气候变化领域集成服务门户(CCPortal): Improving sub-canopy snow depth mapping with unmanned aerial vehicles: Lidar versus structure-from-motion techniques

CCPortal

DOI	10.5194/tc-14-1919-2020
	Improving sub-canopy snow depth mapping with unmanned aerial vehicles: Lidar versus structure-from-motion techniques
	Harder P.; Pomeroy J.W.; Helgason W.D.; Helgason W.D.
发表日期	2020
ISSN	19940416
起始页码	1919
结束页码	1935
卷号	14 期号:6
英文摘要	Vegetation has a tremendous influence on snow processes and snowpack dynamics, yet remote sensing techniques to resolve the spatial variability of sub-canopy snow depth are not always available and are difficult from spacebased platforms. Unmanned aerial vehicles (UAVs) have had recent widespread application to capture high-resolution information on snow processes and are herein applied to the sub-canopy snow depth challenge. Previous demonstrations of snow depth mapping with UAV structure from motion (SfM) and airborne lidar have focussed on non-vegetated surfaces or reported large errors in the presence of vegetation. In contrast, UAV-lidar systems have high-density point clouds and measure returns from a wide range of scan angles, increasing the likelihood of successfully sensing the subcanopy snow depth. The effectiveness of UAV lidar and UAV SfM in mapping snow depth in both open and forested terrain was tested in a 2019 field campaign at the Canadian Rockies Hydrological Observatory, Alberta, and at Canadian prairie sites near Saskatoon, Saskatchewan, Canada. Only UAV lidar could successfully measure the sub-canopy snow surface with reliable sub-canopy point coverage and consistent error metrics (root mean square error (RMSE) <0:17 m and bias-0:03 to-0:13 m). Relative to UAV lidar, UAV SfM did not consistently sense the sub-canopy snow surface, the interpolation needed to account for point cloud gaps introduced interpolation artefacts, and error metrics demonstrated relatively large variability (RMSE<0:33 m and bias 0.08 to-0:14 m). With the demonstration of sub-canopy snow depth mapping capabilities, a number of early applications are presented to showcase the ability of UAV lidar to effectively quantify the many multiscale snow processes defining snowpack dynamics in mountain and prairie environments. © 2020 Author(s).
英文关键词	aerial survey; lidar; maximum likelihood analysis; observatory; remote sensing; snowpack; unmanned vehicle; Alberta; Canada; Saskatchewan; Saskatoon
语种	英语
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/202095
作者单位	Centre for Hydrology, University of Saskatchewan, Saskatoon, SK, Canada; Department of Civil Geological, and Environmental Engineering, University of Saskatchewan, Saskatoon, SK, Canada
推荐引用方式 GB/T 7714	Harder P.,Pomeroy J.W.,Helgason W.D.,et al. Improving sub-canopy snow depth mapping with unmanned aerial vehicles: Lidar versus structure-from-motion techniques[J],2020,14(6).
APA	Harder P.,Pomeroy J.W.,Helgason W.D.,&Helgason W.D..(2020).Improving sub-canopy snow depth mapping with unmanned aerial vehicles: Lidar versus structure-from-motion techniques.Cryosphere,14(6).
MLA	Harder P.,et al."Improving sub-canopy snow depth mapping with unmanned aerial vehicles: Lidar versus structure-from-motion techniques".Cryosphere 14.6(2020).