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An efficient method of monitoring slow-moving landslides with long-range terrestrial laser scanning: a case study of the Dashu landslide in the Three Gorges Reservoir Region, China

Landslides volume 16, pages839–855(2019)Cite this article

Abstract

Accurate measurement of the small pre-failure deformation plays a crucial role in landslide early warning systems. Recently, terrestrial laser scanning (TLS) has been applied to detect the small pre-failure deformation, while the processing of TLS point clouds is still a non-trivial issue, especially for long-range TLS. Therefore, we propose an efficient method to detect the small pre-failure deformation of slow-moving landslides by the use of long-range TLS. To precisely align the point clouds of different epochs, a hybrid-weighted iterative closest point (HWICP) algorithm is proposed after the coarse registration, where the influences of several factors (i.e., deformed areas, point density variations, and observation errors) on the inconsistency of the point correspondence are quantitatively estimated. An adaptive local cloud-to-mesh method (ALC2M) is then proposed for the deformation calculation in the complex topography by defining a local reference frame and removing poor matches between ground points of the two epochs. To validate the performance of the proposed method, the Dashu landslide of the Three Gorges Reservoir Region was selected as a case study. The TLS-derived deformation showed that the landslide was stable in most areas over the study period, except for two abnormal deformation zones with maximum deformations of ~ 6 cm and ~ 3 cm, which agreed well with the ground-based synthetic aperture radar interferometry (GB-InSAR) measurements. A statistical analysis of the stable areas around the landslide illustrated a low uncertainty for the TLS-derived deformation of 6.3 mm. Therefore, the use of the long-range TLS was an efficient way to monitor slow-moving landslides. Moreover, extensive comparative experiments about the co-registration and deformation calculation were conducted, and the results showed that HWICP and ALC2M achieved superior performances.

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References

  1. Abellán A, Jaboyedoff M, Oppikofer T, Vilaplana J (2009) Detection of millimetric deformation using a terrestrial laser scanner: experiment and application to a rockfall event. Nat Hazards Earth Syst Sci 9:365–372

    Article  Google Scholar 

  2. Aiger D, Mitra NJ, Cohen-Or D (2008) 4-points congruent sets for robust pairwise surface registration. ACM Trans Graph 27:1–10

    Article  Google Scholar 

  3. Besl PJ, McKay ND (1992) A method for registration of 3-d shapes. IEEE Trans Pattern Anal Mach Intell 14:239–256

    Article  Google Scholar 

  4. Brodu N, Lague D (2012) 3d terrestrial lidar data classification of complex natural scenes using a multi-scale dimensionality criterion: applications in geomorphology. ISPRS J Photogramm Remote Sens 68:121–134

    Article  Google Scholar 

  5. Caputo T, Marino E, Matano F, Somma R, Troise C, De Natale G (2018) Terrestrial laser scanning (TLS) data for the analysis of coastal tuff cliff retreat: application to Coroglio cliff, Naples, Italy. Ann Geophys 61:1–18

    Article  Google Scholar 

  6. Chen Y, Medioni G (1992) Object modelling by registration of multiple range images. Image Vis Comput 10:145–155

    Article  Google Scholar 

  7. Chen X, Yu K, Wu H (2018) Determination of minimum detectable deformation of terrestrial laser scanning based on error entropy model. IEEE Trans Geosci Remote Sens 56:105–116

    Article  Google Scholar 

  8. Dong Z, Yang B, Liu Y, Liang F, Li B, Zang Y (2017) A novel binary shape context for 3d local surface description. ISPRS J Photogramm Remote Sens 130:431–452

    Article  Google Scholar 

  9. Du S, Liu J, Zhang C, Zhu J, Li K (2015) Probability iterative closest point algorithm for m-d point set registration with noise. Neurocomputing 157:187–198

    Article  Google Scholar 

  10. Franz M, Carrea D, Abellán A, Derron M-H, Jaboyedoff M (2016) Use of targets to track 3d displacements in highly vegetated areas affected by landslides. Landslides 13:821–831

    Article  Google Scholar 

  11. Ge X (2017) Automatic markerless registration of point clouds with semantic-keypoint-based 4-points congruent sets. ISPRS J Photogramm Remote Sens 130:344–357

    Article  Google Scholar 

  12. Gressin A, Mallet C, Demantké J, David N (2013) Towards 3d lidar point cloud registration improvement using optimal neighborhood knowledge. ISPRS J Photogramm Remote Sens 79:240–251

    Article  Google Scholar 

  13. Huang D, Gu DM, Song YX, Cen DF, Zeng B (2018) Towards a complete understanding of the triggering mechanism of a large reactivated landslide in the three gorges reservoir. Eng Geol 238:36–51

    Article  Google Scholar 

  14. Intrieri E, Gigli G, Nocentini M, Lombardi L, Mugnai F, Fidolini F, Casagli N (2015) Sinkhole monitoring and early warning: an experimental and successful GB-InSar application. Geomorphology 241:304–314

    Article  Google Scholar 

  15. Intrieri E, Raspini F, Fumagalli A, Lu P, Del Conte S, Farina P, Allievi J, Ferretti A, Casagli N (2018) The Maoxian landslide as seen from space: detecting precursors of failure with sentinel-1 data. Landslides 15:123–133

    Article  Google Scholar 

  16. Keefer DK, Wilson RC, Mark RK, Brabb EE, Brown WM, Ellen SD, Harp EL, Wieczorek GF, Alger CS, Zatkin RS (1987) Real-time landslide warning during heavy rainfall. Science 238:921–925

    Article  Google Scholar 

  17. Kelbe D, Aardt JV, Romanczyk P, Leeuwen MV, Cawse-Nicholson K (2016) Marker-free registration of forest terrestrial laser scanner data pairs with embedded confidence metrics. IEEE Trans Geosci Remote Sens 54:4314–4330

    Article  Google Scholar 

  18. Kromer R, Abellán A, Hutchinson D, Lato M, Edwards T, Jaboyedoff M (2015) A 4d filtering and calibration technique for small-scale point cloud change detection with a terrestrial laser scanner. Remote Sens 7:13029–13052

    Article  Google Scholar 

  19. Kromer RA, Abellán A, Hutchinson DJ, Lato M, Chanut M-A, Dubois L, Jaboyedoff M (2017) Automated terrestrial laser scanning with near-real-time change detection – monitoring of the Séchilienne landslide. Earth Surf Dyn 5:293–310

    Article  Google Scholar 

  20. Lague D, Brodu N, Leroux J (2013) Accurate 3d comparison of complex topography with terrestrial laser scanner: application to the Rangitikei canyon (n-z). ISPRS J Photogramm Remote Sens 82:10–26

    Article  Google Scholar 

  21. Liu C, Liu Y, Wen M, Li T, Lian J, Qin S (2009) Geo-hazard initiation and assessment in the three gorges reservoir. In: Wang F, Li F (ed) Landslide disaster mitigation in Three Gorges Reservoir, China. Heidelberg, Germany, pp 3–40

  22. Lombardi L, Nocentini M, Frodella W, Nolesini T, Bardi F, Intrieri E, Carla T, Solari L, Dotta G, Ferrigno F, Casagli N (2017) The Calatabiano landslide (southern Italy): preliminary GB-InSar monitoring data and remote 3d mapping. Landslides 14:685–696

    Article  Google Scholar 

  23. Lourakis MI (2005) A brief description of the levenberg-marquardt algorithm implemented by levmar. http://hackage.haskell.org/package/levmar. Accessed 31 Oct 2018

  24. Mayr A, Rutzinger M, Bremer M, Elberink SO, Stumpf F, Geitner C (2017) Object-based classification of terrestrial laser scanning point clouds for landslide monitoring. Photogramm Rec 32:377–397

    Article  Google Scholar 

  25. Monserrat O, Crosetto M (2008) Deformation measurement using terrestrial laser scanning data and least squares 3d surface matching. ISPRS J Photogramm Remote Sens 63:142–154

    Article  Google Scholar 

  26. Olsen MJ, Kuester F, Chang BJ, Hutchinson TC (2010) Terrestrial laser scanning-based structural damage assessment. J Comput Civ Eng 24:264–272

    Article  Google Scholar 

  27. Schürch P, Densmore Alexander L, Rosser Nicholas J, Lim M, McArdell Brian W (2011) Detection of surface change in complex topography using terrestrial laser scanning: application to the Illgraben debris-flow channel. Earth Surf Process Landf 36:1847–1859

    Article  Google Scholar 

  28. Segal A, Haehnel D, Thrun S (2009) Generalized-ICP. Robotics: Sci Syst 1–8

  29. Shi X, Liao M, Li M, Zhang L, Cunningham C (2016) Wide-area landslide deformation mapping with multi-path ALOS PALSAR data stacks: a case study of Three Gorges Area, China. Remote Sens 8:136

    Article  Google Scholar 

  30. Shi X, Zhang L, Zhou C, Li M, Liao M (2018) Retrieval of time series three-dimensional landslide surface displacements from multi-angular SAR observations. Landslides 15:1015–1027

    Article  Google Scholar 

  31. Soudarissanane S, Lindenbergh R, Menenti M, Teunissen P (2011) Scanning geometry: influencing factor on the quality of terrestrial laser scanning points. ISPRS J Photogramm Remote Sens 66:389–399

    Article  Google Scholar 

  32. Wang F, Zhang Y, Huo Z, Peng X, Wang S, Yamasaki S (2008) Mechanism for the rapid motion of the Qianjiangping landslide during reactivation by the first impoundment of the Three Gorges Dam Reservoir, China. Landslides 5:379–386

    Article  Google Scholar 

  33. Wujanz D, Krueger D, Neitzel F (2016) Identification of stable areas in unreferenced laser scans for deformation measurement. Photogramm Rec 31:261–280

    Article  Google Scholar 

  34. Wujanz D, Avian M, Krueger D, Neitzel F (2017a) Identification of stable areas in unreferenced laser scans for automated geomorphometric monitoring. Earth Surf Dynam Discuss 2017:1–23

    Article  Google Scholar 

  35. Wujanz D, Burger M, Mettenleiter M, Neitzel F (2017b) An intensity-based stochastic model for terrestrial laser scanners. ISPRS J Photogramm Remote Sens 125:146–155

    Article  Google Scholar 

  36. Yang B, Zang Y (2014) Automated registration of dense terrestrial laser-scanning point clouds using curves. ISPRS J Photogramm Remote Sens 95:109–121

    Article  Google Scholar 

  37. Yang B, Dong Z, Liang F, Liu Y (2016a) Automatic registration of large-scale urban scene point clouds based on semantic feature points. ISPRS J Photogramm Remote Sens 113:43–58

    Article  Google Scholar 

  38. Yang B, Huang R, Dong Z, Zang Y, Li J (2016b) Two-step adaptive extraction method for ground points and breaklines from lidar point clouds. ISPRS J Photogramm Remote Sens 119:373–389

    Article  Google Scholar 

  39. Yang B, Huang R, Li J, Tian M, Dai W, Zhong R (2017) Automated reconstruction of building lods from airborne lidar point clouds using an improved morphological scale space. Remote Sens 9:14

    Article  Google Scholar 

Download references

Acknowledgements

The authors are thankful to anonymous reviewers for their valuable comments. Special thanks to Lin Liu, Yafei Sun, Binbin Gao of Institute of Geodesy and Geophysics and Daqing Ge, Bin Liu of China Aero Geophysical Survey and Remote Sensing Center for Land and Resources, since their works in capturing the experiment datasets.

Funding

This study was jointly supported by the National Key R&D Program of China (Nos. 2018YFC1406102 and 2017YFA0603103); the China Postdoctoral Science Found (No. 2017M622553); the NSFC project (Nos. 41801398, 41590854, 41531177, 41701530); and the Key Research Program of Frontier Sciences, CAS (Nos. QYZDB-SSW-DQC027, QYZDJ-SSW-DQC042).

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Affiliations

  1. State Key Laboratory of Geodesy and Earth’s Dynamics, Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan, China
    • Ronggang Huang
    • , Liming Jiang
    • , Xiang Shen
    • , Qiaoli Zhou
    •  & Hansheng Wang
  2. University of Chinese Academy of Sciences, Beijing, China
    • Liming Jiang
  3. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China
    • Zhen Dong
    •  & Bisheng Yang
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  1. Ronggang Huang
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  3. Xiang Shen
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Correspondence to Liming Jiang.

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Cite this article

Huang, R., Jiang, L., Shen, X. et al. An efficient method of monitoring slow-moving landslides with long-range terrestrial laser scanning: a case study of the Dashu landslide in the Three Gorges Reservoir Region, China. Landslides 16, 839–855 (2019). https://doi.org/10.1007/s10346-018-1118-6

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Keywords

  • Long-range TLS
  • Co-registration
  • Deformation calculation
  • Dashu landslide
  • Three Gorges Reservoir Region