Event-based landslide susceptibility mapping using weights of evidence (WoE) and modified frequency ratio (MFR) model: a case study of Rangamati district in Bangladesh

Abstract

Landslide hazard of 2017 in Rangamati district had devastating impacts on development, thereby making landslide susceptibility mapping a prerequisite for disaster risk management. This study aims to map the future landslide susceptible areas by overlaying the landslide inventory of 2017 with causative factor maps using WoE and MFR and compare their results to determine that statistical model describes the susceptibility of the landslide occurrence better than the other. The analysis shows that although both models define the spatial relationship of past landslides with the triggering factors in a same way but in case of mapping, MFR had overestimated the high and low susceptible areas and underestimated the moderate susceptible areas than WoE. When validated from success rate curve by plotting the percentage of landslide susceptibility index rank against the percentage of cumulative landslide occurrence, it shows that the WoE model describes the landslides better than the MFR model. About 20% of the high susceptible areas include 85% of the total landslide area in case of the WoE model but the MFR model includes only 20%. On the other hand, the WoE model describes that 30% highly susceptible area covers more than 99% of the total landslide area while MFR defines only 78%.

Remote Sensing-Based Research for Monitoring Progress towards SDG 15 in Bangladesh: A Review

“…We found that out of seven studies only one study produced landslide inventory for the entire CHA [72]. Other studies focused on creating LSMs for the two largest urban parts of the CHA-Chittagong Metropolitan Area (CMA) [73,74] and Cox's Bazar municipality [75], as well as for landslide prone Rangamati district [76]. Landsat TM and OLI images Methodologically, many of these studies used remote sensing products as supporting tools to identify land degradation prone areas and calculate land loss or gain (e.g., [33,64,65].…”

“…Other studies focused on creating LSMs for the two largest urban parts of the CHA-Chittagong Metropolitan Area (CMA) [73,74] and Cox's Bazar municipality [75], as well as for landslide prone Rangamati district [76]. Landsat TM and OLI images and MLC approach were used for mapping land use land cover change and developing indices (i.e., NDVI) [73,77], while Advanced Spaceborn Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM) (30 m) and ALOS-PALSAR DEM (12.5 m) were used for preparing topographic causal factors such as slope, aspect, profile and plan curvature using the GIS platform [76,78]. In these studies, study areas and methods for creating LSMs were different, so it is difficult to say whether the use of different remote sensing products affected the prediction of the LSMs.…”

“…Landslide removes vegetation and the regrowth rate of vegetation is usually very high in the affected areas. Therefore, detecting landslides using automated techniques in moderate resolution (30 m) satellite images is difficult [72,76]. We found that only two studies attempted to use remote sensing methods [72,76], while other studies used field mapping for landslide inventory generation.…”

“…Therefore, detecting landslides using automated techniques in moderate resolution (30 m) satellite images is difficult [72,76]. We found that only two studies attempted to use remote sensing methods [72,76], while other studies used field mapping for landslide inventory generation. Rabby and Li (2019) integrated field mapping and remote sensing-based Google Earth mapping to map 730 landslides over the whole CHA [72].…”

Landslide Inventory (2001–2017) of Chittagong Hilly Areas, Bangladesh

“…Landslide susceptibility mapping in some parts of this area has already been undertaken [8][9][10]. In these works, researchers have generated landslide inventories using field mapping, visual interpretation, and automatic recognition of landslides from satellite images [10,11]. Sifa et al [11] and Comprehensive Disaster Management Plan (CDMP) Phase_II [12] have published landslide inventories in three cities in this area: Cox's Bazar, the Teknaf municipalities, and the Chittagong Metropolitan Area (CMA).…”