A new approach in the development and analysis of the landslide susceptibility map of the hillslopes of Bujumbura, Burundi
Abstract
As in other hilly and mountainous regions of the world, the hillslopes of Bujumbura are prone to landslides. In this area, landslides impact human lives and infrastructures. Despite the high landslide-induced damages, slope instabilities are less investigated. The aim of this research is to assess the landslide susceptibility using a probabilistic/statistical data modeling approach for predicting the initiation of future landslides. A spatial landslide inventory with their physical characteristics through interpretation of high-resolution optic imageries/aerial photos and intensive fieldwork are carried out. Base on in-depth field knowledge and green literature, let’s select potential landslide conditioning factors. A landslide inventory map with 568 landslides is produced. Out of the total of 568 landslide sites, 50 % of the data taken before the 2000s is used for training and the remaining 50 % (post-2000 events) were used for validation purposes. A landslide susceptibility map with an efficiency of 76 % to predict future slope failures is generated. The main landslides controlling factors in ascendant order are the density of drainage networks, the land use/cover, the lithology, the fault density, the slope angle, the curvature, the elevation, and the slope aspect. The causes of landslides support former regional studies which state that in the region, landslides are related to the geology with the high rapid weathering process in tropical environments, topography, and geodynamics. The susceptibility map will be a powerful decision-making tool for drawing up appropriate development plans in the hillslopes of Bujumbura with high demographic exposure. Such an approach will make it possible to mitigate the socio-economic impacts due to these land instabilities
Downloads
References
Broeckx, J., Vanmaercke, M., Duchateau, R., Poesen, J. (2018). A data-based landslide susceptibility map of Africa. Earth-Science Reviews, 185, 102–121. doi: https://doi.org/10.1016/j.earscirev.2018.05.002
Monsieurs, E., Kirschbaum, D., Thiery, W., van Lipzig, N., Kervyn, M., Demoulin, A. et. al. (2017). Constraints on Landslide-Climate Research Imposed by the Reality of Fieldwork in Central Africa. 3rd North American Symposium on Landslides, 158–168. Available at: https://orbi.uliege.be/handle/2268/213093
Dille, A., Kervyn, F., Mugaruka Bibentyo, T., Delvaux, D., Ganza, G. B., Ilombe Mawe, G. et. al. (2019). Causes and triggers of deep-seated hillslope instability in the tropics – Insights from a 60-year record of Ikoma landslide (DR Congo). Geomorphology, 345, 106835. doi: https://doi.org/10.1016/j.geomorph.2019.106835
Dewitte, O., Dille, A., Depicker, A., Kubwimana, D., Maki Mateso, J.-C., Mugaruka Bibentyo, T. et. al. (2020). Constraining landslide timing in a data-scarce context: from recent to very old processes in the tropical environment of the North Tanganyika-Kivu Rift region. Landslides, 18 (1), 161–177. doi: https://doi.org/10.1007/s10346-020-01452-0
Jacobs, L., Dewitte, O., Poesen, J., Maes, J., Mertens, K., Sekajugo, J., Kervyn, M. (2017). Landslide characteristics and spatial distribution in the Rwenzori Mountains, Uganda. Journal of African Earth Sciences, 134, 917–930. doi: https://doi.org/10.1016/j.jafrearsci.2016.05.013
Maes, J., Kervyn, M., de Hontheim, A., Dewitte, O., Jacobs, L., Mertens, K. et. al. (2017). Landslide risk reduction measures: A review of practices and challenges for the tropics. Progress in Physical Geography: Earth and Environment, 41 (2), 191–221. doi: https://doi.org/10.1177/0309133316689344
Wilson, A. M., Jetz, W. (2016). Remotely Sensed High-Resolution Global Cloud Dynamics for Predicting Ecosystem and Biodiversity Distributions. PLOS Biology, 14 (3), e1002415. doi: https://doi.org/10.1371/journal.pbio.1002415
Nobile, A., Dille, A., Monsieurs, E., Basimike, J., Bibentyo, T., d’ Oreye, N. et. al. (2018). Multi-Temporal DInSAR to Characterise Landslide Ground Deformations in a Tropical Urban Environment: Focus on Bukavu (DR Congo). Remote Sensing, 10 (4), 626. doi: https://doi.org/10.3390/rs10040626
Robinson, T. R., Rosser, N., Walters, R. J. (2019). The Spatial and Temporal Influence of Cloud Cover on Satellite-Based Emergency Mapping of Earthquake Disasters. Scientific Reports, 9 (1). doi: https://doi.org/10.1038/s41598-019-49008-0
Depicker, A., Govers, G., Jacobs, L., Campforts, B., Uwihirwe, J., Dewitte, O. (2020). Interactions between deforestation, landscape rejuvenation, and shallow landslides in the North Tanganyika – Kivu Rift region, Africa. Earth Surface Dynamics Discussions. doi: https://doi.org/10.5194/esurf-2020-87
Nibigira, L., Draidia, S., Havenith, H.-B. (2015). GIS-Based Landslide Susceptibility Mapping in the Great Lakes Region of Africa, Case Study of Bujumbura Burundi. Engineering Geology for Society and Territory - Volume 2, 985–988. doi: https://doi.org/10.1007/978-3-319-09057-3_172
Jacobs, L., Dewitte, O., Poesen, J., Sekajugo, J., Nobile, A., Rossi, M. et. al. (2018). Field-based landslide susceptibility assessment in a data-scarce environment: the populated areas of the Rwenzori Mountains. Natural Hazards and Earth System Sciences, 18 (1), 105–124. doi: https://doi.org/10.5194/nhess-18-105-2018
Désiré, K., Lahsen, A. B., Mahfoud, B., Olivier, D., Abdellah, A., Tarik, B. (2018). Landslides susceptibility assessment using AHP method in Kanyosha watershed (Bujumbura-Burundi): Urbanisation and management impacts. MATEC Web of Conferences, 149, 02071. doi: https://doi.org/10.1051/matecconf/201814902071
Moeyersons, J., Trefois, P. (2012). La protection du site CURGO - Kabezi -Bujumbura contre les risques hydrologiques dans le bassin de la rivière Nyabage: Etude de formulation d'une operation de conservation. MRAC Tervuren. Available at: https://repository.tudelft.nl/islandora/object/uuid:3e6a844d-e90e-4234-b83e-6215846ca652?collection=research
Delvaux, D., Mulumba, J.-L., Sebagenzi, M. N. S., Bondo, S. F., Kervyn, F., Havenith, H.-B. (2017). Seismic hazard assessment of the Kivu rift segment based on a new seismotectonic zonation model (western branch, East African Rift system). Journal of African Earth Sciences, 134, 831–855. doi: https://doi.org/10.1016/j.jafrearsci.2016.10.004
Van Westen, C. (2007). Introduction to landslides Part 1: Types and causes. International Institute for Aerospace Survey and Earth Sciences (ITC). Enschede. Available at: https://ru.scribd.com/document/333441109/Landslides-Types-and-Causes#
Hungr, O., Leroueil, S., Picarelli, L. (2014). The Varnes classification of landslide types, an update. Landslides, 11 (2), 167–194. doi: https://doi.org/10.1007/s10346-013-0436-y
Soeters, R., Van Westen, C. J. (1996). Slope instability recognition, analysis, and zonation. National Academy Press, 129–177. Available at: https://research.utwente.nl/en/publications/slope-instability-recognition-analysis-and-zonation
Pradhan, B., Lee, S. (2010). Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling. Environmental Modelling & Software, 25 (6), 747–759. doi: https://doi.org/10.1016/j.envsoft.2009.10.016
Iovine, G. G. R., Greco, R., Gariano, S. L., Pellegrino, A. D., Terranova, O. G. (2014). Shallow-landslide susceptibility in the Costa Viola mountain ridge (southern Calabria, Italy) with considerations on the role of causal factors. Natural Hazards, 73 (1), 111–136. doi: https://doi.org/10.1007/s11069-014-1129-0
Sawatzky, D., Raines, G., Bonham-Carter, G. (2010). Spatial Data Modeller. Available at: https://www.ige.unicamp.br/sdm/ArcSDM10/source/ReadMe.pdf
Sidle, R. C., Bogaard, T. A. (2016). Dynamic earth system and ecological controls of rainfall-initiated landslides. Earth-Science Reviews, 159, 275–291. doi: https://doi.org/10.1016/j.earscirev.2016.05.013
Copyright (c) 2021 Desire Kubwimana, Lahsen Ait Brahim, Abdellah Abdelouafi
This work is licensed under a Creative Commons Attribution 4.0 International License.
Our journal abides by the Creative Commons CC BY copyright rights and permissions for open access journals.
Authors, who are published in this journal, agree to the following conditions:
1. The authors reserve the right to authorship of the work and pass the first publication right of this work to the journal under the terms of a Creative Commons CC BY, which allows others to freely distribute the published research with the obligatory reference to the authors of the original work and the first publication of the work in this journal.
2. The authors have the right to conclude separate supplement agreements that relate to non-exclusive work distribution in the form in which it has been published by the journal (for example, to upload the work to the online storage of the journal or publish it as part of a monograph), provided that the reference to the first publication of the work in this journal is included.
