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Global Pasture Watch - Annual grassland class and extent maps at 30-m spatial resolution (2000—2022) V2-beta
No full textSub-dataset: Dominant grassland class, 2003-2005
Description
Global annual grassland class and extent for 2000—2024 produced by Parente et al. (2024) within the scope of the Global Pasture Watch initiative. 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:
maximum of 50% tree canopy cover (greater than 5 meters),
maximum of 70% of other woody vegetation (scrubs and open shrubland), and
maximum of 50% active cropland cover in mosaic landscapes of cropland & other vegetation.
The grassland extent is classified into two classes:
Cultivated grassland: Areas where grasses and other forage plants have been intentionally planted and managed, as well as areas of native grassland-type vegetation where they clearly exhibit active and 'heavy' management for specific human-directed uses, such as directed grazing of livestock.
Natural/semi-natural grassland: Relatively undisturbed native grasslands/short-height vegetation, such as steppes and tundra, as well as areas that have experienced varying degrees of human activity in the past, which may contain a mix of native and introduced species due to historical land use and natural processes. In general, they exhibit natural-looking patterns of varied vegetation and clearly ordered hydrological relationships throughout the landscape.
Open shrubland (v2-beta): Land on which the vegetation is dominated by low-growing woody plants, characterized by a sparse distribution of shrubs and dominated by woody perennials. Typically covers 50—75% of the area, with significant open ground (with or without herbaceous understory) between them, where shrub canopies are less than 10 meters in diameter, and tree cover is below 10%, meaning they do not form a continuous or semi-continuous canopy.
The dataset is organized in 69 global mosaics (25 years for each time series) in COG (Cloud Optimized GeoTIFF) format, WGS84 Coordinate Systems (EPSG:4326) and pixel size equal to 0.00025 degrees, including:
Probabilities of cultivated grassland (values range from 0–100),
Probabilities of natural/semi-natural grassland (values range from 0–100), and
Probabilities of open shrubland (values range from 0–100), and
Dominant class (0-other land cover, 1-cultivated grassland and 2-natural/semi-natural grassland, 3-open shrubland).
All raster files are in unsigned 8-bit integer format and use 255 as no-data value (pixels ignored by prediction), following an specific naming convention:
Project name: Global Pasture Watch (gpw)
Class name: cultivated grassland (cultiv.grassland), natural/semi-natural grassland (nat.semi.grassland), open shrubland (open.shrubland) and dominant grassland (grassland)
Procedure combination: Random Forest (rf), median filter (med.filt) and balanced threshold (bthr).
Variable type: probability (p) and factor class (c)
Spatial resolution: 30m
Begin of time reference: date of first Landsat composite used by the modeling (20240101)
End of time reference: date of last Landsat composite used by the modeling (20241231)
Spatial extent: global (go)
Coordinate system: World Geodetic System 1984, used in GPS (epsg.4326)
Version: v2
Related resources
Maps of dominant grassland:
2000-2002 2003-2005 2006-2008 2009-2011 2012-2014 2015-2017 2018-2020 2021-2023 2024
Probability maps of cultivated grassland:
2000-2024 (All URLs)
Probability maps of natural/semi-natural grassland:
2000-2024 (All URLs)
Grassland reference samples based on VHR imagery (2000–2024):
GeoPackage files
Global machine learning models (Random Forest):
Parquet and joblib python files
Reference sampling design derived by FSCV:
GeoPackage and raster files
Harmonized reference samples based on existing LULC dataset:
GeoPackage and raster files
Source code for reproducibility:
GitHub release
Mapping feedback tool:
GeoWiki
Data catalogues:
OpenLandMap STAC Google Earth Engine
Support
For questions of bugs/inconsistencies related to the dataset raise a GitHub issue in https://github.com/wri/global-pasture-watc
Supplemental Data for Gidden et al 2025: A prudent planetary limit for geologic carbon storage
No full textBelow are table captions used in the supplementary data of the manuscript:
Table S1. Global storage volumes considering each geospatial risk consideration layer sequentially, starting from the total global technical potential of onshore storage, offshore storage, and both combined as the global total. Summing all exclusions together results in a global planetary limit, provided at the bottom of the table. An overview of the rationale and quantifications of each limit is provided for every risk consideration. Sensitivity values are provided which estimate the difference in storage based on different assumptions compared to our main estimate for each exclusion layer relative to the previous layer in the main analysis. Exclusion layer sensitivities are described in Table S2.
Table S2. Key sensitivities applied to different exclusion layers. Each sensitivity is labeled based on its Risk Consideration, aligning with tabulated values in Table S1. Negative sensitivities result in lower estimates than the central estimates while positive sensitivities result in higher values. Where sensitivities can be binary (included or not), Y (yes) means they have been included and N (no) means they have not been included. Otherwise, numerical values are provided.
Table S3. A review of available literature which estimates either maximum injection depth, minimum injection depth, or estimates both values. Numerical values are harmonized across sources to provide consistent estimates in meters (m). The majority of the literature we assessed finds that maximal storage depth ranges from around 800-3000m. One study claimed storage depth possible in the gulf of Mexico up to 3500m, but noted that it was unclear about this range due to either pressure in the geopressure zone equilibrating with fracture pressure or loss of permeability. The maximum storage depth we could find was from the USGS which uses a boundary of 3962m based on compression requirements. Taken together, and given the large preponderance of the scientific literature, we maintain a central estimate for maximum storage depth of 2500m, but apply a range between 800m and 3500m in our primary analysis to acknowledge and show the uncertainty in this key parameter in our reported results.
Table S4. A review of countries that currently have explicit policies restricting CCS. Expert judgement is used to estimate whether such policies imply major or minor restrictions and to what degree those policies are subject to change.
Table S5. Country-resolved estimates of onshore, offshore, and total carbon storage are provided for: (1) total technical potential (i.e., without any exclusion layers applied), (2) applying all exclusion layers described in Table S1, and (3) prudent storage estimates including only basins with existing oil and gas infrastructure (i.e., in which storage properties of some part of the basin have already been assessed). We additionally provide the IPCC region in which each country is considered. Countries that do not map to IPCC region categories are included at the bottom of the list. The sum of storage potential across all countries results in the planetary limit.
Table S6. IPCC Scenario categories for the scenarios assessed in this analysis. The IPCC uses shorthand labels (e.g., C1, C2, etc.), while we use the temperature outcomes.
Table S7. A mapping table showing which countries are allocated to each IPCC macro region (so called R5 regions)
GLOBIOM output data for the soy supply chains in CLEVER D7.3 deliverable
No full textSupplement for CLEVER D7.3 deliverable including output data for the soy supply chain results. It analyzes future projections to 2050 for the agricultural sector with a focus on soy supply chains, Brazil and EU, and related impacts on biodiversity. The scenarios include:
- 1 business-as-usual future to 2050,
- 3 explorative future scenarios for soy supply chains, picturing a global food system sustainability transition, idealized ambitious conservation effort in Brazil and long-term impacts on US-China trade dispute
- 4 future scenarios focused on EU-related supply chain governance policies (EUDR, EU-MERCOSUR) and possible responses in public and private sector domestic interventions in Brazil (either a strengthening or a weakening of conservarion efforts)
- 2 future scenarios focused on alternatives EU supply chain governance interventions than the EUDR, including an EU biodiversity border adjustment mechanism for selected commodities and EU demand side efforts (waste reduction, dietary transition)
The record contains 4 seperate files: a readme file (readme_D73_SoySupplyChains.docx), 1 model output file at the level of world regions (GLOBIOM_outputs_D73_SoySupplyChains_Global.csv), 1 model output file at the level of Brazilian biomes (GLOBIOM_outputs_D73_SoySupplyChains_Brazil.csv) and 1 model output file for trade of soy-based products between world regions (GLOBIOM_outputs_D73_SoySupplyChains_SoyProdNETT.csv
LIFE Programme Projects Linked to Natura 2000 Sites
No full textThis dataset provides a harmonized table linking EU LIFE Programme projects to their associated Natura 2000 sites. It compiles detailed information on project characteristics, funding, thematic priorities, and biodiversity targets, enabling spatial and thematic analyses of LIFE-funded conservation actions across the European Union.
The following variables are included in the dataset:
Identifiers:
Reference: Official LIFE project code (e.g. LIFE20 NAT/UK/000100)
Project Title: Full project name
Acronym: Short project name
Attributes:
Priority Area: Thematic focus (e.g. Nature, Climate, Environment)
Year: Year of project approval
Lead Partner Country, Location: Geographic information on the coordinating entity
Name of the beneficiaries, Beneficiary types: Organisations involved and their roles
Total Budget, EU Contribution: Financial details
Themes, Keywords: Environmental focus areas (e.g. invasive species, habitat restoration)
Target EU legislative references: Linked EU policy frameworks (e.g. Habitats Directive, Water Framework Directive)
Target habitat types, Species, Red list species: Biodiversity elements addressed
Natura 2000 sites: Codes of linked Natura 2000 areas
Project URL: Direct link to LIFE public project page
Each record corresponds to a unique combination of a LIFE project and its associated Natura 2000 site, allowing cross-referencing between LIFE actions and EU protected area networks.
This dataset can be spatially linked to the EEA 1x1 km² reference grid (see dataset: Natura2000 sites on the EEA reference grid, DOI: 10.5281/zenodo.1722308
Mapping Demand-side Data
No full textThis dataset is the result of a two-year energy demand data mapping project carried out by 24 international experts as part of the Energy Demand changes Induced by Technological and Social innovations (EDITS) project, coordinated by the Research Institute of Innovative Technology for the Earth (RITE) and the International Institute for Applied Systems Analysis (IIASA), and funded by the Ministry of Economy, Trade, and Industry (METI), Japan. A comprehensive systematic review of over 5,400 studies across five key sectors, Buildings, Transport, Food, Urban, and Industry, was conducted, with approximately 1,000 studies coded and analyzed in detail. The results of this project are currently under review at Nature Climate Change
CEDS v_2025_04_18 Gridded Data 0.1 degree
No full textThis Zenodo data entry is a documentation placeholder and diagnostic data release for the v_2025_04_18 0.1 degree gridded data released via ESGF.
This data is derived from CEDS v_2025_03_18 aggregate emissions release, which includes emissions data files by emission species (SO2, NOx, BC, OC, NH3, NMVOC, CO, CO2, CH4, N2O), country, and sector released here:
https://zenodo.org/records/15059443
This data set can be accessed via ESGF, for detailed instructions on how to access and download, as well as data notes, see the README file attached.
The files released here include:
CEDS_v_2025_04_18_0.1_Gridded_README.txt (instructions for access on ESGF and data notes)
v_2025_04_18 gridded 0.1 check sums
See the CEDS GitHub site for details including journal paper reference information and any known issues with this data:
https://github.com/JGCRI/CED
CEDS v_2025_03_18 Aggregate Data
No full textSurface flux data files by species (SO2, NOx, BC, OC, NH3, NMVOC, CO, CO2, CH4, N2O), country, sector, and fuel produced by the March-18-2025 release of CEDS.
Main time series estimates from 1750 - 2023 for all species except CH4 and N2O, which estimate trends starting at 1970. Extended global trends for CH4 and N2O from 1750 are available as supplemental data in the attached files.
The four file bundles are:
CEDS_v_2025_03_18_aggregate.zip (estimates by sector, by country, by fuel, by country and fuel, by country and sector, and by sector and fuel)
CEDS_v_2025_03_18_detailed.zip (estimates by country, sector, and fuel)
CEDS_v_2025_03_18_supplementary_bunkers.zip (Additional detail for aviation and shipping by country)
CEDS_v_2025_03_18_supplementary_extension.zip (Extended global time series for CH4 and N2O)
Associated gridded data for this release is available on ESGF and documented here:
https://zenodo.org/records/15001544
See the CEDS GitHub site for details including journal paper reference information and any known issues with this data:
https://github.com/JGCRI/CED
CEDS v_2025_03_11 Aggregate Data
No full textThis serves as a documentation placeholder for a preliminary data set not publicly released. After minor corrections, this version was superseded by CEDS v_2025_03_18 documented here:
https://zenodo.org/records/1505944
Global Pasture Watch - Annual livestock headcount layers for cattle, goats, sheep, horses, and buffaloes at 1-km 2000–2022 (FAOSTAT-adjusted) (Part-1)
No full textGlobal annual layers of livestock distribution of cattle, goats, sheep, horses and buffaloes at 1-km spatial resolution convering the period of 2000–2022. The layers were produced using harmonized and used as reference data (55,336 census polygons and 939,257 individual data entries), random forest predictive models and a large stack of multi-source harmonized gridded/raster spatial layers (128 individual raster spatial layers harmonized at 1 km spatial resolution).
Raster cell values represent number of livestock animals (headcounts) calibrated according to FAOSTAT database. The adjustment was applied individually to each country, ensuring that the adjusted pixel values accurately reflected the national totals. For applications relying on headcount data from FAOSTAT the provided layers serve as a fully compatible alternative.
In line with a request from our funders, livestock maps will remain under embargo in Zenodo until the final acceptance of peer-reviewed publication. They can be accessed during the reviewing process by filling-in a form via Global Pasture Watch Early Access data program (https://survey.alchemer.com/S3/7859804/Pasture-Early-Adopters). All modeling framework presented in this work is publicly available at: https://github.com/wri/global-pasture-watch. We are currently preparing the data to be ingested in STAC and Google Earth Engine
OPEN, CLOUD-OPTIMIZED, ANALYSIS-READY GLOBAL GEDI SATELLITE LIDAR DATASETS FOR LAND SURFACE APPLICATIONS
No full textCurrent satellite LiDAR missions, such as GEDI and ICESat- 2, provide billions of points annually that are typically not cloud-optimized and require additional quality filtering before any further analysis. In this study, we present Open-LandMap GEDI (OLM-GEDI), a new open, cloud-optimized,
and global GEDI point dataset, for which we establish a spatio-temporal structure to facilitate efficient access. We show random access to OLM-GEDI achieves 20 seconds and a minute for areas around 50-thousand and 3-million km2, respectively. The OLM-GEDI STAC catalog is further established, which can be readily loaded into a local or cloud computing environment, such as openEO. This open GEDI dataset can be beneficial to future studies to enhance their reproducibility and mitigate the complexity of handling large GEDI data volumes (∼ 120 TiB) and quality filters