Mapping Landslide Susceptibility Areas and Influencing Factors in Ngororero District, Rwanda

Abstract

This research focuses on mapping landslide susceptibility areas and influencing factors in the Ngororero District of Rwanda, where landslides are a major threat to both lives and property. This region, characterised by steep terrain and heavy rainfall, faces significant risks from landslides, leading to soil erosion and community disruption. To identify areas at risk of landslides, the researcher analysed various factors influencing landslide occurrences, such as rainfall, slope, elevation, curvature, distance to river and to roads, aspect, land use and land cover, topographic wetness index (TWI) and soil texture. By combining primary data, which was gathered through field surveys and observations, and secondary data utilised, including Landsat imagery, high-resolution DEM data downloaded from the USGS website, where DEM data were used for analysing slope, elevation, aspect, curvature, topographic wetness index, and proximity to river and Landsat imagery for analysing LULC. Climatic data collected from the Rwanda Meteorology Agency was used for generating the spatial distribution of rainfall, and soil data from the Ngororero district office for generating soil texture resulted in an accurate map of the landslide susceptibility area. To effectively prioritise these factors, the Analytical Hierarchy Process (AHP), alongside Geographic Information Systems (GIS) tools and remote sensing, was employed. The results revealed that 8.7% of the area is classified as very high susceptibility to landslides, 18.1% as high, 40.4% as medium, 20% as low and 12% as very low susceptibility. This mapping helps clarify which regions are most at risk, emphasising areas where authorities and communities need to focus their land use planning and risk mitigation efforts. To improve safety and reduce landslide risks, the researcher recommends that local authorities adopt stringent planning and policies to manage human activities and settlement patterns in landslide-prone areas. This proactive approach could help stabilise slopes and minimise the detrimental impacts of future landslides on communities.