2024年6月10日，IEEE 旗下top期刊《IEEE Transactions on Geoscience and Remote Sensing》在线发表了云南师范大学地理学部王金亮教授团队最新研究成果《A Tree-Shrub Layer Separation Method for MLS LiDAR Point Clouds of Typical Tropical Seasonal Rainforests》。云师大地理学部为第一作者单位，通讯作者为云南师范大学地理学部王金亮教授团队。IEEE TGRS是地球科学和遥感领域的世界级顶级期刊，是IEEE地球科学与遥感技术协会（GRSS）会刊，在遥感技术和地球科学领域具有很高影响力。

https://ieeexplore.ieee.org/document/10552301/authors#authors

Abstract:

As the most complex community structure in terrestrial ecosystems, tropical rainforests still employ traditional manual surveys to obtain spatial information on trees, which are time-consuming and laborious with significant errors. LiDAR can provide high-quality 3D point cloud data, but using it to extract structural information of trees and shrubs in complex forests remains a technical challenge for point cloud mapping. Therefore, this paper proposes a solution for realizing the accurate separation of LiDAR point cloud data for trees and shrubs within complex forests such as tropical rainforests. The method first preprocesses the data to obtain understory point cloud data. The local curvature features are then utilized to perform preliminary separation. Then, the growth segmentation is performed based on the normal vector and the curvature magnitude to obtain the clustered objects. Accordingly, trees and shrubs are identified according to the features of the segmented clustered objects. Finally, eight tropical rainforest plots were selected to evaluate and analyze the performance of the method. The research results indicate that the accuracy of tree extraction in tropical rainforests using this method can reach over 91%. The accuracy and efficiency of this method for tree-shrub separation are superior to other methods. This study provides essential support for investigating forest understory vegetation characteristics and the spatial growth distribution of tropical rainforest trees and shrubs. It lays a foundation for the subsequent application and promotion of terrestrial LiDAR when investigating vegetation information in complex forest scenarios.

扩展阅读：

云师大地理学部王金亮教授团队在top期刊《Science of The Total Environment》发表研究成果

Remote sensing estimation of regional PM2.5 based on GTWR model -A case study of southwest China

云南师范大学基础研究代表之-王金亮