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Abstract：
Extracting moso bamboo parameters from single-source point cloud data has limitations. In this article, a new approach for extracting moso bamboo parameters using airborne laser scanning (ALS) and terrestrial laser scanning (TLS) point cloud data is proposed. Using the field-surveyed coordinates of plot corner points and the Iterative Closest Point (ICP) algorithm, the ALS and TLS point clouds were aligned. Considering the difference in point distribution of ALS, TLS, and the merged point cloud, individual bamboo plants were segmented from the ALS point cloud using the point cloud segmentation (PCS) algorithm, and individual bamboo plants were segmented from the TLS and the merged point cloud using the comparative shortest-path (CSP) method. The cylinder fitting method was used to estimate the diameter at breast height (DBH) of the segmented bamboo plants. The accuracy was calculated by comparing the bamboo parameter values extracted by the above methods with reference data in three sample plots. The comparison results showed that by using the merged data, the detection rate of moso bamboo plants could reach up to 97.30%; the R2 of the estimated bamboo height was increased to above 0.96, and the root mean square error (RMSE) decreased from 1.14 m at most to a range of 0.35-0.48 m, while the R2 of the DBH fit was increased to a range of 0.97-0.99, and the RMSE decreased from 0.004 m at most to a range of 0.001-0.003 m. The accuracy of moso bamboo parameter extraction was significantly improved by using the merged point cloud data.
摘要：
从单源点云数据中提取毛竹参数具有局限性。在这篇文章中,提出了一种利用机载激光扫描（ALS）和地面激光扫描（TLS）点云数据提取毛竹参数的新方法。使用现场测量的曲线角点坐标和迭代最近点（ICP）算法，ALS和TLS点云对齐。考虑到ALS点分布的差异，TLS,和合并的点云，使用点云分割（PCS）算法从ALS点云分割出单个竹子植物，使用比较最短路径（CSP）方法从TLS和合并的点云中分割出单个竹子植物。圆柱拟合方法用于估计分段竹子植物的胸高直径（DBH）。通过将上述方法提取的竹子参数值与三个样地中的参考数据进行比较来计算精度。比较结果表明,通过使用合并后的数据,毛竹植物的检出率可达97.30%；估计竹高的R2提高到0.96以上，均方根误差（RMSE）从最多1.14m下降到0.35-0.48m，而DBH拟合的R2提高到0.97-0.99，RMSE从最多0.004m降低到0.001-0.003m。使用合并的点云数据显着提高了毛竹参数提取的精度。
