Close-Range Photogrammetric Techniques for Deformation Measurement: Applications to Landslides

  • Marco Scaioni
  • Tiantian Feng
  • Ping Lu
  • Gang Qiao
  • Xiaohua Tong
  • Ron Li
  • Luigi Barazzetti
  • Mattia Previtali
  • Riccardo Roncella
Part of the Springer Natural Hazards book series (SPRINGERNAT)


In this chapter, the application of close-range photogrammetry for deformation measurements in the field of landslide investigation and monitoring is discussed. Main advantages of this approach are the non-contact operational capability, the large covered area on the slope to analyze, the high degree of automation, the high acquisition rate, the chance to derive information on the whole surface, not limited to a few control points (area-based deformation measurement), and, generally, a lower cost with respect to 3D scanning technology. Applications are organized into two categories: (1) surface-point tracking (SPT) and (2) comparison of surfaces obtained from dense image matching. Different camera configurations and geometric models to transform points from the image space to the object space are also discussed. In the last part of the chapter, a review of the applications reported in the literature and two case studies from the experience of the authors are reported.


Terrestrial photogrammetry Computer vision Deformation measurement Image metrology Landslides 



This research was partially funded by the 863 National High-tech R&D Program of China (No. 2012AA121302) and by the 973 National Basic Research Program of China (No. 2013CB733204). Also, this research was supported by the Italian Ministry of University and Research within the project FIRB—Futuro in Ricerca 2010 (No. RBFR10NM3Z).


  1. Abellán, A., Oppikofer, T., Jaboyedoff, M., Rosser, N. J., Lim, M., & Lato, M. J. (2014). Terrestrial laser scanning of rock slope instabilities. Earth Surface Processes and Landforms, 39, 80–97.CrossRefGoogle Scholar
  2. Akca, D. (2013). Photogrammetric monitoring of an artificially generated shallow landslide. The Photogrammetric Record, 28(142), 178–195.CrossRefGoogle Scholar
  3. Angeli, M., Pasuto, A., & Silvano, S. (2000). A critical review of landslide monitoring experiences. Engineering Geology, 55, 133–147.CrossRefGoogle Scholar
  4. Apollonio, F. I., Ballabeni, A., Gaiani, M., & Remondino, F. (2014). Evaluation of feature-based methods for automated network orientation. International Archives of The Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(5), 47–54.CrossRefGoogle Scholar
  5. Araiba, K., & Sakai, N. (2014). Laser scanner application in monitoring short-term slope deformation. In K. Sassa, et al. (Eds.), Landslide science for a safer geoenvironment (Vol. 2, pp. 5–11). Berlin, Heidelberg: Springer.CrossRefGoogle Scholar
  6. Baker, S., Scharstein, D., Lewis, J. P., Roth, S., Black, M. J., & Szeliski, R. (2011). A database and evaluation methodology for optical flow. International Journal of Computer Vision, 92(1), 1–31.CrossRefGoogle Scholar
  7. Baltsavias, E. P. (1991). Multiphoto geometrically constrained matching. Ph.D dissertation, Mitteilungen Nr. 49, p. 221. Institute of Geodesy and Photogrammetry, ETH Zurich.Google Scholar
  8. Barazzetti, L., Remondino, F., & Scaioni, M. (2010). Orientation and 3D modelling from markerless terrestrial images: combining accuracy with automation. The Photogrammetric Record, 25, 356–381.CrossRefGoogle Scholar
  9. Barbarella, M., Fiani, M., & Lugli, A. (2014). Multi-temporal terrestrial laser scanning survey of a landslide. In M. Scaioni (Ed.), Modern technologies for landslide investigation and prediction(pp. 89–121). Berlin, Heidelberg: Springer.Google Scholar
  10. Bay, H., Ess, A., Tuytelaars, T., & Van Gool, L. (2008). Speeded-up robust features (SURF). Computer Vision and Image Understanding, 110(3), 346–359.CrossRefGoogle Scholar
  11. Bethmann, F., & Luhmann, T. (2010). Least-squares matching with advanced geometric transformation models. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 38(5), 86–91.Google Scholar
  12. Bethmann, F., & Luhmann, T. (2014). Object-based multi-image semi-global matching—concept and first results. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(5), 93–100.CrossRefGoogle Scholar
  13. Bitelli, G., Dubbini, M., & Zanutta, A. (2004). Terrestrial laser scanning and digital photogrammetry techniques to monitor landslide bodies. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 38(7B), 246–251.Google Scholar
  14. Cardenal, J., Mata, E., Perez-Garcia, J., Delgado, J., Andez, M., Gonzales, A., & Diaz-de-Teran, J. (2008). Close range digital photogrammetry techniques applied to landslide monitoring. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 37(B8), 235–240.Google Scholar
  15. Casson, B., Delacourt, C., & Allemand, P. (2005). Contribution of multi-temporal sensing images to characterize landslide slip surface—application to the La Clapiere Landslide (France). Natural Hazards and Earth System Sciences, 5(3), 425–437.CrossRefGoogle Scholar
  16. Colomina, I., & Molina, P. (2014). Unmanned aerial systems for photogrammetry and remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 92, 79–97.CrossRefGoogle Scholar
  17. Crosetto, M., Crippa, B., Biescas, E., Monserrat, O., Agudo, M., & Fernández, P. (2005). State-of-the-art of land deformation monitoring using SAR interferometry. Photogrammetrie, Fernerkundung, Geoinformation, 6, 497–510.Google Scholar
  18. Crosetto, M., Monserrat, O., Cuevas, M., & Crippa, B. (2011). Spaceborne differential SAR interferometry: Data analysis tools for deformation measurement. Remote Sensing, 4, 305–318.CrossRefGoogle Scholar
  19. Crosetto, M., Monserrat, O., Luzi, G., Cuevas-Gonzáles, M., & Devanthéry, N. (2014). Discontinuous GBSAR deformation monitoring. ISPRS Journal of Photogrammetry and Remote Sensing, 93, 136–141.CrossRefGoogle Scholar
  20. Cruden, D. M., & Varnes, D. J. (1996). Landslides types and processes. In A.K. Turner & R.L. Schuster (Eds.), Landslides: Investigation and mitigation (pp. 36–75). Transportation Research Board Special Report No. 247, Washington, DC: National Academy Press.Google Scholar
  21. Dall’Asta, E., & Roncella, R. (2014). A comparison of semiglobal and local dense matching algorithms for surface reconstruction. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(5), 187–194.CrossRefGoogle Scholar
  22. De Agostino, M., Lingua, A., & Piras, M. (2012). Rock face surveys using a LiDAR MMS. Italian Journal of Remote Sensing, 44, 141–151.CrossRefGoogle Scholar
  23. Debella-Gilo, M., & Kääb, A. (2012). Measurement of surface displacement and deformation of mass movements using Least Squares Matching of repeat high resolution satellite and aerial images. Remote Sensing, 4, 43–67.CrossRefGoogle Scholar
  24. Delacourt, C., Allemand, P., Casson, B., & Vadon, H. (2004). Velocity field of the ‘La Clapiere’ landslide measured by the correlation of aerial and QuickBird satellite images. Geophysical Research Letters, 31, paper No. 15619.Google Scholar
  25. Delacourt, C., Allemand, P., Berthier, E., Raucoules, D., Casson, B., Grandjean, P., et al. (2007). Remote-sensing techniques for analysing landslide kinematics: A review. Bulletin de la Société Géologique de France, 178(2), 89–100.CrossRefGoogle Scholar
  26. Dermanis, A. (2011). Fundamentals of surface deformation and application to construction monitoring. Applied Geomatics, 3(1), 9–22.CrossRefGoogle Scholar
  27. Dewez, T. J. B. (2014). Reconstructing 3D coastal cliffs from airborne oblique photographs without ground control points. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2(5), 113–116.CrossRefGoogle Scholar
  28. Eisenbeiss, H., & Sauerbier, M. (2012). Investigation of UAV systems and flight modes for photogrammetric applications. The Photogrammetric Record, 26(136), 400–421.CrossRefGoogle Scholar
  29. Fastellini, G., Radicioni, F., & Stoppini, A. (2011). The Assisi landslide monitoring: A multi-year activity based on geomatic techniques. Applied Geomatics, 3(2), 91–100.CrossRefGoogle Scholar
  30. Feng, T., Liu, X., Scaioni, M., Lin, X., & Li, R. (2012). Real-time landslide monitoring using close-range stereo image sequences analysis. In Systems and Informatics (ICSAI), 2012 International Conference on (ICSAI 2012) (pp. 249–253), Yantai, P.R. China, May 19–21, 2012.Google Scholar
  31. Förstner, W., Gülch, E. (1987, June). A fast operator for detection and precise location of distinct points, corners and centres of circular features. In Proceedings of ISPRS Intercommission Conference on Fast Processing of Photogrammetric Data, (pp. 281–305), Interlaken, Switzerland.Google Scholar
  32. Fraser, C. S. (1996). Network design. In K. B. Atkinson (Ed.), Close range photogrammetry and machine vision (pp. 256–281). Dunbeath, Caithness, Scotland, UK: Whittles Publishing.Google Scholar
  33. Fraser, C. S., Woods, A., & Brizzi, D. (1996). Hyper redundancy for accuracy enhancement in automated close range photogrammetry. The Photogrammetric Record, 20, 205–217.CrossRefGoogle Scholar
  34. Fraser, C. S. (2013). Automatic camera calibration in close range photogrammetry. Photogrammetric Engineering and Remote Sensing, 79, 381–388.CrossRefGoogle Scholar
  35. Froese, C. R., & Moreno, F. (2014). Structure and components for the emergency response and warning system on Turtle Mountain, Alberta, Canada. Natural Hazards, 70(3), 1689–1712.CrossRefGoogle Scholar
  36. Furukawa, Y., & Ponce, J. (2010). Accurate, dense, and robust multi-view stereopsis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(8), 1362–1376.CrossRefGoogle Scholar
  37. Ghuffar, S., Székely, B., Roncat, A., & Pfeifer, N. (2013). Landslide displacement monitoring using 3D range flow on airborne and terrestrial LiDAR data. Remote Sensing, 5(6), 2720–2745.CrossRefGoogle Scholar
  38. Grün, A. (1985). Adaptive least squares correlation: a powerful image matching technique. South African Journal of Photogrammetry, Remote Sensing and Cartography, 14, 175–187.Google Scholar
  39. Grün, A., & Baltsavias, E. P. (1988). Geometrically constrained multiphoto matching. Photogrammetric Engineering and Remote Sensing, 54(5), 633–641.Google Scholar
  40. Grün, A. (2012). Development and status of image matching in photogrammetry. The Photogrammetric Record, 27, 36–57.CrossRefGoogle Scholar
  41. Gu, Z., Feng, T., Scaioni, M., Wu, H., Liu, J., Tong, X., & Li, R. (2014). Experimental results of elevation change analysis in the Antarctic Ice sheet using DEMs from ERS and ICESat data. Annals of Glaciology, 55(66), 198–204.CrossRefGoogle Scholar
  42. Guidi, G., Gonizzi, S., & Micoli, L. L. (2014). Image pre-processing for optimizing automated photogrammetry performances. ISPRS Annals of The Photogrammetry, Remote Sensing and Spatial Information Sciences, 2(5), 145–152. doi: 10.5194/isprsannals-II-5-145-2014.CrossRefGoogle Scholar
  43. Haala N (2013) The landscape of dense image matching algorithms. In Proceedings of Photogrammetric Week 2013, Stuttgart, Germany (pp. 271–284).Google Scholar
  44. Hartley, R., & Zissermann, A. (2006). Multiple view geometry in computer vision. UK: Cambridge University Press.Google Scholar
  45. Heritage, G. L., & Large, A. R. G. (2009). Laser scanning for the environmental sciences (p. 302). Chichester, UK: Wiley.CrossRefGoogle Scholar
  46. Hirschmüller, H. (2005). Accurate and efficient stereo processing by semi-global matching and mutual information. In Proceedings of Conference on Computer Vision and Pattern Recognition (CVPR’05), (p. 8). San Diego, CA, USA, June 20–26, 2005.Google Scholar
  47. Hoffmann, C. M. (1989). Geometric and solid modeling: An introduction. San Francisco: Morgan Kaufmann Publishers Inc.Google Scholar
  48. Hong, Y., He, X., Cerato, A., Zhang, K., Hong, Z., & Liao, Z. (2014). Predictability of a physically-based model for rainfall-induced Shallow Landslides: Model development and case studies. In M. Scaioni (Ed.), Modern technologies for landslide investigation and prediction (pp. 165–178). Berlin, Heidelberg: Springer.Google Scholar
  49. Hungr, O., Leroueil, S., & Picarelli, L. (2014). The Varnes classification of landslide types, an update. Landslides, 11, 167–194.CrossRefGoogle Scholar
  50. Intrieri, E., Gigli, G., Mugnai, F., Fanti, R., & Casagli, N. (2012). Design and implementation of a landslide early warning system. Engineering Geology, 147–148, 124–136.CrossRefGoogle Scholar
  51. Jaboyedoff, M., Oppikofer, T., Abellán, A., Derron, M. H., Loye, A., Metzger, R., & Pedrazzini, A. (2012). Use of LIDAR in landslide investigations: A review. Natural Hazards, 61, 1–24.CrossRefGoogle Scholar
  52. Jazayeri, I., & Fraser, C. S. (2010). Interest operators for feature-based matching in close range photogrammetry. The Photogrammetric Record, 25(129), 24–41.CrossRefGoogle Scholar
  53. Le Moigne, J., Netanyahu, N. S., & Eastman, R. D. (2011). Image registration for remote sensing (p. 484). UK: Cambridge University Press.CrossRefGoogle Scholar
  54. LePrince, S., Berthier, E., Ayoub, F., Delacourt, C., & Avouac, J. P. (2008). Monitoring earth surface dynamics with optical imagery. Eos Transactions, 89, 1–5.CrossRefGoogle Scholar
  55. Li, Z., & Grün, A. (2004). Automatic DSM generation from linear array imagery data. International Archives of The Photogrammetry, Remote Sensing and Spatial Information Sciences, 35(B3), 128–133.Google Scholar
  56. Lindenbergh, R., Pfeifer, N. (2005). A statistical deformation analysis of two epochs of terrestrial laser data of a lock. In Proceedings of 7th Conference on ‘Optical 3-D Measurement Techniques’, (Vol. 2, pp. 61–70). Vienna, October 3–5, 2005.Google Scholar
  57. Lindenbergh, R. (2010). Chapter 7—Engineering applications. In G. Vosselman & H. G. Maas (Eds.), Airborne and terrestrial laser scanning (pp. 237–270). Boca Raton, FL, USA: Taylor and Francis Group.Google Scholar
  58. Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91–110.CrossRefGoogle Scholar
  59. Luhmann, T. (2009). Precision potential of photogrammetric 6DOF pose estimation with a single camera. ISPRS Journal of Photogrammetry and Remote Sensing, 64(3), 275–284.CrossRefGoogle Scholar
  60. Luhmann, T., Robson, S., Kyle, S., & Böhm, J. (2013). Close range photogrammetry: 3D imaging techniques (p. 702). Germany: Walter De Gruyter Inc.Google Scholar
  61. Maas, H. G. (1996). Automatic DEM generation by multi-image feature based matching. International Archives of Photogrammetry and Remote Sensing, 31(3), 484–489.Google Scholar
  62. Mantovani, F., Soeters, R., & van Westen, C. J. (1996). Remote sensing techniques for landslide studies and hazard zonation in Europe. Geomorphology, 15(2), 213–225.CrossRefGoogle Scholar
  63. Mazzanti, P., Brunetti, A., & Bretschneider, A. (2014). A new approach based on terrestrial remote sensing techniques for rock fall hazard assessment. In M. Scaioni (Ed.), Modern technologies for landslide investigation and prediction (pp. 69–87). Berlin, Heidelberg: Springer.Google Scholar
  64. Metternicht, G., Hurni, L., & Gogu, R. (2005). Remote sensing of landslides: An analysis of the potential contribution to geo-spatial systems for hazard assessment in mountainous environments. Remote Sensing of Environment, 98, 284–303.CrossRefGoogle Scholar
  65. Monserrat, O., Moya, J., Luzi, G., Crosetto, M., Gili, J. A., & Corominas, J. (2013). Non-interferometric GB-SAR measurement: application to the Vallcebre landslide (eastern Pyrenees, Spain). Natural Hazards Earth System Science, 13, 1873–1877.CrossRefGoogle Scholar
  66. Motta, M., Gabrieli, F., Corsini, A., Manzi, V., Ronchetti, F., & Cola, S (2013). Landslide displacement monitoring from multi-temporal terrestrial digital images: Case of the Valoria Landslide site. In Margottini et al. (Eds.), Landslide science and practice (Vol. 2, pp. 73–78). Berlin, Heidelberg: Springer.Google Scholar
  67. Nex, F., & Remondino, F. (2014). UAV for 3D mapping applications. Applied Geomatics, 6(1), 1–15.CrossRefGoogle Scholar
  68. Niethammer, U., James, M. R., Rothmund, S., Travelletti, J., & Joswig, M. (2012). UAV-based remote sensing of the Super-Sauze landslide: Evaluation and results. Engineering Geology, 128, 2–11.CrossRefGoogle Scholar
  69. Ohnishi, Y., Nishiyama, S., Yano, T., Matsuyama, H., & Amano, K. (2006). A study of the application of digital photogrammetry to slope monitoring systems. International Journal of Rock Mechanics and Mining Sciences, 43, 756–766.CrossRefGoogle Scholar
  70. Pears, N., Liu, Y., & Bunting, P. (2012). 3D Imaging, Analysis and Applications (p. 499). London: Springer.Google Scholar
  71. Pirotti, F., Guarnieri, A., & Vettore, A. (2013). State of the art of ground and aerial laser scanning technologies for high-resolution topography of the earth surface. European Journal of Remote Sensing, 46, 66–78.CrossRefGoogle Scholar
  72. Pirotti, F., Guarnieri, A., Masiero, A., Gregoretti, C., Degetto, M., & Vettore, A. (2014). Micro-scale landslide displacements detection using Bayesian methods applied to GNSS data. In M. Scaioni (Ed.), Modern technologies for landslide investigation and prediction (pp. 123–138). Berlin, Heidelberg: Springer.Google Scholar
  73. Pomerleau, F., Colas, F., Siegwart, R., & Magnenat, S. (2013). Comparing ICP variants on real-world data sets. Autonomous Robots, 34(3), 133–148.CrossRefGoogle Scholar
  74. Previtali, M., Barazzetti, L., Scaioni, M., & Tian, Y. (2011). An automatic multi-image procedure for accurate 3D object reconstruction. In Proceedings of 4th International Congress on Image and Signal Processing (CISP), Shanghai (Vol. 3, pp. 1400–1404), October 15–17, 2011.Google Scholar
  75. Previtali, M., Barazzetti, L., & Scaioni, M. (2014). Accurate 3D surface measurement of mountain slopes through a fully automated imaged-based technique. Earth Science Informatics, 7(2), 109–122.CrossRefGoogle Scholar
  76. Qiao, G., Lu, P., Scaioni, M., Xu, S., Tong, X., Feng, T., Wu, H., Chen, W., Tian, Y., Wang, W., & Li, R. (2013). Landslide investigation with remote sensing and sensor network: From susceptibility mapping and scaled-down simulation towards in situ sensor network design. Remote Sensing, 5(9), 4319–4346. doi: 10.3390/rs5094319.
  77. Raguse, K., & Heipke, C. (2009). Synchronization of image sequences—a photogrammetric method. Photogrammetric Engineering and Remote Sensing, 75(4), 535–546.CrossRefGoogle Scholar
  78. Remondino, F., & Stoppa, D. (2013). TOF range-imaging cameras. Berlin, Heidelberg: Springer.CrossRefGoogle Scholar
  79. Remondino, F., Spera, M. G., Nocerino, E., Menna, F., & Nez, F. (2014). State of the art in high density image matching. The Photogrammetric Record, 29(146), 144–166.CrossRefGoogle Scholar
  80. Roncella, R., Scaioni, M., & Forlani, G. (2004). Application of digital photogrammetry in geotechnics. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 35(B/V), 93–98.Google Scholar
  81. Roncella, R., & Forlani, G. (2014). A fixed terrestrial photogrammetric system for landslide monitoring. In M. Scaioni (Ed.), Modern technologies for landslide investigation and prediction (pp. 43–67). Berlin, Heidelberg: Springer.Google Scholar
  82. Roncella, R., Romeo, E., Barazzetti, L., Gianinetto, M., & Scaioni, M. (2012). Comparative analysis of digital image correlation techniques for in-plane displacement measurements. In Proceedings of 5 th International Congress on Image and Signal Processing (CISP) (pp. 721–726). Chongqing, P.R. China, October 16–18, 2012.Google Scholar
  83. Rosenfeld, A., & Kak, A. C. (1976). Digital picture processing (Vol. 1). Elsevier.Google Scholar
  84. Rosu, A. M., Pierrot-Deseilligny, M., Delorme, A., Binet, R., & Klinger, Y. (2014). Measurement of ground displacement from optical satellite image correlation using the free open-source software MicMac. ISPRS Journal of Photogrammetry and Remote Sensing, doi: 10.1016/j.isprsjprs.2014.03.002.Google Scholar
  85. Scaioni, M. (2013). Remote sensing for landslide investigations: From research into practice. Remote Sensing, 5(11), 5488–5492.CrossRefGoogle Scholar
  86. Scaioni, M., Roncella, R., & Alba, M. I. (2013a). Change detection and deformation analysis in point clouds: Application to rock face monitoring. Photogrammetric Engineering and Remote Sensing, 79(5), 441–456.CrossRefGoogle Scholar
  87. Scaioni, M., Lu, P., Chen, W., Qiao, G., Wu, H., & Feng, T., et al. (2013b). Analysis of spatial sensor network observations during landslide simulation experiments. European Journal of Environmental and Civil Engineering, 17(9), 802–825.CrossRefGoogle Scholar
  88. Scaioni, M., Tong, X., & Li, R. (2013c). Application of GLAS laser altimetry to detect elevation changes in East Antarctica. ISPRS Annals of The Photogrammetry, Remote Sensing and Spatial Information Sciences, 2(5/W2), 253–258.Google Scholar
  89. Scaioni, M., Feng, T., Barazzetti, L., Previtali, M., Lu, P., & Giao, G., et al. (2014a). Some applications of 2D and 3D photogrammetry during laboratory experiments for hydrogeological risk assessment. Geomatics, Natural Hazards and Risk, doi: 10.1080/19475705.2014.885090.Google Scholar
  90. Scaioni, M., Longoni, L., Melillo, V., & Papini, M. (2014b). Remote sensing for landslide investigations: An overview on recent achievements and perspectives. Remote Sensing, 6(10), 9600–9652. doi: 10.3390/rs6109600.
  91. Scaioni, M., Feng, T., Barazzetti, L., Previtali, M., & Roncella, R. (2014c). Image-based deformation measurement. Applied Geomatics, doi: 10.1007/s12518-014-0152-x.
  92. Scharstein, D., & Szeliski, R. (2002). A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. International Journal of Computer Vision, 47(1–3), 7–42.CrossRefGoogle Scholar
  93. Seitz, S. M., Curless, B., Diebel, J., Scharstein, D., & Szeliski, R. (2006). A comparison and evaluation of multi-view stereo reconstruction algorithms. In Proceedings of Conference on Computer Vision and Pattern Recognition (CVPR), New York (Vol. 1, pp. 519–526), June 17–22, 2006.Google Scholar
  94. Spencer, L., & Shah, M. (2004). Temporal synchronization from camera motion. In Proceedings of 6th Asian Conference on Computer Vision (Vol. 1, pp. 515–520). Jeju Island, Korea, January 27–30, 2004.Google Scholar
  95. Stumpf, A., Malet, J. P., Allemand, P., & Ulrich, P. (2014). Surface reconstruction and landslide displacement measurements with Pléiades satellite images. ISPRS Journal of Photogrammetry and Remote Sensing, 95, 1–12.CrossRefGoogle Scholar
  96. Tao, V., & Li, J. (2007). Advances in mobile mapping technology. ISPRS Book Series No. 4. London: Taylor & Francis Group.Google Scholar
  97. Teunissen, P. J. G. (2000). Testing theory: An introduction. Series on Mathematical geodesy and positioning. The Netherlands: Delft University Press.Google Scholar
  98. Tommaselli, A. M. G., Moraes, M. V. A., Silva, L. S. L., Rubio, M. F., Carvalho, G. J., & Tommaselli, J. T. G. (2014). Monitoring marginal erosion in hydroelectric reservoirs with terrestrial mobile laser scanner. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(5), 589–596.CrossRefGoogle Scholar
  99. Toschi, I., Capra, A., De Luca, L., Beraldin, J. A., & Cournoyer, L. (2014). On the evaluation of photogrammetric methods for dense 3D surface reconstruction in a metrological context. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2(5), 371–378.CrossRefGoogle Scholar
  100. Travelletti, J., Delacourt, C., Allemand, P., Malet, J. P., Schmittbuhl, J., Toussaint, R., & Bastard, M. (2012). Correlation of multi-temporal ground-based optical images for landslide monitoring: Application, potential and limitations. ISPRS Journal of Photogrammetry and Remote Sensing, 70, 39–55.CrossRefGoogle Scholar
  101. Wallis, R. (1976). An approach to the space variant restoration and enhancement of images. In Proceeding of Symposium on Current Mathematical Problems in Image Science, Naval Postgraduate School (pp. 329–340). Monterey, CA, USA. November 11–12, 1976.Google Scholar
  102. Wasowski, J., & Bovenga, F. (2014). Investigating landslides and unstable slopes with satellite Multi Temporal Interferometry: Current issues and future perspectives. Engineering Geology, 174, 103–138.CrossRefGoogle Scholar
  103. Wiegand, C., Rutzinger, M., Heinrich, K., & Geitner, C. (2013). Automated extraction of shallow erosion areas based on multi-temporal ortho-imagery. Remote Sensing, 5, 2292–2307.CrossRefGoogle Scholar
  104. Wu, J., Gilliéron, P. Y., & Merminod, B. (2012). Cell-based automatic deformation computation by analyzing terrestrial LIDAR point clouds. Photogrammetric Engineering and Remote Sensing, 78, 317–329.CrossRefGoogle Scholar
  105. Wujanz, D., Krüger, D., & Neitzel, F. (2013a). Defo scan++: Surface based registration of terrestrial laser scans for deformation monitoring. In Proceedings of 2nd Joint International Symposium on Deformation Measurement (JISDM), Nottingham (p. 7), September 2–6, 2013.Google Scholar
  106. Wujanz, D., Neitzel, F., Hebel, H. P., Linke, J., & Busch, W. (2013b). Terrestrial radar and laser scanning for deformation monitoring: First steps towards assisted radar scanning. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2(5/W2), 325–330.Google Scholar
  107. Xue, Q., Zhang, M., Zhu, L., Cheng, X., Pei, Y., & Bi, J. (2014). Quantitative deformation analysis of landslides based on multi-period DEM data. In K. Sassa et al. (Eds.), Landslide science for a safer geoenvironment (Vol. 2, pp. 201–207). Berlin, Heidelberg: Springer.Google Scholar
  108. Zhang, L. (2005). Automatic digital surface model (DSM) generation from linear array images. Ph.D dissertation, Mitteilungen Nr. 90 (p. 199). Institute of Geodesy and Photogrammetry, ETH Zurich.Google Scholar
  109. Zhao, H., Zhang, B., Wu, C., Zuo, Z., Chen, Z., & Bi, J. (2014). Direct georeferencing of oblique and vertical imagery in different coordinate systems. ISPRS Journal of Photogrammetry and Remote Sensing, 95, 122–133.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Marco Scaioni
    • 1
    • 2
  • Tiantian Feng
    • 1
  • Ping Lu
    • 1
  • Gang Qiao
    • 1
  • Xiaohua Tong
    • 1
  • Ron Li
    • 1
  • Luigi Barazzetti
    • 2
  • Mattia Previtali
    • 2
  • Riccardo Roncella
    • 3
  1. 1.College of Surveying and Geo-informaticsTongji UniversityShanghaiPeople’s Republic of China
  2. 2.Department of Architecture, Built Environment and Construction EngineeringPolitecnico di MilanoMilanItaly
  3. 3.Department of Civil, Environmental, Land Management Engineering and ArchitectureUniversity of ParmaParmaItaly

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