Advertisement

Landslides

, Volume 14, Issue 4, pp 1467–1482 | Cite as

Using multiresolution and multitemporal satellite data for post-disaster landslide inventory in the Republic of Serbia

  • Dragana Đurić
  • Ana Mladenović
  • Milica Pešić-Georgiadis
  • Miloš Marjanović
  • Biljana AbolmasovEmail author
Recent Landslides

Abstract

This paper focuses on a specific event-based landslide inventory compiled after the May 2014 heavy rainfall episode in Serbia as a part of the post-disaster recovery actions. The inventory was completed for a total of 23 affected municipalities, and the municipality of Krupanj was selected as the location for a more detailed study. Three sources of data collection and analysis were used: a visual analysis of the post-event very high and high (VHR-HR) resolution images (Pléiades, WorldView-2 and SPOT 6), semi-automatic landslide recognition in pre- and post-event coarse resolution images (Landsat 8) and a landslide mapping field campaign. The results suggest that the visual and semi-automated analyses significantly contributed to the quality of the final inventory, including the associated planning strategies for conducting future field campaigns (as a final stage of the inventorying process), all the more so because the field-based and image-based inventories were focused on different types of landslides. In the most affected municipalities that had very high resolution satellite image coverage (19.52% of the whole study area), the density of the recognized landslides was approximately three times higher than that in those municipalities without satellite image coverage (where only field data were available). The total number of field-mapped landslides for the 23 municipalities was 1785, while image-based inventories, which were available only for the municipalities with satellite image coverage (77.43% of the study area), showed 1298 landslide records. The semi-automated landslide inventory in the test area (Krupanj municipality), which was based on coarse resolution multitemporal images (Landsat 8), counted 490 landslide instances and was in agreement with the visual analysis of the higher resolution images, with an overlap of approximately 40%. These results justify the use of preliminary inventorying via satellite image analysis and suggest a considerable potential use for preliminary visual and semi-automated landslide inventorying as an important supplement to field mapping.

Keywords

Post-disaster Landslide inventory Remote sensing VHR-HR satellite image Rainfall Serbia 

Notes

Acknowledgements

This research was part of Project BEyond landslide aWAREness (BEWARE) funded by the People of Japan and the UNDP Office in Serbia (grant No. 00094641). The project was implemented by the Geological Survey of Serbia and the University of Belgrade, Faculty of Mining and Geology. All activities are supported by the Ministry for Energy and Mining, the Public Agency for Reconstruction and Ministry for Education, Science and Technological Development of the Republic of Serbia Project No. TR36009. The authors would like to thank reviewers for constructive comments and suggestions.

References

  1. Abolmasov B, Milenković S, Marjanović M, Đurić U, Jelisavac B (2015) A geotechnical model of the Umka landslide with reference to landslides in weathered Neogene marls in Serbia. Landslides 12(4):689–702. doi: 10.1007/s10346-014-0499-4 CrossRefGoogle Scholar
  2. Alkevli T, Ercanoglu M (2011) Assessment of ASTER satellite images in landslide inventory mapping: Yenice-Gökçebey (Western Black Sea Region, Turkey). Bull Eng Geol Environ 70:607–617. doi: 10.1007/s10064-A011-0353-z.A CrossRefGoogle Scholar
  3. Bhambri R, Mehta M, Dobhal DP, Gupta AK, Pratap B, Kesarwani K, Verma A (2016) Devastation in the Kedarnath (Mandakini) Valley, Garhwal Himalaya, during 16–17 June 2013: a remote sensing and ground-based assessment. Nat Hazards 80:1801–1822. doi: 10.1007/s11069-015-2033-y CrossRefGoogle Scholar
  4. Booth AM, Roering JJ, Perron JJ (2009) Automated landslide mapping using spectral analysis and high-resolution topographic data: Puget Sound lowlands, Washington, and Portland Hills, Oregon. Geomorphology 109:132–147. doi: 10.1016/j.geomorph.2009.02.027 CrossRefGoogle Scholar
  5. Bucknam RC, Coe JA, Chavarria MM, Godt JW, Tarr AC, Bradley L, Rafferty S, Hancock D, Dart L, Johnson ML (2001) Landslides triggered by hurricane Mitch in Guatemala—inventory and discussion. U.S. Geological Survey Open File Report 01-443:39. 23 plates at 1:50,000 scale. http://greenwood.cr.usgs.gov/pub/open-file-reports/ofr-01-0443/
  6. Cardinali M, Galli M, Guzzetti F, Ardizzone F, Reichenbach P, Bartoccini P (2006) Rainfall induced landslides in December 2004 in south-western Umbria, central Italy: types, extent, damage and risk assessment. Nat Hazards Earth Syst Sci 6:237–260CrossRefGoogle Scholar
  7. Chavez PS Jr (1996) Image-based atmospheric corrections: revisited and improved. Photogramm Eng Remote Sens 62(9):1025–1036Google Scholar
  8. Chen RF, Chang KJ, Angelier J, Chan YC, Deffontaines B, Lee CT, Lin ML (2006) Topographical changes revealed by high-resolution airborne LiDAR data: the 1999 Tsaoling landslide induced by the Chi–Chi earthquake. Eng Geol 88:160–172. doi: 10.1016/j.enggeo.2006.09.008 CrossRefGoogle Scholar
  9. Ciampalini A, Raspini F, Bianchini S, Frodella W, Bardi F, Lagomarsino D, Traglia F, Moretti S, Proietti C, Pagliara P, Onori R, Corazza A, Duro A, Basile G, Casagli N (2015) Remote sensing as tool for development of landslide databases: the case of the Messina Province (Italy) geodatabase. Geomorphology 249:103–118  http://dx.doi.org/10.1016/j.geomorph.2015.01.029 CrossRefGoogle Scholar
  10. Cruden DM, VanDine DF (2013) Classification, description, causes and indirect effects—Canadian Technical Guidelines and Best Practices related to Landslides: a national initiative for loss reduction, Geological Survey of Canada Open File 7359Google Scholar
  11. Cruden DM, Varnes DJ (1996) Landslide types and processes. In: Turner AK, Schuster RL (eds) Landslide investigation and mitigation. Special Report 247, Transportation Research Board, National Research Council, National Academy Press, Washington, D.C. 1996, Chapter 3: 36–75Google Scholar
  12. Denis G, de Boissezon H, Hosford S, Pasco X, Montfort B, Ranera F (2016) The evolution of earth observation satellites in Europe and its impact on the performance of emergency response services. Acta Astronautica 127:619–633. doi: 10.1016/j.actaastro.2016.06.012 CrossRefGoogle Scholar
  13. Dimitrijević MD (1997) Geology of Yugoslavia. Gemini-Special Publications, 1–187. Belgrade, ISBN 86-7156–016-3, pp. 1–187Google Scholar
  14. Guzzetti F, Cardinali M, Reichenbach P, Cipolla F, Sebastiani C, Galli M, Salvati P (2004) Landslides triggered by the 23 November 2000 rainfall event in the Imperia Province, Western Liguria, Italy. Eng Geol 73(2):229–245. doi: 10.1016/j.enggeo.2004.01.006 CrossRefGoogle Scholar
  15. Guzzetti F, Mondini AC, Cardinali M, Fiorucci F, Santangelo M, Chang KT (2012) Landslide inventory maps: new tools for an old problem. Earth Sci rev 112:42–66. doi: 10.1016/j.earscirev.2012.02.001 CrossRefGoogle Scholar
  16. Haugerud R, Harding DJ, Johnson SY, Harless JL, Weaver CS, Sherrod BL (2003) High-resolution lidar topography of the Puget Lowland, Washington—a bonanza for earth science. GSA Today 13(6):4–10. doi: 10.1130/1052-5173 CrossRefGoogle Scholar
  17. Hungr O, Leroueil L, Picarelli L (2014) The Varnes classification of landslide types, an update. Landslides 11:167–194. doi: 10.1007/s10346-013-0436-y CrossRefGoogle Scholar
  18. Iwahashi J, Kamiya I, Yamagishi H (2012) High-resolution DEMs in the study of rainfall- and earthquake-induced landslides: use of a variable window size method in digital terrain analysis. Geomorphology 153–154: 29–38, doi: 10.1016/j.geomorph.2012.02.002
  19. Jiménez-Muñoz J, Sobrino J, Gillespie A, Sabol D, Gustafson W (2006) Improved land surface emissivities over agricultural areas using ASTER NDVI. Remote Sens Environ 103:474–487. doi: 10.1016/j.rse.2006.04.012 CrossRefGoogle Scholar
  20. Joyce KE, Samsonov SV, Levick SR, Engelbrecht J, Belliss S (2014) Mapping and monitoring geological hazards using optical, LiDAR, and synthetic aperture RADAR image data. Nat Hazards 73:137–163. doi: 10.1007/s11069-014-1122-7 CrossRefGoogle Scholar
  21. Krstić N, Lj S, Jovanović G, Bodor E (2003) Lower Miocene lakes of the Balkan Land. Acta Geol Hung 46:291–299CrossRefGoogle Scholar
  22. Kwan MP, Ransberger DM (2010) LiDAR assisted emergency response: detection of transport network obstructions caused by major disasters. Comput Environ Urban Syst 34:179–188. doi: 10.1016/j.compenvurbsys.2010.02.001 CrossRefGoogle Scholar
  23. Lin CW, Chang WS, Liu SH, Tsai TT, Lee SP, Tsang YC, Shieh CL, Tseng CM (2011) Landslides triggered by the 7 August 2009 Typhoon Morakot in southern Taiwan. Eng Geol 123:3–12. doi: 10.1016/j.enggeo.2011.06.007 CrossRefGoogle Scholar
  24. Lira C, Lousada M, Falcāo AP, Gonҫalves AB, Heleno S, Matias M, Pereira MJ, Pina P, Sousa AJ, Oliveira R, Almeida AB (2013) The 20 February 2010 Madeira Island flash-floods: VHR satellite imagery processing in support of landslide inventory and sediment budget assessment. Nat Hazards Earth Syst Sci 13:709–719. doi: 10.5194/nhess-13-709-2013 CrossRefGoogle Scholar
  25. Marjanović M, Abolmasov B (2015) Evidencija i prostorna analiza klizišta zabeleženih u maju 2014. Časopis Izgradnja 69(5–6):129–134 (in Serbian) Google Scholar
  26. Marjanović M, Vulović N, Đurić U, Božanić B (2016) Coupling field and satellite data for an event-based landslide inventory. Proceedings of the 12th International Symposium on Landslides, Naples, Italy, 12–19 June 2016, pp. 1361–1366Google Scholar
  27. Martha TR, Kumar KV (2013) September, 2012 landslide events in Okhimath, India—an assessment of landslide consequences using very high resolution satellite data. Landslides 10(4):469–479. doi: 10.1007/s10346-013-0420-6 CrossRefGoogle Scholar
  28. Martha TR, Kerle N, Jetten V, van Westen CJ, Kumar VK (2010) Characterizing spectral, spatial and morphometric properties of landslides for semi-automatic detection using object-oriented methods. Geomorphology 116(1–2):24–36. doi: 10.1016/j.geomorph.2009.10.004 CrossRefGoogle Scholar
  29. Martha TR, Kerle N, van Westen CJ, Jetten V, Kumar KV (2012) Object-oriented analysis of multi-temporal panchromatic images for creation of historical landslide inventories. ISPRS J Photogramm Remote Sens 67:105–119. doi: 10.1016/j.isprsjprs.2011.11.004 CrossRefGoogle Scholar
  30. Martha TR, Govindharaj KB, Kumar KV (2015) Damage and geological assessment of the 18 September 2011 Mw 6.9 earthquake in Sikkim, India using very high resolution satellite data. Geosci Front 6:793–805. doi: 10.1016/j.gsf.2013.12.011 CrossRefGoogle Scholar
  31. Martha TR, Roy P, Mazumdar R, Govindharaj KB, Kumar KV (2016) Spatial characteristics of landslides triggered by the 2015 Mw 7.8 (Gorkha) and Mw 7.3 (Dolakha) earthquakes in Nepal. Landslides. doi: 10.1007/s10346-016-0763-x On-line
  32. Menković L, Koščal M, Mijatović M (2003) Geomorfološka karta Srbije, 1:500 000. Geozavod-Gemini, Belgrade (in Serbian) Google Scholar
  33. Metternicht G, Hurni L (2005) Radu Gogu remote sensing of landslides: an analysis of the potential contribution to geo-spatial systems for hazard assessment in mountainous environments. Remote Sens Environ 98:284–303. doi: 10.1016/j.rse.2005.08.004 CrossRefGoogle Scholar
  34. Mihalić Arbanas S, Arbanas Ž, Abolmasov B, Mikoš M, Komac M (2013) The ICL Adriatic-Balkan Network: analysis of current state and planned activities. Landslides 10:103–109. doi: 10.1007/s10346-012-0364-2 CrossRefGoogle Scholar
  35. Minu NS, Bindhu JS (2016) Supervised techniques and approaches for satellite image classification. International Journal of Computer Applications 134(16):1–6. doi: 10.5120/ijca2016908202 CrossRefGoogle Scholar
  36. Mladenović A, Trivić B, Cvetković V (2015) How tectonic controlled post-collisional magmatism within the Dinarides: inferences based on study of tectono-magmatic events in the Kopaonik Mts. (Southern Serbia). Tectonophysics 646:36–49. doi: 10.1016/j.tecto.2015.02.001 CrossRefGoogle Scholar
  37. Mondini AC, Guzzetti F, Reichenbach P, Rossi M, Cardinali M, Ardizzone F (2011) Semi-automatic recognition and mapping of rainfall induced shallow landslides using satellite optical images. Remote Sens Environ 115:1743–1757. doi: 10.1016/j.rse.2011.03.006 CrossRefGoogle Scholar
  38. Moran S, Jackson R, Slater P, Teillet P (1992) Evaluation of simplified procedures for retrieval of land surface reflectance factors from satellite sensor output. Remote Sens Environ 41:169–184. doi: 10.1016/0034-4257(92)90076-V CrossRefGoogle Scholar
  39. Murillo-García FG, Alcántara-Ayala I, Ardizzone F, Cardinali M, Fiourucci F, Guzzetti F (2015) Satellite stereoscopic pair images of very high resolution: a step forward for the development of landslide inventories. Landslides 12:277–291. doi: 10.1007/s10346-014-0473-1 CrossRefGoogle Scholar
  40. Osiñska-Skotak K (2007) Studies of soil temperature on the basis of satellite data. International Agrophysics 21(3):275–284Google Scholar
  41. Prohaska S, Đukić D, Bartoš-Divac V, Todorović N, Božović N (2014) Karakteristike jakih kiša koje su prouzrokovale čestu pojavu poplava na teritoriji Srbije u periodu april-septembar 2014.godine. Vodoprivreda 46:15–26 (in Serbian) Google Scholar
  42. Ray PKC, Chattoraj SL, Bisht MPS, Kannaujiya S, Pandey K, Goswami A (2016) Kedarnath disaster 2013: causes and consequences using remote sensing inputs. Nat Hazards 81:227–243. doi: 10.1007/s11069-015-2076-0 CrossRefGoogle Scholar
  43. Sato HP, Harp EL (2009) Interpretation of earthquake-induced landslides triggered by the 12 May 2008, M7.9 Wenchuan earthquake in the Beichuan area, Sichuan Province, China using satellite imagery and Google Earth. Landslides 6:153–159. doi: 10.1007/s10346-009-0147-6 CrossRefGoogle Scholar
  44. Sato HP, Hasegawa H, Fujiwara S, Tobita M, Koarai M, Une H, Iwahashi J (2007) Interpretation of landslide distribution triggered by the 2005 Northern Pakistan earthquake using SPOT 5 imagery. Landslides 4:113–122. doi: 10.1007/s10346-006-0069-5 CrossRefGoogle Scholar
  45. Schefer S (2010) Tectono-metamorphic and magmatic evolution of the Internal Dinarides (Kopaonik area, southern Serbia) and its significance for the geodynamic evolution of the Balkan Peninsula. PhD thesis, University of Basel, Switzerland, p. 234Google Scholar
  46. Schmid MS, Bernoulli D, Fügenschuh B, Matenco L, Schefer S, Schuster R, Tischler M, Ustaszewski K (2008) The Alps-Carpathians-Dinarides-connection: a correlation of tectonic units. Swiss J Geosci 101(1):139–183. doi: 10.1007/s00015-008 1247 CrossRefGoogle Scholar
  47. Schulz WH (2007) Landslide susceptibility revealed by LIDAR imagery and historical records, Seattle, Washington. Eng Geol 89:67–87. doi: 10.1016/j.enggeo.2006.09.019 CrossRefGoogle Scholar
  48. Shafique M, van der Meijde M, Khan MA (2016) A review of the 2005 Kashmir earthquake-induced landslides; from a remote sensing prospective. J Asian Earth Sci 118:68–80. doi: 10.1016/j.jseaes.2016.01.002 CrossRefGoogle Scholar
  49. Tang C, Ma G, Chang M, Li W, Zhang D, Jia T, Zhou Z (2015) Landslides triggered by the 20 April 2013 Lushan earthquake, Sichuan Province, China. Eng Geol 187:45–55. doi: 10.1016/j.enggeo.2014.12.004 CrossRefGoogle Scholar
  50. Tralli DM, Blom RG, Zlotnicki V, Donnellan A, Evans DL (2005) Satellite remote sensing of earthquake, volcano, flood, landslide and coastal inundation hazards. ISPRS Journal of Photogrammetry & Remote Sensing 59:185–198. doi: 10.1016/j.isprsjprs.2005.02.002 CrossRefGoogle Scholar
  51. UNDAC - UN Disaster Assessment and Coordination (2014) End of mission report. United Nations Office for the Coordination of Humanitarian Affairs—OCHA, p. 78, http://reliefweb.int/report/serbia/undac-mission-serbia-floods-18-31-may-2014-end-mission-report. Access Jul 2016
  52. Ustaszewski K, Kounov A, Schmid S, Schaltegger U, Krenn E, Frank W, Fügenschuh B (2010) Evolution of the Adria–Europe plate boundary in the northern Dinarides: from continent-continent collision to back-arc extension. Tectonics 29:TC6017. doi: 10.1029/2010TC002668 CrossRefGoogle Scholar
  53. Valor E, Caselles V (1996) Mapping land surface emissivity from NDVI: application to European, African, and South American areas. Remote Sens Environ 57:167–184. doi: 10.1016/0034-4257(96)00039-9 CrossRefGoogle Scholar
  54. Voigt S, Kemper T, Riedlinger T, Kiefl R, Scholte K, Mehl H (2007) Satellite image analysis for disaster and crisis-management support. IEEE Trans Geosci Remote Sens 45(6):1520–1528. doi: 10.1109/TGRS.2007.895830 CrossRefGoogle Scholar
  55. Xu C, Xu X, Yu G (2013) Landslides triggered by slipping-fault-generated earthquake on a plateau: an example of the 14 April 2010, Ms 7.1, Yushu, China earthquake. Landslides 10:421–431. doi: 10.1007/s10346-012-0340-x CrossRefGoogle Scholar
  56. Xu C, Xu X, Bruce J, Shyu H, Zheng W, Min W (2014) Landslides triggered by the 22 July 2013 Minxian–Zhangxian, China, Mw 5.9 earthquake: inventory compiling and spatial distribution analysis. J Asian Earth Sci 92:125–142. doi: 10.1016/j.jseaes.2014.06.014 CrossRefGoogle Scholar
  57. Yang X, Chen L (2010) Using multi-temporal remote sensor imagery to detect earthquake-triggered landslides. Int J Appl Earth Obs Geoinf 12:487–495. doi: 10.1016/j.jag.2010.05.006 CrossRefGoogle Scholar

Websites

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Dragana Đurić
    • 1
  • Ana Mladenović
    • 1
  • Milica Pešić-Georgiadis
    • 1
  • Miloš Marjanović
    • 1
  • Biljana Abolmasov
    • 1
    Email author
  1. 1.Faculty of Mining and GeologyUniversity of BelgradeBelgradeSerbia

Personalised recommendations