A novel entropy-based method to quantify forest canopy structural complexity from multiplatform lidar point clouds

doi:10.1016/j.rse.2022.113280

	A novel entropy-based method to quantify forest canopy structural complexity from multiplatform lidar point clouds
	Liu, Xiaoqiang 1; Ma, Qin 1; Wu, Xiaoyong 1; Hu, Tianyu 1; Liu, Zhonghua 1; Liu, Lingli1 ; Guo, Qinghua2,3 ; Su, Yanjun 1,4
	2022
发表期刊	REMOTE SENSING OF ENVIRONMENT
ISSN	0034-4257
卷号	282
摘要	Forest canopy structural complexity (CSC) describes the three-dimensional (3D) arrangement of canopy ele-ments, and has become an emergent forest attribute mediating forest ecosystem functioning along with species diversity. Light detection and ranging (lidar), especially the emerging near-surface lidar platforms (e.g., terrestrial laser scanning/TLS, backpack laser scanning/BLS, unmanned aerial vehicle laser scanning/ULS), can depict 3D canopy information with high efficiency and accuracy, providing an ideal data source for forest CSC quantification. However, current existing lidar-based CSC quantification indices may share common limitations of getting saturated in structurally complex forest stands and not fully capturing within-canopy structural var-iations. In this study, we introduced the concept of entropy into forest CSC quantification, and proposed a new forest CSC index, namely canopy entropy (CE). Two major bottlenecks were addressed in the CE calculation procedure, including (1) using a Mann-Kendall (MK) test-based resampling strategy to address the issue of incongruent sampling chances of canopy elements at different locations from different lidar systems, and (2) using a kernel density estimation (KDE)-based method to reduce its dependence on point density. The effec-tiveness and generality of CE were evaluated by simulating TLS and ULS point clouds from nine forest stands and collecting TLS, BLS, and ULS point clouds from 110 field plots distributed in five forest sites, covering a large variety of forest types and forest CSC conditions. The results showed that CE was an effective forest CSC quantification index that successfully captured CSC variations caused by both tree density and the number of vertical canopy layers. It had significant positive correlations with four widely used CSC indices (i.e., canopy cover, foliage height diversity, canopy top rugosity, and fractal dimension; R2: 0.32 to 0.67), but outperformed them by overcoming their common limitations. CE estimates from multiplatform lidar point clouds agreed well with each other (R2 >= 0.70, RMSE <= 0.10), indicating it has generality in cross-platform forest CSC quantification practices. We believe the proposed CE index has great potential to help us unravel the correlations among forest CSC, species diversity, and forest ecosystem functions, and therefore improve our understanding on forest ecosystem processes.
关键词	Forest canopy structural complexity Entropy Multiplatform lidar point clouds Mann-Kendall test Kernel density estimation
学科领域	Environmental Sciences ; Remote Sensing ; Imaging Science & Photographic Technology
DOI	10.1016/j.rse.2022.113280
收录类别	SCI
语种	英语
WOS关键词	GENERAL QUANTITATIVE THEORY ; LASER-SCANNING DATA ; AIRBORNE LIDAR ; STAND STRUCTURE ; DIVERSITY ; DENSITY ; HETEROGENEITY ; SELECTION
WOS研究方向	Science Citation Index Expanded (SCI-EXPANDED)
WOS记录号	WOS:000862499900003
出版者	ELSEVIER SCIENCE INC
文献子类	Article
出版地	NEW YORK
EISSN	1879-0704
资助机构	Frontier Science Key Programs of the Chinese Academy of Sciences ; National Natural Science Foundation of China ; [QYZDY-SSW-SMC011] ; [41871332] ; [31971575] ; [41901358]
作者邮箱	ysu@ibcas.ac.cn
引用统计	被引频次：14[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ibcas.ac.cn/handle/2S10CLM1/28944
专题	植被与环境变化国家重点实验室
作者单位	1.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing 100093, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Peking Univ, Inst Remote Sensing & Geog Informat Syst, Sch Earth & Space Sci, Beijing 100871, Peoples R China 4.Peking Univ, Inst Ecol, Coll Urban & Environm Sci, Beijing 100871, Peoples R China 5.Chinese Acad Sci, Inst Bot, 20 Nanxincun, Beijing 100093, Peoples R China
推荐引用方式 GB/T 7714	Liu, Xiaoqiang,Ma, Qin,Wu, Xiaoyong,et al. A novel entropy-based method to quantify forest canopy structural complexity from multiplatform lidar point clouds[J]. REMOTE SENSING OF ENVIRONMENT,2022,282.
APA	Liu, Xiaoqiang.,Ma, Qin.,Wu, Xiaoyong.,Hu, Tianyu.,Liu, Zhonghua.,...&Su, Yanjun.(2022).A novel entropy-based method to quantify forest canopy structural complexity from multiplatform lidar point clouds.REMOTE SENSING OF ENVIRONMENT,282.
MLA	Liu, Xiaoqiang,et al."A novel entropy-based method to quantify forest canopy structural complexity from multiplatform lidar point clouds".REMOTE SENSING OF ENVIRONMENT 282(2022).

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