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265 results for “terrain”
Continental Europe Digital Terrain Model geomorphometry derivatives at 30 m, 100 m and 250 m
<p>Digital Terrain Model geomorphometry derivatives based on the DTM for Continental Europe using the <a href="https://epsg.io/3035">EPSG:3035</a> projection system. Processed using <a href="http://www.saga-gis.org/">SAGA GIS</a>, <a href="https://grass.osgeo.org/grass78/">GRASS 7 GIS</a> and <a href="https://gdal.org/programs/gdaldem.html">GDAL</a> at 3 standard spatial resolutions: 30-m, 100-m and 250-m. Derivatives include:</p> <ul> <li>devmean = deviation from mean value derived using <a href="http://www.saga-gis.org/saga_tool_doc/7.4.0/statistics_grid_1.html">SAGA GIS</a>,</li> <li>downlocal / down = downslope local and general curvature derived using <a href="http://www.saga-gis.org/saga_tool_doc/7.1.1/ta_morphometry_26.html">SAGA GIS</a>,</li> <li>hillshade = hillshading derived using using GDAL <a href="https://gdal.org/programs/gdaldem.html">gdaldem</a> functions,</li> <li>mnr = Module Melton Ruggedness Number derived using <a href="http://www.saga-gis.org/saga_tool_doc/2.2.4/ta_hydrology_23.html">SAGA GIS</a>,</li> <li>northerness/easterness = derived using <a href="https://grass.osgeo.org/grass78/manuals/addons/r.northerness.easterness.html">GRASS 7 GIS</a>,</li> <li>openp / openn = openness positive negative derived using <a href="http://www.saga-gis.org/saga_tool_doc/2.2.5/ta_lighting_5.html">SAGA GIS</a>,</li> <li>slope = slope in percent derived using GDAL <a href="https://gdal.org/programs/gdaldem.html">gdaldem</a> functions,</li> <li>topidx = a topographic index (wetness index) derived using <a href="https://grass.osgeo.org/grass76/manuals/r.topidx.html">GRASS 7 GIS</a>,</li> <li>tpi = Topographic Wetness Index derived using <a href="http://www.saga-gis.org/saga_tool_doc/2.1.3/ta_hydrology_20.html">SAGA GIS</a>,</li> <li>vbf = Multiresolution Index of Valley Bottom Flatness derived using <a href="http://www.saga-gis.org/saga_tool_doc/2.2.6/ta_morphometry_8.html">SAGA GIS</a>,</li> </ul> <p>Detailed processing steps can be found <a href="https://gitlab.com/geoharmonizer_inea/spatial-layers"><strong>here</strong></a>. Read more about the processing steps <a href="https://opendatascience.eu/building-continental-europe-digital-terrain-model-30-m-resolution-using-machine-learning"><strong>here</strong></a>.</p> <p>Derivatives were chosen aiming to support soil and vegetation mapping projects. The slope.percent map at 30-m has been converted from 0-100% scale to 0-200% (Byte format) to help decrease the file size.</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>
plant diversity and terrain covariates at OAL-UK
<p>dataset containing information on plant diversity and terrain covariates retrieved from the slopes OAL-UK. Samples were collected using 1 m2 quadrants following a stratified random sampling approach. The data set casts light on the relationship between shallow landslides, terrain covariates and plant diversity. The data set is linked to the following publication <a href="https://doi.org/10.1007/s10346-017-0822-y">https://doi.org/10.1007/s10346-017-0822-y</a></p>
Ensemble Digital Terrain Model (EDTM) of the world
<p>Layers include: Ensemble Digital Terrain Model (EDTM) in 250-m resolution. Unit is in metre(m) and precision is in decimetre (dm). Maps are downscaled from 30-m resolution to 250-m in order to fit the size limit. We provide 30-m EDTM and its standard deviation as links:</p> <ul> <li><strong>30-m EDTM</strong></li> </ul> <p><a href="https://s3.eu-central-1.wasabisys.com/openlandmap/dtm/dtm.bareearth_ensemble_p10_30m_s_2018_go_epsg4326_v20230221.tif">https://s3.eu-central-1.wasabisys.com/openlandmap/dtm/dtm.bareearth_ensemble_p10_30m_s_2018_go_epsg4326_v20230221.tif</a></p> <ul> <li><strong>Standard deviation</strong></li> </ul> <p><a href="https://s3.eu-central-1.wasabisys.com/openlandmap/dtm/dtm.bareearth_ensemble_std_30m_s_2018_go_epsg4326_v20230221.tif"><strong>https://s3.eu-central-1.wasabisys.com/openlandmap/dtm/dtm.bareearth_ensemble_std_30m_s_2018_go_epsg4326_v20230221.tif </strong></a></p> <p>Derived using <a href="https://www.eorc.jaxa.jp/ALOS/en/dataset/aw3d30/aw3d30_e.htm">ALOS AW3D</a>, <a href="https://spacedata.copernicus.eu/collections/copernicus-digital-elevation-model">GLO-30</a>, <a href="http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_DEM/">MERITDEM</a>, and national DTMs. We derived a lower 10% quantile from all maps. In order to create bare earth data, we used <a href="https://glad.umd.edu/dataset/gedi/">canopy height</a> (canopy height > 2m) and standard deviation (sd > 6m) to mask building and forest in AW3D and GLO-30. Practical processing is written <a href="https://gitlab.opengeohub.org/yu-feng.ho/faen-artifact/-/blob/main/ensemble_dtm.ipynb">here</a> in Python.</p> <p>To access and visualize maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>If you discover a bug, artifact or inconsistency, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/-/issues">https://gitlab.com/openlandmap/global-layers/-/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL in Cloud Optimised GeoTiff (COG). File naming convention:</p> <ul> <li>dtm.bareearth = variable: digital terrain model (m), bare earth</li> <li>ensemble = determination method: ensemble of mutli-source dsm and dtm</li> <li>p10/std = aggregation/statistics method: 10th percentile / standard deviation</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>s = vertical reference: at surface,</li> <li>go = bounding box: global land without Antarctica</li> <li>epsg.4326 = ESPG code: epsg.4326</li> <li>v20230221 = version code: creation date 20230221</li> </ul>
Digital terrain models derived from UAV overflights of the fifteen NPP study sites at Jornada Basin LTER in 2019
This data package contains digital terrain models (DTMs) 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 DTMs using a structure from motion method. These models do not include the elevations of aboveground features such as vegetation and large rocks. The raw images are not provided but can be made available via project PIs. This data package includes one centimeter resolution DTMs of all 15 sites as geotiff raster files. Other derived products from these UAV missions, including orthomosaic photos, digital elevation models, and sparse point clouds, are available in other EDI data packages (knb-lter-jrn.210543001, knb-lter-jrn.210543002, 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>
Multi-temporal digital terrain models of the NBS experiment in OAL-Austria
<p>Multi-temporal digital terrain models of the NBS experiment in OAL-Austria with a spatial resolution of 10cm, derived from 3D point clouds acquired with a terrestrial laser scanner (Riegl-VZ2000i); Projection: EPSG 31254</p> <p>The TLS-monitoring is intended for assessing the stability of the embankment at the NBS field demonstrator in OAL-Austria. The digital terrain models were acquired after applying the ground classification filter proposed by Axelsson (2000)</p>
Soil carbon and nitrogen stock data for dominant geomorphological terrain units in Qarlikturvik Valley, Bylot Island, Arctic Canada
<p>Dataset for the manuscript 'The distribution of soil carbon and nitrogen stocks among dominant geomorphological terrain units in Qarlikturvik Valley, Bylot Island, Arctic Canada.' to appear in the 'Journal of Geophysical Research: <em>Biogeosciences'.</em></p>
Identification of Thalweg and Ridge Networks as Landmarks for Terrain Partitioning
<p>Grid digital elevation models having resolution of 1 m or less are increasingly available to scientists and engineers interested in describing current state and evolution of Earth and space topography. Significant information loss is, however, clearly observed when existing terrain analysis methods are used in geophysical modeling, especially when coarse meshes are needed for computational efficiency. The present study shows how thalweg and ridge networks can be extracted automatically from any high-resolution grid digital elevation model without the need to alter the observed topographic data, and how these networks can be used as landmarks for terrain partitioning. The slopeline network extracted in grid digital elevation models is used to determine ridge points, related average rejunction lengths of slopelines extending from ridge points on opposite slopes, exorheic and endorheic basins. Exorheic and endorheic basins are connected through the spilling saddles from endorheic basins to form the thalweg network, and the related ridge network is identified. The obtained thalweg and ridge networks are characterized by using the known concept of drainage area and the new concept of spread area to provide physically meaningful landmarks for terrain partitioning at the desired level of detail. Although the developed methods are inspired by the observation of gravity-driven processes, they support any investigation in Earth and space science where thalweg and ridge networks are relevant topographic features. Potential impacts are exemplified by quantifications of preserved depressions over a mountain area and benefits from physically meaningful unstructured terrain partitioning in surface flow propagation over a complex floodplain.</p>
Datasets of "Terrain-adaptive PCGML in Minecraft"
<p>Datasets of Minecraft world samples created and used for the paper <em>Terrain-adaptive PCGML in Minecraft</em> by Van der Staaij et al., as well as all uncompressed renders made to qualitatively evaluate trained machine learning models for said paper.</p>
Elevation Models for Reproducible Evaluation of Terrain Representation – Multiscale Models – Churfirsten GeoTIFF
<p>Multiscale elevation models centered on Churfirsten, Switzerland</p> <p>Resolutions: 0.5, 2, 5, 10, 15, 30, 60, 120, 250, 500, 1,000, and 2,000 meters, 3,000 × 2,500 height samples each</p> <p>File format: GeoTIFF</p> <p>When using these elevation models in an academic publication, please cite the following article, which describes the process and rationale for compiling these models:</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>
Digital Terrain Model of Edeta's land
<p>Here you can find the Digital Terrain Model -DTM- used to create visibility and network paths. Our DTM has been built merging several sheets -666, 667, 668, 694, 695, 696, 720, 721, 722- of the Digital Terrain Model known as DTM05 that can be downloaded from the Centro Nacional de Información Geográfica -CNIG- "[Centro de Descargas](<a href="https://centrodedescargas.cnig.es/CentroDescargas/locale?request_locale=en">https://centrodedescargas.cnig.es/CentroDescargas/locale?request_locale=en</a>)" -download hub-, the Spanish Government official provider of Geographic Information. The original product is described as a Digital Terrain Model 1st Coverage with 5 m grid spacing. The nine sheets were merged and cropped to a final DTM of 7600 by 5600 with the following corner coordinates:</p> <p><br> Upper Left ( 680000.000, 4403000.000)<br> Lower Left ( 680000.000, 4375000.000)<br> Upper Right ( 718000.000, 4403000.000)<br> Lower Right ( 718000.000, 4375000.000)<br> Center ( 699000.000, 4389000.000)</p>
Multi-temporal digital terrain models of the active deep-seated Vögelsberg landslide (OAL-Austria)
<p>Multi-temporal digital terrain models of the active deep-seated Vögelsberg landslide in OAL Austria (Lat: 47.272°, Lon: 11.597°) with a spatial resolution of 50cm. Raster were derived from classified 3D point clouds acquired with a Riegl VUX-1LR unmanned aerial vehicle laser scanner on August 3<sup>rd</sup> 2018, August 14<sup>th</sup> 2019 and November 6<sup>th</sup> 2020 (Projection: EPSG 31254). Ground classification after Axelsson (2000). Data was used to assess topographic changes at the toe of the active deep-seated Vögelsberg landslide in OAL-Austria.</p>
High-resolution surface wind observations over complex terrain: Big Southern Butte, Salmon River Canyon, Birch Creek
<p>This dataset contains high-resolution wind observations from three field campaigns that took place during 2010-2014 at Big Southern Butte, Salmon River Canyon, and Birch Creek, Idaho. There are three SQLite databases containing 30-s averaged 3-m wind speed, wind direction, and wind gust data from 30-90 cup-and-vane anemometers over a period of 2-4 months at each field site.</p>
Elevation Models for Reproducible Evaluation of Terrain Representation – Archetypal Landforms – Crater Lake GeoTIFF
<p>An elevation model of Crater Lake, Oregon, USA</p> <p>Landform features: caldera, cinder cone, lava flow</p> <p>Resolution: 3.33 meter, 5,200 x 5,200 height samples</p> <p>File format: GeoTIFF</p> <p>This is one model of a set of elevation models: <a href="http://doi.org/10.5281/zenodo.3938020">https://doi.org/10.5281/zenodo.3938020</a>. Please cite the entire set of models.</p> <p>When using this elevation model in an academic publication, please cite the following article, which describes the process and rationale for compiling elevation models:</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>
Elevation Models for Reproducible Evaluation of Terrain Representation – Multiscale Models – Valdez GeoTIFF
<p>Multiscale elevation models centered on Valdez, Alaska, USA</p> <p>Resolutions: 3.3, 7.5, 15, 30, 90, 250, 500, 1,000, and 2,000 meters, 1500 x 1,500 height samples each</p> <p>File format: GeoTIFF</p> <p>When using these elevation models in an academic publication, please cite the following article, which describes the process and rationale for compiling these models:</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>
Elevation Models for Reproducible Evaluation of Terrain Representation – Archetypal Landforms – Great Sand Dunes GeoTIFF
<p>An elevation model of Great Sand Dunes, Colorado, USA</p> <p>Landform features: active dune field, sand sheet, sabkha</p> <p>Resolution: 3.3 meter, 5,300 x 5,300 height samples</p> <p>File format: GeoTIFF</p> <p>This is one model of a set of elevation models: <a href="https://doi.org/10.5281/zenodo.3938020">https://doi.org/10.5281/zenodo.3938020</a>. Please cite the entire set of models.</p> <p>When using this elevation model in an academic publication, please cite the following article, which describes the process and rationale for compiling elevation models:</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>
Elevation Models for Reproducible Evaluation of Terrain Representation – Archetypal Landforms – Massanutten Mountain ASCII
<p>An elevation model of Massanutten Mountain, Virginia, USA</p> <p>Landform features: folded ridges, hogback, water gap, meander</p> <p>Resolution: 10 meter, 3,900 x 3,900 height samples</p> <p>File format: Esri ASCII grid</p> <p>This is one model of a set of elevation models: <a href="https://doi.org/10.5281/zenodo.3938020">https://doi.org/10.5281/zenodo.3938020</a>. Please cite the entire set of models.</p> <p>Version 2.0.0 replaced the previous erroneous elevation model of another geographic area.</p> <p>When using this elevation model in an academic publication, please cite the following article, which describes the process and rationale for compiling elevation models:</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>
Automated vegetation cover estimation from close-range photogrammetric point clouds in mountain terrain for comparison of vegetation location properties - Dataset
<p>Vegetation cover data of the used plots, showing values for manually digitized, in-situ, and photogrammetric methods.</p>
High Resolution Digital Terrain Models of Mercury
<p>Supplementary material of the article:<br> Tenthoff, M.; Wohlfarth, K.; Wöhler, C. High Resolution Digital Terrain Models of Mercury. <em>Remote Sens.</em> <strong>2020</strong>, <em>12</em>, 3989.</p> <p><br> Abstract:<br> We refined our Shape from Shading (SfS) algorithm, which has previously been used to<br> create digital terrain models (DTMs) of the Lunar and the Martian surface, to generate high-resolution<br> DTMs of Mercury from MESSENGER imagery. To adapt the reconstruction procedure to the specific<br> conditions of Mercury and the available imagery, we introduced two methodic innovations. First, we<br> extended the SfS algorithm to enable the 3D-reconstruction from image mosaics. Because most mosaic<br> tiles were acquired at different times and under various illumination conditions, the brightness of<br> adjacent tiles may vary. Brightness variations that are not fully captured by the reflectance model may<br> yield discontinuities at tile borders. We found that the relaxation of the constraint for a continuous<br> albedo map improves the topographic results of an extensive region removing discontinuities at<br> tile borders. The second innovation enables the generation of accurate DTMs from images with<br> substantial albedo variations, such as hollows. We employed an iterative procedure that initializes the<br> SfS algorithm with the albedo map that was obtained by the previous iteration step. This approach<br> converges and yields a reasonable albedo map and topography. With these approaches, we generated<br> DTMs of several science targets such as the Rachmaninoff basin, Praxiteles crater, fault lines, and<br> several hollows. To evaluate the results, we compared our DTMs with stereo DTMs and laser altimeter<br> data. In contrast to coarse laser altimetry tracks and stereo algorithms, which tend to be affected by<br> interpolation artifacts, SfS can generate DTMs almost at image resolution. The root mean squared<br> errors (RMSE) at our target sites are below the size of the lateral image resolution. For some targets,<br> we could achieve an effective resolution of less than 10 m/pixel, which is the best resolution of<br> Mercury to date. We critically discuss the limitations of the evaluation methodology.<br> <br> </p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
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.