Measuring ground deformation is crucial in many fields of Earth sciences (eg. glaciers, landslides or tectonics) to understand the environmental forcing at work. In this work, we present the processing chain Multi-Pairwise Image Correlation for OPTical images (MPIC-OPT) to compute the ground displacement [1]. The chain is accessible on-line through different platforms (Geohazards Exploitation Platform -GEP- and For M@Ter webservice) allowing an easy access High-Performance Computing (HPC) infrastructures and currently ingest Sentinel-2 L1C data. The application of the method is presented for three use cases: a) to measure the co-seismic displacement of the Palu, Sulawesi (Indonesia) 2018 earthquake, b) to monitor ice velocity of the Khumbu glacier (Nepal), c) to monitor the ground displacement of the Slumgullion landslide (USA).

Mapping landslides after major triggering events (earthquake, large rainfall) is crucial for disaster response, hazard assessment, as well as for having benchmark inventories on which landslide models can be tested. Numerous studies have already demonstrated the utility of very-high resolution satellite and aerial images for the elaboration of inventories based on semi-automatic methods or visual image interpretation. However, while manual methods are very time consuming, faster semi-automatic methods are rarely used in an operational contexts, partly caused by data access restrictions on the required input (i.e. VHR satellite images) and by the absence of dedicated services (i.e. processing chain) available for the landslide community. From a data perspective, the free access to the Sentinel-2 and Landsat-8 missions offers opportunities for the design of an operational service that can be deployed for landslide inventory mapping at any time and everywhere on the Earth. From a processing perspective, the Geohazards Exploitation Platform –GEP– of the European Space Agency –ESA– allows the access to processing algorithms in a high computing performance environment. And, from a community perspective, the Committee on Earth Observation Satellites (CEOS) has targeted the take-off of such service as a main objective for the landslide and risk community. Within this context, we present a largely automatic, supervised image processing chain for landslide inventory mapping. The workflow includes: * A segmentation step, which performances is optimized in terms of precision and computing time and with respect to the input data resolution. * A feature extraction step, consisting in the computation of a large set of features (spectral, textural, topographic, morphometric) for the candidate segments to be classified; * A per object classification , based on the training of a random-forest classifier from a sample of manually mapped landslide polygons . The service is able to process both HR (Sentinel-2 or Landsat-8) and VHR (Pléiades, SPOT, Planet, Geo-eyes or every multi-spectral image with 4 bands, blue, green, red, NIR) sensors. The service can be operated in two modes (bi-dates, single-date; the bi-dates mode is based on change detection methods with images before and after a given event, whereas the mono-date mode allows a mapping of landcover at any given time). The service is presented on use cases with both medium resolution (Sentinel-2, Landsat-8) and high-resolution (Spot-6,7, Pléiades) images corresponding landscapes recently impacted by landslide disasters (e.g. Haiti, Mozambique, Kenya). The landslide inventory maps are provided with uncertainty maps that allows identifying areas which might require further considerations. Although the initial focus and the main usage of ALADIM is associated with the landslide analyses, there is a large panel of possible applications. The processing chain was already tested in different others contexts (urbanization, deforestation, agricultural land change, …) with very promising results.

With the CLC+ product suite as part of the Copernicus Land Monitoring Service (CLMS), the European Environment Agency (EEA) has initiated a true paradigm change in European land cover/land use monitoring, building on the 30-years-long rich legacy of the European CORINE Land Cover (CLC) flagship product. The CLC+ Backbone, as first component of the upcoming CLC+ era, will feature an object-oriented wall-to-wall high-resolution inventory of European land cover and its characteristics in unprecedented quality and detail, for the reference year 2018. It will comprise a pan-European combined “hardbone” and “soft-bone” segmentation of vector-based stable landscape objects and a raster-based classification of 11 EAGLE compliant land cover classes at 10m spatial resolution. To this end, a combination of image segmentation and Deep Learning approaches are implemented within a cloud-based infrastructure for a fully integrated analysis of optical/radar time series of Sentinel-1/-2 satellite imagery and auxiliary data. Vector and raster datasets will be fused into a fully attributed, 18 land cover class, vector product with 0.5 ha minimum mapping unit (MMU), additionally incorporating a multitude of further information layers derived from satellite data and various other Copernicus products.

Monitoring the spatial and temporal extents of permanent and temporary bodies of surface water is important for various applications such as water resource management, climate modelling, and biodiversity conservation. Satellite remote sensing is an effective source of information to detect surface water over large areas and document their evolution in time. Recently, the European Space Agency (ESA) launched freely available SAR (Synthetic Aperture Radar) and optical sensors (Sentinel-1 & 2) with high revisiting time and spatial resolution. The objective of this paper is to explore the contribution of multi-temporal and multi-source (passive and active) Sentinel observations for improving the detection and mapping of surface waters by applying decision-level image fusion techniques. The approach is tested over Central Ireland using a time series of 16 Sentinel-1 images and a few Sentinel-2 images for the period 2015-2016. Compared to a mono-date approach, the combination of Sentinel-1 & 2 observations provides better accuracy for mapping permanent surface water. Decision level fusion technique allows mapping temporary surface water (such as flooding) with a high accuracy. It also gives the possibility to monitor their dynamics by providing the probability of occurrence of flooded areas at the pixel level.

Passive sensors such as multi-spectral (e.g., Single Lens Reflex, SLR) cameras are increasingly being used for geohazards monitoring (landslides, cliffs affected by rock falls, ice glaciers, and volcano flanks) because of their low cost compared to expensive terrestrial laser scanner (TLS) or radar imaging (GB-InSAR) systems. Indeed, due to the large consumer market, sensor resolution and quality (e.g., gain, dynamic range, and geometry) are increasing rapidly. For gravitational processes, such as landslides, recent research has focused on the development and implementation of image correlation techniques to estimate the spatial shift between at least a pair of images by maximizing a cross-correlation function. A generic and fully automated pipeline is proposed for the processing of long image time series acquired for several site configurations. The system associates modules for 1) the selection of the image sequences, 2) the registration of the image stacks and the correction of the camera movements, and 3) the calculation of the terrain motion using change detection approaches. The system is based on the open-source photogrammetric library MicMac and tailored for the processing of monoscopic images. A sensitivity analysis is conducted to design and test the image processing for two use cases respectively the Chambon landslide (Isère, France) characterized by slow motion (< 10 cm.day−1), and the Pas de l’Ours landslide (Hautes-Alpes, France) characterized by moderate motion (> 50 cm.day−1). Four categories of parameters are tested: the image modality, the image matching parameters, the size of the stable area used in the co-registration stage, and the strategy used to combine the images in the time series. The application of the pipeline on the two use cases provides information about the kinematics and the spatial behavior of the landslides.

Face aux besoins de suivis hydrométriques et compte tenu des limites d'installation de stations de mesure traditionnelles sur les cours d'eau réunionnais à lit mobile et en tresse, l'Office de l'eau Réunion et les universités de Bretagne Occidentale et de La Réunion ont développé un système de mesure automatique des débits par photogrammétrie. Ce système innovant se base sur la mesure des vitesses d'écoulement de surface par la technique de la PIV (Particle Image Velocimetry), sur l'élaboration d'un modèle numérique de terrain du tronçon de mesure et une détection automatique du masque d'eau. Des algorithmes de calcul ont été développés sous MATLAB de manière (i) à traiter une vidéo acquise en temps réel ou pour réaliser des jaugeages à distance et (ii) à convertir un pack de vidéos acquises automatiquement toutes les 30 minutes afin d'obtenir une chronique de débit en continu. Les comparaisons avec les valeurs de débits jaugés ont montré des écarts maximums de 9.0 % après la calibration du système de mesure. Cette technologie, pouvant être autonome en énergie ou raccordée au réseau électrique, ouvre des perspectives intéressantes pour le suivi des cours d'eau complexe ou des phénomènes hydrologiques extrêmes.

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 RMSE xy = 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.

Reliable information about the spatial distribution of surface waters is critically important in various scientific disciplines. Synthetic Aperture Radar (SAR) is an effective way to detect floods and monitor water bodies over large areas. Sentinel-1 is a new available SAR and its spatial resolution and short temporal baselines have the potential to facilitate the monitoring of surface water changes, which are dynamic in space and time. While several methods and tools for flood detection and surface water extraction already exist, they often comprise a significant manual user interaction and do not specifically target the exploitation of Sentinel-1 data. The existing methods commonly rely on thresholding at the level of individual pixels, ignoring the correlation among nearby pixels. Thus, in this paper, we propose a fully automatic processing chain for rapid flood and surface water mapping with smooth labeling based on Sentinel-1 amplitude data. The method is applied to three different sites submitted to recent flooding events. The quantitative evaluation shows relevant results with overall accuracies of more than 98% and F-measure values ranging from 0.64 to 0.92. These results are encouraging and the first step to proposing operational image chain processing to help end-users quickly map flooding events or surface waters.

Abstract. Rainfall-induced landslides are a common and significant source of damage and fatality worldwide. Still, we have very little understanding of the quantity and properties of landsliding that can be expected for a given storm and a given landscape, mostly because we have very few datasets of rainfall-induced landslides. Here we present six new comprehensive landslide inventories associated to well identified rainfall events. Combining these datasets, with two previously published datasets, we study their statistical properties and their relations to topographic slope distribution and storm properties. Landslide metrics (such as total landsliding, peak landslide density or landslide distribution area) vary across 2 to 3 order of magnitudes but strongly correlate with the storm total rainfall, varying over almost 2 orders of magnitude for these events. Correlation increases when we apply a normalization on the landslide runout distances. The non-linear scaling with total rainfall should be further constrained with additional cases and incorporation of landscape properties such as regolith depth, typical strength or permeability estimates. We also observe that, for storm with longer duration, landslides do not occur preferentially on the steepest slopes of the landscape, contrarily to observations from earthquake-induced landslides, suggesting preferential failures of larger drainage area patches with intermediate slopes. The database could be used for further comparison with spatially resolved rainfall estimates and with empirical or mechanistic landslide event modeling.

Several image analysis methods are used to determine surface displacements and generate 3D surface models (DSM) from multiview photogrammetric techniques. In our case, we focused on the contribution of different softwares to generate 3D models fom multiview images and document the surface motion from a fixed monoscopic camera. The methods are presented in the case of the Chambon landslide (Isère, France).

Rainfall-induced landslides are a common and significant source of damages and fatalities worldwide. Still, we have little understanding of the quantity and properties of landsliding that can be expected for a given storm and a given landscape, mostly because we have few inventories of rainfall-induced landslides caused by single storms. Here we present six new comprehensive landslide event inventories coincident with well identified rainfall events. Combining these datasets, with two previously published datasets, we study their statistical properties and their relations to topographic slope distribution and storm properties. Landslide metrics (such as total landsliding, peak landslide density, or landslide distribution area) vary across 2 to 3 orders of magnitude but strongly correlate with the storm total rainfall, varying over almost 2 orders of magnitude for these events. Applying a normalization on the landslide run-out distances increases these correlations and also reveals a positive influence of total rainfall on the proportion of large landslides. The nonlinear scaling of landslide density with total rainfall should be further constrained with additional cases and incorporation of landscape properties such as regolith depth, typical strength or permeability estimates. We also observe that rainfall-induced landslides do not occur preferentially on the steepest slopes of the landscape, contrary to observations from earthquake-induced landslides. This may be due to the preferential failures of larger drainage area patches with intermediate slopes or due to the lower pore-water pressure accumulation in fast-draining steep slopes. The database could be used for further comparison with spatially resolved rainfall estimates and with empirical or mechanistic landslide event modeling.

The idea to create advanced platforms for the Earth Observation community, where the users can find data but also state-of-art algorithms, processing tools, computing facilities, and instruments for dissemination and sharing, has been launched several years ago. The initiatives developed in this context have been supported firstly by the Framework Programmes of European Commission and the European Space Agency (ESA) and, progressively, by the Copernicus programme. In particular, ESA created and supported the Grid Processing on Demand (G-POD) environment, where the users can access to advanced processing tools implemented in a GRID environment, satellite data and computing facilities. All these components are located in the same datacentre to significantly reduce and make negligible the time to move the satellite data from the archive. From the experience of G-POD was born the idea of ESA to have an ecosystem of Thematic Exploitation Platforms (TEP) focused on the integration of Ground Segment capabilities and ICT technologies to maximize the exploitation of EO data from past and future missions. A TEP refers to a computing platform that deals with a set of user scenarios involving scientists, data providers and ICT developers, aggregated around an Earth Science thematic area. Among the others, the Geohazards Exploitation Platform (GEP) aims at providing on-demand and systematic processing services to address the need of the geohazards community for common information layers and to integrate newly developed processors for scientists and other expert users.Within GEP, the community benefits from a cloud-based environment, specifically designed for the advanced exploitation of EO data. A partner can bring its own tools and processing chains, but also has access in the same workspace to large satellite datasets and shared data processing tools. GEP is currently in the pre-operations phase under a consortium led by Terradue Srl and six pilot projects concerning different EO applications have been selected: time-series stereo-photogrammetric processing using optical images for landslides and tectonics movement monitoring with CNRS/EOST (FR), optical based processing method for volcanic hazard monitoring with INGV (IT), systematic generation of deformation time-series with Sentinel-1 data with CNR-IREA (IT), systematic processing of Sentinel-1 interferometric imagery with DLR (DE), terrain motion velocity map generation based on PSI processing by TRE-ALTAMIRA (ES) and a campaign to test and employ GEP applications with the Corinth Rift EPOS Near Fault Observatory. Finally, GEP is significantly contributing to the development of the satellite component of the European Plate Observing System (EPOS), a long-term plan to facilitate the integrated use of data, data products, and facilities from distributed research infrastructures for solid Earth science in Europe. In particular, GEP has been identified as gateway for the Thematic Core Service “Satellite Data” of EPOS, namely the platform through which the satellite EPOS services will be delivered. In the current work, latest activities and achievements of GEP, including the impact in the context of the distributed Research Infrastructures such as EPOS, will be presented and discussed.

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.