气候变化领域集成服务门户(CCPortal): Individual tree crown segmentation from airborne LiDAR data using a novel Gaussian filter and energy function minimization-based approach

CCPortal

DOI	10.1016/j.rse.2021.112307
	Individual tree crown segmentation from airborne LiDAR data using a novel Gaussian filter and energy function minimization-based approach
	Yun T.; Jiang K.; Li G.; Eichhorn M.P.; Fan J.; Liu F.; Chen B.; An F.; Cao L.
发表日期	2021
ISSN	00344257
卷号	256
英文摘要	Accurate segmentation of individual tree crowns (ITCs) from airborne light detection and ranging (LiDAR) data remains a challenge for forest inventories. Although many ITC segmentation methods have been developed to derive tree crown information from airborne LiDAR data, these algorithms contain uncertainty in processing false treetops because of foliage clumps and lateral branches, overlapping canopies without clear valley-shape areas, and sub-canopy crowns with neighbouring trees that obscure their shapes from an aerial perspective. Here, we propose an approach to crown segmentation using computer vision theories applied in different forest types. First, a dual Gaussian filter was designed with automated adaptive parameter assignment and a screening strategy for false treetops. This preserved the geometric characteristics of sub-canopy trees while eliminating false treetops. Second, anisotropic water expansion controlled by the energy function was applied for accurate crown segmentation. This utilized gradient information from the digital surface model and explored the morphological structures of tree crown boundaries as analogous to the maximal valley height difference from surrounding treetops. We demonstrate the generality of our approach in the subtropical forests within China. Our approach enhanced the detection rate of treetops and ITC segmentation relative to the marker-controlled watershed method, especially in complicated intersections of multiple crowns. A high performance was demonstrated for three pure Eucalyptus plots (a treetop detection rate r ≥ 0.95 and crown width estimation R2 ≥ 0.90 for canopy trees; r ≥ 0.85 and R2 ≥ 0.88 for sub-canopy trees) and three plots dominated by Chinese fir (r ≥ 0.95 and R2 ≥ 0.87 for canopy trees; r ≥ 0.79 and R2 ≥ 0.83 for sub-canopy trees). Finally, in a relatively complex forest park containing a wide range of tree species and sizes, a high performance was also achieved (r = 0.93 and R2 ≥ 0.85 for canopy trees; r = 0.70 and R2 ≥ 0.80 for sub-canopy trees). Our method demonstrates that methods inspired by the computer vision theory can improve on existing approaches, providing the potential for accurate crown segmentation even in mixed forests with complex structures © 2021 Elsevier Inc.
英文关键词	Airborne LiDAR; Computer vision; Energy minimization; Forestry; Individual tree segmentation
语种	英语
scopus关键词	Antennas; Computer vision; Optical radar; Pulse shaping circuits; Digital surface models; Energy function minimization; Geometric characteristics; Gradient informations; Individual tree crown; Light detection and ranging; Marker-controlled watersheds; Morphological structures; Forestry; Cunninghamia lanceolata; Eucalyptus
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/178946
作者单位	Co-Innovation Centre for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, 210037, China; School of Biological, Earth and Environmental Sciences, University College Cork, Distillery Fields, North Mall, Cork, T23 N73K, Ireland; Environmental Research Institute, University College Cork, Lee Road, Cork, T23 XE10, Ireland; National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097, China; Chinese Academy of Tropical Agricultural Sciences, Ministry of Agriculture, Rubber Research Institute, Danzhou Investigation and Experiment Station of Tropical Crops, Danzhou, China
推荐引用方式 GB/T 7714	Yun T.,Jiang K.,Li G.,et al. Individual tree crown segmentation from airborne LiDAR data using a novel Gaussian filter and energy function minimization-based approach[J],2021,256.
APA	Yun T..,Jiang K..,Li G..,Eichhorn M.P..,Fan J..,...&Cao L..(2021).Individual tree crown segmentation from airborne LiDAR data using a novel Gaussian filter and energy function minimization-based approach.Remote Sensing of Environment,256.
MLA	Yun T.,et al."Individual tree crown segmentation from airborne LiDAR data using a novel Gaussian filter and energy function minimization-based approach".Remote Sensing of Environment 256(2021).