Skip to main content

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

Abstract

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.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

References

  • Besl P, Mckay N (1992) A method for registration of 3-D shapes. IEEE T Pattern Anal 14:239–256

    Article  Google Scholar 

  • Chen PQ, Lai XD, Li YX (2019) A thinning algorithm of LiDAR point cloud data in urban area. Remote Sens Technol Appl 34:1245–1251 (in Chinese)

    Google Scholar 

  • Dai WX, Yang BS, Liang XL, Dong Z, Huang RG, Wang YS, Li WY (2019) Automated fusion of forest airborne and terrestrial point clouds through canopy density analysis. Isprs J Photogramm 156:94–107

    Article  Google Scholar 

  • Dong Z, Yang BS, Liang FX, Huang RG, Scherer S (2018) Hierarchical registration of unordered TLS point clouds based on binary shape context descriptor. Isprs J Photogramm 144:61–79

    Article  Google Scholar 

  • Du H, Zhu JF, Zhang L, Sun YS, Luo JH (2016) A thinning method of LiDAR point clouds considering terrain features. Sci Surv Mapp 41:140–146 (in Chinese)

    Google Scholar 

  • Geng L, Li MZ, Fang WY, Wang B (2018) Individual tree structure parameters and effective crown of the stand extraction base on air born LiDAR data. Sci Silvae Sin 54:53–64

    Google Scholar 

  • Guo QH, Su YJ, Hu TY, Liu J (2018) LiDAR principles, processing, and applications in forest ecology. Higher Education Press, Beijing, pp 6−7. (in Chinese)

  • Hu C (2015) Thinning algorithm of LiDAR bare earth surface point cloud under the restriction of precision. Master’s thesis, Southwest Jiaotong University, China. (in Chinese)

  • Li DR, Wang CW, Hu YM, Liu SG (2012) General review on remote sensing-based biomass estimation. Geomat Inf Sci Wuhan Univ 37:631–635 (in Chinese)

    Google Scholar 

  • Li ZY, Liu QW, Pang Y (2016) Review on forest parameters inversion using LiDAR. J Remote Sens 20:138–1150

    Google Scholar 

  • Li RZ, Yang M, Tian Y, Liu YY, Zhang HH (2017) Point cloud registration algorithm based on the ISS feature points combined with improved ICP algorithm. Laser Optoelectron Prog 54:312–319 (in Chinese)

    Google Scholar 

  • Liang XL, Kankare V, Hyyppä J, Wang YS, Kukko A, Haggrén H, Yu XW, Kaartinen H, Jaakkola A, Guan FY, Holopainen M, Vastaranta M (2016) Terrestrial laser scanning in forest inventories. Isprs J Photogramm 115:63–77

    Article  Google Scholar 

  • Lin XJ, Wu G, Shan XF, Zhang Y, Cui T, Hu LY, Yu J (2020) An improved ICP registration algorithm based on CMM measurement data of blade section line. J Mech Eng 56:1–8 (in Chinese)

    Google Scholar 

  • Liu LX, Pang Y, Li ZY (2016) Individual tree DBH and height estimation using Terrestrial Laser Scanning (TLS) in a subtropical forest. Sci Silvae Sin 52:26–37 (in Chinese)

    Google Scholar 

  • Liu LX, Pang Y, Li ZY, Si L, Liao SX (2017) Combining airborne and terrestrial laser scanning technologies to measure forest understory volume. Forests 8:111

    Article  Google Scholar 

  • Liu GJ, Wang JL, Dong PL, Chen Y, Liu ZY (2018) Estimating individual tree height and diameter at breast height (DBH) from Terrestrial Laser Scanning (TLS) data at plot level. Forests 9(398):1–18

    Google Scholar 

  • Liu QW, Ma WF, Zhang JP, Liu YC, Xu DF, Wang JL (2021a) Point-cloud segmentation of individual trees in complex natural forest scenes based on a trunk-growth method. J For Res 32:2403–2414

    Article  Google Scholar 

  • Liu QW, Wang JL, Ma WF, Zhang JP, Deng YC, Shao DJ, Xu DF, Liu YC (2021b) Target-free ULS-TLS point-cloud registration for alpine forest lands. Comput Electron Agr 190:106460

    Article  Google Scholar 

  • Meng XY (2006) Forest Mensuration, 3rd ed. Chinese Forestry Publishing House, Beijing, pp 10–17. (in Chinese)

  • Olofsson K, Holmgren J, Olsson H (2014) Tree stem and height measurements using terrestrial laser scanning and the RANSAC algorithm. J Remote Sens 6:4323–4344

    Article  Google Scholar 

  • Pang Y, Zhao F, Li ZY, Zhou SF, Deng G, Liu QW, Chen EX (2008) Forest height inversion using airborne LiDAR technology. J Remote Sens 1:152–158

    Google Scholar 

  • Polewski P, Ericksonc A, Yao W, Coopsc N, Krzysteka P, Stillab U (2016) Object-based co-registration of terrestrial photo-grammetric and ALS point clouds in forested areas. ISPRS Ann Photogramm, Remote Sens Spat Inf Sci 3:347. https://doi.org/10.5194/isprs-annals-III-3-347-2016

    Article  Google Scholar 

  • Qian JJ, Zhang CS, Wang K, Xu B (2017) Research review on thinning algorithm of airborne LiDAR point cloud data. Bulletin of Surveying and Mapping S1: 33–35+58. (in Chinese)

  • Tao SL, Guo QH, Li L, Xue BL, Kelly M, Li WK, Xu GC, Su YJ (2014) Airborne LiDAR-derived volume metrics for above-ground biomass estimation: a comparative assessment for conifer stands. Agr For Meteorol 198–199:24–32

    Article  Google Scholar 

  • Yu YH, Wang JL, Liu GJ, Cheng F (2019) Forest leaf area index inversion based on landsat OLI data in the Shangri-la city. J Indian Soc Remote 47:967–976

    Article  Google Scholar 

  • Yue CR (2012) Forest biomass estimation in Shangri-La County based on remote sensing. Doctorate thesis, Beijing Forestry University. (in Chinese)

  • Zhang WM, Li D, Chen YM, Shao J, Shen AJ, Yan GJ (2018) Integration between TLS and UAV photogrammetry techniques for retrieving tree height. J Beijing Norm Univ (nat Sci) 54:764–771 (in Chinese)

    Google Scholar 

  • Zhang JP, Wang JL, Liu GJ (2020) Vertical structure classification of a forest sample plot based on point cloud data. J Indian Soc Remote 48:1215–1222

    Article  Google Scholar 

  • Zhang B (2015) Research on spatial registration for three-dimensional laser Scanning data. Master’s thesis, Xi’an University of Science and Technology. (in Chinese)

Download references

Acknowledgements

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

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jinliang Wang.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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.

The online version is available at http://www.springerlink.com

Corresponding editor: Tao Xu

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Zhang, J., Wang, J., Cheng, F. et al. Natural forest ALS-TLS point cloud data registration without control points. J. For. Res. (2022). https://doi.org/10.1007/s11676-022-01499-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11676-022-01499-w

Keywords

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