techniques

Phillip Harder1, John W. Pomeroy1, and Warren D. Helgason1,2 Phillip Harder et al.
  • 1Centre for Hydrology, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
  • 2Department of Civil, Geological, and Environmental Engineering, University of Saskatchewan, Saskatoon, Saskatchewan, Canada

Abstract. 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 lacking. Unmanned Aerial Vehicles (UAV) 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, 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 in a 2019 field campaign in 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 (RMSE < 0.17 m and bias −0.03 m 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 demonstrate relatively large variability (RMSE < 0.33 m and bias 0.08 m 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.

Phillip Harder et al.

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Phillip Harder et al.

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Unmanned aerial vehicle structure from motion and lidar data for sub-canopy snow depth mapping P. Harder, J. Pomeroy, and W. Helgason https://doi.org/10.20383/101.0193

Phillip Harder et al.

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Short summary
Unmanned aerial vehicle (UAV) based structure from motion (SfM) techniques have the ability to map snow depths in open areas. Here UAV lidar and SfM are compared to map sub-canopy snowpacks. Snow depth accuracy was assessed with data from sites in Western Canada collected in 2019. It is demonstrated that UAV-lidar can measure the sub-canopy snow depth at a high accuracy while UAV-SfM cannot. UAV-lidar promises to quantify snow- vegetation interactions at unprecedented accuracy and resolution.
Unmanned aerial vehicle (UAV) based structure from motion (SfM) techniques have the ability to...
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