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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 Pléiades 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. [ABSTRACT FROM AUTHOR]

Vegetation monitoring is becoming a major issue in the urban environment due to the services they procure and necessitates an accurate and up to date mapping. Very High Resolution satellite images enable a detailed mapping of the urban tree and herbaceous vegetation. Several supervised classifications with statistical learning techniques have provided good results for the detection of urban vegetation but necessitate a large amount of training data. In this context, this study proposes to investigate the performances of different sampling strategies in order to reduce the number of examples needed. Two windows based active learning algorithms from state-of-art are compared to a classical stratified random sampling and a third combining active learning and stratified strategies is proposed. The efficiency of these strategies is evaluated on two medium size French cities, Strasbourg and Rennes, associated to different datasets. Results demonstrate that classical stratified random sampling can in some cases be just as effective as active learning methods and that it should be used more frequently to evaluate new active learning methods. Moreover, the active learning strategies proposed in this work enables to reduce the computational runtime by selecting multiple windows at each iteration without increasing the number of windows needed. [ABSTRACT FROM AUTHOR]

Measuring terrain deformation over several spatial and temporal scales is relevant for many applications in Earth Sciences (i.e. active faults, volcanoes, landslides or glaciers understanding). The growing volume of freely available data represents nowadays a challenge in terms of storage capacity and computing resources which, together with the complexity of the processing (code parameterization, combination of the image sequences, co-registration of the images) may prevent the exploitation of long time series. We propose here a new version of the Multiple-Pairwise Image Correlation toolbox for processing OPTical images (MPIC-OPT). The toolbox proposes an end-to-end solution to compute the horizontal sub-pixel ground deformation time series from large Sentinel-2 datasets. In addition to time series inversion, several corrections and filtering options are integrated to reduce the noise and improve the accuracy and precision of the measurements. In particular, an automatic jitter correction based on wavelet filtering is proposed. Moreover, the MPIC-OPT service is deployed on the Tier 1.5 High-Performance Computing cluster (e.g. Datacentre/EOST-A2S) of the University of Strasbourg and is accessible on-line through the ESA Geohazards Exploitation Platform (GEP) and the ForM@Ter Solid Earth computing infrastructure with a user-friendly environment to query the satellite data catalogues, parameterize the processing and visualize the outputs. We test the performances of MPIC-OPT on several use cases: the measurement of the co-seismic ground deformation of the 2019 Ridgecrest earthquake sequence (USA), of the rapid motion of the Slumgullion landslide (USA) and of the glaciers of the Mont-Blanc massif (France/Italy). We show that the results of MPIC-OPT are in agreement with in-situ data. The jitter correction significantly improves the precision (RMSE jitter =0.3 m vs. RMSE nojitter =0.5 m) and the accuracy (RMSE jitter =0.3 m vs. RMSE nojitter =1.3 m) of the measurement of the co-seismic displacement of the Rigdecrest seismic deformation. We show that the precision and accuracy of the terrain deformation estimation depend mainly on the correlation threshold and the temporal matching range parameters and we quantify and discuss their impacts. This work opens new perspectives to monitor automatically surface displacements/velocities of natural hazards over large scales and large periods of time. [Display omitted] [ABSTRACT FROM AUTHOR]