Ahangama Kankanamge Rasika Nishamanie Ranasinghe
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Ranmalee Bandara
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Udeni Gnanapriya Anuruddha Puswewala
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Thilantha Lakmal Dammalage
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.
A Low-Cost Optical Remote Sensing Application for Glacier Deformation Monitoring in an Alpine Environment
