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154 results for “raster”
German weather services (DWD) multi annual meteorological rasters for the climate period 1991-2020 refined to 25m grid
<h1>Overview</h1> <p>These are two multi-annual raster products from the german weather service, that got refined from a 1km grid to a 25m grid, by using a local regression model.</p> <p>The base rasters from DWD are:</p> <ul> <li>HYRAS precipitation</li> <li>REGNIE precipitation</li> <li>DWD-grid (precipitation, potential evapotranspiration and temperature 2m above ground)</li> </ul> <p>To refine the grids the Copernicus DEM with a resolution of 25m got used. For every cell a linear regression model got created, by selecting the multi-annual rasters value and the elevation, from the original digital elevation model that was used by the DWD to create the raster, in a certain window around the cell. This window was at least 2 cells around the considered cell, so 5x5=25 cells. If the standard deviation of the elevation in this window was less than 4m, more neighbooring cells are considered until a maximum of 13x13=169 cells are considered. This widening of the window was necessary for flat regions to get a reasonable regression model.</p> <p>Out of these combinations of elevation and climate parameter a linear regression model was build. These regression models are then applied to the finer digital elevation model with its 25m resolution from Copernicus.</p> <p>The following image illustrates the generation of the refined rasters on a small example window:</p> <p></p>
Hubbard Brook Experimental Forest: Soil type prediction raster files
This dataset consists of raster files predicting spatial patterns in soils for the entire Hubbard Brook Experimental Forest. Eight soil units are used, following a hydropedologic approach, based on relationships between soil genetic horizon presence and thickness, and the frequency and depth of groundwater fluctuations. Nine raster files on a five-meter grid are presented, including one raster each showing the probability of presence of each of the eight soil units; the ninth raster represents the soil unit most likely to be present at each grid cell. The methods section of the metadata includes descriptions of the eight soil units and guidance for users of the model outputs. 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.
Potential forest conservation value rasters for Denmark from Assmann et al. "LiDAR data fusion and machine learning identify temperate forests of high conservation value"
<p>Potential forest conservation value (high / low) rasters for Denmark based on a remote sensing data fusion approach. Please see manuscript (below) for a detailed description of the methods and data products. </p> <p><br>Jakob J. Assmann, Pil B. M. Pedersen, Jesper E. Moeslund, Cornelius Senf, Urs A. Treier, Derek Corcoran, Zsófia Koma, Thomas Nord-Larsen, Signe Normand. In prep. LiDAR data fusion and machine learning identify temperate forests of high conservation value.</p> <p><br>When using the data, please cite the above manuscript. </p> <p><br>Files description:</p> <ul> <li>Compressed and cloud optimised rasters of potential forest conservation value projections for Denmark (10 m res.) in EPSG:3857 <ul> <li>forest_quality_ranger_biowide_10m_cog_epsg3857.tif RandomForest model projections based on BIOWIDE stratification (!! best performing model !!)</li> <li>forest_quality_ranger_sustainscapes_10m_cog_epsg3857.tif RandomForest model projections based on SustainScapes stratification</li> <li>forest_quality_gbm_biowide_10m_cog_epsg3857.tif GBM model projections based on BIOWIDE stratification</li> <li>forest_quality_gbm_sustainscapes_10m_cog_epsg3857.tif GBM model projections based on SustainScapes stratification</li> </ul> </li> </ul> <p> </p> <ul> <li>Aggregated rasters of potential forest conservation value projections for Denmark (100 m res.) in EPSG:25832 <ul> <li>forest_quality_ranger_biowide_100m.tif RandomForest model projections based on BIOWIDE stratification (!! best performing model !!)</li> <li>forest_quality_ranger_sustainscapes_100m.tif RandomForest model projections based on SustainScapes stratification</li> <li>forest_quality_gbm_biowide_100m.tif GBM model projections based on BIOWIDE stratification</li> <li>forest_quality_gbm_sustainscapes_100m.tif GBM model projections based on SustainScapes stratification </li> </ul> </li> </ul> <p> </p> <ul> <li>Uncompressed and tiled rasters of potential forest conservation value projections for Denmark (10 m res.) in EPSG:25832<br>Please note: the archives contain approx. 42k tiles, each 10 x 10 km, as well as a VRT file for covenient loading. <ul> <li>forest_quality_ranger_biowide_10m.zip RandomForest model projections based on BIOWIDE stratification (!! best performing model !!)</li> <li>forest_quality_ranger_sustainscapes_10m.zip RandomForest model projections based on SustainScapes stratification</li> <li>forest_quality_gbm_biowide_10m.zip GBM model projections based on BIOWIDE stratification</li> <li>forest_quality_gbm_sustainscapes_10m.zip GBM model projections based on SustainScapes stratification</li> </ul> </li> </ul>
GRTSmh_base4frac: the raster data source GRTSmaster_habitats converted to base 4 fractions
<p>The data source file is a monolayered GeoTIFF in the <code>FLT8S</code> datatype. In <code>GRTSmh_base4frac</code>, the decimal (i.e. base 10) integer values from the raster data source <code>GRTSmaster_habitats</code> (<a href="https://doi.org/10.5281/zenodo.2682323">link</a>) have been converted into base 4 fractions, using a precision of 13 digits behind the decimal mark (as needed to cope with the range of values). For example, the integer <code>16</code> (<code>= 4^2</code>) has been converted into <code>0.0000000000100</code> and <code>4^12</code> has been converted into <code>0.1000000000000</code>.</p> <p>Long base 4 fractions seem to be handled and stored easier than long (base 4) integers. This approach follows the one of Stevens & Olsen (2004) to represent the reverse hierarchical order in a GRTS sample as base-4-fraction addresses.</p> <p>See R-code in the GitHub repository <a href="https://github.com/inbo/n2khab-preprocessing/tree/ecadaf54d4a5aa662d0d18fbfe59788732bb7182/src/generate_GRTS_10_GRTSmh_base4frac">'n2khab-preprocessing' at commit ecadaf5</a> for the creation from the <code>GRTSmaster_habitats</code> data source.</p> <p>A reading function to return the data source in a standardized way into the R environment is provided by the R-package <a href="https://inbo.github.io/n2khab/">n2khab</a>.</p> <p>Beware that not all GRTS ranking numbers are present in the data source, as the original GRTS raster has been clipped with the Flemish outer borders (i.e., not excluding the Brussels Capital Region).</p>
High resolution shrub cover raster maps of the Jornada Basin LTER, including JER and CDRRC (2011)
This data package contains two raster shrub cover maps of the Jornada Experimental Range (JER) and Chihuahuan Desert Rangeland Research Center (CDRRC) in southern New Mexico, USA. The maps are derived from 1m resolution National Agriculture Imagery Program (NAIP) aerial photos acquired in 2011. A one-meter resolution raster file lists cover as non-shrub or non-shrub. A hectare resolution raster file contains fractional shrub cover values (0-1) calculated from the one-meter raster.
NDVI Raster maps of Scotland for 2013-2016 used to analyse correlations between greenness, mortality and mental health.
<p>These files were used in the analysis for "Greenness, mortality and mental health prescription rates in urban Scotland - a population level, observational study" Hyam. Submitted to RIO 2020.</p> <p><strong>Extract on construction of data</strong></p> <p>NDVI data was downloaded from the United States Geological Survey (USGS) Land Satellites Data System (LSDS) Science Research and Development (LSRD) (United States Geological Survey 2018). Which produces Level 2 and Level 3 data products from the Level 1 data of instruments aboard Landsat Satellites. For this study Surface Reflectance data generated by the Landsat Surface Reflectance Code (LaSRC) from the Operational Land Imager (OLI) instrument aboard the Landsat 8 satellite was used (United States Geological Survey 2018). The Surface Reflectance NDVI (sr_ndvi) product and Level-2 Pixel Quality Assessment band (pixel_qa) were downloaded for Landsat scenes 204/21, 205/21, 206/21, 204/20, 205/20, 206/20 WRS-2 (NASA 2018) for the calendar years 2013 to 2016. These scenes cover most of Scotland and include all the major urban areas. A full list of the 333 products is given in supplemental material. Suppl. material 2</p> <p>All of Scotland is over 54° North and so for many satellite images the sun is at too low an angle to give reliable surface reflectance data especially in the winter months. Scotland also has an oceanic climate so the ground is often obscured by cloud or mist. To build a detailed, contiguous NDVI map of the whole country therefore requires combining images taken on many satellite passes especially if points are to be sampled multiple times to overcome measurement errors. The images downloaded from USGS were therefore combined. A cloud free version of each NDVI image was created by setting the pixels that corresponded to cloud, snow or water in the Quality Assurance Assessment band to NA. These cloud free images were then combined into a single, mosaic stack of images to cover all of the study area and then averaged down to a single layer as a tiff image. This was done for two seasonal periods, Winter (October, November, December of 2013, 2014, 2015 and 2016 combined with January, February, March of 2014, 2015, 2016) and summer (April, May, June, July, August, September of 2014, 2015, and 2016). The resulting two images covering most of Scotland for winters and summers between 2013 and 2016 and formed the basis of subsequent analysis.</p> <p>These two files are included here along with a list of the Landsat products used to produce them.</p>
Horizon Raster Austria
<p>Horizon dataset for Austria on a rasterbasis, stored in a netcdf to combine all viewing directions in 10 degree steps (35 steps in total). The data was calculated from DHM raster data (10mx10m) available here: https://www.data.gv.at/katalog/dataset/b5de6975-417b-4320-afdb-eb2a9e2a1dbf (despite the description it is DHM not DGM).</p>
E4warning_Vector_Raster_Extents_AdminUnits
<p><strong>E4Warning Vector and raster Analysis extents and admin units</strong></p> <p>E4warning standard extents and polygons were defined at the start of the project. There sets of standatd geographies are provided.</p> <p> </p> <p><strong>File Names:</strong></p> <div>a) Folder POLYGONS/e4vnpolygons0921.zip: admin unit Polygon shapes – </div> <div>E4VNDATAPOLYGONS = deslivered NUTS 3 and GAUL 2 depending on country good for standardised data entry. Geographic projection</div> <div>E4VNMAPPOLYGONS = deslivered Admin units chosen to be more or less equal size – best for visibility in maps</div> <div>b) Folder POLYGONS/e4grid10ddclp.zip = decimal degree grids – 0.1 degree (about 10km)</div> <div>c) Folder RASTERS: e4vnextentland10km.zip = 0.08333 deg resolution extent, 1 = land, 0 - water</div> <div>d) Folder RASTERS: e4vnextentlandwater1km.zip = 0.008333 deg resolution extent, 1 = land, 0 - water</div> <div>e) Folder RASTERS: e4vnextentlandwater5km.zip =0.0416667 deg resolution extent, 1 = land, 0 - water</div> <div>g) Folder RASTERS: e4vnextentpcland10km.zip = 0.08333 deg resolution extent, value = PC of pixel that is land</div>
Johann Raster (r2374)
<b>-- <a href="https://doi.org/10.5281/zenodo.11582199">Documentation</a> --</b><br><br><u>Name</u>: Johann Raster<br><u>musiXplora-ID</u>: r2374<br><u>musiXplora-URI</u>: <a href="https://musixplora.de/mxp/r2374">https://musixplora.de/mxp/r2374</a><br><u>Gender</u>: m<br><u>First Mentioned</u>: 1893<br><u>Last Mentioned</u>: 1903<br><u>Sectors</u>: Instrumentenbau<br><u>Professions (Historical)</u>: Saiteninstrumentenmacher<br><u>Professions (Musical)</u>: Zupfinstrumentenbauer<br><u>Other Places of Activity</u>: München<br><br><br><u>Titel/Medien:</u><br><table><tbody><tr><th>Role</th><th>Sigel</th><th>Title</th><th>mXp-ID</th></tr><tr><td>Related</td><td>Henkel 2013</td><td>Zitherbauer und -händler nach den Weltadreßbüchern von Paul de Wit. Unveröffentliches Ms.</td><td><a href="https://musixplora.de/mxp/5001813">5001813</a></td></tr></tbody></table><br><br><u>Changelog</u>:<br> - v0.0.1: Initial Upload.<br>
Supplement - Structure from Motion Raster Data
<p>We created orthorectified images and digital elevation models using Agisoft Metashape, a photogrammetric processing software application that uses SfM. We followed the workflow outlined in Bywater-Reyes and Pratt-Sitaula (2022). Once processed, orthorectified imagery and Digital Elevation Models (DEMs) were exported to ArcGIS Pro for additional analysis. Data collection metadata and postprocessing outcomes can be found in this repository. </p>
waldmonitoring.ch: NDVI difference rasters for annual forest change in Switzerland (Sentinel 2 based): 2016 - 2023
<p><strong>NDVI difference rasters for annual forest change in Switzerland (Sentinel 2 based): 2016 - 2023<br></strong></p> <p><em><strong>Date format:</strong></em> GeoTIFF<br><em><strong>Data type: </strong></em>Int16 - Sixteen bit signed integer*<br><em><strong>Spatial Resolution</strong></em>: 10 x 10 m<br><em><strong>Spatial Extent: </strong></em>Switzerland and Liechtenstein, masked with swisstopo swissTLM3D Forest Mask (2021) <br><em><strong>Coordinate Reference System</strong></em>: EPSG:2056 - CH1903+ / LV95, Swiss. Obl. Mercator<br><br>*:<em> NDVI Difference Values (-1 to 1) are multiplied by 10'000 to allow using Integer 16 bit vs. Float 32 bit while maintaining a precision of 5 digits. The values have to be interpreted accordingly: -10'000 means an NDVI difference of -1, +10'000 an NDVI difference of +0.<br><br></em></p> <p>The NDVI difference rasters for annual forest change in Switzerland are created by using Sentinel 2 based NDVI composites (Normalized Difference Vegetation Index). The code for the generating method can be found in the <a href="https://github.com/HAFL-WWI/Digital-Forest-Monitoring/tree/main/methods/use-case1">waldmonitoring-repository</a>, the method itself is also described and translated in further detail in the <a href="https://wiki.waldmonitoring.ch/index.php/Use_Case_1_-_J%C3%A4hrliche_Waldver%C3%A4nderungen">corresponding waldmonitoring-wiki</a>: For the automatic detection of areas of change, the differences between two years were examined using the NDVI. In order to automatically filter out cloudy images, the maximum NDVI value of all available images of the summer months (June - August) was used for each pixel (10 x 10 m). During this time, practically all the vegetation is green. This results in almost cloud-free, annual raster images with the maximum NDVI ("NDVI maximum composite"). The difference between two years is formed from these composites. The difference values accordingly reflect the strength of the change. </p> <p>As an example for interpretation, values of -0.1 or smaller (closer to -1.0) indicate strong forest changes (e.g. clearing), whereas positive values indicate vegetation regeneration or re-greening of previously unvegetated areas. Using a threshold value (we suggest -0.06 for forest applications), areas with considerable negative change can be separated out and be vectorized (converted to polygons) to create a dataset that can be queried.</p>
GRTSmh_diffres: the raster data source GRTSmaster_habitats converted to 9 hierarchical cell address levels at the corresponding lower resolution
<p>The <code>GRTSmh_diffres</code> data source file is a file collection, composed of nine monolayered GeoTIFF files of the <code>INT4S</code> datatype plus a GeoPackage with six polygon layers:</p> <ul> <li> <p>The polygon layers in the GeoPackage are the dissolved, polygonized versions of levels 4 to 9 of the <code>GRTSmh_brick</code> data source (<a href="https://doi.org/10.5281/zenodo.3354403">link</a>). This means that they provide the decimal (i.e. base 10) integer values of these <em>higher hierarchical levels</em> of the GRTS cell addresses of the raw data source <code>GRTSmaster_habitats</code> (<a href="https://doi.org/10.5281/zenodo.2682323">link</a>). Hence, the polygons are typically squares that correspond to the GRTS cell at the specified hierarchical level. The polygon layer is however restricted to the non-<code>NA</code> cells of the original <code>GRTSmaster_habitats</code> raster. Consequently, a part of the polygons is clipped along the Flemish border. Levels 1 to 3 are not provided for the whole of Flanders, because this would inflate the GPKG file. You can look at the <a href="https://github.com/inbo/n2khab-preprocessing/tree/ecadaf5">source code</a> to do such things.</p> </li> <li> <p>The GeoTIFF files provide the respective levels 1 to 9 of the <code>GRTSmh_brick</code> data source in a raster format, at the resolution that corresponds to the GRTS cell at the specified hierarchical level. The presence of <code>NA</code> cells around Flanders at level 0 implies that, with decreasing resolution, the raster's extent increases and larger areas outside Flanders are covered by non-<code>NA</code> cells along the border.</p> </li> </ul> <p>The higher-level ranking numbers (compared to the original level 0) allow spatially balanced samples at lower spatial resolution than that of 32 m, and can also be used for aggregation purposes.</p> <p>See R-code in the GitHub repository <a href="https://github.com/inbo/n2khab-preprocessing/tree/ecadaf54d4a5aa662d0d18fbfe59788732bb7182/src/generate_GRTS_30_GRTSmh_diffres">'n2khab-preprocessing' at commit ecadaf5</a> for the creation from the <code>GRTSmh_brick</code> data source.</p> <p>A reading function to return the data source in a standardized way into the R environment is provided by the R-package <a href="https://inbo.github.io/n2khab/">n2khab</a>.</p> <p>Beware that not all GRTS ranking numbers at the specified level are provided, as the original GRTS raster has been clipped with the Flemish outer borders (i.e., not excluding the Brussels Capital Region).</p>
GRTSmh_brick: the raster data source GRTSmaster_habitats converted to 10 hierarchical cell address levels at the original resolution
<p>The data source file is a 10-layered GeoTIFF file, derived from the raster data source <code>GRTSmaster_habitats</code> (<a href="https://doi.org/10.5281/zenodo.2682323">link</a>). Both GeoTIFFs (<code>GRTSmaster_habitats</code>, <code>GRTSmh_brick</code>) use the <code>INT4S</code> datatype. The <code>GRTSmh_brick</code> data source (resolution 32 m) holds the decimal integer ranking numbers of 10 hierarchical levels of the GRTS cell addresses, including the one from <code>GRTSmaster_habitats</code> (with GRTS cell addresses at the resolution level).</p> <p>See R-code in the GitHub repository <a href="https://github.com/inbo/n2khab-preprocessing/tree/ecadaf54d4a5aa662d0d18fbfe59788732bb7182/src/generate_GRTS_20_GRTSmh_brick">'n2khab-preprocessing' at commit ecadaf5</a> for its creation from the <code>GRTSmaster_habitats</code> data source.</p> <p>A reading function to return the data source in a standardized way into the R environment is provided by the R-package <a href="https://inbo.github.io/n2khab/">n2khab</a>.</p> <p>The higher-level ranking numbers of the RasterBrick allow spatially balanced samples at lower spatial resolution than that of 32 m, and can also be used for aggregation purposes. The provided hierarchical levels correspond to the resolutions vector <code>32 * 2^(0:9)</code> (minimum: 32 meters, maximum: 16384 meters).</p> <p>Beware that not all GRTS ranking numbers are present in the data source, as the original GRTS raster has been clipped with the Flemish outer borders (i.e., not excluding the Brussels Capital Region).</p>
GRIDCERF: Geospatial Raster Input Datasets for Capacity Expansion Regional Feasibility
<p>Geospatial Raster Input Datasets for Capacity Expansion Regional Feasibility (GRIDCERF) is a data package containing all the necessary input layers for the <a href="https://github.com/IMMM-SFA/cerf">Capacity Expansion Regional Feasibility (CERF) model</a>. The CERF model uses these layers to find feasible power plant siting locations at a 1 kilometer scale across the conterminous United States for renewable and non-renewable electricity production technologies. This package encompasses a wide variety of geospatial layers pertaining to restrictions, regulations, and challenges that come with siting new electricity production facilities.</p>
Water Quality Features' Changes - WQeMS Raster Products
<p>This dataset contains samples of the Water Quality Features' Changes service of the WQeMS H2020 project.</p>
Hubbard Brook Experimental Forest 10m DEM: Raster GeoTiFF
The 7.5-minute digital elevation model (DEM) data are digital representations of cartographic information in a raster form. The DEMs consist of an array of elevations for ground positions at regularly spaced intervals. The data are produced in 7.5- by 7.5-minute blocks from either digitized cartographic map contour overlays of a scanned National Aerial Photography Program (NAPP) photographs. The DEM data are stored as profiles with a 10- or 30-meter square grid spacing along and between each profile. The original USGS DEM data (Mt. Kineo and Woodstock quadrangles) were merged and projected into UTM Zone 19. The resultant grid was clipped to a box surrounding the USDA Forest Service, Hubbard Brook Experimental Forest. Data distributed as GeoTIFF format in Coordinate system EPSG:26919 - NAD83 / UTM zone 19N.
Hubbard Brook Experimental Forest 30m DEM: Raster GeoTIFF
The 7.5-minute digital elevation model (DEM) data are digital representations of cartographic information in a raster form. The DEMs consist of an array of elevations for ground positions at regularly spaced intervals. The data are produced in 7.5- by 7.5-minute blocks from either digitized cartographic map contour overlays of a scanned National Aerial Photography Program (NAPP) photographs. The DEM data are stored as profiles with a 10- or 30-meter square grid spacing along and between each profile. The original USGS DEM data (Mt. Kineo and Woodstock quadrangles) were merged and projected into UTM Zone 19. The resultant grid was clipped to a box surrounding the USDA Forest Service, Hubbard Brook Experimental Forest. Data distributed as GeoTIFF format in Coordinate system EPSG:26919 - NAD83 / UTM zone 19N.
Hydrology - Stream Centerlines - Ipswich Watershed - Idrisi Raster File.
This datalayer is part of a group of layers used for research in the Ipswich River Watershed. This datalayer was rasterized from the vector line data set “ip30_hydrology”, a modified version of the hydrology layer available from MassGIS for the Ipswich Watershed. To be used to generate a hydrologically corrected Digital Elevation Model, and to examine sampling site catchments or sub-watersheds within the Ipswich River Watershed.
Digital Elevation Model - Ipswich Watershed - Idrisi Raster File.
This datalayer is part of a group of layers used for research in the Ipswich River Watershed. This is Digital Elevation Model data for the study area, in a 30-meter grid. The source elevation tile data was provided on the MassGIS website www.state.ma.us/mgis/massgis.htm in ESRI-format shapefile format and imported into IDRISI software using the ShapeIdr command. The resulting vector elevation files were converted to raster format using successive Lineras macro commands. This has the effect of mosaicing the tiles as well. The raster image was filtered once using a low-pass (mean) filter, then masked to the Ipswich study area parameters (extent). This datalayer was produced as part of a research project concerning the Ipswich River Watershed.
Topography Aspect - Ipswich Watershed - Idrisi Raster File
This datalayer is part of a group of layers used for research in the Ipswich River Watershed. This is the aspect model for the study area. The source elevation tile data was provided on the MassGIS website www.state.ma.us/mgis/massgis.htm in ESRI-format shapefile format and imported into IDRISI software using the ShapeIdr command. The resulting vector elevation files were converted to raster format using successive Lineras macro commands. This has the effect of mosaicing the tiles as well. The raster image was filtered once using a low-pass (mean) filter, then masked to the Ipswich study area parameters (extent). The aspect map was created using the surface analysis module. This datalayer was produced as part of a research project concerning the Ipswich River Watershed.
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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
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