Advertisement

Structure-From-Motion Photogrammetry to Support the Assessment of Collapse Risk in Alpine Glaciers

  • Marco ScaioniEmail author
  • Luigi Barazzetti
  • Vasil Yordanov
  • Roberto S. Azzoni
  • Davide Fugazza
  • Massimo Cernuschi
  • Guglielmina A. Diolaiuti
Conference paper
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)

Abstract

The application of Structure-from-Motion (SfM) Photogrammetry with ground-based and UAV camera stations may be exploited for modelling the topographic surface of Alpine glaciers. Multi-temporal repeated surveys lead to geometric models that may be applied to analyze the glacier retreat under global warming conditions. Thanks to the integration of point clouds obtained from ground-based and UAV imaging platforms, a complete 3D reconstruction also including vertical and sub-vertical surfaces may be achieved. These 3D models may be also exploited to understand the precursory signals of local collapse that might represent a risk for tourists and hikers visiting glaciers. In this paper a review on the application of SfM Photogrammetry in the field of glaciological studies is reported. The case of Forni Glacier in the Italian Alps is presented as emblematic study. Photogrammetric data sets obtained from measurement campaigns carried out in 2014, 2016, 2017 and 2018 have been processed using a common workflow. Attention is paid to a few crucial aspects, such as image orientation and calibration, dense surface matching, georeferencing and data fusion. In the end, the use of output point clouds to evaluate the risk of collapse in the Forni Glacier is addressed.

Notes

Acknowledgements

This study was funded by DARA (Department for regional affairs and autonomies) of the Presidency of the Council of the Italian Government). The authors acknowledge the Central Scientific Committee of CAI (Italian Alpine Club) and Levissima San Pellegrino S.P.A. for funding the UAV quadcopter. The authors also thank Stelvio Park Authority for the logistic support and for permitting the UAV surveys. The authors would also like to acknowledge those colleagues, students and friends who helped with different stages of field operations. In particular Manuel Corti and Julién Crippa.

References

  1. Albers B, De Lange N, Xu S (2017) Augmented citizen science for environmental monitoring and education. Int Arch Photogramm Remote Sens Spatial Inf Sci 42(2/W7):1–4.  https://doi.org/10.5194/isprs-archives-xlii-2-w7-1-2017CrossRefGoogle Scholar
  2. Barazzetti L, Forlani G, Remondino F, Roncella R, Scaioni M (2011) Experiences and achievements in automated image sequence orientation for close-range photogrammetric projects. In: Videometrics, range imaging, and applications XI. International Society for Optics and Photonics, paper no. 80850F.  https://doi.org/10.1117/12.890116
  3. Barazzetti L, Remondino F, Scaioni M (2009) Combined use of photogrammetric and computer vision techniques for fully automated and accurate 3D modeling of terrestrial objects. In: Videometrics, range imaging, and applications X. International Society for Optics and Photonics, paper no. 74470 M.  https://doi.org/10.1117/12.825638
  4. Bhardwaj A, Sam L, Martín-Torres FJ, Kumar R (2016) UAVs as semote sensing platform in glaciology: Present applications and future prospects. Remote Sens Env 175:196–204.  https://doi.org/10.1016/j.rse.2015.12.029CrossRefGoogle Scholar
  5. Bühler Y, Marty M, Egli L, Veitinger J, Jonas T, Thee P, Ginzler C (2015) Snow depth mapping in high-Alpine catchments using digital photogrammetry. Cryosphere 9:229–243.  https://doi.org/10.5194/tc-9-229-2015CrossRefGoogle Scholar
  6. Carbonneau PE, Dietrich JT (2017) Cost-effective non-metric photogrammetry from consumer-grade SUAS: implications for direct georeferencing of structure from motion photogrammetry. Earth Surf Proc Land 42(3):473–486.  https://doi.org/10.1002/esp.4012CrossRefGoogle Scholar
  7. Carey M, Mcdowell G, Huggel C, Jackson J, Portocarrero C, Reynolds J M, Vicuña L (2015) Integrated approaches to adaptation and disaster risk reduction in dynamic socio-cryospheric systems. In: Shroder JF, Haeberli W, Whiteman C (eds) Snow and ice-related hazards, risks and disasters. Academic Press, Elsevier, pp 219–261.  https://doi.org/10.1016/B978-0-12-394849-6.00008-1CrossRefGoogle Scholar
  8. Chandler JH, Buckley S (2016) Structure from motion (SfM) photogrammetry vs terrestrial laser scanning. In: Carpenter MB, Keane CM (eds) Geoscience handbook 2016: AGI data sheets, 5th edn. American Geosciences Institute, Alexandria (Virginia-USA), p 4Google Scholar
  9. Clapuyt F, Vanacker V, Van Oost K (2016) Reproducibility of UAV-based earth topography reconstructions based on structure-from-motion algorithms. Geomorph 260:4–15.  https://doi.org/10.1016/j.geomorph.2015.05.011CrossRefGoogle Scholar
  10. Corti M (2017) Analysis of multiple data sources for topographic reconstruction of Forni Glacier (Rhaetian Alps, Italy). Dissertation, Politecnico di Milano, MSc on Civil Engineering for Risk ManagementGoogle Scholar
  11. Dall’Asta E, Forlani G, Roncella R, Santise M, Diotri F, Di Cella UM (2017) Unmanned aerial systems and DSM matching for rock glacier monitoring. ISPRS J Photogramm Remote Sens 127:102–114.  https://doi.org/10.1016/j.isprsjprs.2016.10.003CrossRefGoogle Scholar
  12. Diolaiuti G, Smiraglia C (2010) Changing glaciers in a changing climate: How vanishing geomorphosites have been driving deep changes in mountain landscapes and environments. Geomorphologie: relief, processus, environnement 16(2):131–152.  https://doi.org/10.4000/geomorphologie.7882CrossRefGoogle Scholar
  13. Eltner A, Kaiser A, Castillo C, Rock G, Neugirg F, Abellán A (2016) Image-based surface reconstruction in geomorphometry–merits, limits and developments. Earth Surf Dyn 4:359–389.  https://doi.org/10.5194/esurf-4-359-2016CrossRefGoogle Scholar
  14. Fey C, Wichmann V, Zangerl C (2017) Reconstructing the evolution of a deep seated rockslide (Marzell) and its response to glacial retreat based on historic and remote sensing data. Geomorphology 298:72–85.  https://doi.org/10.1016/j.geomorph.2017.09.025CrossRefGoogle Scholar
  15. Fugazza D, Senese A, Azzoni RS, Smiraglia C, Cernuschi M, Severi D, Diolaiuti GA (2015) High-resolution mapping of glacier surface features. The UAV survey of the Forni glacier (Stelvio National Park, Italy). Geogr Fis Din Quat 38:25–33.  https://doi.org/10.4461/GFDQ.2015.38.03CrossRefGoogle Scholar
  16. Fugazza D, Senese A, Azzoni RS, Maugeri M, Diolaiuti GA (2016) Spatial distribution of surface albedo at the Forni glacier (Stelvio National Park, Central Italian Alps). Cold Reg Sci Technol 125:128–137.  https://doi.org/10.1016/j.coldregions.2016.02.006CrossRefGoogle Scholar
  17. Fugazza D, Scaioni M, Corti M, D’agata C, Azzoni RS, Cernuschi M, Smiraglia C, Diolaiuti GA (2018) Combination of UAV and terrestrial photogrammetry to assess rapid glacier evolution and map glacier hazards. Nat Hazard Earth Sys Sci 18:1055–1071.  https://doi.org/10.5194/nhess-18-1055-2018CrossRefGoogle Scholar
  18. Giordan D, Hayakawa Y, Nex F, Remondino F, Tarolli P (2018) The use of remotely piloted aircraft systems (RPASS) for natural hazards monitoring and management. Nat Hazard Earth Sys Sci 18:1079–1096.  https://doi.org/10.5194/nhess-18-1079-2018CrossRefGoogle Scholar
  19. Gobiet A, Kotlarski S, Beniston M, Beniston M, Heinrich G, Rajczak J, Stoffel M (2014) 21st Century climate change in the European Alps—A review. Sci Total Environ 493:1138–1151.  https://doi.org/10.1016/j.scitotenv.2013.07.050CrossRefGoogle Scholar
  20. Gómez-Gutiérrez Á, De Sanjosé-Blasco JJ, De Matías-Bejarano J et al (2014) Comparing two photo-reconstruction methods to produce high density point clouds and DEMs in the Corral del Veleta rock glacier (Sierra Nevada, Spain). Remote Sens-Basel 6(6):5407–5427.  https://doi.org/10.3390/rs6065407CrossRefGoogle Scholar
  21. Gómez-Gutiérrez Á, De Sanjosé-Blasco JJ, Lozano-Parra J, Berenguer-Sempere F (2015) Does HDR pre-processing improve the accuracy of 3d models obtained by means of two conventional SfM-MVS software packages? The case of the Corral del Veleta rock glacier. Remote Sens-Basel 7(8):10269–10294.  https://doi.org/10.3390/rs70810269CrossRefGoogle Scholar
  22. Gonzalez-Aguilera D, López-Fernández L, Rodriguez-Gonzalvez P, Hernandez-Lopez D, Guerrero D, Remondino F, Menna F, Nocerino E, Toschi I, Ballabeni A (2018) Graphos–open-source software for photogrammetric applications. Photogramm Rec 33:11–29.  https://doi.org/10.1111/phor.12231CrossRefGoogle Scholar
  23. Granshaw SI (2018a) RPV, UAV, UAS, RPAS … or just drone? Photogramm Rec 33:160–170.  https://doi.org/10.1111/phor.12244CrossRefGoogle Scholar
  24. Granshaw SI (2018b) Structure from motion: origins and originality. Photogramm Rec 33:6–10.  https://doi.org/10.1111/phor.12237CrossRefGoogle Scholar
  25. Hartley R, Zisserman A (2003) Multiple view geometry in computer vision. Cambridge University Press, CambridgeGoogle Scholar
  26. Hartmann W, Havlena M, Schindler K (2016) Recent developments in large-scale tie-point matching. ISPRS J Photogramm 115:47–62.  https://doi.org/10.1016/j.isprsjprs.2015.09.005CrossRefGoogle Scholar
  27. Heritage GL, Large AGR (eds) (2009) Laser scanning for the environmental sciences. Wiley, Chichester, p 302Google Scholar
  28. Immerzeel WW, Kraaijenbrink PDA, Shea JM, Shrestha AB, Pellicciotti F, Bierkens MFP, De Jong SM (2014) High-resolution monitoring of Himalayan glacier dynamics using unmanned aerial vehicles. Remote Sens Environ 150:93–103.  https://doi.org/10.1016/j.rse.2014.04.025CrossRefGoogle Scholar
  29. James MR, Robson S, Smith MW (2017) 3-D uncertainty-based topographic change detection with structure-from-motion photogrammetry: Precision maps for ground control and directly georeferenced surveys. Earth Surf Proc Land 42:1769–1788.  https://doi.org/10.1002/esp.4125CrossRefGoogle Scholar
  30. Kääb A, Huggel C, Fischer L, Guex S, Paul F, Roer I, Salzmann N, Schlaefli S, Schmutz K, Schneider D (2005a) Remote sensing of glacier-and permafrost-related hazards in high mountains: an overview. Nat Hazard Earth Sys Sci 5:527–554.  https://doi.org/10.5194/nhess-5-527-2005CrossRefGoogle Scholar
  31. Kääb A, Reynolds JM, Haeberli W (2005b) Glacier and permafrost hazards in high mountains. In: Huber UM, Bugmann HKM & Reasoner MA (eds) Global change and mountain regions. Advances in Global Change Research, Vol 23. Springer, Dordrecht, p 225.  https://doi.org/10.1007/1-4020-3508-X_23Google Scholar
  32. Kerr RA (2012) Experts agree global warming is melting the world rapidly. Science 338(6111):1138.  https://doi.org/10.1126/science.338.6111.1138CrossRefGoogle Scholar
  33. Kraaijenbrink P, Shea J, Pellicciotti F, De Jong S, Immerzeel W (2016) Object-based analysis of unmanned aerial vehicle imagery to map and characterise surface features on a debris-covered glacier. Remote Sens Environ 186:581–595.  https://doi.org/10.1016/j.rse.2016.09.013CrossRefGoogle Scholar
  34. 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 82:10–26.  https://doi.org/10.1016/j.isprsjprs.2013.04.009CrossRefGoogle Scholar
  35. Longoni L, Arosio D, Scaioni M, Papini M, Zanzi L, Roncella R, Brambilla D (2012) Surface and subsurface non-invasive investigations to improve the characterization of a fractured rock mass. J Geophys Eng 9:461.  https://doi.org/10.1088/1742-2132/9/5/461CrossRefGoogle Scholar
  36. Luhmann T, Fraser C, Maas H-G (2016) Sensor modelling and camera calibration for close-range photogrammetry. ISPRS J Photogramm 115:37–46.  https://doi.org/10.1016/j.isprsjprs.2015.10.006CrossRefGoogle Scholar
  37. Luhmann T, Robson S, Kyle S, Boehm J (2013) Close-range Photogrammetry and 3D Imaging. Walter de Gruyter, p 684Google Scholar
  38. Mosbrucker AR, Major JJ, Spicer KR, Pitlick J (2017) Camera system considerations for geomorphic applications of SfM photogrammetry. Earth Surf Proc Land 42:969–986.  https://doi.org/10.1002/esp.4066CrossRefGoogle Scholar
  39. O’Banion MS, Olsen MJ, Rault C, Wartman J, Cunningham K (2018) Suitability of structure from motion for rock-slope assessment. Photogramm Rec 33:217–242.  https://doi.org/10.1111/phor.12241CrossRefGoogle Scholar
  40. O’Connor J, Smith MJ, James MR (2017) Cameras and settings for aerial surveys in the geosciences: optimising image data. Prog Phys Geog 41:325–344.  https://doi.org/10.1177/0309133317703092CrossRefGoogle Scholar
  41. Palomo I (2017) Climate change impacts on ecosystem services in high mountain areas: a literature review. Mt Res Dev 37:179–187.  https://doi.org/10.1659/MRD-JOURNAL-D-16-00110.1CrossRefGoogle Scholar
  42. Pepe M, Fregonese L, Scaioni M (2018) Planning airborne photogrammetry and remote-sensing missions with modern platforms and sensors. Eur J Remote Sens 51:412–435.  https://doi.org/10.1080/22797254.2018.1444945CrossRefGoogle Scholar
  43. Piermattei L, Carturan L, Guarnieri A (2015) Use of terrestrial photogrammetry based on structure-from-motion for mass balance estimation of a small glacier in the Italian Alps. Earth Surf Proc Land 40:1791–1802.  https://doi.org/10.1002/esp.3756CrossRefGoogle Scholar
  44. Piermattei L, Carturan L, De Blasi F, Tarolli P, Fontana GD, Vettore A, Pfeifer N (2016) Suitability of ground-based SfM-MVS for monitoring glacial and periglacial processes. Earth Surf Dynam 4:325–443.  https://doi.org/10.5194/esurf-4-425-2016CrossRefGoogle Scholar
  45. Quincey D, Lucas R, Richardson S, Glasser N, Hambrey M, Reynolds J (2005) Optical remote sensing techniques in high-mountain environments: application to glacial hazards. Prog Phys Geog 29:475–505.  https://doi.org/10.1191/0309133305pp456raCrossRefGoogle Scholar
  46. Remondino F, Spera MG, Nocerino E, Menna F, Nex F (2014) State of the art in high density image matching. Photogramm Rec 29:144–166.  https://doi.org/10.1111/phor.12063CrossRefGoogle Scholar
  47. Roncella R, Forlani G (2015) A fixed terrestrial photogrammetric system for landslide monitoring. In: Scaioni M, (ed) Modern technologies for landslide monitoring and prediction, Springer, Cham.  https://doi.org/10.1007/978-3-662-45931-7_3CrossRefGoogle Scholar
  48. Rutzinger M, Bremer M, Höfle B, Hämmerle M, Lindenbergh R, Oude Elberink S, Pirotti F, Scaioni M, Wujanz D, Zieher T (2018) Training in innovative technologies for close-range sensing in alpine terrain. ISPRS Ann Photogramm Remote Sens Spatial Inf Sci 4(2):239–246.  https://doi.org/10.5194/isprs-annals-iv-2-239-2018CrossRefGoogle Scholar
  49. Ryan JC, Hubbard AL, Box JE, Todd J, Christoffersen P, Carr JR, Holt TO, Snooke NA (2015) UAV photogrammetry and structure from motion to assess calving dynamics at store glacier, a large outlet draining the Greenland ice sheet. Cryosphere 9:1–11.  https://doi.org/10.5194/tc-9-1-2015CrossRefGoogle Scholar
  50. Santangelo M, Alvioli M, Baldo M, Cardinali M, Giordan D, Guzzetti F, Marchesini I, Reichenbach P (2018) Brief communication: Remotely piloted aircraft systems for rapid emergency response: road exposure to rockfall in Villanova di Accumoli (Central Italy). Nat Hazard Earth Sys Sci.  https://doi.org/10.5194/nhess-2018-177 Accessed 18 Oct 2018CrossRefGoogle Scholar
  51. Scaioni M, Corti M, Diolaiuti G, Fugazza D, Cernuschi M (2017) Local and general monitoring of Forni glacier (Italian alps) using multi-platform structure-from-motion photogrammetry. Int Arch Photogramm Remote Sens Spatial Inf Sci 42(2/W7):1547–1554.  https://doi.org/10.5194/isprs-archives-xlii-2-w7-1547-2017CrossRefGoogle Scholar
  52. Smiraglia C, Azzoni RS, D’agata C, Maragno D, Fugazza D, Diolaiuti GA (2015) The evolution of the Italian glaciers from the previous data base to the new Italian inventory. Preliminary considerations and results. Geogr Fis Din Quat 38:79–87.  https://doi.org/10.4461/GFDQ.2015.38.08CrossRefGoogle Scholar
  53. Solbø S, Storvold R (2013) Mapping Svalbard glaciers with the cryowing UAS. Int Arch Photogramm Remote Sens Spatial Inf Sci 40(1/W2):373–377.  https://doi.org/10.5194/isprsarchives-XL-1-W2-373-2013CrossRefGoogle Scholar
  54. Tonkin TN, Midgley NG, Graham DJ, Labadz J (2014) The potential of small unmanned aircraft systems and structure-from-motion for topographic surveys: a test of emerging integrated approaches at CWM Idwal, North Wales. Geomorph 226:35–43.  https://doi.org/10.1016/j.geomorph.2014.07.021CrossRefGoogle Scholar
  55. Wenzel K, Rothermel M, Fritsch D, Haala N (2013) Image acquisition and model selection for multi-view stereo. Int Arch Photogramm Remote Sens Spatial Inf Sci 40(5/W1):251–258.  https://doi.org/10.5194/isprsarchives-XL-5-W1-251-2013CrossRefGoogle Scholar
  56. Westoby MJ, Brasington J, Glasser NF, Hambrey MJ, Reynolds J (2012) ‘Structure-from-Motion’ Photogrammetry: a low-cost, effective tool for Geoscience applications. Geomorph 179:300–314.  https://doi.org/10.1016/j.geomorph.2012.08.021CrossRefGoogle Scholar
  57. Whitehead K, Moorman B, Hugenholtz C (2013) Brief communication: low-cost, on-demand aerial Photogrammetry for glaciological measurement. Cryosphere 7:1879–1884.  https://doi.org/10.5194/tc-7-1879-2013CrossRefGoogle Scholar
  58. Winkler M, Pfeffer WT, Hanke K (2012) Kilimanjaro ice cliff monitoring with close range photogrammetry. Int Arch Photogramm Remote Sens Spatial Inf Sci 39(B5):441–446.  https://doi.org/10.5194/isprsarchives-XXXIX-B5-441-2012CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Marco Scaioni
    • 1
    Email author
  • Luigi Barazzetti
    • 1
  • Vasil Yordanov
    • 2
  • Roberto S. Azzoni
    • 3
  • Davide Fugazza
    • 4
  • Massimo Cernuschi
    • 5
  • Guglielmina A. Diolaiuti
    • 3
  1. 1.Department of Architecture, Built Environment and Construction Engineering (DABC)Politecnico di MilanoMilanItaly
  2. 2.Polo Territoriale di LeccoPolitecnico di MilanoMilanItaly
  3. 3.Department of Environmental Science and Policy (DESP)Università degli studi di MilanoMilanItaly
  4. 4.Department of Earth Sciences “Ardito Desio”Università degli studi di MilanoMilanItaly
  5. 5.Agricola 2000 S.C.P.ATribianoItaly

Personalised recommendations