Arid
DOI10.1016/j.rse.2013.10.021
Quantifying spatial distribution of snow depth errors from LiDAR using Random Forest
Tinkham, Wade T.1; Smith, Alistair M. S.1; Marshall, Hans-Peter2; Link, Timothy E.1; Falkowski, Michael J.3; Winstral, Adam H.4
通讯作者Tinkham, Wade T.
来源期刊REMOTE SENSING OF ENVIRONMENT
ISSN0034-4257
EISSN1879-0704
出版年2014
卷号141页码:105-115
英文摘要

There is increasing need to characterize the distribution of snow in complex terrain using remote sensing approaches, especially in isolated mountainous regions that are often water-limited, the principal source of terrestrial freshwater, and sensitive to climatic shifts and variations. We apply intensive topographic surveys, multi-temporal LiDAR, and Random Forest modeling to quantify snow volume and characterize associated errors across seven land cover types in a semi-arid mountainous catchment at a 1 and 4 m spatial resolution. The LiDAR-based estimates of both snow-off surface topology and snow depths were validated against ground-based measurements across the catchment. LiDAR-derived snow depths estimates were most accurate in areas of low lying vegetation such as meadow and shrub vegetation (RMSE = 0.14 m) as compared to areas consisting of tree cover (RMSE = 0.20-0.35 m). The highest errors were found along the edge of conifer forests (RMSE = 0.35 m), however a second conifer transect outside the catchment had much lower errors (RMSE = 0.21 m). This difference is attributed to the wind exposure of the first site that led to highly variable snow depths at short spatial distances. The Random Forest modeled errors deviated from the field measured errors with-a RMSE of 0.09-0.34 m across the different cover types. The modeling was used to calculate a theoretical lower and upper bound of catchment snow volume error of 21-30%. Results show that snow drifts, which are important for maintaining spring and summer stream flows and establishing and sustaining water-limited plant species, contained 30 +/- 5-6% of the snow volume while only occupying 10% of the catchment area similar to findings by prior physically-based modeling approaches. This study demonstrates the potential utility of combining multi-temporal LiDAR with Random Forest modeling to quantify the distribution of snow depth with a reasonable degree of accuracy. (C) 2013 Elsevier Inc. All rights reserved.


英文关键词LiDAR Snow Snow depth Snow volume Random Forest
类型Article
语种英语
国家USA
收录类别SCI-E
WOS记录号WOS:000331662600009
WOS关键词WATER EQUIVALENT ; MOUNTAIN ; ACCUMULATION ; VARIABILITY ; SUCCESSION ; DEPOSITION ; HYDROLOGY ; PATTERNS ; TERRAIN ; CLIMATE
WOS类目Environmental Sciences ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型期刊论文
条目标识符http://119.78.100.177/qdio/handle/2XILL650/184715
作者单位1.Univ Idaho, Coll Nat Resources, Dept Forest Rangeland & Fire Sci, Moscow, ID 83844 USA;
2.Boise State Univ, Ctr Geophys Invest Shallow Subsurface, Boise, ID 83725 USA;
3.Univ Minnesota, Dept Forest Resources, St Paul, MN 55108 USA;
4.ARS, Northwest Watershed Res Ctr, Boise, ID 83712 USA
推荐引用方式
GB/T 7714
Tinkham, Wade T.,Smith, Alistair M. S.,Marshall, Hans-Peter,et al. Quantifying spatial distribution of snow depth errors from LiDAR using Random Forest[J],2014,141:105-115.
APA Tinkham, Wade T.,Smith, Alistair M. S.,Marshall, Hans-Peter,Link, Timothy E.,Falkowski, Michael J.,&Winstral, Adam H..(2014).Quantifying spatial distribution of snow depth errors from LiDAR using Random Forest.REMOTE SENSING OF ENVIRONMENT,141,105-115.
MLA Tinkham, Wade T.,et al."Quantifying spatial distribution of snow depth errors from LiDAR using Random Forest".REMOTE SENSING OF ENVIRONMENT 141(2014):105-115.
条目包含的文件
条目无相关文件。
个性服务
推荐该条目
保存到收藏夹
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Tinkham, Wade T.]的文章
[Smith, Alistair M. S.]的文章
[Marshall, Hans-Peter]的文章
百度学术
百度学术中相似的文章
[Tinkham, Wade T.]的文章
[Smith, Alistair M. S.]的文章
[Marshall, Hans-Peter]的文章
必应学术
必应学术中相似的文章
[Tinkham, Wade T.]的文章
[Smith, Alistair M. S.]的文章
[Marshall, Hans-Peter]的文章
相关权益政策
暂无数据
收藏/分享

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。