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Landslides

, Volume 13, Issue 3, pp 485–496 | Cite as

Modeling landslide susceptibility over large regions with fuzzy overlay

  • Dalia KirschbaumEmail author
  • Thomas Stanley
  • Soni Yatheendradas
Original Paper

Abstract

Landslide susceptibility mapping is most effective if detailed surface and subsurface information can be combined with authoritative landslide catalogs or a deep understanding of local conditions. However, these types of homogeneous input data and catalogs are frequently not available over large areas. In this study, we model landslide susceptibility in Central America and the Caribbean islands by combining three globally available datasets and one regional dataset with fuzzy overlay. This primarily heuristic model provides the flexibility to test a range of different contributing variables and the capability to compare landslide inventories within the model framework that vary greatly in their size, spatiotemporal scope, and collection methods. We create a regional susceptibility map and evaluate its performance using receiver operating characteristics for both continuous and binned susceptibility values. This susceptibility map forms the basis for a near-real-time landslide hazard assessment system that couples susceptibility with rainfall and soil moisture triggers to estimate potential landslide activity at a regional scale. The application of this susceptibility model at the regional scale provides a foundation for transferring the methodology to other geographic areas.

Keywords

Landslide susceptibility Fuzzy overlay GIS Central America Caribbean 

Notes

Acknowledgments

This work gratefully acknowledges José Cepeda’s (Norwegian Geotechnical Institute) guidance on the expert survey and review of this manuscript. Many thanks go to our insightful colleagues, including Carlos Aguilar (El Salvador Geological Survey), Rex Baum (USGS), Graziella Devoli (Norwegian Water Resources and Energy Directorate), Manuel Diaz (Medio Ambiente y Recursos Naturales, El Salvador), Bruce Harrison (New Mexico Tech), Pavel Havlicek (Czech Geological Survey), Eunjung Lim (University of Maryland), Shlomo Neuman (The University of Arizona), and Jonathan Resop (University of Maryland). The pioneering work of Sergio Mora and Wilhelm-Gunther Vahrson inspired this project. This work was funded by the NASA SERVIR program, NNH11ZDA001N-SERVIR.

References

  1. Akgun A, Sezer EA, Nefeslioglu HA et al (2012) An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm. Comput Geosci 38:23–34. doi: 10.1016/j.cageo.2011.04.012 CrossRefGoogle 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 CrossRefGoogle Scholar
  3. Baeza C, Lantada N, Moya J (2009) Validation and evaluation of two multivariate statistical models for predictive shallow landslide susceptibility mapping of the Eastern Pyrenees (Spain). Environ Earth Sci. doi: 10.1007/s12665-009-0361-5 Google Scholar
  4. Bonham-Carter G (1994) Geographic information systems for geoscientists. Pergamon, New YorkGoogle Scholar
  5. Bucknam RC, Coe JA, Chavarria MM et al (2001) Landslides triggered by hurricane Mitch in Guatemala - inventory and discussion. US Geol Surv Open File Rep 01–443:1–40Google Scholar
  6. Cannon SH, Haller KM, Ekstrom I et al (2001) Landslide response to hurricane mitch rainfall in seven study areas in Nicaragua. Open-File Report 01-412-A. 21Google Scholar
  7. Caraciolo M (2009) Python module for calculating the area under the receive operating characteristic curve (PyRoc.py). GitHub Code Repository. https://github.com/marcelcaraciolo/PyROC/blob/master/pyroc.py. Accessed 3 Nov 2014
  8. Carrara A (1983) Multivariate models for landslide hazard evaluation. Math Geol 15:403–426CrossRefGoogle Scholar
  9. Center for International Earth Science Information Network (CIESIN)/Columbia University, Georgia ITOS (ITOS)/University of (2013) Global roads open access data set, version 1 (gROADSv1). http://sedac.ciesin.columbia.edu/data/set/groads-global-roads-open-access-v1
  10. Cepeda J, Chávez JA, Cruz Martínez C (2010) Procedure for the selection of runout model parameters from landslide back-analyses: application to the metropolitan area of San Salvador, El Salvador. Landslides 7:105–116. doi: 10.1007/s10346-010-0197-9 CrossRefGoogle Scholar
  11. 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 CrossRefGoogle Scholar
  12. Coe JA, Godt JW, Baum RL et al (2004) Landslide susceptibility from topography in Guatemala. In: Lacerda WA, Ehrlich M, Fontura SAB, Sayao ASF (eds) Landslides eval. Stab. Taylor & Francis Group, London, pp 69–78Google Scholar
  13. Crone AJ, Baum RL, Lidke DJ et al (2001) Landslides induced by hurricane Mitch in El Salvador – an inventory and descriptions of selected features. Open File Rep 01–444:28Google Scholar
  14. Gerencia de Geología (2012) Landslide inventory of El Salvador. Medio Ambiente y Recursos Naturales (MARN). El SalvadorGoogle Scholar
  15. Devoli G, Morales A, Hoeg K (2006) Historical landslides in Nicaragua—collection and analysis of data. Landslides. doi: 10.1007/s10346-006-0048-x Google Scholar
  16. Devoli G, Strauch W, Chavez G, Hoeg K (2007) A landslide database for Nicaragua: a tool for landslide-hazard management. Landslides 4:163–176. doi: 10.1007/s10346-006-0074-8 CrossRefGoogle Scholar
  17. Dietrich WE, Real de Asua R, Coyle J, et al. (1998) A validation study of the shallow slope stability model, SHALSTAB, in forested lands of Northern California. BerkeleyGoogle Scholar
  18. ESRI (2013) ArcGIS 10.2 desktop help. http://resources.arcgis.com/en/help/. Accessed July 11 2014.
  19. FAO/IIASA/ISRIC/ISSCAS/JRC (2012) Harmonized world soil database (Version 1.2)Google Scholar
  20. French CD, Schenk CJ (2004) Map showing geology, oil and gas fields, and geologic provinces of the caribbean region. Open File Report 97-470-K. Denver, COGoogle Scholar
  21. Graff JV, Romesburg HC, Ahmad R, McCalpin JP (2012) Producing landslide-susceptibility maps for regional planning in data-scarce regions. Nat Hazards 64:729–749. doi: 10.1007/s11069-012-0267-5 CrossRefGoogle Scholar
  22. Guha-Sapir D, Below R, Hoyois P (2014) EM-DAT: International disaster database. Univ. Cathol. Louvain, Brussels: Belgium. www.em-dat.net. Accessed 20 Mar 2014
  23. Guzzetti F, Reichenbach P, Ardizzone F et al (2006) Estimating the quality of landslide susceptibility models. Geomorphology 81:166–184. doi: 10.1016/j.geomorph.2006.04.007 CrossRefGoogle Scholar
  24. 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 78:173–189CrossRefGoogle Scholar
  25. Haneberg WC (2004) A rational probabilistic method for spatially distributed landslide hazard assessment. Environ Eng Geosci 10:27–43CrossRefGoogle Scholar
  26. Harp EL, Hagaman KW, Held MD, Mckenna JP (2002) Digital inventory of landslides and related deposits in Honduras triggered by hurricane Mitch. US Geol Surv Open-File :1–18Google Scholar
  27. Hong Y, Adler R, 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 CrossRefGoogle Scholar
  28. Jarvis A, Reuter H, Nelson A, Guevara E (2008) Hole-filled SRTM for the globe version 4. CGIAR-CSI SRTM 90 m database. http://srtm.csi.cgiar.org
  29. Kanungo DP, Arora MK, Gupta RP, Sarkar S (2008) Landslide risk assessment using concepts of danger pixels and fuzzy set theory in Darjeeling Himalayas. Landslides 5:407–416CrossRefGoogle Scholar
  30. Kirschbaum DB, Adler R, 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 CrossRefGoogle Scholar
  31. Kirschbaum DB, Adler R, Hong Y et al (2012) Advances in landslide nowcasting: evaluation of a global and regional modeling approach. Environ Earth Sci 66:1683–1696. doi: 10.1007/s12665-011-0990-3 CrossRefGoogle Scholar
  32. Kirschbaum DB, Stanley T, Zhou Y (2015) Spatial and temporal analysis of a global landslide catalog. Geomorphology. doi: 10.1016/j.geomorph.2015.03.016
  33. 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 22:835–848CrossRefGoogle Scholar
  34. Lee S, Min K (2001) Statistical analysis of landslide susceptibility at Yongin, Korea. Environ Geol 40:1095–1113CrossRefGoogle Scholar
  35. Lee S, Ryu J-H, Kim I-S (2007) Landslide susceptibility analysis and its verification using likelihood ratio, logistic regression, and artificial neural network models: case study of Youngin, Korea. Landslides 4:327–338. doi: 10.1007/s10346-007-0088-x CrossRefGoogle Scholar
  36. Matthew J, Jha VK, Rawat GS (2009) Landslide susceptibility zonation mapping and its validation in part of Garhwal Lesser Himalaya, India, using binary logistic regression analysis and receiver operating characteristic curve method. Landslides 6:17–26. doi: 10.1007/s10346-008-0138-z CrossRefGoogle Scholar
  37. Miller AJ (2013) Remote sensing proxies for deforestation and soil degradation in landslide mapping: a review. Geogr Compass 7:489–503CrossRefGoogle Scholar
  38. Mora SC, Vahrson W-G (1994) Macrozonation methodology for landslide hazard determination. Bull Assoc Eng Geol XXXI:49–58Google Scholar
  39. 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 CrossRefGoogle Scholar
  40. Osna T, Sezer EA, Akgun A (2014) GeoFIS: an integrated tool for the assessment of landslide susceptibility. Comput Geosci 66:20–30. doi: 10.1016/j.cageo.2013.12.016 CrossRefGoogle Scholar
  41. Pack RT, Tarboton DG, Goodwin CN (1998) The SINMAP approach to terrain stability mapping. 8th Congr. Int. Assoc. Eng. Geol. Vancouver, Br. Columbia, Canada 21–25 Sept. 1998. pp 1–8Google Scholar
  42. 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, Counture R, Ebergardt E (eds) Landslide risk management. Balkema, Amsterdam, pp 367–374Google Scholar
  43. Pistocchi A, Luzi L, Napolitano P (2002) The use of predictive modeling techniques for optimal exploitation of spatial databases: a case study in landslide hazard mapping with expert system-like methods. Environ Geol 41:765–775. doi: 10.1007/s002540100440 CrossRefGoogle Scholar
  44. Pourghasemi H, Pradhan B, Gokceoglu C (2012) Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Nat Hazards 63:965–996. doi: 10.1007/s11069-012-0217-2 CrossRefGoogle Scholar
  45. Pradhan B, Lee S, Buchroithner MF (2009) Use of geospatial data and fuzzy algebraic operators to landslide-hazard mapping. Appl Geomat 1:3–15. doi: 10.1007/s12518-009-0001-5 CrossRefGoogle Scholar
  46. Remondo J, Bonachea J, Cendrero A (2005) A statistical approach to landslide risk modelling at basin scale: from landslide susceptibility to quantitative risk assessment. Landslides 2:321–328. doi: 10.1007/s10346-005-0016-x CrossRefGoogle Scholar
  47. Slater JA, Garvey G, Johnston C et al (2006) The SRTM data “ Finishing ” process and products. Photogramm Eng Remote Sens 72:237–247CrossRefGoogle Scholar
  48. Vanacker V, Vanderschaeghe M, Govers G et al (2003) Linking hydrological, infinite slope stability and land-use change models through GIS for assessing the impact of deforestation on slope stability in high Andean watersheds. Geomorphology 52:299–315. doi: 10.1016/S0169-555X(02)00263-5 CrossRefGoogle Scholar
  49. Verdin KL, Godt J, Funk C et al (2007) Development of a global slope dataset for estimation of landslide occurrence resulting from earthquakes. US Geol Surv Open-File :1–29Google Scholar
  50. Yesilnacar E, Topal T (2005) Landslide susceptibility mapping: a comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey). Eng Geol 79:251–266CrossRefGoogle Scholar
  51. Zweig MH, Campbell G (1993) Receiver-operating characteristics (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin Chem 39:561–577Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg (outside the USA) 2015

Authors and Affiliations

  • Dalia Kirschbaum
    • 1
    Email author
  • Thomas Stanley
    • 1
    • 2
  • Soni Yatheendradas
    • 1
    • 3
  1. 1.Hydrological Sciences LaboratoryNASA Goddard Space Flight CenterGreenbeltUSA
  2. 2.Universities Space Research AssociationColumbiaUSA
  3. 3.Earth System Science Interdisciplinary CenterUniversity of MarylandCollege ParkUSA

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