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<p>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&#xa0;thickness of 3 to 8&#xa0;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&#xa0;cm vs. 2.48&#xa0;cm). This approach enhances HLS accuracy and provides a&#xa0;promising solution for advancing portable laser scanning technology, &#xa0;which brings opportunity to&#xa0; revolutionize traditional field inventories.</p>

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

  • 期刊论文

  • 发表日期：

    2025年11月25日

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  • Shangshu Cai,
  • Yong Pang

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.