Répertoire du personnel
Axel Deijns
Sciences de la Terre
Risques naturels et Cartographie
Risques naturels et Cartographie
Détails
Niyokwiringirwa, P., Lombardo, L., Dewitte, O., Deijns, AAJ., Wang, N., van Westen, C. & Tanyas, H. 2024. ‘Event-based rainfall-induced landslide inventories and rainfall thresholds for Malawi’. Landslides. DOI: https://doi.org/10.1007/s10346-023-02203-7. I.F. 6.7.
Article dans une revue scientifique / Article dans un périodique
Landslide event inventories are one of the most critical
datasets to increase knowledge on landslide occurrences. However,
they are rarely available in various regions, especially in countries
of the Global South. This study aims to generate rainfall-induced
landslide event inventories and define the rainfall thresholds
responsible for landslide occurrence at the national scale of Malawi,
Africa. We mainly followed a three-step methodology to generate
landslide inventories. First, we went through media reports to
identify documented landslide events. Second, we used Sentinel-2
images to identify possible areas affected by landslides using automated
change detection algorithms based on vegetation indices.
Third, we manually went through optical images provided by Planet
Lab and Google Earth and mapped landslides via visual image
interpretation. Overall, we mapped 27 rainfall-induced landslide
inventories between 2003 and 2022, with a total of 4709 individual
landslides. We then analysed the Malawian terrain and identified
two different landscape clusters (i.e. Cluster 1 and Cluster 2) showing
similar morphometric and climatic conditions. Ultimately, we
calculated the rainfall threshold for each landscape cluster. The
minimum rainfall amounts responsible for landsliding correspond
to 66 mm/two-day and 51 mm/day in Clusters 1 and 2, respectively.
In this context, our paper not only presents and shares the first
national-scale, digital rainfall-induced landslide event inventory
database of Malawi but also suitable rainfall thresholds to be potentially
exploited for a national scale landslide early warning system.
A similar framework could be applied to generate landslide inventories
for other data scarce regions.