Articles | Volume 10, issue 6
The Cryosphere, 10, 2559–2571, 2016
https://doi.org/10.5194/tc-10-2559-2016
The Cryosphere, 10, 2559–2571, 2016
https://doi.org/10.5194/tc-10-2559-2016

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 Harder et al.

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Cited articles

Armstrong, R. and Brun, E.: Snow and Climate: Physical Processes, Surface Energy Exchange and Modeling, Cambridge University Press, Cambridge, UK, 222 pp., 2008.
Bewley, D., Pomeroy, J. W., and Essery, R.: Solar Radiation Transfer Through a Subarctic Shrub Canopy, Arct. Antarct. Alp. Res., 39, 365–374, 2007.
Boufama, B., Mohr, R., and Veillon, F.: Euclidean Constraints for Uncalibrated Reconstruction, in: 4th International Conference on Computer Vision (ICCV '93), IEEE Computer Society, Berlin, Germany, 466–470, 1993.
Bühler, Y., Marty, M., Egli, L., Veitinger, J., Jonas, T., Thee, P., and Ginzler, C.: Snow depth mapping in high-alpine catchments using digital photogrammetry, The Cryosphere, 9, 229–243, https://doi.org/10.5194/tc-9-229-2015, 2015.
Bühler, Y., Adams, M. S., Bösch, R., and Stoffel, A.: Mapping snow depth in alpine terrain with unmanned aerial systems (UASs): potential and limitations, The Cryosphere, 10, 1075–1088, https://doi.org/10.5194/tc-10-1075-2016, 2016.
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Short summary
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.