GIS-Based Analysis of Landslides Susceptibility Mapping: The Case Study of Lushoto District, North Eastern Tanzania.

Abstract

Landslides are becoming increasingly widespread, claiming tens of thousands of fatalities, hundreds of thousands of injuries, and billions of dollars in economic losses each year. Thus, studies for geographically locating landslides vulnerable areas have been increasingly relevant in recent decades. This research is aimed at integrating Geographical Information Systems (GIS) and Remote Sensing (RS) techniques to delineate Landslide Susceptibility Mapping (LSM) of Lushoto District, Tanzania. RS assisted in providing remotely datasets including; Digital Elevation Models (DEM), Landsat 8 OLI imageries, and spatially distributed landslides coordinates with the use of a handheld Global Position System (GPS) receiver while various GIS analysis techniques were used in the preparation and analysis of landslides influencing factors hence, generating LSM index values. However, rainfall, slope’s angle, elevation, soil type, lithology, proximity to roads, rivers, faults, and Normalized Difference Vegetation Index (NDVI) factors were found to have direct influence on the occurrence of landslides. These factors were evaluated, weighted, and ranked using Analytical Hierarchy Process (AHP) technique in which 0.086 (8.6%) consistency ratio (CR) was attained (highly accepted). Findings reveal that, rainfall (29.97%), slopes’ angle (21.72%), elevation (15.68%), and soil types (11.77%) were found to have high influence on the occurrence of landslides while proximity to faults (8.35%), lithology (4.94%), proximity to roads (3.41%), rivers (2.48%) and NDVI (1.69%) had very low influences respectively. The overall results, obtained through Weighted Linear Combination (WLC) analysis indicate that, about 97669.65 hectares (ha) of the land is under very low landslides susceptibility levels which accounts for 24.03% of the total study area. Low susceptibility levels had 123105.84 ha (30.28%) moderate landslides susceptibility areas were found to have 140264.79 ha (34.50%) while high and very high susceptibility areas were found to cover about 45423.43 ha (11.17%) and 57.78 ha (0.01%) respectively. Furthermore, 81% overall model accuracy was obtained as computed from Area under the Curve (AUC) using Receiver Operating Characteristic (ROC) Curve.