Derivation of long-term spatiotemporal landslide activity—A multi-sensor time series approach

Abstract

Abstract This paper presents a remote sensing-based method to efficiently derive multi-temporal landslide inventories over large areas, which allows for the spatiotemporal analysis of landslide activity, which is an important prerequisite in systematic regional landslide hazard and risk assessment. The developed method uses globally archived satellite remote sensing data for a retrospective systematic assessment of past multi-temporal landslide activity. Landslides are automatically identified as spatially explicit objects based on landslide-specific vegetation cover changes using temporal NDVI-trajectories and complementary relief-oriented parameters. To enable the long-term analysis of large areas with highest possible temporal resolution, the developed method facilitates the use of a large amount of optical multi-sensor time series data. The database of this study consists of 212 datasets that comprise freely available Landsat TM & ETM + data and SPOT 1 & 5, IRS1-C LISSIII, ASTER, and RapidEye data. These data were acquired between 1986 and 2013 and cover a landslide-prone area of 2500 km 2 in southern Kyrgyzstan. We identified 1583 landslide objects ranging in size between 50 m 2 and 2.8 km 2 . Spatiotemporal analysis of the landslides that were detected during these 27 years reveals continuous landslide activity of varying intensity. 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 a factor of five. The areas of highest landslide activity are also determined, whereas most of these areas were persistent over time.