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Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/nhess-2018-335
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-2018-335
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 03 Jan 2019

Research article | 03 Jan 2019

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This discussion paper is a preprint. It is a manuscript under review for the journal Natural Hazards and Earth System Sciences (NHESS).

Efficacy of using Radar Induced Factors in Landslide Susceptibility Analysis: case study of Koslanda, Sri Lanka

Ahangama Kankanamge Rasika Nishamanie Ranasinghe1, Ranmalee Bandara1, Udeni Gnanapriya Anuruddha Puswewala2, and Thilantha Lakmal Dammalage1 Ahangama Kankanamge Rasika Nishamanie Ranasinghe et al.
  • 1Department of Surveying and Geodesy, University of Sabaragamuwa, Belihuloya, 70140, Sri Lanka
  • 2Department of Civil Engineering, University of Moratuwa, Moratuwa, 10400, Sri Lanka

Abstract. Through recent technological developments of radar and optical remote sensing in the areas of temporal, spectral, spatial, and global coverage, the availability of such images either at a low cost or free of charge, and the advancement of tools developed in image analysis techniques and GIS for spatial data analysis, a large variety of applications using remote sensing and GIS as tools are possible. Hence, this study aims to assess the efficacy of using Radar Induced Factors (RIF) in identifying landslide susceptibility using bivariate Information Value method (InfoVal method) and multivariate Multi Criteria Decision Analysis based on the Analytic Hierarchy Process statistical analysis. Using identified landslide causative factors, four landslide prediction models as bivariate without and with RIF, multivariate without and with RIF are generated. Twelve factors topographical, hydrological, geological, land cover and soil plus three RIF are considered. The prediction levels of susceptibility regions are distinguished and categorized into four classes as very low, low, moderate, and high susceptibility to landslides. With integration of RIF, boundary detection between high and very low areas increased by 7 %, and 4 % respectively, and there is an improvement of 2.45 % prediction and 1.12 % validation performances of bivariate analysis than multivariate.

Ahangama Kankanamge Rasika Nishamanie Ranasinghe et al.
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Ahangama Kankanamge Rasika Nishamanie Ranasinghe et al.
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Short summary
Through the developments of radar and optical remote sensing in temporal, spectral, spatial, and global coverage, availability of images either at low cost or free of charge, large variety of applications are possible. This study assessed efficacy of Radar Induced Factors in identifying landslide susceptibility regions by considering the significance of radar inherent characteristics for disaster studies. With integration of radar factors, high and very low susceptibility regions are increased.
Through the developments of radar and optical remote sensing in temporal, spectral, spatial, and...
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