Estimating potential landslide sites of an upland sub-watershed in Western Ghat’s of Kerala (India) through frequency ratio and GIS

Vijith, H.; Madhu, G.

doi:10.1007/s00254-007-1090-2

Cited by 91 publications

(40 citation statements)

References 30 publications

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“…1). Here a SW-NE trending tectonic deformation was sealed by the Epi-Ligurian sequence (Vescovi 2002). This framework is the consequence of the EarlyMiddle Eocene tectonic phase which led to the definitive closure of the Ligurian oceanic basin.…”

Section: Main Geological Geomorphological and Climatic Featuresmentioning

confidence: 98%

Landslide failure and runout susceptibility in the upper T. Ceno valley (Northern Apennines, Italy)

et al. 2009

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The 'Conditional Analysis' multivariate statistical method was used to evaluate Landslide Susceptibility (LS) in an area of the Italian Northern Apennines. An Inventory Landslide map, containing 518 landslides, and seven landslide-related factor maps (lithology, elevation, slope angle and aspect, profile and tangential curvatures, bedding/ slope relations) were processed using a shell script that automatically carries out the whole procedure producing a final map with five Failure Susceptibility (FS) classes. The procedure was applied separately to the most frequent landslide typologies, namely, rotational slides, flows, and complex landslides, and for each of the 127 different combinations of the seven factors. To define areas potentially affected by the down-slope movement of the depleted material, four runout belts with different probabilities were distinguished around the classes with highest FS. By overlaying the resulting map with the map of the elements at risk, namely, settlements, roads, and streams, the spatial risk in the area was assessed.

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Section: Main Geological Geomorphological and Climatic Featuresmentioning

confidence: 98%

Landslide failure and runout susceptibility in the upper T. Ceno valley (Northern Apennines, Italy)

et al. 2009

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“…The plan curvature played also an important role in contributing terrain instability (Fernandes et al 2004;Vijith and Madhu 2008) as it may control the surface and subsurface hydrological regime of the slope. The curvature values represent the morphology of the topography; a positive or a negative curvature is an externally convex or concave cell, respectively, while zero indicates a flat surface.…”

Section: Landslide Predisposing Factorsmentioning

confidence: 99%

Application and validation of bivariate GIS-based landslide susceptibility assessment for the Vitravo river catchment (Calabria, south Italy)

et al. 2011

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The Calabria (Southern Italy) region is characterized by many geological hazards among which landslides, due to the geological, geomorphological, and climatic characteristics, constitute one of the major cause of significant and widespread damage. The present work aims to exploit a bivariate statistics-based approach for drafting a landslide susceptibility map in a specific scenario of the region (the Vitravo River catchment) to provide a useful and easy tool for future land planning. Landslides have been detected through air-photo interpretation and field surveys, by identifying both the landslide detachment zones (LDZ) and landslide bodies; a geospatial database of predisposing factors has been constructed using the ESRI ArcView 3.2 GIS. The landslide susceptibility has been assessed by computing the weighting values (Wi) for each class of the predisposing factors (lithology, proximity to fault and drainage line, land use, slope angle, aspect, plan curvature), thus evaluating the distribution of the landslide detachment zones within each class. The extracted predisposing factors maps have then been re-classified on the basis of the calculated weighting values (Wi) and by means of overlay processes. Finally, the landslide susceptibility map has been considered by five classes. It has been determined that a high percentage (61%) of the study area is characterized by a high to very high degree of susceptibility; clay and marly lithologies, and slope exceeding 20°in inclination would be much prone to landsliding. Furthermore, in order to ascertain the proposed landslide susceptibility estimate, a validation procedure has been carried out, by splitting the landslide detachment zones into two groups: a training and a validation set. By means of the training set, the susceptibility map has first been produced; then, it has been compared with the validation set. As a result, a great majority of LDZ-validation set (85%) would be located in highly and very highly susceptible areas. The predictive power of the model is considered reliable, since more than 50% of the LDZ fall into 20% of the most susceptible areas. The reliability of the susceptibility map is also suggested by computing the SCAI index, true positive and false positive rates; nevertheless, the most susceptible areas are overestimated. As a whole, the results indicate that landslide susceptibility assessment based on a bivariate statistics-based method in a GIS environment may be useful for land planning policy, especially when considering its cost/benefit ratio and the need of using an easy tool.

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“…One of the statistical methods available, the logistic regression method, has also been applied to landslide hazard mapping (Dai and Lee 2002;Pradhan et al 2008;Vijith and Madhu 2008). There are other methods for hazard mapping such as geotechnical method and the safety factor method (Gokceoglu et al 2000;Shou and Wang 2003;Remondo et al 2003).…”

Section: Introductionmentioning

confidence: 98%

Manifestation of remote sensing data and GIS on landslide hazard analysis using spatial-based statistical models

2009

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This paper presents landslide hazard analysis at Cameron area, Malaysia, using a geographic information system (GIS) and remote sensing data. Landslide locations were identified from interpretation of aerial photographs and field surveys. Topographical and geological data and satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. The factors chosen that influence landslide occurrence are topographic slope, topographic aspect, topographic curvature, and distance to rivers, all from the topographic database; lithology and distance to faults were taken from the geologic database; land cover from TM satellite image; the vegetation index value was taken from Landsat images; and precipitation distribution from meteorological data. Landslide hazard area was analyzed and mapped using the landslide occurrence factors by frequency ratio and bivariate logistic regression models. The results of the analysis were verified using the landslide location data and compared with the probabilistic models. The validation results showed that the frequency ratio model (accuracy is 89.25%) is better in prediction of landslide than bivariate logistic regression (accuracy is 85.73%) model.

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Estimating potential landslide sites of an upland sub-watershed in Western Ghat’s of Kerala (India) through frequency ratio and GIS

Cited by 91 publications

References 30 publications

Landslide failure and runout susceptibility in the upper T. Ceno valley (Northern Apennines, Italy)

Landslide failure and runout susceptibility in the upper T. Ceno valley (Northern Apennines, Italy)

Application and validation of bivariate GIS-based landslide susceptibility assessment for the Vitravo river catchment (Calabria, south Italy)

Manifestation of remote sensing data and GIS on landslide hazard analysis using spatial-based statistical models

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