Advertisement

Landslides

, Volume 2, Issue 1, pp 61–69 | Cite as

An approach for GIS-based statistical landslide susceptibility zonation—with a case study in the Himalayas

  • Ashis K. Saha
  • Ravi P. Gupta
  • Irene Sarkar
  • Manoj K. Arora
  • Elmar Csaplovics
Original Articles

Abstract

Landslide susceptibility zonation (LSZ) is necessary for disaster management and planning development activities in mountainous regions. A number of methods, viz. landslide distribution, qualitative, statistical and distribution-free analyses have been used for the LSZ studies and they are again briefly reviewed here. In this work, two methods, the Information Value (InfoVal) and the Landslide Nominal Susceptibility Factor (LNSF) methods that are based on bivariate statistical analysis have been applied for LSZ mapping in a part of the Himalayas. Relevant thematic maps representing various factors (e.g., slope, aspect, relative relief, lithology, buffer zones along thrusts, faults and lineaments, drainage density and landcover) that are related to landslide activity, have been generated using remote sensing and GIS techniques. The LSZ derived from the LNSF method, has been compared with that produced from the InfoVal method and the result shows a more realistic LSZ map from the LNSF method which appears to conform to the heterogeneity of the terrain.

Keywords

Landslide susceptibility zonation GIS Remote sensing Himalayas 

Notes

Acknowledgements

A. K. Saha is grateful to the Council of Scientific and Industrial Research (CSIR), New Delhi, India, for Senior Research Fellowship. He is also thankful to German Academic Exchange Service (DAAD), Bonn for the award of DAAD Sandwich Fellowship, during which a part of this work was carried out at the Institute of Photogrammetry and Remote Sensing, Dresden University of Technology, Germany. Thanks are due to Dr. L. Ayalew, Department of Environmental Science, Niigata University, Japan and Dr. R. Anbalagan, Department of Earth Sciences, IIT Roorkee, India, for their valuable comments.

References

  1. Aleotti P, Chowdhury R (1999) Landslide hazard assessment: summary review and new perspectives. Bull Eng Geol Environ 58:21–44CrossRefGoogle Scholar
  2. Anbalagan R (1992) Landslide hazard evaluation and zonation mapping in mountainous terrain. Eng Geol 32:269–277CrossRefGoogle Scholar
  3. Arora MK, Das Gupta AS, Gupta RP (2004) An artificial neural network approach for landslide hazard zonation in the Bhagirathi (Ganga) Valley, Himalayas. Int J Remote Sens 25:559–572CrossRefGoogle Scholar
  4. Ayalew L, Yamagishi H, Ugawa N (2004) Landslide susceptibility mapping using GIS-based weighted linear combination, the case in Tsugawa area of Agano River, Niigata Prefecture, Japan. Landslides 1:73–81CrossRefGoogle Scholar
  5. Bughi S, Aleotti P, Bruschi R, Andrei G, Milani G, Scarpelli G (1996) Slow movements of slopes interfering with pipelines: modelling vs. monitoring. In: Proc 15th Int Conf OMAE, FirenzeGoogle Scholar
  6. Capecchi F, Focardi P (1988) Rainfall and landslides: research into a critical precipitation coefficient in an area of Italy. In: Proc 5th Int Symp on Landslides, Lausanne, Switzerland 2:1131–1136Google Scholar
  7. Carrara AM, Cardinali M, Detti R, Guzzetti F, Pasqui V, Reichenbach P (1991) GIS techniques and statistical models in evaluating landslide hazard. Earth Surf Process Landforms 16:427–445Google Scholar
  8. Chung C-JF, Fabbri AG (1999) Probabilistic prediction models for landslide hazard mapping. Photo Eng Remote Sens 65:1389–1399Google Scholar
  9. Elias PB, Bandis SC (2000) Neurofuzzy systems in landslide hazard assessment. In: Proc 4th Int Symp Spatial Accuracy Assessment in Natural Resources and Environ Sci, pp 199–202Google Scholar
  10. Gupta RP (2003) Remote sensing geology. Springer, Berlin Heidelberg New York, 655 ppGoogle Scholar
  11. Gupta RP, Joshi BC (1990) Landslide hazard zonation using the GIS approach – a case study from the Ramganga Catchment, Himalayas. Eng Geol 28:119–131CrossRefGoogle Scholar
  12. Gupta RP, Saha AK, Arora MK, Kumar A (1999) Landslide hazard zonation in a part of the Bhagirathi Valley, Garhwal Himalayas, using integrated remote sensing- GIS. Himalayan Geol 20:71–85Google Scholar
  13. Lee S, Choi J, Chwae U, Chang B, (2002a) Landslide susceptibility analysis using weight of evidence. In: Proc IEEE Int Geosci Remote Sens Symp, Toronto (CD-ROM)Google Scholar
  14. Lee S, Choi J, Min K (2002b) Landslide susceptibility analysis and verification using the Bayesian probability model. Environ Geol 43:120–131CrossRefGoogle Scholar
  15. Lu PF, An P (1999) A metric for spatial data layers in favorability mapping for geological events. IEEE Tran Geosci Remote Sens 37:1194–1198CrossRefGoogle Scholar
  16. Mantovani F, Soeters R, van Westen CJ (1996) Remote sensing techniques for landslide studies and hazard zonation in Europe. Geomorph 15:213–225CrossRefGoogle Scholar
  17. Nagarajan R, Mukherjee A, Roy A, Khire MV (1998) Temporal remote sensing data and GIS application in landslide hazard zonation of part of Western Ghat, India. Int J Remote Sens 19:573–585CrossRefGoogle Scholar
  18. Okimura T, Kawatani T (1986) Mapping of the potential surface-failure sites on granite mountain slopes. In: Gardiner V (ed) Int Geomorp Part I. Wiley, New York, pp 121–138Google Scholar
  19. Ravindran KV, Philip G (1999) 29 March 1999 Chamoli earthquake: a preliminary report on earthquake-induced landslides using IRS-1C/1D data. Current Sc 77:21–25Google Scholar
  20. Saha AK, Gupta RP, Arora MK (2002) GIS-based landslide hazard zonation in the Bhagirathi (Ganga) Valley, Himalayas. Int J Remote Sens 23:357–369CrossRefGoogle Scholar
  21. Saha AK, Arora MK, Csaplovics E, Gupta RP (2004) Land cover classification using IRS LISS III imagery and DEM in a rugged terrain: a case study in Himalaya. GeoCarto Int (revised and sent)Google Scholar
  22. Sarkar S, Kanungo DP (2004) An integrated approach for landslide susceptibility mapping using remote sensing and GIS. Photo Eng Remote Sens 70:617–625Google Scholar
  23. Valdiya KS (1980) Geology of Kumaun Lesser Himalaya. Wadia Inst of Himalayan Geol, Dehra Dun, 292 ppGoogle Scholar
  24. van Westen CJ (1997) Statistical landslide hazard analysis. In: Application guide, ILWIS 2.1 for Windows. ITC, Enschede, The Netherlands, pp 73–84Google Scholar
  25. van Westen CJ (1994) GIS in landslide hazard zonation: a review, with examples from the Andes of Colombia. In: Price M, Heywood I (eds) Mountain environments and geographic information system. Taylor and Francis, Basingstoke, UK, pp 135–165Google Scholar
  26. Varnes DJ (1984) Landslide hazard zonation: a review of principles and practice. UNESCO, Paris, pp 1–63Google Scholar
  27. Welch R, Ehlers M (1987) Merging multiresolution SPOT HRV and Landsat TM data. Photo Eng Remote Sens 53:301–303Google Scholar
  28. Wieczorek GF (1984) Preparing a detailed landslide-inventory map for hazard evaluation and reduction. Bull Assoc Eng Geol 21:337–342Google Scholar
  29. Yin KL, Yan TZ (1988) Statistical prediction model for slope instability of metamorphosed rocks. In: Proceedings of 5th Int Symp on Landslides, Lausanne, Switzerland 2:1269–1272Google Scholar

Copyright information

© Springer-Verlag 2005

Authors and Affiliations

  • Ashis K. Saha
    • 1
  • Ravi P. Gupta
    • 1
  • Irene Sarkar
    • 1
  • Manoj K. Arora
    • 2
  • Elmar Csaplovics
    • 3
  1. 1.Department of Earth SciencesIndian Institute of Technology RoorkeeRoorkeeIndia
  2. 2.Department of Civil EngineeringIndian Institute of Technology RoorkeeRoorkeeIndia
  3. 3.Institute of Photogrammetry and Remote SensingDresden University of TechnologyDresdenGermany

Personalised recommendations