InSAR Time Series to Characterize Landslide Ground Deformations in a Tropical Urban Environment: Focus on Bukavu, East African Rift System (RD Congo)

The western branch of the East African Rift System, in Central Africa, is a region naturally prone to landslides due to factors such as heavy rainfall, tectonic activity and steep
topography. In addition, sensibility to slope instability is expected to increase in the future in response to increasing demographic pressure and land use/land cover changes.
The Rift flanks west of Lake Kivu (DRC) are one of the Congolese regions most affected by landslides. Although heavy rainfall periods and earthquakes are the main triggering
factors, nothing is known on their potential role on the current dynamics of existing landslides
Here we used InSAR time series to monitor ground deformations associated to large slow-moving landslides that continuously affect highly populated slopes in the city of
Bukavu (DRC). Bukavu is located within the Rift, on the southern shore of Lake Kivu, in a tropical environment.
Using >100 Cosmo-SkyMed SAR images, acquired between March 2015 and June 2016 with a mean revisiting time of 8 days per orbit (ascending and descending), we
produce displacement-rate maps and ground deformation time series using different techniques: Persistent Scatter (PS), Small Baseline Subset (SBAS) and Multidimensional
Small Baseline Subsets (MSBAS). The three techniques provides similar results in areas with relatively small displacements (few mm per months). However, in areas where
displacements are much higher and where coherence is lost by traditional techniques, MSBAS, that process concurrently the two satellite orbits improving temporal resolution,
is more efficient. It allows to measure higher ground deformation rates by keeping the coherence.
For one specific landslide where intense field mapping was done, the results show clearly the pattern of the deformations that divides the landslide in blocks that move with
different velocity (up to 20 cm/yr). This pattern is consistent with field observations and possibly related to the anthropic activity. Furthermore, DGPS measurements, taken at
21 benchmarks in the area during the same period, allow validating the InSAR results.
The combination of InSAR data with rainfall and seismic monitoring, and field observations should help us, when longer time-series will be available, to better understand the
mechanisms (both natural and human) that affect this landslide.