王金亮导师研究小组的论文在SCI期刊Journal of the Indian Society of Remote Sensing上发表

   2020年08月26日，以云南省高校资源与环境遥感重点实验室2018级硕士研究生张建鹏为第一作者、王金亮教授为通讯作者撰写题为“Vertical Structure Classification of a Forest Sample Plot Based on Point Cloud Data”的论文在SCI期刊Journal of the Indian Society of Remote Sensing上发表(DOI: 10.1007/s12524-020-01149-w, 文章链接：https://rdcu.be/b6sEz)。该论文基于地面激光雷达获取的香格里拉森林点云数据，利用形态学滤波和比较最短路径点云分割算法，实现了天然林点云数据在垂直方向上的分层提取：乔木、灌木和地面。

乔木点云提取结果

灌木点云提取结果

地面点云提取结果

该论文得到了王金亮教授主持国家自然科学基金(No.41961060, No.41271230)、欧盟文化执行署(EACEA伊拉斯谟+国际高等教育能力建设项目 “遥感教育与学习创新(No.586037-EPP-1-2017-1-HU-EPPKA2-CBHE-JP）、云南省中青年学术技术带头人（No.2008PY056）、云南省高校科技创新团队支持计划（IRTSTYN）的资助。

标题：Vertical Structure Classification of a Forest Sample Plot Based on Point Cloud Data

作者：Jianpeng Zhang, Jinliang Wang, Guangjie Liu

通讯作者：Jinliang Wang, jlwang@ynnu.edu.cn

作者单位：1. College of Tourism and Geographic Sciences, Yunnan Normal University, Kunming 650500, China; 2. Key Laboratory of Resources and Environmental Remote Sensing, Universities in Yunnan, Kunming 650500, China; 3. Remote Sensing Research Laboratory, Center for Geospatial Information Engineering and Technology of Yunnan Province, Kunming 650500, China.

出版物：Journal of the Indian Society of Remote Sensing

摘要：The vertical structure of forests affects energy transfer and material exchange within forest ecosystems and is of great significance to scientific forestry and ecology research. In this paper, the Shangri-La forest plot in northwestern Yunnan Province of China was the study area. The forest sample plot point cloud data were obtained by terrestrial laser scanning technology. A new method for classifying the vertical structure of forest sample plots based on point cloud data is proposed. The method comprehensively utilizes morphological filtering and comparative shortest-path (CSP) algorithm point cloud segmentation technology. Additionally, the method proposes the concept of secondary CSP segmentation that precisely classifies three types of vertical features in forest point cloud data: trees, shrubs and the ground. Finally, an accuracy analysis showed that the error rate of the tree results was 1.87%, and the error rate of the shrub results was 16.23%.

关键字：Morphological filtering; Point cloud segmentation; Shangri-La; TLS; Vertical structure