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Natural Hazards

, Volume 43, Issue 2, pp 245–256 | Cite as

Use of satellite remote sensing data in the mapping of global landslide susceptibility

  • Yang Hong
  • Robert Adler
  • George Huffman
Original Paper

Abstract

Satellite remote sensing data has significant potential use in analysis of natural hazards such as landslides. Relying on the recent advances in satellite remote sensing and geographic information system (GIS) techniques, this paper aims to map landslide susceptibility over most of the globe using a GIS-based weighted linear combination method. First, six relevant landslide-controlling factors are derived from geospatial remote sensing data and coded into a GIS system. Next, continuous susceptibility values from low to high are assigned to each of the six factors. Second, a continuous scale of a global landslide susceptibility index is derived using GIS weighted linear combination based on each factor’s relative significance to the process of landslide occurrence (e.g., slope is the most important factor, soil types and soil texture are also primary-level parameters, while elevation, land cover types, and drainage density are secondary in importance). Finally, the continuous index map is further classified into six susceptibility categories. Results show the hot spots of landslide-prone regions include the Pacific Rim, the Himalayas and South Asia, Rocky Mountains, Appalachian Mountains, Alps, and parts of the Middle East and Africa. India, China, Nepal, Japan, the USA, and Peru are shown to have landslide-prone areas. This first-cut global landslide susceptibility map forms a starting point to provide a global view of landslide risks and may be used in conjunction with satellite-based precipitation information to potentially detect areas with significant landslide potential due to heavy rainfall.

Keywords

Satellite remote sensing Landslide susceptibility GIS 

Notes

Acknowledgements

This research is supported by NASA’s Applied Sciences program under Steven Ambrose of NASA Headquarters.

References

  1. Anbalagan R (1992) Landslide hazard evaluation and zonation mapping in mountainous terrain. Eng Geol 32:269–277CrossRefGoogle Scholar
  2. 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 Prefecure, Japan. Landslide 1:73–81CrossRefGoogle Scholar
  3. Baum RL, Savage WZ, Godt JW (2002) TRIGRS – A Fortran program for transient rainfall infiltration and grid-based regional slope-stability analysis: U.S. Geological Survey Open-File Report 02-0424, 64 pp. http://pubs.usgs.gov/of/2002/ofr-02-424/
  4. Caine N (1980) The rainfall intensity-duration control of shallow landslides and debris flows. Geografiska Annaler 62A:23–27CrossRefGoogle Scholar
  5. Carrara A, Cardinali M, Detti R, Guzzetti F, Pasqui V, Reichenbach P (1991) GIS techniques and statistical models in evaluating landslide hazard. Earth Surf Proc Land 16:427– 445CrossRefGoogle Scholar
  6. Coe JA, Godt JW, Baum RL, Bucknam RC, Michael JA (2004) Landslide susceptibility from topography in Guatemala. In: Lacerda et al. (eds) Landslides evaluation and stabilization. Taylor and Francis Group, London, pp 69–78Google Scholar
  7. Dai FC, Lee CF (2002) Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong. Geomorphology 42:213–238CrossRefGoogle Scholar
  8. Davis JC (1986) Statistics and data analysis in geology. John Wiley & Sons, New YorkGoogle Scholar
  9. Fabbri AG, Chung CF, Cendrero A, Remondo J (2003) Is prediction of future landslides possible with GIS? Nat Hazards 30:487–499CrossRefGoogle Scholar
  10. Farr T, Kobrick M (2000) Shuttle Radar Topography Mission produces a wealth of data, Eos Trans. AGU 81:583–585Google Scholar
  11. Fernandez T, Irigaray C, El Hamdouni R, Chacon J (2003) Methodology for landslide susceptibility mapping by means of a GIS, application to the contraviesa area (Granada, Spain). Nat Hazards 30:297–308CrossRefGoogle Scholar
  12. Friedl MA, McIver DK, Hodges JCF, Zhang XY, Muchoney D, Strahler AH, Woodcock CE, Gopal S, Schneider A, Cooper A, Baccini A, Gao F, Schaaf C (2002) Global land cover mapping from MODIS: algorithms and early results. Remote Sens Environ 83(1–2):287–302Google Scholar
  13. Godt J (2004) Observed and Modeled conditions for shallow landslide in the Seattle, Washington area. PhD dissertation University of Colorado, Boulder, COGoogle Scholar
  14. Guzzetti F, Carrara A, Cardinali M, Reichenbach P (1999) Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology 31:181–216CrossRefGoogle Scholar
  15. Huffman GJ, Adler RF, Bolvin DT, Gu G, Nelkin EJ, Bowman KP, Hong Y, Stocker EF, Wolff DB (2006) The TRMM Multi-satellite Precipitation Analysis: Quasi-Global, Multi-Year, Combined-Sensor Precipitation Estimates at Fine Scale. J. Hydrometeor., acceptedGoogle Scholar
  16. Larsen MC, Simon A (1993) A rainfall intensity-duration threshold for landslides in a humid-tropical environment, Puerto Rico. Geografiska Annaler 75A:13–23CrossRefGoogle Scholar
  17. Larsen MC, Torres Sanchez AJ (1998) The frequency and distribution of recent landslides in three montane tropical regions of Puerto Rico. Geomorphology 24:309–331CrossRefGoogle Scholar
  18. Lee S, Min K (2001) Statistical analysis of landslide susceptibility at Yongin, Korea. Environ Geol 40:1095–1113CrossRefGoogle Scholar
  19. 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
  20. Saha AK, Gupta RP, Arora MK (2002) GIS-based landslide hazard zonation in the Bagirathi (Ganga) Valley, Himalayas. Int J Remote Sens 23(2):357–369CrossRefGoogle Scholar
  21. Saha AK, Gupta RP, Sarkar I, Arora MK, Csaplovics E (2005) An approach for GIS-based statistical landslide susceptibility zonation—with a case study in the Himalayas. Landslides 2:61–69CrossRefGoogle Scholar
  22. Sidle RC, Ochiai H (2006) Landslide processes, prediction, land use. American Geophysical Union, Washington, DC, pp 1–312Google Scholar

Copyright information

© Springer Science+Business Media, Inc. 2007

Authors and Affiliations

  1. 1.Goddard Earth and Science Technology CenterUniversity of Maryland Baltimore CountyBaltimoreUSA
  2. 2.NASA Goddard Space Flight CenterLaboratory for AtmospheresGreenbeltUSA
  3. 3.Science System Application Inc.GreenbeltUSA

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