Individual tree crown segmentation from airborne LiDAR data using a novel Gaussian filter and energy function minimization-based approach
Individual tree crown segmentation from airborne LiDAR data using a novel Gaussian filter and energy function minimization-based approach
复制标题
使用新型高斯滤波器和基于能量函数最小化的方法对机载 LiDAR 数据进行个体树冠分割
DOI：
10.1016/j.rse.2021.112307
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发表时间：
2021-04
影响因子：
13.5
通讯作者：
Cao Lin
中科院分区：
文献类型：
--
作者：
Yun Ting;Jiang Kang;Li Guangchao;Eichhorn Markus P;Fan Jiangchuan;Liu Fangzhou;Chen Bangqian;An Feng;Cao Lin
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
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影响因子：
2.9
作者：
W. M. Jaafar;I. Woodhouse;C. Silva;H. Omar;K. N. A. Maulud;A. Hudak;Carine Klauberg;A. Cardil;M. Mohan
通讯作者：
W. M. Jaafar;I. Woodhouse;C. Silva;H. Omar;K. N. A. Maulud;A. Hudak;Carine Klauberg;A. Cardil;M. Mohan
影响因子：
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影响因子：
1.3
作者：
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通讯作者：
Michael B. Gousie;W. R. Franklin
DOI：
10.1016/j.isprsjprs.2018.05.006
发表时间：
2018-07
影响因子：
12.7
作者：
N. Amiri;P. Polewski;M. Heurich;P. Krzystek;A. Skidmore
通讯作者：
N. Amiri;P. Polewski;M. Heurich;P. Krzystek;A. Skidmore
影响因子：
56.9
作者：
Azriel Rosenfeld
通讯作者：
Azriel Rosenfeld