{
  "attribution": "Calculated by Environment Dashboard from Hansen/UMD/Google/USGS/NASA (Global Forest Change v1.13), accessed through Global Nature Watch (WRI table gadm__tcl__iso_change v20260424) on 2026-10-05. Tree cover loss due to fire: UMD/GLAD (Tyukavina et al. 2022). Tree cover loss by dominant driver: WRI/Google DeepMind (Sims et al. 2025). Primary humid tropical forest: Turubanova et al. (2018). CC BY 4.0. Changes: Summed WRI's country table by country and year at a tree canopy cover in 2000 of at least 30 percent; world totals of primary forest loss and of loss by driver summed by Environment Dashboard.",
  "description": "Hectares of tree cover lost each year since 2001, worldwide and in each country, split by the dominant driver of loss in each square kilometre: permanent agriculture, mining and energy, shifting cultivation, logging, wildfire, settlements and infrastructure, other natural disturbances, or unknown (WRI and Google DeepMind).",
  "dimensions": [
    {
      "id": "driver",
      "label": "Driver",
      "values": [
        {"id": "permanent-agriculture", "label": "Permanent agriculture"},
        {"id": "hard-commodities", "label": "Mining and energy (hard commodities)"},
        {"id": "shifting-cultivation", "label": "Shifting cultivation"},
        {"id": "logging", "label": "Logging"},
        {"id": "wildfire", "label": "Wildfire"},
        {"id": "settlements-and-infrastructure", "label": "Settlements and infrastructure"},
        {"id": "other-natural-disturbances", "label": "Other natural disturbances"},
        {"id": "unknown", "label": "Unknown"}
      ]
    }
  ],
  "display": {
    "decimals": 0
  },
  "geo_coverage": "mixed",
  "headline_entity": "WLD",
  "id": "forest.gfw.tree-cover-loss-by-driver",
  "kind": "series",
  "latest": {
    "age_bp": null,
    "dims": {
      "driver": "permanent-agriculture"
    },
    "entity": "WLD",
    "period": "2025",
    "status": "final",
    "value": 5728093.192951432
  },
  "licence": {
    "name": "CC BY 4.0",
    "spdx": "CC-BY-4.0",
    "url": "https://creativecommons.org/licenses/by/4.0/"
  },
  "licence_class": "open",
  "notes": [],
  "notice": null,
  "origins": [
    {"acquisition": "automatic", "artifact_id": "loss-global-annual", "bytes": 1620, "citation_full": "Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend. 2013. High-Resolution Global Maps of 21st-Century Forest Cover Change. Science 342 (15 November): 850-53. Data available on-line from: https://glad.earthengine.app/view/global-forest-change. Sims, M.J., R. Stanimirova, A. Raichuk, M. Neumann, J. Richter, F. Follett, J. MacCarthy, K. Lister, C. Randle, L. Sloat, E. Esipova, J. Jupiter, C. Stanton, D. Morris, C. M. Slay, D. Purves, and N. Harris. 2025. \"Global Drivers of Forest Loss at 1 Km Resolution.\" Environmental Research Letters 20 (7): 074027. doi:10.1088/1748-9326/add606.", "date_accessed": "2026-10-05", "date_published": null, "doi": "10.1126/science.1244693", "etag": null, "last_modified": null, "licence": {"name": "CC BY 4.0", "spdx": "CC-BY-4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "producer": "University of Maryland GLAD lab with Google, USGS and NASA; tabulated and served by World Resources Institute", "r2_url": "https://files.environmentdashboard.org/raw/79b791bcb241093320fc0e6297babf3c468e58027c31dc8213c328f68c46053e.zst", "sha256": "79b791bcb241093320fc0e6297babf3c468e58027c31dc8213c328f68c46053e", "source_id": "gfw-tree-cover-loss", "title": "Tree cover loss, Global Forest Change v1.13 (via Global Nature Watch)", "url_download": "https://data-api.globalforestwatch.org/dataset/gadm__tcl__iso_change/v20260424/download/csv?sql=SELECT%20umd_tree_cover_loss__year%2C%20SUM%28umd_tree_cover_loss__ha%29%20AS%20umd_tree_cover_loss__ha%2C%20SUM%28umd_tree_cover_loss_from_fires__ha%29%20AS%20umd_tree_cover_loss_from_fires__ha%20FROM%20data%20WHERE%20umd_tree_cover_density_2000__threshold%20%3D%2030%20GROUP%20BY%20umd_tree_cover_loss__year%20ORDER%20BY%20umd_tree_cover_loss__year", "url_main": "https://globalnaturewatch.org/dashboards/global/", "version_producer": "GFC-2025-v1.13, WRI table gadm__tcl__iso_change v20260424", "wayback_url": null},
    {"acquisition": "automatic", "artifact_id": "loss-drivers-country-annual", "bytes": 1703978, "citation_full": "Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend. 2013. High-Resolution Global Maps of 21st-Century Forest Cover Change. Science 342 (15 November): 850-53. Data available on-line from: https://glad.earthengine.app/view/global-forest-change. Sims, M.J., R. Stanimirova, A. Raichuk, M. Neumann, J. Richter, F. Follett, J. MacCarthy, K. Lister, C. Randle, L. Sloat, E. Esipova, J. Jupiter, C. Stanton, D. Morris, C. M. Slay, D. Purves, and N. Harris. 2025. \"Global Drivers of Forest Loss at 1 Km Resolution.\" Environmental Research Letters 20 (7): 074027. doi:10.1088/1748-9326/add606.", "date_accessed": "2026-10-05", "date_published": null, "doi": "10.1126/science.1244693", "etag": null, "last_modified": null, "licence": {"name": "CC BY 4.0", "spdx": "CC-BY-4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "producer": "University of Maryland GLAD lab with Google, USGS and NASA; tabulated and served by World Resources Institute", "r2_url": "https://files.environmentdashboard.org/raw/2e9dfe9bb94ea744f5f4178fe292ffacc0173caf50609cd1f9981106374a6c63.zst", "sha256": "2e9dfe9bb94ea744f5f4178fe292ffacc0173caf50609cd1f9981106374a6c63", "source_id": "gfw-tree-cover-loss", "title": "Tree cover loss, Global Forest Change v1.13 (via Global Nature Watch)", "url_download": "https://data-api.globalforestwatch.org/dataset/gadm__tcl__iso_change/v20260424/download/csv?sql=SELECT%20iso%2C%20wri_google_tree_cover_loss_drivers__driver%2C%20umd_tree_cover_loss__year%2C%20SUM%28umd_tree_cover_loss__ha%29%20AS%20umd_tree_cover_loss__ha%20FROM%20data%20WHERE%20umd_tree_cover_density_2000__threshold%20%3D%2030%20GROUP%20BY%20iso%2C%20wri_google_tree_cover_loss_drivers__driver%2C%20umd_tree_cover_loss__year%20ORDER%20BY%20iso%2C%20wri_google_tree_cover_loss_drivers__driver%2C%20umd_tree_cover_loss__year", "url_main": "https://globalnaturewatch.org/dashboards/global/", "version_producer": "GFC-2025-v1.13, WRI table gadm__tcl__iso_change v20260424", "wayback_url": null}
  ],
  "processing": [
    {
      "description": "Read loss-drivers-country-annual: WRI's table gadm__tcl__iso_change v20260424 queried through the Global Forest Watch Data API with the SQL in the file's URL (https://data-api.globalforestwatch.org/dataset/gadm__tcl__iso_change/v20260424/download/csv?sql=SELECT%20iso%2C%20wri_google_tree_cover_loss_drivers__driver%2C%20umd_tree_cover_loss__year%2C%20SUM%28umd_tree_cover_loss__ha%29%20AS%20umd_tree_cover_loss__ha%20FROM%20data%20WHERE%20umd_tree_cover_density_2000__threshold%20%3D%2030%20GROUP%20BY%20iso%2C%20wri_google_tree_cover_loss_drivers__driver%2C%20umd_tree_cover_loss__year%20ORDER%20BY%20iso%2C%20wri_google_tree_cover_loss_drivers__driver%2C%20umd_tree_cover_loss__year), at a tree canopy cover in 2000 of at least 30 percent. Hectares as in the file, not rounded.",
      "inputs": ["79b791bcb241093320fc0e6297babf3c468e58027c31dc8213c328f68c46053e", "2e9dfe9bb94ea744f5f4178fe292ffacc0173caf50609cd1f9981106374a6c63"],
      "lock_sha256": "7e110986c30381965c2e6096466c401cbf72bd1b7a828369f6f6c14c909dcbda",
      "script": "pipeline/src/envdash/transforms/nature/gfw_tree_cover_loss.py",
      "transform_sha256": "15a82e1f48690b7a720c6627c6ebe816d1ad424d54de3633a13ab1e900bc7ed0"
    },
    {
      "description": "A country and year with no row in the file has no loss recorded for it; nothing is filled in.",
      "inputs": ["79b791bcb241093320fc0e6297babf3c468e58027c31dc8213c328f68c46053e", "2e9dfe9bb94ea744f5f4178fe292ffacc0173caf50609cd1f9981106374a6c63"],
      "lock_sha256": "7e110986c30381965c2e6096466c401cbf72bd1b7a828369f6f6c14c909dcbda",
      "script": "pipeline/src/envdash/transforms/nature/gfw_tree_cover_loss.py",
      "transform_sha256": "15a82e1f48690b7a720c6627c6ebe816d1ad424d54de3633a13ab1e900bc7ed0"
    },
    {
      "description": "Driver classes as WRI names them; \"Hard commodities\" is labelled \"Mining and energy (hard commodities)\" and \"Settlements & Infrastructure\" \"Settlements and infrastructure\". Each 1 km cell's loss is assigned to its one dominant driver (WRI/Google DeepMind, Sims et al. 2025).",
      "inputs": ["79b791bcb241093320fc0e6297babf3c468e58027c31dc8213c328f68c46053e", "2e9dfe9bb94ea744f5f4178fe292ffacc0173caf50609cd1f9981106374a6c63"],
      "lock_sha256": "7e110986c30381965c2e6096466c401cbf72bd1b7a828369f6f6c14c909dcbda",
      "script": "pipeline/src/envdash/transforms/nature/gfw_tree_cover_loss.py",
      "transform_sha256": "15a82e1f48690b7a720c6627c6ebe816d1ad424d54de3633a13ab1e900bc7ed0"
    },
    {
      "description": "World: the sum of every row of the file by year and driver, including the codes left out below. Checked that the drivers add up to loss-global-annual in every year, to within 0.01 ha.",
      "inputs": ["79b791bcb241093320fc0e6297babf3c468e58027c31dc8213c328f68c46053e", "2e9dfe9bb94ea744f5f4178fe292ffacc0173caf50609cd1f9981106374a6c63"],
      "lock_sha256": "7e110986c30381965c2e6096466c401cbf72bd1b7a828369f6f6c14c909dcbda",
      "script": "pipeline/src/envdash/transforms/nature/gfw_tree_cover_loss.py",
      "transform_sha256": "15a82e1f48690b7a720c6627c6ebe816d1ad424d54de3633a13ab1e900bc7ed0"
    },
    {
      "description": "Not published as countries: GADM codes with no entity in our table (XAD 80 ha; Z01 4,365 ha; Z06 555 ha; Z07 164,532 ha, all years together). Their loss is included in the world totals.",
      "inputs": ["79b791bcb241093320fc0e6297babf3c468e58027c31dc8213c328f68c46053e", "2e9dfe9bb94ea744f5f4178fe292ffacc0173caf50609cd1f9981106374a6c63"],
      "lock_sha256": "7e110986c30381965c2e6096466c401cbf72bd1b7a828369f6f6c14c909dcbda",
      "script": "pipeline/src/envdash/transforms/nature/gfw_tree_cover_loss.py",
      "transform_sha256": "15a82e1f48690b7a720c6627c6ebe816d1ad424d54de3633a13ab1e900bc7ed0"
    }
  ],
  "published_value": null,
  "schema_version": 1,
  "scope": {
    "baseline": null,
    "basis": "Tree cover loss: stand-replacing disturbance, or the complete removal of tree cover canopy, of vegetation taller than 5 m, mapped each year from Landsat at 30 m (Hansen/UMD, Global Forest Change v1.13), where tree canopy cover was at least 30 percent in 2000. Loss is not deforestation: it includes plantation harvest, fire and natural disturbance, and regrowth is not subtracted. Method changes from 2011 and 2015 make early and late years less comparable. Drivers: the dominant driver of loss in each 1 km cell, 2001–2025, from a model trained on labelled samples (WRI/Google DeepMind, Sims et al. 2025). WRI counts permanent agriculture, hard commodities and settlements and infrastructure as permanent conversion to another land use; the other drivers are usually followed by regrowth.",
    "bunkers": null,
    "geography": "Countries and territories (GADM's country codes, mapped to ISO 3166-1) and the world, which also includes areas GADM codes separately (XAD, Z01, Z06, Z07).",
    "gwp": null,
    "lulucf": null
  },
  "superseded_by": null,
  "table": {
    "dims": {
      "driver": ["permanent-agriculture", "permanent-agriculture", "permanent-agriculture", "permanent-agriculture", "permanent-agriculture", "permanent-agriculture", "permanent-agriculture", "permanent-agriculture", "permanent-agriculture", "permanent-agriculture", "permanent-agriculture", "permanent-agriculture", "permanent-agriculture", "permanent-agriculture", "permanent-agriculture", "permanent-agriculture", "permanent-agriculture", "permanent-agriculture", "permanent-agriculture", "permanent-agriculture", "permanent-agriculture", "permanent-agriculture", "permanent-agriculture", "permanent-agriculture", "permanent-agriculture", "hard-commodities", "hard-commodities", "hard-commodities", "hard-commodities", "hard-commodities", "hard-commodities", "hard-commodities", "hard-commodities", "hard-commodities", "hard-commodities", "hard-commodities", "hard-commodities", "hard-commodities", "hard-commodities", "hard-commodities", "hard-commodities", "hard-commodities", 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"logging", "logging", "logging", "logging", "wildfire", "wildfire", "wildfire", "wildfire", "wildfire", "wildfire", "wildfire", "wildfire", "wildfire", "wildfire", "wildfire", "wildfire", "wildfire", "wildfire", "wildfire", "wildfire", "wildfire", "wildfire", "wildfire", "wildfire", "wildfire", "wildfire", "wildfire", "wildfire", "wildfire", "settlements-and-infrastructure", "settlements-and-infrastructure", "settlements-and-infrastructure", "settlements-and-infrastructure", "settlements-and-infrastructure", "settlements-and-infrastructure", "settlements-and-infrastructure", "settlements-and-infrastructure", "settlements-and-infrastructure", "settlements-and-infrastructure", "settlements-and-infrastructure", "settlements-and-infrastructure", "settlements-and-infrastructure", "settlements-and-infrastructure", "settlements-and-infrastructure", "settlements-and-infrastructure", "settlements-and-infrastructure", "settlements-and-infrastructure", "settlements-and-infrastructure", 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  },
  "time_basis": "calendar",
  "title": "Tree cover lost each year, by what drove it",
  "unit": {
    "code": "ha",
    "label": "hectares",
    "short": "ha"
  },
  "vintage": "GFC-2025-v1.13, WRI table gadm__tcl__iso_change v20260424"
}