LAUSR

Abstract Slow-moving landslides are widespread in many landscapes with significant impacts on the topographic relief, sediment transfer and human settlements. Their area-wide mapping and monitoring in mountainous terrain, however, is still challenging. The growing archives of optical remote sensing images offer great potential for the operational detection and monitoring of surface motion in such areas. This study proposes a multiple pairwise image correlation (MPIC) technique to obtain a series of redundant horizontal displacement fields, and different multi-temporal indicators for a more accurate detection and quantification of surface displacement. The technique is developed and tested on a series of monoscopic and stereoscopic Pleiades satellite images at a test site in the South French Alps. Empirical tests confirm that MPIC significantly increased detection accuracy (F − measure = 0.85) and that the measurement error can be reduced by averaging velocities from all pair combinations covering a given time-step (i.e. when stereo-pairs are available for at least one date). The derived inventory and displacement fields of 169 slow-moving landslides show a positive relationship between the landslide size and velocities, as well as a seasonal acceleration of the largest landslides in response to an increase in effective precipitation. The processing technique can be adapted to better exploit increasingly available time-series from a variety of optical satellites for the detection and monitoring of landslide displacement.