Use of satellite remote sensing data in the mapping of global landslide susceptibility
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.
KeywordsSatellite remote sensing Landslide susceptibility GIS
This research is supported by NASA’s Applied Sciences program under Steven Ambrose of NASA Headquarters.
- 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/
- 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
- Davis JC (1986) Statistics and data analysis in geology. John Wiley & Sons, New YorkGoogle Scholar
- Farr T, Kobrick M (2000) Shuttle Radar Topography Mission produces a wealth of data, Eos Trans. AGU 81:583–585Google Scholar
- 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
- Godt J (2004) Observed and Modeled conditions for shallow landslide in the Seattle, Washington area. PhD dissertation University of Colorado, Boulder, COGoogle Scholar
- 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
- 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
- Sidle RC, Ochiai H (2006) Landslide processes, prediction, land use. American Geophysical Union, Washington, DC, pp 1–312Google Scholar