{"help": "https://datasets.wri.org/private-admin/api/3/action/help_show?name=package_search", "success": true, "result": {"count": 29, "facets": {}, "results": [{"approval_status": "approved", "author": null, "author_email": null, "authors": [{"name": "Nancy Harris", "email": "nancy.harris@wri.org"}, {"name": "David Gibbs", "email": "david.gibbs@wri.org"}, {"name": "15 others ...", "email": null}], "cautions": "<ul>\n<li>Data are the product of modeling and thus have an inherent degree of error and uncertainty. Users are strongly encouraged to read and fully comprehend the metadata and other available documentation prior to data use. </li>\n<li>Values are applicable to forest areas (canopy cover &gt;30 percent and &gt;5 m height or areas with tree cover gain). See Harris et al. (2021) for further information on the forest definition used in the analysis. </li>\n<li>Carbon removals reflect the total removals over the model period of 2001-2023, not an annual time series from which a trend can be derived. Thus, values must be divided by 23 to calculate average annual removals. </li>\n<li>Uncertainty is higher in gross removals than emissions, particularly driven by uncertainty in removal factors. </li>\n<li>Carbon removals reflect a gross estimate, i.e., carbon emissions from previous or subsequent loss of tree cover are not included. Instead, gross carbon emissions are accounted for in the companion <a href=\"/datasets/gfw-forest-carbon-gross-emissions\">forest carbon emissions layer</a>.</li>\n<li>Removals data contain temporal inconsistencies because tree cover gain represents a cumulative total from 2000-2020, rather than annual gains as estimated through 2023. </li>\n<li>Forest carbon removals reflect those occurring only within forest ecosystems and do not reflect carbon stock increases in the harvested wood products (HWP) pool. </li>\n<li>Large jumps in removals along some boundaries are due to the use of ecozone-specific removal factors. The changes in removals occur at ecozone boundaries, where different removal factors are applied on each side. </li>\n<li>This dataset has been updated since its original publication.</li>\n</ul>", "citation": "Harris, N.L., D.A. Gibbs, A. Baccini, R.A. Birdsey, S. de Bruin, M. Farina, L. Fatoyinbo, M.C. Hansen, M. Herold, R.A. Houghton, P.V. Potapov, D. Requena Suarez, R.M. Roman-Cuesta, S.S. Saatchi, C.M. Slay, S.A. Turubanova, A. Tyukavina. 2021. Global maps of twenty-first century forest carbon fluxes. Nature Climate Change. https://doi.org/10.1038/s41558-020-00976-6", "creator_user_id": "17daa49a-aa7d-4fea-b856-d3bb9fae7cc8", "draft": false, "featured_dataset": false, "function": "", "has_chart_views": false, "id": "490d4774-ee8d-4cee-b04a-cf2103aa254f", "is_approved": true, "isopen": true, "language": "en", "learn_more": "https://www.globalforestwatch.org/blog/data/whats-new-carbon-flux-monitoring/", "license_id": "cc-by", "license_title": "Creative Commons Attribution", "license_url": "http://www.opendefinition.org/licenses/cc-by", "maintainer": null, "maintainer_email": null, "maintainers": [{"name": "David Gibbs", "email": "david.gibbs@wri.org"}], "metadata_created": "2024-10-10T20:05:31.664854", "metadata_modified": "2025-10-22T20:45:59.723917", "methodology": "<p>The methodology and analytical software used to create these data are available on <a href=\"https://github.com/wri/carbon-budget\">WRI's GitHub</a> and continue to be developed and improved over time.</p>\n<p>This dataset estimates global forest carbon flux following <a href=\"https://www.ipcc.ch/report/2019-refinement-to-the-2006-ipcc-guidelines-for-national-greenhouse-gas-inventories/\">2019 IPCC guidelines for national greenhouse gas inventories</a> as a methodological framework.\nThe approach separately maps greenhouse gas emissions as sources and carbon dioxide removals as sinks within global forest lands.\nThe location and dynamics of forest lands come from the Global Forest Change product, which maps forest extent, loss, and gain at 30-m resolution between 2001 and 2019.\nIntegrating spatially explicit emission and removal factors improves the accuracy of the carbon flux estimates in aggregate.\nEmission and removal factors are a result of ground and air/spaceborne observations on land use and management patterns, forest types and ages, fire history, and biomass and soil carbon stocks.</p>\n<p>Annual carbon removal is calculated within natural, semi-natural, and planted forests.\nSeparately gross annual emissions are calculated for five dominant <a href=\"https://doi.org/10.1126/science.aau3445\">drivers of forest disturbance</a>.\nTo calculate net flux the gross emissions and removals are differenced.\nThe resulting maps of net emissions (+) or removals (-) reveal patterns across highly local to international scales.</p>", "name": "gfw-forest-carbon-gross-removals", "notes": "<p>This carbon removals layer is part of the forest carbon flux model described in\u202f<a href=\"https://www.nature.com/articles/s41558-020-00976-6\">Harris et al. (2021)</a>.\nThis paper introduces a geospatial monitoring framework for estimating global forest carbon fluxes which can assist a variety of actors and organizations with tracking greenhouse gas fluxes from forests and in decreasing emissions or increasing removals by forests.\nForest carbon removals from the atmosphere (sequestration) by forest sinks represent the cumulative carbon captured (megagrams CO2/ha) by the growth of established and newly regrowing forests during the model period between 2001-2023.\nRemovals include accumulation of carbon in both aboveground and belowground live tree biomass.\nFollowing IPCC Tier 1 assumptions for forests remaining forests, removals by dead wood, litter, and soil carbon pools are assumed to be zero.\nIn each pixel, carbon removals are calculated following IPCC Guidelines for\u202f<a href=\"https://www.ipcc.ch/report/2019-refinement-to-the-2006-ipcc-guidelines-for-national-greenhouse-gas-inventories/\">national greenhouse gas inventories</a>\u202fwhere forests existed in 2000 or were established between 2000 and 2020 according to <a href=\"https://www.frontiersin.org/articles/10.3389/frsen.2022.856903/full\">Potapov et al. 2022</a>.\nAtmospheric carbon removed in each pixel is based on maps of forest type (e.g., mangrove, plantation), ecozone (e.g., humid Neotropics), forest age (e.g., primary, old secondary), and number of years of carbon removal.\nThis layer reflects the cumulative removals during the model period (2001-2023) and must be divided by 23 to obtain an annual average during the model duration; removal rates cannot be assigned to individual years of the model.\nAll input layers were resampled to a common resolution of 0.00025 x 0.00025 degrees each to match\u202f<a href=\"https://www.science.org/doi/10.1126/science.1244693\">Hansen et al. (2013)</a>.</p>\n<p><strong>Updates and changes</strong></p>\n<p>Each year the tree cover loss, <a href=\"https://gfw.global/39qbPdC\">drivers of tree cover loss</a>, and burned area are updated. In 2023 and 2024, a few model input data sets and constants were changed as well, as described below.\nPlease refer to <a href=\"https://www.globalforestwatch.org/blog/data/whats-new-carbon-flux-monitoring/\">this blog post</a> for more information. </p>\n<ol>\n<li>The source of the ratio between belowground biomass carbon and aboveground biomass carbon. Previously used one global constant; now uses map from <a href=\"https://essd.copernicus.org/articles/13/4263/2021/\">Huang et al. 2021</a></li>\n<li>The years of tree cover gain. Previously used 2000-2012; now uses 2000-2020 from <a href=\"https://www.frontiersin.org/articles/10.3389/frsen.2022.856903/full\">Potapov et al. 2022</a>. </li>\n<li>The source of fire data. Previously used MODIS burned area; now uses tree cover loss from fires from <a href=\"https://www.frontiersin.org/articles/10.3389/frsen.2022.825190/full\">Tyukavina et al. 2022</a>. </li>\n<li>The source of peat maps. New tropical data sets have been included and the data set above 40 degrees north has been changed. </li>\n<li>Global warming potential (GWP) constants for CH4 and N2O. Previously used GWPs from IPCC Fifth Assessment Report; now uses GWPs from IPCC <a href=\"https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_Chapter07.pdf\">Sixth Assessment Report</a>. </li>\n<li>Removal factors for older (&gt;20 years) secondary temperate forests and their associated uncertainties. Previously used removal factors published in <a href=\"https://www.ipcc-nggip.iges.or.jp/public/2019rf/pdf/4_Volume4/19R_V4_Ch04_Forest%20Land.pdf\">Table 4.9</a> of the 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories; now uses corrected removal factors and uncertainties from the 4th Corrigenda to the 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. </li>\n<li>Planted tree extent and removal factors. Previously used Spatial Database of Planted Trees (SDPT) <a href=\"https://www.wri.org/research/spatial-database-planted-trees-sdpt-version-10\">Version 1.0</a>; now uses <a href=\"https://www.wri.org/research/spatial-database-planted-trees-sdpt-version-2\">SDPT Version 2.0</a> and associated removal factors. </li>\n</ol>\n<p>Removals are available for download in two different area units over the model duration: 1) megagrams of CO2 removed/ha, and 2) megagrams of CO2 removed/pixel.\nThe first is appropriate for visualizing (mapping) removals because it represents the density of removals per hectare.\nThe second is appropriate for calculating the removals in an area of interest (AOI) because the values of the pixels in the AOI can be summed to obtain the total removals for that area.\nThe values in the latter were calculated by adjusting the removals per hectare by the size of each pixel, which varies by latitude.\nWhen estimating removals occurring over a defined number of years between 2001 and 2023 to compare to emissions, divide total carbon removals by the model duration and then multiply by the number of years in the period of interest.\nBoth datasets only include pixels within forests, as defined in the methods of Harris et al. (2021) and updated with tree cover gain through 2020.</p>", "num_resources": 273, "num_tags": 1, "open_in": "[{\"title\":\"Global Forest Watch\",\"url\":\"https://www.globalforestwatch.org/map/?map=eyJkYXRhc2V0cyI6W3siZGF0YXNldCI6ImNhcmJvbi1yZW1vdmFscyIsIm9wYWNpdHkiOjEsInZpc2liaWxpdHkiOnRydWUsImxheWVycyI6WyJjYXJib24tcmVtb3ZhbHMtMjAwMS0yMDIwIl19LHsiZGF0YXNldCI6InBvbGl0aWNhbC1ib3VuZGFyaWVzIiwibGF5ZXJzIjpbImRpc3B1dGVkLXBvbGl0aWNhbC1ib3VuZGFyaWVzIiwicG9saXRpY2FsLWJvdW5kYXJpZXMiXSwib3BhY2l0eSI6MSwidmlzaWJpbGl0eSI6dHJ1ZX1dfQ%3D%3D\"}]", "organization": {"id": "b13bb4e4-2d97-48fa-9cd2-6a051b6d892f", "name": "global-forest-watch", "title": "Global Forest Watch", "type": "organization", "description": "WRI's Global Forest Watch team includes researchers, application developers, and community engagement specialists who seek to protect natural forests by providing up to date information on the locations and causes of forest ecosystem damages worldwide.", "image_url": "gfw-malaysia-kalimantan-border-tropical-forest-1-vcfke8.jpg", "created": "2024-09-25T11:07:46.725373", "is_organization": true, "approval_status": "approved", "state": "active"}, "owner_org": "b13bb4e4-2d97-48fa-9cd2-6a051b6d892f", "private": false, "project": "", "rw_dataset": false, "rw_id": "", "short_description": "This gross removals layer is part of the forest carbon flux model described in Harris et al. (2021). This paper introduces a geospatial monitoring framework for estimating global forest carbon fluxes which can assist a variety of actors and organizations with tracking greenhouse gas fluxes from forests and in decreasing emissions or increasing removals by forests.", "spatial": "null", "spatial_address": "Global", "spatial_type": "global", "state": "active", "technical_notes": "https://doi.org/10.1038/s41558-020-00976-6", "title": "Forest Carbon Removals", "type": "dataset", "update_frequency": "annually", "version": null, "visibility_type": "public", "wri_data": true, "groups": [{"description": "Data related to Earth\u2019s atmosphere, including air quality, GHG fluxes from land, historical weather patterns, and climate model outputs.", "display_name": "Atmosphere", "id": "91fc44b8-b165-4fbf-805d-480ca371a268", "image_display_url": "https://datasets.wri.org/private-admin/uploads/group/atmosphere-mexico-city-volcanes-4iwmbs.png", "name": "atmosphere", "title": "Atmosphere", "type": "group"}, {"description": "Data related to tree cover, deforestation, reforestation, and forest biodiversity.", "display_name": "Forests", "id": "c74fae7a-9b54-4c02-b7b5-e3ca00bff8ef", "image_display_url": "", "name": "forests", "title": "Forests", "type": "group"}, {"description": "Global Forest Watch (GFW) is an online platform that provides data and tools for monitoring forests. By harnessing cutting-edge technology, GFW allows anyone to access near real-time information about where and how forests are changing around the world.", "display_name": "Global Forest Watch", "id": "c3429d38-c9ae-443a-8f7e-e20e6415153c", "image_display_url": "https://datasets.wri.org/private-admin/uploads/group/background-268968d448f189afbf5bfc47eb090ac4-5tjpmt.jpeg", "name": "global-forest-watch-app", "title": "Global Forest Watch", "type": "application", "contact_url": "https://www.globalforestwatch.org/about/?contactUs=true", "help_url": "https://www.globalforestwatch.org/help/", "homepage_url": "https://www.globalforestwatch.org/"}], "resources": [{"cache_last_updated": null, "cache_url": null, "created": "2024-10-10T20:05:31.680735", "datastore_active": false, "description": "Units of Mg_CO2e_ha-1", "format": "TIF", "hash": "", "id": "fb8f38ea-52a3-4f15-8165-80b347ef6051", "is_pending": false, "last_modified": null, "layerObj": null, "layerObjRaw": null, "metadata_modified": 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"created": "2024-10-10T20:05:31.680730", "datastore_active": false, "description": "GHG gross removals from forests. The location of \"forests\" is not universally defined, so this map layer can be parameterized for canopy densities exceeding 30%, 50%, or 75%.", "format": "Layer", "hash": "", "id": "ec5a7d46-e19b-4db5-8824-06e9d7f1652b", "last_modified": null, "layerObj": null, "layerObjRaw": null, "metadata_modified": "2025-10-22T20:45:59.769519", "mimetype": null, "mimetype_inner": null, "name": "Forest Carbon Gross Removals - 2001-2023", "new": false, "not_downloadable": false, "package_id": "490d4774-ee8d-4cee-b04a-cf2103aa254f", "position": 272, "resourceId": "ec5a7d46-e19b-4db5-8824-06e9d7f1652b", "resource_type": null, "rw_id": "73297b00-6c45-4652-8688-c29b46837fa1", "size": null, "state": "active", "title": "Forest Carbon Gross Removals - 2001-2023", "type": "reference-layer", "url": "https://api.resourcewatch.org/v1/dataset/d98dffe9-7ea3-46ca-b378-e89a4cd26bce/layer/73297b00-6c45-4652-8688-c29b46837fa1", "url_type": "reference-layer"}], "tags": [{"display_name": "ghg", "id": "7bd5aa96-9709-4ce7-a17f-e95739bfdf03", "name": "ghg", "state": "active", "vocabulary_id": null}], "relationships_as_subject": [], "relationships_as_object": []}, {"approval_status": "approved", "author": null, "author_email": null, "authors": [{"name": "Nancy Harris", "email": "nancy.harris@wri.org"}, {"name": "David Gibbs", "email": "david.gibbs@wri.org"}, {"name": "15 others ...", "email": null}], "cautions": "<ul>\n<li>This dataset has been updated since its original publication.</li>\n<li>Data are the product of modeling and thus have an inherent degree of error and uncertainty. Users are strongly encouraged to read and fully comprehend the metadata and other available documentation prior to data use.</li>\n<li>Values are applicable to forest areas only (canopy cover &gt;30 percent and &gt;5 m height or areas with tree cover gain). See\u202f<a href=\"https://www.nature.com/articles/s41558-020-00976-6\">Harris et al. (2021)</a>\u202ffor further information on the forest definition used in the analysis.</li>\n<li>Although emissions in each pixel are associated with a specific year of disturbance, emissions over an area of interest reflect the total over the model period of 2001-2023. Thus, values must be divided by 23 to calculate average annual removals.</li>\n<li>Emissions reflect stand-replacing disturbances as observed in Landsat satellite imagery and do not include emissions from unobserved forest degradation.</li>\n<li>Emissions reflect a gross estimate, i.e., carbon removals from any regrowth that occurs after disturbance are not included. Instead, gross carbon removals are accounted for in the companion <a href=\"/datasets/gfw-forest-carbon-gross-removals\">forest carbon removals layer</a>.</li>\n<li>Emissions data contain temporal inconsistencies. Improvements in the detection of tree cover loss due to the incorporation of new satellite data and methodology changes between 2011 and 2015 may result in higher estimates of emissions in recent years compared to earlier years. Refer <a href=\"https://www.globalforestwatch.org/blog/data/20-years-global-tree-cover-loss-data-trends/\">here</a> for additional information.</li>\n<li>Forest carbon emissions do not reflect carbon transfers from ecosystem carbon pools to the harvested wood products (HWP) pool.</li>\n</ul>", "citation": "Harris, N.L., D.A. Gibbs, A. Baccini, R.A. Birdsey, S. de Bruin, M. Farina, L. Fatoyinbo, M.C. Hansen, M. Herold, R.A. Houghton, P.V. Potapov, D. Requena Suarez, R.M. Roman-Cuesta, S.S. Saatchi, C.M. Slay, S.A. Turubanova, A. Tyukavina. 2021. Global maps of twenty-first century forest carbon fluxes. Nature Climate Change. https://doi.org/10.1038/s41558-020-00976-6", "creator_user_id": "17daa49a-aa7d-4fea-b856-d3bb9fae7cc8", "draft": false, "featured_dataset": false, "function": "", "has_chart_views": false, "id": "281e5565-82c1-43fb-9070-be6017c53e73", "is_approved": true, "isopen": true, "language": "en", "learn_more": "https://www.globalforestwatch.org/blog/data/whats-new-carbon-flux-monitoring/", "license_id": "cc-by", "license_title": "Creative Commons Attribution", "license_url": "http://www.opendefinition.org/licenses/cc-by", "maintainer": null, "maintainer_email": null, "maintainers": [{"name": "David Gibbs", "email": "david.gibbs@wri.org"}], "metadata_created": "2024-10-10T20:06:12.365260", "metadata_modified": "2025-10-22T20:39:54.978994", "methodology": "<p>The methodology and analytical software used to create these data are available on <a href=\"https://github.com/wri/carbon-budget\">WRI's GitHub</a> and continue to be developed and improved over time.</p>\n<p>This dataset estimates global forest carbon flux following <a href=\"https://www.ipcc.ch/report/2019-refinement-to-the-2006-ipcc-guidelines-for-national-greenhouse-gas-inventories/\">2019 IPCC guidelines for national greenhouse gas inventories</a> as a methodological framework.\nThe approach separately maps greenhouse gas emissions as sources and carbon dioxide removals as sinks within global forest lands.\nThe location and dynamics of forest lands come from the Global Forest Change product, which maps forest extent, loss, and gain at 30-m resolution between 2001 and 2019.\nIntegrating spatially explicit emission and removal factors improves the accuracy of the carbon flux estimates in aggregate.\nEmission and removal factors are a result of ground and air/spaceborne observations on land use and management patterns, forest types and ages, fire history, and biomass and soil carbon stocks.</p>\n<p>Annual carbon removal is calculated within natural, semi-natural, and planted forests.\nSeparately gross annual emissions are calculated for five dominant <a href=\"https://doi.org/10.1126/science.aau3445\">drivers of forest disturbance</a>.\nTo calculate net flux the gross emissions and removals are differenced.\nThe resulting maps of net emissions (+) or removals (-) reveal patterns across highly local to international scales.</p>", "name": "gfw-forest-carbon-gross-emissions", "notes": "<p>This emissions layer is part of the forest carbon flux model described in\u202f<a href=\"https://www.nature.com/articles/s41558-020-00976-6\">Harris et al. (2021)</a>.\nForest carbon emissions represent the greenhouse gas emissions arising from stand-replacing forest disturbances that occurred in each modeled year (megagrams CO2 emissions/ha, between 2001 and 2023).\nEmissions include all relevant ecosystem carbon pools (aboveground biomass, belowground biomass, dead wood, litter, soil organic carbon) and greenhouse gases (CO2, CH4, N2O).\nEmissions estimates for each pixel are calculated following IPCC Guidelines for\u202f<a href=\"https://www.ipcc.ch/report/2019-refinement-to-the-2006-ipcc-guidelines-for-national-greenhouse-gas-inventories/\">national greenhouse gas inventories</a>\u202fwhere stand-replacing disturbance occurred, as mapped in the Global Forest Change annual tree cover loss data of <a href=\"https://www.science.org/doi/10.1126/science.1244693\">Hansen et al. (2013)</a>.\nThe carbon emitted from each pixel is based on carbon densities in 2000, with adjustment for carbon accumulated between 2000 and the year of disturbance.</p>\n<p>Emissions reflect a gross estimate, i.e., carbon removals from subsequent regrowth are not included.\nInstead, gross carbon removals resulting from subsequent regrowth after clearing are accounted for\u202fin the companion <a href=\"/datasets/gfw-forest-carbon-gross-removals\">forest carbon removals layer</a>.\nThe fraction of carbon emitted from each pixel upon disturbance (emission factor) is affected by several factors, including the direct driver of disturbance, whether fire was observed in the year of or preceding the observed disturbance event,\u202fwhether the disturbance occurred on peat, and more.\nAll emissions are assumed to occur in the year of disturbance.\nEmissions can be assigned to a specific year using the Hansen tree cover loss data; separate rasters for emissions for each year are not available from GFW.\nAll input layers were resampled to a common resolution of 0.00025 \u00d7 0.00025 degrees each to match Hansen et al. (2013).</p>\n<p><strong>Updates and changes</strong></p>\n<p>Each year Global Forest Watch's tree cover loss, <a href=\"https://gfw.global/39qbPdC\">drivers of tree cover loss</a>, and burned area are updated.\nIn 2023 and 2024, a few model input data sets and constants were changed as well, as described below.\nPlease refer to <a href=\"https://www.globalforestwatch.org/blog/data/whats-new-carbon-flux-monitoring/\">this blog post</a> for more information.</p>\n<ol>\n<li>The source of the ratio between belowground biomass carbon and aboveground biomass carbon.\nPreviously used one global constant; now uses map from <a href=\"https://essd.copernicus.org/articles/13/4263/2021/\">Huang et al. 2021</a></li>\n<li>The years of tree cover gain. Previously used 2000-2012; now uses 2000-2020 from <a href=\"https://www.frontiersin.org/articles/10.3389/frsen.2022.856903/full\">Potapov et al. 2022</a>.</li>\n<li>The source of fire data. Previously used MODIS burned area; now uses tree cover loss from fires from <a href=\"https://www.frontiersin.org/articles/10.3389/frsen.2022.825190/full\">Tyukavina et al. 2022</a>.</li>\n<li>The source of peat maps. New tropical data sets have been included and the data set above 40 degrees north has been changed.</li>\n<li>Global warming potential (GWP) constants for CH4 and N2O. Previously used GWPs from IPCC Fifth Assessment Report; now uses GWPs from IPCC <a href=\"https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_Chapter07.pdf\">Sixth Assessment Report</a>.</li>\n<li>Removal factors for older (&gt;20 years) secondary temperate forests and their associated uncertainties. Previously used removal factors published in <a href=\"https://www.ipcc-nggip.iges.or.jp/public/2019rf/pdf/4_Volume4/19R_V4_Ch04_Forest%20Land.pdf\">Table 4.9</a> of the 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories; now uses corrected removal factors and uncertainties from the 4th Corrigenda to the 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories.</li>\n<li>Planted tree extent and removal factors. Previously used Spatial Database of Planted Trees (SDPT) <a href=\"https://www.wri.org/research/spatial-database-planted-trees-sdpt-version-10\">Version 1.0</a>; now uses <a href=\"https://www.wri.org/research/spatial-database-planted-trees-sdpt-version-2\">SDPT Version 2.0</a> and associated removal factors.</li>\n</ol>\n<p>Emissions are available for download in two different area units: 1) megagrams of CO2 emissions/ha, and 2) megagrams of CO2 emissions/pixel.\nThe first is appropriate for visualizing (mapping) emissions because it represents the density of emissions per hectare.\nThe second is appropriate for calculating the emissions in an area of interest (AOI) because the values of the pixels in the AOI can be summed to obtain the total emissions for that area.\nThe values in the latter were calculated by adjusting the emissions per hectare by the size of each pixel, which varies by latitude.\nBoth datasets only include pixels within forests, as defined in the methods of Harris et al. 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A thorough reading of the methodology document can help users evaluate how to interpret the results.</li>\n<li>These are estimates up to the year 2020, for which there is sufficient information about the composition of school bus fleets. Operational changes and the addition of new school bus models after 2020 will have life cycle impacts not accounted here.</li>\n<li>Estimated health impacts of diesel buses only include mortality due to chronic PM2.5 exposure despite the known substantive effects from NOx emissions. Brake- and tire-wear are known to be significant sources of particulate matter but are assumed equal between diesel and electric buses despite operational and weight differences between the classes.</li>\n</ul>", "citation": "Oztaner, Y.B., M. Soltanzadeh, A. Todd, B. Zepka, and A. Hakami. 2025. \u201cModeling the societal health and climate benefits associated with transitioning the US school bus fleet from diesel to electric.\u201d Technical Note. Washington, DC: World Resources Institute. https://doi.org/10.46830/writn.24.00024.", "creator_user_id": "17daa49a-aa7d-4fea-b856-d3bb9fae7cc8", "draft": false, "featured_dataset": false, "function": "", "has_chart_views": false, "id": "de8149ed-9fba-42c1-999c-881fd580cb51", "is_approved": true, "isopen": true, "language": "en", "learn_more": "https://www.wri.org/insights/electric-school-bus-health-climate-benefits", "license_id": "cc-by", "license_title": "Creative Commons Attribution", "license_url": "http://www.opendefinition.org/licenses/cc-by", "maintainer": null, "maintainer_email": null, "maintainers": [{"name": "Electric School Bus Initiative", "email": "esbinfo@wri.org"}], "metadata_created": "2025-10-21T10:31:55.133526", "metadata_modified": "2025-10-21T10:31:59.391385", "methodology": "<p>This modeled data builds on the <a href=\"/datasets/usa-school-bus-fleets\">Dataset of U.S. School Bus Fleets</a> constructed by the Electric School Bus Initiative.</p>\n<p>Estimating the climate impact of a school bus, diesel or electric, is performed by estimating GHG emissions associated with its operation in a straightforward process.\nFor such impacts the effects are not dependent on the location of the release of the GHGs.\nIn contrast the health impacts of pollutants such as fine particulate matter are highly location dependent.\nThe complexity and value of this dataset is primarily in the population health impact estimates and methodological approach.</p>\n<p>The data is the result of a reverse source influence model that estimates the impact of emissions associated with school buses in various counties in the contiguous United States.\nReverse source influence modeling uses an augmented version of a photochemical air quality model (AQM), known as an adjoint model, that traces the overall health impact on the entire population back to individual polluting sources at all locations.\nAs described in the methodology document the model starts from the overall population health impact as characterized by the combination of a regular AQM and an epidemiological model then attributes these impacts to emissions from each school bus at each location (i.e., each US county).\nInformation about input data and models to the impact model including meteorological and emission models is fully described in the WRI Technical Note.</p>", "name": "usa-school-bus-fleet-electrification-impacts", "notes": "<p>This dataset is the output of a model that quantifies the health and climate impacts of replacing aging diesel school buses with either new diesel or new electric buses, incorporating both upstream and tailpipe emissions at the US county level.\nUsing reverse source influence modeling, the research estimates the societal benefits of various fleet renewal scenarios.\nKey questions include which regions would benefit most from electrification and what the national emissions would be under different replacement strategies.</p>\n<p>The findings are intended to inform policymakers, school districts, manufacturers, and utilities by providing monetized estimates of health and climate impacts, enabling data-driven decisions about school bus fleet modernization.</p>\n<p>The datasets produced include the following:</p>\n<ul>\n<li>Diesel school bus impacts are calculated as unit impacts and total impacts.</li>\n<li>Unit impacts of buses for each county are provided per ton-CO2 emitted (i.e., co-benefits, in units of $/ton-CO2 ), per school bus ($/bus-year), and per VMT ($/1,000 VMT).</li>\n<li>Total impacts for each county are impacts from all buses in a model year or subfleet category in that county and are expressed in units of $/year. Total impacts are broken down to health, climate, or overall (health + climate) impacts.</li>\n<li>Electrification impacts of a transition to electric school buses is provided to enable the societal benefits of replacing diesel school buses with electric school buses. Electrification benefits are made up of both climate and health benefits of school bus replacement. Similar to the diesel school bus results, these benefits are provided for all US counties, and as unit impacts (per bus or per VMT) and total impacts ($/year) for model year or subfleet categories.</li>\n</ul>\n<p>The data is distributed as a set of Microsoft Excel files breaking the data down by fleet characteristics related to the year of the diesel school bus.\nThe county-level fleet is available for each year or in clusters of years corresponding to pre-2000, pre-2007, and post-2010.</p>", "num_resources": 1, "num_tags": 4, "open_in": "[{\"title\":\"Electric School Bus Initiative Dashboard\",\"url\":\"https://electricschoolbusinitiative.org/electric-school-bus-data-dashboard\"}]", "organization": {"id": "164c340b-5cf2-49da-98f9-024fdb7e0e42", "name": "electric-school-bus-initiative", "title": "Electric School Bus Initiative", "type": "organization", "description": "WRI's Electric School Bus Initiative accelerates the adoption of electric school buses to deliver on improved health and environmental outcomes in communities across the United States of America.", "image_url": "students-boarding-school-bus-m3277f.jpg", "created": "2024-09-24T18:14:31.852003", "is_organization": true, "approval_status": "approved", "state": "active"}, "owner_org": "164c340b-5cf2-49da-98f9-024fdb7e0e42", "private": false, "rw_dataset": false, "short_description": "Quantification in dollar value of the impacts of transitioning school bus operations from diesel to electric for each county in the contiguous United States.", "spatial_address": "United States", "spatial_type": "address", "state": "active", "technical_notes": "https://doi.org/10.46830/writn.24.00024", "title": "Societal Health and Climate Benefits Associated With Transitioning the US School Bus Fleet From Diesel to Electric", "type": "dataset", "update_frequency": "not_planned", "version": null, "visibility_type": "public", "wri_data": true, "groups": [{"description": "Data related to Earth\u2019s atmosphere, including air quality, GHG fluxes from land, historical weather patterns, and climate model outputs.", "display_name": "Atmosphere", "id": "91fc44b8-b165-4fbf-805d-480ca371a268", "image_display_url": "https://datasets.wri.org/private-admin/uploads/group/atmosphere-mexico-city-volcanes-4iwmbs.png", "name": "atmosphere", "title": "Atmosphere", "type": "group"}, {"description": "Data concerning urban spaces and systems.", "display_name": "Cities", "id": "80539133-5aaa-4257-9aeb-fe6f56e2837f", "image_display_url": "https://datasets.wri.org/private-admin/uploads/group/children-biking-calle-colombia-2-qy6gqa.jpeg", "name": "cities", "title": "Cities", "type": "group"}], "resources": [{"cache_last_updated": null, "cache_url": null, "created": "2025-10-21T10:31:59.425043", "datastore_active": false, "description": "MS Excel documents (23 MB).", "format": "ZIP", "hash": "", "id": "f072f149-9953-4dce-b09e-57130a482e66", "last_modified": "2025-10-21T10:31:59.207920", "metadata_modified": "2025-10-21T10:31:59.403303", "mimetype": "application/zip", "mimetype_inner": null, "name": "Zipfile", "package_id": "de8149ed-9fba-42c1-999c-881fd580cb51", "position": 0, "resource_type": null, "size": 23936665, "state": "active", "title": "Zipfile", "url": "https://datasets.wri.org/private-admin/dataset/de8149ed-9fba-42c1-999c-881fd580cb51/resource/f072f149-9953-4dce-b09e-57130a482e66/download/usa-school-bus-fleet-electrification-impacts.zip", "url_type": "upload"}], "tags": [{"display_name": "ESBi", "id": "91348ad6-1919-456e-8388-c9da333ea9cf", "name": "ESBi", "state": "active", "vocabulary_id": null}, {"display_name": "USA", "id": "7c835d57-3d9c-4615-8e2b-40040119365d", "name": "USA", "state": "active", "vocabulary_id": null}, {"display_name": "electric-vehicles", "id": "96d0ef73-5cc6-4c25-adde-cfa65241f7d8", "name": "electric-vehicles", "state": "active", "vocabulary_id": null}, {"display_name": "schoolbus", "id": "7fac75a7-0532-4ca3-a257-699074499514", "name": "schoolbus", "state": "active", "vocabulary_id": null}], "relationships_as_subject": [], "relationships_as_object": []}, {"approval_status": "approved", "author": null, "author_email": null, "authors": [{"name": "Nancy Harris", "email": "nancy.harris@wri.org"}, {"name": "David Gibbs", "email": "david.gibbs@wri.org"}, {"name": "15 others ...", "email": null}], "cautions": "<p>Data are the product of modeling and thus have an inherent degree of error and uncertainty.\nUsers are strongly encouraged to read and fully comprehend the metadata and other available documentation prior to data use.</p>\n<ul>\n<li>Net flux reflects the total over the model period of 2001-2023, not an annual time series from which a trend can be derived. Thus, values must be divided by 23 to calculate average annual net flux.</li>\n<li>Uncertainty is higher in gross removals than emissions, particularly driven by uncertainty in removal factors. These uncertainties are propagated to the uncertainty in net flux.</li>\n<li>Values are applicable to forest areas (canopy cover &gt;30 percent and &gt;5 m height). See Harris et al. (2021) for further information on the forest definition used in the analysis.</li>\n<li>Emissions reflect stand-replacing disturbances as observed in Landsat satellite imagery and do not include emissions from unobserved forest degradation.</li>\n<li>Activity data used as the basis of the estimates contain temporal inconsistencies:</li>\n<li>Removals data contain temporal inconsistencies because tree cover gain represents a cumulative total from 2000-2020, rather than annual gains as estimated through 2023.</li>\n<li>Improvements in the detection of tree cover loss due to the incorporation of new satellite data and methodology changes between 2011 and 2015 may result in higher estimates of emissions in recent years compared to earlier years. Refer <a href=\"https://www.globalforestwatch.org/blog/data/20-years-global-tree-cover-loss-data-trends/\">here</a> for additional information.</li>\n<li>Large jumps in net flux along some boundary are due to the use of ecozone-specific removal factors. The changes in net flux occur at ecozone boundaries, where different removal factors are applied on each side.</li>\n<li>This dataset has been updated since its original publication. See Overview for more information.</li>\n</ul>", "citation": "Harris, N.L., D.A. Gibbs, A. Baccini, R.A. Birdsey, S. de Bruin, M. Farina, L. Fatoyinbo, M.C. Hansen, M. Herold, R.A. Houghton, P.V. Potapov, D. Requena Suarez, R.M. Roman-Cuesta, S.S. Saatchi, C.M. Slay, S.A. Turubanova, A. Tyukavina. 2021. Global maps of twenty-first century forest carbon fluxes. Nature Climate Change. <https://doi.org/10.1038/s41558-020-00976-6>", "creator_user_id": "17daa49a-aa7d-4fea-b856-d3bb9fae7cc8", "draft": false, "featured_dataset": false, "function": "Displays the net loss of forest ecosystem carbon, calculated as the difference between forest carbon emissions from stand-replacing forest disturbances and carbon removals from forest growth.", "has_chart_views": false, "id": "2fdaa6b5-006f-4d15-8e30-832350644378", "is_approved": true, "isopen": true, "language": "en", "learn_more": "https://www.globalforestwatch.org/blog/data/whats-new-carbon-flux-monitoring/", "license_id": "cc-by", "license_title": "Creative Commons Attribution", "license_url": "http://www.opendefinition.org/licenses/cc-by", "maintainer": null, "maintainer_email": null, "maintainers": [{"name": "David Gibbs", "email": "david.gibbs@wri.org"}], "metadata_created": "2024-10-10T20:04:34.103124", "metadata_modified": "2025-10-15T18:35:22.965813", "methodology": "<p>The methodology and analytical software used to create these data are available on <a href=\"https://github.com/wri/carbon-budget\">WRI's GitHub</a> and continue to be developed and improved over time.</p>\n<p>This dataset estimates global forest carbon flux following <a href=\"https://www.ipcc.ch/report/2019-refinement-to-the-2006-ipcc-guidelines-for-national-greenhouse-gas-inventories/\">2019 IPCC guidelines for national greenhouse gas inventories</a> as a methodological framework.\nThe approach separately maps greenhouse gas emissions as sources and carbon dioxide removals as sinks within global forest lands.\nThe location and dynamics of forest lands come from the Global Forest Change product, which maps forest extent, loss, and gain at 30-m resolution between 2001 and 2019.\nIntegrating spatially explicit emission and removal factors improves the accuracy of the carbon flux estimates in aggregate.\nEmission and removal factors are a result of ground and air/spaceborne observations on land use and management patterns, forest types and ages, fire history, and biomass and soil carbon stocks.</p>\n<p>Annual carbon removal is calculated within natural, semi-natural, and planted forests.\nSeparately gross annual emissions are calculated for five dominant <a href=\"https://doi.org/10.1126/science.aau3445\">drivers of forest disturbance</a>.\nTo calculate net flux the gross emissions and removals are differenced.\nThe resulting maps of net emissions (+) or removals (-) reveal patterns across highly local to international scales.</p>", "name": "gfw-forest-carbon-net-flux", "notes": "<p>Net forest carbon flux represents the net loss of forest ecosystem carbon, calculated as the between carbon emitted by forests and removed by (or sequestered by) forests during the model period.\nNet carbon flux is calculated by subtracting average <a href=\"/datasets/gfw-forest-carbon-gross-removals\">gross removals</a> from annual <a href=\"/datasets/gfw-forest-carbon-gross-emissions\">gross emissions</a> in each forested pixel; negative values are where forests were net sinks of carbon and positive values are where forests were net sources of carbon between 2001 and 2023.</p>", "num_resources": 273, "num_tags": 1, "open_in": "[{\"title\":\"Global Forest Watch\",\"url\":\"https://www.globalforestwatch.org/map/?map=eyJjZW50ZXIiOnsibGF0IjoxNC44ODA2NDQ2MDI3NjY0NTIsImxuZyI6LTQwLjczNDM3NDk5OTk5NjU1fSwiZGF0YXNldHMiOlt7ImRhdGFzZXQiOiJuZXQtY2FyYm9uLWZsdXgiLCJvcGFjaXR5IjoxLCJ2aXNpYmlsaXR5Ijp0cnVlLCJsYXllcnMiOlsibmV0LWNhcmJvbi1mbHV4LTIwMDEtMjAyMCJdfSx7ImRhdGFzZXQiOiJwb2xpdGljYWwtYm91bmRhcmllcyIsImxheWVycyI6WyJkaXNwdXRlZC1wb2xpdGljYWwtYm91bmRhcmllcyIsInBvbGl0aWNhbC1ib3VuZGFyaWVzIl0sIm9wYWNpdHkiOjEsInZpc2liaWxpdHkiOnRydWV9XX0%3D&mapMenu=eyJkYXRhc2V0Q2F0ZWdvcnkiOiJjbGltYXRlIn0%3D\"}]", "organization": {"id": "b13bb4e4-2d97-48fa-9cd2-6a051b6d892f", "name": "global-forest-watch", "title": "Global Forest Watch", "type": "organization", "description": "WRI's Global Forest Watch team includes researchers, application developers, and community engagement specialists who seek to protect natural forests by providing up to date information on the locations and causes of forest ecosystem damages worldwide.", "image_url": "gfw-malaysia-kalimantan-border-tropical-forest-1-vcfke8.jpg", "created": "2024-09-25T11:07:46.725373", "is_organization": true, "approval_status": "approved", "state": "active"}, "owner_org": "b13bb4e4-2d97-48fa-9cd2-6a051b6d892f", "private": false, "project": "", "rw_dataset": false, "rw_id": "", "short_description": "This net flux layer is part of the forest carbon flux model described in Harris et al. (2021). This paper introduces a geospatial monitoring framework for estimating global forest carbon fluxes which can assist a variety of actors and organizations with tracking greenhouse gas fluxes from forests and in decreasing emissions or increasing removals by forests.", "spatial": "null", "spatial_address": "Global", "spatial_type": "global", "state": "active", "technical_notes": "https://doi.org/10.1038/s41558-020-00976-6", "title": "Net Forest Carbon Flux", "type": "dataset", "update_frequency": "annually", "version": null, "visibility_type": "public", "wri_data": true, "groups": [{"description": "Data related to Earth\u2019s atmosphere, including air quality, GHG fluxes from land, historical weather patterns, and climate model outputs.", "display_name": "Atmosphere", "id": "91fc44b8-b165-4fbf-805d-480ca371a268", "image_display_url": "https://datasets.wri.org/private-admin/uploads/group/atmosphere-mexico-city-volcanes-4iwmbs.png", "name": "atmosphere", "title": "Atmosphere", "type": "group"}, {"description": "Data related to tree cover, deforestation, reforestation, and forest biodiversity.", "display_name": "Forests", "id": "c74fae7a-9b54-4c02-b7b5-e3ca00bff8ef", "image_display_url": "", "name": "forests", "title": "Forests", "type": "group"}, {"description": "Global Forest Watch (GFW) is an online platform that provides data and tools for monitoring forests. By harnessing cutting-edge technology, GFW allows anyone to access near real-time information about where and how forests are changing around the world.", "display_name": "Global Forest Watch", "id": "c3429d38-c9ae-443a-8f7e-e20e6415153c", "image_display_url": "https://datasets.wri.org/private-admin/uploads/group/background-268968d448f189afbf5bfc47eb090ac4-5tjpmt.jpeg", "name": "global-forest-watch-app", "title": "Global Forest Watch", "type": "application", "contact_url": "https://www.globalforestwatch.org/about/?contactUs=true", "help_url": "https://www.globalforestwatch.org/help/", "homepage_url": "https://www.globalforestwatch.org/"}], "resources": [{"cache_last_updated": null, "cache_url": null, "created": "2024-10-10T20:04:34.126182", "datastore_active": false, "description": "Units of Mg_CO2e_ha-1", "format": "TIF", "hash": "", "id": "0889941e-08e7-4884-81ad-e07438fd9864", "is_pending": false, "last_modified": null, "layerObj": null, "layerObjRaw": null, "metadata_modified": 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(2013).</li>\n<li>This dataset defines a tree according to both the height and crown diameter. Woody vegetation higher than 5 meters regardless of crown diameter, or between 3 and 5 meters with a minimum crown diameter of 5 meters is considered a tree. This definition is different from Hansen et al. (2013) which defines a tree as any vegetation at least 5 meters in height.</li>\n<li>The tropical tree cover dataset does not disambiguate plantation trees from non-plantation trees.</li>\n<li>Analyses or statistics derived over spatial regions smaller than 0.5 ha may not be accurate.</li>\n</ul>", "citation": "Brandt, Brandt, J., Ertel, J., Spore, J., & Stolle, F. (2023). Wall-to-wall mapping of tree extent in the tropics with sentinel-1 and sentinel-2. Remote Sensing of Environment, 292, 113574. https://doi.org/10.1016/j.rse.2023.113574", "creator_user_id": "17daa49a-aa7d-4fea-b856-d3bb9fae7cc8", "draft": false, "featured_dataset": true, "function": "", "has_chart_views": false, "id": "84267757-8274-4edf-ab2d-05ec43fdbd9b", "is_approved": true, "isopen": true, "language": "en", "learn_more": "https://www.globalforestwatch.org/blog/data-and-tools/tree-cover-data-comparison/", "license_id": "cc-by", "license_title": "Creative Commons Attribution", "license_url": "http://www.opendefinition.org/licenses/cc-by", "maintainer": null, "maintainer_email": null, "maintainers": [{"name": "John Brandt", "email": "john.brandt@wri.org"}], "metadata_created": "2024-10-10T20:18:53.551744", "metadata_modified": "2025-10-06T15:54:39.224682", "methodology": "<p>Please review the <a href=\"https://github.com/wri/sentinel-tree-cover/wiki/Product-Specifications#methodology\">product specification documentation</a> for comprehensive descriptions of the 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If comparing across geographies, refer to the figures and tables in the technical note identifying where regional data was used. </li>\n<li>The definitions of \"natural\" used in the map does not consider or define attributes such as importance of land for biodiversity or the quality of ecosystems.</li>\n</ul>", "citation": "Mazur, E., M. Sims, E. Goldman, M. Schneider, M.D. Pirri, C.R. Beatty, F. Stolle, Stevenson, M. 2024. \"SBTN Natural Lands Map v1: Technical Documentation\". Science Based Targets for Land Version 1-- Supplementary Material. Science Based Targets Network. https://sciencebasedtargetsnetwork.org/wp-content/uploads/2024/09/Technical-Guidance-2024-Step3-Land-v1-Natural-Lands-Map.pdf", "creator_user_id": "17daa49a-aa7d-4fea-b856-d3bb9fae7cc8", "draft": false, "featured_dataset": false, "function": "This dataset is intended to be used in the Science Based Targets Network target on \"no conversion of natural ecosystems.\"", "has_chart_views": false, "id": "80b8f4f3-cedb-4893-87b8-9e3ec4a40c33", "is_approved": true, "isopen": false, "language": "en", "learn_more": "https://sciencebasedtargetsnetwork.org/companies/take-action/set-targets/land-targets/", "license_id": "other-nc", "license_title": "Other (Non-Commercial)", "maintainer": null, "maintainer_email": null, "maintainers": [{"name": "Elise Mazur", "email": "elise.mazur@wri.org"}], "metadata_created": "2024-12-18T18:37:34.596051", "metadata_modified": "2025-10-06T15:54:23.027889", "methodology": "<p>All data processing is performed through Google Earth Engine and written in Javascript.\nThe scripts are available on GitHub:\u00a0<a href=\"https://github.com/wri/natural-lands-map\">https://github.com/wri/natural-lands-map</a>.\nThe full processing methodology is detailed in the SBTN technical documentation.</p>\n<p>To briefly describe the methodology to classify areas:</p>\n<ul>\n<li>Classification into natural and non-natural areas through a decision tree relied on both global and regional land use and land cover maps such as those generated by the University of Maryland (Global Land Use and Land Cover Change) and the European Space Agency (WorldCover) and others.</li>\n<li>Individual land classes are modified by spatial intersection, union, and differencing with global input data that has a focused scope such as known plantations (Spatial Database of Planted Trees v2) and crop maps (USGS GCEP30, WorldCereal)</li>\n<li>Natural forests and short vegetation have 0.5-hectare minimum mapping units applied to meet definitions and remove \"noise.\"</li>\n<li>Regional data superseded global data where available. Some reclassification of regional datasets were applied to improve consistency with the definitions used across datasets.</li>\n</ul>", "name": "sbtn-natural-lands-map", "notes": "<p>The SBTN Natural Lands Map is intended to be used in corporate target setting and evaluation that is aligned with the Science Based Targets Network (SBTN) framework.\nSBTN's Land Hub has developed version 1 of its voluntary corporate target setting methodology and a key target is \"no conversion of natural ecosystems.\"\nThis map enables companies and their stakeholders to estimate their natural land conversion by comparing this 2020 baseline to their current resource sourcing locations.</p>\n<p>The SBTN Natural Lands Map has a two-tier categorization system.\nThe first tier makes a binary disinction between \"natural\" and \"non-natural\" areas according to a very specific definition of natural ecosystems and land.\nThe second tier further refines the binary \"natural or not\" map with context about land use and land cover to provide 20 classes such as \"natural forest\", \"non-natural tree cover\", and \"non-natural cropland\".</p>\n<p>The map relies on over 20 datasets that have either global or regional coverage.\nEach dataset or class-dependent subsets of each dataset is utilized within an objective decision tree that results in per-pixel classification.\nThe map is produced with global coverage and 30-meter resolution reflecting the properties of some critical input data.</p>\n<p>The data is available for access and analysis within <a href=\"https://developers.google.com/earth-engine/datasets/catalog/WRI_SBTN_naturalLands_v1#description\">Google Earth Engine</a>.</p>\n<p>The following land classes are available in the second tier of classification:</p>\n<ul>\n<li>Natural - Forest</li>\n<li>Natural - Short vegetation</li>\n<li>Natural - Water</li>\n<li>Natural - Mangroves</li>\n<li>Natural - Bare</li>\n<li>Natural - Snow/Ice</li>\n<li>Natural - Wetland forest</li>\n<li>Natural - Peat forest</li>\n<li>Natural - Wetland short vegetation</li>\n<li>Natural - Peat short vegetation</li>\n<li>Non-natural - Cropland</li>\n<li>Non-natural - Built-up</li>\n<li>Non-natural - Tree cover</li>\n<li>Non-natural - Short vegetation</li>\n<li>Non-natural - Water</li>\n<li>Non-natural - Wetland tree cover</li>\n<li>Non-natural - Peat tree cover</li>\n<li>Non-natural - Wetland short vegetation</li>\n<li>Non-natural - Peat short vegetation</li>\n</ul>", "num_resources": 2, "num_tags": 2, "open_in": "[{\"title\":\"Google Earth Engine Application\",\"url\":\"https://wri-datalab.earthengine.app/view/sbtn-natural-lands\"},{\"title\":\"Google Earth Engine Data Catalog\",\"url\":\"https://developers.google.com/earth-engine/datasets/catalog/WRI_SBTN_naturalLands_v1#description\"}]", "organization": {"id": "ad461dbd-f646-4080-94a9-c10005c42a43", "name": "land-carbon-lab", "title": "Land & Carbon Lab", "type": "organization", "description": "WRI's Land & Carbon Lab creates datasets to support critical monitoring and decision making needs surrounding land use planning, carbon accounting, and other technical facets of human-land interactions.", "image_url": "1724353785-cover-ethiopia-mosaic-oqiid6.avif", "created": "2024-09-25T11:07:47.708772", "is_organization": true, "approval_status": "approved", "state": "active"}, "owner_org": "ad461dbd-f646-4080-94a9-c10005c42a43", "private": false, "rw_dataset": false, "short_description": "Natural Lands as defined for the Science Based Targets Network (SBTN) target on \"no conversion of natural ecosystems.\"", "spatial_address": "Global", "spatial_type": "address", "state": "active", "technical_notes": "https://sciencebasedtargetsnetwork.org/wp-content/uploads/2024/09/Technical-Guidance-2024-Step3-Land-v1-Natural-Lands-Map.pdf", "title": "SBTN Natural Lands Map", "type": "dataset", "update_frequency": "not_planned", "version": null, "visibility_type": "public", "wri_data": true, "groups": [{"description": "Data concerning land use, land cover, and terrestrial ecosystem dynamics along with the human and environmental drivers of our food, forests, and water systems.", "display_name": "Land", "id": "c1427a08-9de5-4182-9c05-b6a61fda3127", "image_display_url": "https://datasets.wri.org/private-admin/uploads/group/land-peru-amazon-river-jrjo7f.jpg", "name": "land", "title": "Land", "type": "group"}], "resources": [{"cache_last_updated": null, "cache_url": null, "created": "2024-12-18T18:37:34.603405", "datastore_active": false, "description": "The first tier of classification of natural land types.", "format": "Layer", "hash": "", "id": "70a4be84-bfaa-471b-b86e-f186b51af393", "last_modified": null, "metadata_modified": "2025-10-06T15:54:22.184604", "mimetype": null, "mimetype_inner": null, "name": "SBTN Natural Lands Map v1 (binary)", "package_id": "80b8f4f3-cedb-4893-87b8-9e3ec4a40c33", "position": 0, "resource_type": null, "rw_id": "3c722a40-85ee-4544-a850-607d49cc9b18", "size": null, "state": "active", "type": "reference-layer", "url": "https://api.resourcewatch.org/v1/dataset/2c6e8aa3-747d-48c5-b516-285f343a463e/layer/3c722a40-85ee-4544-a850-607d49cc9b18", "url_type": "reference-layer"}, {"cache_last_updated": null, "cache_url": null, "created": "2024-12-18T18:37:34.603413", "datastore_active": false, "description": "The second tier of classification of natural land types.", "format": "Layer", "hash": "", "id": "94588276-9ddf-4825-89ee-708701f95045", "last_modified": null, "metadata_modified": "2025-10-06T15:54:23.033146", "mimetype": null, "mimetype_inner": null, "name": "SBTN Natural Lands Map v1 (classification)", "package_id": "80b8f4f3-cedb-4893-87b8-9e3ec4a40c33", "position": 1, "resource_type": null, "rw_id": "7f6a4033-e702-407f-b28c-0743d700a54b", "size": null, "state": "active", "type": "reference-layer", "url": "https://api.resourcewatch.org/v1/dataset/2c6e8aa3-747d-48c5-b516-285f343a463e/layer/7f6a4033-e702-407f-b28c-0743d700a54b", "url_type": "reference-layer"}], "tags": [{"display_name": "Global Commons Alliance", "id": "d5141253-8d56-4f36-acd6-00527e0f37cb", "name": "Global Commons Alliance", "state": "active", "vocabulary_id": null}, {"display_name": "Supply chains", "id": "18a3c7ff-52db-49fd-a11f-a22e1ae179f2", "name": "Supply chains", "state": "active", "vocabulary_id": null}], "relationships_as_subject": [], "relationships_as_object": []}, {"approval_status": "approved", "author": null, "author_email": null, "authors": [{"name": "Jamie Tolan", "email": null}, {"name": "... and 15 others", "email": null}], "cautions": "<ul>\n<li>This data is based on very high resolution satellite images collected at varying times and atmospheric conditions. As a result the global map does not represent a snapshot in time. Auxiliary files available on AWS include imagery details.</li>\n<li>The machine learning model producing this data is trained and validated on a limited geographic range. Continued comparison and validation is needed to assess and contextualize errors and uncertainties in the estimates across all geographies and input imagery conditions.</li>\n<li>Field validation for very tall trees (&gt;25 meters) was not performed during the development of this dataset. This may affect derivatives of the data such as above ground carbon estimates.</li>\n<li>Terrain slope may influence canopy height estimates by way of modifying the length of a shadow versus totally flat ground. The machine learning model may produce estimates with systematic bias in areas depending on the properties of the surrounding terrain.</li>\n</ul>", "citation": "Tolan, J., Yang, H., Nosarzewski B., et al. (2024). Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on aerial lidar, Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2023.113888", "creator_user_id": "17daa49a-aa7d-4fea-b856-d3bb9fae7cc8", "draft": false, "featured_dataset": false, "function": "Canopy height maps provide spatial information about the elevation difference between the top of tree or other vegetative canopy and the ground underneath the canopy. Such information can be an input to estimates of biomass and carbon stocks.", "has_chart_views": false, "id": "3f3d5dc4-d1db-4fa4-b3cd-3d55136ada23", "is_approved": true, "isopen": true, "language": "en", "learn_more": "https://sustainability.atmeta.com/blog/2024/04/22/using-artificial-intelligence-to-map-the-earths-forests/", "license_id": "cc-by", "license_title": "Creative Commons Attribution", "license_url": "http://www.opendefinition.org/licenses/cc-by", "maintainer": null, "maintainer_email": null, "maintainers": [{"name": "John Brandt", "email": "john.brandt@wri.org"}], "metadata_created": "2024-12-17T19:28:58.980622", "metadata_modified": "2025-10-06T15:54:20.950951", "methodology": "<p>This dataset was created by machine learning analysis of 0.5 meter WorldView imagery from Maxar as released via their Vivid Basemap layer.\nThe underlying imagery spans nearly 600-thousand individual images across multiple years, with 80% of imagery being acquired between 2018 and 2020.\nTo estimate canopy height from the high resolution optical data a machine learning model was developed to relate the optical imagery to reference LiDAR data.\nBecause LiDAR data is expensive to acquire a self-supervised approach known as DiNOv2 was utilized to build high quality and global scale feature representations of the input imagery.\nA secondary model, called a dense prediction transformer, was then trained to relate the feature representations to 1-meter aerial Lidar data.</p>\n<p>The machine learning model weights and inference code is available on GitHub: <a href=\"https://github.com/facebookresearch/HighResCanopyHeight/\">https://github.com/facebookresearch/HighResCanopyHeight/</a>.</p>", "name": "meta-tree-canopy-height", "notes": "<p>This dataset contains estimates of the vertical distance between the ground and the tops of vegetative canopy for all land globally.\nThe canopy height map has sub-meter spatial resolution reflecting the very high resolution Maxar imagery that is used as input to the height estimations.</p>\n<p>The data is currently available in two open access locations - Google Earth Engine and an Amazon Web Services S3.\nThe dataset is in the range of 60 terabytes (TB) so it is suggested to access the data through one of these existing channels rather than obtain a full local copy of the dataset for your own use.</p>\n<p>In Google Earth Engine the dataset is available under the ImageCollection \"projects/meta-forest-monitoring-okw37/assets/CanopyHeight\".\nThe dataset is also described in the Google Earth Engine Community Catalog: <a href=\"https://gee-community-catalog.org/projects/meta_trees/\">https://gee-community-catalog.org/projects/meta_trees/</a></p>\n<p>In AWS the dataset is available as approximately 55-thousand square image tiles that are around 80 km per side near the equator.\nA third-party R module called \"chmloader\" exists to support the fetching and mosaicing of the GeoTIFF tiles provided by Meta on AWS.\nThis is one recommended approach to acquiring local copies of subsets of the data.\nPlease be aware this module was not developed by the authors of this dataset and users should inspect and understand the software prior to use.\nThe R module is available on GitHub: <a href=\"https://github.com/TESS-Laboratory/chmloader\">https://github.com/TESS-Laboratory/chmloader</a></p>", "num_resources": 2, "num_tags": 4, "open_in": "[{\"title\":\"Google Earth Engine Application\",\"url\":\"https://meta-forest-monitoring-okw37.projects.earthengine.app/view/canopyheight\"}]", "organization": {"id": "ad461dbd-f646-4080-94a9-c10005c42a43", "name": "land-carbon-lab", "title": "Land & Carbon Lab", "type": "organization", "description": "WRI's Land & Carbon Lab creates datasets to support critical monitoring and decision making needs surrounding land use planning, carbon accounting, and other technical facets of human-land interactions.", "image_url": "1724353785-cover-ethiopia-mosaic-oqiid6.avif", "created": "2024-09-25T11:07:47.708772", "is_organization": true, "approval_status": "approved", "state": "active"}, "owner_org": "ad461dbd-f646-4080-94a9-c10005c42a43", "private": false, "project": "", "rw_dataset": false, "rw_id": "", "short_description": "Global sub-meter canopy height maps from a machine learning model trained on aerial LiDAR and RGB imagery and applied to very high resolution satellite imagery.", "spatial": "null", "spatial_address": "Global City, Erbil, Iraq", "spatial_type": "address", "state": "active", "technical_notes": "https://doi.org/10.1016/j.rse.2023.113888", "title": "High Resolution Canopy Height Map", "type": "dataset", "update_frequency": "not_planned", "version": null, "visibility_type": "public", "wri_data": true, "groups": [{"description": "Data concerning land use, land cover, and terrestrial ecosystem dynamics along with the human and environmental drivers of our food, forests, and water systems.", "display_name": "Land", "id": "c1427a08-9de5-4182-9c05-b6a61fda3127", "image_display_url": "https://datasets.wri.org/private-admin/uploads/group/land-peru-amazon-river-jrjo7f.jpg", "name": "land", "title": "Land", "type": "group"}], "resources": [{"cache_last_updated": null, "cache_url": null, "created": "2024-12-17T19:28:58.990438", "datastore_active": false, "description": "The data is available from Meta through the Registry of Open Data on AWS.", "format": "", "hash": "", "id": "3a3c429a-0536-430b-9680-02ba89cd9ff5", "last_modified": null, "layerObj": null, "layerObjRaw": null, "metadata_modified": "2025-10-06T15:54:20.965687", "mimetype": null, "mimetype_inner": null, "name": "AWS Open Data Registry", "new": false, "not_downloadable": true, "package_id": "3f3d5dc4-d1db-4fa4-b3cd-3d55136ada23", "position": 0, "resourceId": "3a3c429a-0536-430b-9680-02ba89cd9ff5", "resource_type": null, "size": null, "spatial_address": null, "spatial_coordinates": null, "spatial_geom": null, "spatial_type": null, "state": "active", "title": "AWS Open Data Registry", "type": "link", "url": "https://registry.opendata.aws/dataforgood-fb-forests/", "url_type": "link"}, {"cache_last_updated": null, "cache_url": null, "connectorType": "rest", "connectorUrl": "", "created": "2024-12-17T19:28:58.990434", "datastore_active": false, "description": "Tree canopy height in meters.", "format": "Layer", "hash": "", "id": "ccfb0944-de45-4ea0-9438-c264af07f960", "last_modified": null, "layer": {"id": "9642ef60-e7e9-44df-bce8-c2add48141f4", "env": "production", "iso": [], "name": "Global Canopy Height map", "slug": "Global-Canopy-Height-map", "userId": "5b4fa642e995f09ac0e4a829", "dataset": "75e4feaa-8396-4f97-b48c-caf524a1af98", "default": false, "provider": "gee", "createdAt": "2024-12-12T14:44:02.871Z", "protected": false, "published": true, "updatedAt": "2024-12-12T14:55:03.624Z", "application": ["rw"], "description": "Tree canopy height in meters.", "layerConfig": {"body": {"sldValue": "<RasterSymbolizer><ColorMap 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"staticImageConfig": {}}, "layerObj": null, "layerObjRaw": null, "metadata_modified": "2025-10-06T15:54:20.965836", "mimetype": null, "mimetype_inner": null, "name": "Canopy Height Map", "new": false, "not_downloadable": false, "package_id": "3f3d5dc4-d1db-4fa4-b3cd-3d55136ada23", "position": 1, "provider": "cartodb", "resourceId": "ccfb0944-de45-4ea0-9438-c264af07f960", "resource_type": null, "rw_id": "ad6b562f-da7b-4707-963d-a7fdac92cd2e", "size": null, "state": "active", "tableName": "", "title": "Canopy Height Map", "type": "reference-layer", "url": "https://api.resourcewatch.org/v1/dataset/75e4feaa-8396-4f97-b48c-caf524a1af98/layer/ad6b562f-da7b-4707-963d-a7fdac92cd2e", "url_type": "reference-layer"}], "tags": [{"display_name": "Digital surface model", "id": "5fd9b897-5038-49a6-b656-d4d569e43fa1", "name": "Digital surface model", "state": "active", "vocabulary_id": null}, {"display_name": "Machine learning", "id": "bd190ecb-cb0f-4c96-bfd3-0c0b33d99e91", "name": "Machine learning", "state": "active", "vocabulary_id": null}, {"display_name": "Raster", "id": "02c5322e-f55b-4f58-b473-3f7cf2356a8d", "name": "Raster", "state": "active", "vocabulary_id": null}, {"display_name": "Trees", "id": "4d85cc9f-5c79-4576-be65-00689a2cc336", "name": "Trees", "state": "active", "vocabulary_id": null}], "relationships_as_subject": [], "relationships_as_object": []}, {"approval_status": "approved", "author": null, "author_email": null, "authors": [{"name": "Leandro Parente", "email": "leandro.parente@opengeohub.org"}, {"name": "Lindsey Sloat", "email": "lindsey.sloat@wri.org"}, {"name": "... and 18 others", "email": null}], "cautions": "<ul>\n<li>Many areas of the world are predicted to be cultivated grassland but are actually cropland or some other cyclical short vegetation land cover. The peer-reviewed publication should be consulted for known defects and geographic contextualization including regions of under or over-prediction for certain classes.</li>\n<li>The primary data products are two grassland class probability maps. The dominant grassland class map is based on balanced probability thresholds for each map which results in maps that likely have a conservative estimate for total grassland worldwide.</li>\n<li>The dominant grassland class maps preferentially select for the natural/semi-natural class over the cultivated class when the probability threshold for each is achieved. This is in part because the natural/semi-natural classification model had higher accuracy.</li>\n</ul>", "citation": "Parente, L., Sloat, L., Mesquita, V., et al. (2024). Annual 30-m maps of global grassland class and extent (2000\u20132022) based on spatiotemporal Machine Learning, Scientific Data. http://doi.org/10.1038/s41597-024-04139-6", "creator_user_id": "17daa49a-aa7d-4fea-b856-d3bb9fae7cc8", "draft": false, "featured_dataset": false, "function": "Probabalistic classification and time series to support tracking the intensity and drivers of conversion of land to cultivated grasslands and from natural / semi-natural grasslands into other land use systems.", "has_chart_views": false, "id": "ab526ddc-3954-438a-9a04-2fbb057fa53c", "is_approved": true, "isopen": true, "language": "en", "learn_more": "https://landcarbonlab.org/insights/first-global-annual-cultivated-natural-grassland-data", "license_id": "cc-by", "license_title": "Creative Commons Attribution", "license_url": "http://www.opendefinition.org/licenses/cc-by", "maintainer": null, "maintainer_email": null, "maintainers": [{"name": "Radost Stanimirova", "email": "radost.stanimirova@wri.org"}], "metadata_created": "2024-12-16T18:21:53.206593", "metadata_modified": "2025-10-06T15:54:19.750131", "methodology": "<p>These maps are produced through a complex data pipeline that uses satellite remote sensing products and labeled training data as critical inputs.\nOver <a href=\"/datasets/grassland-dynamics-training-labels\">2.3-million reference samples</a> have been collected using very high resolution imagery and combined with the bi-monthly Landsat ARD2 collection, long-term MODIS temperature and water vapor data, geometric temperature data, global terrain and elevation data, and maps of distance to key land structures such as roads and waterways.\nThe reference labels and Earth observation data are used for the training of spatiotemporal machine learning models with one model per land cover class.\nThese models are applied in a prediction step to create probabalistic estimates of land cover which are then balanced and harmonized to produce maps of the dominant grassland class.</p>\n<p>The full methodology is detailed in the manuscript and should be consulted to best understand specific definitions used in this data product.</p>\n<p>The software supporting the machine learning model development and inference is available on GitHub: <a href=\"https://github.com/wri/global-pasture-watch\">https://github.com/wri/global-pasture-watch</a>.</p>", "name": "grassland-dynamics", "notes": "<p>This product maps global grassland dynamics annually for 2000-2022 at 30 m spatial resolution.\nThe dataset showing the spatiotemporal distribution of cultivated and natural/semi-natural grassland classes was produced by using bi-monthly aggregates of GLAD Landsat ARD-2 image archive, accompanied by climatic, landform and proximity covariates, spatiotemporal machine learning (per-class random forest) and over <a href=\"/datasets/grassland-dynamics-training-labels\">2.3-million reference samples</a> visually interpreted in very high resolution imagery.\nThe suggested uses of data include (1) integration with other compatible land cover products and (2) tracking the intensity and drivers of conversion of land to cultivated grasslands and from natural / semi-natural grasslands into other land use systems.</p>\n<p>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:</p>\n<ul>\n<li>maximum of 50% tree canopy cover (greater than 5 meters),</li>\n<li>maximum of 70% of other woody vegetation (scrubs and open shrubland), and</li>\n<li>maximum of 50% active cropland cover in mosaic landscapes of cropland &amp; other vegetation.</li>\n</ul>\n<p>The grassland extent is classified into two classes:</p>\n<ul>\n<li><strong>Cultivated grassland</strong>: 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>\n<li><strong>Natural/semi-natural grassland</strong>: 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>\n</ul>\n<p>The dataset is organized in 69 global mosaics (23 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:</p>\n<ul>\n<li>probabilities of cultivated grassland (values range from 0\u2013100),</li>\n<li>probabilities of natural/semi-natural grassland (values range from 0\u2013100), and</li>\n<li>dominant class (other land cover, cultivated grassland, or natural/semi-natural grassland).</li>\n</ul>\n<p>These COGs are available in the OpenLandMap STAC: <a href=\"https://stac.openlandmap.org/gpw_ggc-30m/collection.json\">https://stac.openlandmap.org/gpw_ggc-30m/collection.json</a></p>\n<p>The data is available for visualization and analysis within Google Earth Engine by using the following ImageCollections:</p>\n<ul>\n<li><a href=\"https://developers.google.com/earth-engine/datasets/catalog/projects_global-pasture-watch_assets_ggc-30m_v1_grassland_c\">projects/global-pasture-watch/assets/ggc-30m/v1/grassland_c</a></li>\n<li><a href=\"https://developers.google.com/earth-engine/datasets/catalog/projects_global-pasture-watch_assets_ggc-30m_v1_cultiv-grassland_p\">projects/global-pasture-watch/assets/ggc-30m/v1/cultiv-grassland_p</a></li>\n<li><a href=\"https://developers.google.com/earth-engine/datasets/catalog/projects_global-pasture-watch_assets_ggc-30m_v1_nat-semi-grassland_p\">projects/global-pasture-watch/assets/ggc-30m/v1/nat-semi-grassland_p</a></li>\n</ul>\n<p>A Google Earth Engine Application is also available for inspecting the data and investigating custom probability thresholds for class selection: <a href=\"https://global-pasture-watch.projects.earthengine.app/view/ggc-30m\">https://global-pasture-watch.projects.earthengine.app/view/ggc-30m</a>.</p>\n<p>The grassland dynamics maps are created by the Global Pasture Watch (GPW) research consortium initiated by the Land &amp; Carbon Lab.\nGPW consists of experts from the World Resources Institute (WRI), OpenGeoHub Foundation, the Image Processing and GIS Laboratory at the Federal University of Goi\u00e1s (LAPIG/UFG), the International Institute for Applied Systems Analysis (IIASA), the German Center for Integrative Biodiversity Research (iDiv), Cornell University; and the Global Land Analysis and Discovery laboratory of the University of Maryland (GLAD).</p>", "num_resources": 4, "num_tags": 3, "open_in": "[{\"title\":\"Google Earth Engine\",\"url\":\"https://developers.google.com/earth-engine/datasets/publisher/global-pasture-watch\"}]", "organization": {"id": "ad461dbd-f646-4080-94a9-c10005c42a43", "name": "land-carbon-lab", "title": "Land & Carbon Lab", "type": "organization", "description": "WRI's Land & Carbon Lab creates datasets to support critical monitoring and decision making needs surrounding land use planning, carbon accounting, and other technical facets of human-land interactions.", "image_url": "1724353785-cover-ethiopia-mosaic-oqiid6.avif", "created": "2024-09-25T11:07:47.708772", "is_organization": true, "approval_status": "approved", "state": "active"}, "owner_org": "ad461dbd-f646-4080-94a9-c10005c42a43", "private": false, "rw_dataset": false, "short_description": "Global maps of grassland class created from satellite imagery with 30-meter spatial resolution and annual temporal resolution.", "spatial_address": "Global", "state": "active", "technical_notes": "https://doi.org/10.1038/s41597-024-04139-6", "title": "Annual 30-m maps of global grassland class and extent (2000\u20132022)", "type": "dataset", "update_frequency": "not_planned", "version": null, "visibility_type": "public", "wri_data": true, "groups": [{"description": "Data concerning land use, land cover, and terrestrial ecosystem dynamics along with the human and environmental drivers of our food, forests, and water systems.", "display_name": "Land", "id": "c1427a08-9de5-4182-9c05-b6a61fda3127", "image_display_url": "https://datasets.wri.org/private-admin/uploads/group/land-peru-amazon-river-jrjo7f.jpg", "name": "land", "title": "Land", "type": "group"}], "resources": [{"cache_last_updated": null, "cache_url": null, "created": "2024-12-16T18:21:53.213844", "datastore_active": false, "description": "Global raster with 3 classes - other land cover (0), cultivated (1), or natural/semi-natural grassland (2) based on balanced and harmonized thresholds for the grassland class probabilities.", "format": "Layer", "hash": "", "id": "16af6998-0343-4ea0-b2f0-a7d6424ba823", "last_modified": null, "metadata_modified": "2025-10-06T15:54:17.514186", "mimetype": null, "mimetype_inner": null, "name": "Cultivated and natural/semi-natural grassland map", "package_id": "ab526ddc-3954-438a-9a04-2fbb057fa53c", "position": 0, "resource_type": null, "rw_id": "8656f38d-f826-4660-b83f-f5c3eedbd430", "size": null, "state": "active", "type": "reference-layer", "url": "https://api.resourcewatch.org/v1/dataset/f127ef4d-91ae-4398-b4d7-57a157b31990/layer/8656f38d-f826-4660-b83f-f5c3eedbd430", "url_type": "reference-layer"}, {"cache_last_updated": null, "cache_url": null, "created": "2024-12-16T18:21:53.213852", "datastore_active": false, "description": "Probability between 0% and 100% that the land is natural/semi-natural grassland or pasture.", "format": "Layer", "hash": "", "id": "32417676-ff67-4113-8cf2-67d142227914", "last_modified": null, "metadata_modified": "2025-10-06T15:54:18.651560", "mimetype": null, "mimetype_inner": null, "name": "Natural/semi-natural probability", "package_id": "ab526ddc-3954-438a-9a04-2fbb057fa53c", "position": 1, "resource_type": null, "rw_id": "31f4b063-0a43-4cde-a178-8470b514fb51", "size": null, "state": "active", "type": "reference-layer", "url": "https://api.resourcewatch.org/v1/dataset/52ba9748-107f-4b2a-94ec-e9d5f16598ba/layer/31f4b063-0a43-4cde-a178-8470b514fb51", "url_type": "reference-layer"}, {"cache_last_updated": null, "cache_url": null, "created": "2024-12-16T18:21:53.213855", "datastore_active": false, "description": "Probability between 0% and 100% that the land is cultivated grassland or pasture.", "format": "Layer", "hash": "", "id": "64fe432d-4546-4808-b330-6c97287d6d98", "last_modified": null, "metadata_modified": "2025-10-06T15:54:19.760278", "mimetype": null, "mimetype_inner": null, "name": "Cultivated probability", "package_id": "ab526ddc-3954-438a-9a04-2fbb057fa53c", "position": 2, "resource_type": null, "rw_id": "c3b7723f-e1e4-4ae2-b7af-86b0115d0dd1", "size": null, "state": "active", "type": "reference-layer", "url": "https://api.resourcewatch.org/v1/dataset/4849f6a8-f43a-432a-96a0-aebfef74aeea/layer/c3b7723f-e1e4-4ae2-b7af-86b0115d0dd1", "url_type": "reference-layer"}, {"cache_last_updated": null, "cache_url": null, "created": "2024-12-16T18:21:53.213858", "datastore_active": false, "description": "This data product has been archived with Zenodo and you are able to access the GeoTIFFs using this link.", "format": "", "hash": "", "id": "cac29106-e4de-4815-8ef7-8dcd421aad2e", "last_modified": null, "metadata_modified": "2025-10-06T15:54:17.514437", "mimetype": null, "mimetype_inner": null, "name": "Cloud Optimized GeoTIFFs (COGs)", "package_id": "ab526ddc-3954-438a-9a04-2fbb057fa53c", "position": 3, "resource_type": null, "size": null, "spatial_address": null, "spatial_coordinates": null, "spatial_geom": null, "spatial_type": null, "state": "active", "title": "Cloud Optimized GeoTIFFs (COGs)", "url": "https://doi.org/10.5281/zenodo.13890401", "url_type": "link"}], "tags": [{"display_name": "Global Pasture Watch Consortium", "id": "230efe08-344a-463e-962c-76523382326a", "name": "Global Pasture Watch Consortium", "state": "active", "vocabulary_id": null}, {"display_name": "Raster", "id": "02c5322e-f55b-4f58-b473-3f7cf2356a8d", "name": "Raster", "state": "active", "vocabulary_id": null}, {"display_name": "STAC", "id": "0ada3f2d-f22a-411c-89cd-6474ebf57cf5", "name": "STAC", "state": "active", "vocabulary_id": null}], "relationships_as_subject": [], "relationships_as_object": []}, {"approval_status": "approved", "author": null, "author_email": null, "authors": [{"name": "Logan Byers", "email": "logan.byers@wri.org"}, {"name": "Johannes Friedrich", "email": null}, {"name": "Roman Hennig", "email": null}, {"name": "Aaron Kressig", "email": null}, {"name": "Xinyue Li", "email": null}, {"name": "Laura Malaguzzi Valeri", "email": null}], "cautions": "<p>The \"Primary Fuel\" is the fuel that has been identified to provide the largest portion of generated electricity for the plant or has been identified as the primary fuel by the data source.</p>\n<p>For power plants that have data in multiple Other Fuel fields, the ordering of the fuels should not be taken to indicate any priority or preference of the fuel for operating the power plant or generating units.\nThough the \"Other Fuel\" columns in the database are numbered sequentially from 1, the ordering is insignificant.\nGeneration is provided at the year scale for the years 2013-2017.\nThe generation values may correspond to a calendar year or a fiscal or regulatory year; no distinction is provided in the database.</p>", "citation": "Global Energy Observatory, Google, KTH Royal Institute of Technology in Stockholm, Enipedia, World Resources Institute. 2021. Global Power Plant Database version 1.3.0. Accessed through https://datasets.wri.org/datasets/global-power-plant-database on <date>", "creator_user_id": "17daa49a-aa7d-4fea-b856-d3bb9fae7cc8", "draft": false, "featured_dataset": false, "function": "A comprehensive, global, open source database of power plants.", "has_chart_views": false, "id": "53623dfd-3df6-4f15-a091-67457cdb571f", "is_approved": true, "isopen": true, "language": "en", "learn_more": "http://www.wri.org/publication/global-power-plant-database", "license_id": "cc-by", "license_title": "Creative Commons Attribution", "license_url": "http://www.opendefinition.org/licenses/cc-by", "maintainer": null, "maintainer_email": null, "maintainers": [{"name": "Logan Byers", "email": "logan.byers@wri.org"}], "metadata_created": "2024-10-16T13:22:14.708510", "metadata_modified": "2025-10-06T15:54:16.241513", "methodology": "<p>The Global Power Plant Database leverages existing data sources and methodologies to build a comprehensive and open-access power sector database.\nThe database was built entirely from open sources, which are publicly available on the internet, including data from national government agencies, reports from companies that build power plants or provide their components, data from public utilities, and information from multinational organizations.\nCurrently the Global Power Plant Database uses more than 600 sources to create the database.</p>", "name": "global-power-plant-database", "notes": "<p>The Global Power Plant Database is a comprehensive, open source database of power plants around the world.\nIt centralizes power plant data to make it easier to navigate, compare, and draw insights for one\u2019s own analysis.\nThe database covers approximately 30,000 power plants from 164 countries and includes thermal plants (e.g., coal, gas, oil, nuclear, biomass, waste, geothermal) and renewables (e.g., hydro, wind, solar).\nEach power plant is geolocated, and entries contain information on plant capacity, generation, ownership, and fuel type.</p>", "num_resources": 4, "num_tags": 1, "open_in": "[]", "organization": {"id": "afe1d718-7853-4a88-9386-cd81459f0519", "name": "climate-economics-finance", "title": "Climate, Economics, and Finance", "type": "organization", "description": "WRI's Climate, Economics, and Finance team uses research and data to inform the direction of progress on global and local greenhouse gas emissions and attempts to shift financial and governance systems towards more environmentally positive results.", "image_url": "unfccc-1-oo2qts.jpg", "created": "2024-09-25T11:07:45.706411", "is_organization": true, "approval_status": "approved", "state": "active"}, "owner_org": "afe1d718-7853-4a88-9386-cd81459f0519", "private": false, "rw_dataset": false, "short_description": "A comprehensive, global, open source database of power plants.", "spatial_address": "Global", "state": "active", "technical_notes": "http://www.wri.org/publication/global-power-plant-database", "title": "Global Power Plant Database", "type": "dataset", "update_frequency": "not_planned", "version": null, "visibility_type": "public", "wri_data": true, "extras": [{"key": "application", "value": "rw"}], "groups": [{"description": "Data pertaining to how economies function and the drivers of production and consumption patterns.", "display_name": "Economy & Industry", "id": "bcf5398c-4880-4678-a934-f741e1b426bc", "image_display_url": "https://datasets.wri.org/private-admin/uploads/group/industry-woman-working-1-gcjlff.jpg", "name": "economy-industry", "title": "Economy & Industry", "type": "group"}, {"description": "Data about the energy systems that underpin all economies.", "display_name": "Power Sector", "id": "a82e7d91-8c0f-429f-b145-ab7f52ea9723", "image_display_url": "", "name": "power-sector", "title": "Power Sector", "type": "group"}], "resources": [{"cache_last_updated": null, "cache_url": null, "created": "2024-10-16T13:22:14.713319", "datastore_active": false, "description": "Global power plants by capacity in megawatts and fuel type. Includes coal, oil, gas, hydro, nuclear, solar, waste, wind, geothermal, and biomass.", "format": "Layer", "hash": "", "id": "67b08651-b6a9-4765-be71-ebca1692c5f7", "last_modified": null, "metadata_modified": "2025-10-06T15:54:16.251516", "mimetype": null, "mimetype_inner": null, "name": "Global Power Plant Database", "package_id": "53623dfd-3df6-4f15-a091-67457cdb571f", "position": 0, "resource_type": null, "rw_id": "2a694289-fec9-4bfe-a6d2-56c3864ec349", "size": null, "state": "active", "type": "reference-layer", "url": "https://api.resourcewatch.org/v1/dataset/a86d906d-9862-4783-9e30-cdb68cd808b8/layer/2a694289-fec9-4bfe-a6d2-56c3864ec349", "url_type": "reference-layer"}, {"cache_last_updated": null, "cache_url": null, "created": "2024-10-16T13:22:15.442305", "datastore_active": false, "description": "Version 1.1.0 (June 2018)", "format": "ZIP", "hash": "", "id": "26669133-9493-4f38-a32f-ce35d5ea6ab9", "last_modified": "2024-10-16T13:22:15.388472", "metadata_modified": "2024-10-16T13:22:16.337146", "mimetype": "application/zip", "mimetype_inner": null, "name": "Version 1.1.0 (deprecated)", "package_id": "53623dfd-3df6-4f15-a091-67457cdb571f", "position": 1, "resource_type": null, "size": 1310707, "state": "active", "title": "Version 1.1.0 (deprecated)", "url": "https://datasets.wri.org/private-admin/dataset/53623dfd-3df6-4f15-a091-67457cdb571f/resource/26669133-9493-4f38-a32f-ce35d5ea6ab9/download/globalpowerplantdatabasev110.zip", "url_type": "upload"}, {"cache_last_updated": null, "cache_url": null, "created": "2024-10-16T13:22:16.353811", "datastore_active": false, "description": "Version 1.2.0 (June 2019)", "format": "ZIP", "hash": "", "id": "9a7587a6-964e-4bfc-8c09-0ba1f611f18b", "last_modified": "2024-10-16T13:22:16.236110", "metadata_modified": "2024-10-16T13:22:17.346279", "mimetype": "application/zip", "mimetype_inner": null, "name": "Version 1.2.0 (deprecated)", "package_id": "53623dfd-3df6-4f15-a091-67457cdb571f", "position": 2, "resource_type": null, "size": 2632855, "state": "active", "title": "Version 1.2.0 (deprecated)", "url": "https://datasets.wri.org/private-admin/dataset/53623dfd-3df6-4f15-a091-67457cdb571f/resource/9a7587a6-964e-4bfc-8c09-0ba1f611f18b/download/globalpowerplantdatabasev120.zip", "url_type": "upload"}, {"cache_last_updated": null, "cache_url": null, "created": "2024-10-16T13:22:17.356484", "datastore_active": false, "description": "Version 1.3.0 (June 2021)", "format": "ZIP", "hash": "", "id": "66bcdacc-3d0e-46ad-9271-a5a76b1853d2", "last_modified": "2024-10-16T13:22:17.257329", "metadata_modified": "2025-10-06T15:54:16.251804", "mimetype": "application/zip", "mimetype_inner": null, "name": "Version 1.3.0", "package_id": "53623dfd-3df6-4f15-a091-67457cdb571f", "position": 3, "resource_type": null, "size": 4178889, "state": "active", "title": "Version 1.3.0", "url": "https://datasets.wri.org/private-admin/dataset/53623dfd-3df6-4f15-a091-67457cdb571f/resource/66bcdacc-3d0e-46ad-9271-a5a76b1853d2/download/globalpowerplantdatabasev130.zip", "url_type": "upload"}], "tags": [{"display_name": "powerplant", "id": "3ea4e1fb-c351-4077-82c3-2f609c76b328", "name": "powerplant", "state": "active", "vocabulary_id": null}], "relationships_as_subject": [], "relationships_as_object": []}, {"approval_status": "approved", "author": null, "author_email": null, "authors": [{"name": "Michelle Sims", "email": "michelle.sims@wri.org"}, {"name": "Radost Stanimirova", "email": "radost.stanimirova@wri.org"}], "cautions": "<p>This data is part of a research archive and represents outdated or legacy data. The <a href=\"/datasets/dominant-drivers-of-tree-cover-loss-at-1km\">latest update of this dataset</a> is always available in another catalog entry.</p>\n<ul>\n<li>\n<p>This product shows the dominant driver in each 1 km cell over the entire period. It does not show multiple drivers if they occur in the same cell at smaller scales, nor does it detail the sequence of drivers if multiple occurred at different times within the period.</p>\n</li>\n<li>\n<p>This product does not distinguish between the loss of natural forest and planted trees (e.g., plantations, tree crops, or agroforestry systems). While tree cover loss associated with the permanent agriculture, hard commodities, and settlements &amp; infrastructure classes represent a close approximation of deforestation, they do not always represent the conversion of natural forests to other land uses and in some cases may represent loss of planted trees. Similarly, replacement of natural forest with wood fiber plantations is not distinguished from routine harvesting within existing plantations established before 2000, as these are both included in the logging class.</p>\n</li>\n<li>\n<p>These data are limited in scope to attributing drivers to tree cover loss as mapped by the Hansen et al. (2013) tree cover loss product, and therefore the detection of loss is subject to the accuracy of that product. A full description of limitations is included in the publication.</p>\n</li>\n</ul>", "citation": "Sims, M.J., R. Stanimirova, A. Raichuk, M. Neumann, J. Richter, F. Follett, J. MacCarthy, K. Lister, C. Randle, L. Sloat, E. Esipova, J. Jupiter, C. Stanton, D. Morris, C. M. Slay, D. Purves, and N. Harris. 2025. \u201cGlobal Drivers of Forest Loss at 1 Km Resolution.\u201d Environmental Research Letters 20 (7): 074027. doi:10.1088/1748-9326/add606.", "creator_user_id": "17daa49a-aa7d-4fea-b856-d3bb9fae7cc8", "draft": false, "featured_dataset": false, "function": "Global map of the dominant driver of tree cover loss at 0.01\u00b0 resolution (~1km) for the period 2001-2023.", "has_chart_views": false, "id": "38e6fe0c-3d9e-45d4-aedb-ddc1314c1e0f", "is_approved": true, "isopen": true, "language": "en", "learn_more": "https://www.globalforestwatch.org/blog/data-and-tools/new-drivers-data-forest-loss", "license_id": "cc-by", "license_title": "Creative Commons Attribution", "license_url": "http://www.opendefinition.org/licenses/cc-by", "maintainer": null, "maintainer_email": null, "maintainers": [{"name": "Michelle Sims", "email": "michelle.sims@wri.org"}, {"name": "Radost Stanimirova", "email": "radost.stanimirova@wri.org"}], "metadata_created": "2025-06-17T13:17:27.228102", "metadata_modified": "2025-10-06T15:51:02.063256", "methodology": "<p>These data were produced by the World Resources Institute and Google DeepMind. The data were developed using a global neural network model (ResNet) trained on a <a href=\"https://zenodo.org/records/15225267\">set of 6,955 samples collected through visual interpretation</a> of very high-resolution satellite imagery. The model used satellite imagery (Landsat 7 &amp; 8, Sentinel-2) and ancillary data to classify the seven driver categories.</p>\n<p>Overall accuracy of the model is 90.5%, with regional accuracies varying from 82.8% in Southeast Asia to 94.1% in Asia. Global per class producer\u2019s and user\u2019s accuracy are highest for the permanent agriculture, logging, and wildfire classes (over 90%), and generally lower for rarer classes, such as hard commodities, settlements and infrastructure, and other natural disturbances. A full description of the methods and accuracy statistics are available in the publication.</p>", "name": "dominant-drivers-of-tree-cover-loss-at-1km-v1-1", "notes": "<p>This dataset contains the dominant driver of tree cover loss from 2001-2023. A <em>driver</em> is defined as the direct cause of tree cover loss, and can include both temporary disturbances (natural or anthropogenic) or permanent loss of tree cover due to a change to a non-forest land use (e.g., deforestation). The <em>dominant</em> driver is defined as the direct driver that caused the majority of tree cover loss within each 1 km cell over a time period.</p>\n<p>Classes of drivers are defined as follows:</p>\n<ul>\n<li>Permanent agriculture: Long-term, permanent tree cover loss for small- to large-scale agriculture.</li>\n<li>Hard commodities: Loss due to the establishment or expansion of mining or energy infrastructure.</li>\n<li>Shifting cultivation: Tree cover loss due to small- to medium-scale clearing for temporary cultivation that is later abandoned and followed by subsequent regrowth of secondary forest or vegetation.</li>\n<li>Logging: Forest management and logging activities occurring within managed, natural or semi-natural forests and plantations, often with evidence of forest regrowth or planting in subsequent years.</li>\n<li>Wildfire: Tree cover loss due to fire with no visible human conversion or agricultural activity afterward. Fires may be started by natural causes (e.g. lightning) or may be related to human activities (accidental or deliberate).</li>\n<li>Settlements and infrastructure: Tree cover loss due to expansion and intensification of roads, settlements, urban areas, or built infrastructure (not associated with other classes).</li>\n<li>Other natural disturbances: Tree cover loss due to other non-fire natural disturbances (e.g., landslides, insect outbreaks, river meandering). If loss due to natural causes is followed by salvage or sanitation logging, it is classified as logging.</li>\n</ul>\n<p>The data is available for download as a global raster file with 8 bands, 8-bit pixel values, and 0.01-degree spatial resolution. The first band is a classification of the dominant driver for the full time period (2001-2023). The pixel values 1 through 7 correspond to the seven classifications of drivers and 255 corresponds to NoData. The other seven bands contain the probability of each driver. Probabilities should be interpreted as a relative confidence between the classes, rather than a true estimate of the likelihood of each class.</p>\n<p>The pixel values for the probability bands should NOT be interpreted as an integer percentage (i.e. <em>1 means 1%</em>, <em>2 means 2%</em>, ... is false). Instead more dynamic range is provided and the percentage between 0%-100% is quantized into 250 levels each corresponding to a 0.4% increment rather than a 1% increment. The formula that can be used to calculate the percentage probability for a driver is <code>driver_probability_pct = uint8(pixel_value) * 0.4</code> . Valid pixel values are between 0 and 250 inclusive. The pixel value of 255 is NoData.</p>\n<p>The Cloud Optimized GeoTIFF (COG) available for download:</p>\n<ul>\n<li>Band 1: Dominant driver classification (categorical) over the time period</li>\n<li>1 = Permanent agriculture</li>\n<li>2 = Hard commodities</li>\n<li>3 = Shifting cultivation</li>\n<li>4 = Logging</li>\n<li>5 = Wildfire</li>\n<li>6 = Settlements and infrastructure</li>\n<li>7 = Other natural disturbances</li>\n<li>Band 2: Permanent agriculture - probability percentage (numeric) scaled over [0-250]</li>\n<li>Band 3: Hard commodities - probability percentage (numeric) scaled over [0-250]</li>\n<li>Band 4: Shifting cultivation - probability percentage (numeric) scaled over [0-250]</li>\n<li>Band 5: Logging - probability percentage (numeric) scaled over [0-250]</li>\n<li>Band 6: Wildfire - probability percentage (numeric) scaled over [0-250]</li>\n<li>Band 7: Settlements and infrastructure - probability percentage (numeric) scaled over [0-250]</li>\n<li>Band 8: Other natural disturbances - probability percentage (numeric) scaled over [0-250]</li>\n</ul>", "num_resources": 2, "num_tags": 1, "open_in": "[{\"title\":\"Google Earth Engine\",\"url\":\"https://developers.google.com/earth-engine/datasets/catalog/projects_landandcarbon_assets_wri_gdm_drivers_forest_loss_1km_v1_1_2001_2023\"}]", "organization": {"id": "ad461dbd-f646-4080-94a9-c10005c42a43", "name": "land-carbon-lab", "title": "Land & Carbon Lab", "type": "organization", "description": "WRI's Land & Carbon Lab creates datasets to support critical monitoring and decision making needs surrounding land use planning, carbon accounting, and other technical facets of human-land interactions.", "image_url": "1724353785-cover-ethiopia-mosaic-oqiid6.avif", "created": "2024-09-25T11:07:47.708772", "is_organization": true, "approval_status": "approved", "state": "active"}, "owner_org": "ad461dbd-f646-4080-94a9-c10005c42a43", "private": false, "rw_dataset": false, "short_description": "Global map of the dominant driver of tree cover loss at 0.01\u00b0 resolution (~1km) for the period 2001-2023.", "spatial_address": "Global", "spatial_type": "address", "state": "active", "technical_notes": "https://doi.org/10.1088/1748-9326/add606", "title": "Global drivers of forest loss at 1 km resolution - Version 1.1", "type": "dataset", "update_frequency": "annually", "version": null, "visibility_type": "public", "wri_data": true, "groups": [{"description": "Data related to tree cover, deforestation, reforestation, and forest biodiversity.", "display_name": "Forests", "id": "c74fae7a-9b54-4c02-b7b5-e3ca00bff8ef", "image_display_url": "", "name": "forests", "title": "Forests", "type": "group"}], "resources": [{"cache_last_updated": null, "cache_url": null, "created": "2025-06-17T13:17:27.230780", "datastore_active": false, "description": "Version 1.1 available as a web map layer.", "format": "Layer", "hash": "", "id": "c5891398-d58a-4709-a33e-39f35cd9edde", "last_modified": null, "metadata_modified": "2025-10-06T15:51:02.069271", "mimetype": null, "mimetype_inner": null, "name": "Dominant Driver Map Layer", "package_id": "38e6fe0c-3d9e-45d4-aedb-ddc1314c1e0f", "position": 0, "resource_type": null, "rw_id": "a66fdca9-aa28-42da-a175-ccedb584ba56", "size": null, "state": "active", "type": "reference-layer", "url": "https://api.resourcewatch.org/v1/dataset/b34ef0b2-fd10-4adc-809d-5fd33f2521c9/layer/a66fdca9-aa28-42da-a175-ccedb584ba56", "url_type": "reference-layer"}, {"cache_last_updated": null, "cache_url": null, "created": "2025-06-17T13:17:45.161683", "datastore_active": false, "description": "Version 1.1 (2001-2023). Raster file in EPSG:4326 (WGS84) coordinate system at 0.01\u00b0 resolution. See dataset description for how to interpret the 8 raster bands.", "format": "tif", "hash": "", "id": "81122270-3e14-4cc0-b5b4-8679d0a4b725", "last_modified": "2025-06-17T13:17:44.955355", "metadata_modified": "2025-10-06T15:51:02.069436", "mimetype": "image/tiff", "mimetype_inner": null, "name": "Cloud Optimized Geotiff - v1.1", "package_id": "38e6fe0c-3d9e-45d4-aedb-ddc1314c1e0f", "position": 1, "resource_type": null, "size": 289622025, "state": "active", "title": "Cloud Optimized Geotiff - v1.1", "url": "https://datasets.wri.org/private-admin/dataset/38e6fe0c-3d9e-45d4-aedb-ddc1314c1e0f/resource/81122270-3e14-4cc0-b5b4-8679d0a4b725/download/drivers_forest_loss_1km_2001_2023_v1_1.tif", "url_type": "upload"}], "tags": [{"display_name": "raster", "id": "36f3d667-e68f-402e-86d3-0fe96419476f", "name": "raster", "state": "active", "vocabulary_id": null}], "relationships_as_subject": [], "relationships_as_object": []}], "sort": "score desc, metadata_modified desc", "search_facets": {}}}