A Multi-sensOr approach to characterize ground Displacements in Urban Sprawling contexts

Ground deformations in urban areas are environmental constraints that need to be considered in planning and risk management. Characterization of such deformations requires combination of several techniques, adapted to the deformation dynamics and urban context. Here, the city of Bukavu (DR Congo) is chosen as experimental test site for comparing and integrating deformation monitoring by multiple sensors and techniques. This rapidly expanding city is set in a landslide-prone environment. In the mother-RESIST project, the InSAR PSI, SBAS and MSBAS techniques provided promising preliminary results on landslide displacements within Bukavu. Here, in the framework of a PhD research, we will continue using these techniques with additional image acquisitions allowing us to extent deformation time series, in combination with techniques using optical imageries. Satellite COSMO-SkyMED, Sentinel-1, Pléiades and SPOT-6, SPOT-7 data will be used in combination. Targeted ground-based stereo time-lapse photogrammetry, UAV, ground-based LiDAR and repeated DGPS measurement campaigns using the RESIST GPS reference network will provide topographic data at higher spatial and temporal resolutions to complement these time series and validate displacement results. Image correlation methods will be applied on both SAR (amplitude images) and optical data (high-resolution satellite, time-lapse and UAV images). The time series will be complemented with old aerial photographs (digital photogrammetry) to assess the changes over the last 60 years, enabling to compare long- and short-term trends with climatic and land use change data. All these dynamics data, together with high topographic elevation models derived from optical data and LiDAR will be used to better characterize the landslide mechanisms and forecast the evolution of hazard in space and time. We expect the comparison of methods to provide insights into the most suitable (combination of) techniques according to the landslide type, dynamics and environmental context.


2017 2020

External collaborators:

Université de Strasbourg