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528 results for “Land cover”
Copernicus Global Land Cover data from 2015-01-01 to 2019-12-31 for Troms and Finnmark (Norway)
<p>This dataset contains 100m x 100m maps of cover fraction expressed in % ground cover per pixel for 10 base classes including moss & lichen for years 2015 to 2019.</p> <p>The geographical area of interest corresponds to the Troms and Finnmark counties in Norway.</p> <p>Along with 10.5281/zenodo.8142713 this is to be used as input to forecast vegetation browning in Troms and Finnmark using machine learning.</p>
Regional greenhouse gas net emission intensities by land cover category in Finland
<p>The methods related to the data published herein are described in detail in the associated publications (Holmberg et al. 2023, Junttila et al. 2023). This file describes the datasets and the data preparation steps. The aim of this data publication is to provide regional assessments of the role of land cover in greenhouse gas emissions in Finland. The results in the publications are reported for the large administrative divisions, the NUTS 3 regions of mainland Finland (Statistics Finland 2023a). While limited by the accuracy of the methods and source data involved, these data can also be used for more local assessments, e.g., at the scale of municipalities. The data represent a temporal snapshot of land cover. Except for the soil maps, rivers and lakes, all land cover data are from the period 2015-2020 and are based on registry data or remote sensing.</p> <p><strong>Data description</strong></p> <p><em>Data format.</em> The data are distributed as GeoTiff raster files, which can be read using most GIS-software.</p> <p><em>Units and definitions</em>. The land cover net emission intensities are shared as raster data with a 250m-by-250m resolution in the ETRS-TM35FIN projected coordinate system. Negative values correspond to sinks (only sinks of C/CO<sub>2</sub> considered). The emission intensities are reported as total emission intensities in carbon dioxide equivalents (gCO<sub>2</sub>-eq m<sup>-2</sup>) based on the 100-year global warming potential as reported in the IPCC 5<sup>th</sup> assessment report (Myhre et al. 2013, p. 73). All cells which do not include emissions from the corresponding land use are classified as <code class="language-sql">NULL</code>s or <em>no data</em>, which should be taken into account if combining raster layers. Where the source data report emission coefficients in the amount of the main element (e.g. C for CO<sub>2</sub> or N for N<sub>2</sub>O) they have been converted to the amounts of the corresponding gas using the standard atomic weights of the relevant atoms (C: 12.011, O: 15.999, N: 14.007, H: 1.008) before conversion to carbon dioxide equivalents. See the related publication for the values of the emission coefficients used and further methodological details (Holmberg et al. 2023).</p> <p><em>Data processing</em>. Data processing for the production of the 250m-by-250m emission intensity raster maps was conducted using GRASS GIS 8.2 (GRASS Development Team, 2022).</p> <p>Land cover emissions derived from vector data (rivers, lakes, agricultural land) were rasterized at a resolution of 1m<sup>2</sup> with the emission intensity as the raster cell value. For rivers, linear features representing rivers having a width of 2 to 5 meters were converted first to areal features by creating a buffer of 1.75 meters to represent an average width of 3.5 meters (see <em>Rivers</em> below). The buffer was created <em>without caps</em> so that the total length of the linear segments was not changed. The buffered river features were merged with the areal features removing the potentially overlapping parts.</p> <p>For all source raster data, the data were available at a 16m-by-16m meter resolution. Emission intensities were aggregated to 250m-by-250m by first summing over the original raster cells intersecting with each aggregate cell while accounting for the proportion of each cell overlapping with the aggregated cell and then multiplying by the area of the original cell. The resulting raster values were divided by the total area of the aggregate cell to acquire average emission intensities. Hence, rasters including a lower proportion of the corresponding land use have lower emission intensities.</p> <p><strong>Thematic layers</strong></p> <p><em>Cropland</em>. CO<sub>2</sub> emissions from cropland were estimated for mineral soils and organic soils separately using emission coefficients from the national greenhouse gas inventory report for 2023. Averaged emission coefficients for the years 2010–2020 for southern and northern Finland were used for mineral soils (Statistics Finland 2023b, Table 3_App_6j). For organic soils separate emission coefficients were used for annual and perennial crops (IPCC 2014, Table 2.1). Cropland and crop data were acquired from the Finnish Food Authority’s Land parcel register for year 2020. Soils were classified into mineral and organic soils by intersecting the field parcels with the soil body layer of the Finnish soil database (Lilja et al. 2006, Lilja et al. 2017).</p> <p>Data files:</p> <ul> <li>Net missions from cropland on mineral soils: <code>cropland_mineral_250m_250m_mean.tif</code></li> <li>Net missions from cropland on organic soils: <code>cropland_organic_250m_250m_mean.tif</code></li> </ul> <p><em>Forests</em>. The net emissions from forests are estimated as the balance of carbon sequestration due to gross primary production of trees and understory vegetation and carbon loss due to harvested biomass, and emission from decomposition of harvest residues, litter, and soil organic matter. Forest productivity is modelled using the process-based forest growth model PREBAS (Minunno et al. 2016, 2019, Junttila et al. 2023, Mäkelä et al. 2023). The initial state for the forest model for the three main forestry species Scots pine, Norway spruce, and Silver birch is derived from the multi-source national forest inventory (<a href="http://kartta.luke.fi/">MS-NFI; version 2015</a>) and harvesting intensities are modelled on the basis of the Finnish national statistics (National Resources Institute Finland 2023). The PREBAS forest net emissions represent annual averages for the period 2017–2025.</p> <p>CO<sub>2</sub> emissions from decomposition on mineral soils are estimated with the soil carbon model YASSO07 (Liski et al. 2005, Tuomi et al. 2009). On drained peatlands, in addition to CO<sub>2</sub> emissions due to peat and litter decomposition, the soil emissions include the CH<sub>4</sub> and N<sub>2</sub>O emissions. The net emissions due to CO<sub>2</sub>, CH<sub>4</sub> and N<sub>2</sub>O from drained peatland (Ojanen et al. 2010, Ojanen and Minkkinen 2019, Minkkinen et al. 2020, Junttila et al. 2023) are calculated using emission coefficients for nutrient rich sites (herb-rich and blueberry type), and nutrient poor sites (lingonberry, dwarf-shrub, and lichen type).</p> <p>Data files:</p> <ul> <li>Net missions from forest on mineral soils: <code>forest_mineral_250m_250m_mean.tif</code></li> <li>Net missions from forest on organic soils: <code>forest_organic_250m_250m_mean.tif</code></li> </ul> <p><em>Lakes</em>. Emissions of CO<sub>2</sub> and CH<sub>4</sub> were estimated for lakes using size dependent emission coefficients. The lakes were classified into five size classes with emission coefficients for CO<sub>2</sub> evasion (Kortelainen et al. 2006), CH<sub>4</sub> diffusion (Juutinen et al. 2009) and ebullition (Bastviken et al. 2004) as well as the CH<sub>4</sub> emissions due to the macrophytes <em>Phragmites australis</em> and <em>Equisetum fluviatile</em> (Juutinen et al. 2003, Bergström et al. 2007, 2011). The lake date was from the <a href="https://ckan.ymparisto.fi/dataset/ranta10-rantaviiva-1-10-000">Shoreline10</a> data by the Finnish Environment Institute.</p> <p>Data files:</p> <ul> <li>Net emissions from lakes: <code>lakes_250m_250m_mean.tif</code></li> </ul> <p><em>Rivers</em>. CO<sub>2</sub> emissions from rivers were estimated using emission coefficients based on the width of the stream. The width dependent emission coefficients were derived from stream order specific emission coefficients of Swedish rivers (Humborg et al. 2010) by classifying the rivers into width groups and with the emission coefficients chosen based on the stream order specific coefficient of corresponding average width. The river emissions were calculated from the <a href="https://ckan.ymparisto.fi/dataset/ranta10-rantaviiva-1-10-000">Shoreline10</a> data by the Finnish Environment Institute which represents rivers wider than 5 m as areal features, and rivers < 5 m wide as linear features. For rivers < 5m wide, an average width of 3.5 m was assumed.</p> <p>Data files:</p> <ul> <li>Net emssions from rivers: <code>rivers_250m_250m_mean.tif</code></li> </ul> <p><em>Undrained mires</em>. Total net emissions were estimated for undrained mires in Finland using average emission coefficients for CH<sub>4</sub> (Minkkinen and Ojanen 2013), CO<sub>2</sub> (Sallantaus 1994 , Turunen et al. 2002), and N<sub>2</sub>O (Minkkinen et al. 2020). The emission coefficients represent the long term accumulation of carbon as well as the emission of CH<sub>4</sub> and from N<sub>2</sub>O peatland. Peatland sites were extracted from the multi-source national forest inventory (<a href="http://kartta.luke.fi/">MS-NFI; version 2019</a>; see also Mäkisara et al. 2022) and undrained mires were delineated using data provided by the Natural Resources Institute Finland. The undrained mires were classified into four classes using the MS-NFI data: 1) productive forested mires, 2) sedge fens, 3) other open and sparsely treed fens and 4) ombrotrophic bogs, which mainly differ in their emission coefficients for methane (Minkkinen and Ojanen 2013).</p> <p>Data files:</p> <ul> <li>Net missions from undrained mires: <code>undrained_mires_250m_250m_mean.tif</code></li> </ul>
Zambia land use and land cover field samples
<p><strong>General Description</strong></p> <p>This data set consists of 697 land use and land cover (LULC) sample locations and reference labelling collected over Muchinga and Copperbelt provinces, Zambia. The data available in ESRI shapefile format (spatial reference system: WGS 84, EPSG: 4326) was collected in a field campaign realized between May and June 2023. The dataset can be used for training and to evaluate the performance of the classification models.</p> <p><strong>Land Use and Land Cover Classes</strong></p> <p>The five LULC classes collected are:</p> <table> <tbody> <tr> <td>Label</td> <td>Code</td> </tr> <tr> <td>Forest land</td> <td>1</td> </tr> <tr> <td>Cropland</td> <td>2</td> </tr> <tr> <td>Grassland</td> <td>3</td> </tr> <tr> <td>Wetland</td> <td>4</td> </tr> <tr> <td>Other land</td> <td>5</td> </tr> </tbody> </table> <p><strong>Data set Details</strong></p> <ul> <li><strong>Time period:</strong> May / June 2023</li> <li><strong>Type of data:</strong> land use and land cover samples</li> <li><strong>How the data was collected:</strong> KoboCollect app</li> <li><strong>Coordinate reference system:</strong> EPSG:4326</li> <li><strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (27.5472562965098682,-13.8722138995139996 : 32.0291093994995890,-11.2161515458659427)</li> <li><strong>File format:</strong> shapefile</li> </ul> <p> </p> <p> </p>
Database of a study on long-term land cover changes in Thừa Thien Huế Province, Central Vietnam
<p>Data provided here forms the basis of a study which is published in the journal Land Use Policy (year 2023) under the title 'The nature of a ‘forest transition’ in Thừa Thien Huế Province, Central Vietnam – A study of land cover changes over five decades'. The database contains the image files of the maps (and associated legends) shown in the publication. Further data (currently under use for other related research) will eventually be added to the repository.</p>
A Land-use/Land Cover Classification of Baltimore City in 1927
Land-use and land cover classifications are typically created using automated methods to analyze modern, spatially explicit color aerial imagery. However, creating classifications from black and white historical aerial imagery presents a number of challenges that require a combination of more traditional, manual techniques and approaches. A georectified mosaic of 93 aerial images was digitized in ArcGIS to create a land-use/land cover classification. The analyzed area covered 585 km2 (226 mi2) including all of Baltimore City, and an area immediately adjacent to the city known at the time as the Metropolitan District of Baltimore County. A combination of 8 land-use and land cover classes were used: Agriculture, Barren, Built (Other), Forest, Grass/Shrubland, Industrial, Residential, and Water. This geospatial data set captures a moment of dynamic expansion in the city, just prior to the Great Depression and can be used to examine relationships between property ownership and forest patch dynamics across time. These insights may help inform future environmental planning, conservation, management, and stewardship goals for Baltimore City forest patches, and other cities throughout the region.
Patch occupancy of stream fauna across a land cover gradient in the southern Appalachians, USA
Field sampling of four functionally important focal stream consumers within the Little Tennessee River Basin took place in thirty-seven stream reaches between May - July 2009. Sampled reaches all drained an area less than 17 km2, and land cover varied among these reaches. This data was used to model patch occupancy to examine factors that best predicted the prevalence of the four groups.
Theia OSO Land Cover Map 2018
<p>Land cover map of France based on Sentinel-2 satellite images with iota² chain (https://framagit.org/iota2-project/iota2/)</p>
Global land use/land cover and soils relations from 850 to 2015 (LUSoils v.1)
<p>This dataset links human land use and land cover types from the Land-Use Harmonization (LUH2) dataset (Lawrence et al., 2016) to four hydrologic soil groups from 850 to 2015 derived from the SoilGrids250m soils dataset (Hengl et al., 2017). These groups represent sandy soils (hydrologic group A) consisting of texture classes sand, sandy loam, and loamy sand; silty soils (hydrologic group B) consisting of loam, silty-loam, and silt; a mixed sand-silt-clay soils (hydrologic group C); and clayey soils (hydrologic group D) represented by clay, sandy-clay, clay-loam, silty-clay, and silt-clay-loam texture classes from the SoilGrids250m dataset. This dataset makes it possible to better link LULCs to soil types typically used for these activities potentially improving the simulation of water, energy and biogeochemical processes in Earth System Models. Additionally, it lays the foundation for simulating LULC impacts on soils that have different vulnerabilities and responses to human uses of soils.</p>
Data from : Classifying wetland‐related land cover types and habitats using fine‐scale lidar metrics derived from country‐wide Airborne Laser Scanning
<p>This data repository contains the processed lidar metrics for characterizing the habitat structure for classifying main land cover and habitat types in the Lauwersmeer area in the northern part of the Netherlands in the province of Groningen (5754 ha). The lidar metrics were derived from Airborne Laser Scanning (ALS) data using the Actueel Hoogtebestand Nederland 2 (AHN2) openly available dataset from https://www.pdok.nl/. </p> <p>The derived lidar metrics saved in *.grd file format and contain 32 bands. Each band represents a lidar metric and the water surface was masked out in the dataset. The *l1* in the file name indicates that the file was used for level 1 (wetland) classification and *l23* used for level 2 (land cover types within wetland) and level 3 (reedbed habitats) classification. The lidar metrics were calculated using lidR (<a href="https://github.com/Jean-Romain/lidR">https://github.com/Jean-Romain/lidR</a>) software package. Further details related to the lidar metrics extraction can be found at <a href="https://github.com/eEcoLiDAR/PhDPaper1_Classifying_wetland_habitats">https://github.com/eEcoLiDAR/PhDPaper1_Classifying_wetland_habitats</a> Github repository.</p> <p> </p>
Snow depth and land surface cover in Tuolumne basin (California) from Pléiades images
<p>This dataset contains products calculated from Pléiades images.</p> <p>Details about the products are available in https://doi.org/10.5194/tc-2020-15.</p> <p>These products were used in Figure 4.</p> <p>- pleiades_elevation_difference_raw_winter_minus_summer.tif : raw difference of digital elevation models (DEMs) calculated from Pléiades stereo images.</p> <p>- pleiades_snow_depth_winter.tif : difference of DEMs on snow terrain only (where pleiades_land_surface_cover_winter.tif==1 with morphological erosion)</p> <p>- pleiades_land_surface_cover_winter.tif : land cover surface in the winter images (1= snow, 2=forest, 3= stable terrain, 4=water)</p> <p>- pleiades_land_surface_cover_summer.tif : land cover surface in the summer images (1= snow, 2=forest, 3= stable terrain, 4=water) </p> <p>- elevation_difference_style.qml : qgis style used for elevation difference and snow depth.</p> <p>- land_surface_cover_style.qml : qgis style used for land cover surface.</p> <p> </p>
GLC_FCS30-2020:Global Land Cover with Fine Classification System at 30m in 2020
<p>The new GLC_FCS30-2020 products were produced based on Global 30-m land-cover product with fine classification system in 2015 (GLC_FCS30-2015) and combined with the 2019-2020 time series Landsat surface reflectance data, Sentinel-1 SAR data, DEM terrain elevation data, global thematic auxiliary dataset and prior knowledge dataset. </p>
Oso to Corine Land cover dataset
<p>Dataset used in the paper "Toward a yearly country-scale CORINE Land-Cover map without using images: a map translation approach".</p>
Kaduna state Land Use Land Cover 2022
<p>The map shows the land use land Cover of Kaduna state as at 2022 showing Build up area, water body,green areas, and open space which was achieved by the use of ESRI LULC data 2019-2022 open Atlas, the data was process using ArcGIS 10.8 using arctoolbox techniques which includes Extract by mask, Conversion from raster to polygon, working with add field from the attribute table and geoprocessing tool i.e Dissolve where used to analyze the data, and finally the data was switched to layout view to add the map element and the work was exported to a Soft copy for usages in form of an A4 ISO standard respectively</p>
Land cover map of the Greater Paramaribo region 2019
<p>This map shows the land cover of the Greater Paramaribo region for 2019 based on Sentinel 2 images. See details in the metadata document. </p><p>The map was made by Razi Taus for the Tropenbos Suriname and the University of Twente-Faculty Geo-information Science and Earth Observation (ITC) project "Naar een groen en leefbaarder Paramaribo" and must be accredited as follows.</p><p><i>Razia Taus, Lisa Best, Rudi van Kanten, Nina Schwarz, Louise Willemen, 2020, Land cover map of the Greater Paramaribo Region 2019, product of 'Naar een groen en leefbaarder Paramaribo' by Tropenbos Suriname and University of Twente-ITC. DOI: 10.5281/zenodo.7696951</i></p>
Code and data for 'Human modification of land cover alters net primary productivity, species richness and their relationship' manuscript
<p>The data and scripts in this database are analyses for a research paper in Global Ecology and Biogeography in 2023: Human modification of land cover alters net primary productivity, species richness and their relationship. Please refer to the README file and the paper for details about the usage of the data and methodology.</p>
Globe230k: A Benchmark Dense-Pixel Annotation Dataset for Global Land Cover Mapping
<p>We (Intelligent Mining and Analysis of Remote Sensing big data, IMARS) create a large-scale annotated dataset (Globe230k) for land use/land cover (LULC) mapping, which is annotated on Google Earth image of 1 m spatial resolution. Globe230k is annotated by numerous experts and students major in survey and mapping after necessary training, through visual interpretation on very high-resolution images, as well as in-situ field survey, under the guidance of the organized annotation pipeline. Globe230k has three superiorities:</p> <p>1) Large scale: the Globe230k includes 232,819 annotated images with the size of 512x512 and spatial resolution of 1 m, with more than 3x1010 annotated pixels, and it includes 10 first-level categories. </p> <p>2) Rich diversity: the annotated images are sampled from worldwide regions, with coverage area of over 60,000 km2, indicating a high variability and diversity. Besides, in order to ensure the category balance, we intentionally give more chance to the rare categories to be sampled, such as wetland, ice/snow, etc.</p> <p>3) Multi-modal: Globe230k not only contains RGB bands, but also include other important features for Earth system research, such as Normalized differential vegetation index (NDVI), digital elevation model (DEM), vertical-vertical polarization (VV) bands, vertical-horizontal polarization (VH) bands, which can facilitate the multi-modal data fusion research. Due to the large size of the multi-modal dataset (DEM 1.91G, NDVI 164G, VVVH 372G), these dataset are stored on Baidu Yunpan, the download link is :https://pan.baidu.com/s/12AKbiqOXSf4fnm7mYkCE0g?pwd=230k, the extraction code is 230k.</p> <p>The image patches and their corresponding annotated patches are respectively stored in "image_patch.zip" and "label_patch.zip" file. The RGB image is in forms of ".jpg", with size of 512x512, the pixel value is ranged from 0-255. The annotated patches is in forms of ".png", also with size of 512x512, the pixel value is ranged from 1-10, which respectively represent 1#cropland, 2#forest, 3#grass, 4#shrubland, 5#wetland, 6#water, 7#tundra, 8#impervious, 9#bareland, 10#ice/snow. The corresponding DEM, NDVI and VVVH patches are all in form of ".tif", with size of 512x512 (due to the different resolution of DEM, NDVI and VVVH patches, they are all uniformly resized to the same scale as the image patch). </p> <p>The total 232,819 pairs are officially divided into training set, validation set, and test set, based on ratio of 7:1:2, which can be find in "train_num.txt","val_num.txt","test_num.txt" file. Based on this division, the official baseline accuracy of several state-of-the-art semantic segmentation can be found in the related arcticle (https://spj.science.org/doi/10.34133/remotesensing.0078).</p> <p>We hope it can be used as a benchmark to promote further development of global land cover mapping and semantic segmentation algorithm development.</p>
Land cover preferences and spatiotemporal associations of ungulates within a Scottish mammal community
<p>In the degraded and modified environment of the Scottish Highlands, novel ungulate communities have arisen following local extinctions, reintroductions, and the introduction of non-native species. An understanding of the dynamics and interactions within these unique mammal communities is important as many of these mammals represent keystone species with disproportionate impacts on the environment. Using a camera trap survey, we investigated land cover preferences and spatiotemporal interactions within a Scottish ungulate community: the sika deer (<em>Cervus nippon</em>), the roe deer (<em>Capreolus capreolus</em>), the red deer (<em>Cervus elaphus</em>), and the wild boar (<em>Sus scrofa</em>). We used generalised linear models to assess land cover preferences and the effect of human disturbance; spatiotemporal interactions were characterised using time interval modelling. We found sika deer and roe deer preferred coniferous plantations and grasslands, with sika deer additionally preferring woodland. For red deer, we found a slight preference for wetland over woodland; however, the explained variance was low. Finally, wild boar preferred grassland and woodland and avoided coniferous plantations, heathland, and shrubland. Contrary to our expectations, we found no evidence that human disturbance negatively impacted ungulates distributions, potentially because ungulates temporally avoid humans or because dense vegetation cover mitigates the impacts of humans on their distributions. Furthermore, we detected a spatiotemporal association between sika deer and roe deer. Although the underlying cause of this is unknown, we hypothesise that interactions such as grazing facilitation or an anti-predator response to culling could be driving this pattern. Our work provides a preliminary analysis of the dynamics occurring within a novel ungulate community, but also highlights current knowledge gaps in our understanding of the underlying mechanisms dictating the observed spatiotemporal associations.</p>
BSRLC+: An annual land cover dataset for the Baltic Sea Region with crop types and peat bogs at 30 m from 2000 to 2022
<p><strong>(NEW) </strong>Baltic Sea Region Land Cover <em>Urban</em> (BSRLC-U) focusing on urban built-up types now available: <a href="https://zenodo.org/records/17347941">https://zenodo.org/records/17347941 </a></p> <p><strong>Baltic Sea Region Land Cover <em>Plus </em>(BSRLC+) </strong>is annual land cover mapping (30 m) dataset in Europe from 2000 to 2022. The maps contain detailed information of 18 land cover (LC) types, including 9 crop types and 2 peat bog types.</p> <p>Input data : Optical multi-temporal remote sensing imageries (Landsat 5 (TM) / 7 (ETM+) / 8 (OLI) / 9 (OLI+) and Sentinel 2 (A / B ) from 2000 to 2022. Data is processed to surface reflectance and tiled into datacube structure using <a href="https://doi.org/10.3390/rs11091124">Framework for Operational Radiometric Correction for Environmental monitoring - FORCE.</a></p> <p>Mapping method: Maps are produced using data encoding and deep learning classification according to <a href="https://doi.org/10.1016/j.jag.2024.103867">Pham et al. 2024</a></p> <p>Validation: Maps have been rigorously validated using independent in-situ data <a href="https://doi.org/10.1038/s41597-020-00675-z">The Land Use/Cover Area frame Survey (LUCAS)</a>. </p> <p>Traing data and validation data are available: <a href="https://zenodo.org/records/11073291">https://zenodo.org/records/11073291</a></p> <p>This dataset contains:</p> <ul> <li><strong>00_preview.png</strong>: Preview map (2022) of the Baltic Sea region</li> <li><strong>BSRLC_{year}.tif</strong>: Annual map data (30 m) in GeoTIFF format (projection ETRS89 / EPSG:3035)</li> <li><strong>BSRLC_legend.xlss</strong>: Land cover codes and class names</li> <li><strong>BSRLC_qgis_style.qml</strong>: Map style to be used in QGIS</li> <li><strong>BSRLC_arcgis_style.lyrx</strong>: Map style to be used in ArcGIS</li> </ul> <p>Land cover codes (can also be found in <strong>BSRLC_legend.xlss</strong>):</p> <ul> <li>1: Built-up</li> <li>2: Bareland</li> <li>3: Water</li> <li>4: Shrubland</li> <li>5: Broadleaf forest</li> <li>6: Coniferous forest</li> <li>7: Wetland marsh</li> <li>8: Exploited peat bog</li> <li>9: Unexploited peat bog</li> <li>10: Wheat</li> <li>11: Barley</li> <li>12: Rye</li> <li>13: Oat</li> <li>14: Maize</li> <li>15: Seed crops</li> <li>16: Root crops</li> <li>17: Pulses, vegetable</li> <li>18: Grassland</li> <li>255: Nodata</li> </ul> <p> </p> <p><strong>Publication (please cite this publication if you are using the dataset):</strong></p> <ul> <li>Pham, V.-D., de Waard, F., Thiel, F., Bobertz, B., Hellmann, C., Nguyen, D.-V., Beer, F., Arasumani, M., Schwieder, M., Hartleib, J., Frantz, D., & van der Linden, S. (2024). An annual land cover dataset for the Baltic Sea Region with crop types and peat bogs at 30 m from 2000 to 2022. <em>Scientific Data, 11</em>, 1242, <a href="https://doi.org/10.1038/s41597-024-04062-w">https://doi.org/10.1038/s41597-024-04062-w</a></li> </ul> <p> </p> <p><strong>Other related publications:</strong></p> <ul> <li><em>Pham, V.-D., Tetteh, G., Thiel, F., Erasmi, S., Schwieder, M., Frantz, D., & van der Linden, S. (2024). Temporally transferable crop mapping with temporal encoding and deep learning augmentations. International Journal of Applied Earth Observation and Geoinformation, 129, 103867, <a href="https://doi.org/10.1016/j.jag.2024.103867">https://doi.org/10.1016/j.jag.2024.103867</a></em></li> <li><em>Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11, <a href="https://doi.org/10.3390/rs11091124">https://doi.org/10.3390/rs11091124</a></em></li> </ul> <p> </p> <p><strong>Funding</strong></p> <p>This datatset is created in the frame of the Interdisciplinary Research Center for the Baltic Sea Region Research (IFZO) of University of Greifswald, Germany, and the research project Fragmented Transformations, which is funded by the German Federal Ministry of Education and Research (FKZ 01UC2102). </p>
Comparison of Cropland Maps Derived from Land Cover Maps in Sub-Saharan Africa
<p>This data repository provides the datasets associated with analysis conducted in Kerner et al. (2024), citation below.</p> <p>The CSV file `intercomparison-results.csv` gives a table metrics for each of 11 land cover maps (plus a majority vote ensemble of all maps) evaluated using the reference dataset for each of 8 countries (Kenya, Rwanda, Uganda, Tanzania, Mali, Malawi, Togo, and Zambia). The reference datasets are provided as zip files. To ensure these datasets can be used for independent evaluation and comparison between maps in the future, the reference datasets should ONLY be used for final, independent evaluation of data products/model outputs; they should NOT be used for training models, tuning hyperparameters, or any other decisions during model/map development. </p> <p>The provided tif files contain the consensus maps (sum of all 11 maps) used to compute consensus statistics in Kerner et al. (2024).</p> <p>Kerner, H., Nakalembe, C., Yang, A., Zvonkov, I., McWeeny, R., Tseng, G., and Becker-Reshef, I. (2024). How accurate are existing land cover maps for agriculture in Sub-Saharan Africa? <em>Under review</em>. </p>
High resolution land cover 2017 Ile-de-France
<p><strong>High resoultion land cover for the Ile-de-France, 2017.</strong></p> <p>The rasters are mostly based on vector files, which were rasterised, merged and resampled to 5m. The encoded values represent the different thematic classes in the dataset. </p> <p>For ease of processing and computation, all datasets were tiled and are provided as a single continuous raster. This map shows the extents of the datasets “Hauteur vegetation” and “cadastre vert”, which were only available for a limited region within the Ile- de- France region. </p> <p></p> <p>The datasets used in the production of this map were MosPlus 2017-81, Cadastre Verte, Copernicus Small and Woody features, Copernicus Street tree layer, Hauteur vegetation and the densibati dataset. </p> <p> </p> <p>Class Codec </p> <table> <tbody> <tr> <td> <p><strong>Class</strong><strong> </strong></p> </td> <td> <p><strong>NumCodec</strong><strong> </strong></p> <p><strong>8bit compatible</strong><strong> </strong></p> </td> </tr> <tr> <td> <p>Building<strong> </strong></p> </td> <td> <p>10 </p> </td> </tr> <tr> <td> <p>Built parcel<strong> </strong></p> </td> <td> <p>19 </p> </td> </tr> <tr> <td> <p>Mineral surface<strong> </strong></p> </td> <td> <p>21 </p> </td> </tr> <tr> <td> <p>Bare soil<strong> </strong></p> </td> <td> <p>22 </p> </td> </tr> <tr> <td> <p>Grass<strong> </strong></p> </td> <td> <p>31 </p> </td> </tr> <tr> <td> <p>Shrub<strong> </strong></p> </td> <td> <p>40 </p> </td> </tr> <tr> <td> <p>Shrub round<strong> </strong></p> </td> <td> <p>41 </p> </td> </tr> <tr> <td> <p>Shrub linear<strong> </strong></p> </td> <td> <p>42 </p> </td> </tr> <tr> <td> <p>Tree<strong> </strong></p> </td> <td> <p>50 </p> </td> </tr> <tr> <td> <p>Water<strong> </strong></p> </td> <td> <p>60 </p> </td> </tr> <tr> <td> <p>Lake<strong> </strong></p> </td> <td> <p>61 </p> </td> </tr> <tr> <td> <p>River<strong> </strong></p> </td> <td> <p>62 </p> </td> </tr> <tr> <td> <p>Agriculture<strong> </strong></p> </td> <td> <p>80/81</p> </td> </tr> <tr> <td> <p>NonAOI/ unclassified<strong> </strong></p> </td> <td> <p>99 /0 </p> </td> </tr> </tbody> </table> <p>Data sources </p> <p>European Union's Copernicus Land Monitoring Service information (2018), Small Woody Features 2018,<a href="https://doi.org/10.2909/7fd9d32e-8c2f-42b2-b959-c8e12b843821" target="_blank" rel="noopener">https://doi.org/10.2909/7fd9d32e-8c2f-42b2-b959-c8e12b843821</a> </p> <p>European Union's Copernicus Land Monitoring Service information (2018), Urban Atlas Street Tree Layer 2018, <a href="https://doi.org/10.2909/205691b3-7ae9-41dd-abf1-1fbf60d72c8c" target="_blank" rel="noopener">https://doi.org/10.2909/205691b3-7ae9-41dd-abf1-1fbf60d72c8c</a> </p> <p>Atelier Parisien d'Urbanisme 2017 HAUTEUR VEGETATION 2015 </p> <p><a href="https://opendata.apur.org/datasets/Apur::hauteur-vegetation-2015/about" target="_blank" rel="noopener">https://opendata.apur.org/datasets/Apur::hauteur-vegetation-2015/about</a> </p> <p>Département des Hauts-de-Seine (2012) Cadastre vert - Masses vertes. https://opendata.hauts-de-seine.fr/explore/dataset/cadastre-vert-masses-vertes/information/?disjunctive.commune&basemap=mapbox.streets-satellite&location=11,48.83991,2.24091 </p> <p>L'Institut Paris Region (2017) Mode d'occupation du sol (MOS) 2017 a 81 postes. <a href="https://www.institutparisregion.fr/vente-de-donnees/" target="_blank" rel="noopener">https://www.institutparisregion.fr/vente-de-donnees/</a> </p> <p>L'Institut Paris Region (2018) Densibati 2018. <a href="https://www.institutparisregion.fr/vente-de-donnees/" target="_blank" rel="noopener">https://www.institutparisregion.fr/vente-de-donnees/</a> </p>
ScienceDex guides
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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.