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111 results for “Flanders”
Orthophotos and DSMs derived from RPAS flights over the nature reserve Zwin in Flanders, Belgium
<p><strong>Study area</strong></p> <p>The Zwin is a nature reserve situated along the Belgian North Sea coast, northeast of Knokke, in the province of West-Flanders, Flanders, Belgium. The area is managed by the Flemish Agency for Nature and Forest and consists of a tidal marsh, coastal dunes with <em>Ammophila arenaria</em>, dune grasslands and/or shrub (<em>Hippophae rhamnoides</em>, <em>Salix repens</em>), and a transitional grassland zone that stretches from the inner edge of the coastal dunes into the polders.</p> <p><strong>Data collection</strong></p> <p>Data were collected by the <a href="http://www.inbo.be/en">Research Institute for Nature and Forest (INBO)</a> with a fixed wing drone Gatewing X100 in 2014 and 2015 (15 flights). RGB data were acquired using an off-the-shelf Ricoh GR Digital IV camera, with the following image bands: 1: red, 2: green, 3: blue, 4: alpha channel. CIR (color-infrared) data were acquired using a NIR-enabled Ricoh GR Digital IV camera, with the following info bands: 1: NIR, 2: red, 3: green, 4: alpha channel.</p> <p><strong>Data processing</strong></p> <p>The raw data were processed to Digital Surface Models and orthophotos by the <a href="https://vito.be/en">Flemish Institute for Technological Research (VITO)</a> in 2017. Images with coarse GPS coordinates were imported and processed in Agisoft PhotoScan Pro 1.4.x, a structure-from-motion (SfM) based photogrammetry software program. After extraction and matching of tie points, a bundle adjustment leads to a sparse point cloud and a refined set of camera position and orientation values. Ground control points (either artificially installed markers on the terrain, or other photo-identifiable points, measured on the ground with RTK GNSS) were used to further refine the camera calibration and obtain a pixel-level georeferencing accuracy. From there, a point cloud densification and classification into ground and non-ground points was performed, leading to a rasterized digital surface model (DSM) and digital terrain model (DTM). Finally, a true orthomosaic was projected onto the DTM.</p> <p><strong>Coordinate reference system</strong></p> <p>All geospatial data have the coordinate reference system <code>EPSG:31370 - Belgian Lambert 72</code>.</p> <p><strong>Files</strong></p> <ul> <li><strong>Raw flight data</strong>: images and logs collected by the drone during flight. These files are zipped per flight, with the date (<code>yyyymmdd</code>) and flight number (<code>x</code>) indicated in the file name (<code>flight_yyyymmdd_Zwin_x.zip</code>).</li> <li><strong>Processed data</strong>: Digital Surface Models (<code>filename_DSM.tif</code>) and orthophotos (<code>filename_Ortho.tif</code>) stitched together from the raw data. The included flights are indicated in the file name (e.g. 6 flights for <code>20150709_Zwin_1-3_20150710_Zwin_1-3_DSM.tif</code>).</li> <li><strong>Ground control points</strong>: fixed ground control points (GCP) were placed on 2014-04-07, coordinates of which are available in <code>GCP_20140407_Zwin_fixed.tsv</code>. These GCPs are visible (but fading over time) in all orthophotos except <code>20151012_Zwin_1-4_Ortho.tif</code> which covers a different area. Additional temporary GCPs were placed on 2014-04-07, 2014-04-10 and 2015-07-09 (visible in orthophotos of those dates), coordinates of which are available in the respective <code>GCP_yyyymmdd_Zwin.tsv</code> file.</li> </ul> <p><strong>Cloud Optimized GeoTIFF</strong></p> <p>The most efficient way to explore the processed data is by loading the <a href="https://www.cogeo.org/">Cloud Optimized GeoTIFFs</a> we created for each processed file. Copy one of the file URLs below and follow e.g. the <a href="https://www.cogeo.org/qgis-tutorial.html">QGIS tutorial</a> to load this type of file.</p> <ul> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20140407_Zwin_1-2_DSM.tif</code></li> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20140407_Zwin_1-2_Ortho.tif</code> CIR</li> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20140410_Zwin_1-3_DSM.tif</code></li> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20140410_Zwin_1-3_Ortho.tif</code> CIR</li> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20150709_Zwin_1-3_20150710_Zwin_1-3_DSM.tif</code></li> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20150709_Zwin_1-3_20150710_Zwin_1-3_Ortho.tif</code> RGB</li> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20151012_Zwin_1-4_DSM.tif</code></li> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20151012_Zwin_1-4_Ortho.tif</code> RGB</li> </ul> <p>See <a href="https://s3-eu-west-1.amazonaws.com/lw-remote-sensing/index.html">this page</a> for an overview of public INBO RPAS data.</p>
Orthophotos and DSMs/DTM derived from RPAS flights over the nature reserve Landschap De Liereman in Flanders, Belgium
<p><strong>Study area</strong></p> <p>Landschap De Liereman is a nature reserve situated in Oud-Turnhout, in the province of Antwerp, Flanders, Belgium. The area is managed by the nature conservation NGO Natuurpunt and consists of a diverse landscape of wet and dry heathlands, <em>Nardus</em> grasslands on siliceous soils, forests and transition mires, as well as some remaining arable fields and high-intensity agricultural grasslands.</p> <p><strong>Data collection</strong></p> <p>Data were collected by the <a href="http://www.inbo.be/en">Research Institute for Nature and Forest (INBO)</a> with a fixed wing drone Gatewing X100 in 2015 (7 flights). RGB data were acquired using an off-the-shelf Ricoh GR Digital IV camera, with the following image bands: 1: red, 2: green, 3: blue, 4: alpha channel.</p> <p>An additional flight campaign was commissioned by INBO and carried out by the <a href="https://vito.be/en">Flemish Institute for Technological Research (VITO)</a> in 2017 with a fixed wing drone SenseFly eBee. Multispectral data were acquired using a Parrot Sequoia camera, with the following image bands: 1: green, 2: red, 3: red edge, 4: NIR, 5: alpha channel. with a Parrot Sequoia camera. Raw data for this flight campaign are not available.</p> <p><strong>Data processing</strong></p> <p>The raw data were processed to Digital Surface Models and orthophotos by VITO in 2017. For the 2017 campaign by VITO, the outputs also include a Digital Terrain Model (DTM). Images with coarse GPS coordinates were imported and processed in Agisoft PhotoScan Pro 1.4.x, a structure-from-motion (SfM) based photogrammetry software program. After extraction and matching of tie points, a bundle adjustment leads to a sparse point cloud and a refined set of camera position and orientation values. From there, a point cloud densification and classification into ground and non-ground points was performed, leading to a rasterized digital surface model (DSM) and digital terrain model (DTM). Finally, a true orthomosaic was projected onto the DTM.</p> <p><strong>Coordinate reference system</strong></p> <p>All geospatial data have the coordinate reference system <code>EPSG:31370 - Belgian Lambert 72</code>.</p> <p><strong>Files</strong></p> <ul> <li><strong>Raw flight data</strong>: images and logs collected by the drone during flight. These files are zipped per flight, with the date (<code>yyyymmdd</code>) and flight number (<code>x</code>) indicated in the file name (<code>flight_yyyymmdd_Liereman_x.zip</code>). Raw data for the eBee flights are not available.</li> <li><strong>Processed data</strong>: Digital Surface Models (<code>filename_DSM.tif</code>) and orthophotos (<code>filename_Ortho.tif</code>) stitched together from the raw data. The included flights are indicated in the file name (7 flights for <code>20151008_Liereman_1-4_20151009_Liereman_1-3_DSM.tif)</code>.</li> <li><strong>Ground control points</strong>: not applicable for this dataset.</li> </ul> <p><strong>Cloud Optimized GeoTIFF</strong></p> <p>The most efficient way to explore the processed data is by loading the <a href="https://www.cogeo.org/">Cloud Optimized GeoTIFFs</a> we created for each processed file. Copy one of the file URLs below and follow e.g. the <a href="https://www.cogeo.org/qgis-tutorial.html">QGIS tutorial</a> to load this type of file.</p> <ul> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20151008_Liereman_1-4_20151009_Liereman_1-3_DSM.tif</code></li> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20151008_Liereman_1-4_20151009_Liereman_1-3_Ortho.tif</code> RGB</li> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20170718_Liereman_eBee_DSM.tif</code></li> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20170718_Liereman_eBee_DTM.tif</code></li> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20170718_Liereman_eBee_Ortho.tif</code> multispec</li> </ul> <p>See <a href="https://s3-eu-west-1.amazonaws.com/lw-remote-sensing/index.html">this page</a> for an overview of public INBO RPAS data.</p>
Orthophotos and DSMs derived from RPAS flights over wild boar damaged fields in Eigenbilzen in Flanders, Belgium
<p><strong>Study area</strong></p> <p>The study area in Eigenbilzen is situated in the agricultural zone east of the locality of Eigenbilzen, in the province of Limburg, Flanders, Belgium. The flights picture a wheat field where damage by wild boar is apparent.</p> <p><strong>Data collection</strong></p> <p>Data were collected by the <a href="https://www.inbo.be/en">Research Institute for Nature and Forest (INBO)</a> with a fixed wing drone Gatewing X100 in 2015 (2 flights). RGB data were acquired using an off-the-shelf Ricoh GR Digital IV camera, with the following image bands: 1: red, 2: green, 3: blue, 4: alpha channel. CIR (color-infrared) data were acquired using a NIR-enabled Ricoh GR Digital IV camera, with the following info bands: 1: NIR, 2: red, 3: green, 4: alpha channel.</p> <p><strong>Data processing</strong></p> <p>The raw data were processed to Digital Surface Models and orthophotos by INBO in 2015 using Agisoft PhotoScan Pro 1.0.4, a structure-from-motion (SfM) based photogrammetry software program.</p> <p><strong>Coordinate reference system</strong></p> <p>All geospatial data have the coordinate reference system <code>EPSG:31370 - Belgian Lambert 72</code>.</p> <p><strong>Files</strong></p> <ul> <li><strong>Raw flight data</strong>: images and logs collected by the drone during flight. These files are zipped per flight, with the date (<code>yyyymmdd</code>) and flight number (<code>x</code>) indicated in the file name (<code>flight_yyyymmdd_Bilzen_x.zip</code>).</li> <li><strong>Processed data</strong>: Digital Surface Models (<code>filename_DSM.tif</code>) and orthophotos (<code>filename_Ortho.tif</code>) stitched together from the raw data. The included flight is indicated in the file name (e.g. <code>20150728_Bilzen_1_DSM.tif</code>).</li> <li><strong>Ground control points</strong>: not applicable for this dataset.</li> </ul> <p><strong>Cloud Optimized GeoTIFF</strong></p> <p>The most efficient way to explore the processed data is by loading the <a href="https://www.cogeo.org/">Cloud Optimized GeoTIFFs</a> we created for each processed file. Copy one of the file URLs below and follow e.g. the <a href="https://www.cogeo.org/qgis-tutorial.html">QGIS tutorial</a> to load this type of file.</p> <ul> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20150728_Bilzen_1_Ortho.tif</code> RGB</li> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20150728_Bilzen_2_DSM.tif</code></li> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20150728_Bilzen_2_Ortho.tif</code> CIR</li> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20150728_Bilzen_1_DSM.tif</code></li> </ul> <p>See <a href="https://s3-eu-west-1.amazonaws.com/lw-remote-sensing/index.html">this page</a> for an overview of public INBO RPAS data.</p>
Orthophotos and DSMs derived from RPAS flights over the nature reserve Averbode Bos & Heide in Flanders, Belgium
<p><strong>Study area</strong></p> <p>Averbode Bos & Heide is a nature reserve situated near the locality of Averbode, in the province of Flemish-Brabant, Flanders, Belgium. The area is managed by the nature conservation NGO Natuurpunt and consists of wet and dry heathlands, inland dunes, forests and moorland pools. In the years prior to the drone flights, large stands of mostly coniferous trees were cut to enable ecological restoration of heathlands and moorland pools. The drone flight was triggered by a particular interest to monitor the effects of this restoration.</p> <p><strong>Data collection</strong></p> <p>Data were collected by the <a href="https://www.inbo.be/en">Research Institute for Nature and Forest (INBO)</a> with a fixed wing drone Gatewing X100 in 2015 and 2016 (6 flights). RGB data were acquired using an off-the-shelf Ricoh GR Digital IV camera, with the following image bands: 1: red, 2: green, 3: blue, 4: alpha channel. CIR (color-infrared) data were acquired using a NIR-enabled Ricoh GR Digital IV camera, with the following info bands: 1: NIR, 2: red, 3: green, 4: alpha channel.</p> <p><strong>Data processing</strong></p> <p>The raw data were processed to Digital Surface Models and orthophotos by the <a href="https://vito.be/en">Flemish Institute for Technological Research (VITO)</a> in 2017. Images with coarse GPS coordinates were imported and processed in Agisoft PhotoScan Pro 1.4.x, a structure-from-motion (SfM) based photogrammetry software program. After extraction and matching of tie points, a bundle adjustment leads to a sparse point cloud and a refined set of camera position and orientation values. From there, a point cloud densification and classification into ground and non-ground points was performed, leading to a rasterized digital surface model (DSM) and digital terrain model (DTM). Finally, a true orthomosaic was projected onto the DTM.</p> <p><strong>Coordinate reference system</strong></p> <p>All geospatial data have the coordinate reference system <code>EPSG:31370 - Belgian Lambert 72</code>.</p> <p><strong>Files</strong></p> <ul> <li><strong>Raw flight data</strong>: images and logs collected by the drone during flight. These files are zipped per flight, with the date (<code>yyyymmdd</code>) and flight number (<code>x</code>) indicated in the file name (<code>flight_yyyymmdd_ABH_x.zip</code>).</li> <li><strong>Processed data</strong>: Digital Surface Models (<code>filename_DSM.tif</code>) and orthophotos (<code>filename_Ortho.tif</code>) stitched together from the raw data. The included flights are indicated in the file name (e.g. 3 flights for <code>20150928_ABH_1-2_20151001_ABH_1_DSM.tif</code>).</li> <li><strong>Ground control points</strong>: not applicable for this dataset.</li> </ul> <p><strong>Cloud Optimized GeoTIFF</strong></p> <p>The most efficient way to explore the processed data is by loading the <a href="https://www.cogeo.org/">Cloud Optimized GeoTIFFs</a> we created for each processed file. Copy one of the file URLs below and follow e.g. the <a href="https://www.cogeo.org/qgis-tutorial.html">QGIS tutorial</a> to load this type of file.</p> <ul> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20150928_ABH_1-2_20151001_ABH_1_DSM.tif</code></li> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20150928_ABH_1-2_20151001_ABH_1_Ortho.tif</code> RGB</li> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20151001_ABH_2_DSM.tif</code></li> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20151001_ABH_2_Ortho.tif</code> RGB</li> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20160401_ABH_1_DSM.tif</code></li> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20160401_ABH_1_Ortho.tif</code> RGB</li> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20160401_ABH_2_DSM.tif</code></li> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20160401_ABH_2_Ortho.tif</code> CIR</li> </ul> <p>See <a href="https://s3-eu-west-1.amazonaws.com/lw-remote-sensing/index.html">this page</a> for an overview of public INBO RPAS data.</p>
Orthophotos and DSMs derived from RPAS flights over the nature reserve Kalmthoutse Heide in Flanders, Belgium
<p><strong>Study area</strong></p> <p>The Kalmthoutse Heide is a nature reserve situated north of Kalmthout, in the province of Antwerp, Flanders, Belgium. It is part of the Cross-Border Nature Park De Zoom - Kalmthoutse Heide in the Netherlands and Belgium. The Kalmthoutse Heide is managed by the Flemish Agency for Nature and Forest and consists of wet and dry heathlands, inland dunes, forests and moorland pools. In this area, there is particular interest in monitoring the encroachment of the heathlands by <em>Molinia caerulea</em> and <em>Campylopus introflexus</em>.</p> <p><strong>Data collection</strong></p> <p>Data were collected by the <a href="http://www.inbo.be/en">Research Institute for Nature and Forest (INBO)</a> with a fixed wing drone Gatewing X100 in 2015 and 2016 (8 flights). RGB data were acquired using an off-the-shelf Ricoh GR Digital IV camera, with the following image bands: 1: red, 2: green, 3: blue, 4: alpha channel.</p> <p><strong>Data processing</strong></p> <p>The raw data were processed to Digital Surface Models and orthophotos by the <a href="https://vito.be/en">Flemish Institute for Technological Research (VITO)</a> in 2017. Images with coarse GPS coordinates were imported and processed in Agisoft PhotoScan Pro 1.4.x, a structure-from-motion (SfM) based photogrammetry software program. After extraction and matching of tie points, a bundle adjustment leads to a sparse point cloud and a refined set of camera position and orientation values. Ground control points (either artificially installed markers on the terrain, or other photo-identifiable points, measured on the ground with RTK GNSS) were used to further refine the camera calibration and obtain a pixel-level georeferencing accuracy. From there, a point cloud densification and classification into ground and non-ground points was performed, leading to a rasterized digital surface model (DSM) and digital terrain model (DTM). Finally, a true orthomosaic was projected onto the DTM.</p> <p><strong>Coordinate reference system</strong></p> <p>All geospatial data have the coordinate reference system <code>EPSG:31370 - Belgian Lambert 72</code>.</p> <p><strong>Files</strong></p> <ul> <li><strong>Raw flight data</strong>: images and logs collected by the drone during flight. These files are zipped per flight, with the date (<code>yyyymmdd</code>) and flight number (<code>x</code>) indicated in the file name (<code>flight_yyyymmdd_KH_x.zip</code>).</li> <li><strong>Processed data</strong>: Digital Surface Models (<code>filename_DSM.tif</code>) and orthophotos (<code>filename_Ortho.tif</code>) stitched together from the raw data. The included flights are indicated in the file name (e.g. 3 flights for <code>20150717_KH_1-3_DSM.tif</code>).</li> <li><strong>Ground control points</strong>: temporary ground control points were placed for the first flights on 2015-07-17 (visible in <code>20150717_KH_1-3_Ortho.tif</code>). Coordinates for these are available in <code>GCP_20150717_KH.tsv</code>.</li> </ul> <p><strong>Cloud Optimized GeoTIFF</strong></p> <p>The most efficient way to explore the processed data is by loading the <a href="https://www.cogeo.org/">Cloud Optimized GeoTIFFs</a> we created for each processed file. Copy one of the file URLs below and follow e.g. the <a href="https://www.cogeo.org/qgis-tutorial.html">QGIS tutorial</a> to load this type of file.</p> <ul> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20150717_KH_1-3_DSM.tif</code></li> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20150717_KH_1-3_Ortho.tif</code> RGB</li> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20151020_KH_1-2_DSM.tif</code></li> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20151020_KH_1-2_Ortho.tif</code> RGB</li> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20151020_KH_3_DSM.tif</code></li> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20151020_KH_3_Ortho.tif</code> RGB</li> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20160205_KH_1-2_DSM.tif</code></li> <li><code>http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20160205_KH_1-2_Ortho.tif</code> RGB</li> </ul> <p>See <a href="https://s3-eu-west-1.amazonaws.com/lw-remote-sensing/index.html">this page</a> for an overview of public INBO RPAS data.</p>
Redistribution of the map with the flood prone areas in Flanders (status 2017-07-13)
<p>This is a redistribution of the data source '<a href="http://www.geopunt.be/catalogus/datasetfolder/f5b2c84c-0d78-4efa-a97d-7cd172726572">Overstromingsgevoelige gebieden 2017 - (Watertoets), correctie 13/07/2017</a>', originally published by 'Vlaamse Milieumaatschappij - afdeling Operationeel Waterbeheer' and ‘Departement Mobiliteit en Openbare Werken, afdeling Waterbouwkundig Laboratorium’, and distributed by 'Informatie Vlaanderen' under a CC-BY compatible license. It is redistributed for reproducible, analytical workflows on Flemish Natura 2000 habitats and regionally important biotopes.</p> <p>In the context of the Flemish Water Assay (Watertoets), a fourth version of a map has been created that shows flood prone areas up to the plot level for the entire Flemish Region. The map contains the effectively flood prone areas (‘effectief overstromingsgevoelig’) and the potentially flood prone areas (‘mogelijk overstromingsgevoelig’). </p> <p>In this new version, the effectively flood prone areas were processed with information from new and updated modeled flood areas, in addition to the registered local floods between 2006 and now. These modifications honour the changes to the implementing decision that the Flemish Government approved on 15 May 2017. Unlike previous versions that were raster files, the 2017 version is a vector file.</p> <p>The data source is a coproduction of the Hydraulic Engineering Laboratory of the Department of Mobility and Public Works (Departement Mobiliteit en Openbare Werken, afdeling Waterbouwkundig Laboratorium) and the division Operational Water Management of the Flemish Environmental Agency (Vlaamse Milieumaatschappij - VMM, afdeling Operationeel Waterbeheer), and is owned and administered by the latter.</p>
Redistribution of the map with the Special Areas of Conservation in Flanders with respect to Directive 92/43/EEC (status 2013-01-18)
<p>This is a redistribution of a subdataset of the data source '<a href="http://www.geopunt.be/catalogus/datasetfolder/a84a87f5-5607-4019-a8db-9d52a827786b">Habitatrichtlijn(deel)gebieden</a>' (Special Areas of Conservation), originally published by 'Agentschap Natuur en Bos' (Agency Nature and Forest) and distributed by 'Informatie Vlaanderen' under a CC-BY compatible license. It is redistributed for reproducible, analytical workflows on Flemish Natura 2000 habitats and regionally important biotopes.</p> <p>This dataset contains the delimitation of the Special Areas of Conservation (SAC) with respect to the EC Habitats Directive (92/43/EEC) as approved by the Flemish government on 4 May 2001, ratified on 24 May 2002 and published in the Belgian Official Gazette on 17 August 2002. On 15 February 2008, the Flemish Government also proposed the water zone of the IJzer and Scheldt estuary to the Commission as additional areas of Community interest. After public investigation, the Flemish government decides on January 15, 2013 (published in the Belgian Official Gazette on February 4, 2013) to finalize the delimitation of the sub-area 11 'Boterakker' of the special protection zone BE2200037 'Uiterwaarden van de Limburgse Maas met Vijverbroek' as SAC in application of the Habitats Directive. This zone is now being proposed to the European Commission as an additional area of Community interest. The Habitats Directive aims to preserve biodiversity in the EU Member States and seeks to conserve and restore the natural habitats and the wild fauna and flora that are part of them. The most important measure is the designation of Special Areas of Conservation.</p> <p>This subdataset is a shapefile of geospatial polygons that delimit the (subareas of the) Special Areas of Conservation in the Flemish region with respect to the European directive 92/43/EEC, identical to the shapefile <code>ps_hbtrl_deel</code> in the original data source.</p> <p>The data source is produced, owned and administered by the Agency Nature and Forest of the Flemish government.</p>
Linked collectors and determiners for: Florabank2: a grid-based database on distribution of bryophytes in the northern part of Belgium (Flanders and the Brussels Capital region).
Natural history specimen data linked to collectors and determiners held within, "Florabank2: a grid-based database on distribution of bryophytes in the northern part of Belgium (Flanders and the Brussels Capital region)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/1e9b6eff-af44-4e48-90f0-35ca8d2cdb7b">https://bionomia.net/dataset/1e9b6eff-af44-4e48-90f0-35ca8d2cdb7b</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/1e9b6eff-af44-4e48-90f0-35ca8d2cdb7b">https://gbif.org/dataset/1e9b6eff-af44-4e48-90f0-35ca8d2cdb7b</a>. Formatted as a Frictionless Data package.
SERENA EJPSOIL BE Flanders EROSION SOILLOSS cookbook
<p>The internal EJP SOIL project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national, and European scales.</p> <p>The dataset (5m resolution) was mainly produced to test the methodology of the SERENA soil erosion cookbook of the European SERENA EJP SOIL project. The data was prepared according to the methodology of SERENA soil erosion cookbook. The objective of SERENA project was to develop methods to calculate and map soil-based ecosystem services and soil threats. Soil loss was used as an indicator for soil erosion. For Belgium, the application was carried out at regional scale for the Flanders region. To create the soil erosion map, RULSE modelling was done according to the SERENA cookbook using several publicly available datasets. The following auxiliary data was used: K-Factor, Land-Cover Map, Long-term 30 yr averaged monthly rainfall, <br>LS-Factor given by Panagos et al. (2015). The dataset will be mostly useful as a reference result for actors that want to learn to implement the part of the soil erosion cookbook of SERENA dealing with the creation of an erosion map. It has limited use as an erosion map for Flanders because a more detailed and accurate map exists for Flanders based on regional covariates.</p> <p>This dataset is originally hosted at DOV (<a href="https://www.dov.vlaanderen.be/">https://www.dov.vlaanderen.be/</a>), for the most up to date version of the dataset access the data from the DOV repository through the DOV services. The original metdata is accesible through the DOV metadata catalog: <a href="https://www.dov.vlaanderen.be/geonetwork/srv/dut/catalog.search#/metadata/37337449-8bee-420e-9943-81f074c4cbe8">SERENA EJPSOIL BE Flanders EROSION SOILLOSS cookbook</a></p> <p>The DOV services:</p> <ul> <li><a href="https://www.dov.vlaanderen.be/geoserver/bodem/wms?SERVICE=WMS&version=1.3.0&request=GetMap">WMS </a>( OGC:WMS-1.3.0-http-get-map ) </li> <li><a href="https://www.dov.vlaanderen.be/geoserver/bodem/wcs?SERVICE=WCS&version=2.0.1&request=GetCoverage">WCS </a>( OGC:WCS )</li> </ul>
SERENA EJPSOIL BE Flanders soil sealing cookbook
<div> <p>The internal EJP SOIL project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national and European scales.<br><br>The data was prepared according to the Level 2 methodology of the SERENA soil sealing cookbook. For Belgium, the application was carried out at the regional scale for the Flanders region. <br> <br>The automatically generated yearly soil sealing maps (1 m resolution GeoTIFF rasters) combine “known” sealing from administrative databases (buildings and transport infrastructure) with modelled sealing based on artificial intelligence. Administrative databases do not (adequately) cover parking lots, private driveways and garden terraces, which are a substantial part of the sealed area in Flanders. Hence, a machine learning model was built for deriving this remaining sealing from aerial imagery. For this purpose, an assessor manually labeled the sealed parts on a subset of the images. Based on this training set, a convolutional neural network model was used to produce a sealing probability map, which was converted to a binary modelled sealing map. Finally, a continuity correction was applied to ensure a temporally consistent result across the yearly maps. <br><br>The objective of the SERENA project was to develop methods to calculate and map soil-based ecosystem services and soil threats. The selected indicator was the degree of soil sealing. By evaluating this degree at two moments in time, the change in soil sealing can be determined. <br> <br>The following data were used: </p> </div> <div> <ul> <li> <p><a href="https://www.vlaanderen.be/digitaal-vlaanderen/onze-oplossingen/basiskaart-vlaanderen-grb" target="_blank" rel="noopener">Large-scale Reference Database</a> (Grootschalig Referentiebestand or Basiskaart), the digital topographic reference map for Flanders (vector) </p> </li> </ul> </div> <div> <ul> <li> <p><a href="https://www.vlaanderen.be/datavindplaats/catalogus/orthofotomozaiek-middenschalig-winteropnamen-0" target="_blank" rel="noopener">Medium-scale annual winter aerial images</a> of Flanders (15 or 25 cm raster resolution)</p> </li> </ul> <p><br>This dataset is originally hosted at Geopunt (<a href="https://www.geopunt.be/" target="_blank" rel="noopener">www.geopunt.be</a>). For the most up-to-date version of the dataset, please access the data from the <a href="https://www.vlaanderen.be/datavindplaats/catalogus/jaarlijkse-bodemafdekkingskaart-jaarbak-1-m-resolutie-2021" target="_blank" rel="noopener">Geopunt repository</a>.</p> </div>
Change in relative abundance of the second generation (third flight peak) of peacock butterflies in Flanders (northern Belgium) since 1950.
<p>To investigate historical changes, we calculate for each year with sufficient data the proportional abundance of the third flight peak since 1950 for Flanders (northern part of Belgium) (1950–2008: 26,933 records; 2009–2020: 319,684 records).</p>
GIS Data for Optimising Vaccination Center Placement in Flanders and Brussels
<p>This dataset collection contains geospatial data that was used in a study on optimising vaccination center placement in Flanders and Brussels Capital Region (Belgium). The collection includes five raster datasets, a road network dataset in vector format, and datasets of potential vaccination facilities in vector format.</p> <p>The raster datasets provide information on population density, mean age, proximity to the nearest N-road, travel time to the nearest hospital, and node value of collective transport. These datasets cover the region of Flanders and the Brussels Capital Region, and have been normalised on a scale of 0 to 1. The population density data was sourced from Statbel [1], while the road network and the hospital locations were queried from OpenStreetMap [2]. The node value of collective transport dataset was obtained from a study by Verachtert et al. [3].</p> <p>The road network dataset is a multilinestring vector dataset that includes all of the roads in Belgium. This dataset can be used to analyse traffic flow and identify optimal locations for vaccine centers. The two point vector datasets contain the locations of potential vaccination facilities within the province of Antwerp, with one dataset including 14 facilities and the other including 7 facilities. These datasets can be used to evaluate the effectiveness of different vaccine center placement strategies.</p> <p>The geospatial data in this collection is stored in GeoJSON format for the road network and the potential vaccination facilities and GeoTIFF format for the raster datasets and can be accessed and analyzed using a variety of geospatial tools and software.</p>
Agricultural yield database Flanders
<p>Database containing the evolution of yield of agricultural crops in Flanders under ‘conventional’ farming practices, as part of the project <a href="https://www.peilimpact.be/">PEILIMPACT</a>. This database contains yield in ton/ha of the most important crops in Flanders. When available, it includes the planting and harvesting dates, and mowing times in case of grass. At the moment, the crops included in the database are maize, winter wheat, sugar beet, potato and grass. The data is collected from different research departments at ILVO and other governmental and private Flemish institutions.</p>
Energy Balance Flanders quarterly and monthly data and related auxiliary data
<p>This data set is used in the VITO pilot study of the UNECE Machine Learning project 2019-2020. </p>
Dietary composition and overlap among small- and medium-sized carnivores in Flanders, Belgium
<p class="MsoNoSpacing"><span>There are increasing concerns about the status and population trends carnivores around the world. Carnivores play an important role in terrestrial ecosystems, as their interactions influence the composition and function of ecological communities. In the context of global change, it is essential to understand the interactions and resource use of carnivores. In this study, we explore the dietary ecology of six small- and medium-sized carnivores (red fox, European badger, European polecat, stoat, stone marten and least weasel) to determine their main food resources and the degree of food overlap in Flanders, Belgium. <span>The studied species differed in their food consumption pattern with some being generalist and some more specialist.</span> The dietary composition among the species was clearly different, although certain species showed considerable overlap for different food types. We also conclude that the differences and overlap in dietary habits among the species were consistent across seasons. Understanding these relationships among species' populations and their dietary ecology is essential for biodiversity conservation and nature management.</span></p>
Descriptive representation of immigrant-origin citizens at the 2018 Local elections in Flanders
<p>This dataset contains information about the descriptive representation of immigrant-origin citizens at the 2018 local elections in Flanders. It covers 31,176 candidates over 1,312 lists. For each candidate list, this dataset contains information about the percentage of immigrant-origin candidates, their average relative list position and the proportion of elected immigrant-origin candidates. Extra variables are added at the party level (e.g. whether it was part of the local government prior to the 2018 local elections or whether the head of list had an immigrant background) and municipality level (e.g. population density or percentage of the immigrant-origin population) to allow a profound examination of which factors steer the descriptive representation of immigrant-origin citizens.</p> <p>The updated version also includes information about the gendered selection of the immigrant-origin candidates. During this additional coding process, I discovered some small coding errors included in the original version. These errors are corrected in the updated version. </p>
Dataset Political Gender Stereotypes in Flanders
<p>Dataset of online survey experiment conducted in 2017 among 2 500 Flemish (Belgian) respondents on the topic of political gender stereotypes. </p>
Political ethnic stereotypes in Flanders - Student sample
<p>Dataset of an online survey experiment conducted in 2017 among Flemish university students on the topic of political ethnic stereotypes.</p>
Dataset for proceedings paper: How international is co-authorship outside of Web of Science? The case of social sciences and humanities in Flanders, Belgium
<p>This is an anonymized dataset containing data on international collaboration in the social sciences and humanities (SSH) in Flanders, Belgium. It is based on all peer-reviewed publications in <a href="https://www.ecoom.be/en/data-collections/vabb-shw">VABB-SHW</a>, regardless of their indexation in WoS or other databases.</p> <p>The dataset accompanies the conference paper 'How international is co-authorship outside of Web of Science? The case of social sciences and humanities in Flanders, Belgium,' which was submitted to <a href="https://cns-iu.github.io/workshops/2023-07-02_issi/index.html">ISSI 2023</a>.</p>
Vestibular Infants Screening-Flanders
ClinicalTrials.gov study NCT05061069. IPD Sharing: NO. Countries: 1. Publications: 4.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.