Set data ini menyediakan peta kelas dominan tahunan global padang rumput (yang dibudidayakan dan alami/semi-alami) dari tahun 2000 hingga 2022 pada resolusi spasial 30 m.
Diproduksi oleh inisiatif Land & Carbon Lab Global Pasture Watch, luas padang rumput yang dipetakan mencakup semua jenis cakupan lahan, yang berisi setidaknya 30% vegetasi rendah kering atau basah, yang didominasi oleh rumput dan forba (kurang dari 3 meter) dan:
maksimum 50% tutupan kanopi pohon (lebih dari 5 meter),
maksimum 70% vegetasi berkayu lainnya (semak dan semak terbuka), dan
maksimum 50% tutupan lahan pertanian aktif dalam lanskap mosaik lahan pertanian & vegetasi lainnya.
Luas padang rumput diklasifikasikan menjadi dua kelas:
- Padang rumput yang dibudidayakan: Area tempat rumput dan tanaman pakan lainnya telah ditanam dan dikelola secara sengaja, serta area vegetasi tipe padang rumput asli yang jelas menunjukkan pengelolaan aktif dan berat untuk penggunaan khusus yang diarahkan oleh manusia, seperti penggembalaan ternak yang diarahkan.
- Padang rumput alami/semi-alami: Padang rumput asli/vegetasi pendek yang relatif tidak terganggu, seperti stepa dan tundra, serta area yang telah mengalami berbagai tingkat aktivitas manusia di masa lalu, yang mungkin berisi campuran spesies asli dan spesies pendatang karena penggunaan lahan historis dan proses alami.
Secara umum, mereka menunjukkan pola yang tampak alami dari beragam vegetasi dan hubungan hidrologi yang jelas di seluruh lanskap.
Metodologi yang diterapkan mempertimbangkan gambar GLAD Landsat ARD-2
(diproses menjadi gabungan dua bulanan bebas awan, lihat Consoli et al, 2024
), yang disertai dengan kovariat iklim, bentuk lahan, dan kedekatan,
machine learning spatiotemporal (Random Forest per kelas) dan lebih dari
2,3 juta sampel referensi (ditafsirkan secara visual dalam gambar Beresolusi Sangat Tinggi). Ambang batas probabilitas kustom (berdasarkan validasi silang spasial lima kali lipat serta nilai presisi dan recall yang seimbang) digunakan untuk mendapatkan peta kelas dominan, yaitu 0,32 dan 0,42 untuk ambang batas probabilitas padang rumput yang dibudidayakan dan padang rumput alami/semi-alami.
Batasan: Luas padang rumput sebagian diprediksi lebih rendah di Afrika tenggara (Zimbabwe dan Mozambik) dan di Australia timur (semak dan hutan di ekoregion Mulga). Lahan tanaman diklasifikasikan secara keliru sebagai padang rumput
di sebagian Afrika utara, Semenanjung Arab, Australia Barat,
Selandia Baru, pusat Bolivia, dan negara bagian Mato Grosso (Brasil). Karena kegagalan SLC Landsat 7, garis-garis reguler probabilitas padang rumput terlihat di tingkat bidang tanah, terutama pada tahun 2012. Penggunaan lapisan resolusi yang lebih kasar (peta aksesibilitas dan produk MODIS) menyebabkan kesalahan makroskopik kurvilinear (karena strategi penurunan skala berdasarkan cubicspline) di Uruguay, Argentina Barat Daya, Angola Selatan, dan di wilayah Sahel di Afrika. Pengguna harus mengetahui batasan dan masalah umum; sekaligus mempertimbangkannya dengan cermat untuk memastikan penggunaan peta yang tepat pada tahap prediksi awal ini. GPW berupaya secara aktif mengumpulkan masukan sistematis melalui platform Geo-Wiki, memvalidasi versi saat ini, dan meningkatkan kualitas versi mendatang dari set data tersebut.
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).
Peta tahunan 30 m tentang kelas dan luas padang rumput global (2000–2022)
berdasarkan Machine Learning spatiotemporal, Data Ilmiah.
doi: http://doi.org/10.1038/s41597-024-04139-6
Set data ini menyediakan peta kelas dominan tahunan global padang rumput (yang dibudidayakan dan alami/semi-alami) dari tahun 2000 hingga 2022 pada resolusi spasial 30 m. Diproduksi oleh inisiatif Land & Carbon Lab Global Pasture Watch, luas padang rumput yang dipetakan mencakup semua jenis penutup lahan, yang berisi setidaknya 30% lahan kering atau basah rendah …
[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)"]]
