文献类型：期刊文献

英文题名：Trunk cross-section reconstruction and DBH calculation from handheld laser scanning data using Kalman filter

作者：Cai, Shangshu[1,2] Pang, Yong[1,2]

第一作者：Cai, Shangshu

机构：[1] Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, 100091, China; [2] Key Laboratory of Forestry Remote Sensing and Information System, National Forestry and Grassland Administration, Beijing, 100091, China

年份：2026

卷号：37

期号：1

外文期刊名：Journal of Forestry Research

收录：EI(收录号：20254819597813);Scopus(收录号：2-s2.0-105022782429)

语种：英文

外文关键词：Forestry - Laser applications - Mean square error - Remote sensing - Surface analysis - Three dimensional computer graphics - Timber - Trees (mathematics)

摘要：Tree trunk cross-sections are essential for forest condition analysis. Handheld laser scanning (HLS), a portable three-dimensional digitization technology, has captured considerable interest, due to the efficiency brought by its portability and flexibility. However, the accuracy of HLS is limited by its lightweight design, leading to point clouds with a?thickness of 3 to 8?cm for tree trunk surfaces. This reduces the accuracy of cross-section characterization. Here, we propose a Kalman filter-based reconstruction algorithm to improve HLS-derived tree trunk cross-sections. The method transforms the point cloud from Cartesian to polar coordinates based on a density-based reference direction, and applies Kalman filtering for reconstruction. Diameter at breast height (DBH) is then calculated using a simulated diameter tape. Validation with HLS data at different accuracy levels shows that the proposed algorithm outperforms traditional geometric fitting methods, providing more accurate representations of irregular trunk cross-sections. It achieves a higher intersection over union of 86.98 ± 7.67% vs. 84.33 ± 11.08%, and a lower root mean square error in DBH estimation (1.96?cm vs. 2.48?cm). This approach enhances HLS accuracy and provides a?promising solution for advancing portable laser scanning technology, which brings opportunity to revolutionize traditional field inventories. ? Northeast Forestry University 2025.