中南林业科技大学学报

基于无人机-手持激光雷达点云的天然林单木和林分尺度乔木碳储量估测

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关宏韬 , 赵颖慧 , 甄贞

中南林业科技大学学报 | 林学 2026,46(10): 37-49

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中南林业科技大学学报 |林学 2026 , 46 (10) : 37 -49

基于无人机-手持激光雷达点云的天然林单木和林分尺度乔木碳储量估测

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关宏韬, 赵颖慧, 甄贞

作者信息

东北林业大学　a. 森林生态系统可持续管理教育部重点实验室；b. 林学院，黑龙江　哈尔滨　150040

通讯作者:

甄贞（zhenzhen@nefu.edu.cn），教授，博士。

作者简介:

关宏韬（yuguan@nefu.edu.cn），硕士研究生。

Estimation of tree carbon storage at individual tree- and stand levels in natural forests based on fused UAV and handheld LiDAR point clouds

Hongtao Guan, Yinghui Zhao, Zhen Zhen

Affiliations

^a.Key Laboratory of Sustainable Forest Ecosystem Management-Ministry of Education; b. School of Forestry, Northeast Forestry University, Harbin 150040, Heilongjiang, China

出版时间: 2026-10-25 doi: 10.14067/j.cnki.1673-923x.2026.10.003

文章导航

摘要

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【目的】

解决当前森林资源调查中人工实测获取数据效率低下的问题。

【方法】

以大兴安岭地区天然林为研究对象，获取无人机激光雷达（Unmanned aerial vehicle laser scanning，ULS）与手持激光雷达（Handheld laser scanning，HLS）点云数据，进行轨迹解算、滤波与噪声剔除后，使用3D Harris算法提取关键点实现粗配准，进一步利用迭代最近点（Iterative closest point，ICP）算法完成精配准。采用基于冠层高度模型（Canopy height model，CHM）的单木分割方法和基于种子点的K-means层次聚类方法对融合点云进行单木分割，基于最优的单木分割结果提取树高和胸径，再结合二元可加性生物量模型估测单木和样地尺度的生物量和碳储量，验证多平台U-HLS数据在森林精细化监测中的适用性。

【结果】

1）不同分割方法与数据源的单木分割精度差异显著。基于CHM的分割在无人机点云中虽识别出5 790株个体，但虚假识别率较高，召回率仅0.61，F-score仅0.43；基于种子点的K均值层次聚类法分割在树干位置约束下，识别的完整性与精度明显提升，F-score达0.87，召回率、精确率和F-score分别高107.07%、70.46%、87.49%。2）基于融合点云和K均值层次聚类法得到的单木提取参数中，胸径和树高的总体精度均较高，系统性偏差较小。决定系数均达到0.93，RMSE分别为1.6 cm和1.15 m，相对RMSE均控制在16%以下，偏差仅为0.37 cm和0.02 m。树干位置约束在一定程度上削弱了冠层遮挡造成的误差，使单木参数的提取更加可靠。3）基于U-HLS数据的单木和样地尺度碳储量的估测精度均较高，R²均为0.97，单木尺度的碳储量估测的RMSE为7.14 kg，偏差仅为-0.68 kg，但在大径组（26~36 cm）和小径组（<12 cm）仍表现出一定程度的低估和高估现象。样地尺度碳储量估测的R²为0.97，而RMSE和偏差仅为3.62和-1.27 tC·hm^-2，尤其是rRMSE（6.22%），远小于单木尺度rRMSE（23.32%）。说明在样地层面，单木个体的估测误差被进一步平衡，局部的高估与低估被部分抵消，使样地尺度的碳储量估测结果与实测值高度一致。

【结论】

多平台激光雷达融合能够有效提升单木参数提取及碳储量估测的精度，在复杂林分条件下保持稳定可靠，能够在单木及样地尺度部分替代传统每木检尺调查，为高效地开展精细化森林资源调查和监测提供技术支持。

关键词

森林资源调查 / 激光雷达 / K均值层次聚类 / 点云融合 / 单木参数

Abstract

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【Objective】

This study aimed to address the inefficiency of manual measurements in current forest resource surveys.

【Method】

The LiDAR point cloud data of the natural forests of the Greater Khingan mountains were obtained from unmanned aerial vehicle laser scanning (ULS) and handheld laser scanning (HLS). After trajectory solution, filtering, and noise removal, coarse registration of ULS and HLS was performed using the 3D Harris algorithm for key point extraction, followed by fine registration through the Iterative Closest Point (ICP) algorithm. Based on ULS and fused point cloud data (U-HLS), individual trees were segmented using two approaches: the canopy height model (CHM)-based segmentation and a seed-based K-means hierarchical clustering method. Tree height and diameter at breast height (DBH) were extracted from the optimal segmentation results, and a two-variable additive biomass model was applied to estimate biomass and carbon storage at both individual-tree and plot levels, thereby verifying the applicability of multi-platform LiDAR data for refined forest monitoring.

【Result】

1) Significant differences were observed in individual tree segmentation accuracy among different methods and data sources. The CHM-based method identified 5 790 individual trees from ULS data but exhibited a high false detection rate, with a recall of only 0.61 and an F-score of 0.43. In contrast, the seed-based K-means hierarchical clustering method, constrained by stem position, achieved notably higher completeness and accuracy, with an F-score of 0.87 and recall, precision, and F-score improved by 107.07%, 70.46%, and 87.49%, respectively; 2) Using U-HLS data and K-means hierarchical clustering method, the tree parameters achieved high accuracy with minimal systematic bias. Both DBH and tree height reached R²=0.93, with RMSE values of 1.6 cm and 1.15 m, respectively, and relative RMSEs below 16%. The inclusion of stem position constraints effectively mitigates errors caused by canopy occlusion, improving the reliability of individual-tree parameters extraction; 3) The estimation accuracy of carbon storage based on U–HLS data is high at both the individual-tree and plot levels, with R² values of 0.97. The carbon storage estimation at the individual tree level has small RMSE (7.14 kg) and bias (-0.68 kg), although underestimation and overestimation occur in large (26-36 cm) and small (<12 cm) DBH classes, respectively. At the plot scale, the R² of carbon storage estimation achieves 0.97, and the RMSE and bias were only 3.62 t C·hm^-2 and -1.27 t C·hm^-2, respectively. The rRMSE, in particular, was 6.22%, significantly smaller than that at the individual tree-level (23.32%). This suggests that at the plot level, individual tree estimation errors are further balanced, partially offsetting local overestimations and underestimations, resulting in high consistency between carbon storage estimates and observed values.

【Conclusion】

Multi-platform LiDAR data effectively enhances the accuracy of individual-tree parameter extraction and carbon storage estimation, maintaining stable and reliable performance under complex forest conditions. It can partially replace traditional field measurements at both individual-tree and plot scales, providing a valuable reference for efficient and refined forest resource surveys and carbon sink monitoring.

Key words

forest resource inventory / LiDAR / K-means hierarchical clustering / point cloud fusion / individual tree parameters

引用本文

关宏韬, 赵颖慧, 甄贞. 基于无人机-手持激光雷达点云的天然林单木和林分尺度乔木碳储量估测. 中南林业科技大学学报, 2026 , 46 (10) : 37 -49 . DOI: 10.14067/j.cnki.1673-923x.2026.10.003

Hongtao Guan, Yinghui Zhao, Zhen Zhen. Estimation of tree carbon storage at individual tree- and stand levels in natural forests based on fused UAV and handheld LiDAR point clouds[J]. Journal of Central South University of Forestry & Technology, 2026 , 46 (10) : 37 -49 . DOI: 10.14067/j.cnki.1673-923x.2026.10.003

基金

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国家重点研发计划青年科学家项目(2023YFF1305900)
黑龙江省自然科学基金项目(LH2023C040)

参考文献

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2026年第46卷第10期

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doi: 10.14067/j.cnki.1673-923x.2026.10.003

接收时间：2025-10-09
首发时间：2026-10-30
出版时间：2026-10-25

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收稿日期：2025-10-09
修回日期：2025-12-18

基金

国家重点研发计划青年科学家项目(2023YFF1305900)

黑龙江省自然科学基金项目(LH2023C040)

作者信息

东北林业大学　a. 森林生态系统可持续管理教育部重点实验室；b. 林学院，黑龙江　哈尔滨　150040

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甄贞（zhenzhen@nefu.edu.cn），教授，博士。

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2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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