Search

The large availability of Synthetic Aperture Radar (SAR) data collected over the last decade by several satellite missions, such as COSMO-SkyMed, TerraSAR-X and Sentinel-1 constellations, has been pushing toward the present Earth Observation (EO) scenario into a "golden age", which is rapidly moving towards a real Big Data scenario. The widespread use of satellite SAR data have fostered the development of several SAR applications, one of those referred to as Differential SAR Interferometry (DInSAR) technology, which has deeply demonstrated to profitably detect the surface deformations over a wide spatial extent in both natural and anthropic hazard scenarios, through the generation of spatially dense deformation maps with millimetric accuracies. In particular, the advanced DInSAR algorithm referred to as Parallel Small BAseline Subset (P-SBAS) approach allows retrieving the temporal and spatial characteristics of the detected displacements at two spatial resolution scales, referred to as regional (spatial resolution in the 30-90 m range) and local (spatial resolution in the 3-10 m range) scales, suitable for a large variety of application fields, from natural hazards (volcano eruptions, seismic events, landslides) to anthropic contexts (urban areas, archaeological sites, oil-gas extraction, structures and transport infrastructures).However, the interferometric processing performed at local scale needs to deal with hundreds of SAR acquisitions at full spatial resolution, i.e. to manage several hundreds of million points; consequently, the processing of such a data amount is particularly heavy from a computational point of view and can not be carried out in reasonable time frames through the traditional (sequential) implementation of the full resolution DInSAR processing chains.Accordingly, to profitably benefit from the current SAR scenario, it is crucial to develop innovative solutions to automatically and efficiently handle large DInSAR data stacks. These solutions are principally based on the exploitation of advanced distributed computing environments, to achieve high efficiency in terms of scalability performances, as well as on the development of much more advanced DInSAR methodologies (and codes) able to effectively maximize the information related to these huge amount of DInSAR data.This work is aimed at describing an innovative DInSAR solution, based on the exploitation of distributed HPC and Cloud Computing environments, which benefits from parallel programming techniques (multi-node, multi-threads, GPUs) implemented within an automatic full resolution P-SBAS processing pipeline. Starting from large SAR datasets acquired by the COSMO-SkyMed constellation, the developed parallel full resolution P-SBAS processing chain allows retrieving in short time frame (less than 24 hours) displacement time series and deformation maps, at the single buildings/infrastructure level, relevant to extended urban areas. The presented experimental results and the related performance analyses are achieved by applying the developed parallel P-SBAS pipeline to a number of large full resolution COSMO-SkyMed datasets acquired over some important Italian cities (e.g. Rome and Naples urban areas).

In this work we present a cloud-computing based strategy for generating surface displacement time series and mean deformation velocity maps of very large areas by exploiting Sentinel-1 (S1) data. Our approach relies on the simultaneous exploitation of a fast access to the S1 data archives, High Performance Computing resources and external geodetic data. The presented pipeline is based on an advanced cloud- computing implementation of the differential SAR interferometry (DInSAR) Parallel Small BAseline Subset (P-SBAS) processing chain, which allows the fully unsupervised processing of huge Interferometric Wide Swath (IWS) Sentinel-1 data volumes. In particular, for what concerns the S1 data archives, we benefited from the NASA’s Alaska Satellite Facility (ASF) Distributed Active Archive Center (DAAC), which stores and distributes S1 SAR data from Amazon Web Service (AWS) for advancing Earth science research. The ASF- DAAC allowed us to reach a very high performance level in terms of downloading time and system reliability. Moreover, we exploited the geodetic measurements provided from the Magnet + Global GPS Network Map of the Nevada Geodetic Laboratory at the University of Nevada, Reno, USA (UNR-NGL), which supply GPS measurements daily updated and continuosly available. The GPS measurements were used to account for regional trends and to better discriminate the low (tectonic) and high (local) frequency deformation patterns. The presented solution is highly scalable and has been migrated to the Amazon Web Service (AWS) environment. It runs out along an automatic routine to generate the mean deformation velocity maps of the vertical and horizontal (East-West) displacement components of the whole investigated area. The developed pipeline has been tested on ascending and descending Sentinel-1 archives acquired over a large area of Southern California (US), which extends over about 150,000 square kilometers.

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.

Trabajo presentado en la European Geosciences Union General Assembly, celebrada en Viena (Austria), del 23 al 28 de abril de 2017, EPOS, the European Plate Observing System, is a long-term plan to facilitate the integrated use of data, data products, software and services, available from distributed Research Infrastructures (RI), for solid Earth science in Europe. Indeed, EPOS integrates a large number of existing European RIs belonging to several fields of the Earth science, from seismology to geodesy, near fault and volcanic observatories as well as anthropogenic hazards. The EPOS vision is that the integration of the existing national and trans-national research infrastructures will increase access and use of the multidisciplinary data recorded by the solid Earth monitoring networks, acquired in laboratory experiments and/or produced by computational simulations. The establishment of EPOS will foster the interoperability of products and services in the Earth science field to a worldwide community of users. Accordingly, the EPOS aim is to integrate the diverse and advanced European Research Infrastructures for solid Earth science, and build on new e-science opportunities to monitor and understand the dynamic and complex solid-Earth System. One of the EPOS Thematic Core Services (TCS), referred to as Satellite Data, aims at developing, implementing and deploying advanced satellite data products and services, mainly based on Copernicus data (namely Sentinel acquisitions), for the Earth science community. This work intends to present the technological enhancements, fostered by EPOS, to deploy effective satellite services in a harmonized and integrated way. In particular, the Satellite Data TCS will deploy five services, EPOSAR, GDM, COMET, 3D-Def and MOD, which are mainly based on the exploitation of SAR data acquired by the Sentinel-1 constellation and designed to provide information on Earth surface displacements. In particular, the planned services will provide both advanced DInSAR products (deformation maps, velocity maps, deformation time series) and value-added measurements (source model, 3D displacement maps, seismic hazard maps). Moreover, the services will release both on-demand and systematic products. The latter will be generated and made available to the users on a continuous basis, by processing each Sentinel-1 data once acquired, over a defined number of areas of interest; while the former will allow users to select data, areas, and time period to carry out their own analyses via an on-line platform. The satellite components will be integrated within the EPOS infrastructure through a common and harmonized interface that will allow users to search, process and share remote sensing images and results. This gateway to the satellite services will be represented by the ESA- Geohazards Exploitation Platform (GEP), a new cloud-based platform for the satellite Earth Observations designed to support the scientific community in the understanding of high impact natural disasters. Satellite Data TCS will use GEP as the common interface toward the main EPOS portal to provide EPOS users not only with data products but also with relevant processing and visualisation software, thus allowing users to gather and process on a cloud-computing infrastructure large datasets without any need to download them locally

In the context of space-borne geodetic techniques, Differential Synthetic Aperture Radar Interferometry (DInSAR) has demonstrated its high performance in measuring surface displacements in different conditions and scenarios, both natural and anthropic. In particular, the advanced DInSAR time series processing method referred to as Small BAseline Subset (SBAS), that allows studying both the spatial and temporal variability of the surface displacements, has proven to be particularly suitable in different contexts, as for natural hazards (volcanoes, earthquakes and landslides) and human-induced deformation (subsidence due to aquifer exploitation, mining operations, and building of large infrastructures). Recently, an efficient implementation of this algorithm (referred to as P-SBAS approach) has been fully integrated within the Geohazards Thematic Exploitation Platform (GEP) of ESA by the CNR IREA team. The GEP is devoted to the exploitation of EO data resources in the context of the Geohazard Supersites & Natural Laboratories as well as on the CEOS Pilots on Seismic Hazards and Volcanoes. The GEP (https://geohazards-tep.eo.esa.int/7) is sourced with elements, data and processing, including P-SBAS, relevant to the geohazards theme. The integration of the P-SBAS algorithm within GEP resulted in a web-based tool freely available to the scientific community. This tool allows users to process, from their own laptops, the European SAR data archives (ERS, ENVISAT and Sentinel-1) for obtaining surface displacement maps and time series in a completely unsupervised way, without caring about data download and processing facility procurements. The course is organized in four parts. First, a short overview on the DInSAR processing methods allowing retrieving mean surface deformation maps and displacement time series will be provided, with a specific focus on the SBAS-DInSAR technique. Secondly, the GEP and G-POD environments will be introduced and the P-SBAS web tool will be presented. Subsequently, the advanced features as well as some case studies and results achieved via the web tool are also shown. Finally, some case studies relevant to mountain environments will be presented and analysed. This is the agenda of the Workshop that was held on the 25th of January at ETH Zurich (https://www.ethz.ch/4), in the Department of Earth Sciences: 09:30-10:15 -- Registration of the participants 10:15-11:00 -- Part 1: Introduction to Differential SAR Interferometry 11:15-12:00 -- Part 2: ESA Platforms for Automatic Web Processing 13:15-14:00 -- Part 3: New frontiers in Earth Observation research 14:00-14:45 -- Part 4: Case studies in mountain environments 14:45-15:30 -- Open Discussion Recorded Live streaming of the event is available at http://www.video.ethz.ch/events/2017/esa.html

The Geohazards Exploitation Platform (GEP) is a European Space Agency (ESA) initiative within the ecosystem of Thematic Exploitation Platforms (TEP) focuses 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. The Exploitation Platforms are targeted to cover different capacities and they define, implement and validate a platform for effective EO data exploitation in a given thematic area. In this framework, the 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. The GEP offers this expanding community a unique set of tools to forge new applications in direct collaboration with a large number of players. In particular, the community will benefit from a cloud-based workspace, allowing advanced EO data exploitation activities and offering access to a broad range of shared processing tools. Each partner brings their own tools and processing chains, but also has access in the same workspace to large data sets and shared processing tools. The GEP has now on-boarded over 70+ early adopters and is entered in the pre-operations phase during 2016 by developing new Pilot applications that will significantly augment the Platform’s capabilities for systematic production and community building. Each project on the Platform is concerned with either integrating an application, running on demand processing using an application available in the platform or systematically generating a new product collection. Under a Consortium lead by Terradue Srl, six new pilot projects have been taken on board: 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 this work we show how the GEO Geohazards Supersites community can fully benefit from availability of an advanced IT infrastructure, where satellite and in-situ data, advanced satellite processing tools and web-based visualization instruments (Figure 1) are at the disposal of users to address scientific questions. In particular, we focus on the contributions provided by GEP for the management of EO data, for the implementation of a European e-infrastructure, and for the monitoring and modelling of ground deformations (Figure 2) and seismic activity (Figure 3).

In this work we present an advanced interferometric processing chain, which is based on the DInSAR algorithm referred to as Parallel Small BAseline Subset (P-SBAS) approach, for the massive processing of SENTINEL-1 (S1) Interferometric Wide Swath (IWS) data. The P-SBAS S1 processing chain produces surface deformation time series, and the relevant mean velocity maps, in automatic and systematic way by efficiently exploiting Cloud Computing infrastructures, thus allowing us to perform DInSAR analyses at very large scale in reduced time frames. As experimental results, the overall mean deformation velocity map relevant to the Central and Southern Italy zone (from Lazio to Sicily), generated by processing in parallel about 300 S1 acquisitions within the Amazon Web Services Cloud Computing platform, is presented. Moreover, the displacement time series of some pixels located in volcanic deforming areas such as the Campi Flegrei Caldera (Napoli Bay area) and the Mt. Etna (Sicily) are shown.

In the context of space-borne geodetic techniques, Differential Synthetic Aperture Radar Interferometry (DInSAR) has demonstrated its high performance in measuring surface displacements in different conditions and scenarios, both natural and anthropic. In particular, the advanced DInSAR time series processing method referred to as Small BAseline Subset (SBAS), that allows studying both the spatial and temporal variability of the surface displacements, has proven to be particularly suitable in different contexts, as for natural hazards (volcanoes, earthquakes and landslides) and human-induced deformation (subsidence due to aquifer exploitation, mining operations, and building of large infrastructures). Recently, this algorithm has been fully integrated within the ESA’s Grid Processing on Demand (G-POD) environment, which is part of the Geohazards Thematic Exploitation Platform (GEP) of ESA. The GEP is devoted to the exploitation of EO data resources in the context of the Geohazard Supersites & Natural Laboratories as well as on the CEOS Pilots on Seismic Hazards and Volcanoes. The GEP is sourced with elements, data and processing, including SBAS-DInSAR, relevant to the geohazards theme. The integration of the SBAS-DInSAR algorithm within GEP resulted in a web-based tool freely available to the scientific community. This tool allows users to process, from their own laptops, the European SAR data archives (ERS, ENVISAT and Sentinel-1) for obtaining surface displacement maps and time series in a completely unsupervised way, without caring about data download and processing facility procurements. The course is organized in two parts. First, a short overview on the DInSAR processing methods allowing to retrieve mean surface deformation maps and displacement time series will be provided, with a specific focus on the SBAS-DInSAR technique. Secondly, the GEP and G-POD environments will be introduced and the SBAS-DInSAR web tool will be presented. Basic knowledge of SAR data analysis is envisaged.

In this thesis the modeling of SAR (Synthetic Aperture Radar) images of natural surfaces described via fractal models is dealt with. A complete theoretical forward model linking the parameters describing the scene observed by the sensor to the stochastic characterization of the relevant SAR image is provided. The inverse problem is treated as well: a SAR image post-processing able to automatically retrieve - operating on an amplitude single SAR image - the fractal parameters of the scene, is presented. The developed imaging model is based on sound geometrical and electromagnetic models that are combined according to the SAR impulse response function. The power spectral densities of appropriate cuts of the SAR image are evaluated in closed form in terms of the surface fractal parameters. The theoretical results are here conceptually assessed, analytically derived, graphically validated and numerically verified. Moreover, based on the inversion of the forward theoretical model, an innovative SAR image post-processing for the fractal parameters estimation is implemented. It is firstly tested on simulated SAR images, then it is applied to different types of new generation (i.e. high resolution) SAR images. The generated fractal maps show themselves to be very useful for a wide range of application, e.g. prevention and monitoring of environmental disasters, geodynamic processes interpretation, land classification, rural planning, and so on.