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


Airborne laser scanning (ALS) and terrestrial laser scanning (TLS) has attracted attention due to their forest parameter investigation and research applications. ALS is limited to obtaining fine structure information below the forest canopy due to the occlusion of trees in natural forests. In contrast, TLS is unable to gather fine structure information about the upper canopy. To address the problem of incomplete acquisition of natural forest point cloud data by ALS and TLS on a single platform, this study proposes data registration without control points. The ALS and TLS original data were cropped according to sample plot size, and the ALS point cloud data was converted into relative coordinates with the center of the cropped data as the origin. The same feature point pairs of the ALS and TLS point cloud data were then selected to register the point cloud data. The initial registered point cloud data was finely and optimally registered via the iterative closest point (ICP) algorithm. The results show that the proposed method achieved high-precision registration of ALS and TLS point cloud data from two natural forest plots of Pinus yunnanensis Franch. and Picea asperata Mast. which included different species and environments. An average registration accuracy of 0.06 m and 0.09 m were obtained for P. yunnanensis and P. asperata, respectively.

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We would like to thank Yuncheng Deng, Dajiang Shao, Hui Ye, Jie Li and Jieying Lao for their help in data collecting.

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Correspondence to Jinliang Wang.

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Project Funding This study was supported by the National Natural Science Foundation of China, Grant Number 41961060, by the Program for Innovative Research Team (in Science and Technology) in the University of Yunnan Province, Grant Number IRTSTYN, by the Scientific Research Fund Project of the Education Department of Yunnan Province, Grant Numbers 2020J0256 and 2021J0438, and by the Postgraduate Scientific Research and Innovation Fund Project of Yunnan Normal University, Grant Number YJSJJ21-A08.

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Corresponding editor: Tao Xu

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Zhang, J., Wang, J., Cheng, F. et al. Natural forest ALS-TLS point cloud data registration without control points. J. For. Res. (2022).

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  • Airborne laser scanning (ALS)
  • Terrestrial laser scanning (TLS)
  • Registration
  • Natural forest
  • Iterative closest point (ICP) algorithm