A novel method for ULS-TLS forest point cloud registration based on height context descriptor

• Published in: Forest ecosystems Vol. 14; p. 100369
• Main Authors: Dai, Xusong, Qi, Hanwen, Wang, Xiaochen, Xu, Yaozhan, Jiang, Qinghu, Zhang, Qingjun, Wang, Xu, Chen, Jianchang, Liu, Guangzu, Liang, Xinlian
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.12.2025
• Subjects: Ground points; Height context descriptor; Light detection and ranging (LiDAR); Registration; Terrestrial laser scanning (TLS); Unmanned aerial vehicle; Light detection and ranging (LiDAR); Ground points; Unmanned aerial vehicle; Registration; Height context descriptor; Terrestrial laser scanning (TLS)
• ISSN: 2197-5620; 2197-5620
• DOI: 10.1016/j.fecs.2025.100369

Unmanned aerial vehicle laser scanning (ULS) and terrestrial laser scanning (TLS) systems are effective ways to capture forest structures from top and side views, respectively. The registration of TLS and ULS data is a prerequisite for a comprehensive forest structure representation. Conventional registration methods based on geometric features (e.g., points, lines, and planes) are likely to fail due to the irregular natural point distributions of forest point clouds. Currently, automatic registration methods for forest point clouds typically rely on tree attributes (such as tree position and stem diameter). However, these methods are often unsuitable for forests with diverse compositions, complex terrains, irregular tree layouts, and insufficient common trees. In this study, an automated method is proposed to register ULS and TLS forest point clouds using ground points as registration primitives, which operates independently of tree attribute extraction and is estimated to reduce processing time by over 50%. A new evaluation method for registration accuracy evaluation is proposed, where transformation parameters from each TLS scan to the ULS obtained by the proposed registration algorithm are used to derive transformation parameters between TLS scans, which are then compared to reference parameters obtained using artificial spherical targets. Conventional ULS-TLS registration evaluation methods mostly rely on the manual corresponding points selection that is subject to inherent subjective errors, or control points in both TLS and ULS data that are difficult to collect. The proposed method presents an objective and accurate solution for ULS-TLS registration accuracy evaluation that effectively eliminates these limitations. The proposed method was tested on 12 plots with diverse stem densities, tree species, and altitudes located in a mountain forest. A total of 124 TLS scans were successfully registered to ULS data. The registration accuracy was assessed using both the conventional evaluation method and the proposed new evaluation method, with average rotation errors of 2.03 and 2.06 ​mrad, and average translation errors of 7.63 and 6.51 ​cm, respectively. The registration accuracies demonstrate that the proposed algorithm effectively and accurately registers TLS to ULS point clouds.