Monitoring recent activity of the Koytash Landslide (Kyrgyzstan) using radar and optical remote sensing techniques

Landslides are recurrent in most mountainous areas of the world where they frequently have catastrophic consequences. Around the Fergana Basin and in the Maily-Say Valley (Kyrgyzstan), landslides are often reactivated due to intense rainfalls, especially during spring, and as a consequence of the high seismicity characterizing the region. In spring 2017, Kyrgyzstan suffered a massive activation event which caused 160 emergency situations, including the reactivation of Koytash, one of the largest deep-seated mass movements of the Maily-Say area. In this region, risks related to landslides are accentuated by the presence of uranium tailings, remnants of the former nuclear mining activity. In this study, we used multiple satellite remote sensing techniques to highlight deformation zones and identify displacements prior to the collapse of Koytash. The comparison of multi-temporal digital elevation models (DEMs; satellite and UAV-based) enabled us to highlight areas of depletion and accumulation, in the scarp and foothill zones respectively. A differential synthetic aperture radar interferometry (D-InSAR) analysis and the computation of deformation time series allowed us to identify slope displacements and estimate the evolution of the displacement rates over time. This analysis identified slow displacements during the months preceding the reactivation, indicating the long-term sliding activity of Koytash, well before the reactivation in April 2017. This was confirmed by the computation of deformation time series, showing a positive velocity anomaly on the upper part of Koytash. Furthermore, the use of optical imagery, through the difference of NDVIs (Normalized Difference Vegetation Index), revealed landcover changes associated to the sliding process. In addition to remote sensing techniques, we performed a meteorological analysis to identify the conditions that triggered the massive failure of Koytash. In-situ data from a local station highlighted the important contribution of precipitations as a trigger of the landslide movement. Indeed, despite a relative decrease in annual rainfall in 2017 compared to the previous years, the month of April 2017 was characterised by heavy rains, including a major peak of rainfall the day of Koytash’s failure. The multidirectional approach used in this study, demonstrated the efficiency of using multiple remote sensing techniques, combined to a meteorological analysis, to identify triggering factors and monitor the activity of landslides.