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24 results for “Digital Elevation Model (DEM)”
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.
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>
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>
Copernicus Digital Elevation Model (DEM) for Europe at 100 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 100 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 100 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> 100 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>
Historical digital elevation models (DEMs) and orthoimage mosaics for North American Glacier Aerial Photography (NAGAP) program, version 1.0
<p>This data archive contains digital elevation models (DEMs) and orthoimages generated from scanned historical aerial photographs from the North American Glacier Aerial Photography program available from the NSF Arctic Data Center (ADC, arcticdata.io). </p> <p>The scanned images were preprocessed using the <a href="https://github.com/friedrichknuth/hipp">Historical Image Pre-Processing</a> v0.1 software. Photogrammetric processing was performed with the <a href="https://github.com/friedrichknuth/hsfm">Historical Structure from Motion</a> v0.1 software. </p> <p>All DEM and orthoimage products are provided in the UTM Zone 10N (EPSG:32610) projected coordinate system. Elevation values are in meters above the WGS84 ellipsoid. </p> <p>See <a href="https://www.sciencedirect.com/science/article/pii/S0034425722004850">manuscript</a> and <a href="https://ars.els-cdn.com/content/image/1-s2.0-S0034425722004850-mmc1.pdf">supplement</a> for processing details and further dataset description.</p> <p>This release contains data products for two study sites in Washington state, USA:</p> <p><strong>Mount Baker</strong><br> 1970-09-09<br> 1970-09-29<br> 1974-08-10<br> 1977-09-27<br> 1979-10-06<br> 1987-08-21<br> 1990-09-05<br> 1991-09-09<br> 1992-09-15<br> 1992-09-18</p> <p><strong>South Cascade</strong><br> 1967-09-21<br> 1970-09-29<br> 1974-08-10<br> 1977-10-03<br> 1979-08-20<br> 1979-10-06<br> 1984-08-14<br> 1986-09-05<br> 1987-08-21<br> 1990-09-05<br> 1991-09-09<br> 1992-07-28<br> 1992-09-15<br> 1992-09-18<br> 1992-10-06<br> 1994-09-06<br> 1996-09-10<br> 1997-09-23</p> <p>The 00_thumbnails.jpg provides a quicklook overview at both sites.</p> <p><strong>The DEM and ortho file names are structured as follows:</strong><br> hsfm_NAGAP_[site-name]_[date]_[type].tif</p> <p><strong>For example:</strong><br> hsfm_NAGAP_south-cascade_19670921_ortho.tif</p> <p><strong>Where:</strong><br> [site-name] = Either mount-baker or south-cascade<br> [date] = Image acquisition date in YYYYMMDD format<br> [type] = File type</p> <p><strong>For each DEM and ortho pair, we provide the following:</strong><br> _1m_dem.tif = Digital elevation model posted at 1 m resolution <br> _ortho.tif = Orthoimage mosaic posted at the median image ground sample distance, rounded up to the nearest second decimal place.<br> _metadata.tar.gz = Metadata tarball containing:<br> _ortho_footprints.geojson = Orthoimage mosaic footprint polygons provided in GeoJSON format (EPSG:4326)<br> _dem_footprints.geojson = DEM footprint polygons provided in GeoJSON format (EPSG:4326)<br> _cameras.csv = Image file names, positions, and orientations</p>
Local digital elevation model for the Ayeyarwady Delta in Myanmar (AD-DEM) derived from digitised spot and contour heights of topographic maps
<p><strong>Title:</strong></p> <p>Local digital elevation model for the Ayeyarwady Delta in Myanmar (AD-DEM) derived from digitised spot and contour heights of topographic maps</p> <p><strong>Citation:</strong></p> <p>Seeger, K.; Minderhoud, P. S. J., Peffeköver, A., Vogel, A., Brückner, H., Kraas, F., Nay Win Oo, Brill, D. (2023): Local digital elevation model for the Ayeyarwady Delta in Myanmar (AD-DEM) derived from digitised spot and contour heights of topographic maps. Zenodo, <a href="https://doi.org/10.5281/zenodo.7875965">https://doi.org/10.5281/zenodo.7875965</a>.</p> <p><strong>Supplement to:</strong></p> <p>Seeger, K., Minderhoud, P. S. J., Peffeköver, A., Vogel, A., Brückner, H., Kraas, F., Nay Win Oo, and Brill, D. (2023): Assessing land elevation in the Ayeyarwady Delta (Myanmar) and its relevance for studying sea level rise and delta flooding. EGUsphere [preprint], <a href="https://doi.org/10.5194/egusphere-2022-1425">https://doi.org/10.5194/egusphere-2022-1425</a>.</p> <p><strong>Abstract:</strong></p> <p>The local digital elevation model (DEM) of the Ayeyarwady Delta, referred to as AD-DEM, was generated based on elevation data of topographic maps at scale of 1:50,000 published in 2014 while source data was compiled between 2000 and 2004. Empirical Bayesian Kriging with empirical data transformation and exponential modelling was applied to interpolate ~5100 elevation points (spot heights) and ~13600 elevation points extracted from contour data of the topographic maps. Elevation values higher than 10 m were excluded from interpolation and the SRTM water body mask created in 2000 was applied to the processed AD-DEM. The AD-DEM was transformed from its original vertical reference of local mean sea level at Kyaikkhami tide gauge to continuous mean sea level based on the mean dynamic topography data (CNES-CLS18 dataset of Mulet et al. (2021; <a href="https://doi.org/10.5194/os-17-789-2021">https://doi.org/10.5194/os-17-789-2021</a>) that we transposed to EGM96) in order to account for sea level variations along the Myanmar coast.</p> <p>The AD-DEM contains itself some uncertainty due to the lack of evenly distributed spot heights in areas of the upper delta, for which a separate shapefile is provided. However, we highlight to consider the AD-DEM as being the currently best available model against the background of the lacking possibility of ground truthing and being independent from satellite-based measurements.</p> <p>For further information on data processing, including DEM interpolation, determination of local mean sea level and vertical datum conversions, as well as DEM performance, see the corresponding paper and supplementary material.</p> <p>File name: ADDEM_Con250m_lesseq10_MDT_AD_MMR2000_masked_maskedSRTM.tif</p> <p>File format: GEOTIFF file</p> <p>Spatial reference: MMR2000_46N</p> <p>Vertical reference: local continuous mean sea level, i.e., mean dynamic topography (CNES-CLS18 dataset of Mulet et al. (2021; <a href="https://doi.org/10.5194/os-17-789-2021">https://doi.org/10.5194/os-17-789-2021</a>) transposed to EGM96</p> <p>Cell size: 750 × 750 m</p> <p>File name: DataPoorAreas_MMR2000.shp</p> <p>File format: ESRI Shapefile</p> <p>Spatial reference: MMR2000_46N</p>
30 meter digital elevation model (DEM) clipped to the Andrews Experimental Forest, 1996
Elevation Model for the HJ Andrews Experimental Forest (30 meter DEM). This dataset includes the raw DEM, and several value added products. The products are contour lines, aspect, percent slope, and a hill shade for relief mapping.
10 meter digital elevation model (DEM) clipped to the Andrews Experimental Forest, 1998
A Digital Elevation Model (DEM) is a digital data file containing an array of elevation information over a portion of the earth's surface. This array is developed using information extracted from digitized elevation contours from Primary Base Series (PBS) maps. FSTopo or PBS are 1:24,000 scale topographic maps. This dataset is a digital elevation model grid at a resolution of 10 meters by 10 meters. The data was originated from 1:24,000 scale topographic maps (primarily contours). The base data is in the form of an esri lattice file. Derived datasets include generated contours at 10, 25, and 50 meter intervals, degree slope, aspects, and a hillshade for topographic visualization.
Caribou Poker Creek Research Watershed GIS Data: Digital Elevation Model (DEM)
This file contains one of many raster grids of the Elevation Derivatives for National Applications (EDNA), a multi-layered database that provides systematic and consistent topographically-derived hydrologic derivatives. The filled DEM grid was created from the original elevation data by filling all of the depressions, or sinks, in the original DEM. To create this grid, an algorithm was used to loacted and fill all depressions or sinks where there was no flow from pixel to pixel. During this process, efforts were made to maintain natural sink features. Originator: U.S. Geological Survey. Publication_Date: 2006. Title: cpcrw_dem.tif. Edition: Stage I Data. Geospatial_Data_Presentation_Form: Remote-sensing image. Series_Information: Series_Name: Elevation Derivatives for National Applications (EDNA). Publication_Information: Publication_Place: USGS EROS, Sioux Falls, South Dakota. Publisher: U.S. Geological Survey.
Bonanza Creek Experimental Forest GIS Data: Digital Elevation Model (DEM)
This file contains one of many raster grids of the Elevation Derivatives for National Applications (EDNA), a multi-layered database that provides systematic and consistent topographically-derived hydrologic derivatives. The filled DEM grid was created from the original elevation data by filling all of the depressions, or sinks, in the original DEM. To create this grid, an algorithm was used to loacted and fill all depressions or sinks where there was no flow from pixel to pixel. During this process, efforts were made to maintain natural sink features. Originator: U.S. Geological Survey. Publication_Date: 2006. Title: bcef_dem.tif. Edition: Stage I Data. Geospatial_Data_Presentation_Form: Remote-sensing image. Series_Information: Series_Name: Elevation Derivatives for National Applications (EDNA). Publication_Information: Publication_Place: USGS EROS, Sioux Falls, South Dakota. Publisher: U.S. Geological Survey.
1-km bed topography digital elevation model (DEM) of the Weddell Sea sector, West Antarctica
<p>We present a new bed elevation digital elevation model (DEM) of the Weddell Sea sector, West Antarctica. The DEM consists a total area of ~125,000 km<sup>2</sup> covering the Institute, Möller and Foundation ice streams and the Bungenstock Ice Rise with a 1 km spatial resolution. </p> <p>In order to produce the bed elevation DEM, ice thickness DEM was formed from the available radio-echo sounding (RES) data using the 'Topo to Raster' function in ArcGIS. The RES data used in this study were compiled from four main sources which are (1) Scott Polar Research Institute (SPRI) survey collected during several campaigns in the 1970s (Drewry, 1983), (2) British Antarctic Survey (BAS) airborne radar survey conducted during the austal summer 2006/07 (GRADES/IMAGE) (Ashmore et al., 2014). , (3) BAS airborne survey accomplished during the Institute and Möller Antarctic Funding Initiative (IMAFI) in 2010/2011 (Ross et al., 2012) and (4) Center for the Remote Sensing of Ice Sheet (CReSIS) data during the NASA Operation IceBridge (OIB) programme in 2012, 2014 and 2016 (Gogineni, 2012). The ice thickness picks were gridded at a uniform 1-km spacing using the Nearest Neighbour interpolation within the Topo to Raster. The ice thickness DEM was later subtracted from the 1-km ice-sheet surface elevation derived from the combined European Remote Sensing Satellite-1 (ERS-1) radar and Ice, Cloud and land Elevation Satellite (ICESat) laser satellite altimetry DEM (Bamber et al., 2009), to produce the bed topography referenced to the Polar Stereographic projection (Snyder, 1987). </p>
Spot6 and Pléiades Digital Elevation Models (DEMs) and elevation change grid to understand the South Lhonak lake (Sikkim) flood of October 2023
<p>===========================</p> <p>Introduction</p> <p> </p> <p>On 3 October 2023, a multi-hazard cascade in the Sikkim Himalaya, India, was triggered by the collapse of a frozen lateral moraine into South Lhonak Lake</p> <p>We share here the SPOT6 and Pléiades DEMs and the elevation change maps derived from these images. The collection includes four files:</p> <p>Two pre-event DEMs from SPOT6</p> <p>2018-12-01_Lhonak_DEM_SGM_4m_on_Cop30_BiasCorr_NoData_aligned.tif</p> <p>2018-12-08_Lhonak_DEM_SGM_4m_on_Cop30_BiasCorr_NoData_aligned.tif</p> <p>One post-event mosaic DEM from Pléiades </p> <p>Post-Event_DEM_SGM_october2023.tif</p> <p>One grid of elevation change from 2018 to 2023</p> <p>dh_mosaic_Lhonak_SGM_median.tif</p> <p> </p> <p>===========================</p> <p>Methods</p> <p>The SPOT6 and Pléiades stereo-images were processed using the Ames Stereo Pipeline (ASP, Beyer et al., 2018), yielding a DEM in 4x4 m GSD. The processing was done using as only input the stereo-images and their orientation information, as Rational Polynomial Coefficients (RPCs). The parallel_stereo routine performs all the steps needed in the correlation of the stereo-images, yielding a pointcloud which is then interpolated using the routine point2dem. We used the semi global matching algorithm and the set of processing parameters from Deschamps-Berger et al. (2020)</p> <p>Beyer, R. A., Alexandrov, O., and McMichael, S.: The Ames Stereo Pipeline: NASA’s Open Source Software for Deriving and Processing Terrain Data, Earth and Space Science, 5, 537–548, https://doi.org/10.1029/2018EA000409, 2018.</p> <p>Deschamps-Berger, C., Gascoin, S., Berthier, E., Deems, J., Gutmann, E., Dehecq, A., Shean, D., and Dumont, M.: Snow depth mapping from stereo satellite imagery in mountainous terrain: evaluation using airborne laser-scanning data, The Cryosphere, 14, 2925–2940, https://doi.org/10.5194/tc-14-2925-2020, 2020.</p> <p> </p> <p>===========================</p> <p>Data Specifications:</p> <p>Cartographic projection: UTM zone 45N (EPSG:32645)</p> <p>Origin of Elevation: meters above WGS84 ellipsoid</p> <p>Raster data format: GeoTIFF</p> <p>NoData value : -9999</p> <p> </p> <p>===========================</p> <p>Acknowledgements:</p> <p>Pléiades and SPOT6 images were acquired thanks to the CIEST2 service developed and performed with the French Space Agency (CNES) by FormaTerre, Solid Earth component of the Data Terra Research Infrastructure</p> <p> </p> <p>===========================</p> <p>Dataset Attribution:</p> <p>This dataset is licensed under a Creative Commons CC BY-NC 4.0 International License (Attribution-NonCommercial).</p> <p> </p>
Data obtained during classification of intertidal habitats using UAV imagery in the Galapagos Archipelago (Orthophotos, digital elevation models (DEM) and orthophoto-draped 3D models)
<p>In the repository 5 folders exist. 1) Digital elevation models (DEMs), 2) Intertidal habitat map, 3) Othophoto draped 3D models, 4) Orthophotos, and 5) Processing reports. The data has been collected in Puerto Ayora at Santa Cruz in August 2017, the most urbanized island of the Galapagos Archipelago. The purpose of this study was to investigate the image classification opportunities for these intertidal habitats using Uncrewed Aerial Vehicle (UAV) imagery. This dataset is cited in an open-access publication: https://doi.org/10.3390/drones7070416. </p>
Digital Elevation Models (DEMs) after the Shovi (Caucasus) debris flow, 13 August 2023
<p>===========================</p> <p>Introduction</p> <p>On 4 August 2023, a large debris flow struck the mountain resort town of Shovi in Georgia. More on this event here : <a href="https://eos.org/thelandslideblog/the-4-august-2023-debris-flow-at-shovi-in-georgia">https://eos.org/thelandslideblog/the-4-august-2023-debris-flow-at-shovi-in-georgia</a></p> <p>Two Pléiades stereo images were acquired after the event on 13 August 2023, thanks to the activation of the CIEST² (<a href="https://www.poleterresolide.fr/ciest-2-nouvelle-generation-2/">https://www.poleterresolide.fr/ciest-2-nouvelle-generation-2/</a>) scheme. 2023-08-13a covers the lower part, 2023-08-13b the upper/source area.</p> <p>We share here the Pléiades DEMs derived from these images. The collection includes four files:</p> <p>Shovi_2023-08-13a_DEM_2m.tif</p> <p>Shovi_2023-08-13a_DEM_20m.tif</p> <p>Shovi_2023-08-13b_DEM_2m.tif</p> <p>Shovi_2023-08-13b_DEM_20m.tif</p> <p> </p> <p>===========================</p> <p>Methods</p> <p>The Pléiades stereo-images were processed using the Ames Stereo Pipeline (ASP, Beyer et al., 2018), yielding a DEM in 2x2m and 20x20 m GSD and an orthoimage in 0.5x0.5m GSD. The processing was done using as only input the stereo-images and their orientation information, as Rational Polynomial Coefficients (RPCs). The parallel_stereo routine performs all the steps needed in the correlation of the stereo-images, yielding a pointcloud which is then interpolated using the routine point2dem. We used the block matching algorithm and the set of processing parameters from Marti et al. (2016)</p> <p>Beyer, R. A., Alexandrov, O., and McMichael, S.: The Ames Stereo Pipeline: NASA’s Open Source Software for Deriving and Processing Terrain Data, Earth and Space Science, 5, 537–548, https://doi.org/10.1029/2018EA000409, 2018.</p> <p>Marti, R. et al.: Mapping snow depth in open alpine terrain from stereo satellite imagery, The Cryosphere, 10(4), 1361–1380, doi:10.5194/tc-10-1361-2016, 2016.</p> <p> </p> <p>===========================</p> <p>Data Specifications:</p> <p>Cartographic projection: UTM zone 38N (EPSG:32638)</p> <p>Origin of Elevation: meters above WGS84 ellipsoid</p> <p>Raster data format: GeoTIFF</p> <p>NoData value : -9999</p> <p>Pléiades dataset includes only DEMs because a licence needs to be signed with CNES to access Pléiades imagery. Please contact the authors for further information on this.</p> <p> </p> <p>===========================</p> <p>Acknowledgements:</p> <p>Pléiades images were provided under the CIEST² initiative (CIEST2 is part of ForM@Ter (https://en.poleterresolide.fr/) (Pléiades © CNES 2023, distribution AIRBUS DS)</p> <p> </p> <p>===========================</p> <p>Dataset Attribution:</p> <p>This dataset is licensed under a Creative Commons CC BY-NC 4.0 International License (Attribution-NonCommercial).</p> <p> </p> <p>===========================</p> <p>Citation:</p> <p>Please cite this repository as :</p> <p>Berthier E. (2023). Digital Elevation Models (DEMs) after the Shovi flood (Caucasus), 13 August 2023, Dataset distributed on Zenodo: 10.5281/zenodo.8252339</p>
Digital Elevation Models (DEMs) after the Shovi (Caucasus) debris flow, 13 August 2023
<p>===========================</p><p>Introduction</p><p>On 4 August 2023, a large debris flow struck the mountain resort town of Shovi in Georgia. More on this event here : <a href="https://eos.org/thelandslideblog/the-4-august-2023-debris-flow-at-shovi-in-georgia">https://eos.org/thelandslideblog/the-4-august-2023-debris-flow-at-shovi-in-georgia</a></p><p>Two Pléiades stereo images were acquired after the event on 13 August 2023, thanks to the activation of the CIEST² (<a href="https://www.poleterresolide.fr/ciest-2-nouvelle-generation-2/">https://www.poleterresolide.fr/ciest-2-nouvelle-generation-2/</a>) scheme.</p><p>We share here the Pléiades DEMs derived from these images. The collection includes four files:</p><p>Shovi_2023-08-13a_DEM_2m.tif</p><p>Shovi_2023-08-13a_DEM_20m.tif</p><p>Shovi_2023-08-13b_DEM_2m.tif</p><p>Shovi_2023-08-13b_DEM_20m.tif</p><p>===========================</p><p>Methods</p><p>The Pléiades stereo-images were processed using the Ames Stereo Pipeline (ASP, Beyer et al., 2018), yielding a DEM in 2x2m and 20x20 m GSD and an orthoimage in 0.5x0.5m GSD. The processing was done using as only input the stereo-images and their orientation information, as Rational Polynomial Coefficients (RPCs). The parallel_stereo routine performs all the steps needed in the correlation of the stereo-images, yielding a pointcloud which is then interpolated using the routine point2dem. We used the semi global matching algorithm and the set of processing parameters from Deschamps-Berger et al. (2020)</p><p>Beyer, R. A., Alexandrov, O., and McMichael, S.: The Ames Stereo Pipeline: NASA's Open Source Software for Deriving and Processing Terrain Data, Earth and Space Science, 5, 537–548, https://doi.org/10.1029/2018EA000409, 2018.</p><p>Deschamps-Berger, C., Gascoin, S., Berthier, E., Deems, J., Gutmann, E., Dehecq, A., Shean, D., and Dumont, M.: Snow depth mapping from stereo satellite imagery in mountainous terrain: evaluation using airborne laser-scanning data, The Cryosphere, 14, 2925–2940, https://doi.org/10.5194/tc-14-2925-2020, 2020.<br><br>===========================</p><p>Data Specifications:</p><p>Cartographic projection: UTM zone 38N (EPSG:32638)</p><p>Origin of Elevation: meters above WGS84 ellipsoid</p><p>Raster data format: GeoTIFF</p><p>NoData value : -9999</p><p>Pléiades dataset includes only DEMs because a licence needs to be signed with CNES to access Pléiades imagery. Please contact the authors for further information on this.</p><p>===========================</p><p>Acknowledgements: </p><p>Pléiades images were provided under the CIEST² initiative (CIEST2 is part of ForM@Ter (https://en.poleterresolide.fr/) (Pléiades © CNES 2023, distribution AIRBUS DS)</p><p>===========================</p><p>Dataset Attribution:</p><p>This dataset is licensed under a Creative Commons CC BY-NC 4.0 International License (Attribution-NonCommercial).</p><p>===========================</p><p>Citation:</p><p>Please cite this repository as described below:</p><p>Etienne Berthier. (2023). Digital Elevation Models (DEMs) after the Shovi flood (Caucasus), 13 August 2023</p>
Digital Elevation Model (DEM) raster layer for interior Alaska
This is a raster file in .e00 file that has a number of values that represent a range of elevations across Interior Alaska.
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.