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Global Pasture Watch - Annual grassland class and extent maps at 30-m spatial resolution (2000—2024)

<h2>Sub-dataset: Dominant grassland class, 2018-2020</h2> <h2>Description</h2> Global annual grassland class and extent for 2000—2024 produced by <a href="https://doi.org/10.1038/s41597-024-04139-6">Parente et al. (2024)</a> within the scope of the <a href="https://landcarbonlab.org/data/global-grassland-and-livestock-monitoring/">Global Pasture Watch initiative</a>. The mapped grassland extent includes any land cover type, which contains at least 30% of dry or wet low vegetation, dominated by grasses and forbs (less than 3 meters) and a: <ul> <li>maximum of 50% tree canopy cover (greater than 5 meters),</li> <li>maximum of 70% of other woody vegetation (scrubs and open shrubland), and</li> <li>maximum of 50% active cropland cover in mosaic landscapes of cropland & other vegetation.</li> </ul> The grassland extent is classified into two classes: <ul> <li>Cultivated grassland: Areas where grasses and other forage plants have been intentionally planted and managed, as well as areas of native grassland-type vegetation where they clearly exhibit active and 'heavy' management for specific human-directed uses, such as directed grazing of livestock.</li> <li>Natural/semi-natural grassland: Relatively undisturbed native grasslands/short-height vegetation, such as steppes and tundra, as well as areas that have experienced varying degrees of human activity in the past, which may contain a mix of native and introduced species due to historical land use and natural processes. In general, they exhibit natural-looking patterns of varied vegetation and clearly ordered hydrological relationships throughout the landscape.</li> <li>Open shrubland (v2-beta): Land on which the vegetation is dominated by low-growing woody plants, characterized by a sparse distribution of shrubs and dominated by woody perennials. Typically covers 50—75% of the area, with significant open ground (with or without herbaceous understory) between them, where shrub canopies are less than 10 meters in diameter, and tree cover is below 10%, meaning they do not form a continuous or semi-continuous canopy.</li> </ul> The dataset is organized in 69 global mosaics (25 years for each time series) in COG (Cloud Optimized GeoTIFF) format, WGS84 Coordinate Systems (EPSG:4326) and pixel size equal to 0.00025 degrees, including: <ul> <li>Probabilities of cultivated grassland (values range from 0–100),</li> <li>Probabilities of natural/semi-natural grassland (values range from 0–100), and</li> <li>Probabilities of open shrubland (values range from 0–100), and</li> <li>Dominant class (0-other land cover, 1-cultivated grassland and 2-natural/semi-natural grassland, 3-open shrubland).</li> </ul> All raster files are in unsigned <code>8-bit integer format</code> and use <code>255</code> as no-data value (pixels ignored by prediction), following an specific naming convention: <ol> <li>Project name: Global Pasture Watch (<code>gpw</code>)</li> <li>Class name: cultivated grassland (<code>cultiv.grassland</code>), natural/semi-natural grassland (<code>nat.semi.grassland</code>), open shrubland (<code>open.shrubland</code>)  and dominant grassland (<code>grassland</code>)</li> <li>Procedure combination: Random Forest (<code>rf</code>), median filter (med.filt) and balanced threshold (<code>bthr</code>).</li> <li>Variable type: probability (<code>p</code>) and factor class (<code>c</code>)</li> <li>Spatial resolution: 30m</li> <li>Begin of time reference: date of first Landsat composite used by the modeling (<code>20240101</code>)</li> <li>End of time reference: date of last Landsat composite used by the modeling (<code>20241231</code>)</li> <li>Spatial extent: global (<code>go</code>)</li> <li>Coordinate system: World Geodetic System 1984, used in GPS (<code>epsg.4326</code>)</li> <li>Version: v2</li> </ol> <h3>Related resources</h3> <ul> <li>Maps of dominant grassland: <a href="http://doi.org/10.5281/zenodo.15646181">2000-2002</a><a href="http://doi.org/10.5281/zenodo.15644486"> 2003-2005</a><a href="http://doi.org/10.5281/zenodo.15644623"> 2006-2008</a><a href="http://doi.org/10.5281/zenodo.15647042"> 2009-2011</a><a href="http://doi.org/10.5281/zenodo.15647681"> 2012-2014</a><a href="http://doi.org/10.5281/zenodo.15648306"> 2015-2017</a><a href="http://doi.org/10.5281/zenodo.15648551"> 2018-2020</a><a href="http://doi.org/10.5281/zenodo.15648751"> 2021-2023</a> <a href="http://doi.org/10.5281/zenodo.15649332">2024</a></li> <li>Probability maps of cultivated grassland: <a href="https://zenodo.org/records/13890401/files/ggc-30m.csv?download=1">2000-2024 (All URLs)</a></li> <li>Probability maps of natural/semi-natural grassland: <a href="https://zenodo.org/records/13890401/files/ggc-30m.csv?download=1">2000-2024 (All URLs)</a></li> <li> <div>Grassland reference samples based on VHR imagery (2000–2024): <a href="https://doi.org/10.5281/zenodo.15631655">GeoPackage files</a></div> </li> <li>Global machine learning models (Random Forest): <a href="https://doi.org/10.5281/zenodo.13952806">Parquet and joblib python files</a></li> <li>Reference sampling design derived by FSCV: <a href="https://doi.org/10.5281/zenodo.11391517">GeoPackage and raster files</a></li> <li>Harmonized reference samples based on existing LULC dataset: <a href="https://doi.org/10.5281/zenodo.13951976">GeoPackage and raster files</a></li> <li>Source code for reproducibility: <a href="https://doi.org/10.5281/zenodo.13952867">GitHub release</a> </li> <li>Mapping feedback tool: <a href="https://geo-wiki.org">GeoWiki</a></li> <li>Data catalogues: <a href="https://stac.openlandmap.org/gpw_ggc-30m/collection.json?.language=en">OpenLandMap STAC</a> <a href="https://global-pasture-watch.projects.earthengine.app/view/ggc-30m">Google Earth Engine</a></li> </ul> Support For questions of bugs/inconsistencies related to the dataset raise a GitHub issue in <a href="https://github.com/wri/global-pasture-watch">https://github.com/wri/global-pasture-watch</a>

Global Pasture Watch - Annual grassland class and extent maps at 30-m spatial resolution (2000—2024)

<h2>Sub-dataset: Dominant grassland class, 2021-2023</h2> <h2>Description</h2> Global annual grassland class and extent for 2000—2024 produced by <a href="https://doi.org/10.1038/s41597-024-04139-6">Parente et al. (2024)</a> within the scope of the <a href="https://landcarbonlab.org/data/global-grassland-and-livestock-monitoring/">Global Pasture Watch initiative</a>. The mapped grassland extent includes any land cover type, which contains at least 30% of dry or wet low vegetation, dominated by grasses and forbs (less than 3 meters) and a: <ul> <li>maximum of 50% tree canopy cover (greater than 5 meters),</li> <li>maximum of 70% of other woody vegetation (scrubs and open shrubland), and</li> <li>maximum of 50% active cropland cover in mosaic landscapes of cropland & other vegetation.</li> </ul> The grassland extent is classified into two classes: <ul> <li>Cultivated grassland: Areas where grasses and other forage plants have been intentionally planted and managed, as well as areas of native grassland-type vegetation where they clearly exhibit active and 'heavy' management for specific human-directed uses, such as directed grazing of livestock.</li> <li>Natural/semi-natural grassland: Relatively undisturbed native grasslands/short-height vegetation, such as steppes and tundra, as well as areas that have experienced varying degrees of human activity in the past, which may contain a mix of native and introduced species due to historical land use and natural processes. In general, they exhibit natural-looking patterns of varied vegetation and clearly ordered hydrological relationships throughout the landscape.</li> <li>Open shrubland (v2-beta): Land on which the vegetation is dominated by low-growing woody plants, characterized by a sparse distribution of shrubs and dominated by woody perennials. Typically covers 50—75% of the area, with significant open ground (with or without herbaceous understory) between them, where shrub canopies are less than 10 meters in diameter, and tree cover is below 10%, meaning they do not form a continuous or semi-continuous canopy.</li> </ul> The dataset is organized in 69 global mosaics (25 years for each time series) in COG (Cloud Optimized GeoTIFF) format, WGS84 Coordinate Systems (EPSG:4326) and pixel size equal to 0.00025 degrees, including: <ul> <li>Probabilities of cultivated grassland (values range from 0–100),</li> <li>Probabilities of natural/semi-natural grassland (values range from 0–100), and</li> <li>Probabilities of open shrubland (values range from 0–100), and</li> <li>Dominant class (0-other land cover, 1-cultivated grassland and 2-natural/semi-natural grassland, 3-open shrubland).</li> </ul> All raster files are in unsigned <code>8-bit integer format</code> and use <code>255</code> as no-data value (pixels ignored by prediction), following an specific naming convention: <ol> <li>Project name: Global Pasture Watch (<code>gpw</code>)</li> <li>Class name: cultivated grassland (<code>cultiv.grassland</code>), natural/semi-natural grassland (<code>nat.semi.grassland</code>), open shrubland (<code>open.shrubland</code>)  and dominant grassland (<code>grassland</code>)</li> <li>Procedure combination: Random Forest (<code>rf</code>), median filter (med.filt) and balanced threshold (<code>bthr</code>).</li> <li>Variable type: probability (<code>p</code>) and factor class (<code>c</code>)</li> <li>Spatial resolution: 30m</li> <li>Begin of time reference: date of first Landsat composite used by the modeling (<code>20240101</code>)</li> <li>End of time reference: date of last Landsat composite used by the modeling (<code>20241231</code>)</li> <li>Spatial extent: global (<code>go</code>)</li> <li>Coordinate system: World Geodetic System 1984, used in GPS (<code>epsg.4326</code>)</li> <li>Version: v2</li> </ol> <h3>Related resources</h3> <ul> <li>Maps of dominant grassland: <a href="http://doi.org/10.5281/zenodo.15646181">2000-2002</a><a href="http://doi.org/10.5281/zenodo.15644486"> 2003-2005</a><a href="http://doi.org/10.5281/zenodo.15644623"> 2006-2008</a><a href="http://doi.org/10.5281/zenodo.15647042"> 2009-2011</a><a href="http://doi.org/10.5281/zenodo.15647681"> 2012-2014</a><a href="http://doi.org/10.5281/zenodo.15648306"> 2015-2017</a><a href="http://doi.org/10.5281/zenodo.15648551"> 2018-2020</a><a href="http://doi.org/10.5281/zenodo.15648751"> 2021-2023</a> <a href="http://doi.org/10.5281/zenodo.15649332">2024</a></li> <li>Probability maps of cultivated grassland: <a href="https://zenodo.org/records/13890401/files/ggc-30m.csv?download=1">2000-2024 (All URLs)</a></li> <li>Probability maps of natural/semi-natural grassland: <a href="https://zenodo.org/records/13890401/files/ggc-30m.csv?download=1">2000-2024 (All URLs)</a></li> <li> <div>Grassland reference samples based on VHR imagery (2000–2024): <a href="https://doi.org/10.5281/zenodo.15631655">GeoPackage files</a></div> </li> <li>Global machine learning models (Random Forest): <a href="https://doi.org/10.5281/zenodo.13952806">Parquet and joblib python files</a></li> <li>Reference sampling design derived by FSCV: <a href="https://doi.org/10.5281/zenodo.11391517">GeoPackage and raster files</a></li> <li>Harmonized reference samples based on existing LULC dataset: <a href="https://doi.org/10.5281/zenodo.13951976">GeoPackage and raster files</a></li> <li>Source code for reproducibility: <a href="https://doi.org/10.5281/zenodo.13952867">GitHub release</a> </li> <li>Mapping feedback tool: <a href="https://geo-wiki.org">GeoWiki</a></li> <li>Data catalogues: <a href="https://stac.openlandmap.org/gpw_ggc-30m/collection.json?.language=en">OpenLandMap STAC</a> <a href="https://global-pasture-watch.projects.earthengine.app/view/ggc-30m">Google Earth Engine</a></li> </ul> Support For questions of bugs/inconsistencies related to the dataset raise a GitHub issue in <a href="https://github.com/wri/global-pasture-watch">https://github.com/wri/global-pasture-watch</a>

Global Pasture Watch - Annual grassland class and extent maps at 30-m spatial resolution (2000—2024)

<h2>Sub-dataset: Dominant grassland class, 2015-2017</h2> <h2>Description</h2> Global annual grassland class and extent for 2000—2024 produced by <a href="https://doi.org/10.1038/s41597-024-04139-6">Parente et al. (2024)</a> within the scope of the <a href="https://landcarbonlab.org/data/global-grassland-and-livestock-monitoring/">Global Pasture Watch initiative</a>. The mapped grassland extent includes any land cover type, which contains at least 30% of dry or wet low vegetation, dominated by grasses and forbs (less than 3 meters) and a: <ul> <li>maximum of 50% tree canopy cover (greater than 5 meters),</li> <li>maximum of 70% of other woody vegetation (scrubs and open shrubland), and</li> <li>maximum of 50% active cropland cover in mosaic landscapes of cropland & other vegetation.</li> </ul> The grassland extent is classified into two classes: <ul> <li>Cultivated grassland: Areas where grasses and other forage plants have been intentionally planted and managed, as well as areas of native grassland-type vegetation where they clearly exhibit active and 'heavy' management for specific human-directed uses, such as directed grazing of livestock.</li> <li>Natural/semi-natural grassland: Relatively undisturbed native grasslands/short-height vegetation, such as steppes and tundra, as well as areas that have experienced varying degrees of human activity in the past, which may contain a mix of native and introduced species due to historical land use and natural processes. In general, they exhibit natural-looking patterns of varied vegetation and clearly ordered hydrological relationships throughout the landscape.</li> <li>Open shrubland (v2-beta): Land on which the vegetation is dominated by low-growing woody plants, characterized by a sparse distribution of shrubs and dominated by woody perennials. Typically covers 50—75% of the area, with significant open ground (with or without herbaceous understory) between them, where shrub canopies are less than 10 meters in diameter, and tree cover is below 10%, meaning they do not form a continuous or semi-continuous canopy.</li> </ul> The dataset is organized in 69 global mosaics (25 years for each time series) in COG (Cloud Optimized GeoTIFF) format, WGS84 Coordinate Systems (EPSG:4326) and pixel size equal to 0.00025 degrees, including: <ul> <li>Probabilities of cultivated grassland (values range from 0–100),</li> <li>Probabilities of natural/semi-natural grassland (values range from 0–100), and</li> <li>Probabilities of open shrubland (values range from 0–100), and</li> <li>Dominant class (0-other land cover, 1-cultivated grassland and 2-natural/semi-natural grassland, 3-open shrubland).</li> </ul> All raster files are in unsigned <code>8-bit integer format</code> and use <code>255</code> as no-data value (pixels ignored by prediction), following an specific naming convention: <ol> <li>Project name: Global Pasture Watch (<code>gpw</code>)</li> <li>Class name: cultivated grassland (<code>cultiv.grassland</code>), natural/semi-natural grassland (<code>nat.semi.grassland</code>), open shrubland (<code>open.shrubland</code>)  and dominant grassland (<code>grassland</code>)</li> <li>Procedure combination: Random Forest (<code>rf</code>), median filter (med.filt) and balanced threshold (<code>bthr</code>).</li> <li>Variable type: probability (<code>p</code>) and factor class (<code>c</code>)</li> <li>Spatial resolution: 30m</li> <li>Begin of time reference: date of first Landsat composite used by the modeling (<code>20240101</code>)</li> <li>End of time reference: date of last Landsat composite used by the modeling (<code>20241231</code>)</li> <li>Spatial extent: global (<code>go</code>)</li> <li>Coordinate system: World Geodetic System 1984, used in GPS (<code>epsg.4326</code>)</li> <li>Version: v2</li> </ol> <h3>Related resources</h3> <ul> <li>Maps of dominant grassland: <a href="http://doi.org/10.5281/zenodo.15646181">2000-2002</a><a href="http://doi.org/10.5281/zenodo.15644486"> 2003-2005</a><a href="http://doi.org/10.5281/zenodo.15644623"> 2006-2008</a><a href="http://doi.org/10.5281/zenodo.15647042"> 2009-2011</a><a href="http://doi.org/10.5281/zenodo.15647681"> 2012-2014</a><a href="http://doi.org/10.5281/zenodo.15648306"> 2015-2017</a><a href="http://doi.org/10.5281/zenodo.15648551"> 2018-2020</a><a href="http://doi.org/10.5281/zenodo.15648751"> 2021-2023</a> <a href="http://doi.org/10.5281/zenodo.15649332">2024</a></li> <li>Probability maps of cultivated grassland: <a href="https://zenodo.org/records/13890401/files/ggc-30m.csv?download=1">2000-2024 (All URLs)</a></li> <li>Probability maps of natural/semi-natural grassland: <a href="https://zenodo.org/records/13890401/files/ggc-30m.csv?download=1">2000-2024 (All URLs)</a></li> <li> <div>Grassland reference samples based on VHR imagery (2000–2024): <a href="https://doi.org/10.5281/zenodo.15631655">GeoPackage files</a></div> </li> <li>Global machine learning models (Random Forest): <a href="https://doi.org/10.5281/zenodo.13952806">Parquet and joblib python files</a></li> <li>Reference sampling design derived by FSCV: <a href="https://doi.org/10.5281/zenodo.11391517">GeoPackage and raster files</a></li> <li>Harmonized reference samples based on existing LULC dataset: <a href="https://doi.org/10.5281/zenodo.13951976">GeoPackage and raster files</a></li> <li>Source code for reproducibility: <a href="https://doi.org/10.5281/zenodo.13952867">GitHub release</a> </li> <li>Mapping feedback tool: <a href="https://geo-wiki.org">GeoWiki</a></li> <li>Data catalogues: <a href="https://stac.openlandmap.org/gpw_ggc-30m/collection.json?.language=en">OpenLandMap STAC</a> <a href="https://global-pasture-watch.projects.earthengine.app/view/ggc-30m">Google Earth Engine</a></li> </ul> Support For questions of bugs/inconsistencies related to the dataset raise a GitHub issue in <a href="https://github.com/wri/global-pasture-watch">https://github.com/wri/global-pasture-watch</a>

Global Pasture Watch - Annual grassland class and extent maps at 30-m spatial resolution (2000—2024)

<h2>Sub-dataset: Dominant grassland class, 2009-2011</h2> <h2>Description</h2> Global annual grassland class and extent for 2000—2024 produced by <a href="https://doi.org/10.1038/s41597-024-04139-6">Parente et al. (2024)</a> within the scope of the <a href="https://landcarbonlab.org/data/global-grassland-and-livestock-monitoring/">Global Pasture Watch initiative</a>. The mapped grassland extent includes any land cover type, which contains at least 30% of dry or wet low vegetation, dominated by grasses and forbs (less than 3 meters) and a: <ul> <li>maximum of 50% tree canopy cover (greater than 5 meters),</li> <li>maximum of 70% of other woody vegetation (scrubs and open shrubland), and</li> <li>maximum of 50% active cropland cover in mosaic landscapes of cropland & other vegetation.</li> </ul> The grassland extent is classified into two classes: <ul> <li>Cultivated grassland: Areas where grasses and other forage plants have been intentionally planted and managed, as well as areas of native grassland-type vegetation where they clearly exhibit active and 'heavy' management for specific human-directed uses, such as directed grazing of livestock.</li> <li>Natural/semi-natural grassland: Relatively undisturbed native grasslands/short-height vegetation, such as steppes and tundra, as well as areas that have experienced varying degrees of human activity in the past, which may contain a mix of native and introduced species due to historical land use and natural processes. In general, they exhibit natural-looking patterns of varied vegetation and clearly ordered hydrological relationships throughout the landscape.</li> <li>Open shrubland (v2-beta): Land on which the vegetation is dominated by low-growing woody plants, characterized by a sparse distribution of shrubs and dominated by woody perennials. Typically covers 50—75% of the area, with significant open ground (with or without herbaceous understory) between them, where shrub canopies are less than 10 meters in diameter, and tree cover is below 10%, meaning they do not form a continuous or semi-continuous canopy.</li> </ul> The dataset is organized in 69 global mosaics (25 years for each time series) in COG (Cloud Optimized GeoTIFF) format, WGS84 Coordinate Systems (EPSG:4326) and pixel size equal to 0.00025 degrees, including: <ul> <li>Probabilities of cultivated grassland (values range from 0–100),</li> <li>Probabilities of natural/semi-natural grassland (values range from 0–100), and</li> <li>Probabilities of open shrubland (values range from 0–100), and</li> <li>Dominant class (0-other land cover, 1-cultivated grassland and 2-natural/semi-natural grassland, 3-open shrubland).</li> </ul> All raster files are in unsigned <code>8-bit integer format</code> and use <code>255</code> as no-data value (pixels ignored by prediction), following an specific naming convention: <ol> <li>Project name: Global Pasture Watch (<code>gpw</code>)</li> <li>Class name: cultivated grassland (<code>cultiv.grassland</code>), natural/semi-natural grassland (<code>nat.semi.grassland</code>), open shrubland (<code>open.shrubland</code>)  and dominant grassland (<code>grassland</code>)</li> <li>Procedure combination: Random Forest (<code>rf</code>), median filter (med.filt) and balanced threshold (<code>bthr</code>).</li> <li>Variable type: probability (<code>p</code>) and factor class (<code>c</code>)</li> <li>Spatial resolution: 30m</li> <li>Begin of time reference: date of first Landsat composite used by the modeling (<code>20240101</code>)</li> <li>End of time reference: date of last Landsat composite used by the modeling (<code>20241231</code>)</li> <li>Spatial extent: global (<code>go</code>)</li> <li>Coordinate system: World Geodetic System 1984, used in GPS (<code>epsg.4326</code>)</li> <li>Version: v2</li> </ol> <h3>Related resources</h3> <ul> <li>Maps of dominant grassland: <a href="http://doi.org/10.5281/zenodo.15646181">2000-2002</a><a href="http://doi.org/10.5281/zenodo.15644486"> 2003-2005</a><a href="http://doi.org/10.5281/zenodo.15644623"> 2006-2008</a><a href="http://doi.org/10.5281/zenodo.15647042"> 2009-2011</a><a href="http://doi.org/10.5281/zenodo.15647681"> 2012-2014</a><a href="http://doi.org/10.5281/zenodo.15648306"> 2015-2017</a><a href="http://doi.org/10.5281/zenodo.15648551"> 2018-2020</a><a href="http://doi.org/10.5281/zenodo.15648751"> 2021-2023</a> <a href="http://doi.org/10.5281/zenodo.15649332">2024</a></li> <li>Probability maps of cultivated grassland: <a href="https://zenodo.org/records/13890401/files/ggc-30m.csv?download=1">2000-2024 (All URLs)</a></li> <li>Probability maps of natural/semi-natural grassland: <a href="https://zenodo.org/records/13890401/files/ggc-30m.csv?download=1">2000-2024 (All URLs)</a></li> <li> <div>Grassland reference samples based on VHR imagery (2000–2024): <a href="https://doi.org/10.5281/zenodo.15631655">GeoPackage files</a></div> </li> <li>Global machine learning models (Random Forest): <a href="https://doi.org/10.5281/zenodo.13952806">Parquet and joblib python files</a></li> <li>Reference sampling design derived by FSCV: <a href="https://doi.org/10.5281/zenodo.11391517">GeoPackage and raster files</a></li> <li>Harmonized reference samples based on existing LULC dataset: <a href="https://doi.org/10.5281/zenodo.13951976">GeoPackage and raster files</a></li> <li>Source code for reproducibility: <a href="https://doi.org/10.5281/zenodo.13952867">GitHub release</a> </li> <li>Mapping feedback tool: <a href="https://geo-wiki.org">GeoWiki</a></li> <li>Data catalogues: <a href="https://stac.openlandmap.org/gpw_ggc-30m/collection.json?.language=en">OpenLandMap STAC</a> <a href="https://global-pasture-watch.projects.earthengine.app/view/ggc-30m">Google Earth Engine</a></li> </ul> Support For questions of bugs/inconsistencies related to the dataset raise a GitHub issue in <a href="https://github.com/wri/global-pasture-watch">https://github.com/wri/global-pasture-watch</a>

Global Pasture Watch - Annual grassland class and extent maps at 30-m spatial resolution (2000—2024)

<h2>Sub-dataset: Dominant grassland class, 2012-2014</h2> <h2>Description</h2> Global annual grassland class and extent for 2000—2024 produced by <a href="https://doi.org/10.1038/s41597-024-04139-6">Parente et al. (2024)</a> within the scope of the <a href="https://landcarbonlab.org/data/global-grassland-and-livestock-monitoring/">Global Pasture Watch initiative</a>. The mapped grassland extent includes any land cover type, which contains at least 30% of dry or wet low vegetation, dominated by grasses and forbs (less than 3 meters) and a: <ul> <li>maximum of 50% tree canopy cover (greater than 5 meters),</li> <li>maximum of 70% of other woody vegetation (scrubs and open shrubland), and</li> <li>maximum of 50% active cropland cover in mosaic landscapes of cropland & other vegetation.</li> </ul> The grassland extent is classified into two classes: <ul> <li>Cultivated grassland: Areas where grasses and other forage plants have been intentionally planted and managed, as well as areas of native grassland-type vegetation where they clearly exhibit active and 'heavy' management for specific human-directed uses, such as directed grazing of livestock.</li> <li>Natural/semi-natural grassland: Relatively undisturbed native grasslands/short-height vegetation, such as steppes and tundra, as well as areas that have experienced varying degrees of human activity in the past, which may contain a mix of native and introduced species due to historical land use and natural processes. In general, they exhibit natural-looking patterns of varied vegetation and clearly ordered hydrological relationships throughout the landscape.</li> <li>Open shrubland (v2-beta): Land on which the vegetation is dominated by low-growing woody plants, characterized by a sparse distribution of shrubs and dominated by woody perennials. Typically covers 50—75% of the area, with significant open ground (with or without herbaceous understory) between them, where shrub canopies are less than 10 meters in diameter, and tree cover is below 10%, meaning they do not form a continuous or semi-continuous canopy.</li> </ul> The dataset is organized in 69 global mosaics (25 years for each time series) in COG (Cloud Optimized GeoTIFF) format, WGS84 Coordinate Systems (EPSG:4326) and pixel size equal to 0.00025 degrees, including: <ul> <li>Probabilities of cultivated grassland (values range from 0–100),</li> <li>Probabilities of natural/semi-natural grassland (values range from 0–100), and</li> <li>Probabilities of open shrubland (values range from 0–100), and</li> <li>Dominant class (0-other land cover, 1-cultivated grassland and 2-natural/semi-natural grassland, 3-open shrubland).</li> </ul> All raster files are in unsigned <code>8-bit integer format</code> and use <code>255</code> as no-data value (pixels ignored by prediction), following an specific naming convention: <ol> <li>Project name: Global Pasture Watch (<code>gpw</code>)</li> <li>Class name: cultivated grassland (<code>cultiv.grassland</code>), natural/semi-natural grassland (<code>nat.semi.grassland</code>), open shrubland (<code>open.shrubland</code>)  and dominant grassland (<code>grassland</code>)</li> <li>Procedure combination: Random Forest (<code>rf</code>), median filter (med.filt) and balanced threshold (<code>bthr</code>).</li> <li>Variable type: probability (<code>p</code>) and factor class (<code>c</code>)</li> <li>Spatial resolution: 30m</li> <li>Begin of time reference: date of first Landsat composite used by the modeling (<code>20240101</code>)</li> <li>End of time reference: date of last Landsat composite used by the modeling (<code>20241231</code>)</li> <li>Spatial extent: global (<code>go</code>)</li> <li>Coordinate system: World Geodetic System 1984, used in GPS (<code>epsg.4326</code>)</li> <li>Version: v2</li> </ol> <h3>Related resources</h3> <ul> <li>Maps of dominant grassland: <a href="http://doi.org/10.5281/zenodo.15646181">2000-2002</a><a href="http://doi.org/10.5281/zenodo.15644486"> 2003-2005</a><a href="http://doi.org/10.5281/zenodo.15644623"> 2006-2008</a><a href="http://doi.org/10.5281/zenodo.15647042"> 2009-2011</a><a href="http://doi.org/10.5281/zenodo.15647681"> 2012-2014</a><a href="http://doi.org/10.5281/zenodo.15648306"> 2015-2017</a><a href="http://doi.org/10.5281/zenodo.15648551"> 2018-2020</a><a href="http://doi.org/10.5281/zenodo.15648751"> 2021-2023</a> <a href="http://doi.org/10.5281/zenodo.15649332">2024</a></li> <li>Probability maps of cultivated grassland: <a href="https://zenodo.org/records/13890401/files/ggc-30m.csv?download=1">2000-2024 (All URLs)</a></li> <li>Probability maps of natural/semi-natural grassland: <a href="https://zenodo.org/records/13890401/files/ggc-30m.csv?download=1">2000-2024 (All URLs)</a></li> <li> <div>Grassland reference samples based on VHR imagery (2000–2024): <a href="https://doi.org/10.5281/zenodo.15631655">GeoPackage files</a></div> </li> <li>Global machine learning models (Random Forest): <a href="https://doi.org/10.5281/zenodo.13952806">Parquet and joblib python files</a></li> <li>Reference sampling design derived by FSCV: <a href="https://doi.org/10.5281/zenodo.11391517">GeoPackage and raster files</a></li> <li>Harmonized reference samples based on existing LULC dataset: <a href="https://doi.org/10.5281/zenodo.13951976">GeoPackage and raster files</a></li> <li>Source code for reproducibility: <a href="https://doi.org/10.5281/zenodo.13952867">GitHub release</a> </li> <li>Mapping feedback tool: <a href="https://geo-wiki.org">GeoWiki</a></li> <li>Data catalogues: <a href="https://stac.openlandmap.org/gpw_ggc-30m/collection.json?.language=en">OpenLandMap STAC</a> <a href="https://global-pasture-watch.projects.earthengine.app/view/ggc-30m">Google Earth Engine</a></li> </ul> Support For questions of bugs/inconsistencies related to the dataset raise a GitHub issue in <a href="https://github.com/wri/global-pasture-watch">https://github.com/wri/global-pasture-watch</a>

Global Pasture Watch - Annual grassland class and extent maps at 30-m spatial resolution (2000—2024)

<h2>Sub-dataset: Dominant grassland class, 2006-2008</h2> <h2>Description</h2> Global annual grassland class and extent for 2000—2024 produced by <a href="https://doi.org/10.1038/s41597-024-04139-6">Parente et al. (2024)</a> within the scope of the <a href="https://landcarbonlab.org/data/global-grassland-and-livestock-monitoring/">Global Pasture Watch initiative</a>. The mapped grassland extent includes any land cover type, which contains at least 30% of dry or wet low vegetation, dominated by grasses and forbs (less than 3 meters) and a: <ul> <li>maximum of 50% tree canopy cover (greater than 5 meters),</li> <li>maximum of 70% of other woody vegetation (scrubs and open shrubland), and</li> <li>maximum of 50% active cropland cover in mosaic landscapes of cropland & other vegetation.</li> </ul> The grassland extent is classified into two classes: <ul> <li>Cultivated grassland: Areas where grasses and other forage plants have been intentionally planted and managed, as well as areas of native grassland-type vegetation where they clearly exhibit active and 'heavy' management for specific human-directed uses, such as directed grazing of livestock.</li> <li>Natural/semi-natural grassland: Relatively undisturbed native grasslands/short-height vegetation, such as steppes and tundra, as well as areas that have experienced varying degrees of human activity in the past, which may contain a mix of native and introduced species due to historical land use and natural processes. In general, they exhibit natural-looking patterns of varied vegetation and clearly ordered hydrological relationships throughout the landscape.</li> <li>Open shrubland (v2-beta): Land on which the vegetation is dominated by low-growing woody plants, characterized by a sparse distribution of shrubs and dominated by woody perennials. Typically covers 50—75% of the area, with significant open ground (with or without herbaceous understory) between them, where shrub canopies are less than 10 meters in diameter, and tree cover is below 10%, meaning they do not form a continuous or semi-continuous canopy.</li> </ul> The dataset is organized in 69 global mosaics (25 years for each time series) in COG (Cloud Optimized GeoTIFF) format, WGS84 Coordinate Systems (EPSG:4326) and pixel size equal to 0.00025 degrees, including: <ul> <li>Probabilities of cultivated grassland (values range from 0–100),</li> <li>Probabilities of natural/semi-natural grassland (values range from 0–100), and</li> <li>Probabilities of open shrubland (values range from 0–100), and</li> <li>Dominant class (0-other land cover, 1-cultivated grassland and 2-natural/semi-natural grassland, 3-open shrubland).</li> </ul> All raster files are in unsigned <code>8-bit integer format</code> and use <code>255</code> as no-data value (pixels ignored by prediction), following an specific naming convention: <ol> <li>Project name: Global Pasture Watch (<code>gpw</code>)</li> <li>Class name: cultivated grassland (<code>cultiv.grassland</code>), natural/semi-natural grassland (<code>nat.semi.grassland</code>), open shrubland (<code>open.shrubland</code>)  and dominant grassland (<code>grassland</code>)</li> <li>Procedure combination: Random Forest (<code>rf</code>), median filter (med.filt) and balanced threshold (<code>bthr</code>).</li> <li>Variable type: probability (<code>p</code>) and factor class (<code>c</code>)</li> <li>Spatial resolution: 30m</li> <li>Begin of time reference: date of first Landsat composite used by the modeling (<code>20240101</code>)</li> <li>End of time reference: date of last Landsat composite used by the modeling (<code>20241231</code>)</li> <li>Spatial extent: global (<code>go</code>)</li> <li>Coordinate system: World Geodetic System 1984, used in GPS (<code>epsg.4326</code>)</li> <li>Version: v2</li> </ol> <h3>Related resources</h3> <ul> <li>Maps of dominant grassland: <a href="http://doi.org/10.5281/zenodo.15646181">2000-2002</a><a href="http://doi.org/10.5281/zenodo.15644486"> 2003-2005</a><a href="http://doi.org/10.5281/zenodo.15644623"> 2006-2008</a><a href="http://doi.org/10.5281/zenodo.15647042"> 2009-2011</a><a href="http://doi.org/10.5281/zenodo.15647681"> 2012-2014</a><a href="http://doi.org/10.5281/zenodo.15648306"> 2015-2017</a><a href="http://doi.org/10.5281/zenodo.15648551"> 2018-2020</a><a href="http://doi.org/10.5281/zenodo.15648751"> 2021-2023</a> <a href="http://doi.org/10.5281/zenodo.15649332">2024</a></li> <li>Probability maps of cultivated grassland: <a href="https://zenodo.org/records/13890401/files/ggc-30m.csv?download=1">2000-2024 (All URLs)</a></li> <li>Probability maps of natural/semi-natural grassland: <a href="https://zenodo.org/records/13890401/files/ggc-30m.csv?download=1">2000-2024 (All URLs)</a></li> <li> <div>Grassland reference samples based on VHR imagery (2000–2024): <a href="https://doi.org/10.5281/zenodo.15631655">GeoPackage files</a></div> </li> <li>Global machine learning models (Random Forest): <a href="https://doi.org/10.5281/zenodo.13952806">Parquet and joblib python files</a></li> <li>Reference sampling design derived by FSCV: <a href="https://doi.org/10.5281/zenodo.11391517">GeoPackage and raster files</a></li> <li>Harmonized reference samples based on existing LULC dataset: <a href="https://doi.org/10.5281/zenodo.13951976">GeoPackage and raster files</a></li> <li>Source code for reproducibility: <a href="https://doi.org/10.5281/zenodo.13952867">GitHub release</a> </li> <li>Mapping feedback tool: <a href="https://geo-wiki.org">GeoWiki</a></li> <li>Data catalogues: <a href="https://stac.openlandmap.org/gpw_ggc-30m/collection.json?.language=en">OpenLandMap STAC</a> <a href="https://global-pasture-watch.projects.earthengine.app/view/ggc-30m">Google Earth Engine</a></li> </ul> Support For questions of bugs/inconsistencies related to the dataset raise a GitHub issue in <a href="https://github.com/wri/global-pasture-watch">https://github.com/wri/global-pasture-watch</a>

Global Pasture Watch - Annual grassland class and extent maps at 30-m spatial resolution (2000—2024)

<h2>Sub-dataset: Dominant grassland class, 2003-2005</h2> <h2>Description</h2> Global annual grassland class and extent for 2000—2024 produced by <a href="https://doi.org/10.1038/s41597-024-04139-6">Parente et al. (2024)</a> within the scope of the <a href="https://landcarbonlab.org/data/global-grassland-and-livestock-monitoring/">Global Pasture Watch initiative</a>. The mapped grassland extent includes any land cover type, which contains at least 30% of dry or wet low vegetation, dominated by grasses and forbs (less than 3 meters) and a: <ul> <li>maximum of 50% tree canopy cover (greater than 5 meters),</li> <li>maximum of 70% of other woody vegetation (scrubs and open shrubland), and</li> <li>maximum of 50% active cropland cover in mosaic landscapes of cropland & other vegetation.</li> </ul> The grassland extent is classified into two classes: <ul> <li>Cultivated grassland: Areas where grasses and other forage plants have been intentionally planted and managed, as well as areas of native grassland-type vegetation where they clearly exhibit active and 'heavy' management for specific human-directed uses, such as directed grazing of livestock.</li> <li>Natural/semi-natural grassland: Relatively undisturbed native grasslands/short-height vegetation, such as steppes and tundra, as well as areas that have experienced varying degrees of human activity in the past, which may contain a mix of native and introduced species due to historical land use and natural processes. In general, they exhibit natural-looking patterns of varied vegetation and clearly ordered hydrological relationships throughout the landscape.</li> <li>Open shrubland (v2-beta): Land on which the vegetation is dominated by low-growing woody plants, characterized by a sparse distribution of shrubs and dominated by woody perennials. Typically covers 50—75% of the area, with significant open ground (with or without herbaceous understory) between them, where shrub canopies are less than 10 meters in diameter, and tree cover is below 10%, meaning they do not form a continuous or semi-continuous canopy.</li> </ul> The dataset is organized in 69 global mosaics (25 years for each time series) in COG (Cloud Optimized GeoTIFF) format, WGS84 Coordinate Systems (EPSG:4326) and pixel size equal to 0.00025 degrees, including: <ul> <li>Probabilities of cultivated grassland (values range from 0–100),</li> <li>Probabilities of natural/semi-natural grassland (values range from 0–100), and</li> <li>Probabilities of open shrubland (values range from 0–100), and</li> <li>Dominant class (0-other land cover, 1-cultivated grassland and 2-natural/semi-natural grassland, 3-open shrubland).</li> </ul> All raster files are in unsigned <code>8-bit integer format</code> and use <code>255</code> as no-data value (pixels ignored by prediction), following an specific naming convention: <ol> <li>Project name: Global Pasture Watch (<code>gpw</code>)</li> <li>Class name: cultivated grassland (<code>cultiv.grassland</code>), natural/semi-natural grassland (<code>nat.semi.grassland</code>), open shrubland (<code>open.shrubland</code>)  and dominant grassland (<code>grassland</code>)</li> <li>Procedure combination: Random Forest (<code>rf</code>), median filter (med.filt) and balanced threshold (<code>bthr</code>).</li> <li>Variable type: probability (<code>p</code>) and factor class (<code>c</code>)</li> <li>Spatial resolution: 30m</li> <li>Begin of time reference: date of first Landsat composite used by the modeling (<code>20240101</code>)</li> <li>End of time reference: date of last Landsat composite used by the modeling (<code>20241231</code>)</li> <li>Spatial extent: global (<code>go</code>)</li> <li>Coordinate system: World Geodetic System 1984, used in GPS (<code>epsg.4326</code>)</li> <li>Version: v2</li> </ol> <h3>Related resources</h3> <ul> <li>Maps of dominant grassland: <a href="http://doi.org/10.5281/zenodo.15646181">2000-2002</a><a href="http://doi.org/10.5281/zenodo.15644486"> 2003-2005</a><a href="http://doi.org/10.5281/zenodo.15644623"> 2006-2008</a><a href="http://doi.org/10.5281/zenodo.15647042"> 2009-2011</a><a href="http://doi.org/10.5281/zenodo.15647681"> 2012-2014</a><a href="http://doi.org/10.5281/zenodo.15648306"> 2015-2017</a><a href="http://doi.org/10.5281/zenodo.15648551"> 2018-2020</a><a href="http://doi.org/10.5281/zenodo.15648751"> 2021-2023</a> <a href="http://doi.org/10.5281/zenodo.15649332">2024</a></li> <li>Probability maps of cultivated grassland: <a href="https://zenodo.org/records/13890401/files/ggc-30m.csv?download=1">2000-2024 (All URLs)</a></li> <li>Probability maps of natural/semi-natural grassland: <a href="https://zenodo.org/records/13890401/files/ggc-30m.csv?download=1">2000-2024 (All URLs)</a></li> <li> <div>Grassland reference samples based on VHR imagery (2000–2024): <a href="https://doi.org/10.5281/zenodo.15631655">GeoPackage files</a></div> </li> <li>Global machine learning models (Random Forest): <a href="https://doi.org/10.5281/zenodo.13952806">Parquet and joblib python files</a></li> <li>Reference sampling design derived by FSCV: <a href="https://doi.org/10.5281/zenodo.11391517">GeoPackage and raster files</a></li> <li>Harmonized reference samples based on existing LULC dataset: <a href="https://doi.org/10.5281/zenodo.13951976">GeoPackage and raster files</a></li> <li>Source code for reproducibility: <a href="https://doi.org/10.5281/zenodo.13952867">GitHub release</a> </li> <li>Mapping feedback tool: <a href="https://geo-wiki.org">GeoWiki</a></li> <li>Data catalogues: <a href="https://stac.openlandmap.org/gpw_ggc-30m/collection.json?.language=en">OpenLandMap STAC</a> <a href="https://global-pasture-watch.projects.earthengine.app/view/ggc-30m">Google Earth Engine</a></li> </ul> Support For questions of bugs/inconsistencies related to the dataset raise a GitHub issue in <a href="https://github.com/wri/global-pasture-watch">https://github.com/wri/global-pasture-watch</a>

Global Pasture Watch - Annual grassland class and extent maps at 30-m spatial resolution (2000—2024)

<h2>Sub-dataset: Dominant grassland class, 2000-2002</h2> <h2>Description</h2> Global annual grassland class and extent for 2000—2024 produced by <a href="https://doi.org/10.1038/s41597-024-04139-6">Parente et al. (2024)</a> within the scope of the <a href="https://landcarbonlab.org/data/global-grassland-and-livestock-monitoring/">Global Pasture Watch initiative</a>. The mapped grassland extent includes any land cover type, which contains at least 30% of dry or wet low vegetation, dominated by grasses and forbs (less than 3 meters) and a: <ul> <li>maximum of 50% tree canopy cover (greater than 5 meters),</li> <li>maximum of 70% of other woody vegetation (scrubs and open shrubland), and</li> <li>maximum of 50% active cropland cover in mosaic landscapes of cropland & other vegetation.</li> </ul> The grassland extent is classified into two classes: <ul> <li>Cultivated grassland: Areas where grasses and other forage plants have been intentionally planted and managed, as well as areas of native grassland-type vegetation where they clearly exhibit active and 'heavy' management for specific human-directed uses, such as directed grazing of livestock.</li> <li>Natural/semi-natural grassland: Relatively undisturbed native grasslands/short-height vegetation, such as steppes and tundra, as well as areas that have experienced varying degrees of human activity in the past, which may contain a mix of native and introduced species due to historical land use and natural processes. In general, they exhibit natural-looking patterns of varied vegetation and clearly ordered hydrological relationships throughout the landscape.</li> <li>Open shrubland (v2-beta): Land on which the vegetation is dominated by low-growing woody plants, characterized by a sparse distribution of shrubs and dominated by woody perennials. Typically covers 50—75% of the area, with significant open ground (with or without herbaceous understory) between them, where shrub canopies are less than 10 meters in diameter, and tree cover is below 10%, meaning they do not form a continuous or semi-continuous canopy.</li> </ul> The dataset is organized in 69 global mosaics (25 years for each time series) in COG (Cloud Optimized GeoTIFF) format, WGS84 Coordinate Systems (EPSG:4326) and pixel size equal to 0.00025 degrees, including: <ul> <li>Probabilities of cultivated grassland (values range from 0–100),</li> <li>Probabilities of natural/semi-natural grassland (values range from 0–100), and</li> <li>Probabilities of open shrubland (values range from 0–100), and</li> <li>Dominant class (0-other land cover, 1-cultivated grassland and 2-natural/semi-natural grassland, 3-open shrubland).</li> </ul> All raster files are in unsigned <code>8-bit integer format</code> and use <code>255</code> as no-data value (pixels ignored by prediction), following an specific naming convention: <ol> <li>Project name: Global Pasture Watch (<code>gpw</code>)</li> <li>Class name: cultivated grassland (<code>cultiv.grassland</code>), natural/semi-natural grassland (<code>nat.semi.grassland</code>), open shrubland (<code>open.shrubland</code>)  and dominant grassland (<code>grassland</code>)</li> <li>Procedure combination: Random Forest (<code>rf</code>), median filter (med.filt) and balanced threshold (<code>bthr</code>).</li> <li>Variable type: probability (<code>p</code>) and factor class (<code>c</code>)</li> <li>Spatial resolution: 30m</li> <li>Begin of time reference: date of first Landsat composite used by the modeling (<code>20240101</code>)</li> <li>End of time reference: date of last Landsat composite used by the modeling (<code>20241231</code>)</li> <li>Spatial extent: global (<code>go</code>)</li> <li>Coordinate system: World Geodetic System 1984, used in GPS (<code>epsg.4326</code>)</li> <li>Version: v2</li> </ol> <h3>Related resources</h3> <ul> <li>Maps of dominant grassland: <a href="http://doi.org/10.5281/zenodo.15646181">2000-2002</a><a href="http://doi.org/10.5281/zenodo.15644486"> 2003-2005</a><a href="http://doi.org/10.5281/zenodo.15644623"> 2006-2008</a><a href="http://doi.org/10.5281/zenodo.15647042"> 2009-2011</a><a href="http://doi.org/10.5281/zenodo.15647681"> 2012-2014</a><a href="http://doi.org/10.5281/zenodo.15648306"> 2015-2017</a><a href="http://doi.org/10.5281/zenodo.15648551"> 2018-2020</a><a href="http://doi.org/10.5281/zenodo.15648751"> 2021-2023</a> <a href="http://doi.org/10.5281/zenodo.15649332">2024</a></li> <li>Probability maps of cultivated grassland: <a href="https://zenodo.org/records/13890401/files/ggc-30m.csv?download=1">2000-2024 (All URLs)</a></li> <li>Probability maps of natural/semi-natural grassland: <a href="https://zenodo.org/records/13890401/files/ggc-30m.csv?download=1">2000-2024 (All URLs)</a></li> <li> <div>Grassland reference samples based on VHR imagery (2000–2024): <a href="https://doi.org/10.5281/zenodo.15631655">GeoPackage files</a></div> </li> <li>Global machine learning models (Random Forest): <a href="https://doi.org/10.5281/zenodo.13952806">Parquet and joblib python files</a></li> <li>Reference sampling design derived by FSCV: <a href="https://doi.org/10.5281/zenodo.11391517">GeoPackage and raster files</a></li> <li>Harmonized reference samples based on existing LULC dataset: <a href="https://doi.org/10.5281/zenodo.13951976">GeoPackage and raster files</a></li> <li>Source code for reproducibility: <a href="https://doi.org/10.5281/zenodo.13952867">GitHub release</a> </li> <li>Mapping feedback tool: <a href="https://geo-wiki.org">GeoWiki</a></li> <li>Data catalogues: <a href="https://stac.openlandmap.org/gpw_ggc-30m/collection.json?.language=en">OpenLandMap STAC</a> <a href="https://global-pasture-watch.projects.earthengine.app/view/ggc-30m">Google Earth Engine</a></li> </ul> Support For questions of bugs/inconsistencies related to the dataset raise a GitHub issue in <a href="https://github.com/wri/global-pasture-watch">https://github.com/wri/global-pasture-watch</a>

opencc-by-4.0Apr 2024View details →

Maps of the detailed spatially and temporally attributed emission for area of Legerova and Sokolska (TURBAN-D18)

<h3>Basic information</h3> This dataset contains six folders with maps of input data for simulations published in project TURBAN as result D17 (see <a href="../records/10982836">https://zenodo.org/records/10982836</a>). Each folder contains air quality inputs for the so-called Legerova domain, an area in the city of Prague, Czech Republic, centred around the traffic-heavy streets Legerova and Sokolská. All times are in UTC (local time in winter, CET, is UTC +01:00, summer time, CEST, is UTC +02:00). In total 6 episodes in 2022 and 2023 were selected: <ol> <li>s1 2022-07-17 00:00:00 - 2022-07-20 00:00:00</li> <li>s2: 2022-08-02 00:00:00 - 2022-08-05 00:00:00</li> <li>s3: 2022-09-22 00:00:00 - 2022-09-25 00:00:00</li> <li>s4: 2022-12-08 00:00:00 - 2022-12-11 00:00:00</li> <li>s5: 2023-01-27 00:00:00 - 2023-01-30 00:00:00</li> <li>s6: 2023-02-13 00:00:00 - 2023-02-16 00:00:00</li> </ol> For more detailed description of the experiments see the TURBAN project website at <a href="https://www.project-turban.eu/">https://www.project-turban.eu/</a>. <h3>General organisation, variables and file nomenclature</h3> Each selected epizode (s1-s6) has three subfolders; input files in ASCII (output-ascii) or GeoTiff (output-gis) formats that can be viewed in many GIS applications. In the third subfolder are maps in the PNG format (output-png). Each subfolder includes 4 subfolders with emissions summarized in all layers above ground. Variable vsrc_PM10 is the concentration of volume source emissions (VSRC) of the PM10, vsrc_PM25 is the concentration of PM2.5, vsrc_NO is the concentration of NO and vsrc_NO2 is the concentration of NO2. Each file (PRJ, TIF, ASC or PNG) has the same nomenclature. An example (vsrc_NO_abs-01h_20220717_1200-1300.png) could be parsed as: variable name (vsrc_NO), processed input (abs-01h), date (20220717) and period (1200-1300). So, the result is a map with emission fluxes of NO between 12:00 and 13:00 UTC 24 Jul 2019. <h3>Emissions (see section 2.4.3 in Resler et al., 2024)</h3> The data were processed from datasets published by CHMI, data collected by the Municipality of Prague and its organizations, data obtained by the researcher (ATEM) while providing expert studies in the past, and results of previous research projects. The input data of the used emission sources can be divided into two basic groups: emission from local heating and transport sources. Emissions for local heating were determined by calculations based on data from CHMI and the Czech Statistical Office (CZSO). Emissions from the transport sources were modeled using the MEFA transportation emission model which is recommended for the use in the Czech Republic by the Ministry of Environment of the Czech Republic. The model takes into account factors such as road gradient, the number of vehicles on the road, the flow of traffic, the composition of car types, and the emission characteristics of the individual car types. The emission calculation is based on data from the traffic census provided by the Prague Technical Administration of Roads (TSK Praha) and on data from the census of the composition of the transportation fleet in Prague built in the MEFA emission model. The data are based on regular surveys of the fleet composition carried out in Prague (Karel et al., 2021). The dust resuspension was computed according to the methodology published by the Ministry of Environment (Karel et. al., 2015). This methodology is based on US EPA methodology AP-42 (EPA, 2011) and was adjusted for the conditions of the Czech Republic. For the garages and parking lots, the results of the project TH03030496 (Karel et al., 2020) were used and for the bus stations, publicly available data about transportation were gathered from the Prague Public Transit Company (DPP). The disaggregation of the annual emissions into hourly intervals was then performed according to the type of source. For combustion sources distribution of emissions to days was done according to natural gas supply profiles for category DOM4 were used (OTE, 2024) and complemented by daily profiles for SNAP 2 (van der Gon, 2011). For transport sources, the census data from TSK Praha was utilized for all streets where it was available. For Legerova and Sokolská streets, hourly traffic intensity data were obtained and used directly for the selected episodes. For streets that were not covered by regular traffic surveys, the spatial and temporal distribution of the traffic intensities were based on analysis and evaluation of the relevant studies for the particular area (e.g. urban planning studies, Environmental Impact Assessment (EIA), etc.) and combined with information like street type, location, traffic regime, and pavement type. This approach allowed us to specify the distribution of the transportation intensities on smaller streets. For the detailed modeling of emissions from rail transport (diesel locomotives), the data of train rides were obtained from the Railway Administration (SŽ) and emission factors from the EMEP/EEA Air Pollutant Emission Inventory Guidebook 2019 (EEA, 2019) were used. Emissions from river ships were obtained from the CHMI national database and spatially distributed to the area of the river. Spatial transformation of the line and point emission into the corresponding areas was done with the utilization of the surrogates representing corresponding areas (e.g. areas of the street traffic lines and parking places for traffic emission and areas of the building roofs for local heating sources). This not only ensured the reasonable spatial distribution of the emission in the street canyon but also decreased the gradients of the emission field and with this proneness of the model to numerical inaccuracy of the micro-scale model. The processing of the emission sources into hourly emission flows was done in the emission model FUME recently extended for processing of the PALM emission (Belda et al., 2024). <h3>Acknowledgements</h3> The PALM simulations, and pre- and postprocessing were performed partially on the HPC infrastructure of the Institute of Computer Science of the Czech Academy of Sciences (ICS), supported by the long-term strategic development financing of the ICS (RVO:67985807) and partially on the IT4I HPC infrastructure supported by the Ministry of Education, Youth and Sports of the Czech Republic through the e-INFRA CZ (ID:90254). The work was performed within the project TURBAN (TO01000219; TURBAN – Turbulent-resolving urban modelling of air quality and thermal comfort) supported by Norway Grants and Technology Agency of the Czech Republic. <h3>Literature</h3> Note that some sources are available only in Czech language. Belda, M., et al. (2024) FUME 2.0 – Flexible Universal processor for Modeling Emissions, EGUsphere [preprint]. <a href="https://doi.org/10.5194/egusphere-2023-2740">https://doi.org/10.5194/egusphere-2023-2740</a> Karel, J., et al. (2020) Projekt TH03030496 - Zmapování a emisní bilance neevidovaných zdrojů emisí zneči&scaron;ťujících látek na území městských aglomerací. Mapa neevidovaných zdrojů emisí zneči&scaron;ťujících látek na území aglomerace CZ01 Praha. Partially available at: <a href="https://www.atem.cz/neevidovane_zdroje.php">https://www.atem.cz/neevidovane_zdroje.php</a> Karel, J., et al. (2015) Metodika pro výpočet emisí částic pocházejících z resuspenze ze silniční dopravy, CENEST, s. r. o., Prague. Available at: <a href="https://www.mzp.cz/C1257458002F0DC7/cz/doprava/$FILE/OOO-resuspenze_metodika-20190708.pdf">https://www.mzp.cz/C1257458002F0DC7/cz/doprava/$FILE/OOO-resuspenze_metodika-20190708.pdf</a> Karel J., et. al. (2021) Zpráva o dynamické skladbě vozového parku na území hlavního města Prahy v roce 2020, Prague 2021. Available upon request from the Environmental Protection Division of the Prague Municipality. EPA (2011) Compilation of Air Pollutant Emission Factors, Volume I, AP-42. Section 13.2.1. Paved roads. EPA Research Triangle Park, US, 2003, updated 2011. Available at: <a href="https://www.epa.gov/air-emissions-factors-and-quantification/ap-42-compilation-air-emissions-factors-stationary-sources">https://www.epa.gov/air-emissions-factors-and-quantification/ap-42-compilation-air-emissions-factors-stationary-sources</a> van der Gon, H.D., et al. (2011) Description of Current Temporal Emission Patterns and Sensitivity of Predicted AQ for Temporal Emission Patterns. EU FP7 MACC Deliverable Report D_D-EMIS_1.3. Available at: <a href="https://atmosphere.copernicus.eu/sites/default/files/2019-07/MACC_TNO_del_1_3_v2.pdf">https://atmosphere.copernicus.eu/sites/default/files/2019-07/MACC_TNO_del_1_3_v2.pdf</a> EEA (2019) European Environment Agency, EMEP/EEA air pollutant emission inventory guidebook 2019 – Technical guidance to prepare national emission inventories, Publications Office. Available at: <a href="https://data.europa.eu/doi/10.2800/293657">https://data.europa.eu/doi/10.2800/293657</a> OTE (2024) Gas Load Profiles - temperature and recalculated TDD. Available at: <a href="https://www.ote-cr.cz/en/statistics/gas-load-profiles/normalized-lp?set_language=en">https://www.ote-cr.cz/en/statistics/gas-load-profiles/normalized-lp?set_language=en</a>

opencc-by-4.0Nov 2014View details →

Development of a global inundation map at high spatial resolution from topographic downscaling of coarse-scale remote sensing data

Overview: The Global Inundation Extent from Multi-Satellites (GIEMS; Prigent et al. 2007, Papa et al. 2010) downscaled at 15 arc-second (GIEMS-D15; Fluet-Chouinard et al. 2015) was produced through the downscaling of the GIEMS database (natively at 0.25°).  The downscaling procedure predicts the location of surface water cover with an inundation ranking surface generated by bagged decision trees. The decision trees were trained on binary presence/absence of wetland in the GLC2000 global land cover map (Bartholomé & Belward 2005) and used 13 topographic and hydrographic predictors derived from the SRTM-derived HydroSHEDS database (Lehner, Verdin & Jarvis 2008). The downscaling technique to three temporal aggregation of the GIEMS dataset representing three states of land surface inundation extents: mean annual minimum (MAMin; total area, 6.5 × 106 km2), mean annual maximum (MAMax; 12.1 × 106 km2), and long-term maximum (LTMax; 17.3 × 106 km2). The area of MAMin and MAMax from GIEMS were supplemented with the minimum area value from lakes, river and reservoirs from GLWD (Lehner & Döll 2004; classes 1,2,3). LTMax was corrected as the mean area from 3-year rolling maximum from GIEMS and the total wetland area from GLWD (classes 1-12). The accuracy of GIEMS-D15 reflects distribution errors introduced by the downscaling process as well as errors from the original satellite estimates. Yet, a comparison against independent regional wetland maps showed adequate agreement over large floodplains and wetlands. GIEMS-D15 offers a higher resolution delineation of inundated areas than originally offered by GIEMS, allowing for the assessment of global freshwater resources and the study of large floodplain and wetland ecosystems. Projection: WGS84 (EPSG:4326) Geographic extent: <ul> <li>Longitude: -180° to 180°</li> <li>Latitude: -56° to 84°</li> </ul> Spatial resolution: 15 arc-second (500m at equator) Legend (for discrete pixel values): <ul> <li>0 = Upland</li> <li>1 = Mean Annual Minimum (MAMin)</li> <li>2 = Mean Annual Maximum (MAMax)</li> <li>3 = Long Term Maximum (LTMax)</li> </ul>

opencc-by-4.0Dec 2023View details →

SHIFT: A DEM-Based Spatial Heterogeneity Improved Mapping of Global Geomorphic Floodplains

<h2>Description</h2> SHIFT (Spatial Heterogeneity Improved Floodplain by Terrain analysis) is a 90-m resolution global geomorphic floodplain map based on terrain analysis. It takes MERIT-Hydro as the terrain input and Floodplain Hydraulic Geometry (FHG) as the thresholding scheme, with the scaling parameters estimated by a stepwise framework that both respects the power law and approximates the spatial extent of hydrodynamic modeling. SHIFT effectively captures the global patterns of the geomorphic floodplains, with better regional details than existing data. <h2>Data Structure</h2> We provide 2 resolutions of data for different needs. <ul> <li>SHIFT_v3_90m: The original SHIFT data derived from MERIT-Hydro, with lakes and reservoirs removed. The resolution is 0.000833333333333 degrees under geographic coordinate system (EPSG:4326), approximately 90 meters at the equator. Pixels with value 1 are floodplains, 2 are lakes and reservoirs and 0 are non-floodplains, with empty values set as 255 (denoting pixels not within any watersheds under the threshold of 1000 km2).</li> <li>SHIFT_v3_1km: The resampled SHIFT data with lakes and reservoirs marked. The resolution is 0.00833333333333 degrees under geographic coordinate system (EPSG:4326), approximately 1 km at the equator. Pixels with value 1 are floodplains, 2 are lakes and reservoirs and 0 are non-floodplains, with empty values set as 255 (denoting pixels not within any watersheds under the threshold of 1000 km2).</li> </ul> Also, we provide our derived spatially-varying parameters in all Level-3 basins to support future studies. Parameters are provided in a shapefile, with 'a' denotes the proportional parameter and 'b' denotes the exponent. We aggregated MERIT-Basins based on its spatial relationship with basins from Level-3 HydroBASINS, ensuring that the centroid of a MERIT-Basin falls within the corresponding boundary. This approach accounts for slight differences in boundaries due to the use of different terrain data, preventing confusion in hydrological representation. For more details, please refer to: <ul> <li>Zheng, K., Lin, P., and Yin, Z.: SHIFT: a spatial-heterogeneity improvement in DEM-based mapping of global geomorphic floodplains, Earth Syst. Sci. Data, 16, 3873–3891, <a href="https://doi.org/10.5194/essd-16-3873-2024" rel="noopener">https://doi.org/10.5194/essd-16-3873-2024</a>, 2024.</li> </ul> <h2>Development Log</h2> <ol> <li>Changes in v3 compared to v2: <ol> <li> Inclusion of Missing Level-3 Basin: We have added a previously missing Level-3 basin (PFAF ID: 242) that covers an area in Eastern Europe, specifically from Warsaw to Minsk. This omission was due to a technical problem that has now been resolved. Data are now still available in two resolutions: 90-meter and 1-kilometer. </li> <li> Updated Parameters: Along with the new boundaries, updated parameters are provided in the shapefile. </li> <li> Re-estimated Global Floodplain Area: Based on the new data, we have re-estimated the global total floodplain area from 9.9 × 10^6 km² to 9.92 × 10^6 km². This area still represents approximately 6.6% of the total land mass. </li> </ol> </li> <li>Changes in v2 compared to v1: <ol> <li>Parameter 'b' Estimation: We modified the technical details of parameter 'b' estimation, specifically the binning parameter, adding a constraining mechanism to handle data noise. This resulted in stabler estimates for large basins and a clearer pattern of global residual uncertainty.</li> <li>Target Function for Parameter 'a': We changed our target function to balance information from both datasets, using Fleiss’s Kappa (FK) and a penalty term to reduce bias.</li> </ol> </li> </ol> <h2>Contacts</h2> <ul> <li>Kaihao Zheng, <a href="mailto:Mostaly@pku.edu.cn" target="_blank" rel="noopener">Mostaly@pku.edu.cn</a></li> <li>Peirong Lin, <a href="mailto:peironglinlin@pku.edu.cn" target="_blank" rel="noopener">peironglinlin@pku.edu.cn</a></li> </ul>

opencc-by-4.0Jul 2023View details →

Spatial models of topsoil properties in Romania using digital soil mapping techniques

The database includes the selected spatial models of soil variables for the Romanian territory derived by digital soil mapping techniques, accepted for publication in: Cristian Valeriu Patriche, Bogdan Roșca, Radu Gabriel Pîrnău, Ionuț Vasiliniuc, Spatial modelling of topsoil properties in Romania using geostatistical methods and machine learning, PLOS ONE, 2023 The database includes the selected spatial models of soil variables for the Romanian territory derived by digital soil mapping techniques. The file names indicate the soil variable and the method used for interpolation (RK – regression - kriging, EML – ensemble machine learning, GWR_OK – Geographically Weighted Regression – Ordinary kriging). The raster data is classified and saved in tif format with a resolution of 100 x 100 m. The spatial reference is Stereographic projection 1970 (Pulkovo_1942_Adj_58_Stereo_70). The soil variables are classified as follows: <table> <tbody> <tr> <td> Variable </td> <td> Classes </td> </tr> <tr> <td> 1 </td> <td> 2 </td> <td> 3 </td> <td> 4 </td> <td> 5 </td> <td> 5 </td> <td> 7 </td> </tr> <tr> <td> pH </td> <td> ≤ 5 (strongly acid) </td> <td> 5.1 – 5.8 (moderately acid) </td> <td> 5.9 – 6.8 (weakly acid) </td> <td> 6.9 – 7.2 (neutral) </td> <td> 7.3 – 8.4 (weakly alkaline) </td> <td> 8.5 – 8.8 (moderately alkaline) </td> <td>   </td> </tr> <tr> <td> EC (mS m-1) </td> <td> ≤ 12.75 </td> <td> 12.76 – 16.49 </td> <td> 16.50 – 20.04 </td> <td> 20.05 – 24.18 </td> <td> 24.19 – 29.11 </td> <td> 29.12 – 35.23 </td> <td> ≤ 35.24 </td> </tr> <tr> <td> OC (g kg-1) </td> <td> < 7.5 (very low) </td> <td> 7.5 – 17.4 (low) </td> <td> 17.4 – 37.8 (moderate) </td> <td> 37.8 – 61.0 (high) </td> <td> > 61 (very high) </td> <td>   </td> <td>   </td> </tr> <tr> <td> CaCO3 (g kg-1) </td> <td> 0 (no carbonates) </td> <td> 1 – 10 (low) </td> <td> 11 – 40 (medium 1) </td> <td> 41 – 80 (medium 2) </td> <td> 81 – 107 (medium 3) </td> <td>   </td> <td>   </td> </tr> <tr> <td> P (mg kg-1) </td> <td> < 4 (extremely low) </td> <td> 4 – 8 (very low) </td> <td> 8 – 18 (low) </td> <td> 18 – 36 (medium) </td> <td> 36 – 72 (high) </td> <td> > 72 (very high) </td> <td>   </td> </tr> <tr> <td> N (g kg-1) </td> <td> ≤ 1 (very low) </td> <td> 1.1 – 1.4 (low) </td> <td> 1.5 – 2.0 (medium 1) </td> <td> 2.1 – 2.7 (medium 2) </td> <td> 2.8 – 6.0 (high) </td> <td> > 6 (very high) </td> <td>   </td> </tr> <tr> <td> K (mg kg-1) </td> <td> ≤ 40 * (extremely low) </td> <td> 41 – 65 * (very low) </td> <td> 66 – 130 (low) </td> <td> 131 – 200 (medium) </td> <td> 201 – 300 (high) </td> <td> > 300  (very high) </td> <td>   </td> </tr> <tr> <td> Clay (%) </td> <td> ≤ 25  (low 1) </td> <td> 26 – 32 (low 2) </td> <td> 33 – 40 (medium 1) </td> <td> 41 – 45 (medium 2) </td> <td> ≥ 46  (high) </td> <td>   </td> <td>   </td> </tr> <tr> <td> Silt (%) </td> <td> < 25 (medium 1) </td> <td> 25 – 32 (medium 2) </td> <td> 33 – 40 (high 1) </td> <td> 41 – 50 (high 2) </td> <td> > 50 (high 3) </td> <td>   </td> <td>   </td> </tr> <tr> <td> Sand (%) </td> <td> < 15 (low 1) </td> <td> 15 – 25 (low 2) </td> <td> 26 – 35 (low 3) </td> <td> 36 – 56 (medium) </td> <td> > 56 (high) </td> <td>   </td> <td>   </td> </tr> </tbody> </table>   * classes not present on the Romanian territory

opencc-by-4.0Apr 2023View details →

Data Used for "Mapping Tribes: Ottoman Spatial Thinking in Iraq and Arabia, c. 1910"

This repository contains data used for our article, "Mapping Tribes: Ottoman Spatial Thinking in Iraq and Arabia, c. 1910". The README.md file explains the contents. This data can also be found at https://github.com/opengulf/ottoman-map.

opencc-by-4.0Mar 2023View details →

The administrative topography of Rome. Mapping administrative space and the spatial dynamics of Roman Republicanism

This dataset contains the following figures: Table 1, The Radar Chart Map 1, ROME, 2nd CENTURY BCE Map 2, ROME, 1st CENTURY BCE Map 3, ROME, 1st CENTURY ACE Map 4, ROME, 2nd CENTURY ACE Map 5, ROME, 3rd CENTURY ACE

opencc-by-4.0Dec 2019View details →

A method for generating coherent spatially explicit maps of seasonal palaeoclimates from site-based reconstructions

Reconstruction of climate anomalies in southern Europe for the Last Glacial Maximum (LGM, ca 21,000 years ago), made by combining pollen based reconstructions (from Bartlein et al. 2011) and averaged outputs of LGM simulations from the 3rd round of the Palaeoclimate Model Intercomparison Project (PMIP, Braconnot et al. 2011), under a variational data assimilation technique. Reconstructions made using this technique are designed to be used for data-model comparison, specifically against the results of PMIP4. The dataset consists of 6 variables: moisture index (the ratio precipitation and equilibrium evapotranspiration), mean annual precipitation (mm), mean annual temperature (degrees C), mean temperature of the coldest month (degrees C), mean temperature of the warmest month (degrees C), growing degree days above 5 degrees C (day degrees C). The standard deviation of these variables is also given.

opencc-by-4.0Jan 2020View details →

Map 1 in Taxonomic Accounts With Notes On Spatial Diversity And Relative Abundance Pattern Of Horseflies (Diptera: Tabanidae) From Sonamukhi Protected Forest Area Of West Bengal, India

Map 1. GIS map showing distribution and richness of family Tabanidae in Sonamukhi protected forest, on the basis of eco-regions in Indo-malayan biome of arid region of west Bengal below and satellite map of all the stations showing study sites of Tabanidae above.

opencc-by-4.0Jun 2017View details →

IVMOOC 2017 - GloBI Data for Interactive Tableau Map of Spatial and Temporal Distribution of Interactions

Global Biotic Interactions (GloBI, www.globalbioticinteractions.org) provides an infrastructure and data service that aggregates and archives known biotic interaction databases to provide easy access to species interaction data. This project explores the coverage of GloBI data against known taxonomic catalogues in order to identify ‘gaps’ in knowledge of species interactions. We examine the richness of GloBI’s datasets using itself as a frame of reference for comparison and explore interaction networks according to geographic regions over time. The resulting analysis and visualizations intend to provide insights that may help to enhance GloBI as a resource for research and education. Spatial and temporal biotic interactions data were used in the construction of an interactive Tableau map. The raw data (IVMOOC 2017 GloBI Kingdom Data Extracted 2017 04 17.csv) was extracted from the project-specific SQL database server. The raw data was clean and preprocessed (IVMOOC 2017 GloBI Cleaned Tableau Data.csv) for use in the Tableau map. Data cleaning and preprocessing steps are detailed in the companion paper. The interactive Tableau map can be found here: https://public.tableau.com/profile/publish/IVMOOC2017-GloBISpatialDistributionofInteractions/InteractionsMapTimeSeries#!/publish-confirm The companion paper can be found here: doi.org/10.5281/zenodo.814979 Complementary high resolution visualizations can be found here: doi.org/10.5281/zenodo.814922 Project-specific data can be found here: doi.org/10.5281/zenodo.804103 (SQL server database)