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

• Published in: The Cryosphere
• Main Authors: C. De Michele, F. Avanzi, D. Passoni, R. Barzaghi, L. Pinto, P. Dosso, A. Ghezzi, R. Gianatti, G. Della Vedova
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2016
• Subjects: Environmental sciences; GE1-350; Geology; QE1-996.5; The Cryosphere
• Online Access: https://doi.org/10.5194/tc-10-511-2016; https://doaj.org/article/f65fc2c9fe7a409c96f4851a9b222ed5

We investigate snow depth distribution at peak accumulation over a small Alpine area ( ∼ 0.3 km 2 ) 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 −0.073 m and a RMSE equal to 0.14 m. We have also explored how some basic snow depth statistics (e.g., mean, standard deviation, minima and maxima) change with sampling resolution (from 5 cm up to ∼ 100 m): for 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. This provides a possible indication of sampling resolution in similar conditions.