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

We investigate snow depth distribution at peak accumulation over a small Alpine area ( ∼  0.3 km<sup>2</sup>) using photogrammetry-based surveys with a fixed-wing unmanned aerial system (UAS). These devices are growing in popularity as inexpensive alternatives to existing techniques with...

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Main Authors: C. De Michele, F. Avanzi, D. Passoni, R. Barzaghi, L. Pinto, P. Dosso, A. Ghezzi, R. Gianatti, G. Della Vedova
Format: Article
Language:English
Published: Copernicus Publications 2016-03-01
Series:The Cryosphere
Online Access:http://www.the-cryosphere.net/10/511/2016/tc-10-511-2016.pdf
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spelling doaj-f65fc2c9fe7a409c96f4851a9b222ed52020-11-24T23:59:31ZengCopernicus PublicationsThe Cryosphere1994-04161994-04242016-03-0110251152210.5194/tc-10-511-2016Using a fixed-wing UAS to map snow depth distribution: an evaluation at peak accumulationC. De Michele0F. Avanzi1D. Passoni2R. Barzaghi3L. Pinto4P. Dosso5A. Ghezzi6R. Gianatti7G. Della Vedova8Politecnico di Milano, Department of Civil and Environmental Engineering, Piazza Leonardo da Vinci 32, 20133 Milan, ItalyPolitecnico di Milano, Department of Civil and Environmental Engineering, Piazza Leonardo da Vinci 32, 20133 Milan, ItalyUniversity of Genova, Department of Civil, Chemical and Environmental Engineering, Via Montallegro 1, 16145 Genoa, ItalyPolitecnico di Milano, Department of Civil and Environmental Engineering, Piazza Leonardo da Vinci 32, 20133 Milan, ItalyPolitecnico di Milano, Department of Civil and Environmental Engineering, Piazza Leonardo da Vinci 32, 20133 Milan, ItalyStudio di Ingegneria Terradat, Paderno Dugnano, ItalyPolitecnico di Milano, Department of Civil and Environmental Engineering, Piazza Leonardo da Vinci 32, 20133 Milan, Italya2a Group, Grosio, Italya2a Group, Grosio, ItalyWe investigate snow depth distribution at peak accumulation over a small Alpine area ( ∼  0.3 km<sup>2</sup>) 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.http://www.the-cryosphere.net/10/511/2016/tc-10-511-2016.pdf
collection DOAJ
language English
format Article
sources DOAJ
author C. De Michele
F. Avanzi
D. Passoni
R. Barzaghi
L. Pinto
P. Dosso
A. Ghezzi
R. Gianatti
G. Della Vedova
spellingShingle C. De Michele
F. Avanzi
D. Passoni
R. Barzaghi
L. Pinto
P. Dosso
A. Ghezzi
R. Gianatti
G. Della Vedova
Using a fixed-wing UAS to map snow depth distribution: an evaluation at peak accumulation
The Cryosphere
author_facet C. De Michele
F. Avanzi
D. Passoni
R. Barzaghi
L. Pinto
P. Dosso
A. Ghezzi
R. Gianatti
G. Della Vedova
author_sort C. De Michele
title Using a fixed-wing UAS to map snow depth distribution: an evaluation at peak accumulation
title_short Using a fixed-wing UAS to map snow depth distribution: an evaluation at peak accumulation
title_full Using a fixed-wing UAS to map snow depth distribution: an evaluation at peak accumulation
title_fullStr Using a fixed-wing UAS to map snow depth distribution: an evaluation at peak accumulation
title_full_unstemmed Using a fixed-wing UAS to map snow depth distribution: an evaluation at peak accumulation
title_sort using a fixed-wing uas to map snow depth distribution: an evaluation at peak accumulation
publisher Copernicus Publications
series The Cryosphere
issn 1994-0416
1994-0424
publishDate 2016-03-01
description We investigate snow depth distribution at peak accumulation over a small Alpine area ( ∼  0.3 km<sup>2</sup>) 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.
url http://www.the-cryosphere.net/10/511/2016/tc-10-511-2016.pdf
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