Journal cover Journal topic
Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
Nat. Hazards Earth Syst. Sci., 18, 105-124, 2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
09 Jan 2018
Field-based landslide susceptibility assessment in a data-scarce environment: the populated areas of the Rwenzori Mountains
Liesbet Jacobs1,2, Olivier Dewitte1, Jean Poesen3, John Sekajugo4, Adriano Nobile1, Mauro Rossi5, Wim Thiery6,7, and Matthieu Kervyn2 1Royal Museum for Central Africa, Department of Earth Sciences, Leuvensesteenweg 13, 3080 Tervuren, Belgium
2Vrije Universiteit Brussel, Department of Geography, Earth System Science, Pleinlaan 2, 1050 Brussels, Belgium
3KU Leuven, Division of Geography and Tourism, Celestijnenlaan 200E, 3001 Leuven, Belgium
4Busitema University, Department of Natural Resource Economics, P.O. Box 236, Tororo, Uganda
5CNR-IRPI, Geomorphology division, via Madonna Alta 126, 06128 Perugia, Italy
6ETH Zurich, Institute for Atmospheric and Climate Science, Universitaetstrasse 16, 8092 Zurich, Switzerland
7Vrije Universiteit Brussel, Department of Hydrology and Hydraulic Engineering, Pleinlaan 2, 1050 Brussels, Belgium
Abstract. The inhabited zone of the Ugandan Rwenzori Mountains is affected by landslides, frequently causing loss of life, damage to infrastructure and loss of livelihood. This area of ca. 1230 km2 is characterized by contrasting geomorphologic, climatic and lithological patterns, resulting in different landslide types. In this study, the spatial pattern of landslide susceptibility is investigated based on an extensive field inventory constructed for five representative areas within the region (153 km2) and containing over 450 landslides. To achieve a reliable susceptibility assessment, the effects of (1) using different topographic data sources and spatial resolutions and (2) changing the scale of assessment by comparing local and regional susceptibility models on the susceptibility model performances are investigated using a pixel-based logistic regression approach. Topographic data are extracted from different digital elevation models (DEMs) based on radar interferometry (SRTM and TanDEM-X) and optical stereophotogrammetry (ASTER DEM). Susceptibility models using the radar-based DEMs tend to outperform the ones using the ASTER DEM. The model spatial resolution is varied between 10, 20, 30 and 90 m. The optimal resolution depends on the location of the investigated area within the region but the lowest model resolution (90 m) rarely yields the best model performances while the highest model resolution (10 m) never results in significant increases in performance compared to the 20 m resolution. Models built for the local case studies generally have similar or better performances than the regional model and better reflect site-specific controlling factors. At the regional level the effect of distinguishing landslide types between shallow and deep-seated landslides is investigated. The separation of landslide types allows us to improve model performances for the prediction of deep-seated landslides and to better understand factors influencing the occurrence of shallow landslides such as tangent curvature and total rainfall. Finally, the landslide susceptibility assessment is overlaid with a population density map in order to identify potential landslide risk hotspots, which could direct research and policy action towards reduced landslide risk in this under-researched, landslide-prone region.

Citation: Jacobs, L., Dewitte, O., Poesen, J., Sekajugo, J., Nobile, A., Rossi, M., Thiery, W., and Kervyn, M.: Field-based landslide susceptibility assessment in a data-scarce environment: the populated areas of the Rwenzori Mountains, Nat. Hazards Earth Syst. Sci., 18, 105-124,, 2018.
Publications Copernicus
Short summary
While country-specific, continental and global susceptibility maps are increasingly available, local and regional susceptibility studies remain rare in remote and data-poor settings. Here, we provide a landslide susceptibility assessment for the inhabited region of the Rwenzori Mountains. We find that higher spatial resolutions do not necessarily lead to better models and that models built for local case studies perform better than aggregated susceptibility assessments on the regional scale.
While country-specific, continental and global susceptibility maps are increasingly available,...