Description: Landslides are a worldwide natural hazard causing thousands of fatalities and severe monetary losses every year. To predict and thus reduce the landslide risk in the future, a profound knowledge about the past and recent landslide activity is of utmost importance. For this purpose, the records about the landslide activity have to be as complete as possible in time and space, in order to derive spatial and temporal probabilities of landslide occurrence as a crucial prerequisite of landslide hazard and risk assessment. However, for most regions of the world such comprehensive landslide records are not available, because the conventional manual mapping of landslides is an extremely time-consuming and labor-intensive task. This study presents an automated approach for efficient multi-temporal identification of landslides at regional scale based on optical remote sensing time series data. The developed approach allows for retrospective analysis of long-term landslide occurrence and for monitoring recent landslide activity. In case of the long-term analysis, a combined usage of multiple optical sensors is required to achieve best possible temporal data coverage for the longest possible time span. For this study, such a database has been established for a landslide-affected area of 12000 km² in Southern Kyrgyzstan, Central Asia. It consists of about 900 orthorectified multispectral satellite remote sensing datasets acquired by Landsat-(E)TM, SPOT, IRS-1C (LISS3), ASTER and RapidEye during the last 30 years. For monitoring the landslide activity of the last 5 years, high spatial and temporal resolution RapidEye data have been acquired in the frame of the RapidEye Science Archive (RESA) program. The developed approach comprises automated multi-sensor pre-processing and multi-temporal change detection methods allowing spatiotemporal identification of landslides in an object-based form. The change detection builds on the analysis of temporal NDVI-trajectories, representing footprints of vegetation changes over time. Landslide-specific trajectories are characterized by short-term vegetation cover destruction and longer-term revegetation rates resulting from landslide related disturbance and dislocation of the fertile soil cover. In combination with DEM-derivatives the developed approach enables automated identification of landslides of different sizes, shapes and in different stages of development under varying natural conditions. The multi-sensor long-term analysis of a 2500 km² region resulted in the identification of 1583 landslides ranging in size between 50 m² and 2.8 km². The highest overall landslide rates occurred in 2003 and 2004 exceeding the long-term annual average rate of 57 landslides per year by more than five times. For monitoring the recent landslide activity the approach has been applied to the RapidEye time series acquired between 2009 and 2015 for the whole 12000 km² study area. The combination of high spatial resolution (5 m) and frequent data acquisition (up to several days/weeks) of the RapidEye data has allowed for the systematic assessment of the whole variety of landslide processes also including small slope failures, which often represent precursors for subsequent larger and more hazardous landslides. Thus, the approach can provide valuable information in the context of early warning. Currently, the applicability of the approach is investigated for assessing the aftereffects of the disastrous Nepal earthquakes of April and May 2015 that triggered thousands of landslides. First results have shown the general transferability of the approach to the differing natural environment of Nepal and its general suitability to operate within a rapid response system. Together with the newly available Sentinel-2 data, this approach has the potential to be developed into a globally applicable landslide mapper, which will open up new opportunities to analyze spatiotemporal landslide activity over large areas facilitating further development of probabilistic landslide hazard and risk assessments.