基于网络图的地基激光雷达复杂树木点云枝叶分离方法

doi:10.11873/j.issn.1004-0323.2022.1.0161

遥感技术与应用

2022, Vol. 37

Issue (1): 161-172 DOI: 10.11873/j.issn.1004-0323.2022.1.0161

青促会十周年专栏

基于网络图的地基激光雷达复杂树木点云枝叶分离方法

林筱涵^1,²(

),李爱农¹(

),边金虎¹,张正建^1,²,南希¹

^1.中国科学院水利部成都山地灾害与环境研究所数字山地与遥感应用中心，四川成都 610041
^2.中国科学院大学资源与环境学院，北京 100049

A Method for Separating Leaf and Wood Components of Complex Tree Point Cloud Data based on Network Graph with Terrestrial Laser Scanning

Xiaohan Lin^1,²(

),Ainong Li¹(

),Jinhu Bian¹,Zhengjian Zhang^1,²,Xi Nan¹

^1.Center for Digital Mountain and Remote Sensing Application，Institute of Mountain Hazards and Environment，Chinese Academy of Sciences，Chengdu 610041，China
^2.University of Chinese Academy of Sciences，Beijing 100049，China

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摘要：

地基激光雷达树木点云数据的枝叶分离是精确计算地上生物量和叶面积指数的重要前提，也是树木三维建模的重要步骤。然而，山地复杂树木冠幅大且结构复杂，从而造成树叶与枝干之间的相互遮挡，因此很难获取高质量的点云数据，目前对其实现枝叶分离依然存在较大的困难。利用地基激光雷达FARO Focus^3D X330获取三维激光点云数据，提出了一种基于网络图的树木点云枝叶分离方法。首先，采用LeWos模型对点云进行初步的枝叶分离，分离出枝干和叶片点云。在此基础上，针对枝干和叶片混合点云通过路径追踪检测算法来精细分离枝干和叶片。随着路径长度从10增加到100，枝干点不断增加，叶片点不断减少，枝叶分离精确度、枝干F分数、叶片F分数、Kappa系数均先增加后减少。综合这4项精度评价指标，选取各个树木最优路径长度执行路径追踪检测算法。通过与LeWos模型、Tlseparation模型和高斯混合模型等主流枝叶分离方法比较发现，该方法精度更优，精确度为91.97%。而且，该方法的枝干F分数和叶片F分数均大于85%，这表明该方法具有很好的平衡性。该方法仅使用路径长度，不考虑几何特征，因此极大地提高了针叶树木的枝叶分离精度。同时，该方法对不同树种、不同密度点云枝叶分离效果均较好，鲁棒性强。精确的树木点云枝叶分离对森林资源管理和生物多样性研究等具有重要意义。

关键词： 地基激光雷达; 枝叶分离; 网络图; LeWos模型; 路径追踪检测算法

Abstract:

The leaf and wood separation of the terrestrial laser scanning tree point cloud data is an important prerequisite for the accurate estimation of above-ground biomass and leaf area index， and it is also an important step for three-dimensional modeling of a tree. However， complex trees in mountain areas have large crowns and complex structures， resulting in mutual occlusion between leaves and branches. Therefore， it is difficult to obtain high-quality point cloud data. At present， it is still difficult for complex trees to separate leaf and wood components. High-resolution point clouds were acquired with Faro Focus^3D X330. This paper proposes a method for leaf and wood separation of tree point cloud based on network graph. First， the LeWos model is used to perform preliminary leaf and wood separation on the point cloud， and separate the wood and leaf point cloud. On this basis， the path retrace detection algorithm is used to finely separate the leaf and wood for the mixed point clouds. As the retrace steps increases from 10 to 100， the wood points continue to increase， the leaf points continue to decrease， the accuracy， wood F-score decreases， leaf F-score and the Kappa coefficient first increases and then decreases. By comparing with LeWos model， Tlseparation model and Gaussian mixture model， it is found that the research method in this paper has the better precision， with an accuracy of 91.97%. Moreover， the wood F-score and leaf F-score of the proposal method are both greater than 85%， which means that the proposal method has a good balance when classifying wood and leaf. The proposal method only uses the retrace steps and does not consider the geometric characteristics， so the accuracy of branch and leaf separation of coniferous trees is greatly improved. At the same time， the method in this paper has a good effect on the separation of wood and leaf components of point clouds with different densities and different species. Therefore， the method in this paper is more robust. Accurate wood and leaf separation of tree point clouds is of great significance to forest resource management and biodiversity research.

Key words: Terrestrial laser scanning Leaf and wood separation Network graph LeWos model Path trace detection algorithm

收稿日期: 2021-10-08 出版日期: 2022-04-08

ZTFLH:

TN958.98

基金资助: 国家重点研发计划项目“山地生态系统全球变化关键参数立体观测与高分辨率产品研制”(2020YFA0608700);国家自然科学基金项目“山地典型生态参量遥感反演建模及其时空表征能力研究”(41631180)

通讯作者: 李爱农 E-mail: linxiaohan@imde.ac.cn;ainongli@imde.ac.cn

作者简介: 林筱涵（1996—），山东威海人，男，硕士研究生，主要从事激光雷达遥感研究。E?mail：linxiaohan@imde.ac.cn

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引用本文:

林筱涵,李爱农,边金虎,张正建,南希. 基于网络图的地基激光雷达复杂树木点云枝叶分离方法[J]. 遥感技术与应用, 2022, 37(1): 161-172.

Xiaohan Lin,Ainong Li,Jinhu Bian,Zhengjian Zhang,Xi Nan. A Method for Separating Leaf and Wood Components of Complex Tree Point Cloud Data based on Network Graph with Terrestrial Laser Scanning. Remote Sensing Technology and Application, 2022, 37(1): 161-172.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2022.1.0161 或 http://www.rsta.ac.cn/CN/Y2022/V37/I1/161

图1 研究区审图号：GS（2019）3266

表1 FARO Focus3D X330技术参数

图2 样方点云

表2 树木数据描述

特征	公式	参考文献
显著特征1	$λ 2$	[25]
显著特征2	$λ 0 - λ 1$	[25]
显著特征3	$λ 1 - λ 2$	[25]
平面度	$(λ 1 - λ 2) / λ 1$	[37]
线性度	$(λ 0 - λ 1) / λ 0$	[37]

表2 几何特征

表3 逐点评估枝叶分离结果的混淆矩阵

图3 精确度、枝干F分数、叶片F分数及Kappa系数随路径长度变化图

图4 样方枝叶分离结果

表4 本实验方法精度指标计算结果 (%)

表5 精确度、枝干F分数、叶片F分数和kappa系数平均值比较 (%)

表6 一类错误和二类错误比较 (%)

图5 6棵树木样本4种模型枝叶分离结果精确度

图6 6颗树木样本4种模型枝叶分离结果枝干F分数

图7 6颗树木样本4种模型枝叶分离结果叶片F分数

图8 6颗树木样本4种模型枝叶分离结果叶片kappa系数

图9 三个样本枝叶分离错误分布（第一列为Lewos模型，第二列为Tlseparation模型，第三列为高斯混合模型，第四列为本文方法。棕色点为分类正确枝干的点，绿色点为分类正确叶片的点，蓝色点为分类错误的枝干点，红色点为分类错误的叶片点）

1	Dassort M， Constant T， Fournier M. The use of terrestrial LiDAR technology in forest science： application fields， benefits and challenges［J］. Annal of Forest Science， 2011， 68（5）：959-974. DOI： 10.1007/s13595-011-0102-2 . doi: 10.1007/s13595-011-0102-2
2	Ma Y C， Piao S L， Sun Z Z， et al. Stand ages regulate the response of soil respiration to temperature in a Larix Principis-rupprechtii plantation［J］. Agricultural and Forest Meteorology，2014，184：179-187. DOI：10.1016/j.agrformet.2013. 10.008 . doi: 10.1016/j.agrformet.2013. 10.008
3	Srinivasan S， Popescu S C， Eriksson M， et al. Multi-temporal terrestrial laser scanning for modeling tree biomass change［J］. Foestr Ecology and Management， 2014， 318： 304-317. DOI： 10.1016/j.foreco.2014.01.038 . doi: 10.1016/j.foreco.2014.01.038
4	Liang X L， Kankare V， Hyyppa J， et al. Terrestrial laser scanning in forest inventories［J］. ISPRS Journal of Photogrammetry and Remote Sensing， 2016， 115：63-77. DOI： 10.1016/j.isprsjprs.2016.01.006 . doi: 10.1016/j.isprsjprs.2016.01.006
5	Lin Y. LiDAR： An important tool for next-generation phenotyping technology of high potential for plant phenomics？［J］. Computers and Electronics and Agriculture， 2015， 119：61-73.DOI： 10.1016/j.compag.2015.10.011 . doi: 10.1016/j.compag.2015.10.011
6	Llorens J， Gil E， Llop J， et al. Ultrasonic and LiDAR sensors for electronic canopy characterization in vineyards： advances to improve pesticide application methods［J］. Sensors， 2011， 11（2）： 2177-2194.DOI： 10.3390/s110202177 . doi: 10.3390/s110202177
7	Msdec S， Baret F， de Solan B， et al. High-throughput phenotyping of plant height： comparing unmanned aerial vehicles and ground LiDAR estimates［J］. Frontiers in Plant Science， 2017， 8：14.DOI： 10.3389/fpls.2017.02002 . doi: 10.3389/fpls.2017.02002
8	Berk P， Hocevar M， Stajnko D， et al. Development of alternative plant protection product application techniques in Orchards， based on measurement sensing systems： a review［J］. Computers and Electronics in Agriculture， 2016， 124： 273-288.DOI： 10.1016/j.compag.2016.04.018 . doi: 10.1016/j.compag.2016.04.018
9	Yu Y， Gao T， Zhu J， et al. Terrestrial laser scanning‐derived Canopy interception index for predicting rainfall interception［J］. Ecohydrology，2020，13（5）：15. DOI：10.1002/eco. 2212 . doi: 10.1002/eco. 2212
10	Li Y M， Su Y J， ZHao X X， et al. Retrieval of tree branch architecture attributes from terrestrial laser scan data using a Laplacian Algorithm［J］. Agricultural and Forest Meteorology， 2020， 284：107874.DOI： 10.1016/j.agrformet.2019.107874 . doi: 10.1016/j.agrformet.2019.107874
11	Li Y M， Guo Q H， Su Y J， et al. Retrieving the gap fraction， element clumping index， and leaf area index of individual trees using single-scan data from a terrestrial laser scanner［J］. ISPRS Journal of Photogrammetry and Remote Sensing， 2017， 130：308-316.DOI： 10.1016/j.isprsjprs.2017.06.006 . doi: 10.1016/j.isprsjprs.2017.06.006
12	Zhao K， Garcia M， Liu S， et al. Terrestrial Lidar remote sensing of forests： maximum likelihood estimates of canopy profile， leaf area index， and leaf angle distribution［J］. Agricultural and Forest Meteorology，2015，209-210：100-113. DOI： 10.1016/j.agrformet.2015.03.008 . doi: 10.1016/j.agrformet.2015.03.008
13	Bailey B N， Mahaffee W F. Rapid measurement of the three-dimensional distribution of leaf orientation and the leaf angle probability density function using terrestrial LiDAR scanning［J］.Remote Sensing of Environment，2017，194：63-76. DOI： 10.1088/1361-6501/aa5cfd . doi: 10.1088/1361-6501/aa5cfd
14	Liu J， Wang T， Skidmore A K， et al. Comparison of terrestrial LiDAR and digital hemispherical photography for estimating leaf angle distribution in european broadleaf beech forests［J］. ISPRS Journal of Photogrammetry and Remote Sensing， 2019， 158：76-89. DOI： 10.1016/j.isprsjprs.2019.09.015 . doi: 10.1016/j.isprsjprs.2019.09.015
15	Côté J F， Widlowski J L， Fournier R A， et al. The structural and radiative consistency of three-dimensional tree reconstructions from terrestrial LiDAR［J］. Remote Sensing of Environment，2009，113（5）：1067-1081. DOI：10.1016/j.rse.2009. 01.017 . doi: 10.1016/j.rse.2009. 01.017
16	Hu S J， Li Z R， Zhang Z Y， et al. Efficient tree modeling from airborne LiDAR point clouds［J］. Computers & Graphics， 2017， 67：1-13.DOI： 10.1016/j.cag.2017.04.004 . doi: 10.1016/j.cag.2017.04.004
17	Calders K， Origo N， Burt A， et al. Realistic forest stand reconstruction from terrestrial LiDAR for radiative transfer modelling［J］. Remote Sensing， 2018， 10（6）： 15.DOI： 10.3390/rs10060933 . doi: 10.3390/rs10060933
18	Tao S L， Guo Q H， Xu S W， et al. A geometric method for wood-leaf separation using terrestrial and simulated LiDAR data［J］. Photogrammetric Engineering & Remote Sensing， 2015， 81（10）： 767-776.DOI： 10.14358/pers.81.10.767 . doi: 10.14358/pers.81.10.767
19	Strahler A H， Jupp D L B， Woodcock C E， et al. Retrieval of forest structural parameters using a ground-based LiDAR instrument （Echidna （R））［J］. Canadian Journal of Remote Sensing， 2008， 34：S426-S40.DOI： 10.5589/m08-046 . doi: 10.5589/m08-046
20	Danson F M， Sasse F， Schofield L A. Spectral and spatial information from a novel dual-wavelength full-waveform terrestrial laser scanner for forest ecology［J］.Interface Focus，2018， 8（2）： 20170049.DOI： 10.1098/rsfs.2017.0049 . doi: 10.1098/rsfs.2017.0049
21	Raumonen P， Kaasalainen M， Akerblom M， et al. Fast automatic precision tree models from terrestrial laser scanner data［J］. Remote Sensing， 2013， 5（2）： 491-520.DOI： 10.3390/rs5020491 . doi: 10.3390/rs5020491
22	Calders K， Disney M I， Armston J， et al. Evaluation of the range accuracy and the radiometric calibration of multiple terrestrial laser scanning instruments for data interoperability［J］. IEEE Transactions on Geoscience and Remote Sensing， 2017，55（5）：2716-2724. DOI：10.1109/tgrs.2017.2652721 . doi: 10.1109/tgrs.2017.2652721
23	Disney M I， Vicari M B， Burt A， et al. Weighing trees with lasers： advances， challenges and opportunities［J］. Interface Focus， 2018， 8（2）： 14.DOI： 10.1098/rsfs.2017.0048 . doi: 10.1098/rsfs.2017.0048
24	Yun T， An F， Li W， et al. A novel approach for retrieving tree leaf area from ground-based LiDAR［J］. Remote Sensing， 2016， 8（11）： 21.DOI： 10.3390/rs8110942 . doi: 10.3390/rs8110942
25	Ma L X， Zheng G， Eitel J U H， et al. Improved salient feature-based approach for automatically separating photosynthetic and nonphotosynthetic components within terrestrial LiDAR point cloud data of forest canopies［J］. IEEE Transactions on Geoscience and Remote Sensing， 2016， 54（2）： 679-696.DOI： 10.1109/tgrs.2015.2459716 . doi: 10.1109/tgrs.2015.2459716
26	Wang D， Hollaus M， Pfeifer N. Feasibility of machine learning methods for separating wood and leaf points from terrestrial laser scaning data［J］. ISPRS Annals of Photogrammetry， Reomte Sensing and Spatial Information Sciences，2017： IV-2/W4：157-164.
27	Li S H， Dai L Y， Wang H S， et al. Estimating leaf area density of individual trees using the point cloud segmentation of terrestrial LiDAR data and a Voxel-based model［J］. Remote Sensing， 2017， 9（11）： 1202.DOI： 10.3390/rs9111202 .. doi: 10.3390/rs9111202
28	Vicari M B， Disney M， Wilkes P， et al. Leaf and wood classification framework for terrestrial LiDAR point clouds［J］. Methods in Ecology and Evolution， 2019， 10（5）： 680-694.DOI： 10.1111/2041-210x.13144 . doi: 10.1111/2041-210x.13144
29	Su Z H， Li S H， Liu H H， et al. Extracting wood point cloud of individual trees based on geometric features［J］. IEEE Geoscience and Remote Sensing Letters， 2019， 16（8）： 1294-1298.DOI： 10.1109/lgrs.2019.2896613 . doi: 10.1109/lgrs.2019.2896613
30	Wang Hongshu， Li Shihua， Guo Jiawei， et al. Retrieval of the leaf area density of magnolia woody canopy with terrestrial laser-scanning data［J］.Journal of Remote Sensing，2016，20（4）：570-578.
30	王洪蜀，李世华，郭加伟，等. 地基激光雷达的玉兰林冠叶面积密度反演［J］.遥感学报，2016，20（4）：570-578
31	Wang D， Takoudjou S M， Casella E. LeWoS： A universal leaf-wood classification method to facilitate the 3D modelling of large tropical trees using terrestrial LiDAR［J］. Methods in Ecology and Evolution，2020，11（3）：376-389. DOI：10.1111/2041-210x.13342 . doi: 10.1111/2041-210x.13342
32	Guinard S， Landrieu L. Weakly supervised segementation-alded classification of urban scences from 3D LIDAR point clouds［M］. Gottingen. Copernicus Gesellschaft Mbh. 2017.
33	Wang D， Brunner J， Ma Z Y， et al. Separating tree photosynthetic and non-photosynthetic components from point cloud data using dynamic segment merging［J］. Forests，2018，9（5）：23. DOI： 10.3390/f9050252 . doi: 10.3390/f9050252
34	Klasing K， Wollherr D， Buss M， et al. A clustering method for efficient segmentation of 3 D laser data［M］. New York； IEEE. 2008.
35	Raumonen P， Tarvainen T. Segmentation of vessel structures from photoacoustic images with reliability assessment［J］. Biomedical Optics Express，2018，9（7）：2887-2904. DOI： 10. 1364/boe.9.002887 . doi: 10. 1364/boe.9.002887
36	Landrieu L， Raguet H， Vallet B， et al. A structured regularization framework for spatially smoothing semantic labelings of 3D point clouds［J］. ISPRS Journal of Photogrammetry and Remote Sensing，2017，132：102-118. DOI：10.1016/j.isprsjprs. 2017.08.010 . doi: 10.1016/j.isprsjprs. 2017.08.010
37	Wang Z， Zhang L Q， Fang T， et al. A multiscale and hierarchical feature extraction method for terrestrial laser scanning point cloud classification［J］. IEEE Transactions on Geoscience and Remote Sensing，2015，53（5）：2409-2425. DOI：10. 1109/tgrs.2014.2359951 . doi: 10. 1109/tgrs.2014.2359951
38	Sokolova M， Japkowicz N， Szpakowicz S. Beyond accuracy， F-Score and ROC： a family of discriminant measures for performance evaluation［C］∥ Australian Joint Conference on Artificial Intelligence： Advances in Artificial Intelligence，Hobart Australia， 2006.
39	de Tanago J G， Lau A， Bartholomeus H， et al. Estimation of above-ground biomass of large tropical trees with terrestrial LiDAR［J］. Methods in Ecology and Evolution， 2018， 9（2）： 223-234. DOI： 10.1111/2041-210x.12904 . doi: 10.1111/2041-210x.12904
40	Paynter I， Genest D， Saenz E， et al. Quality assessment of terrestrial laser scanner ecosystem observations using pulse trajectories［J］. IEEE Transactions on Geoscience and Remote Sensing， 2018， 56（11）： 6324-6333. DOI： 10.1109/tgrs.2018.2836947 . doi: 10.1109/tgrs.2018.2836947
41	Wan P， Zhang W M， Jin S N， et al. Plot-level wood-leaf separation of trees using terrestrial LiDAR data based on a segmentwise geometric feature classification method［J］. 2020. DOI： 10.21203/rs.3.rs-42032/v1 . doi: 10.21203/rs.3.rs-42032/v1

[1]	陈磊,赵书河. 一种改进的基于平面拟合的机载LiDAR点云滤波方法[J]. 遥感技术与应用, 2011, 26(1): 117-122.

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