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The Cryosphere An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/tc-2017-57
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/tc-2017-57
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

  24 Apr 2017

24 Apr 2017

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This preprint was under review for the journal TC but the revision was not accepted.

Measuring the snowpack depth with Unmanned Aerial System photogrammetry: comparison with manual probing and a 3D laser scanning over a sample plot

Francesco Avanzi1, Alberto Bianchi1, Alberto Cina2, Carlo De Michele1, Paolo Maschio2, Diana Pagliari1, Daniele Passoni3, Livio Pinto1, Marco Piras2, and Lorenzo Rossi1 Francesco Avanzi et al.
  • 1Politecnico di Milano, Department of Civil and Environmental Engineering, Piazza Leonardo da Vinci 32, 20133 Milano
  • 2Politecnico di Torino, Department of Environment, Land and Infrastructure Engineering, Corso Duca degli Abruzzi 24, 10129 Torino
  • 3Università degli Studi di Genova, Laboratory of Geodesy, Geomatics and GIS, Via Montallegro 1, 16145 Genova

Abstract. Photogrammetric surveys using Unmanned Aerial Systems (UAS) may represent an alternative to existing methods for measuring the distribution of snow, but additional efforts are still needed to establish this technique as a low-cost, yet precise tool. Importantly, existing works have mainly used sparse evaluation datasets that limit the insight into UAS performance at high spatial resolutions. Here, we compare a UAS-based photogrammetric map of snow depth with data acquired with a MultiStation and with manual probing over a sample plot. The relatively high density of manual data (135 pt over 6700 m2, i.e., 2 pt/100 m2) enables to assess the performance of UAS in capturing the marked spatial variability of snow. The use of a MultiStation, which exploits a scanning principle, also enables to compare UAS data on snow with a frequently used instrument in high-resolution applications. Results show that the Root Mean Square Error (RMSE) between UAS and MultiStation data on snow is equal to 0.036 m when comparing the two point clouds. A large fraction of this difference may be, however, due to spurious differences between datasets due to simultaneous snowmelt, as the RMSE on bare soil is equal to 0.02 m. When comparing UAS data with manual probing, the RMSE is equal to 0.31 m, whereas the median difference is equal to 0.12 m. The statistics significantly decrease up to RMSE = 0.17 m when excluding areas of likely water accumulation in snow and ice layers. These results suggest that UAS represent a competitive choice among existing techniques for high-precision, high-resolution remote sensing of snow.

Francesco Avanzi et al.

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Francesco Avanzi et al.

Francesco Avanzi et al.

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Short summary
We compare three different instruments used to collect snow depth, i.e., photogrammetric surveys using Unmanned Aerial Systems (UAS), a 3D laser scanning, and manual probing. The relatively high density of manual data (135 pt over 6700 m2, i.e., 2 pt/100 m2) enables to assess the performance of UAS in capturing the marked spatial variability of snow. Results suggest that UAS represent a competitive choice among existing techniques for high-precision, high-resolution remote sensing of snow.
We compare three different instruments used to collect snow depth, i.e., photogrammetric surveys...
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