DOI: 10.5281/zenodo.15649331
title: Global Pasture Watch - Annual grassland class and extent maps at 30-m spatial resolution (2000—2024)

description: Sub-dataset: Dominant grassland class, 2024

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-watch