Remote sensing and GIS-based landslide susceptibility mapping using frequency ratio and analytical hierarchy methods in Rize province (NE Turkey)


Reis S., Yalcin A., Atasoy M., Nisanci R., Bayrak T., Erduran M. , ...Daha Fazla

ENVIRONMENTAL EARTH SCIENCES, cilt.66, ss.2063-2073, 2012 (SCI İndekslerine Giren Dergi) identifier identifier

  • Cilt numarası: 66 Konu: 7
  • Basım Tarihi: 2012
  • Doi Numarası: 10.1007/s12665-011-1432-y
  • Dergi Adı: ENVIRONMENTAL EARTH SCIENCES
  • Sayfa Sayıları: ss.2063-2073

Özet

The northeast part of Turkey is prone to landslides because of the climatic conditions, as well as geologic and geomorphologic characteristics of the region. Especially, frequent landslides in the Rize province often result in significant damage to people and property. Therefore, in order to mitigate the damage from landslides and help the planners in selecting suitable locations for implementing development projects, especially in large areas, it is necessary to scientifically assess susceptible areas. In this study, the frequency ratio method and the analytical hierarchy process (AHP) were used to produce susceptibility maps. Especially, AHP gives best results because of allowing better structuring of various components, including both objective and subjective aspects and comparing them by a logical and thorough method, which involves a matrix-based pairwise comparison of the contribution of different factors for landslide. For this purpose, lithology, slope angle, slope aspect, land cover, distance to stream, drainage density, and distance to road were considered as landslide causal factors for the study area. The processing of multi-geodata sets was carried out in a raster GIS environment. Lithology was derived from the geological database and additional field studies; slope angle, slope aspect, distance to stream, distance to road and drainage density were invented from digital elevation models; land cover was produced from remote sensing imagery. In the end of study, the results of the analysis were verified using actual landslide location data. The validation results showed satisfactory agreement between the susceptibility map and the existing data on landslide locations.