气候变化领域集成服务门户(CCPortal): Monitoring snow depth change across a range of landscapes with ephemeral snowpacks using structure from motion applied to lightweight unmanned aerial vehicle videos

CCPortal

DOI	10.5194/tc-12-3535-2018
	Monitoring snow depth change across a range of landscapes with ephemeral snowpacks using structure from motion applied to lightweight unmanned aerial vehicle videos
	Fernandes R.; Prevost C.; Canisius F.; Leblanc S.G.; Maloley M.; Oakes S.; Holman K.; Knudby A.
发表日期	2018
ISSN	19940416
卷号	12 期号:11
英文摘要	Differencing of digital surface models derived from structure from motion (SfM) processing of airborne imagery has been used to produce snow depth (SD) maps with between ∼ 2 and ∼ 15 cm horizontal resolution and accuracies of ±10 cm over relatively flat surfaces with little or no vegetation and over alpine regions. This study builds on these findings by testing two hypotheses across a broader range of conditions: (i) that the vertical accuracy of SfM processing of imagery acquired by commercial low-cost unmanned aerial vehicle (UAV) systems can be adequately modelled using conventional photogrammetric theory and (ii) that SD change can be more accurately estimated by differencing snow-covered elevation surfaces rather than differencing a snow-covered and snow-free surface. A total of 71 UAV missions were flown over five sites, ranging from short grass to a regenerating forest, with ephemeral snowpacks. Point cloud geolocation performance agreed with photogrammetric theory that predicts uncertainty is proportional to UAV altitude and linearly related to horizontal uncertainty. The root-mean-square difference (RMSD) over the observation period, in comparison to the average of in situ measurements along ∼ 50 m transects, ranged from 1.58 to 10.56 cm for weekly SD and from 2.54 to 8.68 cm for weekly SD change. RMSD was not related to microtopography as quantified by the snow-free surface roughness. SD change uncertainty was unrelated to vegetation cover but was dominated by outliers corresponding to rapid in situ melt or onset; the median absolute difference of SD change ranged from 0.65 to 2.71 cm. These results indicate that the accuracy of UAV-based estimates of weekly snow depth change was, excepting conditions with deep fresh snow, substantially better than for snow depth and was comparable to in situ methods. © 2018 Author(s).
学科领域	airborne sensing; alpine environment; depth determination; digital photogrammetry; digital terrain model; hypothesis testing; landscape structure; microtopography; snow cover; snowpack; unmanned vehicle; vegetation cover
语种	英语
scopus关键词	airborne sensing; alpine environment; depth determination; digital photogrammetry; digital terrain model; hypothesis testing; landscape structure; microtopography; snow cover; snowpack; unmanned vehicle; vegetation cover
来源期刊	Cryosphere
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/119034
作者单位	Canada Centre for Remote Sensing, Natural Resources Canada, Ottawa, K1A0Y7, Canada; Department of Geography, Environment and Geomatics, University of Ottawa, Ottawa, K1N6Y5, Canada
推荐引用方式 GB/T 7714	Fernandes R.,Prevost C.,Canisius F.,et al. Monitoring snow depth change across a range of landscapes with ephemeral snowpacks using structure from motion applied to lightweight unmanned aerial vehicle videos[J],2018,12(11).
APA	Fernandes R..,Prevost C..,Canisius F..,Leblanc S.G..,Maloley M..,...&Knudby A..(2018).Monitoring snow depth change across a range of landscapes with ephemeral snowpacks using structure from motion applied to lightweight unmanned aerial vehicle videos.Cryosphere,12(11).
MLA	Fernandes R.,et al."Monitoring snow depth change across a range of landscapes with ephemeral snowpacks using structure from motion applied to lightweight unmanned aerial vehicle videos".Cryosphere 12.11(2018).