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.
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References
Anbalagan R (1992) Landslide hazard evaluation and zonation mapping in mountainous terrain. Eng Geol 32:269–277
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 1:73–81
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/
Caine N (1980) The rainfall intensity-duration control of shallow landslides and debris flows. Geografiska Annaler 62A:23–27
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– 445
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 69–78
Dai FC, Lee CF (2002) Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong. Geomorphology 42:213–238
Davis JC (1986) Statistics and data analysis in geology. John Wiley & Sons, New York
Fabbri AG, Chung CF, Cendrero A, Remondo J (2003) Is prediction of future landslides possible with GIS? Nat Hazards 30:487–499
Farr T, Kobrick M (2000) Shuttle Radar Topography Mission produces a wealth of data, Eos Trans. AGU 81:583–585
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 30:297–308
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):287–302
Godt J (2004) Observed and Modeled conditions for shallow landslide in the Seattle, Washington area. PhD dissertation University of Colorado, Boulder, CO
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 31:181–216
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., accepted
Larsen MC, Simon A (1993) A rainfall intensity-duration threshold for landslides in a humid-tropical environment, Puerto Rico. Geografiska Annaler 75A:13–23
Larsen MC, Torres Sanchez AJ (1998) The frequency and distribution of recent landslides in three montane tropical regions of Puerto Rico. Geomorphology 24:309–331
Lee S, Min K (2001) Statistical analysis of landslide susceptibility at Yongin, Korea. Environ Geol 40:1095–1113
Sarkar S, Kanungo DP (2004) An integrated approach for landslide susceptibility mapping using remote sensing and GIS. Photo Eng Remote Sens 70:617–625
Saha AK, Gupta RP, Arora MK (2002) GIS-based landslide hazard zonation in the Bagirathi (Ganga) Valley, Himalayas. Int J Remote Sens 23(2):357–369
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 2:61–69
Sidle RC, Ochiai H (2006) Landslide processes, prediction, land use. American Geophysical Union, Washington, DC, pp 1–312
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This research is supported by NASA’s Applied Sciences program under Steven Ambrose of NASA Headquarters.
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Hong, Y., Adler, R. & Huffman, G. Use of satellite remote sensing data in the mapping of global landslide susceptibility. Nat Hazards 43, 245–256 (2007). https://doi.org/10.1007/s11069-006-9104-z
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DOI: https://doi.org/10.1007/s11069-006-9104-z