[null,null,[],[[["\u003cp\u003eThis dataset offers annual probability maps of global cultivated grassland from 2000 to 2022 at a 30-meter resolution.\u003c/p\u003e\n"],["\u003cp\u003eThe dataset, produced by the Land & Carbon Lab Global Pasture Watch initiative, classifies grassland into cultivated and natural/semi-natural categories based on vegetation cover and management practices.\u003c/p\u003e\n"],["\u003cp\u003eIt utilizes GLAD Landsat ARD-2 images, climatic and landform data, and over 2.3 million reference samples for grassland classification through machine learning.\u003c/p\u003e\n"],["\u003cp\u003eLimitations exist, including under-prediction in some regions and potential misclassification of cropland as grassland, and users should be aware of these limitations when using the data.\u003c/p\u003e\n"],["\u003cp\u003eThe dataset is available under the CC-BY-4.0 license and is accompanied by detailed documentation and citations for further reference.\u003c/p\u003e\n"]]],["The Land & Carbon Lab's Global Pasture Watch dataset offers annual probability maps of cultivated grasslands from 2000-2022 at 30-meter resolution. It differentiates between cultivated and natural/semi-natural grasslands, considering factors like vegetation type, tree cover, and cropland presence. The data, derived from GLAD Landsat ARD-2 images and machine learning, is accessible via Google Earth Engine. Users can explore cultivated grassland probabilities and it has a minimum probability threshold of 32. Contact the Lab for bugs and explore more.\n"],null,["# GPW Annual Probabilities of Cultivated Grasslands v1\n\ninfo\n\n\nThis dataset is part of a Publisher Catalog, and not managed by Google Earth Engine.\n\nContact [Land \\& Carbon Lab](https://landcarbonlab.org/subscribe)\n\nfor bugs or [view more datasets](https://developers.google.com/earth-engine/datasets/publisher/global-pasture-watch)\nfrom the Global Pasture Watch Catalog. [Learn more about Publisher datasets](/earth-engine/datasets/publisher). \n[](https://landcarbonlab.org/data/global-grassland-and-livestock-monitoring) \n\nCatalog Owner\n: Global Pasture Watch\n\nDataset Availability\n: 2000-01-01T00:00:00Z--2023-01-01T00:00:00Z\n\nDataset Provider\n:\n\n\n [Land and Carbon Lab Global Pasture Watch](https://landcarbonlab.org/data/global-grassland-and-livestock-monitoring)\n\nContact\n: [Land \\& Carbon Lab](https://landcarbonlab.org/subscribe)\n\nCadence\n: 1 Year\n\nTags\n:\n[global](/earth-engine/datasets/tags/global) [global-pasture-watch](/earth-engine/datasets/tags/global-pasture-watch) [land](/earth-engine/datasets/tags/land) [landcover](/earth-engine/datasets/tags/landcover) [landuse](/earth-engine/datasets/tags/landuse) [landuse-landcover](/earth-engine/datasets/tags/landuse-landcover) [pasture](/earth-engine/datasets/tags/pasture) [publisher-dataset](/earth-engine/datasets/tags/publisher-dataset) [rangeland](/earth-engine/datasets/tags/rangeland) [vegetation](/earth-engine/datasets/tags/vegetation) \n\n#### Description\n\nThis dataset provides global annual probability maps of cultivated\ngrassland from 2000 to 2022 at 30-m spatial resolution.\nProduced by Land \\&\nCarbon Lab Global Pasture Watch initiative, the mapped grassland extent\nincludes any land cover type, which contains at least 30% of dry or wet\nlow vegetation, dominated by grasses and forbs (less than 3 meters)\nand a:\n\n- maximum of 50% tree canopy cover (greater than 5 meters),\n- maximum of 70% of other woody vegetation (scrubs and open shrubland), and\n- maximum of 50% active cropland cover in mosaic landscapes of cropland \\& other vegetation.\n\nThe grassland extent is classified into two classes:\n- **Cultivated grassland** : Areas where grasses and other forage plants have\nbeen intentionally planted and managed, as well as areas of native\ngrassland-type vegetation where they clearly exhibit active and\nheavy management for specific human-directed uses, such as directed\ngrazing of livestock.\n- **Natural/Semi-natural grassland**: Relatively undisturbed native\ngrasslands/short-height vegetation, such as steppes and tundra,\nas well as areas that have experienced varying degrees of human\nactivity in the past, which may contain a mix of native and\nintroduced species due to historical land use and natural processes.\nIn general, they exhibit natural-looking patterns of varied vegetation\nand clearly ordered hydrological relationships throughout the landscape.\n\nThe implemented methodology considered [GLAD Landsat ARD-2 images](https://glad.umd.edu/ard) (processed into cloud-free bi-monthly\naggregates, see [Consoli et al, 2024](https://doi.org/10.7717/peerj.18585)\n), accompanied by climatic, landform and proximity covariates,\nspatiotemporal machine learning (per-class Random Forest) and over\n2.3 million reference samples (visually interpreted in Very High\nResolution imagery). Custom probability thresholds (based on five-fold\nspatial cross-validation and balanced precision and recall values)\nwere used to derive dominant class maps, 0.32 and 0.42 for\ncultivated and natural/semi-natural grassland probability thresholds, respectively.\n\n**Limitations:** Grassland extent is partly under-predicted in southeastern\nAfrica (Zimbabwe and Mozambique) and in eastern Australia (shrublands and\nwoodlands of the Mulga ecoregion). Cropland is misclassified as grassland\nin parts of northern Africa, the Arabian Peninsula, Western Australia,\nNew Zealand, the center of Bolivia, and Mato Grosso state (Brazil). Due\nto the Landsat 7 SLC failure, regular stripes of grassland probabilities\nare visible at parcel-level, particularly in the year 2012. The usage of\ncoarser resolution layers (accessibility maps and MODIS products)\nintroduced curvilinear macroscopic errors (due to the downscaling\nstrategy based on cubicspline) in Uruguay, Southwest Argentina, South\nof Angola and in the Sahel region in Africa. Users need to be aware\nof the limitations and known issues; whilst considering them\ncarefully to ensure appropriate use of maps at this initial prediction\nstage. GPW is working actively to collect systematic feedback via the [Geo-Wiki\nplatform](https://www.geo-wiki.org), validate the current version\nand improve future versions of the dataset.\n\n**For more information see [Parente et. al, 2024](http://doi.org/10.1038/s41597-024-04139-6),\n[Zenodo](https://zenodo.org/records/13890401) and\n\u003chttps://github.com/wri/global-pasture-watch\u003e**\n\n### Bands\n\n**Bands**\n\n| Name | Min | Max | Pixel Size | Description |\n|---------------|-----|-----|------------|-----------------------------------------------------------------------|\n| `probability` | 0 | 100 | 30 meters | Cultivated grassland probability value derived through Random Forest. |\n\n### Image Properties\n\n**Image Properties**\n\n| Name | Type | Description |\n|---------|------|-----------------|\n| version | INT | Product version |\n\n### Terms of Use\n\n**Terms of Use**\n\n[CC-BY-4.0](https://spdx.org/licenses/CC-BY-4.0.html)\n\n### Citations\n\nCitations:\n\n- Parente, L., Sloat, L., Mesquita, V., et al. (2024)\n Global Pasture Watch - Annual grassland class and extent\n maps at 30-m spatial resolution (2000---2022) (Version v1)\n \\[Data set\\]. Zenodo\n [doi:https://doi.org/10.5281/zenodo.13890401](https://doi.org/10.5281/zenodo.13890401)\n- Parente, L., Sloat, L., Mesquita, V., et al. (2024).\n Annual 30-m maps of global grassland class and extent (2000--2022)\n based on spatiotemporal Machine Learning, Scientific Data.\n [doi: http://doi.org/10.1038/s41597-024-04139-6](http://doi.org/10.1038/s41597-024-04139-6)\n\n### DOIs\n\n- \u003chttps://doi.org/10.1038/s41597-024-04139-6\u003e\n- \u003chttps://doi.org/10.5281/zenodo.13890401\u003e\n\n### Explore with Earth Engine\n\n| **Important:** Earth Engine is a platform for petabyte-scale scientific analysis and visualization of geospatial datasets, both for public benefit and for business and government users. Earth Engine is free to use for research, education, and nonprofit use. To get started, please [register for Earth Engine access.](https://console.cloud.google.com/earth-engine)\n\n### Code Editor (JavaScript)\n\n```javascript\nMap.setCenter(-49.265188, -16.602052, 4);\n\nvar cultiv_grassland = ee.ImageCollection(\n \"projects/global-pasture-watch/assets/ggc-30m/v1/cultiv-grassland_p\"\n)\nvar min_prob = 32 // Probability threshold\nvar visParams = {min: 15, max: 85, palette: 'f5f5f5,fdaf27,ae7947,3a2200'}\n\nvar cultiv_grassland_2022 = cultiv_grassland.filterDate('2022-01-01', '2023-01-01').first();\nMap.addLayer(\n cultiv_grassland_2022.mask(cultiv_grassland_2022.gte(min_prob)), \n visParams, 'Cultivated grassland prob. (2022)'\n);\n\nvar cultiv_grassland_2000 = cultiv_grassland.filterDate('2000-01-01', '2001-01-01').first();\nMap.addLayer(\n cultiv_grassland_2000.mask(cultiv_grassland_2000.gte(min_prob)), \n visParams, 'Cultivated grassland prob. (2000)'\n);\n```\n[Open in Code Editor](https://code.earthengine.google.com/?scriptPath=Examples:Datasets/global-pasture-watch/projects_global-pasture-watch_assets_ggc-30m_v1_cultiv-grassland_p) \n[GPW Annual Probabilities of Cultivated Grasslands v1](/earth-engine/datasets/catalog/projects_global-pasture-watch_assets_ggc-30m_v1_cultiv-grassland_p) \nThis dataset provides global annual probability maps of cultivated grassland from 2000 to 2022 at 30-m spatial resolution. Produced by Land \\& Carbon Lab 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 ... \nprojects/global-pasture-watch/assets/ggc-30m/v1/cultiv-grassland_p, global,global-pasture-watch,land,landcover,landuse,landuse-landcover,pasture,publisher-dataset,rangeland,vegetation \n2000-01-01T00:00:00Z/2023-01-01T00:00:00Z \n-90 -180 90 180 \nGoogle Earth Engine \nhttps://developers.google.com/earth-engine/datasets\n\n- [https://doi.org/10.5281/zenodo.13890401](https://doi.org/https://landcarbonlab.org/data/global-grassland-and-livestock-monitoring)\n- [https://doi.org/10.5281/zenodo.13890401](https://doi.org/https://developers.google.com/earth-engine/datasets/catalog/projects_global-pasture-watch_assets_ggc-30m_v1_cultiv-grassland_p)"]]
