A Novel Framework to Automatically Fuse Multiplatform LiDAR Data in Forest Environments Based on Tree Locations

作者：Hongcan Guan, Yanjun Su, Tianyu Hu, Rui Wang, Qin Ma, Qiuli Yang, Xiliang Sun, Yumei Li, Shichao Jin, Jing Zhang, Qin Ma, Min Liu, Fayun Wu, Qinghua Guo · 发表于：IEEE Transactions on Geoscience and Remote Sensing · 年份：2019 · DOI：10.1109/tgrs.2019.2953654 · 被引用次数：62 · 研究领域：Remote Sensing and LiDAR Applications、3D Surveying and Cultural Heritage、Remote Sensing in Agriculture

The emerging near-surface light detection and ranging (LiDAR) platforms [e.g., terrestrial, backpack, mobile, and unmanned aerial vehicle (UAV)] have shown great potential for forest inventory. However, different LiDAR platforms have limitations either in data coverage or in capturing undercanopy information. The fusion of multiplatform LiDAR data is a potential solution to this problem. Because of the complexity and irregularity of forests and the inaccurate positioning information under forest canopies, current multiplatform data fusion still involves substantial manual efforts. In this article, we proposed an automatic multiplatform LiDAR data registration framework based on the assumption that each forest has a unique tree distribution pattern. Five steps are included in the proposed framework, i.e., individual tree segmentation, triangulated irregular network (TIN) generation, TIN matching, coarse registration, and fine registration. TIN matching, as the essential step to find the corresponding tree pairs from multiplatform LiDAR data, uses a voting strategy based on the similarity of triangles composed of individual tree locations. The proposed framework was validated by fusing backpack and UAV LiDAR data and fusing multiscan terrestrial LiDAR data in coniferous forests. The results showed that both registration experiments could reach a satisfying data registration accuracy (horizontal root-mean-square error (RMSE) <; 30 cm and vertical RMSE <; 20 cm). Moreover, the pr...