王金亮教授团队学术论文在SCIE二区期刊《Forests》上线发表

2023 年 3 月 28 日，以张建鹏（云南师范大学地理学部2021级地图学与地理信息系统专业博士研究生）为第一作者，王金亮教授为通讯作者所撰写的题为“Vegetation Extraction from Airborne Laser Scanning Data of Urban Plots Based on Point Cloud Neighborhood Features”的学术论文在SCIE期刊《Forests》上线发表(https://doi.org/10.3390/f14040691)。该期刊2022年中科院SCI期刊分区为二区，IF 3.282.

城市绿色植被在美化空间环境、降低城市温度、吸收噪声、储存碳等方面对城市建设和环境保护具有至关重要的作用。本研究提出了一种基于点云邻域特征的机载激光雷达城市样地植被提取方法，克服了目前城市样地植被精准提取研究的不足。首先，将一定 R 邻域内提取的的平面特征与欧氏距离聚类相结合，准确提取建筑点云，并利用一定 R 邻域内的离散特征提取粗糙植被点云。然后，在建筑点云约束下，结合欧氏距离聚类方法，去除粗糙植被点云中剩余的建筑边界点。最后，以去除建筑边界点云后的植被点云作为约束条件，从原始数据中提取特定半径 r内的植被点，得到完整的城市样地植被提取结果。研究选取了 2 个机载激光雷达城市样地，计算了R = 0.6 m的点云平面特征和离散特征，从城市样地数据中准确提取了植被点云。通过比较了r = 0.5 m、r = 1 m、r = 1.5 m和r = 2 m四种不同半径范围下植被提取的视觉效果和精度分析结果。当 r = 1 m时，研究认为两个样地植被提取效果最佳。样地1的查全率和查准率分别为92.19%和98.74%，样地2的查全率和查准率分别为94.30%和98.73%。

图1. 技术流程图

图2. 两个样地的植被提取结果图. (a)和(c)为两个样地的原始数据；(b)和(d)为两个样地的植被提取结果

该论文得到了王金亮教授主持的国家自然科学基金项目“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）”、国家重点研发计划多政府国际科技创新合作重点项目 “利用地理空间技术进行土地利用/土地覆盖变化对生态安全影响的环境监测与评估（2018YFE0184300）”和张建鹏同学主持的云南省教育厅项目“基于点云邻域特征的机载激光雷达城市样地植被精确提取研究（2023Y0521）”共同资助。

这是张建鹏同学博士期间发表的第3篇SCIE学术论文（详见录 1，2），也是王金亮教授导师团队 2023年发表的第3篇 SCI/SCIE 论文(（详见录 3）)，让我们恭喜张建鹏同学！希望他再接再厉！也热烈祝贺团队取好成绩！

附录 1 论文相关信息

标题：Vegetation Extraction from Airborne Laser Scanning Data of Urban Plots Based on Point Cloud Neighborhood Features

作者：Jianpeng Zhang ^1,2,3, Jinliang Wang ^1,2,3,*, Weifeng Ma^1,2,3,4 , Yuncheng Deng^1,2,3, Jiya Pan^1,2,3and Jie Li^1,2,3

通讯作者：Jinliang Wang

作者单位： 

1  Faculty of Geography , Yunnan Normal University , Kunming 650500, China; jpzhang@ynnu.edu.cn (J.Z.)

2  Key Laboratory of Resources and Environmental Remote Sensing for Universities in Yunnan,
Kunming 650500, China.

3 Center for Geospatial Information Engineering and Technology of Yunnan Province,
Kunming 650500, China.

4  Power China Kunming Engineering Co., Ltd., Kunming 650051, China.

*  Correspondence: jlwang@ynnu.edu.cn

出版物：Forests

摘要：This study proposes an accurate vegetation extraction method used for airborne laser scanning data of an urban plot based on point cloud neighborhood features to overcome the deficiencies in the current research on the precise extraction of vegetation in urban plots. First, the plane features in the R-neighborhood are combined with Euclidean distance clustering to extract the building point cloud accurately, and the rough vegetation point cloud is extracted using the discrete features in the R-neighborhood. Then, under the building point cloud constraints, combined with the Euclidean distance clustering method, the remaining building boundary points in the rough vegetation point cloud are removed. Finally, based on the vegetation point cloud after removing the building boundary point cloud, points within a specific radius r are extracted from the vegetation point cloud in the original data, and a complete urban plot vegetation extraction result is obtained. Two urban plots of airborne laser scanning data are selected to calculate the point cloud plane features and discrete features with R = 0.6 m and accurately extract the vegetation point cloud from the urban point cloud data. The visual effect and accuracy analysis results of vegetation extraction are compared under four different radius ranges of r = 0.5 m, r = 1 m, r = 1.5 m and r = 2 m. The best vegetation extraction results of the two plots are obtained for r = 1 m. The recall and precision are obtained as 92.19% and 98.74% for plot 1 and 94.30% and 98.73% for plot 2, respectively.

关键词：airborne laser scanning; Euclidean distance clustering; point cloud plane features; point cloud discrete features; urban plots; vegetation extraction

附录2 张建鹏同学博士期间发表论文清单

张建鹏同学在王金亮教授指导下，博士期间共发表了3篇SCI学术论文，信息如下：

[3] Jianpeng Zhang, Jinliang Wang*, Weifeng Ma, Yuncheng Deng, Jiya Pan, Jie Li. Vegetation Extraction from Airborne Laser Scanning Data of Urban Plots Based on Point Cloud Neighborhood Features [J]. Forests, 2023, 14(4), 691. DOI: https://doi.org/10.3390/f14040691.( 2022年中科院SCI期刊分区二区，IF 3.282)

[2] Jianpeng Zhang, Jinliang Wang*, Feng Cheng, Weifeng Ma, Qianwei Liu, Guangjie Liu. Natural forest ALS-TLS point cloud data registration without control points[J]. Journal of Forestry Research, 2022, Published: 17 June 2022.DOI：https://doi.org/10.1007/s11676-022-01499-w. （2021年12月基础版二区，升级版二区，2021年IF 2.149）

[1] 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, 2022, Online.DOI: https://doi.org/10.1080/10106049.2022.2034988. (2021年12月基础版二区，升级版三区，2021年IF 4.889)

附录 3 王金亮教授团队 2023 年发表论文清单

自2023年01月01日至3月28日，王金亮教授团队发表学术论文3篇，具体信息如下：

[3] Jianpeng Zhang, Jinliang Wang*, Weifeng Ma, Yuncheng Deng, Jiya Pan, Jie Li. Vegetation Extraction from Airborne Laser Scanning Data of Urban Plots Based on Point Cloud Neighborhood Features [J]. Forests, 2023, 14(4), 691. DOI: https://doi.org/10.3390/f14040691. ( 2022年中科院SCI期刊分区二区，IF 3.282)

[2] Jun Ma, Jianpeng Zhang, Jinliang Wang *, Vadim Khromykh, Jie Li, Xuzheng Zhong. Global Leaf Area Index Research over the Past 75 Years: A Comprehensive Review and Bibliometric Analysis [J]. Sustainability, 2023. DOI: https://doi.org/10.3390/su15043072. (中科院SCI期刊分区：2021年12月基础版三区，2022年12月最新升级版三区，2022年IF 3.889)

[1] Lei Liang, Jinliang Wang*, Fei Deng, Deyang Kong.Mapping Pu'er tea plantations from GF-1 images using Object-Oriented Image Analysis (OOIA) and Support Vector Machine (SVM)[J]. PLOS ONE, 2023. DOI: https://doi.org/10.1371/journal.pone. 0263969. (中科院SCI期刊分区：2021年12月最新基础版三区，升级版三区，2022年IF 3.752)

（云南高校资源与环境遥感重点实验室  供稿）