Landslide Hazard and Exposure Modelling in Data-Poor Regions: The Example of the Rohingya Refugee Camps in Bangladesh

Landslide hazards significantly affect economies and populations around the world, but locations where the greatest proportional losses occur are in data-poor regions where capacity to estimate and prepare for these hazards is most limited. Earth observation (EO) data can fill key knowledge gaps, and can be rapidly used in settings with lower analytical capacity. In this study, we describe a novel series of methods designed to analyze landslide susceptibility, hazard and exposure in the region in and around the Rohingya refugee camps in Bangladesh, where limited data is juxtaposed with a major humanitarian crisis. We demonstrate that a high degree of accuracy is possible even when estimating susceptibility of relatively small landslides. In the context of this example, we also explore how estimates of landslide hazard and exposure are most beneficial to decisions made by humanitarian stakeholders relevant to natural hazards and risk. The unique opportunity to work alongside humanitarian end-users has allowed us to produce focused products that can be tested while in development. In particular, we stress the importance of communicating the difference between a landslide early warning system-for which satellite data may be unsuitable at local scales-and a model that provides relative hazard estimates, where EO may be valuable. The toolbox of methods presented here could be used to generate landslide hazard and exposure maps in other data-poor regions around the globe.

Automated summary

This study focuses on analyzing landslide susceptibility, hazard, and exposure in the region around Rohingya refugee camps in Bangladesh, where limited data and a major humanitarian crisis exist. The research demonstrates high accuracy in estimating susceptibility of relatively small landslides, and emphasizes the importance of communication with humanitarian stakeholders for decision-making related to natural hazards and risk. The methods presented in this study could be applied to generate landslide hazard and exposure maps in other data-poor regions globally.