Skip to main content

A heuristic approach to global landslide susceptibility mapping

Abstract

Landslides can have significant and pervasive impacts to life and property around the world. Several attempts have been made to predict the geographic distribution of landslide activity at continental and global scales. These efforts shared common traits such as resolution, modeling approach, and explanatory variables. The lessons learned from prior research have been applied to build a new global susceptibility map from existing and previously unavailable data. Data on slope, faults, geology, forest loss, and road networks were combined using a heuristic fuzzy approach. The map was evaluated with a Global Landslide Catalog developed at the National Aeronautics and Space Administration, as well as several local landslide inventories. Comparisons to similar susceptibility maps suggest that the subjective methods commonly used at this scale are, for the most part, reproducible. However, comparisons of landslide susceptibility across spatial scales must take into account the susceptibility of the local subset relative to the larger study area. The new global landslide susceptibility map is intended for use in disaster planning, situational awareness, and for incorporation into global decision support systems.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

References

  1. Ahmed MF, Rogers JD, Ismail EH (2014) A regional level preliminary landslide susceptibility study of the upper Indus river basin. Eur J Remote Sens 47:343–373. doi:10.5721/EuJRS20144721

    Article  Google Scholar 

  2. Ayalew L, Yamagishi H (2005) The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology 65:15–31. doi:10.1016/j.geomorph.2004.06.010

    Article  Google Scholar 

  3. Beguería S (2006) Changes in land cover and shallow landslide activity: a case study in the Spanish Pyrenees. Geomorphology 74:196–206. doi:10.1016/j.geomorph.2005.07.018

    Article  Google Scholar 

  4. Bhatt BP, Awasthi KD, Heyojoo BP et al (2013) Using geographic information system and analytical hierarchy process in landslide hazard zonation. Appl Ecol Environ Sci 1:14–22. doi:10.12691/aees-1-2-1

    Google Scholar 

  5. BMTPC (Building Materials and Technology Promotion Council Ministry of Urban Development and Poverty Alleviation Government of India), CDMM (Centre for Disaster Mitigation and Management, Anna University) (2003) Landslide Hazard Zonation Atlas of India. New Delhi

  6. Bonham-Carter G (1994) Geographic information systems for geoscientists: modelling with GIS. Elsevier, Amsterdam

    Google Scholar 

  7. Bouysse P (2009) Geological map of the world at 1:50 000 000. Commission for the Geological Map of the World

  8. Brabb EE, Colgan JP, Best TC (1999) Map showing inventory and regional susceptibility for Holocene debris flows and related fast moving landslides in the conterminous United States. Accessed http://pubs.usgs.gov/mf/1999/2329/

  9. Bucknam RC, Coe JA, Chavarría MM et al (2001) Landslides triggered by Hurricane Mitch in Guatemala—inventory and discussion. US Geological Survey Open File Report 01-443:38

  10. Center for International Earth Science Information Network, Information Technology Outreach Services (2013) Global roads open access data set, version 1. http://sedac.ciesin.columbia.edu/data/set/groads-global-roads-open-access-v1. Accessed 1 Jan 2015

  11. Cepeda J, Smebye, H, Vangelsten, B et al (2010) Landslide risk in Indonesia. Global assessment report on disaster risk reduction. United Nations

  12. Champati ray PK, Dimri S, Lakhera RC, Sati S (2007) Fuzzy-based method for landslide hazard assessment in active seismic zone of Himalaya. Landslides 4:101–111. doi:10.1007/s10346-006-0068-6

    Article  Google Scholar 

  13. Dahal RK, Hasegawa S, Nonomura A et al (2008) Predictive modelling of rainfall-induced landslide hazard in the Lesser Himalaya of Nepal based on weights-of-evidence. Geomorphology 102:496–510. doi:10.1016/j.geomorph.2008.05.041

    Article  Google Scholar 

  14. de Ferranti J (2014a) Digital Elevation Data—with SRTM voids filled using accurate topographic mapping. http://www.viewfinderpanoramas.org/dem3.html. Accessed 17 Nov 2015

  15. de Ferranti J (2014b) Digital Elevation Data: SRTM void fill. http://viewfinderpanoramas.org/voidfill.html. Accessed 19 May 2016

  16. Devoli G, Morales A, Høeg K (2007a) Historical landslides in Nicaragua—collection and analysis of data. Landslides 4:5–18. doi:10.1007/s10346-006-0048-x

    Article  Google Scholar 

  17. Devoli G, Strauch W, Chávez G, Høeg K (2007b) A landslide database for Nicaragua: a tool for landslide-hazard management. Landslides 4:163–176. doi:10.1007/s10346-006-0074-8

    Article  Google Scholar 

  18. DOGAMI (Oregon Department of Geology and Mineral Industries) (2015) SLIDO: statewide landslide information layer for Oregon. http://www.oregongeology.org/sub/slido/data.htm. Accessed 11 Oct 2015

  19. dos Santos Alvalá RC, Camarinha PIM, Canavesi V (2013) Landslide susceptibility mapping in the coastal region in the State of São Paulo, Brazil. In: American Geophysical Union, Spring Meeting

  20. Elliott AH, Harty KM (2010) Landslide maps of Utah. Utah Geological Survey Map 246DM:14. 46 plates. 1:100,000 scale. DVD

  21. ESRI (2013) ArcGIS Desktop, version 10.2. Environmental Systems Research Institute, Redlands, California

  22. Frolova JV, Gvozdeva IP, Kuznetsov NP (2015) Effects of Hydrothermal Alterations on Physical and Mechanical Properties of Rocks in the Geysers Valley (Kamchatka Peninsula) in Connection with Landslide Development. In: Proceedings World Geothermal Congress 2015, pp 1–6

  23. Gerencia de Geología (2012) Landslide inventory of El Salvador. Ministerio de Medio Ambiente y Recursos Naturales, El Salvador

  24. Günther A, Van Den Eeckhaut M, Malet J-P et al (2014) Climate-physiographically differentiated Pan-European landslide susceptibility assessment using spatial multi-criteria evaluation and transnational landslide information. Geomorphology 224:69–85. doi:10.1016/j.geomorph.2014.07.011

    Article  Google Scholar 

  25. Guzzetti F, Cardinali M, Reichenbach P (1994) The AVI project: a bibliographical and archive inventory of landslides and floods in Italy. Environ Manag 18:623–633. doi:10.1007/BF02400865

    Article  Google Scholar 

  26. Haigh MJ, Rawat JS, Bartarya SK (1989) Environmental indicators of landslide activity along the Kilbury road, Nainital, Kumaun lesser Himalaya. Mt Res Dev 9:25–33

    Article  Google Scholar 

  27. Haigh MJ, Rawat JS, Rawat MS et al (1995) Interactions between forest and landslide activity along new highways in the Kumaun Himalaya. For Ecol Manag 78:173–189

    Article  Google Scholar 

  28. Hansen MC, Potapov PV, Moore R et al (2013) High-resolution global maps of 21st-century forest cover change. Science 342:850–853. doi:10.1126/science.1244693

    Article  Google Scholar 

  29. Haque U, Blum P, da Silva PF et al (2016) Fatal landslides in Europe. Landslides. doi:10.1007/s10346-016-0689-3

    Google Scholar 

  30. Hijmans RJ (2015) Raster: geographic data analysis and modeling. R package version 2.4-15. https://CRAN.R-project.org/package=raster

  31. Hirano A, Welch R, Lang H (2003) Mapping from ASTER stereo image data: DEM validation and accuracy assessment. ISPRS J Photogramm Remote Sens 57:356–370. doi:10.1016/S0924-2716(02)00164-8

    Article  Google Scholar 

  32. Hong Y, Adler RF, Huffman G (2007) Use of satellite remote sensing data in the mapping of global landslide susceptibility. Nat Hazards 43:245–256. doi:10.1007/s11069-006-9104-z

    Article  Google Scholar 

  33. Hosmer DW, Lemeshow S (2005) Assessing the fit of the model. In: Applied logistic regression, 2nd edn. Wiley, Inc., Hoboken, NJ, USA, pp 143–202

  34. ICIMOD (International Centre for Integrated Mountain Development) (1992) Landslides in Koshi River Basin of 1990. http://rds.icimod.org/Home/DataDetail?metadataId=23175&searchlist=True. Accessed 7 Jan 2015

  35. ICIMOD (International Centre for Integrated Mountain Development) (2010) Landslides in Koshi River Basin of 2010. http://rds.icimod.org/Home/DataDetail?metadataId=23176&searchlist=True. Accessed 7 Jan 2015

  36. Jarvis A, Reuter H, Nelson A, Guevara E (2008) Hole-filled SRTM for the globe version 4. Available from the CGIAR-CSI SRTM 90 m Database

  37. Jezek KC (2002) RADARSAT-1 Antarctic mapping project: change-detection and surface velocity campaign. Ann Glaciol 34:263–268. doi:10.3189/172756402781818030

    Article  Google Scholar 

  38. Keefer DK (1994) The importance of earthquake-induced landslides to long-term slope erosion and slope-failure hazards in seismically active regions. Geomorphology 10:265–284. doi:10.1016/0169-555X(94)90021-3

    Article  Google Scholar 

  39. Kirschbaum DB, Adler RF, Hong Y et al (2010) A global landslide catalog for hazard applications: method, results, and limitations. Nat Hazards 52:561–575. doi:10.1007/s11069-009-9401-4

    Article  Google Scholar 

  40. Kirschbaum D, Stanley T (2016) A satellite-based global landslide hazard assessment model for situational awareness. In: Geological society of america abstracts with programs, vol 48. doi:10.1130/abs/2016AM-279271

  41. Kirschbaum DB, Stanley T, Yatheendradas S (2015a) Modeling landslide susceptibility over large regions with fuzzy overlay. Landslides. doi:10.1007/s10346-015-0577-2

    Google Scholar 

  42. Kirschbaum DB, Stanley T, Zhou Y (2015b) Spatial and temporal analysis of a global landslide catalog. Geomorphology 249:4–15. doi:10.1016/j.geomorph.2015.03.016

    Article  Google Scholar 

  43. Korup O, Stolle A (2014) Landslide prediction from machine learning. Geol Today 30:26–33. doi:10.1111/gto.12034

    Article  Google Scholar 

  44. Larsen IJ, Montgomery DR (2012) Landslide erosion coupled to tectonics and river incision. Nat Geosci 5:468–473. doi:10.1038/ngeo1479

    Article  Google Scholar 

  45. Larsen MC, Parks JE (1997) How wide is a road? The association of roads and mass-wasting in a forested montane environment. Earth Surf Process Landf 22:835–848. doi:10.1002/(SICI)1096-9837(199709)22:9<835:AID-ESP782>3.0.CO;2-C

    Article  Google Scholar 

  46. Lehner B, Verdin K, Jarvis A (2008) New global hydrography derived from spaceborne elevation data. EOS Trans Am Geophys Union 89:93. doi:10.1029/2008EO100001

    Article  Google Scholar 

  47. Liu C, Li W, Wu H et al (2013) Susceptibility evaluation and mapping of China’s landslides based on multi-source data. Nat Hazards 69:1477–1495. doi:10.1007/s11069-013-0759-y

    Article  Google Scholar 

  48. Nadim F, Kjekstad O, Peduzzi P et al (2006) Global landslide and avalanche hotspots. Landslides 3:159–173. doi:10.1007/s10346-006-0036-1

    Article  Google Scholar 

  49. NIMA (National Imagery and Mapping Agency) (1993) Vector map (VMap) level 0. http://earth-info.nga.mil/publications/vmap0.html. Accessed 1 Jan 2014

  50. Okamoto T, Sakurai M, Tsuchiya S (2013) Secondary hazards associated with coseismic landslide. In: Ugai K, Yagi H, Wakai A (eds) Earthquake-induced landslides. Springer, Berlin, pp 77–82

    Chapter  Google Scholar 

  51. OpenStreetMap contributors (2015) OpenStreetMap. http://osm-x-tractor.org/Data.aspx. Accessed 7 Jun 2015

  52. Petley DN (2012) Global patterns of loss of life from landslides. Geology 40:927–930. doi:10.1130/G33217.1

    Article  Google Scholar 

  53. Petley DN, Dunning SA, Rosser NJ (2005) The analysis of global landslide risk through the creation of a database of worldwide landslide fatalities. In: Hungr O, Fell R, Couture R, Eberhardt E (eds) Landslide risk management. CRC Press, Boca Raton, p 776

    Google Scholar 

  54. Petley DN, Hearn GJ, Hart A et al (2007) Trends in landslide occurrence in Nepal. Nat Hazards 43:23–44. doi:10.1007/s11069-006-9100-3

    Article  Google Scholar 

  55. Pradhan B (2011) Use of GIS-based fuzzy logic relations and its cross application to produce landslide susceptibility maps in three test areas in Malaysia. Environ Earth Sci 63:329–349. doi:10.1007/s12665-010-0705-1

    Article  Google Scholar 

  56. Rabus B, Eineder M, Roth A, Bamler R (2003) The shuttle radar topography mission—a new class of digital elevation models acquired by spaceborne radar. ISPRS J Photogramm Remote Sens 57:241–262. doi:10.1016/S0924-2716(02)00124-7

    Article  Google Scholar 

  57. Radbruch-Hall DH, Colton RB, Davies WE et al (1982) Landslide overview map of the conterminous United States. U.S Government Printing Office, Washington

    Google Scholar 

  58. Regmi AD, Devkota KC, Yoshida K et al (2013) Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya. Arab J Geosci 7:725–742. doi:10.1007/s12517-012-0807-z

    Article  Google Scholar 

  59. Reid ME, Sisson TW, Brien DL (2001) Volcano collapse promoted by hydrothermal alteration and edifice shape, Mount Rainier, Washington. Geology 29:779. doi:10.1130/0091-7613(2001)029<0779:VCPBHA>2.0.CO;2

    Article  Google Scholar 

  60. Rubel Y, Ahmed B (2013) Understanding the issues involved in urban landslide vulnerability in Chittagong metropolitan area. Association of American Geographers (AAG), Bangladesh

    Google Scholar 

  61. Scheidegger AE, Ai NS (1986) Tectonic processes and geomorphological design. Tectonophysics 126:285–300. doi:10.1016/0040-1951(86)90234-9

    Article  Google Scholar 

  62. Schutz BE, Zwally HJ, Shuman CA et al (2005) Overview of the ICESat mission. Geophys Res Lett 32:L21S01. doi:10.1029/2005GL024009

    Article  Google Scholar 

  63. Sidle RC, Pearce AJ, O’Loughlin CL (1985) Effects of land management on soil mass movement. In: Sidle RC, Pearce AJ, O’Loughlin CL (eds) Hillslope stability and land use. American Geophysical Union, Washington, pp 73–88

    Chapter  Google Scholar 

  64. Sidle RC, Ziegler AD, Negishi JN et al (2006) Erosion processes in steep terrain—truths, myths, and uncertainties related to forest management in Southeast Asia. For Ecol Manag 224:199–225. doi:10.1016/j.foreco.2005.12.019

    Article  Google Scholar 

  65. Srivastava V, Srivastava HB, Lakhera RC (2010) Fuzzy gamma based geomatic modelling for landslide hazard susceptibility in a part of Tons river valley, northwest Himalaya, India. Geomat Nat Hazards Risk 1:225–242. doi:10.1080/19475705.2010.490103

    Article  Google Scholar 

  66. Steger S, Brenning A, Bell R et al (2016a) Exploring discrepancies between quantitative validation results and the geomorphic plausibility of statistical landslide susceptibility maps. Geomorphology 262:8–23. doi:10.1016/j.geomorph.2016.03.015

    Article  Google Scholar 

  67. Steger S, Brenning A, Bell R, Glade T (2016b) The impact of systematically incomplete and positionally inaccurate landslide inventories on statistical landslide susceptibility models. In: EGU general assembly conference abstracts 18:6666

  68. Tangestani MH (2004) Landslide susceptibility mapping using the fuzzy gamma approach in a GIS, Kakan catchment area, southwest Iran. Aust J Earth Sci 51:439–450. doi:10.1111/j.1400-0952.2004.01068.x

    Article  Google Scholar 

  69. USGS (United States Geological Survey) (2008) Global land survey digital elevation model. Global Land Cover Facility, University of Maryland, College Park, Maryland. http://glcf.umd.edu/data/glsdem/

  70. van Westen CJ, Castellanos E, Kuriakose SL (2008) Spatial data for landslide susceptibility, hazard, and vulnerability assessment: an overview. Eng Geol 102:112–131. doi:10.1016/j.enggeo.2008.03.010

    Article  Google Scholar 

  71. Verdin KL, Godt JW, Funk C et al (2007) Development of a global slope dataset for estimation of landslide occurrence resulting from earthquakes: U.S. Geological Survey, Colorado. Open-File Report 2007–1188:25

  72. Weirich F, Blesius L (2007) Comparison of satellite and air photo based landslide susceptibility maps. Geomorphology 87:352–364. doi:10.1016/j.geomorph.2006.10.003

    Article  Google Scholar 

  73. Zhang J, Gurung DR, Liu R et al (2015) Abe Barek landslide and landslide susceptibility assessment in Badakhshan Province, Afghanistan. Landslides 12:597–609. doi:10.1007/s10346-015-0558-5

    Article  Google Scholar 

  74. Zweig MH, Campbell G (1993) Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin Chem 39:561–577

    Google Scholar 

Download references

Acknowledgements

Thank you to all of the contributors to the Global Landslide Catalog since its creation in 2007. Thank you also to all of those who provided landslide inventories for analysis, including Deo Raj Gurung and Jianqiang Zhang (ICIMOD), Mauro Rossi (CNR IRPI), Graziella Devoli, Manuel Diaz (MARN), the Oregon DOGAMI, the USGS, and the Utah Geological Survey. This work was supported by NASA’s Precipitation Measurement Missions.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Dalia B. Kirschbaum.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Stanley, T., Kirschbaum, D.B. A heuristic approach to global landslide susceptibility mapping. Nat Hazards 87, 145–164 (2017). https://doi.org/10.1007/s11069-017-2757-y

Download citation

Keywords

  • Landslide
  • Landslide susceptibility
  • Remote sensing
  • GIS
  • Fuzzy logic