globalchange  > 气候变化与战略
DOI: 10.5194/tc-14-2925-2020
论文题名:
Snow depth mapping from stereo satellite imagery in mountainous terrain: Evaluation using airborne laser-scanning data
作者: Deschamps-Berger C.; Gascoin S.; Berthier E.; Deems J.; Gutmann E.; Dehecq A.; Shean D.; Dumont M.
刊名: Cryosphere
ISSN: 19940416
出版年: 2020
卷: 14, 期:9
起始页码: 2925
结束页码: 2940
语种: 英语
英文关键词: accuracy assessment ; airborne survey ; laser method ; mapping method ; model validation ; mountain environment ; photogrammetry ; Pleiades ; satellite imagery ; snow ; spatial resolution ; stereo image ; terrain ; unmanned vehicle ; California ; Tuolumne River ; United States ; United States
英文摘要: Accurate knowledge of snow depth distributions in mountain catchments is critical for applications in hydrology and ecology. Recently, a method was proposed to map snow depth at meter-scale resolution from very-highresolution stereo satellite imagery (e.g., Pléiades) with an accuracy close to 0.5 m. However, the validation was limited to probe measurements and unmanned aircraft vehicle (UAV) photogrammetry, which sampled a limited fraction of the topographic and snow depth variability. We improve upon this evaluation using accurate maps of the snow depth derived from Airborne Snow Observatory laser-scanning measurements in the Tuolumne river basin, USA. We find a good agreement between both datasets over a snow-covered area of 138 km2 on a 3m grid, with a positive bias for a Pléiades snow depth of 0.08 m, a root mean square error of 0.80m and a normalized median absolute deviation (NMAD) of 0.69 m. Satellite data capture the relationship between snow depth and elevation at the catchment scale and also small-scale features like snow drifts and avalanche deposits at a typical scale of tens of meters. The random error at the pixel level is lower in snow-free areas than in snow-covered areas, but it is reduced by a factor of 2 (NMAD of approximately 0.40m for snow depth) when averaged to a 36m grid. We conclude that satellite photogrammetry stands out as a convenient method to estimate the spatial distribution of snow depth in high mountain catchments. © Author(s) 2020.
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被引频次[WOS]:45   [查看WOS记录]     [查看WOS中相关记录]
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/164443
Appears in Collections:气候变化与战略

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作者单位: Centre d'Etudes Spatiales de la Biosphère, CESBIO, Univ. Toulouse, CNES/CNRS/INRA/IRD/UPS, Toulouse, 31401, France; Université Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, CNRM, Centre d'Etudes de la Neige, Grenoble, 38000, France; Centre National de la Recherche Scientifique (CNRS-LEGOS), Toulouse, 31400, France; National Snow and Ice Data Center, Boulder, CO, United States; Research Applications Lab, National Center for Atmospheric Research (NCAR), Boulder, CO, United States; Laboratory of Hydraulics, Hydrology and Glaciology (VAW), ETH Zurich, Zurich, Switzerland; Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), Birmensdorf, Switzerland; Dept. of Civil and Environmental Engineering, University of Washington, Seattle, WA, United States

Recommended Citation:
Deschamps-Berger C.,Gascoin S.,Berthier E.,et al. Snow depth mapping from stereo satellite imagery in mountainous terrain: Evaluation using airborne laser-scanning data[J]. Cryosphere,2020-01-01,14(9)
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