Optical and Radar Satellite Remote Sensing for Large Area Analysis of Landslide Activity in Southern Kyrgyzstan, Central Asia

Monday, 15 December 2014
Sigrid Roessner, Robert Behling, Kanayim O Teshebaeva, Mahdi Motagh and Hans-Ulrich Wetzel, Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences, Potsdam, Germany
The presented work has been investigating the potential of optical and radar satellite remote sensing for the spatio-temporal analysis of landslide activity at a regional scale along the eastern rim of the Fergana Basin representing the area of highest landslide activity in Kyrgyzstan. For this purpose a multi-temporal satellite remote sensing database has been established for a 12.000 km2 study area in Southern Kyrgyzstan containing a multitude of optical data acquired during the last 28 years as well as TerraSAR-X and ALOS-PALSAR acquired since 2007. The optical data have been mainly used for creating a multi-temporal inventory of backdated landslide activity. For this purpose an automated approach for object-oriented multi-temporal landslide detection has been developed which is based on the analysis of temporal NDVI-trajectories complemented by relief information to separate landslide-related surface changes from other land cover changes. Applying the approach to the whole study area using temporal high resolution RapidEye time series data has resulted in the automated detection of 612 landslide objects covering a total area of approx. 7.3 km². Currently, the approach is extended to the whole multi-sensor time-series database for systematic analysis of longer-term landslide occurrence at a regional scale. Radar remote sensing has been focussing on SAR Interferometry (InSAR) to detect landslide related surface deformation. InSAR data were processed by repeat-pass interferometry using the DORIS and SARScape software. To better assess ground deformation related to individual landslide objects, InSAR time-series analysis has been applied using the Small Baseline Subset (SBAS) method. Analysis of the results in combination with optical data and DEM information has revealed that most of the derived deformations are caused by slow movements in areas of already existing landslides indicating the reactivation of older slope failures. This way, InSAR analysis can contribute to the early recognition of landslide activation prior to the onset of larger slope failure and thus support early warning. Overall, combined analysis of optical and radar data enables systematic spatio-temporal derivatioin of ongoing and backdated landslide activity for subsequent GIS-based landslide hazard assessment.