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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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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