气候变化领域集成服务门户(CCPortal): Detection of coarse woody debris using airborne light detection and ranging (LiDAR)

CCPortal

DOI	10.1016/j.foreco.2018.11.049
	Detection of coarse woody debris using airborne light detection and ranging (LiDAR)
	Joyce M.J.; Erb J.D.; Sampson B.A.; Moen R.A.
发表日期	2019
ISSN	0378-1127
起始页码	678
结束页码	689
卷号	433
英文摘要	Coarse woody debris (CWD) is an essential component of forest ecosystems that provides habitat for diverse species, functions in water and nutrient cycling, and can be a potential surface fuel in wildfires. CWD detection and mapping would enhance forestry and wildlife research and management but passive remote sensing technologies cannot provide information on features beneath forest canopy, while field-based CWD inventories are not practical for mapping CWD over large areas. Airborne light detecting and ranging (LiDAR) is a remote sensing technology that provides detailed information on three-dimensional vegetation structure that could overcome limitations of passive remote sensing technologies and field-based inventories. Our objectives were to evaluate whether airborne LiDAR could be used to detect individual pieces of CWD. We measured 1679 pieces of CWD at 144 field plots from 2015 to 2016. We acquired high-density (∼24 first returns/m2) LiDAR data in 2014, filtered out canopy and sub-canopy returns using a height threshold based on field measurements of CWD, and used height-filtered data to determine which field-measured pieces of CWD were visible in the resulting point cloud. CWD pieces that were detected constituted 50% of plot CWD volume, and there was a strong, positive correlation between total plot CWD volume and volume of detected pieces (r = 0.96). Overall, we detected 23% of the individual pieces of CWD we measured. Large pieces of CWD were most likely to be detected, with the majority of pieces ≥30 cm diameter or ≥13.9 m long detected. Canopy density, shrub density, and forest type did not influence detection probability. CWD detection rates increased from 1 pulses/m2 to 16 pulses/m2, and CWD detection rate was constant from 16 pulses/m2 to 24 pulses/m2. Our results demonstrate that airborne LiDAR can be used to detect CWD. LiDAR-based detection and mapping of CWD will be most useful for applications that focus on larger and longer pieces of CWD or applications focused on total CWD volume. © 2018 Elsevier B.V.
英文关键词	Coarse woody debris; LiDAR; Remote sensing; Wildlife habitat
语种	英语
scopus关键词	Animals; Debris; Ecosystems; Forestry; Mapping; Passive filters; Remote sensing; Coarse woody debris; Detection probabilities; Light detection and ranging; Passive remote sensing; Positive correlations; Remote sensing technology; Vegetation structure; Wildlife habitats; Optical radar; canopy architecture; coarse woody debris; correlation; detection method; ecosystem function; forest ecosystem; inventory; lidar; nutrient cycling; probability; remote sensing; species diversity; vegetation structure; Animals; Density; Detection; Ecosystems; Forestry; Mapping; Remote Sensing; Volume
来源期刊	Forest Ecology and Management
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/156236
作者单位	Integrated Biosciences Graduate Program, University of Minnesota, 1035 Kirby Dr, Duluth, MN 55812, United States; Forest Wildlife and Populations Research Group, Minnesota Department of Natural Resources, 1201 E. Hwy 2, Grand Rapids, MN 55744, United States; Natural Resources Research Institute, University of Minnesota Duluth, 5013 Miller Trunk Hwy, Duluth, MN 55811, United States; Deptartment of Biology, University of Minnesota Duluth, 1035 Kirby Drive, Duluth, MN 55812, United States
推荐引用方式 GB/T 7714	Joyce M.J.,Erb J.D.,Sampson B.A.,et al. Detection of coarse woody debris using airborne light detection and ranging (LiDAR)[J],2019,433.
APA	Joyce M.J.,Erb J.D.,Sampson B.A.,&Moen R.A..(2019).Detection of coarse woody debris using airborne light detection and ranging (LiDAR).Forest Ecology and Management,433.
MLA	Joyce M.J.,et al."Detection of coarse woody debris using airborne light detection and ranging (LiDAR)".Forest Ecology and Management 433(2019).