气候变化领域集成服务门户(CCPortal): Tree canopy and snow depth relationships at fine scales with terrestrial laser scanning

CCPortal

DOI	10.5194/tc-15-2187-2021
	Tree canopy and snow depth relationships at fine scales with terrestrial laser scanning
	Hojatimalekshah A.; Uhlmann Z.; Glenn N.F.; Hiemstra C.A.; Tennant C.J.; Graham J.D.; Spaete L.; Gelvin A.; Marshall H.-P.; McNamara J.P.; Enterkine J.
发表日期	2021
ISSN	19940416
起始页码	2187
结束页码	2209
卷号	15 期号:5
英文摘要	Understanding the impact of tree structure on snow depth and extent is important in order to make predictions of snow amounts and how changes in forest cover may affect future water resources. In this work, we investigate snow depth under tree canopies and in open areas to quantify the role of tree structure in controlling snow depth, as well as the controls from wind and topography. We use fine-scale terrestrial laser scanning (TLS) data collected across Grand Mesa, Colorado, USA (winter 2016-2017), to measure the snow depth and extract horizontal and vertical tree descriptors (metrics) at six sites. We utilize these descriptors along with topographical metrics in multiple linear and decision tree regressions to investigate snow depth variations under the canopy and in open areas. Canopy, topography, and snow interaction results indicate that vegetation structural metrics (specifically foliage height diversity; FHD) along with local-scale processes like wind and topography are highly influential in snow depth variation. Our study specifies that windward slopes show greater impact on snow accumulation than vegetation metrics. In addition, the results indicate that FHD can explain up to 27 % of sub-canopy snow depth variation at sites where the effect of topography and wind is negligible. Solar radiation and elevation are the dominant controls on snow depth in open areas. Fine-scale analysis from TLS provides information on local-scale controls and provides an opportunity to be readily coupled with lidar or photogrammetry from uncrewed aerial systems (UASs) as well as airborne and spaceborne platforms to investigate larger-scale controls on snow depth. © Author(s) 2021. This work is distributed under the Creative Commons Attribution 4.0 License.
英文关键词	forest cover; laser method; lidar; photogrammetry; seasonal variation; snow accumulation; tree; vegetation type; Colorado; Grand Mesa; United States
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/202332
作者单位	Department of Geosciences, Boise State University, Boise, ID 83725, United States; US Department of Agriculture, Forest Service, Geospatial Management Office, Salt Lake, UT 84138, United States; US Army Corps of Engineers, Sacramento, CA 95814, United States; Minnesota Department of Natural Resources, Division of Forestry, Resource Assessment, Grand Rapids, MN 55744, United States; US Army Corps of Engineer, Cold Regions Research and Engineering Laboratory, Hanover, NH 03755, United States
推荐引用方式 GB/T 7714	Hojatimalekshah A.,Uhlmann Z.,Glenn N.F.,et al. Tree canopy and snow depth relationships at fine scales with terrestrial laser scanning[J],2021,15(5).
APA	Hojatimalekshah A..,Uhlmann Z..,Glenn N.F..,Hiemstra C.A..,Tennant C.J..,...&Enterkine J..(2021).Tree canopy and snow depth relationships at fine scales with terrestrial laser scanning.Cryosphere,15(5).
MLA	Hojatimalekshah A.,et al."Tree canopy and snow depth relationships at fine scales with terrestrial laser scanning".Cryosphere 15.5(2021).