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

Abstract

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.

Assessing Soil Erosion Hazards Using Land-Use Change and Landslide Frequency Ratio Method: A Case Study of Sabaragamuwa Province, Sri Lanka

“…Most of these studies applied the RUSLE model for soil erosion assessments in different watersheds i.e., Kotmale watershed [33], Kelani river basin [12], Kiridi Oya river basin [36], and Kalu ganga river basin [37]. The landslide susceptibility assessments have been explored and suggested the spatial prediction of landslides using geo-informatics technology, i.e., logistic regression [32,38], analytic hierarchy process [39,40], entropy method [31], and spatial multi criteria evaluation [39]. Although numerous factors such as slope, elevation, aspect, profile curvature, topography were evaluated, rainfall and land-use change were recognized as a significant contribution on landslide initiation in Central Highlands of Sri Lanka [29,32,41].…”

Spatial understanding of historical and future landslide variation in Africa

“…The distribution of African soil texture and its classes is provided in Fig.7 (b).Soil moistureThe soil moisture indicates the water content in the soil and is among the major parameters for land susceptibility analysis. Although several studies utilize the insitu measurement while estimating the surface soil moisture, it is cost and time consuming mainly when the study area is large(Ranasinghe et al 2019;Wicki et al 2020). This fact is recognized by authors and then the available shape le form the Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC)(Ross et al 2018) is utilized.…”