基于地基激光雷达点云的桉树单木参数提取与地上碳储量测定

doi:10.11707/j.1001-7488.LYKX20240701

Abstract

Abstract:

Objective: The differences in stem morphology and canopy structure among individuals of the same tree species result in the uncertainties in estimating individual tree volume or carbon stock based on allometric growth equations. To address the uncertainties, this study developed a new method for three-dimensional measurement of individual tree aboveground carbon stock based on terrestrial laser scanning (TLS) point clouds. Method: The concept of space colonization modeling was based to reconstruct individual Eucalyptus trees in 3D and determine their aboveground carbon storage. The workflow included branch-stem separation, skeleton extraction and optimization, 3D reconstruction, and branch-stem volume calculation. Layer-by-layer judgment method combined with clustering analysis was used to separate trunk and branch point clouds, avoiding misclassification caused by drooping branches. Skeleton optimization removed redundant fine branches and merged nearly parallel branches belonging to the same branch. Cardinal curve interpolation algorithm was used to fill the missing skeleton segments, and a smoothed, completed skeleton was expanded to generate high-precision 3D geometric models of individual trees. Based on the three-dimensional structure extraction of branch and stem volumes, and combined with destructively sampling 41 Eucalyptus trees for wood density and carbon content measurements, the volume was further converted into individual tree aboveground carbon storage. Result: The accuracy of the method for extracting individual tree parameters was as follows: The linear fit of tree height measurements and reference values had an R² of 0.94 and CV(RMSE) of 19.00%, the linear fit of diameter at breast height (DBH) measurements and reference values had an R² of 0.94 and CV(RMSE) of 19.00%, the linear fit of stem volume measurements and reference values had an R² of 0.94 and CV(RMSE) of 19.00%, and the linear fit of branch volume measurements and reference values had an R² of 0.95 and CV(RMSE) of 38.84%. The method for estimating individual tree aboveground carbon stock had an accuracy of: R² of 0.96 and CV(RMSE) of 16.23% for the linear fit of measured and reference values. Conclusion: The carbon storage is directly measured by extracting individual Eucalyptus volume parameters and combining them with measured density and carbon content rate. This study focuses on addressing the differences in aboveground carbon storage measurement caused by variations in the morphology and structure of different Eucalyptus individuals, providing a technical basis for forest inventory compilation, smart forestry applications, and carbon sink accounting.

Key words: terrestrial laser scanning (TLS) point clouds, tree structure, 3D reconstruction, space colonization algorithm, aboveground carbon stock

CLC Number:

S757.3

Guangpeng Fan,Liangliang Xu,Huide Cai,Zhanyong Xu,Xiang Meng,Yakui Shao,Feng Lu. Extraction of Individual Eucalyptus Tree Parameters and Determination of Aboveground Carbon Stock Based on Terrestrial LiDAR Point Clouds[J]. Scientia Silvae Sinicae, 2026, 62(3): 88-99.

Figures/Tables 12

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References 0

	曹　林, 佘光辉, 代劲松, 等. 激光雷达技术估测森林生物量的研究现状及展望. 南京林业大学学报: 自然科学版, 2013, 37 (3): 163- 169.
	Cao L, She G H, Dai J S, et al. Status and prospects of the LiDAR-based forest biomass estimation. Journal of Nanjing Forestry University: Natural Sciences, 2013, 37 (3): 163- 169.
	董利虎, 刘永帅, 宋　博, 等. 立木含碳量估算方法比较. 林业科学, 2020, 56 (4): 46- 54.
	Dong L H, Liu Y S, Song B, et al. Comparison of individual tree carbon estimation approaches. Scientia Silvae Sinicae, 2020, 56 (4): 46- 54.
	黄华国. 林业定量遥感研究进展和展望. 北京林业大学学报, 2019, 41 (12): 1- 14.
	Huang H G. Progress and perspective of quantitative remote sensing of forestry. Journal of Beijing Forestry University, 2019, 41 (12): 1- 14.
	李平昊, 申　鑫, 代劲松, 等. 机载激光雷达人工林单木分割方法比较和精度分析. 林业科学, 2018, 54 (12): 127- 136.
	Li P H, Shen X, Dai J S, et al. Comparisons and accuracy assessments of LiDAR-based tree segmentation approaches in planted forests. Scientia Silvae Sinicae, 2018, 54 (12): 127- 136.
	李　响, 甄　贞, 赵颖慧. 基于局域最大值法单木位置探测的适宜模型研究. 北京林业大学学报, 2015, 37 (3): 27- 33.
	Li X, Zhen Z, Zhao Y H. Suitable model of detecting the position of individual treetop based on local maximum method. Journal of Beijing Forestry University, 2015, 37 (3): 27- 33.
	李增元, 刘清旺, 庞　勇. 激光雷达森林参数反演研究进展. 遥感学报, 2016, 20 (5): 1138- 1150.
	Li Z Y, Liu Q W, Pang Y. Review on forest parameters inversion using LiDAR. Journal of Remote Sensing, 2016, 20 (5): 1138- 1150.
	师　翊, 何　鹏, 胡少军, 等. 基于角度约束空间殖民算法的树点云几何结构重建方法. 农业机械学报, 2018, 49 (2): 207- 216. doi: 10.6041/j.issn.1000-1298.2018.02.027
	Shi Y, He P, Hu S J, et al. Reconstruction method of tree geometric structures from point clouds based on angle-constrained space colonization algorithm. Transactions of the Chinese Society for Agricultural Machinery, 2018, 49 (2): 207- 216. doi: 10.6041/j.issn.1000-1298.2018.02.027
	王姣娇, 高唤唤, 康宏樟. 杉木人工林碳储量影响因素研究进展. 西北林学院学报, 2018, 33 (3): 74- 81. doi: 10.3969/j.issn.1001-7461.2018.03.12
	Wang J J, Gao H H, Kang H Z. A review on influencing factors of carbon storage in Chinese fir plantations. Journal of Northwest Forestry University, 2018, 33 (3): 74- 81. doi: 10.3969/j.issn.1001-7461.2018.03.12
	谢贤胜, 苏宏新, 杨元征, 等. 基于地基激光雷达估测广西桉树人工林的林分参数. 林业资源管理, 2022 (2): 100- 108.
	Xie X S, Su H X, Yang Y Z, et al. Estimation of forest parameters of Guangxi Eucalyptus plantation based on terrestrial laser scanning. Forest Resources Management, 2022 (2): 100- 108.
	曾伟生. 遥感技术在森林资源清查中的应用问题探讨. 中南林业调查规划, 2004, 23 (1): 47- 49. doi: 10.3969/j.issn.1003-6075.2004.01.014
	Zeng W S. Discussion on application of remote sensing in forest inventories. Central South Forest Inventory and Planning, 2004, 23 (1): 47- 49. doi: 10.3969/j.issn.1003-6075.2004.01.014
	郑晓璐, 潘广贞, 杨　剑, 等. 基于Hausdorff距离改进的ICP算法. 计算机工程与设计, 2015, 36 (9): 2481- 2484, 2489. doi: 10.16208/j.issn1000-7024.2015.09.032
	Zheng X L, Pan G Z, Yang J, et al. Improved ICP algorithm based on Hausdorff distance. Computer Engineering and Design, 2015, 36 (9): 2481- 2484, 2489. doi: 10.16208/j.issn1000-7024.2015.09.032
	朱泊东, 罗洪斌, 金　京, 等. 高郁闭度人工林无人机激光雷达单木分割方法优化. 林业科学, 2022, 58 (9): 48- 59.
	Zhu B D, Luo H B, Jin J, et al. Optimization of individual tree segmentation methods for high canopy density plantation based on UAV LiDAR. Scientia Silvae Sinicae, 2022, 58 (9): 48- 59.
	Asadilla Y, Hu X M, Halik Ü, et al. Developing new allometric models for estimating aboveground woody biomass and volume of Populus euphratica using terrestrial laser scanning. Journal of Remote Sensing, 2025, 5, 697. doi: 10.34133/remotesensing.0697
	Bauwens S, Bartholomeus H, Calders K, et al. Forest inventory with terrestrial LiDAR: a comparison of static and hand-held mobile laser scanning. Forests, 2016, 7 (6): 12. doi: 10.3390/f7060127
	Brede B, Calders K, Lau A, et al. Non-destructive tree volume estimation through quantitative structure modelling: comparing UAV laser scanning with terrestrial LIDAR. Remote Sensing of Environment, 2019, 233, 111355. doi: 10.1016/j.rse.2019.111355
	Chave J, Réjou-Méchain M, Búrquez A, et al. Improved allometric models to estimate the aboveground biomass of tropical trees. Glob Chang Biol, 2014, 20 (10): 3177- 3190. doi: 10.1111/gcb.12629
	Delagrange S, Jauvin C, Rochon P. PypeTree: a tool for reconstructing tree perennial tissues from point clouds. Sensors, 2014, 14 (3): 4271- 4289. doi: 10.3390/s140304271
	Du H, Zeng F, Peng W, et al. Carbon storage in a Eucalyptus plantation chronosequence in southern China. Forests, 2015, 6, 1763- 1778. doi: 10.3390/f6061763
	Du S, Lindenbergh R, Ledoux H, et al. AdTree: accurate, detailed, and automatic modelling of laser-scanned trees. Remote Sensing, 2019, 11 (18): 2074. doi: 10.3390/rs11182074
	Fan G P, Nan L L, Dong Y Q, et al. AdQSM: a new method for estimating above-ground biomass from TLS point clouds. Remote Sensing, 2020, 12 (18): 3089. doi: 10.3390/rs12183089
	Guo J W, Xu S B, Yan D M, et al. Realistic procedural plant modeling from multiple view images. IEEE Transactions on Visualization and Computer Graphics, 2018, 26 (2): 1372- 1384. doi: 10.1109/tvcg.2018.2869784
	Kandare K, Orka H O, Chan J C W, et al. Effects of forest structure and airborne laser scanning point cloud density on 3D delineation of individual tree crowns. European Journal of Remote Sensing, 2016, 49 (1): 337- 359. doi: 10.5721/EuJRS20164919
	Li J T, Wu H B, Xiao Z H, et al. 2022. 3D modeling of laser-scanned trees based on skeleton refined extraction, International Journal of Applied Earth Observation and Geoinformation, 112: 102943.
	Qin H M, Zhou W Q, Qian Y G et al. Estimating aboveground carbon stocks of urban trees by synergizing ICESat-2 LiDAR with GF-2 data. Urban Forestry & Urban Greening, 2022, 76, 127728. doi: 10.1016/j.ufug.2022.127728
	Raumonen P, Kaasalainen M, Åkerblom M, et al. Fast automatic precision tree models from terrestrial laser scanner data. Remote Sensing, 2013, 5 (2): 491- 520. doi: 10.3390/rs5020491
	Runions A, Fuhrer M, Lane B, et al. Modeling and visualization of leaf venation patterns. Acm Transactions on Graphics, 2005, 24 (3): 702- 711.. doi: 10.1145/1073204.1073251
	Singh V, Tewari A, Kushwaha S P, et al. Formulating allometric equations for estimating biomass and carbon stock in small diameter trees. Forest Ecology and Management, 2011, 261 (11): 1945- 1949. doi: 10.1016/j.foreco.2011.02.019
	Verroust, AnneLazarus, Francis. Extracting skeletal curves from 3D scattered data. The Visual Computer, 2000, 16 (1): 15- 25. doi: 10.1109/sma.1999.749340
	Yu X, Hyyppä J, Vastaranta M, et al. Predicting individual tree attributes from airborne laser point clouds based on the random forests technique. ISPRS Journal of Photogrammetry and Remote Sensing, 2011, 66 (1): 28- 37. doi: 10.1016/j.isprsjprs.2010.08.003
	Zhao D, Pang Y, Li Z Y, et al. Isolating individual trees in a closed coniferous forest using small footprint LiDAR data. International Journal of Remote Sensing, 2014, 35 (20): 7199- 7218. doi: 10.1080/01431161.2014.967886

树号Tree ID	$ \rho $=0.12	$ \rho $=0.20	$ \rho_1 $=0.12， $ \rho_2 $=0.20
1	0.925 0	0.988 0	0.988 0
2	0.922 6	0.974 6	0.974 6
3	0.855 2	0.985 1	0.977 7
4	0.955 6	0.995 2	0.996 4

树号 Tree ID	距离类型 Type of distance	AdTree	PypeTree	原空间殖民算法 Original algorithm	改进算法 Improved algorithm
1	$ h\left({S}_{\rm o},{S}_{\rm std}\right) $	0.720 3	0.729 5	0.642 2	0.299 7
	$ h\left({S}_{\rm std},{S}_{\rm o}\right) $	0.378 2	1.253 6	1.314 4	1.281 8
	$ H({S}_{\rm \text{std}},{S}_{\rm o}) $	0.720 4	1.253 6	1.314 4	1.281 8
	$ {H}_{\rm avg}({S}_{\rm o},{S}_{\rm std}) $	0.113 7	0.173 4	0.090 6	0.087 4
	$ F({S}_{\rm o},{S}_{\rm std}) $	0.075 9	0.090 0	0.035 9	0.034 8
2	$ h\left({S}_{\rm std},{S}_{\rm o}\right) $	0.232 8	0.229 1	0.199 3	0.139 0
	$ h\left({S}_{\rm std},{S}_{\rm o}\right) $	0.111 6	0.329 2	0.674 6	0.675 8
	$ H({S}_{\rm \text{std}},{S}_{\rm o}) $	0.232 8	0.329 2	0.674 6	0.675 8
	$ {H}_{\rm avg}({S}_{\rm o},{S}_{\rm std}) $	0.054 1	0.086 0	0.043 8	0.042 0
	$ F({S}_{\rm o},{S}_{\rm std}) $	0.033 4	0.031 2	0.016 3	0.012 1

类别 Category	Bias/kg	rBias(%)	RMSE/kg	CV(RMSE)(%)
单木 Total tree	0.609 72	0.66	14.943 91	16.23
树干 Trunk	?2.338 48	?2.85	15.035 35	18.35
树枝 Branch	1.832 95	18.02	3.904 54	38.39

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Extraction of Individual Eucalyptus Tree Parameters and Determination of Aboveground Carbon Stock Based on Terrestrial LiDAR Point Clouds

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