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...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2016-03-01
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| Series: | The Cryosphere |
| Online Access: | http://www.the-cryosphere.net/10/511/2016/tc-10-511-2016.pdf |
| Summary: | 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. |
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| ISSN: | 1994-0416 1994-0424 |