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462 results for “Volcano”
Caldera resurgence during the 2018 eruption of Sierra Negra volcano, Galápagos Islands
<p>Key datasets associated with the 'Caldera resurgence during the 2018 eruption of Sierra Negra volcano, Galápagos Islands'. This are pre-eruption and co-eruption interferograms, IGUANA earthquake catalogue, list of earthquake times and magnitudes picked from station VCH1, and cGPS baseline timeseries.</p>
Uplift and Seismicity driven by Magmatic Inflation at Sierra Negra Volcano, Galápagos Islands
<p>Catalogue of detected earthquakes and cGPS uplift timeseries for Sierra Negra Volcano, Galapagos Islands</p>
Systematic PSI processing over Santorini volcano: the 2011-2012 unrest period
<p>In the framework of the ESA funded project “Disaster Risk Reduction using innovative data exploitation methods and space assets”, the National Observatory of Athens together with Terradue customized a GEP implementation of the StaMPS suite of s/w modules, to automatically process stacks of SAR imagery using Persistent Scatterer Interferometry. The engine is now able to seamlessly and timely process data from Envisat, ERS-1,2, TerraSAR-X, COSMO-SkyMed and Radarsat-2 platforms, for user-defined Regions Of Interest. Hence, the concept of project’s “Trial Case VO-1” is to modify, customize, execute and test a fully automatic processing chain over the GEP, starting from raw/SLC SAR data, to ground velocities generation.</p> <p>The new capabilities have been tested for an historic volcano in Santorini island, Greece, aiming to create a unique database of diachronic InSAR measurements spanning from 1992 to 2015 (~23 years of data). The processing of the five different SAR stacks was done entirely within the GEP environment, in the context of the Trial Case VO-1. The thematic map included herein contains the estimated Line-Of-Sight ground velocities (mm/yr) for the <strong>period 2011-2012, based on 12 descending ASAR Envisat images</strong>. Kindly note that the product is raw and has not gone through any manual post-processing. Therefore, adjustments may need to be made to address reference point issues, isolated phase unwrapping errors, etc. </p>
Systematic PSI processing over Santorini volcano: the 2012-2013 post unrest period
<p>In the framework of the ESA funded project “Disaster Risk Reduction using innovative data exploitation methods and space assets”, the National Observatory of Athens together with Terradue customized a GEP implementation of the StaMPS suite of s/w modules, to automatically process stacks of SAR imagery using Persistent Scatterer Interferometry. The engine is now able to seamlessly and timely process data from Envisat, ERS-1,2, TerraSAR-X, COSMO-SkyMed and Radarsat-2 platforms, for user-defined Regions of Interest. Hence, the concept of project’s “Trial Case VO-1” is to modify, customize, execute and test a fully automatic processing chain over the GEP, starting from raw/SLC SAR data, to ground velocities generation. The new capabilities have been tested for an historic volcano in Santorini island, Greece, aiming to create a unique database of diachronic InSAR measurements spanning from 1992 to 2015 (~23 years of data). The processing of the five different SAR stacks was done entirely within the GEP environment, in the context of the Trial Case VO-1. The thematic map included herein contains the estimated Line-Of-Sight ground velocities (mm/yr) for the period 2012-2013, based on 25 descending TerraSAR-X images. Kindly note that the product is raw and has not gone through any manual post-processing. Therefore, adjustments may need to be made to address reference point issues, isolated phase unwrapping errors, etc. </p>
Systematic PSI processing over Santorini volcano: the 1992-2000 pre-unrest period
<p>In the framework of the ESA funded project “Disaster Risk Reduction using innovative data exploitation methods and space assets”, the National Observatory of Athens together with Terradue customized a GEP implementation of the StaMPS suite of s/w modules, to automatically process stacks of SAR imagery using Persistent Scatterer Interferometry. The engine is now able to seamlessly and timely process data from Envisat, ERS-1,2, TerraSAR-X, COSMO-SkyMed and Radarsat-2 platforms, for user-defined Regions Of Interest. Hence, the concept of project’s “Trial Case VO-1” is to modify, customize, execute and test a fully automatic processing chain over the GEP, starting from raw/SLC SAR data, to ground velocities generation.</p> <p>The new capabilities have been tested for an historic volcano in Santorini island, Greece, aiming to create a unique database of diachronic InSAR measurements spanning from 1992 to 2015 (~23 years of data). The processing of the five different SAR stacks was done entirely within the GEP environment, in the context of the Trial Case VO-1. The thematic map included herein contains the estimated Line-Of-Sight ground velocities (mm/yr) for the <strong>period 1992-2000, based on 37 descending ERS-1,2 images</strong>. Kindly note that the product is raw and has not gone through any manual post-processing. Therefore, adjustments may need to be made to address reference point issues, isolated phase unwrapping errors, etc. </p>
Systematic PSI processing over Santorini volcano: the 2015 post unrest period
<p>In the framework of the ESA funded project “Disaster Risk Reduction using innovative data exploitation methods and space assets”, the National Observatory of Athens together with Terradue customized a GEP implementation of the StaMPS suite of s/w modules, to automatically process stacks of SAR imagery using Persistent Scatterer Interferometry. The engine is now able to seamlessly and timely process data from Envisat, ERS-1,2, TerraSAR-X, COSMO-SkyMed and Radarsat-2 platforms, for user-defined Regions Of Interest. Hence, the concept of project’s “Trial Case VO-1” is to modify, customize, execute and test a fully automatic processing chain over the GEP, starting from raw/SLC SAR data, to ground velocities generation.</p> <p>The new capabilities have been tested for an historic volcano in Santorini island, Greece, aiming to create a unique database of diachronic InSAR measurements spanning from 1992 to 2015 (~23 years of data). The processing of the five different SAR stacks was done entirely within the GEP environment, in the context of the Trial Case VO-1. The thematic map included herein contains the estimated Line-Of-Sight ground velocities (mm/yr) for the <strong>period 2015, based on 30 ascending COSMO-SkyMed</strong> <strong>images</strong>. Kindly note that the product is raw and has not gone through any manual post-processing. Therefore, adjustments may need to be made to address reference point issues, isolated phase unwrapping errors, etc. </p>
Systematic PSI processing over Santorini volcano: the 2012-2016 post unrest period
<p>In the framework of the ESA funded project “Disaster Risk Reduction using innovative data exploitation methods and space assets”, the National Observatory of Athens together with Terradue customized a GEP implementation of the StaMPS suite of s/w modules, to automatically process stacks of SAR imagery using Persistent Scatterer Interferometry. The engine is now able to seamlessly and timely process data from Envisat, ERS-1,2, TerraSAR-X, COSMO-SkyMed and Radarsat-2 platforms, for user-defined Regions Of Interest. Hence, the concept of project’s “Trial Case VO-1” is to modify, customize, execute and test a fully automatic processing chain over the GEP, starting from raw/SLC SAR data, to ground velocities generation.</p> <p>The new capabilities have been tested for an historic volcano in Santorini island, Greece, aiming to create a unique database of diachronic InSAR measurements spanning from 1992 to 2015 (~23 years of data). The processing of the five different SAR stacks was done entirely within the GEP environment, in the context of the Trial Case VO-1. The thematic map included herein contains the estimated Line-Of-Sight ground velocities (mm/yr) for the <strong>period 2012-2016, based on 20 descending Radarsat-2 images</strong>. Kindly note that the product is raw and has not gone through any manual post-processing. Therefore, adjustments may need to be made to address reference point issues, isolated phase unwrapping errors, etc. </p>
Digital Elevation Models of Hunga Volcano, Tonga, from the MAX2201 voyage, July-August 2022
<p>This dataset contains digital elevation models (DEM) of the Hunga Volcano complex, These DEM are from the MAX2201 voyage of the USV <i>Maxlimer</i> which surveyed the volcano July-August 2022.</p><p>Hunga Volcano is a volcanic complex near the island of Tongatapu in the Kingdom of Tonga. The volcano rises from ~2,500 m depth, a caldera at its summit, and two islands, Hunga Tonga and Hunga-Ha'apai, at the on the rim of the caldera. An eruption during December 2014-January 2015 was centered between the islands and combined them into one larger structure named Hunga Tonga – Hunga Ha'apai (HTHH). HTHH erupted violently on 15th January 2022, sending large clouds of ash into the atmosphere, triggering a tsunami, and reducing the size of the islands of Hunga Tonga and Hunga Ha'apai. </p><p>As a result of this event, the NIWA-Nippon Foundation Tonga Eruption Seabed Mapping Project (<strong>TESMaP</strong>) is a multidisciplinary research plan involving geological, oceanographic and biological studies that centered around three objectives: </p><ol><li>To determine the impacts of volcanic ash on ocean productivity, species composition, and biogeochemical cycling in the water column.</li><li>To determine the immediate nature and extent of the impact of ash fall/turbidity flows on deep-sea sediments and benthic ecosystems.</li><li>To determine the recovery potential of the deep-sea ecosystem.</li></ol><p>This project involved two survey voyages of the volcano and its surrounding waters. The first was carried out from <i>RV Tangaroa </i>(TAN2206) in April and May 2022 (Mackay et al., 2022) on the flanks of Hunga volcano and its surrounds; and the second was carried out over the summit of Hunga volcano by the <i>USV Maxlimer</i> (MAX2201) in August 2022.</p><p>TESMaP was funded from a combination of sources including The Nippon Foundation, Japan; the Natural Environmental Research Council, UK, Japan Agency for Marine Earth Science and Technology, the Tangaroa Reference Group (TRG) for ship time and the NIWA Oceans Centre. Support was given by The Nippon Foundation Seabed 2030 project and by GEBCO Alumni.</p>
Fig. 1 in First Confirmed Japanese Record of Suttonia lineata (Perciformes: Serranidae) from Iwo Island, Volcano Islands
Fig. 1. Color photograph of Suttonia lineata. KAUM–I. 99999, 57.3 mm SL, off Mount Suribachi, Iwo Island, Volcano Islands, Japan. A, left lateral view; B, dorsal view of head.
pY and pSTY phosphoproteomic data for interactive volcano plots - by Glykofridis et al.
<p>These CSV files are generated by Glykofridis et al. (2021) and part of the supplementary data of "<strong>Phosphoproteomic analysis of FLCN inactivation highlights differential kinase pathways and regulatory TFEB phosphoserines</strong>" to be published in Molecular and Cellular Proteomics.</p> <p>The CSV files are used as input to generate (interactive) volcano plots, using the web app VolcanoNoseR. The code is archived here: https://zenodo.org/record/3625858</p> <p>The most up-to-date version of the interactive web app is available here: <a href="https://huygens.science.uva.nl/VolcaNoseR/">https://huygens.science.uva.nl/VolcaNoseR/</a></p>
Figs 33–36. Trechus spp., elytra. 33. T in Revision of Trechus Clairville, 1806 of the Bale Mountains and adjacent volcanos, Ethiopia (Coleoptera, Carabidae, Trechini)
Figs 33–36. Trechus spp., elytra. 33. T. dodola sp. nov., holotype. 34. T. adaba sp. nov., paratype, ³. 35. T. harryi sp. nov., paratype, ³. 36. T. bayedika sp. nov., paratype, ³. The arrows point to the insertions of the discal setae and the preapical seta.
Pléiades co- and post-eruption survey in Cumbre Vieja volcano, La Palma, Spain
<p><strong>Introduction</strong>: </p> <p>This repository consists of a series of topographic surfaces of the Cumbre Vieja volcano (La Palma, Spain), presented as a series of Digital Elevation Models (DEMs) obtained from multiple Pléiades stereoscopic surveys acquired from the 22<sup>nd</sup> of September 2021 until the 14<sup>th</sup> of January 2022. We also present a series of grids showing the difference of elevation between the pre-eruption surface and the co- and post-eruption surface, which reveal the lava thickness of the eruption. This was used to calculate the lava volume and effusion rate or Time Average Discharge Rate (TADR) at the time of the Pléiades surveys.</p> <p> </p> <p><strong>Data</strong>: </p> <p>1 – Pléiades stereo images: </p> <p>A pre-eruption Pléiades stereopair was collected from 2013. A total of ten stereopairs were collected between the 23<sup>th</sup> of September 2021 and 2<sup>nd</sup> of October 2021 as part of the CIEST<sup>2</sup> initiative (https://www.poleterresolide.fr/ciest-2-nouvelle-generation-2/). Four additional pairs were acquired between the 11<sup>th</sup> of December 2021 and the 14<sup>th</sup> of January 2022 as part of the Dinamis initiative (https://dinamis.data-terra.org/). However, only some of these stereopairs were acquired with sufficiently large cloud-free areas around the eruption site. The following Pléiades stereo images were processed and are presented in this repository: </p> <p> </p> <table> <tbody> <tr> <td> <p>Date </p> </td> <td> <p>Sensor </p> </td> <td> <p>IDs </p> </td> </tr> <tr> <td> <p>2013-06-30, 12h02m </p> </td> <td> <p>PHR1B </p> </td> <td> <p>5944045101 & 5944046101 </p> </td> </tr> <tr> <td> <p>2021-09-26, 11h58m </p> </td> <td> <p>PHR1B </p> </td> <td> <p>5962414101 & 5962415101 </p> </td> </tr> <tr> <td> <p>2021-10-02, 12h02m </p> </td> <td> <p>PHR1B </p> </td> <td> <p>5988066101 & 5988067101 </p> </td> </tr> <tr> <td> <p>2022-01-01, 12h02m </p> </td> <td> <p>PHR1A </p> </td> <td> <p>6122469101 & 6122470101 </p> </td> </tr> <tr> <td> <p>2022-01-14, 12h02m </p> </td> <td> <p>PHR1B </p> </td> <td> <p>6135055101 & 6135057101 </p> </td> </tr> </tbody> </table> <p>Table 1: Date, sensor and image ID of the Pléiades stereoimages used in this repository. </p> <p> </p> <p>2 – Lidar pre-eruption surface: </p> <p>A lidar survey acquired in 2016 by the Spanish Mapping Agency (IGN, Spain) was downloaded through the portal: <a href="http://centrodedescargas.cnig.es/CentroDescargas/catalogo.do?Serie=LIDAR">http://centrodedescargas.cnig.es/CentroDescargas/catalogo.do?Serie=LIDAR</a>#. Specifically, we used the Digital Surface Model (DSM) product, available in 2x2 m Ground Sampling Distance (GSD). This means that trees and human structures were removed based using classification of the multiple returns of the lidar pulses. The coordinate reference system is REGCAN (UTM zone 28N, EPSG: 32628), and the heights are orthometric, using the height reference system REDNAP, built upon the geoid EGM08. Using the REDNAP geoid model, we converted the heights to meters above ellipsoid (WGS84), since the Pléiades data is acquired with satellite attitudes referred to the ellipsoid WGS84. </p> <p> </p> <p><strong>Methods</strong>:</p> <p>The Pléiades stereoimages were processed using the Ames StereoPipeline (ASP, Shean et al., 2016, see ASP branch in repository), yielding a DEM in 2x2m GSD and an orthoimage in 0.5x0.5m GSD. The processing was done using as only input the stereoimages and their orientation information, as Rational Polynomial Coefficients (RPCs). The <em>parallel_stereo </em>routine performs all the steps needed in the correlation of the stereoimages, yielding a pointcloud which is then interpolated using the routine <em>point2dem</em>. Besides default parameters, the <em>parallel_stereo</em> parameters used for creation of the DEMs were the standard parameters, plus the following ones: </p> <p><em>--corr-tile-size 2048 --sgm-collar-size 256 --corr-seed-mode 3 --corr-max-levels 2 --corr-timeout 900 --cost-mode 3 --subpixel-mode 9 --corr-kernel 7 7 --subpixel-kernel 15 15</em></p> <p>Once the DEM was created, DEM co-registration was applying in order to align and minimize positional biases between the pre-eruption DEM and the Pléiades DEMs. We followed the co-registration method of Nuth & Kääb (2011), implemented by David Shean’s co-registration routines (<a href="https://github.com/dshean/demcoreg">https://github.com/dshean/demcoreg</a>, Shean et al., 2016). The co-registration involved a horizontal and vertical shift of the Pléiades DEMs, as well as a planar tilt correction. The horizontal offset obtained from the DEM co-registration was also applied to the Pléiades orthoimages.</p> <p>Lava outlines were manually digitized from the co-registered Pléiades orthoimages, excluding kipukas and major building constructions which were not covered by the lavas. The lava outlines are available as GeoPackages in the “GPKG” branch of the repository.</p> <p>Lava volume calculations were done using the average lava thickness, multiplied by the area covered by the lavas. The uncertainty in volume was assumed to be the Normalized Mean Absolute Deviation (NMAD, Höhle and Höhle, 2009), multiplied by the lava area. The TADR was calculated as the total volume divided by the time, in seconds, between the start of the eruption, defined as 2021-09-19 11:58:00 local time, and the acquisition of the Pléiades images. For the total TADR, we used the volume extracted from the Pléiades images from the 1<sup>st</sup> of January 2022, divided by the observed time of beginning and end of the eruption, defined as 2021-12-13 22:21:00, local time. The TADR values shown in this repository do not account for submarine lavas nor tephra deposits.</p> <p>In addition, another set of DEMs were produced automatically as soon as the images were made available by the on-demand processing service DSM-OPT provided by <a href="mailto:ForM@Ter">ForM@Ter</a> (https://en.poleterresolide.fr/on-demand-processing/#/mns). This processing is based on Micmac (D. Michéa and J.-P. Malet / EOST; E. Pointal, IPGP, Rupnik, 2017). The DEMs produced correspond to the file created automatically “A2_dsm_denoised.tif”. They were obtained in 1x1 m GSD, and they were cropped over the area of interest. These DEMs have not been co-registered. These data are available in the “MM” branch in the repository. </p> <p> </p> <p><strong>Results: Lava area, volumes and effusion rate:</strong> </p> <p> </p> <table> <tbody> <tr> <td> <p>Date </p> </td> <td> <p>Lava Area (km2) </p> </td> <td> <p>Lava thickness (m) </p> </td> <td> <p>Lava volume (10e+6 m3) </p> </td> <td> <p>TADR </p> <p>(m3 s-1) </p> </td> </tr> <tr> <td> <p>2021-09-26, 11h58m </p> </td> <td> <p>2.6 </p> </td> <td> <p>11.4±1.1 </p> </td> <td> <p>29.8±2.8 </p> </td> <td> <p>49.2±4.7 </p> </td> </tr> <tr> <td> <p>2021-10-02, 12h02m </p> </td> <td> <p>4.3 </p> </td> <td> <p>10.0±1.4 </p> </td> <td> <p>43.0±6.1 </p> </td> <td> <p>38.2±5.4 </p> </td> </tr> <tr> <td> <p>2022-01-01, 12h02m </p> </td> <td> <p>12.25 </p> </td> <td> <p>16.6±1.1 </p> </td> <td> <p>203.3±13.9 </p> </td> <td> <p>27.5±1.9 </p> </td> </tr> </tbody> </table> <p>Table 2: results of lava area, thickness, lava volume and TADR since the start of the eruption. </p> <p> </p> <p><strong>Repository structure:</strong></p> <p>zenodo_lapalma/<br> ├── ASP<br> │ ├── 20130630_1202_lapalma_PL_2x2m_UTM28N_ASP_DEM.tif<br> │ ├── 20210926_1158_lapalma_PL_2x2m_UTM28N_ASP_DEM.tif<br> │ ├── 20210926_1158_lapalma_PL_2x2m_UTM28N_thickness.tif<br> │ ├── 20211002_1202_lapalma_PL_2x2m_UTM28N_ASP_DEM.tif<br> │ ├── 20211002_1202_lapalma_PL_2x2m_UTM28N_thickness.tif<br> │ ├── 20220101_1202_lapalma_PL_2x2m_UTM28N_ASP_DEM.tif<br> │ ├── 20220101_1202_lapalma_PL_2x2m_UTM28N_thickness.tif<br> │ ├── 20220114_1202_lapalma_PL_2x2m_UTM28N_ASP_DEM.tif<br> │ └── 20220114_1202_lapalma_PL_2x2m_UTM28N_thickness.tif<br> ├── GPKG<br> │ ├── 20210925_1202_lapalma_PL_UTM28N_outline.gpkg<br> │ ├── 20211002_1202_lapalma_PL_UTM28N_outline.gpkg<br> │ └── 20220101_1202_lapalma_PL_UTM28N_outline.gpkg<br> └── MM<br> ├── 20130630_1202_PL_1x1m_UTM28N_MM_DEM.tif<br> ├── 20210926_1158_PL_1x1m_UTM28N_MM_DEM.tif<br> ├── 20211002_1230_PL_1x1m_UTM28N_MM_DEM.tif<br> ├── 20220101_1202_PL_1x1m_UTM28N_MM_DEM.tif<br> └── 20220114_1202_PL_1x1m_UTM28N_MM_DEM.tif</p> <p> </p> <p><strong>Acknowledgements</strong>: </p> <p>Pléiades images were provided under the CIEST² initiative (CIEST2 is part of ForM@Ter (<a href="https://en.poleterresolide.fr/">https://en.poleterresolide.fr/</a> ) and supported by ISDeform National Service of Observation) for the reference image acquired in 2013 and from the 23<sup>rd</sup> of September to the 2<sup>nd</sup> of October 2021, and through the Dinamis program (CNES, France) from the 12<sup>th</sup> of December 2021 to the 14<sup>th</sup> of January 2022 (image Pléiades©CNES2013,©CNES2021,©CNES2022, distribution AIRBUS DS) </p> <p> </p> <p><strong>Dataset Attribution</strong> </p> <p>This dataset is licensed under a <a href="https://creativecommons.org/licenses/by-nc/4.0/">Creative Commons CC BY-NC 4.0 International License</a> (Attribution-NonCommercial).<br> Attribution required for copies and derivative works:</p> <p>The underlying dataset from which this work has been derived includes Pleiades material ©CNES (2013,2021,2022), distributed by AIRBUS DS, and data provided by the Spanish Mapping Agency (IGN, Spain), all rights reserved.</p> <p> </p> <p><strong>Dataset Citation</strong> </p> <p>Belart and Pinel (2022). “Pléiades co- and post-eruption survey in Cumbre Vieja volcano, La Palma, Spain”. Dataset distributed on Zenodo: 10.5281/zenodo.5833771</p> <p> </p> <p><strong>References:</strong> </p> <p>Höhle, J. and Höhle, M.: Accuracy assessment of digital elevation models by means of robust statistical methods, ISPRS J. Photogramm. Remote Sens., 64, 398–406, https://doi.org/10.1016/j.isprsjprs.2009.02.003, 2009.</p> <p>Nuth, C. and Kääb, A.: Co-registration and bias corrections of satellite elevation datasets for quantifying glacier thickness change, The Cryosphere, 5, 271–290, https://doi.org/10.5194/tc-5-271-2011, 2011.</p> <p>Rupnik, E., Daakir, M., & Deseilligny, M. P.: MicMac – a free, open-source solution for photogrammetry. Open Geospatial Data, Software and Standards, 2(1), 1-9, 2017.</p> <p>Shean, D. E., Alexandrov, O., Moratto, Z. M., Smith, B. E., Joughin, I. R., Porter, C., and Morin, P.: An automated, open-source pipeline for mass production of digital elevation models (DEMs) from very-high-resolution commercial stereo satellite imagery, ISPRS J. Photogramm. Remote Sens., 116, 101–117, https://doi.org/10.1016/j.isprsjprs.2016.03.012, 2016. </p>
Fatiando a Terra Data: Sierra Negra volcano, Ecuador - Topography
<p>This is a topography point cloud of the 2018 lava flows of the Sierra Negra volcano, located on the Galápagos islands, Ecuador. The data are generated using structure from motion (SFM) and shows nice topographic features and different roughness of the lava flows. Good to show examples of calculating slope and other terrain properties from the point cloud or gridded data.</p> <p><strong>Note:</strong> This is a processed and formatted version of the source dataset below. It's meant for use in documentation and tutorials of the <a href="https://www.fatiando.org">Fatiando a Terra</a> project. Please <strong>cite the original authors</strong> when using this dataset.</p> <p><strong>Changes made: </strong>Data were cropped to smaller region to align with previously published studies of the data and make file sizes under 10 Mb. Coordinates converted from UTM to WGS84 geographic. Export to a compressed CSV for easier loading with Pandas.</p> <p><strong>Source: </strong>Carr, B. (2020). Sierra Negra Volcano (TIR Flight 3): Galápagos, Ecuador, October 22 2018. Distributed by OpenTopography. <a href="https://doi.org/10.5069/G957196P">https://doi.org/10.5069/G957196P</a></p> <p><strong>Additional reference:</strong> Carr, B. B., Lev, E., Sawi, T., Bennett, K. A., Edwards, C. S., Soule, S. A., et al. (2021). Mapping and classification of volcanic deposits using multi-sensor unoccupied aerial systems. Remote Sensing of Environment. <a href="https://doi.org/10.1016/j.rse.2021.112581">https://doi.org/10.1016/j.rse.2021.112581</a></p> <p><strong>Source license: </strong><a href="https://doi.org/10.5069/G957196P">CC-BY</a></p> <p><strong>Repository: </strong><a href="https://github.com/fatiando-data/sierra-negra-topography">https://github.com/fatiando-data/sierra-negra-topography</a></p>
Tracking magma spine extrusion from space: Implications for conduit and topography complexity at Shiveluch volcano, Kamchatka - Photogrammetric data repository
<p>This is a dataset relevant for a paper on lava spine extrusion at Shieveluch volcano, Kamchatka. Data was used to show that the spine elongates along a previously identified fracture line and bends to a preferred northerly direction. By repeated morphology analysis and feature tracking, we constrain a spine diameter of ~300 m, extruding at a velocity of 1.7 m/day and discharge rate of 0.3-0.7 m³/s. Results are relevant for understanding the growth and collapse hazards of spines and provide unique insights into the hidden magma-conduit architecture.</p> <p>The data consists of three parts. First, we provide the filtered and corrected three dimensional point clouds generated from Pleiades tristereo data. These 3D point clouds were co-aligned and now allow analysing subtle changes. Point clouds are provided in .las format. Second, we provide the filtered and corrected digital elevation models generated from the point cloud data, these DEMs are provided in geotiff format. The name of the files indicates the dates of their acquisition. Third and lastly, we provide an orthomap stack used to estimate displacements by tracking offsets.</p> <p> </p>
Thermal demagnetization data of Risica et al. (Deposit-derived block-and-ash flows: the hazard posed by perched temporary tephra accumulations on volcanoes; 2018 Fuego disaster, Guatemala)
<p>Thermal demagnetization data (repository data) of Risica et al. "Deposit-derived block-and-ash flows: the hazard posed by perched temporary tephra accumulations on volcanoes; 2018 Fuego disaster, Guatemala".</p>
Figs 32–33 in A new subspecies of Bembidion sanatum (Coleoptera: Carabidae) endemic to the Mendeleev Volcano (Kunashir Island, Russia)
Figs 32–33. Habitat of Bembidion sanatum iwanai, ssp. n.: 32 – upstream of Kislaya River; 33 – the bank of Kislaya River in the middle reaches.
Dataset for the paper "Accelerating Seafloor Uplift of Submarine Caldera near Sofugan Volcano, Japan, Resolved by Distant Tsunami Recordings"
<p>The results of the analysis in the paper "Accelerating Seafloor Uplift of Submarine Caldera near Sofugan Volcano, Japan, Resolved by Distant Tsunami Recordings" published in Geophysical Research Letters are available here. For the details of the file, please see Readme.pdf.</p>
Text-fig. 3. Distribution of main types of volcanoes in the NearShore Volcanic Belt of Eastern Sikhote-Alin' (Eocene–Neogene). 1 – Central volcanoes (partly preserved); 2 – Central volcanoes (destructed); 3 – Shield and gentle sloping volcanoes with a dolerite or trachy-basaltic neck on the top; 4 – Lava and scoria cones; 5 – Pyroclastic, tuffaceous coarse- and fine-grained terrigenous sedimentary rocks, partly with plant-bearing levels; 6 – Eruption centers of plateau-basalts and the direction of lava flows; 7 – Main Late Cenozoic basaltic plateaus; 8 – Fumarol fields; 9 – Hot springs. in Mid-Latitude Palaeogene Floras Of Eurasia Bound To Volcanic Settings And Palaeoclimatic Events - Experience Obtained From The Far East Of Russia (Sikhote-Alin') And Central Europe (Bohemian Massif)
Text-fig. 3. Distribution of main types of volcanoes in the NearShore Volcanic Belt of Eastern Sikhote-Alin' (Eocene–Neogene). 1 – Central volcanoes (partly preserved); 2 – Central volcanoes (destructed); 3 – Shield and gentle sloping volcanoes with a dolerite or trachy-basaltic neck on the top; 4 – Lava and scoria cones; 5 – Pyroclastic, tuffaceous coarse- and fine-grained terrigenous sedimentary rocks, partly with plant-bearing levels; 6 – Eruption centers of plateau-basalts and the direction of lava flows; 7 – Main Late Cenozoic basaltic plateaus; 8 – Fumarol fields; 9 – Hot springs.
Text-fig. 4. A typical Oligocene–Early Miocene gentle-sloping Central volcano with an eruptive dolerite neck near the top – Nevelskoy "Cap". in Mid-Latitude Palaeogene Floras Of Eurasia Bound To Volcanic Settings And Palaeoclimatic Events - Experience Obtained From The Far East Of Russia (Sikhote-Alin') And Central Europe (Bohemian Massif)
Text-fig. 4. A typical Oligocene–Early Miocene gentle-sloping Central volcano with an eruptive dolerite neck near the top – Nevelskoy "Cap".
Numerical simulations of the latest caldera-forming eruption of Okmok volcano, Alaska
<p>Raw outputs of the numerical simulations of an explosive volcanic eruption.</p> <p>The file "source.zip" includes all the source files modified compared to the default<br>MFIX 2016-1 files (www.mfix.netl.doe.gov, v.2016). It also includes an example of the input file mfix.dat (run 3). These<br>are raw file intended for enabling result reproductibility. They contain unused <br>optional variables and are not intented (and commented) to serve as tutorials. The<br>subfolder "postmfix" includes the source file for option 6 of the post processing<br>program. Some options are user-defined, and others are hard coded.</p> <p>The file "AllRuns.zip" inculde all the Okmok runs. All runs were generated with <br>MFIX 2016-1 in TFM mode. They are organized in folders corresponding to the naming<br>convention of Table 1. Some runs were generated in several sequences (e.g., 0-300 s,<br>then 300-700 s) that were stitched into a single output bundle. Other sequences were<br>kept separated.</p> <p>The files OK.RES can be openend with Paraview >5.6 and selecting the MFIXReader.</p> <p>The variables are:</p> <p>EP_g = Gas volume fraction<br>Gas Velocity = Gas velocity vector (m/s)<br>P_g = Pressure (Pa)<br>P_star = Solid pressure (Pa)<br>ROP_s_m = Solid density times particle volume fraction of solid phase m (kg/m3)<br>RRates_1 = Gas density (kg/m3)<br>RRates_2 = Gas viscosity (Pa s)<br>Solids_Velocity_x = Velocity vector of solid phase x (m/s)<br>T_g = Gas temperature (K)<br>T_s_x = Temperature of solid phase x (K)<br>Theta_m_x = Granular temperature of solid phase x (m2/s2)<br>U_g = Horizontal component of gas velocity (m/s)<br>V_g = Vertical component of gas velocity (m/s)<br>W_g = Z component of gas velocity (0 because axisymmetric domain)<br>U_s_x = Horizontal component of velocity of solid phase x (m/s)<br>V_s_x = Vertical component of velocity of solid phase x (m/s)<br>W_s_x = Z component of velocity of solid phase x (0 because axisymmetric domain)<br>X_g_x, X_s_x = unused<br>k_turb_g (not always recorded) = Turbulent kinetic energy (J/kg)<br>e_turb_g = duplicate of k_turb_g</p> <p><br>Refer to the MFIX documentation for more details on the format and on the model itself.</p> <p> </p>
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