Questo set di dati fornisce mappe globali annuali delle classi dominanti di praterie
(coltivate e naturali/seminaturali) dal 2000 al 2022 con una risoluzione spaziale di 30 metri.
Prodotto dall'iniziativa Land &
Carbon Lab Global Pasture Watch, l'estensione dei pascoli mappata
include qualsiasi tipo di copertura del suolo che contenga almeno il 30% di vegetazione bassa secca o umida, dominata da erbe e piante erbacee (meno di 3 metri)
e un:
massimo 50% di copertura arborea (superiore a 5 metri),
un massimo del 70% di altra vegetazione legnosa (arbusti e boscaglia aperta) e
massimo 50% di copertura di colture attive in paesaggi a mosaico di colture
e altra vegetazione.
L'estensione delle praterie è classificata in due classi:
- Praterie coltivate: aree in cui erbe e altre piante foraggere sono state piantate e gestite intenzionalmente, nonché aree di vegetazione nativa di tipo prateria in cui è chiaramente visibile una gestione attiva e intensa per usi specifici diretti dall'uomo, come il pascolo diretto del bestiame.
- Praterie naturali/seminaturali: praterie/vegetazione di bassa altezza autoctone relativamente indisturbate, come steppe e tundra, nonché aree che hanno subito vari gradi di attività umana in passato, che possono contenere un mix di specie autoctone e introdotte a causa dell'uso storico del territorio e dei processi naturali.
In generale, mostrano schemi dall'aspetto naturale di vegetazione variegata
e relazioni idrologiche chiaramente ordinate in tutto il paesaggio.
La metodologia implementata ha preso in considerazione le immagini GLAD Landsat ARD-2
(elaborate in aggregati bimestrali senza nuvole, vedi Consoli et al, 2024), accompagnate da covariate climatiche, di morfologia e di prossimità, machine learning spaziotemporale (Random Forest per classe) e oltre 2,3 milioni di campioni di riferimento (interpretati visivamente in immagini ad altissima risoluzione). Sono state utilizzate soglie di probabilità personalizzate (basate sulla convalida incrociata spaziale a cinque fold e su valori di precisione e richiamo bilanciati) per derivare mappe delle classi dominanti, 0,32 e 0,42 per le soglie di probabilità di prati coltivati e naturali/seminaturali, rispettivamente.
Limitazioni: l'estensione delle praterie è in parte sottostimata nel sud-est
dell'Africa (Zimbabwe e Mozambico) e nell'Australia orientale (arbusteti e
boschi dell'ecoregione di Mulga). I terreni coltivati sono classificati erroneamente come praterie
in alcune parti del Nord Africa, della penisola arabica, dell'Australia occidentale,
della Nuova Zelanda, del centro della Bolivia e dello stato di Mato Grosso (Brasile). A causa del guasto dello scanner SLC di Landsat 7, a livello di parcella sono visibili strisce regolari di probabilità di praterie, in particolare nell'anno 2012. L'utilizzo di
livelli di risoluzione più grossolana (mappe di accessibilità e prodotti MODIS)
ha introdotto errori macroscopici curvilinei (a causa della strategia di riduzione della scala basata su spline cubica) in Uruguay, Argentina sudoccidentale, Sud
dell'Angola e nella regione del Sahel in Africa. Gli utenti devono essere consapevoli
delle limitazioni e dei problemi noti e devono valutarli
attentamente per garantire un utilizzo appropriato delle mappe in questa fase iniziale di previsione. GPW sta lavorando attivamente per raccogliere feedback sistematici tramite la piattaforma Geo-Wiki, convalidare la versione attuale e migliorare le versioni future del set di dati.
Parente, L., Sloat, L., Mesquita, V., et al. (2024)
Global Pasture Watch - Annual grassland class and extent
maps at 30-m spatial resolution (2000—2022) (Version v1)
[Data set]. Zenodo
doi:https://doi.org/10.5281/zenodo.13890401
Parente, L., Sloat, L., Mesquita, V., et al. (2024).
Mappe annuali a 30 m della classe e dell'estensione dei prati globali (2000-2022)
basate sul machine learning spaziotemporale, Scientific Data.
doi: http://doi.org/10.1038/s41597-024-04139-6
Questo set di dati fornisce mappe globali annuali della classe dominante di praterie (coltivate e naturali/seminaturali) dal 2000 al 2022 con una risoluzione spaziale di 30 metri. Prodotto dall'iniziativa Global Pasture Watch del Land & Carbon Lab, l'estensione dei pascoli mappata include qualsiasi tipo di copertura del suolo che contenga almeno il 30% di bassa vegetazione secca o umida…
[null,null,[],[[["\u003cp\u003eThis dataset provides annual maps of global grassland types (cultivated and natural/semi-natural) at 30-meter resolution from 2000 to 2022.\u003c/p\u003e\n"],["\u003cp\u003eDeveloped by the Land & Carbon Lab Global Pasture Watch initiative, it identifies areas with at least 30% low vegetation dominated by grasses and forbs, with specific tree and shrubland cover limitations.\u003c/p\u003e\n"],["\u003cp\u003eThe mapping methodology uses Landsat imagery, environmental covariates, and machine learning, validated with over 2.3 million reference samples.\u003c/p\u003e\n"],["\u003cp\u003eKnown limitations include potential under-prediction in certain regions and misclassification of cropland as grassland in others.\u003c/p\u003e\n"],["\u003cp\u003eThe dataset is available under a CC-BY-4.0 license and users are encouraged to consider the limitations for appropriate application.\u003c/p\u003e\n"]]],["The dataset offers annual global grassland maps from 2000-2022, at 30-meter resolution, classifying grasslands into cultivated and natural/semi-natural types. Created by the Land & Carbon Lab Global Pasture Watch, the maps identify areas with at least 30% low vegetation, with limitations on tree cover, woody vegetation, and cropland. The data utilizes GLAD Landsat ARD-2 images, machine learning, and over 2.3 million reference samples. Users can access the data through Google Earth Engine using the provided code and should be aware of the documented limitations.\n"],null,["# GPW Annual Dominant Class of 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 dominant class maps of grasslands\n(cultivated and natural/semi-natural) from 2000 to 2022 at 30-m spatial\nresolution.\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| `dominant_class` | 0 | 2 | 30 meters | Dominant class derived through Random Forest and probability maps. |\n\n**dominant_class Class Table**\n\n| Value | Color | Description |\n|-------|---------|--------------------------------|\n| 0 | #ffffff | Other |\n| 1 | #ffcd73 | Cultivated grassland |\n| 2 | #ff9916 | Natural/Semi-natural grassland |\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 domi_grassland = ee.ImageCollection(\n \"projects/global-pasture-watch/assets/ggc-30m/v1/grassland_c\"\n)\nvar visParams = {\"opacity\":1, \"min\":1,\"max\":2,\"palette\":[\"ffcd73\",\"ff9916\"]};\n\nvar domi_grassland_2022 = domi_grassland.filterDate('2022-01-01', '2023-01-01').first();\nMap.addLayer(\n domi_grassland_2022.selfMask(), \n visParams, 'Dominant grassland class (2022)'\n);\n\nvar domi_grassland_2000 = domi_grassland.filterDate('2000-01-01', '2001-01-01').first();\nMap.addLayer(\n domi_grassland_2000.selfMask(), \n visParams, 'Dominant grassland class (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_grassland_c) \n[GPW Annual Dominant Class of Grasslands v1](/earth-engine/datasets/catalog/projects_global-pasture-watch_assets_ggc-30m_v1_grassland_c) \nThis dataset provides global annual dominant class maps of grasslands (cultivated and natural/semi-natural) 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 ... \nprojects/global-pasture-watch/assets/ggc-30m/v1/grassland_c, 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_grassland_c)"]]
