中南林业科技大学学报

融合无人机和手持激光雷达数据的思茅松单木地上生物量估测

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张虎 ¹ , 李世明 ²^,³ , 姬永杰 ¹ , 张王菲 ¹ , 张浩芫 ²^,³ , 刘晴 ²^,³ , 侬庆龙 ¹ , 张译 ²^,³

中南林业科技大学学报 | 林学 2026,46(7): 33-46

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中南林业科技大学学报 |林学 2026 , 46 (7) : 33 -46

融合无人机和手持激光雷达数据的思茅松单木地上生物量估测

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张虎¹, 李世明²^,³, 姬永杰¹, 张王菲¹, 张浩芫²^,³, 刘晴²^,³, 侬庆龙¹, 张译²^,³

作者信息

^1.西南林业大学，云南　昆明　650224

^2.中国林业科学研究院　资源信息研究所，北京　100091

^3.国家林业和草原局　林业遥感与信息技术重点实验室，北京　100091

通讯作者:

李世明（lism@ifrit.ac.cn），研究员，博士。

作者简介:

张虎（zhanghu123619@163.com），硕士研究生。

Estimation of aboveground biomass of individual Pinus kesiya trees based on fused UAV and handheld LiDAR data

Hu Zhang¹, Shiming Li²^,³, Yongjie Ji¹, Wangfei Zhang¹, Haoyan Zhang²^,³, Qing Liu²^,³, Qinglong Nong¹, Yi Zhang²^,³

Affiliations

^1.Southwest Forestry University, Kunming 650224, Yunnan, China

^2.Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China

^3.Key Laboratory of Forestry Remote Sensing and Information System, National Forestry and Grassland Administration, Beijing 100091, China

出版时间: 2026-07-25 doi: 10.14067/j.cnki.1673-923x.2026.07.004

文章导航

摘要

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【目的】

探讨无人机激光雷达、手持激光雷达以及二者融合点云数据在单木参数提取及地上生物量估测中的适用性，提升复杂林分中单木尺度建模的准确性。

【方法】

以云南省林业和草原科学院普文试验林场的5块思茅松Pinus kesiya样地为研究对象，分别采集无人机激光雷达点云、手持激光雷达点云2类单一数据源，并完成2类数据融合处理。采用分水岭算法对无人机激光雷达点云进行单木分割，手持激光雷达及融合点云则通过距离判别聚类算法进行单木分割。随后，从单木点云提取4个结构特征、40个高度变量、10个密度变量和17个体素特征，共计71个变量。通过Boruta算法与随机森林变量重要性排序进行特征筛选，最终选取关键变量构建基于随机森林的单木地上生物量估测模型，并对不同数据源的建模精度进行对比分析。

【结果】

1）无人机、手持和融合点云数据单木分割综合分割精度（F）分别为0.90、0.95、0.97。2）基于无人机、手持和融合点云数据提取的单木树高的决定系数（R²）分别为0.93、0.94、0.94，均方根误差（RMSE）分别为1.43、1.74、1.68 m。融合点云数据单木胸径估测精度为：R²=0.95，RMSE为2.13 cm。3）3类点云各提取的71个变量经Boruta算法与随机森林筛选，每类数据均独立筛选得到8个核心变量，用于单木生物量建模。4）基于无人机点云数据的单木地上生物量估测精度为：R²=0.83，RMSE为85.69 kg/株，rRMSE为44.53%；手持点云数据的单木地上生物量估测精度为：R²=0.87，RMSE为72.02 kg/株，rRMSE为36.34%；融合点云数据的单木地上生物量估测精度为：R²=0.86，RMSE为70.65 kg/株，rRMSE为36.72%。

【结论】

基于随机森林方法构建的单木生物量模型对不同数据源均表现出良好的预测效果，其中融合无人机与手持激光雷达的点云数据在预测精度和稳定性方面表现更优。

关键词

无人机激光雷达 / 手持激光雷达 / 点云融合 / 单木分割 / 单木结构参数 / 单木地上生物量

Abstract

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【Objective】

To investigate the applicability of unmanned aerial vehicle (UAV) LiDAR, handheld LiDAR, and their fused point cloud data for individual tree parameter extraction and aboveground biomass estimation, and to improve the accuracy of single-tree scale modeling in complex forest stands.

【Method】

The study was conducted in five plots of Pinus kesiya at the Puwen experimental forest of the Yunnan Academy of Forestry and Grassland. UAV LiDAR point cloud data and handheld LiDAR point cloud data were collected separately as two independent data sources, followed by data fusion of the two datasets. The watershed algorithm was applied to segment individual trees from the UAV LiDAR point cloud, whereas a distance-based clustering algorithm was used for the handheld and fused point clouds. Then, a total of 71 variables were extracted from individual tree point clouds, including 4 structural features, 40 height-related variables, 10 density variables, and 17 voxel-based features. The Boruta algorithm and random forest feature importance ranking were used for variable selection, and key variables were then used to build random forest models for AGB estimation. The modeling accuracy of different data sources was compared.

【Result】

1) The overall segmentation F-scores for UAV, handheld, and fused point cloud data were 0.90, 0.95 and 0.97, respectively; 2) The R² values of tree height estimation from UAV, handheld, and fused data were 0.93, 0.94 and 0.94, respectively, with RMSEs of 1.43 m, 1.74 m, and 1.68 m. The fused data achieved a DBH estimation accuracy of R²=0.95 and RMSE is 2.13 cm; 3) For the three types of point cloud data, 71 variables were extracted and independently selected using the Boruta algorithm and Random forest. For each dataset, eight key variables were identified separately and used for single-tree aboveground biomass modeling; 4) The estimation accuracy of individual tree above-ground biomass based on UAV point cloud data was R²=0.83, RMSE is 85.69 kg/plant and rRMSE is 44.53%; based on handheld point cloud data, R²=0.87, RMSE is 72.02 kg/plant and rRMSE is 36.34%; and based on fused point cloud data, R²=0.86, RMSE is 70.65 kg/plant and rRMSE is 36.72%.

【Conclusion】

The individual tree biomass model constructed based on the random forest method demonstrated good predictive performance across different data sources, with the fused point cloud data from UAV and handheld LiDAR showing superior accuracy and stability.

Key words

UAV LiDAR / handheld LiDAR / point cloud fusion / individual tree segmentation / individual tree structural parameters / individual tree aboveground biomass

引用本文

张虎, 李世明, 姬永杰, 张王菲, 张浩芫, 刘晴, 侬庆龙, 张译. 融合无人机和手持激光雷达数据的思茅松单木地上生物量估测. 中南林业科技大学学报, 2026 , 46 (7) : 33 -46 . DOI: 10.14067/j.cnki.1673-923x.2026.07.004

Hu Zhang, Shiming Li, Yongjie Ji, Wangfei Zhang, Haoyan Zhang, Qing Liu, Qinglong Nong, Yi Zhang. Estimation of aboveground biomass of individual Pinus kesiya trees based on fused UAV and handheld LiDAR data[J]. Journal of Central South University of Forestry & Technology, 2026 , 46 (7) : 33 -46 . DOI: 10.14067/j.cnki.1673-923x.2026.07.004

基金

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中央级公益性科研院所基本科研业务专项(CAFYBB2022ZB00105)
国家自然科学基金项目(42161059; 3216365; 32371869; 32371865)
国家重点研发计划项目(2023YFB3905801)

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2026年第46卷第7期

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文章信息

doi: 10.14067/j.cnki.1673-923x.2026.07.004

接收时间：2025-06-23
首发时间：2026-10-30
出版时间：2026-07-25

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出版历史

收稿日期：2025-06-23
修回日期：2025-09-25

基金

中央级公益性科研院所基本科研业务专项(CAFYBB2022ZB00105)

国家自然科学基金项目(42161059; 3216365; 32371869; 32371865)

国家重点研发计划项目(2023YFB3905801)

作者信息

^1.西南林业大学，云南　昆明　650224

^2.中国林业科学研究院　资源信息研究所，北京　100091

^3.国家林业和草原局　林业遥感与信息技术重点实验室，北京　100091

通讯作者:

李世明（lism@ifrit.ac.cn），研究员，博士。

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https://castjournals.cast.org.cn/joweb/znlykj/CN/10.14067/j.cnki.1673-923x.2026.07.004

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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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