pp 1–17 | Cite as

Calculation of 3D displacement and time to failure of an earth dam using DIC analysis of hillshade images derived from high temporal resolution point cloud data

  • Nancy Berg
  • Toshikazu Hori
  • W. Andy TakeEmail author
Original Paper


Point cloud data capturing land surface elevation at two instants in time are commonly used to detect landslides occurrence. In this paper, it is hypothesised that this same point cloud data has the potential to yield much more valuable quantitative information regarding landslide behaviour, including the direction, magnitude, and rate of surface displacement. Given point cloud data contains roughness information, shaded projections (hillshade images) of the slope at two or more instants in time can be processed using digital image correlation (DIC) to track displacement in the plane of the projection. If multiple view angles are used to generate the hill shade images, 3D surface displacements of the landslide surface should theoretically be resolved. Furthermore, if point clouds are generated with sufficiently high temporal resolution, it should be possible to estimate the time to failure. The objective of this paper is to test this hypothesis using point clouds generated at high temporal resolution using digital images of a 3.5 m high earth dam field experiment brought to failure under high reservoir water conditions and an extreme rainfall event. This experiment indicates that the proposed method was successful in generating 3D displacement estimates within 2 cm of collected total station data and an estimated time to failure was within four minutes of the observed slope failure.


Deformation montoring Remote sensing Photogrammetry Time to failure Digital image correlation 



The authors gratefully acknowledge funding from the Natural Sciences and Engineering Research Council of Canada, the Japan Society for the Promotion of Science, and Thurber Engineering. The authors would also like to thank the staff of Tokyo Soil Research Co for constructing the full-scale earth dams used in these trials. The support provided by the staff of the National Institute for Rural Engineering in Tsukuba, in particular, Yusaku Mukae, is gratefully acknowledged.


  1. Agisoft LLC (2011) Agisoft PhotoScan user manual. Professional edition, version 0.8.0 (accessed 03.05. 2011.). Online at: http://www.Agisoft.Ru/pscan/help/en/pscan_pro.pdf
  2. Aryal A, Brooks BA, Reid ME, Bawden GW, Pawlak GR (2012) Displacement fields from point cloud data: application of particle imaging velocimetry to landslide geodesy. J Geophys Res Earth Surf 117(F1)CrossRefGoogle Scholar
  3. Aryal A, Brooks BA, Reid ME (2015) Landslide subsurface slip geometry inferred from 3-D surface displacement fields. Geophys Res Lett 42(5):1411–1417CrossRefGoogle Scholar
  4. Avery TE, Berlin GL (1992) Fundamentals of remote sensing and airphoto interpretation, Fifth edn. Maxwell Macmillan Canada, TorontoGoogle Scholar
  5. Booth AM, McCarley J, Hinkle J, Shaw S, Ampuero JP, Lamb MP (2018) Transient reactivation of a deep-seated landslide by undrained loading captured with repeat airborne and terrestrial lidar. Geophys Res Lett 45(10):4841–4850CrossRefGoogle Scholar
  6. Bozzano F, Cipriani I, Mazzanti P, Prestininzi A (2014) A field experiment for calibrating landslide time-of-failure prediction functions. Int J Rock Mech Min Sci 67:69–77CrossRefGoogle Scholar
  7. Brunner FK, Woschitz H and Macheiner K (2007) November. Monitoring of deep-seated mass movements. In proceedings of the third international conference on structural health monitoring of intelligent infra-structure (SHMII-3), Vancouver, Canada: 3-16Google Scholar
  8. Carlà T, Intrieri E, Di Traglia F, Nolesini T, Gigli G, Casagli N (2017) Guidelines on the use of inverse velocity method as a tool for setting alarm thresholds and forecasting landslides and structure collapses. Landslides 14(2):1–18CrossRefGoogle Scholar
  9. Fey C, Rutzinger M, Wichmann V, Prager C, Bremer M, Zangerl C (2015) Deriving 3D displacement vectors from multi-temporal airborne laser scanning data for landslide activity analyses. GIScience & Remote Sensing 52(4):437–461CrossRefGoogle Scholar
  10. Fukuzono T (1985) A new method for predicting the failure time of a slope. In proceedings of the 4th international conference and field workshop in landslides, Tokyo, pp 145-150Google Scholar
  11. Girardeau-Montaut D (2015) Cloud Compare version 2.6. 1-user manual. Online at:,6, pp 20
  12. Greene C (2015) Shadem Matlab function – Mathworks file exchange. Online at:
  13. Guthrie RH and Nicksiar M (2016) Time to failure – practical improvements of an analytical tool. In proceedings of the 69th Canadian geotechnical conference, VancouverGoogle Scholar
  14. Hashimoto M (1991) Geology of Japan: developments in earth and planetary sciences. Terra Scientific Publishing Company, TokyoGoogle Scholar
  15. Haugen BD (2016) Qualitative and quantitative comparative analyses of 3D lidar landslide displacement field measurements (Doctoral dissertation, Colorado School of Mines)Google Scholar
  16. Hori T, Mohri Y Kohgo Y (2006) Model test and deformation analysis for failure of a loose sandy embankment dam by seepage. American Society of Civil EngineersGoogle Scholar
  17. Jaboyedoff M, Ornstein P, Rouiller JD (2004) Design of a geodetic database and associated tools for monitoring rock-slope movements: the example of the top of Randa rockfall scar. Nat Hazards Earth Syst Sci 4(2):187–196CrossRefGoogle Scholar
  18. Jaboyedoff M, Oppikofer T, Abellán A, Derron MH, Loye A, Metzger R, Pedrazzini A (2012) Use of LIDAR in landslide investigations: a review. Nat Hazards 61(1):5–28CrossRefGoogle Scholar
  19. Javernick L, Brasington J, Caruso B (2014) Modeling the topography of shallow braided rivers using structure-from-motion photogrammetry. Geomorphology 213:166–182CrossRefGoogle Scholar
  20. Kasperski J, Delacourt C, Allemand P, Potherat P, Jaud M, Varrel E (2010) Application of a terrestrial laser scanner (TLS) to the study of the Séchilienne landslide (Isère, France). Remote Sens 2(12):2785–2802CrossRefGoogle Scholar
  21. Kromer R, Abellan A, Hutchinson J, Lato M, Chanut M, Dubois L, Jaboyedoff M (2017) Automated terrestrial laser scanning with real-time change detection – monitoring of the Sechilienne landslide. Earth Surface Dynamics DiscussionsGoogle Scholar
  22. Laribi A, Walstra J, Ougrine M, Seridi A, Dechemi N (2015) Use of digital photogrammetry for the study of unstable slopes in urban areas: case study of the El Biar landslide, Algiers. Eng Geol 187:73–83CrossRefGoogle Scholar
  23. Lucieer A, de Jong S, Turner D (2014) Mapping landslide displacements using structure from motion (SfM) and image correlation of multi-temporal UAV photography. Prog Phys Geogr 38(1):97–116CrossRefGoogle Scholar
  24. Malet JP, Maquaire O, Calais E (2002) The use of global positioning system techniques for the continuous monitoring of landslides: application to the super-Sauze earthflow (Alpes-de-haute-Provence, France). Geomorphology 43(1):33–54CrossRefGoogle Scholar
  25. Moya L, Yamazaki F, Liu W, Chiba T (2017) Calculation of coseismic displacement from lidar data in the 2016 Kumamoto, Japan, earthquake. Nat Hazards Earth Syst Sci 17(1):143–156CrossRefGoogle Scholar
  26. Peppa MV, Mills JP, Moore P, Miller PE, Chambers JE (2017) Brief communication: landslide motion from cross correlation of UAV-derived morphological attributes. Nat Hazards Earth Syst Sci 17:2143–2150CrossRefGoogle Scholar
  27. Prokop A, Panholzer H (2009) Assessing the capability of terrestrial laser scanning for monitoring slow moving landslides. Nat Hazards Earth Syst Sci 9(6):1921–1928CrossRefGoogle Scholar
  28. Saito M (1965) Forecasting the time of occurrence of a slope failure. Proceedings of the 6th International Conference on Soil Mechanics and Foundation Engineering, (2): 537–539Google Scholar
  29. Schürch P, Densmore AL, Rosser NJ, Lim M, McArdell BW (2011) Detection of surface change in complex topography using terrestrial laser scanning: application to the Illgraben debris-flow channel. Earth Surf Process Landf 36(14):1847–1859CrossRefGoogle Scholar
  30. Stanier SA, Blaber J, Take WA, White DJ (2016) Improved image-based deformation measurement for geotechnical applications. Can Geotech J 53(5):727–739CrossRefGoogle Scholar
  31. Stiros SC, Vichas C, Skourtis C (2004) Landslide monitoring based on geodetically derived distance changes. J Surv Eng 130(4):156–162CrossRefGoogle Scholar
  32. Stumpf A, Malet JP, Allemand P, Pierrot-Deseilligny M, Skupinski G (2015) Ground-based multi-view photogrammetry for the monitoring of landslide deformation and erosion. Geomorphology 231:130–145CrossRefGoogle Scholar
  33. Take WA (2015) Thirty-sixth Canadian geotechnical colloquium: advances in visualization of geotechnical processes through digital image correlation 1. Can Geotech J 52(9):1199–1220CrossRefGoogle Scholar
  34. Travelletti J, Delacourt C, Allemand P, Malet JP, Schmittbuhl J, Toussaint R, Bastard M (2012) Correlation of multi-temporal ground-based optical images for landslide monitoring: application, potential and limitations. ISPRS J Photogramm Remote Sens 70:39–55CrossRefGoogle Scholar
  35. Travelletti J, Malet JP, Delacourt C (2014) Image-based correlation of laser scanning point cloud time series for landslide monitoring. Int J Appl Earth Obs Geoinf 32:1–18CrossRefGoogle Scholar
  36. Turner D, Lucieer A, Watson C (2012) An automated technique for generating georectified mosaics from ultra-high resolution unmanned aerial vehicle (UAV) imagery, based on structure from motion (SfM) point clouds. Remote Sens 4(5):1392–1410CrossRefGoogle Scholar
  37. Turner D, Lucieer A, Wallace L (2014) Direct georeferencing of ultrahigh-resolution UAV imagery. IEEE Trans Geosci Remote Sens 52(5):2738–2745CrossRefGoogle Scholar
  38. Turner D, Lucieer A, De Jong SM (2015) Time series analysis of landslide dynamics using an unmanned aerial vehicle (UAV). Remote Sens 7(2):1736–1757CrossRefGoogle Scholar
  39. van Veen M, Hutchinson DJ, Kromer R, Lato M, Edwards T (2017) Effects of sampling interval on the frequency-magnitude relationship of rockfalls detected from terrestrial laser scanning using semi-automated methods. Landslides 14(5):1579–1592CrossRefGoogle Scholar
  40. Verhoeven G (2011) Taking computer vision aloft–archaeological three-dimensional reconstructions from aerial photographs with photoscan. Archaeol Prospect 18(1):67–73CrossRefGoogle Scholar
  41. Westoby MJ, Brasington J, Glasser NF, Hambrey MJ, Reynolds JM (2012) Structure-from-motion photogrammetry: a low-cost, effective tool for geoscience applications. Geomorphology 179:300–314CrossRefGoogle Scholar
  42. White DJ, Take WA, Bolton MD (2003) Soil deformation measurement using particle image velocimetry (PIV) and photogrammetry. Geotechnique 53(7):619–632CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Civil EngineeringQueen′s UniversityKingstonCanada
  2. 2.Department of Geotechnical and Hydraulic EngineeringNational Institute for Rural EngineeringTsukubaJapan

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