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

被引:76
作者
De Michele, Carlo [1 ]
Avanzi, Francesco [1 ]
Passoni, Daniele [2 ]
Barzaghi, Riccardo [1 ]
Pinto, Livio [1 ]
Dosso, Paolo [3 ]
Ghezzi, Antonio [1 ]
Gianatti, Roberto [4 ]
Della Vedova, Giacomo [4 ]
机构
[1] Politecn Milan, Dept Civil & Environm Engn, Piazza Leonardo da Vinci 32, I-20133 Milan, Italy
[2] Univ Genoa, Dept Civil Chem & Environm Engn, Via Montallegro 1, I-16145 Genoa, Italy
[3] Studio Ingn Terradat, Paderno Dugnano, Italy
[4] A2a Grp, Grosio, Italy
关键词
TELEMETRY SNOTEL DATA; WATER EQUIVALENT; SPATIAL VARIABILITY; ALPINE TERRAIN; MOUNTAIN CATCHMENT; RIVER-BASIN; COVER; MODEL; LIDAR; SCALE;
D O I
10.5194/tc-10-511-2016
中图分类号
P9 [自然地理学];
学科分类号
0705 ; 070501 ;
摘要
We investigate snow depth distribution at peak accumulation over a small Alpine area (similar to aEuro-0.3aEuro-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 -0.073aEuro-m and a RMSE equal to 0.14aEuro-m. We have also explored how some basic snow depth statistics (e.g., mean, standard deviation, minima and maxima) change with sampling resolution (from 5aEuro-cm up to similar to aEuro-100aEuro-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 1aEuro-m. This provides a possible indication of sampling resolution in similar conditions.
引用
收藏
页码:511 / 522
页数:12
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