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185 results for “Elevation model”
UAV-based colour-infrared orthomosaics and digital elevation models of basalts and rock glaciers on Disko Island, West Greenland
<p><span>This data set contains multispectral surveys conducted with an unoccupied aerial vehicle over rock glaciers and steep mafic outcrops (intrusive and flood volcanics) near the coastline of Disko Island.</span></p> <ul> <li><span>Acquisition date: 07.08.2019 – 10.08.2019</span></li> <li><span>Location: Illukunnguaq, Disko Island, Greenland</span></li> <li><span>UAV: SenseFly eBee Plus</span></li> <li><span>Flight altitude above ground level: >100m</span></li> <li><span>Image Overlap forward/side: various</span></li> <li><span>Camera: Parrot Sequoia multispectral</span></li> <li><span>EPSG: 32622</span></li> <li><span>Center coordinates: 69.885277°N, -52.577724°E</span></li> <li><span>Flight mode: automatic flight plan</span></li> </ul> <p><span>Data products: </span></p> <ul> <li><span>Orthomosaic colour-infrared, 10-16 cm pixel resolution</span></li> <li><span>Colour-infrared spectral bands: 790nm, 660nm, 550nm</span></li> <li><span>DEM, 20-30cm pixel resolution</span></li> <li><span>Data coverage: approx. 5500 x 2500 m</span></li> <li><span>Elevation profile: 20-680m </span></li> <li><span>Processing in Agisoft Metashape</span></li> </ul> <p><span>Additional data supplement for article:<br>Barnes, E. (2020). Assessment of Drone-Borne Multispectral Mapping in the Exploration of Magmatic Ni-Cu Sulphides–an Example from Disko Island, West Greenland. <br><em>URN: urn:nbn:se:uu:diva-418858</em></span></p> <p>MULSEDRO field campaign was conducted under scientific survey licence (VU-00158-2019) within mineral exploration licence MEL 2018-16 by Blue Jay Mining PLC. This research has been supported by the project MULSEDRO, funded by HZDR-HIF & EITRawMaterials (project ID 16193) and the European Union.</p>
Uncertainty Analysis of Digital Elevation Models by Spatial Inference From Stable Terrain – Dataset
<p><strong>Dataset of <a href="https://doi.org/10.1109/jstars.2022.3188922">Hugonnet et al. (2022), Uncertainty Analysis of Digital Elevation Models by Spatial Inference From Stable Terrain</a>.</strong></p> <p>The data is composed of:</p> <ul> <li><strong>For the Mont-Blanc case study: </strong>the Pléiades reference DEM, the SPOT-6 DEM, the Pléiades–SPOT-6 elevation difference, and the forest mask generated from the ESA CCI landcover (delainey polygonization);</li> <li><strong>For the Northern Patagonian Icefield case study: </strong>the ASTER reference DEM, the SPOT-5 DEM, the ASTER–SPOT-5 elevation difference, and the quality of stereo-correlation of the ASTER DEM from MicMac.</li> </ul> <p>The filenames correspond to those used in the <strong>associated GitHub repository</strong>: <a href="https://github.com/rhugonnet/dem_error_study">https://github.com/rhugonnet/dem_error_study</a>. The shapefiles used for masking glaciers are available directly from the <strong>Randolph Glacier Inventory 6.0</strong> at <a href="https://www.glims.org/RGI/">https://www.glims.org/RGI/</a>.</p> <p>The date of the DEMs is in their original format: <strong>year-month-day for all but ASTER</strong> that has the original naming of <a href="https://lpdaac.usgs.gov/products/ast_l1av003/">AST L1A products</a>. <strong>Units are meters</strong> for the DEMs and elevation differences, <strong>and percentages</strong> for the quality of stereo-correlation.</p>
Modeling the effect of explicit vs implicit representation of grazing on ecosystem carbon and nitrogen cycling in response to elevated carbon dioxide and warming in arctic tussock tundra, Alaska - Dataset A
We use a simple model of coupled carbon and nitrogen cycles in terrestrial ecosystems to examine how explicitly representing grazers versus having grazer effects implicitly aggregated in with other biogeochemical processes in the model alters predicted responses to elevated carbon dioxide and warming. The aggregated approach can affect model predictions because grazer-mediated processes can respond differently to changes in climate from the processes with which they are typically aggregated. We use small-mammal grazers in arctic tundra as an example and find that the typical three-to-four-year cycling frequency is too fast for the effects of cycle peaks and troughs to be fully manifested in the ecosystem biogeochemistry. We conclude that implicitly aggregating the effects of small-mammal grazers with other processes results in an underestimation of ecosystem response to climate change relative to estimations in which the grazer effects are explicitly represented. The magnitude of this underestimation increases with grazer density. We therefore recommend that grazing effects be incorporated explicitly when applying models of ecosystem response to global change.
Modeling the effect of explicit vs implicit representation of grazing on ecosystem carbon and nitrogen cycling in response to elevated carbon dioxide and warming in arctic tussock tundra, Alaska - Dataset B
We use a simple model of coupled carbon and nitrogen cycles in terrestrial ecosystems to examine how explicitly representing grazers versus having grazer effects implicitly aggregated in with other biogeochemical processes in the model alters predicted responses to elevated carbon dioxide and warming. The aggregated approach can affect model predictions because grazer-mediated processes can respond differently to changes in climate from the processes with which they are typically aggregated. We use small-mammal grazers in arctic tundra as an example and find that the typical three-to-four-year cycling frequency is too fast for the effects of cycle peaks and troughs to be fully manifested in the ecosystem biogeochemistry. We conclude that implicitly aggregating the effects of small-mammal grazers with other processes results in an underestimation of ecosystem response to climate change relative to estimations in which the grazer effects are explicitly represented. The magnitude of this underestimation increases with grazer density. We therefore recommend that grazing effects be incorporated explicitly when applying models of ecosystem response to global change.
The dataset and model code pertinent to the Everglades Peat Elevation Model (EvPEM): The salinity and inundation mesocosm experiment in freshwater and brackish water sawgrass wetlands in Florida Coastal Everglades (2015-2017).
This is an assembled data and Everglades Peat Elevation Model (EvPEMv1.0) Stella code used to estimate and simulate net ecosystem carbon balance (NECB) and peat elevation change in response to saltwater intrusion and level of inundations. Data from several studies were combined for the estimation of NECB, model parameterization, and calibration (Wilson, 2018; Wilson et al., 2018, 2019; Charles et al., 2019; Servais et al., 2020). The reported data includes aboveground net primary productivity (ANPP), belowground net primary productivity (BNPP), peat elevation change, and decomposition rates that were collected from outdoor laboratory mesocosm experiments conducted at the Florida Bay Interagency Science Center in Key Largo, Florida during 2015-17. The plant-soil monoliths were obtained from a freshwater peat and a brackish water peat marsh located within the Florida Coastal Everglades and transported to the Key Largo facility for the experimental manipulations. In experiments focused on the brackish water marsh, three experiments were carried out reflecting the combined effect of salinity, inundation, and peat exposure to air. The brackish water experiments characterized submerged (SUB), exposed (EXP), and extended depth of exposure of peat surface (EXTEXP) conditions, as we varied water depth relative to the peat surface. Each experiment was subjected to two salinity manipulations: (1) ambient (~10 ppt) porewater salinity (AMB) and (2) elevated (~20 ppt) salinity (SALT). The experimental design included six (2 X 3) treatments: (1) submerged ambient salinity (AMB.SUB), (2) submerged elevated salinity (SALT.SUB.), (3) exposed ambient salinity (AMB.EXP), (4) exposed elevated salinity (SALT.EXP), (5) exposed with extended exposure/dry-down ambient salinity (AMB.EXTEXP), and (6) exposed with extended exposure/dry-down elevated salinity (SALT.EXTEXP). The water level was kept 4 cm above the peat surface for the brackish water SUB treatments. Exposure for the EXP treatment
UAV-based orthomosaic and digital elevation model of a basalt outcrop on Disko Island, West Greenland
<p><span>This data set contains an RGB survey conducted with an unoccupied aerial vehicle (UAV) over a flat basaltic outcrop (intrusive and flood volcanics), surrounded by boreal vegetation (Salix species).</span></p> <ul> <li><span>Acquisition date: 13.08.2019</span></li> <li><span>Location: Qullissat (Qutdlikssat), Disko Island, Greenland</span></li> <li><span>UAV: DJI Mavic 1 Pro</span></li> <li><span>Flight altitude above ground level: 75 m</span></li> <li><span>Image Overlap forward/side: 70 % / 70 %</span></li> <li><span>Camera: RGB</span></li> <li><span>EPSG: 32622</span></li> <li><span>Center coordinates: 70.05330°N, -52.97780°E</span></li> <li><span>Flight mode: manual image acquisition</span></li> </ul> <p><span>Data products: </span></p> <ul> <li><span>Orthomosaic RGB 2.3 cm pixel resolution</span></li> <li><span>DEM 5cm pixel resolution</span></li> <li><span>Processing in Agisoft Metashape</span></li> <li><span>Data coverage: approx. 250 x 360 m</span></li> </ul> <p>Acknowledgements</p> <p><span>MULSEDRO field campaign was conducted under scientific survey licence (VU-00158-2019) within mineral exploration licence MEL 2018-16 by Blue Jay Mining PLC. This research has been supported by the project MULSEDRO, funded by HZDR-HIF & EITRawMaterials (project ID 16193) and the European Union.</span></p>
DATABASE OF THE DIGITAL ELEVATION MODELS OF THE SKEIÐARÁRSANDUR KETTLE-HOLES (S ICELAND), JUNE 2022 - PART I
<p>The database concerns kettle-holes of glacial flood origin. They are located at various outwash levels of Skeiðarársandur in S Iceland. The database contains 87 digital elevation models (DEM) with a minimum resolution of 0.05 m and additional files, e.g. field measurements data, frames selected from the video, errors calculation, point cloud, 3D view. These data document the process of obtaining the material using the photogrammetric ‘Structure from Motion’ method from fieldwork conducted in June 2022 through the processing stages in free, mainly open-source software. The data is prepared in the local Cartesian system and includes relative heights, where 0 m is the lowest point of the kettle-hole. The simple technique used, based on filming the landforms with a digital camera, enables mapping of depressions up to 1250 m<sup>2</sup> in the area and a maximum depth of up to 8 m with the assumed high accuracy.</p>
Digital Elevation Models of Hunga Volcano; pre- and post- 15 January 2022 eruption
<p>This dataset contains digital elevation models (DEM) of the Hunga Volcano complex. The first is a pre-2022 eruption elevation model. The second is a post-2022 eruption elevation model.</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 (TESMaP) 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> in April and May 2022 (Mackay et al., 2022) and the second was carried out by the <i>USV Maxlimer</i> 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>
Digital Elevation Model (DEM) of the Duplin River and adjacent intertidal areas near Sapelo Island, Georgia
Topographic and bathymetric data were collected for the Duplin River and adjacent intertidal areas near Sapelo Island, Georgia, using high-precision multibeam SONAR equipment. The bathymetric survey was performed from 09-Dec-2009 to 12-Dec-2009. This study was conducted to create a base map of bathymetry, morphology and physical habitat of the Duplin River, which is a primary study site of the Georgia Coastal Ecosystems Long Term Ecological Research (GCE-LTER) project.
Digital Elevation Model (DEM) of Doboy Sound at the mouth of the Duplin River near Sapelo Island, Georgia
The purpose of this study was to map the bathymetry of Doboy Sound near the mouth of the Duplin River adjacent to Sapelo Island, Georgia. This study extends a previous bathymetry mapping project conducted in 2009. The primary objective of the Duplin River project in 2009 was to provide data in support of understanding the sediment and water exchange process between intertidal areas and tidal creeks of the Duplin River. The Center for Marine and Wetland Studies (CMWS) surveyed the Doboy Sound using the Simrad EM3002D Multibeam Echosounder (MBES) in April 2011. A digital elevation model (DEM) was then developed based on the depth survey data.
Corrected LIDAR-derived digital elevation model of the Duplin River salt marshes
LIDAR (light detection and ranging) data were acquired on March 9-10, 2009 by the National Center for Airborne Laser Mapping (NCALM) for the Duplin River (35 km2). A 1 m spatial resolution gridded digital elevation models (DEM) was produced from these data. The accuracy of the DEM was assessed using real time kinematic (RTK) GPS ground reference data and elevations were corrected following the method of Hladik and Alber (2012).
Hubbard Brook Experimental Forest: 1 meter LiDAR-derived and Hydro-enforced Digital Elevation Models, 2012
This data package contains a 1 m LiDAR-derived digital elevation model (DEM) and a 1 m hydro-enforced DEM across Hubbard Brook EF. The LiDAR was collected during leaf-off and snow-free conditions by Photo Science, Inc. in April 2012 for the White Mountain National Forest (WMNF). These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Digital elevation models derived from UAV overflights of the fifteen NPP study sites at Jornada Basin LTER in 2019
This data package contains digital elevation models (DEMs) of each of the 15 NPP study sites at the Jornada Basin LTER in southern New Mexico, USA. The models were derived from raw images collected during uncrewed aerial vehicle (UAV) overflight missions conducted in late summer and early autumn of 2019 (1 overflight day per site). For each site one or two missions were flown during an afternoon using a DJI Phantom 4 UAV, and between 450 and 1300 12.4 megapixel RGB images were captured. A subset of images captured at each site were loaded into Agisoft Metashape software to derive digital elevation models using a structure from motion method. These models include elevations of vegetation and other aboveground features. The raw images are not provided but can be made available via project PIs. This data package includes one centimeter resolution DEMs of all 15 sites as geotiff raster files. Other derived products from these UAV missions, including orthomosaic photos, digital terrain models, and sparse point clouds, are available in other EDI data packages (knb-lter-jrn.210543001, knb-lter-jrn.210543003, and knb-lter-jrn.210543004, respectively). This study is complete.
Elevation Models for Reproducible Evaluation of Terrain Representation - Inventory of Renderings
<p>This is an inventory of 155 renderings from 78 publications on terrain visualization techniques. The renderings guided the selection of landform types in the elevation models that are proposed in the following article:</p> <p><em>Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: <a href="http://doi.org/10.1080/15230406.2020.1830856">10.1080/15230406.2020.1830856</a></em></p> <p>Visualization techniques include colored aspect, contour lines, hypsometric tints, plan oblique relief, relief shading, rock and scree representation, and spot heights. The inventory contains information about display scale of the sample renderings, landform types, cell size, and geographic location of the digital elevation models. Also inventoried are how authors evaluated their renderings, how scale was indicated on the renderings, and whether the cell size and source of elevation data was included.</p> <p>The inventory is formatted as a single table in CSV UTF-8 and MS Excel .xlsx formats. Papers are grouped by visualization type; attributes of each rendering are stored on a single row.</p>
Digital Elevation Models (DEMs) and lava outlines from the 2023 Litla-Hrútur eruption, Iceland, from Pléiades satellite stereoimages
<p><strong>Introduction:</strong></p><p>On the 10th of July 2023, at 16:40, an eruption started in the Reykjanes Peninsula, Iceland, next to the mountain "Litla-Hrútur". As part of the response, the CIEST2 french initiative was activated (Gouhier et al., 2022). Once activated, Pléiades stereoimages were tasked and scheduled for fast delivery within the area of Interest. On the 20th of August 2023 an additional stereopair of images from Pléiades was acquired and processed after the eruption had stopped.</p><p>Once acquired and delivered, the Pléiades images were processed following the methods described in the section below. This repository contains the near-real time results of DEMs, difference maps compared to a pre-eruption DEM, and lava outlines digitized from the difference map and the orthoimage.</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 <i>parallel_stereo </i>routine performs all the steps needed in the correlation of the stereoimages, yielding a pointcloud which is then interpolated using the routine <i>point2dem</i>. Besides default parameters, the <i>parallel_stereo</i> parameters used for creation of the DEMs were the standard parameters, plus the following ones: </p><p><i>--stereo-algorithm asp_mgm --corr-tile-size 300 --corr-timeout 900 --cost-mode 3 --subpixel-mode 9 --corr-kernel 7 7 --subpixel-kernel 15 15</i></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>The pre-eruption DEM used for this study is a survey done on the 27th of September 2022, data collected Birgir Óskarsson and Robert A. Askew (Icelandic Institute of Natural History) and processed by Sydney R. Gunnarsson and Joaquín M.C. Belart (National Land Survey of Iceland). Metadata of this dataset is available here: https://gatt.lmi.is/geonetwork/srv/eng/catalog.search#/metadata/c59da6cf-18ee-44af-a085-afbad0de029a</p><p>Lava outlines were manually digitized from the co-registered Pléiades orthoimages, The lava outlines are available as GeoPackages in the "GPKG" branch of the repository.</p><p>At the moment, the results from Pléiades are used by the Institute of Earth Sciences of the Univesity of Iceland (Jarðvisindustofnun Háskoli Íslands) to estimate lava volumes and effusion rate, following the methods described in Pedersen et al. (2022). Please contact the authors if these data are intended to be used for a similar purpose, in order to avoid conflict of interests or duplicate work. We encourage collaboration and data sharing for the purpose of the monitoring of the eruption and for research applications.</p><p><strong>Data naming convention:</strong></p><p>faf_YYYYMMDD_hhmmss_hhmmss_*align.tif: DEM obtained from the processing, co-registered to the reference pre-eruption DEM.</p><p>faf_YYYYMMDD_hhmmss_hhmmss_*align_diff.tif: Difference of elevation between the Pléiades DEM and the pre-eruption DEM.</p><p>faf_YYYYMMDD_hhmm.gpkg: Polygon containing the lava outlines, extracted from the Pléiades orthoimage and the map of elevation difference.</p><p>0_faf_YYYYMMDD_hhmmss_hhmmss_*fig.png: A figure showing the latest map of elevation difference, overlaid with a hillshade of the latest Pléiades DEM and the latest lava outlines, result of the processing of the Pléiades stereoimages. The figure was created using the tool imviewer.py from the GitHub repository https://github.com/dshean/imview (Shean et al., 2016).</p><p><strong>Data Specifications:</strong></p><ul><li>Cartographic projection: ISN93 / Lambert 1993 (EPSG:3057, <a href="http://https:/epsg.io/3057">https://epsg.io/3057</a>)</li><li>Origin of Elevation: meters above GRS80 ellipsoid (WGS84)</li><li>Raster data format: GeoTIFF</li><li>Raster compression system: LZW</li><li>Vector data format: GeoPackage (<a href="https://www.geopackage.org/">https://www.geopackage.org/</a>)</li><li>Pléiades dataset includes only DEMs because the Pléiades ortho imagery is for licensed use only. Please contact the authors for further information on this.</li></ul><p><strong>Acknowledgements</strong>: </p><p>Pléiades images from July 2023 were provided under the CIEST² initiative (CIEST2 is part of ForM@Ter (<a href="https://en.poleterresolide.fr/">https://en.poleterresolide.fr/</a>). Pléiades images from August 2023 were provided under the CEOS Volcano Supersite (https://ceos.org/ourwork/workinggroups/disasters/gsnl/). Image Pléiades©CNES2023, distribution AIRBUS DS.</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).</p><p><strong>Citation:</strong></p><p>Please cite this repository as described below:</p><p>Joaquin M.C. Belart, Virginie Pinel, Hannah. I. Reynolds, Etienne Berthier, & Sydney R. Gunnarson. (2023). Digital Elevation Models (DEMs) and lava outlines from the 2023 Litla-Hrútur eruption, Iceland, from Pléiades satellite stereoimages (1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10133203</p>
Digital Elevation Model (DEM) of northern Brøggerhalvøya (Svalbard, Norway) with Ground Sampling Distance (GSD) of 50 cm
<p>HRSC is a multisensor pushbroom instrument with 9 CCD line sensors mounted in parallel that has been in orbit around Mars since January 2004 on ESA’s Mars Express spacecraft (Gwinner et al., 2016). It simultaneously obtains high-resolution stereo, multicolor, and multiphase images. Digital photogrammetric techniques are used to reconstruct the topography on the basis of five stereo channels, which provide five different views of the ground.</p> <p>An airborne version of the HRSC was used for the acquisition of stereo and color images in Svalbard. Since 1997, different airborne versions of HRSC have been developed. The principles of HRSC-AX data processing are described by Gwinner et al. (2006). The orientation data of the camera are reconstructed from a global positioning system inertial navigation system (GPS INS). HRSC-AX has been applied in diverse technical and scientific applications (e.g., Gwinner et al., 1999, 2000; Hauber et al., 2001; Otto et al., 2007) and has also been successfully used to investigate rock glacier activity (Roer and Nyenhuis, 2007). The flight campaign in July–August 2008 covered a total of seven regions in Svalbard: (1) Longyearbyen and the surroundings of Adventfjorden, (2) large parts of Adventdalen, (3) large parts of the Brøggerhalvøya (halvøya = peninsula) in western Spitsbergen (this dataset), (4) the Bockfjorden area in northern Spitsbergen, (5) the northeastern shore of the Palanderbukta and the margin of the adjacent ice cap in Nordaustlandet, (6) an area on Prins Karls Forland, and (7) the area of the abandoned Russian mining settlement of Pyramiden together with the nearby Ebbedalen. </p> <p>This dataset is a Digital Elevation Model (DEM) derived from HRSC-AX stereo images. The elevations recorded in the DEM are ellipsoid heights; i.e., they are not computed with respect to a geoid but to a mathematically defined reference surface, which is a<br>rotational ellipsoid with the equatorial A and B axes both having a radius of 6378.14 km and the polar<br>C axis having a radius of 6356.75 km. This results in an offset of about 36.5m with respect to geoid<br>heights; i.e., sea level in the HRSC-AX DEM is not at 0 m, but at ~36.5 m.</p> <p><strong>References</strong></p> <p>Gwinner, K., Hauber, E., Hoffmann, H., Scholten, F., Jaumann, R., Neukum, G.,<br>Coltelli, M., and Puglisi, G., 1999, The HRSC-A experiment on high reso-<br>lution imaging and DEM generation at the Aeolian Islands, in Proceedings<br>of the 13th International Conference on Applied Geologic Remote Sens-<br>ing: Ann Arbor, Michigan, ERIM International, v. I, p. 560–569.</p> <p>Gwinner, K., Hauber, E., Jaumann, R., and Neukum, G., 2000, High-resolution,<br>digital photogrammetric mapping: A tool for earth science: Eos<br>(Transactions, American Geophysical Union), v. 81, no. 44, p. 513–520,<br>doi:10.1029/00EO00364.</p> <p>Gwinner, K., Coltelli, M., Flohrer, J., Jaumann, R., Matz, K.-D., Marsella, M.,<br>Roatsch, T., Scholten, F., and Trauthan, F., 2006, The HRSC-AX Mt.<br>Etna Project: High-Resolution Orthoimages and 1 m DEM at Regional<br>Scale: International Archives of Photogrammetry and Remote Sensing,<br>v. XXXVI, Part 1, http://isprs.free.fr/documents/Papers/T05-23.pdf.</p> <p>Gwinner, K., Scholten, F., Spiegel, M., Schmidt, R., Giese, B., Oberst,<br>J., Heipke, C., Jaumann, R., and Neukum, G., 2009, Derivation and<br>validation of high-resolution digital elevation models from Mars Express<br>HRSC data: Photogrammetric Engineering and Remote Sensing, v. 75,<br>no. 9, p. 1127–1142.</p> <p>Gwinner, K., Jaumann, R., Hauber, E., et al., 2016, The High Resolution Stereo Camera (HRSC) of Mars Express and its<br>approach to science analysis and mapping for Mars and its satellites: Planetary and Space Science, v. 126, p. 93–138. http://dx.doi.org/10.1016/j.pss.2016.02.014</p> <p>Hauber, E., Slupetzky, H., Jaumann, R., Wewel, F., Gwinner, K., and Neukum,<br>G., 2001, Digital and automated high resolution stereo mapping of the<br>Sonnblick glacier: EARSeL eProceedings, v. 1, no. 1, p. 246–254.</p> <p>Jaumann, R., Neukum, G., Behnke, T., Duxbury, T.C., Eichentopf, K., Flohrer,<br>J., van Gasselt, S., Giese, B., Gwinner, K., Hauber, E., Hoffmann, H., Hoff-<br>meister, A., Köhler, U., Matz, K.-D., McCord, T.B., Mertens, V., Oberst,<br>J., Pischel, R., Reiss, D., Ress, E., Roatsch, T., Saiger, P., Scholten, F.,<br>Schwarz, G., Stephan, K., Wählisch, M., and the HRSC Co-Investigator<br>Team, 2007, The high-resolution stereo camera (HRSC) experiment on<br>Mars Express: instrument aspects and experiment conduct from interplan-<br>etary cruise through the nominal mission: Planetary and Space Science, v.<br>55, p. 928–952, doi:10.1016/j.pss.2006.12.003.</p> <p>Otto, J.-C., Kleinod, K., König, O., Krautblatter, M., Nyenhuis, M., Roer,<br>I., Schneider, M., Schreiner, B., and Dikau, R., 2007, HRSC-A data:<br>A new high-resolution data set with multipurpose applications in physi-<br>cal geography: Progress in Physical Geography, v. 31, no. 2, p. 179–197,<br>doi:10.1177/0309133307076479.</p> <p>Roer, I., and Nyenhuis, M., 2007, Rockglacier activity studies on a regional<br>scale: Comparison of geomorphological mapping and photogrammetric<br>monitoring: Earth Surface Processes and Landforms, v. 32, p. 1747–1758,<br>doi:10.1002/esp.1496.</p>
WINTERC-G: a global upper mantle thermochemical model from coupled geophysical–petrological inversion of seismic waveforms, heat flow, surface elevation and gravity satellite data
<p>WINTERC-G: A global, temperature and compositional model of the lithosphere<br> and upper mantle.<br> Version: v5.4, December 2020, J. Fullea, S. Lebedev, Z. Martinec, N. Celli<br> <br> Contact: Javier Fullea (jfullea@ucm.es)<br> Facultad de Fisica,<br> Universidad Complutense de Madrid (UCM),<br> Spain<br> ////////<br> Geophysics Section,<br> Dublin Institute for Advanced Studies<br> Dublin, Ireland<br> </p> <p>TYPE:<br> This contains files with:<br> i) the model directly on the triangular grid solved for in the surface wave inversion.</p> <p> ii) an interpolated grid at 0.5 deg lateral resolution for the density and density discontinuities used in the gravity field data inversion<br> </p> <p>If you have any questions regarding the methodology or the construction<br> of the model, please contact the authors. If you use the model, we would<br> request that you cite the reference indicated below, and appreciate<br> your feedback regarding the model and its application.</p> <p>Citation:</p> <p>Fullea, J., Lebedev, S., Martinec, Z., & Celli, N. L. (2021). WINTERC-G: mapping the upper mantle thermochemical heterogeneity from coupled geophysical–petrological inversion of seismic waveforms, heat flow, surface elevation and gravity satellite data. Geophysical Journal International, 226(1), 146-191.</p> <p>*******************************<br> Summary: construction of the model.<br> WINTERC-G is a Waveform tomography and Gravity (geoid and gravity anomalies and gradiometric measurements<br> from ESA's GOCE mission) INversion model of the TEmpeRature and Composition of the lithosphere and upper mantle at<br> global scale. WINTERC-G is based on upon the integrated geophysical-petrological<br> approach LitMod (Afonso et al., 2008; Fullea et al. 2009) and, hence, all<br> relevant mantle rock physical properties modelled (seismic velocities and density) are<br> computed within a thermodynamically self-consistent framework allowing for a direct<br> parameterization in terms of the temperature and composition of the lithosphere-upper<br> mantle. The inversion is a two-step procedure. In a first step, we invert surface-wave, Rayleigh and Love<br> fundamental mode dispersion curves from a high resolution global dataset measured using waveform inversion,<br> along with surface heat flow and elevation (isostasy) for temperature and crustal structure<br> using a point-wise, non-linear, gradient-search inversion<br> over a triangular grid with an average 225 km lateral inter-knot spacing. In a second step we<br> use a fully parallelized spherical harmonic formalism to invert satellite gravity field data in<br> order to refine the initial crustal density and mantle composition distributions from the step 1<br> for a fixed temperature field.</p> <p>The parameter space in step 1 includes crust (densities and S-wave velocities for a three-layered crust)<br> and mantle variables (the depth of the thermal Lithosphere-Athenosphere-Boundary,<br> the thickness of the sublithospheric thermal buffer, the sublithospheric temperatures at 3 different<br> equispaced nodes down to 400 km, the lithospheric and sublithospheric mantle compositon, and<br> the the radial anisotropy at the 3 crustal layers and at 56, 80, 110, 150, 200, 260, 330,<br> and 400 km depths.</p> <p>The parameter space in step 2 is defined by the average crustal density, and the<br> mantle composition in the lithosphere and sublithosphere.<br> We use the output crustal density from step 1 as the<br> initial value in step 2 inversion. Mantle densities are derived based on the output temperature<br> field from step 1 (kept fixed) and the bulk mantle composition inversion variables.</p> <p> </p> <p> </p> <p>*******************************</p> <p>This archive contains the following files:<br> README (this file)<br> WINTERC-G_Vp-Vs.lis (triangular grid)<br> WINTERC-G_rad_anis_Vs.lis (triangular grid)<br> WINTERC-G_Temperature.lis (triangular grid)<br> WINTERC-G_Density.lis (triangular grid)<br> WINTERC-G_LAB.lis (triangular grid)<br> WINTERC_T_rho_1D.z (1D average model of temperature and density)<br> rho_*_out.xyz (0.5 deg egular grid for gravity field)<br> ETOPO2_km_continental.xyz (0.5 deg egular grid for gravity field)<br> ETOPO2_km_depth_Ice.xyz (0.5 deg egular grid for gravity field)<br> ETOPO2_km_depth_Bed.xyz (0.5 deg egular grid for gravity field)<br> Global_Moho_WINTERC-G.xyz (0.5 deg egular grid for gravity field)</p> <p><br> Files in the triangular grid with an average 225 km lateral inter-knot spacing (12232 grid points):</p> <p>* WINTERC-G_Vp-Vs.lis: Vp and Vs (in km/s) in all model columns with a vertical grid step of 2 km<br> Format for each column:<br> #Column number longitude latitude depth(km, <0 downwards) Vp (km/s) Vs(km/s)<br> 5640 93.72 4.135 -5.0 3.91 2.11</p> <p><br> * WINTERC-G_rad_anis_Vs.lis: radial anisotropy, (Vsh-Vsv)/Vs_iso (in %) in all model columns with a vertical grid step of 2 km<br> Format for each column:<br> #Column number longitude latitude depth(km, <0 downwards) anisotropy (%)</p> <p>* WINTERC-G_Temperature.lis: temperature (in ºC) in all model columns with a vertical grid step of 2 km<br> Format for each column:<br> #Column number longitude latitude depth (km, <0 downwards) T (ºC) dT (%) dT(K) <br> 6437 297.20 -2.524 -259.000 1431.9 -1.91 -27.9<br> The anomalies dT are in % and K with respect to the 1D model in WINTERC_T_rho_1D.z (column 2).</p> <p>* WINTERC-G_Density.lis: density (in kg/m3) in all model columns with a vertical grid step of 2 km<br> Format for each column:<br> #Column number longitude latitude depth(km, <0 downwards) rho (kg/m3) drho(%) drho(kg/m3)<br> The anomalies drho are in % and kg/m3 with respect to the 1D model in WINTERC_T_rho_1D.z (column 3).</p> <p>* WINTERC_T_rho_1D.z: 1D average model of temperature (column 2 in ºC) and density (column 3 in kg/m3) with a vertical grid step of 2 km <br> 5.00000000 0.0000000000000000 6.0259973839110526<br> 3.00000000 0.0000000000000000 38.960571309690394<br> 1.00000000 0.33634006819423840 174.42296045978722<br> -1.00000000 3.8888495253719624 1692.8437489147236<br> -3.00000000 23.974111923225379 1863.8834351235944<br> -5.00000000 47.727920701943034 2568.2414495590924<br> -7.00000000 89.633398074381162 2819.8386016341910<br> -9.00000000 137.01489361657013 2839.5325893195904<br> -11.0000000 182.35233447017222 2897.6600872935287<br> -13.0000000 224.46247069572485 2945.2036923862997<br> -15.0000000 260.63395547331390 3069.6809340323475<br> -17.0000000 292.28175449521456 3132.4574175461721<br> -19.0000000 322.29571965406632 3145.5747337463940<br> -21.0000000 351.58698283375054 3157.2401512748038<br> -23.0000000 380.30002225705056 3177.0000420059773<br> -25.0000000 408.50259805632055 3183.6651032398490<br> -27.0000000 436.22632217636487 3190.9586785996116<br> -29.0000000 463.48733903170023 3198.9369509456310<br> -31.0000000 490.29841705549831 3209.7229872383764<br> -33.0000000 516.71149258457456 3221.6329506091679<br> ...</p> <p>Files in the interpolated regular grid at 0.5 deg lateral resolution used for gravity field data inversion:</p> <p> * rho_c_out.xyz: average crustal density<br> * rho_submoho_out.xyz: mantle density below the Moho discontinuity<br> * rho_*_out.xyz: mantle density defined at different model depths: 20, 35, 56, 80, 110, 150, 200, 260, 330 and 400 km.</p> <p> Format for the density files:<br> # longitude latitude density (kg/m3)<br> <br> Files containing layer discontinuities:</p> <p> * ETOPO2_km_continental.xyz: surface elevation including ice sheet and 0 in marine areas (km, <0 upwards)</p> <p> * ETOPO2_km_depth_Ice.xyz: surface elevation including ice sheet (km, >0 downwards, <0 above sea level)</p> <p> * ETOPO2_km_depth_Bed.xyz: bedrock surface elevation without ice sheet (km, >0 downwards, <0 above sea level)</p> <p> * Global_Moho_WINTERC-G.xyz: crust-mantle discontinuity depth (km, >0 downwards)</p> <p> Format for the discontinuity files:<br> # longitude latitude depth (km)<br> <br> <br> The gravity field in WINTERC-G is computed using an spherical harmonic formalism and a model discretization<br> in 13 layers with laterally varying density. The first 7 layers are characterized by top and bottom boundaries with laterally varying radius whereas the last 6 layers are defined by top and bottom boundaries with constant radius:</p> <p>1/ Water: from ETOPO2_km_continental.xyz to ETOPO2_km_depth_Ice.xyz with rho=1030 kg/m3 (constant vertically)</p> <p>2/ Ice: from ETOPO2_km_depth_Ice.xyz to ETOPO2_km_depth_Bed.xyz with rho=910 kg/m3 (constant vertically)</p> <p>3/ Crust: from ETOPO2_km_depth_Bed to Global_Moho_WINTERC-G.xyz with rho=rho_c_out.xyz (constant vertically)</p> <p>4/ submoho-20km: from Global_Moho_WINTERC-G.xyz to z_20km (file with 20 km everywhere except where z_moho>20km) with rho=rho_submoho_out.xyz (top) and rho=rho_20km_out.xyz (bottom)</p> <p>5/ 20km-36km: from z_20km (file with 20 km everywhere except where z_moho>20km) to z_36km (file with 36 km everywhere except where z_moho>36km) with rho=rho_20km_out.xyz (top) and rho=rho_36km_out.xyz (bottom)</p> <p>6/ 36km-56km: from z_36km (file with 36 km everywhere except where z_moho>36km) to z_56km (file with 56 km everywhere except where z_moho>56km) with rho=rho_36km_out.xyz (top) and rho=rho_56km_out.xyz (bottom)</p> <p>7/ 56km-80km: from z_56km (file with 56 km everywhere except where z_moho>56km) to 80 km depth with rho=rho_56km_out.xyz (top) and rho=rho_80km_out.xyz (bottom)</p> <p>The next 6 layers are computed using the constant radius option:</p> <p>8/ 80km-110km: from z=80km to z=110 km with rho=rho_80km_out.xyz (top) and rho=rho_110km_out.xyz (bottom)</p> <p>9/ 110km-150km: from z=110km to z=150 km with rho=rho_110km_out.xyz (top) and rho=rho_150km_out.xyz (bottom)</p> <p>10/ 150km-200km: from z=150km to z=200 km with rho=rho_150km_out.xyz (top) and rho=rho_200km_out.xyz (bottom)</p> <p>11/ 200km-260km: from z=200km to z=260 km with rho=rho_200km_out.xyz (top) and rho=rho_260km_out.xyz (bottom)</p> <p>12/ 260km-330km: from z=260km to z=330 km with rho=rho_260km_out.xyz (top) and rho=rho_330km_out.xyz (bottom)</p> <p>13/ 330km-400km: from z=330km to z=400 km with rho=rho_330km_out.xyz (top) and rho=rho_400km_out.xyz (bottom)</p> <p> </p> <p> </p>
Copernicus Digital Elevation Model (DEM) for Europe at 3 arc seconds (ca. 90 meter) resolution derived from Copernicus Global 30 meter DEM dataset
<p>Overview:<br> The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. The original GLO-30 provides worldwide coverage at 30 meters (refers to 10 arc seconds). Note that ocean areas do not have tiles, there one can assume height values equal to zero. Data is provided as Cloud Optimized GeoTIFFs. Note that the vertical unit for measurement of elevation height is meters.</p> <p>The Copernicus DEM for Europe at 3 arcsec (0:00:03 = 0.00083333333 ~ 90 meter) in COG format has been derived from the Copernicus DEM GLO-30, mirrored on Open Data on AWS, dataset managed by Sinergise (https://registry.opendata.aws/copernicus-dem/).</p> <p>Processing steps:<br> The original Copernicus GLO-30 DEM contains a relevant percentage of tiles with non-square pixels. We created a mosaic map in <a href="https://gdal.org/drivers/raster/vrt.html">VRT</a> format and defined within the VRT file the rule to apply cubic resampling while reading the data, i.e. importing them into GRASS GIS for further processing. We chose cubic instead of bilinear resampling since the height-width ratio of non-square pixels is up to 1:5. Hence, artefacts between adjacent tiles in rugged terrain could be minimized:</p> <p><code>gdalbuildvrt -input_file_list list_geotiffs_MOOD.csv -r cubic -tr 0.000277777777777778 0.000277777777777778 Copernicus_DSM_30m_MOOD.vrt </code></p> <p>In order to reduce the spatial resolution to 3 arc seconds, weighted resampling was performed in GRASS GIS (using <code>r.resamp.stats -w</code> and the pixel values were scaled with 1000 (storing the pixels as integer values) for data volume reduction. In addition, a hillshade raster map was derived from the resampled elevation map (using <code>r.relief</code>, GRASS GIS). Eventually, we exported the elevation and hillshade raster maps in Cloud Optimized GeoTIFF (COG) format, along with SLD and QML style files.</p> <p>Projection + EPSG code:<br> Latitude-Longitude/WGS84 (EPSG: 4326)</p> <p>Spatial extent:<br> north: 82:00:30N<br> south: 18N<br> west: 32:00:30W<br> east: 70E</p> <p>Spatial resolution:<br> 3 arc seconds (approx. 90 m)</p> <p>Pixel values:<br> meters * 1000 (scaled to Integer; example: value 23220 = 23.220 m a.s.l.)</p> <p>Software used:<br> GDAL 3.2.2 and GRASS GIS 8.0.0 (r.resamp.stats -w; r.relief)</p> <p>Original dataset license:<br> <a href="https://spacedata.copernicus.eu/documents/20126/0/CSCDA_ESA_Mission-specific+Annex.pdf">https://spacedata.copernicus.eu/documents/20126/0/CSCDA_ESA_Mission-specific+Annex.pdf</a></p> <p>Processed by:<br> mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p>
Copernicus Digital Elevation Model (DEM) for Europe at 30 arc seconds (ca. 1000 meter) resolution derived from Copernicus Global 30 meter DEM dataset
<p>Overview:<br> The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. The original GLO-30 provides worldwide coverage at 30 meters (refers to 10 arc seconds). Note that ocean areas do not have tiles, there one can assume height values equal to zero. Data is provided as Cloud Optimized GeoTIFFs. Note that the vertical unit for measurement of elevation height is meters.</p> <p>The Copernicus DEM for Europe at 30 arcsec (0:00:30 = 0.0083333333 ~ 1000 meter) in COG format has been derived from the Copernicus DEM GLO-30, mirrored on Open Data on AWS, dataset managed by Sinergise (https://registry.opendata.aws/copernicus-dem/).</p> <p>Processing steps:<br> The original Copernicus GLO-30 DEM contains a relevant percentage of tiles with non-square pixels. We created a mosaic map in <a href="https://gdal.org/drivers/raster/vrt.html">VRT</a> format and defined within the VRT file the rule to apply cubic resampling while reading the data, i.e. importing them into GRASS GIS for further processing. We chose cubic instead of bilinear resampling since the height-width ratio of non-square pixels is up to 1:5. Hence, artefacts between adjacent tiles in rugged terrain could be minimized:</p> <p><code>gdalbuildvrt -input_file_list list_geotiffs_MOOD.csv -r cubic -tr 0.000277777777777778 0.000277777777777778 Copernicus_DSM_30m_MOOD.vrt </code></p> <p>In order to reduce the spatial resolution to 30 arc seconds, weighted resampling was performed in GRASS GIS (using <code>r.resamp.stats -w</code> and the pixel values were scaled with 1000 (storing the pixels as integer values) for data volume reduction. In addition, a hillshade raster map was derived from the resampled elevation map (using <code>r.relief</code>, GRASS GIS). Eventually, we exported the elevation and hillshade raster maps in Cloud Optimized GeoTIFF (COG) format, along with SLD and QML style files.</p> <p>Projection + EPSG code:<br> Latitude-Longitude/WGS84 (EPSG: 4326)</p> <p>Spatial extent:<br> north: 82:00:30N<br> south: 18N<br> west: 32:00:30W<br> east: 70E</p> <p>Spatial resolution:<br> 30 arc seconds (approx. 1000 m)</p> <p>Pixel values:<br> meters * 1000 (scaled to Integer; example: value 23220 = 23.220 m a.s.l.)</p> <p>Software used:<br> GDAL 3.2.2 and GRASS GIS 8.0.0 (r.resamp.stats -w; r.relief)</p> <p>Original dataset license:<br> <a href="https://spacedata.copernicus.eu/documents/20126/0/CSCDA_ESA_Mission-specific+Annex.pdf">https://spacedata.copernicus.eu/documents/20126/0/CSCDA_ESA_Mission-specific+Annex.pdf</a></p> <p>Processed by:<br> mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p> <p> </p>
Copernicus Digital Elevation Model (DEM) for Europe at 1000 meter resolution (EU-LAEA) derived from Copernicus Global 30 meter DEM dataset
<p>Overview:<br> The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. The original GLO-30 provides worldwide coverage at 30 meters (refers to 10 arc seconds). Note that ocean areas do not have tiles, there one can assume height values equal to zero. Data is provided as Cloud Optimized GeoTIFFs. Note that the vertical unit for measurement of elevation height is meters.</p> <p>The Copernicus DEM for Europe at 1000 meter resolution (EU-LAEA projection) in COG format has been derived from the Copernicus DEM GLO-30, mirrored on Open Data on AWS, dataset managed by Sinergise (https://registry.opendata.aws/copernicus-dem/).</p> <p>Processing steps:<br> The original Copernicus GLO-30 DEM contains a relevant percentage of tiles with non-square pixels. We created a mosaic map in <a href="https://gdal.org/drivers/raster/vrt.html">VRT</a> format and defined within the VRT file the rule to apply cubic resampling while reading the data, i.e. importing them into GRASS GIS for further processing. We chose cubic instead of bilinear resampling since the height-width ratio of non-square pixels is up to 1:5. Hence, artefacts between adjacent tiles in rugged terrain could be minimized:</p> <p><code>gdalbuildvrt -input_file_list list_geotiffs_MOOD.csv -r cubic -tr 0.000277777777777778 0.000277777777777778 Copernicus_DSM_30m_MOOD.vrt </code></p> <p>In order to reproject the data to EU-LAEA projection while reducing the spatial resolution to 1000 m, bilinear resampling was performed in GRASS GIS (using <code>r.proj</code> and the pixel values were scaled with 1000 (storing the pixels as Integer values) for data volume reduction. In addition, a hillshade raster map was derived from the resampled elevation map (using <code>r.relief</code>, GRASS GIS). Eventually, we exported the elevation and hillshade raster maps in Cloud Optimized GeoTIFF (COG) format, along with SLD and QML style files.</p> <p>Projection + EPSG code:<br> ETRS89-extended / LAEA Europe (EPSG: 3035)</p> <p>Spatial extent:<br> north: 6874000<br> south: -485000<br> west: 869000<br> east: 8712000</p> <p>Spatial resolution:<br> 1000 m</p> <p>Pixel values:<br> meters * 1000 (scaled to Integer; example: value 23220 = 23.220 m a.s.l.)</p> <p>Software used:<br> GDAL 3.2.2 and GRASS GIS 8.0.0 (r.proj; r.relief)</p> <p>Original dataset license:<br> <a href="https://spacedata.copernicus.eu/documents/20126/0/CSCDA_ESA_Mission-specific+Annex.pdf">https://spacedata.copernicus.eu/documents/20126/0/CSCDA_ESA_Mission-specific+Annex.pdf</a></p> <p>Processed by:<br> mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p>
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