Natural forest ALS-TLS point cloud data registration without control points

Zhang, Jianpeng; Wang, Jinliang; Cheng, Feiyan; Ma, Weifeng; Liu, Qianwei; Liu, Guangjie

doi:10.1007/s11676-022-01499-w

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…The traditional method for obtaining forest parameters is in-field survey, but this method has many problems, such as a long operation cycle, low efficiency, and high labor intensity. Therefore, it is not suitable for large-area forest surveys [9]. In recent years, airborne laser scanning (ALS) and terrestrial laser scanning (TLS), which are rapidly developing active remote sensing techniques, have been widely used in the acquisition of forest information [10], e.g., individual tree height, crown width, and DBH [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

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Extraction of Moso Bamboo Parameters Based on the Combination of ALS and TLS Point Cloud Data

Fan,

Jing,

et al. 2024

Sensors

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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.

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Section: Introductionmentioning

confidence: 99%

“…However, the disparate scanning modes of ALS and TLS lead to differences in point distribution characteristics between ALS and TLS data. Therefore, how to merge ALS and TLS data to employ the complementary characteristics of these two data sets has become one of the key issues in current forest research [9].…”

Section: Introductionmentioning

confidence: 99%