Effectiveness of Remote Sensing and GIS-Based Landslide Susceptibility Zonation Mapping Using Information Value Method

<p><strong>Abstract.</strong> Landslides are very common problem in hilly terrain. Chamoli region of Himalaya is highest sensitive zone of the landslide hazards. The purpose of Chamoli landslide study, to observe the important terrain factors and parameters responsible for landslide initiation. Lithological, geomorphological, slope, aspect, landslide, drainage density and lineament density map generated in remote sensing and GIS environment. Data information of related geological terrain obtain through topographic maps, remote sensing images, field visits and geological maps. Geodatabases of all thematic layers prepared through digitization of topographic map and satellite imageries (LISS-III, LISS-IV &amp; ASTER DEM). Integrated all thematic layers applying information value method under GIS environment to map the zonation of landslide hazard zonation map validation and verification completed by field visit. The landslide hazard zonation map classified in four classes very high, high, medium and low.</p>

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Efficacy of using radar-derived factors in landslide susceptibility analysis: case study of Koslanda, Sri Lanka

Natural Hazards and Earth System Science ◽

10.5194/nhess-19-1881-2019 ◽

2019 ◽

Vol 19 (8) ◽

pp. 1881-1893 ◽

Cited By ~ 1

Author(s):

Ahangama Kankanamge Rasika Nishamanie Ranasinghe ◽

Ranmalee Bandara ◽

Udeni Gnanapriya Anuruddha Puswewala ◽

Thilantha Lakmal Dammalage

Keyword(s):

Remote Sensing ◽

Spatial Data ◽

Landslide Susceptibility ◽

Prediction Models ◽

Low Cost ◽

Information Value ◽

Spatial Data Analysis ◽

Optical Remote Sensing ◽

Analytic Hierarchy ◽

Technological Developments

Abstract. Through the recent technological developments of radar and optical remote sensing in (i) the areas of temporal, spectral, spatial, and global coverage; (ii) the availability of such images either at a low cost or free of charge; and (iii) the advancement of tools developed in image analysis techniques and GIS for spatial data analysis, there is a vast potential for landslide studies using remote sensing and GIS as tools. Hence, this study aimed to assess the efficacy of using radar-derived factors (RDFs) in identifying landslide susceptibility using the bivariate information value method (InfoVal method) and the multivariate multi-criteria decision analysis based on the analytic hierarchy process statistical analysis. Using identified landslide causative factors, four landslide prediction models – bivariate with and without RDFs as well as multivariate with and without RDFs – were generated. Twelve factors such as topographical, hydrological, geological, land cover and soil plus three RDFs are considered. The weight of index for landslide susceptibility is calculated by using the landslide failure map, and susceptibility regions are categorized into four classes as very low, low, moderate, and high susceptibility to landslides. With the integration of RDFs, boundary detection between high- and very-low-susceptibility regions are increased by 7 % and 4 % respectively.

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