王金亮导师研究小组的论文在SCIE期刊Journal of Forestry Research上发表

        2021年3月4日，以云南省高校资源与环境遥感重点实验室2019级硕士研究生刘钱威为第一作者、王金亮教授为通讯作者撰写题为“Point‑cloud segmentation of individual trees in complex natural forest scenes based on a trunk‑growth method”的论文在SCIE期刊Journal of Forestry Research（SCI三区,2020年影响因子1.689）上发表（DOI: 10.1007/s11676-021-01303-1，文章链接https://doi.org/10.1007/s11676-021-01303-1）。该论文基于“主干生长枝干，枝干连接枝叶”的思想，提出主干生长算法（Trunk-Growth，TG)，并将其应用在香格里拉获取的地面激光点云数据中，实现了单木分割。

  论文得到了王金亮教授主持的国家自然科学基金项目(名称：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;批准编号：41961060)、云南省重点项目（No. 2019FA017）、云南省高校科技创新团队支持计划（IRTSTYN）和云南师范大学研究生创新项目（No. ysdyjs 2020058）的资助。

标题：Point‑cloud segmentation of individual trees in complex natural forest scenes based on a trunk‑growth method

作者：Qianwei Liu, Weifeng Ma, Jianpeng Zhang, Yicheng Liu, Dongfan Xu, Jinliang Wang

通讯作者：Jinliang Wang（王金亮）

作者单位：

1. Faculty of Geography, Yunnan Normal University, Kunming 650092, Yunnan, People’s Republic of China

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

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

4.Forestry College, Southwest Forestry University, Kunming 650224, Yunnan, People’s Republic of China

出版物：Journal of Forestry Research（中科院SCI期刊分区三区（2020年12月最新基础版， 2020年12月最新升级版），2020年影响因子：1.689）

网址https://www.springer.com/journal/11676）

摘要：Forest resource management and ecological assessment have been recently supported by emerging technologies. Terrestrial laser scanning (TLS) is one that can be quickly and accurately used to obtain three-dimensional forest information, and create good representations of forest vertical structure. TLS data can be exploited for highly significant tasks, particularly the segmentation and information extraction for individual trees. However, the existing single-tree segmentation methods suffer from low segmentation accuracy and poor robustness, and hence do not lead to satisfactory results for natural forests in complex environments. In this paper, we propose a trunk-growth (TG) method for single-tree point-cloud segmentation, and apply this method to the natural forest scenes of Shangri-La City in Northwest Yunnan, China. First, the point normal vector and its Z-axis component are used as trunk-growth constraints. Then, the points surrounding the trunk are searched to account for regrowth. Finally, the nearest distributed branch and leaf points are used to complete the individual tree segmentation. The results show that the TG method can effectively segment individual trees with an average F-score of 0.96. The proposed method applies to many types of trees with various growth shapes, and can effectively identify shrubs and herbs in complex scenes of natural forests. The promising outcomes of the TG method demonstrate the key advantages of combining plant morphology theory and LiDAR technology for advancing and optimizing forestry systems.

关键词：Terrestrial laser scanning • Point-cloud • Northwest Yunnan • Natural forests • Single-tree segmentation • Trunk-growth