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The constellation of Landsat-8 and Sentinel-2 optical satellites offers opportunities for a wide range of Earth Observation (EO) applications and scientific studies in Earth sciences mainly related to geohazards. The multi-temporal co-registration accuracy of images provided by both missions is, however, currently not fully satisfactory for change detection, time-series analysis and in particular Earth surface motion measurements. The objective of this work is the development, implementation and test of an automatic processing chain for correcting co-registration artefacts targeting accurate alignment of Sentinel-2 and Landsat-8 imagery for time series analysis. The method relies on dense sub-pixel offset measurements and robust statistics to correct for systematic offsets and striping artefacts. Experimental evaluation at sites with diverse environmental settings is conducted to evaluate the efficiency of the processing chain in comparison with previously proposed routines. The experimental evaluation suggests lower residual offsets than existing methods ranging between RMSExy = 2.30 and 2.91 m remaining stable for longer time series. A first case study demonstrates the utility of the processor for the monitoring of continuously active landslides. A second case study demonstrates the use of the processor for measuring co-seismic surface displacements indicating an accuracy of 1/5 th of a pixel after corrections and 1/10th of a pixel after calibration with ground measurements. The implemented processing chain is available as an open source tool to support a better exploitation of the growing archives of Sentinel-2 and Landsat-8. [ABSTRACT FROM AUTHOR]

Monitoring the endogenous seismicity of volcanoes helps to forecast eruptions and prevent their related risks, and also provides critical information on the eruptive processes. Due the high number of events recorded during pre-eruptive periods by the seismic monitoring networks, cataloging each event can be complex and time-consuming if done by human operators. Automatic seismic signal processing methods are thus essential to build consistent catalogs based on objective criteria. We evaluated the performance of the “Random Forests” (RF) machine-learning algorithm for classifying seismic signals recorded at the Piton de la Fournaise volcano, La Réunion Island (France). We focused on the discrimination of the dominant event types (rockfalls and volcano-tectonic earthquakes) using over 19,000 events covering two time periods: 2009–2011 and 2014–2015. We parametrized the seismic signals using 60 attributes that were then given to RF algorithm. When the RF classifier was given enough training samples, its sensitivity (rate of good identification) exceeded 99%, and its performance remained high (above 90%) even with few training samples. The sensitivity collapsed when using an RF classifier trained with data from 2009 to 2011 to classify data from 2014 to 2015 catalog, because the physical characteristics of the rockfalls and hence their seismic signals had evolved between the two time-periods. The main attribute families (waveform, spectrum, spectrogram or polarization) were all found to be useful for event discrimination. Our work validates the performance of the RF algorithm and suggests it could be implemented at other volcanic observatories to perform automatic, near real-time, classification of seismic events. [ABSTRACT FROM AUTHOR]