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The Cryosphere An interactive open-access journal of the European Geosciences Union
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Volume 10, issue 6
The Cryosphere, 10, 2559-2571, 2016
https://doi.org/10.5194/tc-10-2559-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
The Cryosphere, 10, 2559-2571, 2016
https://doi.org/10.5194/tc-10-2559-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 02 Nov 2016

Research article | 02 Nov 2016

Accuracy of snow depth estimation in mountain and prairie environments by an unmanned aerial vehicle

Phillip Harder1, Michael Schirmer1,a, John Pomeroy1, and Warren 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
  • anow at: WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland

Abstract. Quantifying the spatial distribution of snow is crucial to predict and assess its water resource potential and understand land–atmosphere interactions. High-resolution remote sensing of snow depth has been limited to terrestrial and airborne laser scanning and more recently with application of structure from motion (SfM) techniques to airborne (manned and unmanned) imagery. In this study, photography from a small unmanned aerial vehicle (UAV) was used to generate digital surface models (DSMs) and orthomosaics for snow cover at a cultivated agricultural Canadian prairie and a sparsely vegetated Rocky Mountain alpine ridgetop site using SfM. The accuracy and repeatability of this method to quantify snow depth, changes in depth and its spatial variability was assessed for different terrain types over time. Root mean square errors in snow depth estimation from differencing snow-covered and non-snow-covered DSMs were 8.8cm for a short prairie grain stubble surface, 13.7cm for a tall prairie grain stubble surface and 8.5cm for an alpine mountain surface. This technique provided useful information on maximum snow accumulation and snow-covered area depletion at all sites, while temporal changes in snow depth could also be quantified at the alpine site due to the deeper snowpack and consequent higher signal-to-noise ratio. The application of SfM to UAV photographs returns meaningful information in areas with mean snow depth > 30cm, but the direct observation of snow depth depletion of shallow snowpacks with this method is not feasible. Accuracy varied with surface characteristics, sunlight and wind speed during the flight, with the most consistent performance found for wind speeds <10ms−1, clear skies, high sun angles and surfaces with negligible vegetation cover.

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This paper assesses the accuracy of high-resolution snow depth maps generated from unmanned aerial vehicle imagery. Snow depth maps are generated from differencing snow-covered and snow-free digital surface models produced from structure from motion techniques. On average, the estimated snow depth error was 10 cm. This technique is therefore useful for observing snow accumulation and melt in deep snow but is restricted to observing peak snow accumulation in shallow snow.
This paper assesses the accuracy of high-resolution snow depth maps generated from unmanned...
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