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

文献类型：期刊论文

作者Liu, Xiaoqiang5; Ma, Qin5; Wu, Xiaoyong5; Hu, Tianyu5; Liu, Zhonghua5; Liu, Lingli5; Guo, Qinghua1,3; Su, Yanjun4,5
刊名REMOTE SENSING OF ENVIRONMENT
出版日期2022
卷号282
ISSN号0034-4257
关键词Forest canopy structural complexity Entropy Multiplatform lidar point clouds Mann-Kendall test Kernel density estimation
DOI10.1016/j.rse.2022.113280
文献子类Article
英文摘要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.
学科主题Environmental Sciences ; Remote Sensing ; Imaging Science & Photographic Technology
电子版国际标准刊号1879-0704
出版地NEW YORK
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)
语种英语
出版者ELSEVIER SCIENCE INC
WOS记录号WOS:000862499900003
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]
源URL[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.Peking Univ, Inst Remote Sensing & Geog Informat Syst, Sch Earth & Space Sci, Beijing 100871, Peoples R China
2.Chinese Acad Sci, Inst Bot, 20 Nanxincun, Beijing 100093, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
4.Peking Univ, Inst Ecol, Coll Urban & Environm Sci, Beijing 100871, Peoples R China
5.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, 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).

入库方式： OAI收割

来源：植物研究所

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