王金亮导师团队论文在SCIE期刊Geocarto International上线发表

2022年1月26日，以张建鹏（云南师范大学地理学部地图学与地理信息系统专业2021级博士研究生）为第一作者，王金亮教授为通讯作者撰写题为 “Tree stem extraction from TLS point-cloud data of natural forests based on geometric features and DBSCAN” 论文在SCI/SCIE期刊Geocarto International （2021年12月基础版二区，升级版三区，2021年IF 4.889）上线发表（https://doi.org/10.1080/10106049.2022.2034988）。

  树木主干在养分运输中起着关键作用，具有重要的经济和生态价值，是天然林树木的重要组成部分。传统的从天然林点云数据中提取树干的方法存在精度低、通用性差的问题。这篇文章提出了一种从地面激光雷达（TLS）收集的点云数据中提取树干的新方法。首先，通过主成分分析计算点云数据的特征值和特征向量，以最小化信息熵准则实现最佳邻域尺度选择。然后，将三维空间几何森林特征与法向量的 Z 轴分量相结合。这些几何特征用于粗略提取树干点，同时通过阈值化过滤掉大量非干点。最后采用DBSCAN算法实现树干点的准确提取。研究采用云南省香格里拉地区的一片高山松样地和一片云冷杉样地作为实验数据，结果表明：与实际提取的主干结果相比，研究提出的方法高山松树干提取的R²值为 0.990，云冷杉树干提取的R²值为 0.982，该方法在两片不同树种和不同生长环境的样地中都取得了较高的树干提取精度。研究可为以树干为基础的精准的森林参数提取、生物量和碳储量等研究提供一定的参考。

Figure 1. Stem clustering and precise extraction results for the Pinus densata Mast. sample plot. (a) DBSCAN stem clustering results; (b) Precise stem extraction results.

Figure2. Stem clustering and precise extraction results for the Picea asperata Mast. sample plot. (a) DBSCAN stem clustering results; (b) Precise stem extraction results.

该论文得到了王金亮教授主持的国家自然基金项目：联合ULS与TLS点云数据的滇西北天然林单木生物量估算研究（41961060）、国家重点研发计划政府间国际科技创新合作重点专项：用地理空间技术监测和评估土地利用/土地覆被变化对区域生态安全的影响（2018YFE0184300）、云南省高校高原山地资源环境遥感监测与评估科技创新团队（IRTSTYN）和云南省教育厅科学研究基金项目（NO.2020J0256）的资助。

这是王金亮教授导师团队2022年的第二篇SCIE论文，是张建鹏同学发表的第二篇SCIE学术论文（详见录1），让我们恭喜张建鹏同学！希望他再接再厉！也热烈祝贺团队取得好成绩！

论文相关信息

标题：Tree stem extraction from TLS point-cloud data of natural forests based on geometric features and DBSCAN

作者：Jianpeng Zhang^a,b,c, Jinliang Wang^a,b,c*, Pinliang Dong^d, Weifeng Ma^a,b,c, Yicheng Liu^e, Qianwei Liu^a,b,c, Zhiyan Zhang^a,b,c

通讯作者：Jinliang Wang

作者单位：

^aFaculty of Geography, Yunnan Normal University, Kunming 650500, China;

^bKey Laboratory of Resources and Environmental Remote Sensing for Universities in Yunnan, Kunming 650500, Yunnan, China;

^cCenter for Geospatial Information Engineering and Technology of Yunnan Province, Kunming 650500, Yunnan, China；

^dDepartment of Geography and the Environment, University of North Texas, 1155 Union Circle #305279, Denton, TX76203, USA;

^eYunnan Institute of Water & Hydropower Engineering Investigation, Design and Research.

出版物：Geocarto International

摘要：Conventional methods for tree stem extraction from point-cloud data of natural forests suffer from problems of low accuracy and poor universality. In this paper, an enhanced method is proposed for tree stem extraction from point-cloud data collected by terrestrial laser scanning (TLS). First, principal component analysis is used to calculate the eigenvalues and eigenvectors of the point-cloud data, and an information entropy criterion is minimized in order to achieve the best neighborhood scale selection. Then, three-dimensional spatial geometric forest features are combined with the Z-axis component of the normal vector. These geometric features are used for rough extraction of tree stem points, while a large number of non-stem points is filtered out by thresholding. Finally, the DBSCAN algorithm is used to achieve accurate extraction of tree stem points. The proposed method for tree stem detection and extraction is experimentally evaluated in the case of two representative natural-forest plots of Pinus densata Mast. and Picea asperata Mast. in the Shangri-La City in China. All stem points in these two plots were detected and extracted with a reference method to create a ground-truth dataset. Correlation analysis was carried out between the stem points extracted by the proposed and reference methods for the two plots. This analysis resulted in an R² value of 0.990 for the Pinus densata Mast. sample plot, and an R² value of 0.982 for the Picea asperata Mast. sample plot which has a more complex growth environment.

关键词：tree stem; point cloud; terrestrial laser scanning (TLS); geometric feature; DBSCAN

附录1 张建鹏同学发表SCI论文清单

2018年9月硕士入学至今，张建鹏在王金亮教授指导下发表了3篇学术论文，其中的SCI论文2篇，信息如下：

[2] Jianpeng Zhang, Jinliang Wang^*, Pinliang Dong, Weifeng Ma, Yicheng Liu, Qianwei Liu, Zhiyan Zhang. Tree stem extraction from TLS point-cloud data of natural forests based on geometric features and DBSCAN[J]. Geocarto International, posted online: 26 Jan 2022

DOI: https://doi.org/10.1080/10106049.2022.2034988. (2021年12月基础版二区，2021年12月升级版三区，2021年IF 4.889)

[1] Zhang, Jianpeng, Wang, Jinliang^*, Liu, Guangjie. Vertical Structure Classification of a Forest Sample Plot Based on Point Cloud Data[J]. Journal of the Indian Society of Remote Sensing, 2020, 48(8), 1215–1222.

DOI: https://doi.org/10.1007/s12524-020-01149-w. (SCI 四区, 2020年IF: 0.997)