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  • 2019-2023
  • Publications
  • Research software
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  • European Commission
  • EPOS IP
  • English
  • European Plate Observing System

  • Authors: F. Casu1; M. Bonano1; 2; R. Castaldo1; +11 Authors

    We present an unsupervised and automatic system for volcano deformation monitoring via the Copernicus Sentinel-1 data. The system relies on the Parallel Small BAseline Subset (P-SBAS) approach, permitting us to generate updated displacement time series at every new Sentinel-1 acquisition over a selected area of interest in a fast and accurate way. The service is currently operative to monitor the main active Italian volcanoes in the framework of cooperation with the Italian Department of Civil Protection. The system is potentially extendable to every area on the Earth, thus making it suitable for surface displacement monitoring of a large variety of phenomena. Finally, the obtained results are made available to the scientific community through the EPOS Research Infrastructure.

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    Authors: DeFelipe, I; Alcalde, J; Baykiev, E; Bernal, I; +19 Authors

    The immense advances in computer power achieved in the last decades have had a significant impact in Earth science, providing valuable research outputs that allow the simulation of complex natural processes and systems, and generating improved forecasts. The development and implementation of innovative geoscientific software is currently evolving towards a sustainable and efficient development by integrating models of different aspects of the Earth system. This will set the foundation for a future digital twin of the Earth. The codification and update of this software require great effort from research groups and therefore, it needs to be preserved for its reuse by future generations of geoscientists. Here, we report on Geo-Soft-CoRe, a Geoscientific Software & Code Repository, hosted at the archive DIGITAL.CSIC. This is an open source, multidisciplinary and multiscale collection of software and code developed to analyze different aspects of the Earth system, encompassing tools to: 1) analyze climate variability; 2) assess hazards, and 3) characterize the structure and dynamics of the solid Earth. Due to the broad range of applications of these software packages, this collection is useful not only for basic research in Earth science, but also for applied research and educational purposes, reducing the gap between the geosciences and the society. By providing each software and code with a permanent identifier (DOI), we ensure its self-sustainability and accomplish the FAIR (Findable, Accessible, Interoperable and Reusable) principles. Therefore, we aim for a more transparent science, transferring knowledge in an easier way to the geoscience community, and encouraging an integrated use of computational infrastructure.

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  • Authors: Fernando Monterroso Tobar1; 2; Claudio de Luca2; Manuela Bonano2; +7 Authors

    Earth's surface deformation that occur as a consequence of an earthquake is a crucial information for investigating the causative source of the seismic event. In this context, the space-borne Differential Synthetic Aperture Radar Interferometry (DInSAR) has proven to be one of the key methods for the quantitative measurement of the Earth's surface deformation, with centimetres to millimetres accuracy [1]. DInSAR relies on the evaluation of the phase difference between two SAR images, acquired from different orbital positions and at different times [1]. Depending on the system configuration, the footprint of space-borne SAR acquisitions can span from a few kilometres up to hundreds of kilometres, making it particularly suitable for accurate investigations of wide areas at relative low cost. In these sense, according to USGS records [2], from 1992 to 2016, about 3700 earthquakes with significant magnitudes (Mw > 6.0) have occurred, while only a limited number of them has been successfully investigated through DInSAR [3]. This is mainly due, apart the intrinsic limitation of the DInSAR technique, to the lack of a satellite program with a systematic and global acquisition policy, which are fundamental characteristics to allow creating DInSAR operational services at global scale. However, since the launch of the Copernicus Sentinel-1 SAR satellite missions in 2014 and 2016, the availability of SAR images dramatically increased. Indeed, this constellation acquires, with global coverage policy, radar images every 6/12 days over the same area, allowing us to dispose of a huge archive of SAR data that can be processed for obtaining co-seismic displacement maps in a short time frame and anywhere in the world. Considering the relevance of the satellite interferometric analysis for the hazards monitoring, as well as the availability of new radar systems as Sentinel-1, which are characterized by a high reliability level, is it therefore possible the development of operational services for the generation of DInSAR products, some of them being already in place [4, 5]. In this work an unsupervised and automatic tool for the generation of DInSAR co-seismic displacement maps is presented. Benefiting from the mostly global availability of Sentinel-1 SAR data and the on-line earthquake catalogues, the tool retrieves information about the depth and magnitude of recent earthquakes and triggers, if necessary, the interferometric process over the area affected by the seismic event. The workflow process is the following (Figure 1). First, the extraction of earthquake information (epicenter location, magnitude, time, ...) from the on-line public available web catalogues, as those provided by main international geophysical institutions (e.g. USGS [2], INGV [6]), is performed (Block A of Figure 1). The retrieved information is provided according to different standard formats (QuakeML, geoJSON, ...) and is accessible via subscription feeds that are updated with a defined frequency. The system is not limited to a single earthquake catalog interface. The relevant earthquake information is collected in accordance to an empirical magnitude and depth relation, which considers that only high magnitude (> Mw 6.0) and relatively shallow earthquakes (typically < 20 km) very likely induce a surface deformation that is detectable via DInSAR [7] (Block B). Among the earthquakes that respect the relation, only those with the epicentre on land (or even on water but that can likely induce detectable deformation on land) are processed. Once the occurred earthquake has been selected, the SAR data retrieval is performed via an automatic query to the open access Sentinel-1 catalogue (Block C). The query is performed over an area whose extension depends of the relation between magnitude, depth and epicenter location, which is derived from theoretical and empirical considerations and is susceptible of further tuning and refinement. Once all the tracks covering the earthquake area have been identified, the system retrieves all the available SAR Sentinel-1 data (from both ascending and descending passes) up to 30 days before the event (or at least 1 pre-event image even in a larger time span), in order to allow the generation of the co-seismic interferograms. The data retrieval, and accordingly the subsequent DInSAR processing, remains active up to 30 days after the event. Once the data are downloaded, they are processed through an efficient DInSAR algorithm [8] (Block D). According to this scenario and taking benefit from the operational capability of the Sentinel-1 constellation, the processing of the different tracks can be carried out in parallel, while actually their execution depends on the available computing resources and on the effective temporal acquisition of the SAR data. A processing prioritization of the different tracks on the basis of the post-event acquisition time has been implemented (according to a First come-First served policy). The tool provides wrapped interferograms and displacement maps (unwrapped interferograms converted in centimetres) in the satellite Line of Sight (LOS). The output data are provided according to the specification of the European Plate Observing System (EPOS) [9] research infrastructure, and will be made openly available through the EPOS portal, to be investigated and interpreted by the scientific community. The system has been implemented on in-house computing facilities and has been tested through a controlled experiment with several significant earthquakes. Although tested with Sentinel-1 data, the implemented tool is independent from the exploited SAR acquisitions, thus increasing the number of data to be processed. Indeed, the only dependency is on the catalog interface that, if does not respect an Open standard, requires the implementation of an appropriate wrapper. It is also worth noting that the presented tool, since it takes benefit from efficient and scalable DInSAR algorithms, can be exploited to perform large processing campaigns of all the co-seismic DInSAR pairs acquired by the Sentinel-1, and even ERS and ENVISAT, since their respective launch. To do this, disposing of proper computing facilities, such as those provided by the DIAS [10] platforms where data and processing are co-located, is strongly envisaged.

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    Authors: Shevchenko, A.; Dvigalo, V.; Walter, T.; Mania, R.;

    Decades of photogrammetric records at Bezymianny, one of the most active volcanoes on Earth, allow unveiling morphological changes, eruption and intrusion dynamics, erosion, lava and tephra deposition processes. This data publication releases an almost 7-decade long record, retrieved from airborne, satellite, and UAV platforms. The Kamchatkan Institute of Volcanology and Seismology released archives of high-resolution aerial images acquired in 1967-2013. We complemented the aerial datasets with 2017 Pleiades tri-stereo satellite and UAV images. The images were processed using Erdas Imagine and Photomod software. Here we publish nine quality-controlled point clouds in LAS format referenced to the WGS84 (UTM zone 57N). By comparing the point clouds we were able to describe topographic changes and calculate volumetric differences, details of which were further analyzed in Shevchenko et al. (2020, https://doi.org/...). The ~5-decade-long photogrammetric record was achieved by 8 aerial and 1 satellite-UAV datasets. The 8 sets of near nadir aerial photographs acquired in 1967, 1968, 1976, 1977, 1982, 1994, 2006, and 2013 were taken with various photogrammetry cameras dedicated for topographic analysis, specifically the AFA 41-10 camera (1967, 1968, 1976, and 1977; focal length = 99.086 mm), the TAFA 10 camera (1982 and 1994; focal length = 99.120 mm), and the AFA TE-140 camera (2006 and 2013; focal length = 139.536 mm). These analog cameras have all an 18×18 cm frame size. The acquisition flight altitude above the mean surface of Bezymianny varied from 1,500-2,500 m above mean surface elevation, translating up to >5,000 m above sea level. For photogrammetric processing, we used 3-4 consecutive shots that provided a 60-70% forward overlap. The analog photo negatives were digitized by scanning with Epson Perfection V750 Pro scanner in a resolution of 2,400 pixels/inch (approx. pixel (px) size = 0.01 mm). The mean scale within a single photograph depends on the distance to the surface and corresponds on average to 1:10,000-1:20,000. Thus, each px in the scanned image represents about 10-20 cm resolution on the ground. The coordinates of 12 ground control points were derived from a Theo 010B theodolite dataset collected at geodetic benchmarks during a 1977 fieldwork. These benchmarks were established on the slopes of Bezymianny before the 1977 aerial survey and then captured with the AFA 41-10 aerial camera. The most recent was a satellite dataset acquired on 2017-09-09 by the PHR 1B sensor aboard the Pleiades satellite (AIRBUS Defence & Space) operated by the French space agency (CNES). The forward, nadir and backward camera configuration allows revisiting any point on earth and was tasked for the acquisition of Bezymianny to provide a 0.5 m resolution panchromatic imagery dataset. In order to improve the Pleiades data, we complemented them with UAV data collected on 2017-07-29 with DJI Mavic Pro during fieldwork at Bezymianny. This data publication includes a description of the data (in pdf format) and the nine processed and controlled three-dimensional point clouds (in LAS format). The point clouds can be easily interpolated and imported into most open and commercially available geographic information system (GIS) software. Further details on data and data handling are provided in Shevchenko et al. (2020).

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    Authors: Manunta, Michele; De Luca, Claudio; Zinno, Ivana; Casu, Francesco; +8 Authors

    We present an advanced differential synthetic aperture radar (SAR) interferometry (DInSAR) processing chain, based on the Parallel Small BAseline Subset (P-SBAS) technique, for the efficient generation of deformation time series from Sentinel-1 (S-1) interferometric wide (IW) swath SAR data sets. We first discuss an effective solution for the generation of high-quality interferograms, which properly accounts for the peculiarities of the terrain observation with progressive scans (TOPS) acquisition mode used to collect S-1 IW SAR data. These data characteristics are also properly accounted within the developed processing chain, taking full advantage from the burst partitioning. Indeed, such data structure represents a key element in the proposed P-SBAS implementation of the S-1 IW processing chain, whose migration into a cloud computing (CC) environment is also envisaged. An extensive experimental analysis, which allows us to assess the quality of the obtained interferometric products, is presented. To do this, we apply the developed S-1 IW P-SBAS processing chain to the overall archive acquired from descending orbits during the March 2015-April 2017 time span over the whole Italian territory, consisting in 2740 S-1 slices. In particular, the quality of the final results is assessed through a large-scale comparison with the GPS measurements relevant to nearly 500 stations. The mean standard deviation value of the differences between the DInSAR and the GPS time series (projected in the radar line of sight) is less than 0.5 cm, thus confirming the effectiveness of the implemented solution. Finally, a discussion about the performance achieved by migrating the developed processing chain within the Amazon Web Services CC environment is addressed, highlighting that a two-year data set relevant to a standard S-1 IW slice can be reliably processed in about 30 h. The presented results demonstrate the capability of the implemented P-SBAS approach to efficiently and effectively process large S-1 IW data sets relevant to extended portions of the earth surface, paving the way to the systematic generation of advanced DInSAR products to monitor ground displacements at a very wide spatial scale.

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    Authors: Theodoridou, Maria; Ivanovic, Dragan; Martin, Paul; Remy, Laurent; +1 Authors

    This archive contains a set of mappings in X3ML format between a number of metadata record schemes and CERIF RDF (based on CERIF 1.6). These mapping files were created during the EU Horizon 2020 VRE4EIC project as part of the development of a joint catalogue for resources hosted by various European research infrastructures serving the environmental and earth sciences. The following source metadata schemes are supported: Dublin Core DCAT-AP EPOS DCAT-AP (extension of DCAT-AP used within the EPOS research infrastructure) ISO 19139 OIL-E (Open Information Linking for Environmental research infrastructures) The scheme used by EUDAT's B2FIND service (based on CKAN) The scheme used within the D4Science platform (based on CKAN) In addition, test mappings have been developed for the transformation of RBAC data, WADL specifications, and WSDL (both 1.1 and 2.0), transformation of CERIF and VRE4EIC classification schemes into CERIF RDF, and generation of additional provenance data for data originating from EPOS or the ENVRI environmental research infrastructure cluster. To make use of these mappings, it is necessary to have a means of interpreting X3ML documents. See: https://github.com/isl/x3ml To directly view and edit these mappings, as well as to test them, it is best to use the 3M editor (which interprets X3ML). See: https://github.com/isl/Mapping-Memory-Manager 3M can be set up and run as an online service in Apache Tomcat (version 8 or higher). These mappings are provided to interested parties who wish to replicate or build upon the metadata mapping activity of the VRE4EIC project, or wish to import metadata records from external sources into a single RDF triple store structured according to the CERIF 1.6 standard. They represent the best state of mappings as of the end of the VRE4EIC project, in October 2018, but are provided as-is, with no assertion of completeness, soundness or fitness-for-purpose out of the box. For queries regarding the mappings in this archive, or for information about the VRE4EIC project in general, email vre4eic-contact at ercim dot eu.

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  • Authors: Casu, Francesco1; Bonano, Manuela1,2; Buonanno, Sabatino1; De Luca, Claudio1; +5 Authors
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    Authors: Lazzeri, Emma; Cocco, Massimo; Bailo, Daniele; Sarretta, Alessandro; +1 Authors

    A cycle of four webinars on Open Science and Open Access for earth and environmental sciences, with discipline-specific tools and practical resources. Course outline: Module 1: - Introduction and motivations - Open Science in Solid Earth Science Module 2: - Research Data Management - OS in solid Earth sciences: the EPOS research infrastructure experience Module 3: - FAIR principles and Open Data - Implementing FAIR. Considerations from the solid Earth domain Module 4: - The Data Management Plan - The adoption of Open Science Paradigm at INGV - Practical Tips Scientific committee: Maria Silvia Giamberini, IGG/CNR Gina Pavone, ISTI/CNR

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  • Authors: Vincenzo De Novellis (1); Simone Atzori (2); Manuela Bonano (3); Raffaele Castaldo (1); +12 Authors
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  • Authors: Fernando Monterroso (1; 2); Manuela Bonano (2; 3); +9 Authors

    During the last decades, the availability of Synthetic Aperture Radar (SAR) satellite missions, such as the ERS-1/2 and ENVISAT ones operating at C-band who have worked since 1992 to 2011, as well as the X-band COSMOSkyMed and TerraSAR-X constellations, up to the brand new Sentinel-1 mission, have strongly contributed to SAR data diffusion and popularity in the generation of different studies at different scales and in different research fields. One of the most popular SAR technique is the one referred to as Differential SAR Interferometry (DInSAR), which allows measuring with centimeter accuracy the Earth's surface deformation entity related to both natural and man-made hazards. Nowadays, with the increasing of SAR data availability provided by Sentinel-1 constellation of Copernicus European Program, which is composed by two twin satellites operating in C-band since 2014 and 2016, with a repeat pass of 6 days and with a global (i.e. worldwide) data acquisition policy, the SAR EO scenario is becoming more and more operational, thus mainly providing support for natural hazards monitoring. This allows, in theory, and disposing of sufficient computing power, the EO community to monitor, for instance, the deformation of every volcano or to obtain co-seismic displacement maps in a short time frame and anywhere in the world. Accordingly, in this work, we present a fully automatic and fast processing service for the generation of co-seismic displacement maps by using Sentinel-1 data. The implemented system is completely unsupervised and is triggered by the all significant (i.e. larger than a defined magnitude) seismic event registered by the online catalog as those provided by the United States Geological Survey (USGS) and the National Institute of Geophysics and Volcanology of Italy (INGV). The service has been specifically designed to operate for Civil Protection purposes. The generated DInSAR measurements are made available to the geoscience community through the EPOS Research Infrastructure and they will contribute to the creation of a global database of co-seismic displacement maps. Finally, it is worth noting that the developed system relies on widely common IT methods and protocols and is not specifically tied to a defined computing architecture, thus implying its portability, in view also of the European Commission Data and Information Access Services (DIAS) where satellite data (mainly Sentinel) and processing facilities are co-located to reduce the data transfer time during their processing.

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  • Authors: F. Casu1; M. Bonano1; 2; R. Castaldo1; +11 Authors

    We present an unsupervised and automatic system for volcano deformation monitoring via the Copernicus Sentinel-1 data. The system relies on the Parallel Small BAseline Subset (P-SBAS) approach, permitting us to generate updated displacement time series at every new Sentinel-1 acquisition over a selected area of interest in a fast and accurate way. The service is currently operative to monitor the main active Italian volcanoes in the framework of cooperation with the Italian Department of Civil Protection. The system is potentially extendable to every area on the Earth, thus making it suitable for surface displacement monitoring of a large variety of phenomena. Finally, the obtained results are made available to the scientific community through the EPOS Research Infrastructure.

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    Authors: DeFelipe, I; Alcalde, J; Baykiev, E; Bernal, I; +19 Authors

    The immense advances in computer power achieved in the last decades have had a significant impact in Earth science, providing valuable research outputs that allow the simulation of complex natural processes and systems, and generating improved forecasts. The development and implementation of innovative geoscientific software is currently evolving towards a sustainable and efficient development by integrating models of different aspects of the Earth system. This will set the foundation for a future digital twin of the Earth. The codification and update of this software require great effort from research groups and therefore, it needs to be preserved for its reuse by future generations of geoscientists. Here, we report on Geo-Soft-CoRe, a Geoscientific Software & Code Repository, hosted at the archive DIGITAL.CSIC. This is an open source, multidisciplinary and multiscale collection of software and code developed to analyze different aspects of the Earth system, encompassing tools to: 1) analyze climate variability; 2) assess hazards, and 3) characterize the structure and dynamics of the solid Earth. Due to the broad range of applications of these software packages, this collection is useful not only for basic research in Earth science, but also for applied research and educational purposes, reducing the gap between the geosciences and the society. By providing each software and code with a permanent identifier (DOI), we ensure its self-sustainability and accomplish the FAIR (Findable, Accessible, Interoperable and Reusable) principles. Therefore, we aim for a more transparent science, transferring knowledge in an easier way to the geoscience community, and encouraging an integrated use of computational infrastructure.

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  • Authors: Fernando Monterroso Tobar1; 2; Claudio de Luca2; Manuela Bonano2; +7 Authors

    Earth's surface deformation that occur as a consequence of an earthquake is a crucial information for investigating the causative source of the seismic event. In this context, the space-borne Differential Synthetic Aperture Radar Interferometry (DInSAR) has proven to be one of the key methods for the quantitative measurement of the Earth's surface deformation, with centimetres to millimetres accuracy [1]. DInSAR relies on the evaluation of the phase difference between two SAR images, acquired from different orbital positions and at different times [1]. Depending on the system configuration, the footprint of space-borne SAR acquisitions can span from a few kilometres up to hundreds of kilometres, making it particularly suitable for accurate investigations of wide areas at relative low cost. In these sense, according to USGS records [2], from 1992 to 2016, about 3700 earthquakes with significant magnitudes (Mw > 6.0) have occurred, while only a limited number of them has been successfully investigated through DInSAR [3]. This is mainly due, apart the intrinsic limitation of the DInSAR technique, to the lack of a satellite program with a systematic and global acquisition policy, which are fundamental characteristics to allow creating DInSAR operational services at global scale. However, since the launch of the Copernicus Sentinel-1 SAR satellite missions in 2014 and 2016, the availability of SAR images dramatically increased. Indeed, this constellation acquires, with global coverage policy, radar images every 6/12 days over the same area, allowing us to dispose of a huge archive of SAR data that can be processed for obtaining co-seismic displacement maps in a short time frame and anywhere in the world. Considering the relevance of the satellite interferometric analysis for the hazards monitoring, as well as the availability of new radar systems as Sentinel-1, which are characterized by a high reliability level, is it therefore possible the development of operational services for the generation of DInSAR products, some of them being already in place [4, 5]. In this work an unsupervised and automatic tool for the generation of DInSAR co-seismic displacement maps is presented. Benefiting from the mostly global availability of Sentinel-1 SAR data and the on-line earthquake catalogues, the tool retrieves information about the depth and magnitude of recent earthquakes and triggers, if necessary, the interferometric process over the area affected by the seismic event. The workflow process is the following (Figure 1). First, the extraction of earthquake information (epicenter location, magnitude, time, ...) from the on-line public available web catalogues, as those provided by main international geophysical institutions (e.g. USGS [2], INGV [6]), is performed (Block A of Figure 1). The retrieved information is provided according to different standard formats (QuakeML, geoJSON, ...) and is accessible via subscription feeds that are updated with a defined frequency. The system is not limited to a single earthquake catalog interface. The relevant earthquake information is collected in accordance to an empirical magnitude and depth relation, which considers that only high magnitude (> Mw 6.0) and relatively shallow earthquakes (typically < 20 km) very likely induce a surface deformation that is detectable via DInSAR [7] (Block B). Among the earthquakes that respect the relation, only those with the epicentre on land (or even on water but that can likely induce detectable deformation on land) are processed. Once the occurred earthquake has been selected, the SAR data retrieval is performed via an automatic query to the open access Sentinel-1 catalogue (Block C). The query is performed over an area whose extension depends of the relation between magnitude, depth and epicenter location, which is derived from theoretical and empirical considerations and is susceptible of further tuning and refinement. Once all the tracks covering the earthquake area have been identified, the system retrieves all the available SAR Sentinel-1 data (from both ascending and descending passes) up to 30 days before the event (or at least 1 pre-event image even in a larger time span), in order to allow the generation of the co-seismic interferograms. The data retrieval, and accordingly the subsequent DInSAR processing, remains active up to 30 days after the event. Once the data are downloaded, they are processed through an efficient DInSAR algorithm [8] (Block D). According to this scenario and taking benefit from the operational capability of the Sentinel-1 constellation, the processing of the different tracks can be carried out in parallel, while actually their execution depends on the available computing resources and on the effective temporal acquisition of the SAR data. A processing prioritization of the different tracks on the basis of the post-event acquisition time has been implemented (according to a First come-First served policy). The tool provides wrapped interferograms and displacement maps (unwrapped interferograms converted in centimetres) in the satellite Line of Sight (LOS). The output data are provided according to the specification of the European Plate Observing System (EPOS) [9] research infrastructure, and will be made openly available through the EPOS portal, to be investigated and interpreted by the scientific community. The system has been implemented on in-house computing facilities and has been tested through a controlled experiment with several significant earthquakes. Although tested with Sentinel-1 data, the implemented tool is independent from the exploited SAR acquisitions, thus increasing the number of data to be processed. Indeed, the only dependency is on the catalog interface that, if does not respect an Open standard, requires the implementation of an appropriate wrapper. It is also worth noting that the presented tool, since it takes benefit from efficient and scalable DInSAR algorithms, can be exploited to perform large processing campaigns of all the co-seismic DInSAR pairs acquired by the Sentinel-1, and even ERS and ENVISAT, since their respective launch. To do this, disposing of proper computing facilities, such as those provided by the DIAS [10] platforms where data and processing are co-located, is strongly envisaged.

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    Authors: Shevchenko, A.; Dvigalo, V.; Walter, T.; Mania, R.;

    Decades of photogrammetric records at Bezymianny, one of the most active volcanoes on Earth, allow unveiling morphological changes, eruption and intrusion dynamics, erosion, lava and tephra deposition processes. This data publication releases an almost 7-decade long record, retrieved from airborne, satellite, and UAV platforms. The Kamchatkan Institute of Volcanology and Seismology released archives of high-resolution aerial images acquired in 1967-2013. We complemented the aerial datasets with 2017 Pleiades tri-stereo satellite and UAV images. The images were processed using Erdas Imagine and Photomod software. Here we publish nine quality-controlled point clouds in LAS format referenced to the WGS84 (UTM zone 57N). By comparing the point clouds we were able to describe topographic changes and calculate volumetric differences, details of which were further analyzed in Shevchenko et al. (2020, https://doi.org/...). The ~5-decade-long photogrammetric record was achieved by 8 aerial and 1 satellite-UAV datasets. The 8 sets of near nadir aerial photographs acquired in 1967, 1968, 1976, 1977, 1982, 1994, 2006, and 2013 were taken with various photogrammetry cameras dedicated for topographic analysis, specifically the AFA 41-10 camera (1967, 1968, 1976, and 1977; focal length = 99.086 mm), the TAFA 10 camera (1982 and 1994; focal length = 99.120 mm), and the AFA TE-140 camera (2006 and 2013; focal length = 139.536 mm). These analog cameras have all an 18×18 cm frame size. The acquisition flight altitude above the mean surface of Bezymianny varied from 1,500-2,500 m above mean surface elevation, translating up to >5,000 m above sea level. For photogrammetric processing, we used 3-4 consecutive shots that provided a 60-70% forward overlap. The analog photo negatives were digitized by scanning with Epson Perfection V750 Pro scanner in a resolution of 2,400 pixels/inch (approx. pixel (px) size = 0.01 mm). The mean scale within a single photograph depends on the distance to the surface and corresponds on average to 1:10,000-1:20,000. Thus, each px in the scanned image represents about 10-20 cm resolution on the ground. The coordinates of 12 ground control points were derived from a Theo 010B theodolite dataset collected at geodetic benchmarks during a 1977 fieldwork. These benchmarks were established on the slopes of Bezymianny before the 1977 aerial survey and then captured with the AFA 41-10 aerial camera. The most recent was a satellite dataset acquired on 2017-09-09 by the PHR 1B sensor aboard the Pleiades satellite (AIRBUS Defence & Space) operated by the French space agency (CNES). The forward, nadir and backward camera configuration allows revisiting any point on earth and was tasked for the acquisition of Bezymianny to provide a 0.5 m resolution panchromatic imagery dataset. In order to improve the Pleiades data, we complemented them with UAV data collected on 2017-07-29 with DJI Mavic Pro during fieldwork at Bezymianny. This data publication includes a description of the data (in pdf format) and the nine processed and controlled three-dimensional point clouds (in LAS format). The point clouds can be easily interpolated and imported into most open and commercially available geographic information system (GIS) software. Further details on data and data handling are provided in Shevchenko et al. (2020).

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    Authors: Manunta, Michele; De Luca, Claudio; Zinno, Ivana; Casu, Francesco; +8 Authors

    We present an advanced differential synthetic aperture radar (SAR) interferometry (DInSAR) processing chain, based on the Parallel Small BAseline Subset (P-SBAS) technique, for the efficient generation of deformation time series from Sentinel-1 (S-1) interferometric wide (IW) swath SAR data sets. We first discuss an effective solution for the generation of high-quality interferograms, which properly accounts for the peculiarities of the terrain observation with progressive scans (TOPS) acquisition mode used to collect S-1 IW SAR data. These data characteristics are also properly accounted within the developed processing chain, taking full advantage from the burst partitioning. Indeed, such data structure represents a key element in the proposed P-SBAS implementation of the S-1 IW processing chain, whose migration into a cloud computing (CC) environment is also envisaged. An extensive experimental analysis, which allows us to assess the quality of the obtained interferometric products, is presented. To do this, we apply the developed S-1 IW P-SBAS processing chain to the overall archive acquired from descending orbits during the March 2015-April 2017 time span over the whole Italian territory, consisting in 2740 S-1 slices. In particular, the quality of the final results is assessed through a large-scale comparison with the GPS measurements relevant to nearly 500 stations. The mean standard deviation value of the differences between the DInSAR and the GPS time series (projected in the radar line of sight) is less than 0.5 cm, thus confirming the effectiveness of the implemented solution. Finally, a discussion about the performance achieved by migrating the developed processing chain within the Amazon Web Services CC environment is addressed, highlighting that a two-year data set relevant to a standard S-1 IW slice can be reliably processed in about 30 h. The presented results demonstrate the capability of the implemented P-SBAS approach to efficiently and effectively process large S-1 IW data sets relevant to extended portions of the earth surface, paving the way to the systematic generation of advanced DInSAR products to monitor ground displacements at a very wide spatial scale.

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      ZENODO
      Article . 2019
      License: CC BY
      Data sources: Datacite
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      ZENODO
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      ZENODO; CNR ExploRA
      Article . 2019
      License: CC BY
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Theodoridou, Maria; Ivanovic, Dragan; Martin, Paul; Remy, Laurent; +1 Authors

    This archive contains a set of mappings in X3ML format between a number of metadata record schemes and CERIF RDF (based on CERIF 1.6). These mapping files were created during the EU Horizon 2020 VRE4EIC project as part of the development of a joint catalogue for resources hosted by various European research infrastructures serving the environmental and earth sciences. The following source metadata schemes are supported: Dublin Core DCAT-AP EPOS DCAT-AP (extension of DCAT-AP used within the EPOS research infrastructure) ISO 19139 OIL-E (Open Information Linking for Environmental research infrastructures) The scheme used by EUDAT's B2FIND service (based on CKAN) The scheme used within the D4Science platform (based on CKAN) In addition, test mappings have been developed for the transformation of RBAC data, WADL specifications, and WSDL (both 1.1 and 2.0), transformation of CERIF and VRE4EIC classification schemes into CERIF RDF, and generation of additional provenance data for data originating from EPOS or the ENVRI environmental research infrastructure cluster. To make use of these mappings, it is necessary to have a means of interpreting X3ML documents. See: https://github.com/isl/x3ml To directly view and edit these mappings, as well as to test them, it is best to use the 3M editor (which interprets X3ML). See: https://github.com/isl/Mapping-Memory-Manager 3M can be set up and run as an online service in Apache Tomcat (version 8 or higher). These mappings are provided to interested parties who wish to replicate or build upon the metadata mapping activity of the VRE4EIC project, or wish to import metadata records from external sources into a single RDF triple store structured according to the CERIF 1.6 standard. They represent the best state of mappings as of the end of the VRE4EIC project, in October 2018, but are provided as-is, with no assertion of completeness, soundness or fitness-for-purpose out of the box. For queries regarding the mappings in this archive, or for information about the VRE4EIC project in general, email vre4eic-contact at ercim dot eu.

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    ZENODO
    Other ORP type . 2019
    Data sources: Datacite
    ZENODO
    Other ORP type . 2019
    Data sources: ZENODO
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  • Authors: Casu, Francesco1; Bonano, Manuela1,2; Buonanno, Sabatino1; De Luca, Claudio1; +5 Authors
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    Conference object . 2019
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