Mailuu-Suu is a former uranium mining area in Kyrgyzstan (Central Asia) at the northern border of the Fergana Basin. This region is particularly prone to landslide hazards and, during the last 50 years, has experienced severe landslide disasters in the vicinity of numerous nuclear waste tailing dams. Due to its critical situation, the Mailuu-Suu region was and still is the target area for several risk assessment projects. This paper provides a brief review of previous studies, past landslide events and a discussion on possible future risk scenarios. Various aspects of landslide hazard and related impacts in the Mailuu-Suu Valley are analyzed in detail: landslide susceptibility, historical evolution of landslide activity, size-frequency relationship, river damming and flooding as well as impacts on inhabited areas and nuclear waste storage zones. The study was carried out with standard remote sensing tools for the processing of satellite imagery and the construction of digital elevation models (DEMs). The processed inputs were combined on a GIS platform with digital landslide distribution maps of 1962, 1977, and 2003, digitized geological and geographic maps, and information from landslide monitoring and geophysical investigation.As a result, various types of landslide susceptibility maps based on conditional analysis (CA) are presented as well as predictions of future landslide activity and related damming potential and their possible impact on the population. For some risk scenarios, remediation and prevention measures are suggested.

S U M M A R YThe presence of massive Quaternary loess units at the eastern border of the Fergana Basin (Kyrgyzstan, Central Asia) makes this area particularly prone to the development of catastrophic loess earthflows, causing damages and injuries almost every year. Efficient disaster management requires a good understanding of the main causes of these mass movements, that is, increased groundwater pressure and seismic shaking. This paper focuses on the Kainama earthflow, mainly composed of loess, which occurred in 2004 April. Its high velocity and the long run-out zone caused the destruction of 12 houses and the death of 33 people. In summer 2005, a field survey consisting of geophysical and seismological measurements was carried out along the adjacent slope. By combination and geostatistical analysis of these data, a reliable 3-D model of the geometry and properties of the subsurface layers, as shown in the first part of the paper, was created. The analysis of the seismological data allowed us to point out a correlation between the thickness of the loess cover and the measured resonance frequencies and associated amplification potential. The second part of this paper is focused on the study of the seismic response of the slope by numerical simulations, using a 2-D finite difference code named FLAC. Modelling of the seismic amplification potential along the slope confirmed the results obtained from the seismological survey-strong amplifications at the crest and bottom of the slope where there is a thick loess cover and almost no amplification in the middle part of the slope. Furthermore, dynamic slope stability analyses were conducted to assess the influence of local amplifications and increased groundwater pressures on the slope failure. The results of the dynamic modelling, although preliminary, show that a combination of seismic and hydrologic origin (pore pressure build-up during the seismic shaking) is the most probable scenario responsible for the 2004 failure.

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This paper presents results of research undertaken on the creation and filtering of digital elevation models (DEMs) from a stereo pair of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images. The raw DEM, created by automatic image matching, appears to be very noisy. Two types of irregularities can be observed. First, a random occurrence of small sinks and mounds with high amplitude is observed. Secondly, a more regular east–west-oriented pattern of noise is present. Many DEM-creation programs provide some editing tools to smooth out the irregularities, including some noise removal, smoothing and interpolation algorithms. However, the application of these algorithms has an important impact on the values of the parameters derived from the elevation, such as slope, aspect and curvature. In this study we propose a filtering algorithm based on morphological greyscale reconstruction in order to remove the sinks and mounds. This technique is very effective in mitigating the artefacts while preserving the remaining structures. For the regular pattern, a linear north–south-oriented low-pass filtering showed the best results. This approach was compared with a median filter and proved to be more effective in terms of both elevation and slope parameters.