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The Cryosphere An interactive open-access journal of the European Geosciences Union
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Volume 10, issue 2
The Cryosphere, 10, 511-522, 2016
https://doi.org/10.5194/tc-10-511-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
The Cryosphere, 10, 511-522, 2016
https://doi.org/10.5194/tc-10-511-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 04 Mar 2016

Research article | 04 Mar 2016

Using a fixed-wing UAS to map snow depth distribution: an evaluation at peak accumulation

Carlo De Michele1, Francesco Avanzi1, Daniele Passoni2, Riccardo Barzaghi1, Livio Pinto1, Paolo Dosso3, Antonio Ghezzi1, Roberto Gianatti4, and Giacomo Della Vedova4 Carlo De Michele et al.
  • 1Politecnico di Milano, Department of Civil and Environmental Engineering, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
  • 2University of Genova, Department of Civil, Chemical and Environmental Engineering, Via Montallegro 1, 16145 Genoa, Italy
  • 3Studio di Ingegneria Terradat, Paderno Dugnano, Italy
  • 4a2a Group, Grosio, Italy

Abstract. We investigate snow depth distribution at peak accumulation over a small Alpine area ( ∼ 0.3km2) using photogrammetry-based surveys with a fixed-wing unmanned aerial system (UAS). These devices are growing in popularity as inexpensive alternatives to existing techniques within the field of remote sensing, but the assessment of their performance in Alpine areas to map snow depth distribution is still an open issue. Moreover, several existing attempts to map snow depth using UASs have used multi-rotor systems, since they guarantee higher stability than fixed-wing systems. We designed two field campaigns: during the first survey, performed at the beginning of the accumulation season, the digital elevation model of the ground was obtained. A second survey, at peak accumulation, enabled us to estimate the snow depth distribution as a difference with respect to the previous aerial survey. Moreover, the spatial integration of UAS snow depth measurements enabled us to estimate the snow volume accumulated over the area. On the same day, we collected 12 probe measurements of snow depth at random positions within the case study to perform a preliminary evaluation of UAS-based snow depth. Results reveal that UAS estimations of point snow depth present an average difference with reference to manual measurements equal to −0.073m and a RMSE equal to 0.14m. We have also explored how some basic snow depth statistics (e.g., mean, standard deviation, minima and maxima) change with sampling resolution (from 5cm up to  ∼ 100m): for this case study, snow depth standard deviation (hence coefficient of variation) increases with decreasing cell size, but it stabilizes for resolutions smaller than 1m. This provides a possible indication of sampling resolution in similar conditions.

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We investigate snow depth distribution at peak accumulation over a small Alpine area using photogrammetry-based surveys with a fixed wing unmanned aerial system. Results reveal that UAS estimations of point snow depth present an average difference with reference to manual measurements equal to -0.073 m. Moreover, in this case study snow depth standard deviation (hence coefficient of variation) increases with decreasing cell size, but it stabilizes for resolutions smaller than 1 m.
We investigate snow depth distribution at peak accumulation over a small Alpine area using...
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