Data and Methods

Last Updated on April 4, 2024

Every year, the Global Forest Review (GFR) provides an independent assessment of the state of the world’s forests based on the best available geospatial data and analysis. A key distinguishing element of the GFR is its focus on insights derived from analysis of geospatial data and maps. In 2014, breakthroughs in global forest monitoring using satellite data, computer algorithms, and cloud computing resulted in the first global map of forest change at 30-meter resolution, depicting tree cover loss annually since 2001 and tree cover gain cumulatively over the same time period. Analysis of these data, combined with hundreds of other spatial data sets, allows for granular, timely, and consistent monitoring of global forest trends over time and space.

 

The Global Forest Change data set, with its annual updates on tree cover loss and gain, provides a critical input to the report. The GFR also draws on spatial data and analysis techniques that are rapidly improving with the evolution of forest monitoring technologies and scientific methods. The report and the Data and Methods section will be updated annually to reflect the latest advances in data and data science.

 

Data and Methods is organized into three subsections: Data Sets, Methodology, and Indicators Overview. The Data Sets section describes the spatial data used in the GFR. The Methodology section describes techniques underpinning any calculations and analysis of the data conducted by World Resources Institute to derive and report results. The Indicators Overview section builds on these two sections by summarizing the data sets and methods used for each calculation.

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Data Sets

The Global Forest Review (GFR) uses the best-available global spatial data on forests. Over 20 different global data sets come together to help us understand why our forests are changing and the impacts these changes have on people, climate, and biodiversity. Unless otherwise specified, the data descriptions below summarize definitions and methods outlined in published papers. Additional manipulation or processing of the data sets was not done for GFR analyses. The data sets are divided between the following types:

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Data SetSourceSpatial ResolutionTemporal ResolutionYears of CoverageSpatial Coverage
Tree cover lossHansen et al. (2013)30-meterAnnual2001-23Global
Tree cover loss by dominant driverCurtis et al. (2018)10-kilometer23 years2001-23Global
Tree cover gainPotapov et al. (2022)30-meter20 years2000-2020Global 
Lower Mekong height and canopyPotapov et al. (2019)30-meterAnnual2001-17Lower Mekong
Net tree cover changePotapov et al. (2022)30-meter20 years2000-2020Global
Hot spots of primary forest lossHarris et al. (2017)Vector22 years2002–23Tropics
Tree cover loss due to fireTyukavina et al. (2022)30-meterAnnual2001–23Global

 

 

Tree cover loss.  This data set measures areas of tree cover loss across all global land at 30-meter (m) resolution. The data were generated using multispectral satellite imagery from the Landsat 5 Thematic Mapper, the Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and the Landsat 8 Operational Land Imager sensors. Over 1 million satellite images were processed and analyzed, including over 600,000 Landsat 7 images for the 2000–12 interval and more than 400,000 Landsat 5, 7, and 8 images for updates for the 2011–23 interval. The clear land surface observations in the satellite images were assembled and a supervised learning algorithm was applied to identify per-pixel tree cover loss.

In this data set, tree cover is defined as all vegetation greater than 5 m in height and greater than 30 percent tree canopy density, and it may take the form of natural forests or plantations across a range of canopy densities. Tree cover loss is defined as “stand replacement disturbance,” or the complete removal of tree cover canopy at the Landsat pixel scale (i.e., tree cover from more than 30 percent to about 0 percent). Tree cover loss may be the result of human activities, including forestry practices such as timber harvesting or deforestation (the conversion of natural forest to other land uses) as well as natural causes such as disease or storm damage. Fire is another widespread cause of tree cover loss and can be either natural or human induced. Forest degradation —for example, selective removals from within forests that do not lead to a nonforest state—are not included in the loss data. Therefore, partial reductions in canopy cover (e.g., from 70 percent to 40 percent) are not included in the loss data.

Data for 2011–23 were produced as annual updates, while 2001-2012 were produced as a block as part of the original publication. Recent years of data are also more sensitive to changes due to the incorporation of data from Landsat 8 (2013) and improvements to the method (most notably in 2015). Comparisons between older and more recent data should be performed with caution.

At the global scale, for the original 2001–12 product, the overall prevalence of false positives (detected as tree cover loss but, in reality, is not, also known as commission errors) in this data is 13 percent, and the prevalence of false negatives (not detected as tree cover loss but, in reality, is lost, also known as omission errors) is 12 percent, though the accuracy varies by biome and thus may be higher or lower in any particular location. The model often misses disturbances in smallholder landscapes, resulting in lower accuracy of the data in sub-Saharan Africa, where this type of disturbance is more common. There is 75 percent confidence that the loss occurred within the stated year, and 97 percent confidence that it occurred within a year before or after. The data also does not detect sparse and scattered trees in the agricultural landscape. Additional accuracy assessments for the updated algorithm and additional years of loss data beyond 2012 are not available.

Tree cover loss by dominant driver.  This data set shows the dominant driver of tree cover loss from 2001 to 2023 using the following five categories:

  • Commodity-driven deforestation: Long-term, permanent conversion of forest and shrubland to a nonforest land use such as agriculture (including oil palm), mining, or energy infrastructure.
  • Shifting agriculture: Small- to medium-scale forest and shrubland conversion for agriculture that is later abandoned and followed by subsequent forest regrowth.
  • Forestry: Large-scale forestry operations occurring within managed forests and tree plantations.
  • Wildfire: Large-scale forest loss resulting from the burning of forest vegetation with no visible human conversion or agricultural activity afterward.
  • Urbanization: Forest and shrubland conversion for the expansion and intensification of existing urban centers. 

For the purposes of statistics generated in the Global Forest Review, commodity-driven deforestation, urbanization, and shifting agriculture with primary forest are considered to represent permanent deforestation, whereas tree cover usually regrows in the other categories (forestry, wildfire, and shifting agriculture outside of primary forests).

The data were generated using decision tree models to separate each 10-kilometer (km) grid cell into one of the five categories. The decision trees were created using 4,699 sample grid cells and use metrics derived from the following data sets: Hansen et al. (2013) tree cover, tree cover gain, and tree cover loss; National Aeronautics and Space Administration fires; global land cover; and population count. Separate decision trees were created for each driver and each region (North America, South America, Europe, Africa, Eurasia, Southeast Asia, Oceania), for a total of 35 decision trees. The final outputs were combined into a global map that is then overlaid with tree cover loss data to indicate the intensity of loss associated with each driver around the world.

Regional models were created, and training samples allowed for the interpretation of local land uses or management styles. A cell was categorized as commodity-driven deforestation if it contained clearings that showed signs of existing agriculture, pasture, or mining in the most recent imagery (after the tree cover loss occurred) as well as zero or minimal regrowth in subsequent years. Cells were categorized as shifting agriculture if the cell contained clearings that showed signs of existing agriculture or pasture in most recent imagery (after the tree cover loss occurred) as well as past clearings that contained visible forest or shrubland regrowth (gain) in historical imagery spanning 2001–15. Tree crops typically considered as agricultural commodities, such as oil palm, were classified accordingly as commodity-driven deforestation. The forestry class reflects a combination of wood fiber plantations and other forestry activity, including clear-cutting and selective cuts. Cells were categorized as wildfire when large swaths of fire scarring were visible in cleared areas, indicating that the loss event was driven by wildfire. The wildfire class excludes fire used to clear land for agriculture. Cells were categorized as urbanization if the loss of tree cover coincided with visible urban expansion or intensification.

This data set is intended for use at the global or regional scale, not for individual pixels. Individual grid cells may have more than one driver of tree cover loss, with variation over space and time.

Aside from commodity-driven deforestation, urbanization, and shifting agriculture with primary forest, which are assumed to represent permanent conversion from a forest to nonforest state, this data set does not indicate the stability or changing condition of the forest land use after the tree cover loss occurs. The data set does not distinguish between natural or anthropogenic wildfires, but it does distinguish fires for conversion or agricultural activity, which are not included in the wildfire class. Only direct drivers of forest disturbance are considered, and not indirect drivers such a demographic pressures or economic markets.

The accuracy of the data was assessed using a validation sample of 1,565 randomly selected grid cells. At the global scale, overall accuracy of the model was 89 percent, with individual class accuracies ranging from 55 percent (urbanization) to 94 percent (commodity-driven deforestation). The data has been updated since the original publication to include tree cover loss data from 2016 to 2023.

Tree cover gain.  This data set measures areas of tree cover gain across all land globally based on data at 30-meter resolution, displayed as the total area with tree cover in 2020 that did not have tree cover in 2000. The data was developed by Potapov et al. (2022) through the integration of the Global Ecosystem Dynamics Investigation (GEDI) lidar forest structure measurements and Landsat analysis-ready data time-series. The NASA GEDI is a spaceborne lidar instrument that provides point-based measurements of vegetation structure, including forest canopy height at latitudes between 52°N and 52°S globally. The Landsat multi-temporal metrics that represent the surface phenology serve as the independent variables for global forest height modeling with the GEDI data as the dependent reference data. The model was extrapolated to the boreal regions (beyond the GEDI data range).

Tree cover gain is defined as land cover with tree canopy height of at least five meters tall in 2020 but not in 2000 at the Landsat pixel scale. Tree cover gain may indicate a number of potential activities, including natural forest growth or the rotation cycle of tree plantations.

Lower Mekong height and canopy.  This data set measures the annual tree canopy extent and height for the lower Mekong region (Cambodia, Laos, Myanmar, Thailand, and Vietnam) at 30 m resolution for the years 2000–17. The data were generated from the University of Maryland’s Landsat Analysis Ready Data, a time-series data set of 16-day normalized surface reflectance composites, to produce regional woody vegetation structure mapping and change detection. A semiautomatic algorithm was used to map woody vegetation canopy cover and height. It used automatic data processing and mapping using a set of lidar-based vegetation structure prediction models. Any changes in vegetation cover were detected separately and then integrated into the structure time series.

Tree cover change.  This data set measures the net areas of tree cover change (loss or gain) across all land globally, and by country, between 2000 and 2020 based on data at 30-meter resolution. The data was developed by Potapov et al. (2022) through the integration of the Global Ecosystem Dynamics Investigation (GEDI) lidar forest structure measurements and Landsat analysis-ready data time-series. The NASA GEDI is a spaceborne lidar instrument that provides point-based measurements of vegetation structure, including forest canopy height at latitudes between 52°N and 52°S globally. The Landsat multi-temporal metrics that represent the surface phenology serve as the independent variables for global forest height modeling with the GEDI data as the dependent reference data. The model was extrapolated to the boreal regions (beyond the GEDI data range).

Net tree cover change is defined as the difference between tree cover gain and loss (that is, the amount of tree cover gain minus the amount of tree cover loss) between 2000 and 2020. Tree cover gain is defined as land cover with tree canopy height of at least five meters tall in 2020 but not in 2000 at the Landsat pixel scale (30 meters). Conversely, tree cover loss is defined as an area with tree canopy height greater than five meters in 2000 and less than five meters in 2020. Tree cover disturbance, in which tree cover is lost and regrown repeatedly during the 20-year time period, is tracked separately and not considered in the net tree cover change total.

Hot spots of primary forest loss.  The emerging hot spots data set identifies the most significant clusters of primary humid tropical forest loss between 2002 and 2023 within each country. The term hot spot is defined as an area that exhibits statistically significant clustering in the spatial patterns of loss. In this analysis, observed patterns of primary forest loss are likely to be attributable to underlying, as opposed to random, spatial processes. 

The emerging hot spots analysis uses the annual Hansen et al. (2013) tree cover loss data set between the years 2002 and 2023, the Turubanova et al. (2018) primary forest extent data set for the year 2001, and the Esri ArcGIS Emerging Hot Spot Analysis geoprocessing tool. The tool uses a combination of two statistical measures: the Getis-Ord Gi* statistic to identify the location and degree of spatial clustering of forest loss and the Mann-Kendall trend test to evaluate the temporal trend over time. The analysis was run for individual countries, and its results are relative to the patterns and amount of loss in each country. It has been updated since the original publication to include the latest tree cover loss data. 

Tree cover loss due to fire.  This data set measures areas of tree cover loss due to fires across all global land (except Antarctica and other Arctic islands) at approximately 30-meter resolution. Tree cover loss is defined, following Hansen et al. 2013, as “stand replacement disturbance,” or the complete removal of tree cover canopy at the Landsat pixel scale. Stand replacement forest fires are defined as natural or human-ignited fires resulting in direct loss of tree canopy cover exceeding 5 meters in height. This can include wildfires, intentionally set fires, or escaped fires from human activities, such as hunting or agriculture. It does not include burning of felled trees, since the direct cause of loss in these cases is mechanical removal. Therefore, trees that are cut down and later burned to clear land for agriculture would not be classified as tree cover loss due to fire in this dataset. It does not include low-intensity and understory forest fires that do not result in substantial tree canopy loss at the scale of a 30-meter pixel.

The data were generated using global Landsat-based annual change detection metrics as input data to a set of regionally calibrated classification tree ensemble models. Tree cover loss due to fire was mapped only within the extent of the global 30-m resolution tree cover loss data set (Hansen et al., 2013). The result of the mapping process can be viewed as a set of binary maps (tree cover loss due to fire vs. tree cover loss due to all other drivers).

 
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Data SetSourceSpatial ResolutionTemporal ResolutionYears of CoverageSpatial Coverage
Tree cover extentPotapov et al. (2022)30-meter1 year2020Global
Tree cover extentHansen et al. (2013)30-meter1 year2000Global
Primary forestTurubanova et al. (2018)30-meter1 year2001Tropics
Tropical Tree CoverBrandt et al. 202310-meter1 year2020Tropics (-23.44° to 23.44° latitude)
Intact forest landscapesPotapov et al. (2017)Vector3 years2000, 2013, 2016, 2020Global
Tree plantationsHarris et al. (2019)Vector1 year2015Global
MangrovesBunting et al. (2022)Vector11 years1996, 2007-2010, 2015-2020Global

 

Tree cover extent.  Tree cover is defined as all woody vegetation greater than five meters in height, and can include tree plantations as well as unmanaged natural forestsmanaged natural forests and urban forests. The tree cover extent data set (Potapov et al. 2022) covers all global land for the year 2020 at 30-meter resolution.

Tree cover extent for the year 2000 serves as the baseline for most of the tree cover and forest loss calculations in the Global Forest Review. Data set values represent 0–100 percent tree canopy cover, with percent tree cover defined as the density of tree canopy coverage of the land surface. This data set was generated using multispectral satellite imagery from the Landsat 7 Enhanced Thematic Mapper Plus sensor. The clear surface observations from over 600,000 images were analyzed using Google Earth Engine, a cloud platform for Earth observation and data analysis, to determine per-pixel tree cover using a supervised learning algorithm. For the Global Forest Review, greater than 30 percent tree canopy density threshold was used to define tree cover extent baseline, unless otherwise noted.

Tree cover extent for the year 2020 is used to report on the most recent global extent data available. The data was developed through the integration of the Global Ecosystem Dynamics Investigation (GEDI) lidar forest structure measurements and Landsat analysis-ready data time-series. The NASA GEDI is a spaceborne lidar instrument that provides point-based measurements of vegetation structure, including forest canopy height at latitudes between 52°N and 52°S globally. The Landsat multi-temporal metrics that represent the surface phenology serve as the independent variables for global forest height modeling, with the GEDI data as the dependent reference data. The model was extrapolated to the boreal regions (beyond the GEDI data range).

Primary forest.  Primary forests are among the most biodiverse forests, providing a multitude of ecosystem services, making them crucial for monitoring national land-use planning and carbon accounting. This data set defines primary forest as "mature natural humid tropical forest cover that has not been completely cleared and regrown in recent history" (approximately 30 years before the year 2001, when primary forests were mapped as part of this data). Researchers classified Landsat images into primary forest data, using a separate algorithm for each region.

Tropical Tree Cover.  The Tropical Tree Cover (TTC) data set maps tree cover and tree extent in the tropics for the year 2020. For the Global Forest Review, it is used exclusively for the Trees Outside Forests Indicator to quantify tree cover on human-managed urban and agricultural land, and a 10 percent threshold is applied to include trees in open canopy systems. The 10-meter (m) data set is derived from Sentinel-1 and Sentinel-2 satellites using a convolutional neural network to perform image segmentation on monthly composite images. A full description of the methodology can be found in Brandt et al. (2023).  TTC defines a tree as woody vegetation that is either >5 m in height regardless of canopy diameter, or is between 3 and 5 m in height with a crown of at least a 5-m diameter. Tall herbaceous vegetation such as sugarcane, bananas and cacti, and short woody crops such as tea and coffee are excluded. Trees on non-forested land such as agroforestry, rotational and non-rotational tree plantations and trees in urban areas are included as trees. TTC covers 4.35 billion hectares of land in the tropics (-23.44° to 23.44° latitude) and is currently available for the year 2020.

Intact forest landscapes.  The intact forest landscapes (IFLs) data set identifies unbroken expanses of natural ecosystems within the zone of forest extent that show no signs of significant human activity and are large enough that all native biodiversity, including viable populations of wide-ranging species, could be maintained. They can include temporary treeless areas after natural disturbances, water bodies, or treeless intact ecosystems where climate, soil, or hydrological conditions prevent forest growth. 

To map IFL areas, the extent of forest areas was identified using greater than 20 percent tree canopy density in the Hansen et al. (2013)  data set. Then a set of criteria was developed and designed to be globally applicable and easily replicable, the latter to allow for repeated assessments over time as well as verification. IFL areas were defined as unfragmented landscapes, at least 50,000 hectares in size, and with a minimum width of 10 kilometers. For the most part, once an area is disturbed, it is no longer considered intact and any regrowth of IFLs is not measured in the data. These were then mapped from Landsat satellite imagery for the year 2000.

Changes in the extent of IFLs were identified within the year 2000 IFL boundary using the global wall-to-wall Landsat image composite for 2016 and the global forest cover loss data set (Hansen et al. 2013). Areas identified as “reduction in extent” met the IFL criteria in 2000, but they no longer met the criteria in 2020. The main causes of change were clearing for agriculture and tree plantations, industrial activity such as logging and mining, fragmentation due to infrastructure and new roads, and fires assumed to be caused by humans.

The world IFL map was created through visual interpretation of Landsat images by experts. The map may contain inaccuracies due to limitations in the spatial resolution of the imagery and lack of ancillary information about local land-use practices in some regions. In addition, the methodology assumes that fires near roads or other infrastructure may have been caused by humans and therefore constitute a form of anthropogenic disturbance. This assumption could result in an underestimation of IFL extent in the boreal biome.

Tree plantations.  The Spatial Database of Planted Trees (SDPT) was compiled by World Resources Institute using data obtained from national governments, nongovernmental organizations, and independent researchers. Data were compiled for 82 countries around the world, through a procedure that included cleaning and processing each individual data set before creating a harmonized attribute table. Most country maps originated from supervised classification or manual polygon delineation of Landsat, SPOT, or RapidEye satellite imagery. The data is nominally representative of the year 2015, although years for individual countries vary. 

The planted trees category in the SDPT includes forest plantations of native or introduced species, established through deliberate human planting or seeding. Sometimes called tree farms, these forests infuse the global economy with a constant stream of lumber for construction, pulp for paper, and fuelwood for energy. The data set also includes agricultural tree crops such as oil palm plantations, avocado farms, apple orchards, and even Christmas tree farms. The SDPT makes it possible to identify planted forests and tree crops as separate from natural forests and enables changes in these planted areas to be monitored independently from changes in global natural forest cover.

Mangrove forests.  This data set (version 3.0) depicts the global extent of mangrove forests for the years 1996, 2007-2010, and 2015-2020 derived by L-band Synthetic Aperture Radar (SAR) global mosaic data sets from the Japan Aerospace Exploration Agency (JAXA), thus developing a long-term timeseries of global mangrove extent and change. The study used a map-to-image approach to change detection where the baseline map (Global Mangrove Watch v2.5)  was updated using thresholding and a contextual mangrove change mask.

The classification was confined using a mangrove habitat mask, which defined regions where mangrove ecosystems can be expected to exist. The mangrove habitat definition was based on geographical parameters such as latitude, elevation, and distance from ocean water. The habitat mask was initially developed in Global Mangrove Watch (GMW) v2.0  and has been revised in subsequent versions. 

A source of error in v3.0 included a misregistration in the SAR mosaic data sets, resulting in the omission of known change events and commissions where change was known not to have occurred. This was partially corrected for using tie points automatically generated via the method of Bunting et al. (2010).  The changes associated with misregistration errors are considered to be similar in terms of gain and loss due to the random nature of the input data, so it is recommended that the observed net change statistics are used for analysis rather than individual gain and loss statistics. Mangrove extent maps in v3.0 have an estimated overall accuracy of 87.4% (95th CI: 86.2 - 88.6%). 

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Data SetSourceSpatial ResolutionTemporal ResolutionYears of CoverageSpatial Coverage
Global cocoa, coffee, soyMapSPAM10-kilometer1 year2010Global
Global pastureRamankutty et al. (2008)10-kilometer1 year2000Global
Brazilian pastureLaboratório de Processamento de Imagens e Geoprocessamento (LAPIG)30-meter1 year2018Brazil
South America SoySong et al. (2021)30-meterAnnual2001-18South America
Oil palm, rubber, wood fiberHarris et al. (2019)Vector1 year2015Select countries

Global cocoa, coffee, soy.  For the Global Forest Review (GFR), we use cocoa, arabica and robusta coffee, and soy maps from MapSPAM to assess which crops have replaced forests; the exception is for soy in South America, where higher-resolution and more recent data are available. The MapSPAM data maps crop area for 42 crops in the year 2010 at a spatial resolution of 10 kilometers (km). Physical crop area was used in all analyses, as opposed to harvested area, to account for all land occupied by a specific crop. The data combines country and subnational reported production statistics, an agriculture land cover map, and crop-specific suitability information based on climate, landscape, and soil conditions into a spatial model. Suitable areas for each crop are identified in the MapSPAM data by using existing land resources and biophysical limitations to provide suitable crops areas.  Each 10 km grid cell contains the estimated area of each of the 42 crops, further broken into physical and harvested area of irrigated high input, rain-fed high input, rain-fed low input, and rain-fed subsistence. Whereas high input includes the use of high-yield crop varieties, optimal application of fertilizer, chemical pest disease and weed controls, and might be fully mechanized, low input uses traditional varieties of crops with manual labor and minimal or no applications of fertilizers or pest control measures. Subsistence refers to crop production by small-scale farmers largely for their own consumption under rain-fed and low-input conditions, regardless of the suitability of land. It is assumed to happen more intensively in areas with large rural populations, so rural population density from the Global Rural-Urban Mapping Project (Version 1) helps to further identify subsistence farming.    

Global pasture.  This data set maps global pastureland at a 10 km resolution for the year 2000. In the GFR, we use EarthStat pasture data to assess where pasture has replaced forests; the exception is for Brazil, where there is higher-resolution and more recent data available. EarthStat uses the definition of permanent pasture used by the Food and Agriculture Organization of the United Nations (FAO): “land used permanently (5 years or more) for herbaceous forage crops, either cultivated or growing wild.” Agricultural inventory data from a variety of sources, including country and FAOSTAT data, were modeled onto land-use and land cover maps of agriculture and pasture derived from Moderate Resolution Imaging Spectroradiometer and SPOT imagery. The definition of pasture causes some known inconsistencies because some countries distinguish between grassland pasture and grazed land, but most do not in their reporting. 

 

Brazil pasture.  This data set maps annual pasture extent in Brazil at a 30 m resolution. In the GFR, this data is used preferentially over the global pasture map to assess where pasture replaced forests in Brazil. The data were derived from Landsat imagery using automatic, random forest classification. Historical maps from 1985 to 2018 are available from the Image Processing and Geoprocessing Laboratory (Laboratório de Processamento de Imagens e Geoprocessamento; LAPIG) as part of the MapBiomas initiative, but only the 2018 extent was used in the GFR calculations.  

South America soy.  This data set maps annual soy extent from 2001 to 2018. We use this data set for all calculations to assess where soy replaced forests in South America. The data were derived from Landsat imagery to map the harvest season of soy annually from 2001 to 2018. All years were combined to estimate forest loss on land that was eventually used for soy production. 

Oil palm, rubber, wood fiber.  Oil palm, rubber, and wood fiber plantations from the Spatial Database of Planted Trees (SDPT) were used for all calculations to assess where oil palm, rubber, and wood fiber replaced forests. Oil palm is thought to be a comprehensive data set for the year 2015, whereas rubber and wood fiber plantation data is only available for specific countries (for rubber, Brazil, Cambodia, Cameroon, the Democratic Republic of the Congo, India, Indonesia, and Malaysia; and for wood fiber, Argentina, Brazil, Cambodia, China, India, Indonesia, Malaysia, Rwanda, South Africa, and Vietnam). See above for more information about the SDPT. 

 

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Data SetSourceSpatial ResolutionTemporal ResolutionYears of CoverageSpatial Coverage
Protected areasWorld Database on Protected AreasVectorUpdated monthly2024Global
Logging concessionsVaries, see belowVector1 yearVaries, see belowSelect countries

 

 

Protected areas.  The World Database on Protected Areas (WDPA) is the most comprehensive global spatial data set on marine and terrestrial protected areas available. Protected area data are provided via Protected Planet, the online interface for the WDPA, and are updated monthly (the February 2024 data update was used in the Global Forest Review). The WDPA is a joint initiative of the International Union for Conservation of Nature (IUCN) and the United Nations Environment Programme World Conservation Monitoring Centre to compile spatially referenced information about protected areas. All IUCN categories were used as part of any Global Forest Review analysis unless otherwise specified. 

Logging concessions. Managed forests refers to areas allocated by a government for harvesting timber and other wood products in a public forest. Managed forests are distinct from wood fiber concessions, where tree plantations are established for the exclusive production of pulp and paper products. Concession is used as a general term for licenses, permits, or other contracts that confer rights to private companies to manage and extract timber and other wood products from public forests; terminology varies at the national level, however, and includes forest permits, tenures, licenses, and other terms. 

This data set is assembled by aggregating data for multiple countries. Source and date information can be found in the table below. 

Logging concession data sources and dates 

CountrySourceDate
CameroonMinistry of Forestry and Wildlife and World Resources Institute (WRI) Unknown
CanadaGlobal Forest Watch Canada 2016
Central African RepublicMinistry of Water, Forests, Hunting, and Fishing and WRI Unknown
Democratic Republic of the CongoMinistry of the Environment, Nature Conservation, and Tourism and WRI Unknown
Equatorial GuineaMinistry of Agriculture and Forests and WRI 2013
GabonMinistry of Forest Economy, Water, Fisheries, and Aquaculture and WRI Unknown
IndonesiaMinistry of Environment and Forestry 2018
LiberiaGlobal Witness 2016
Sarawak, MalaysiaEarthsight and Global Witness 2010
Republic of the CongoMinistry of Forest Economy and Sustainable Development and WRI2013
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Data SetSourceSpatial ResolutionTemporal ResolutionYears of CoverageSpatial Coverage
Biodiversity intactnessHill et al. (2019) 1-kilometer1 year2018Global
Biodiversity significanceHill et al. (2019) 1-kilometer1 year2018Global
Key Biodiversity AreasBirdLife International Vector1 year2021Global
Alliance for Zero ExtinctionAlliance for Zero ExtinctionVectorUpdated every 5 years2020Global
International Union for Conservation of Nature (IUCN) Red List of Threatened SpeciesIUCN Red List of Threatened SpeciesVectorRegular updatesVaries, see belowGlobal

 

Biodiversity intactness.  This data set quantifies the impact humans have had on the intactness of species communities. Anthropogenic pressures such as land-use conversion have caused dramatic changes to the composition of species communities, and this layer illustrates these changes by focusing on the impact of forest change on biodiversity intactness. The maximum value indicates no human impact, whereas lower values indicate that intactness has been reduced. 

The Projecting Responses of Ecological Diversity in Changing Terrestrial Systems (PREDICTS) database comprises over 3 million records of geographically and taxonomically representative data of land-use impacts to local biodiversity.  A subset of the PREDICTS database, including data pertaining to forested biomes only, was employed to model the impacts of land-use change and human population density on the intactness of local species communities. 

First, a relevant land-use map was produced by selecting all forested biomes and each 30-by-30-meter (m) pixel within the biomes was assigned a land-use category based upon inputs from the Global Forest Watch forest change database and a downscaled land-use map.  The modeled results of biodiversity intactness derived from the PREDICTS database are projected onto the land-use and human population density maps, and the final product is aggregated to match the resolution of the downscaled land-use map.  The final output models the impacts of forest change on local biodiversity intactness within forested biomes.

The metric assumes that the biodiversity found in a perfectly intact site is equivalent to the biodiversity that would be present without human interference. Human impacts on biodiversity intactness are quantified through models that extrapolate results from site-specific studies across large areas, and there is always a degree of uncertainty in such extrapolations.

Biodiversity significance.  This data set shows the significance of each forest location for biodiversity in terms of the relative contribution of each pixel to the global distributions of all forest-dependent mammals, birds, amphibians, and conifers worldwide. To calculate it, species that are coded in the International Union for Conservation of Nature (IUCN) Red List of Threatened Species as forest dependent are selected and their distribution maps are clipped by their known altitudinal ranges (note, the altitudinal range for amphibians has not been assessed) using a digital elevation model data set, and overlapped with the layer of forest cover. For each species, the relative “significance” of each forest pixel in their range is calculated as one divided by the total number of pixels of forest in their range. These values are summed for all species occurring within the pixel to give an overall value to the pixel. This metric is also sometimes termed range rarity.

This data set includes several caveats. There are many ways to define biodiversity significance, and this layer is based on one approach. Only forest-dependent bird, mammal, amphibian, and conifer species were included in the analysis. The individual species range maps upon which this layer is based show distributional boundaries, not occupancy, and so contain commission errors. However, when more than 15,000 species ranges are combined into this single layer, such errors become largely irrelevant. Historical ranges were excluded. Hence, the value of each pixel is related to the global loss of species richness if the pixel is deforested. Locations of high species richness do not necessarily have high scores if most of the species in the location have large global distributions. All species are treated equally, so the evolutionary distinctiveness of different taxa is not considered. When overlaid with maps of forest loss, forest gain is ignored. It is assumed that tree cover gain over the analysis period is unlikely to translate into significant gain in forest-dependent species given the natural time lags in regeneration of forest ecosystems. Finally, the data set provides a broad picture of variation in biodiversity significance of different forests globally. It is not intended to be used in isolation for priority setting or decision-making, for which additional information is typically needed.

Key Biodiversity Areas.  Key Biodiversity Areas (KBAs) are “sites contributing significantly to the global persistence of biodiversity.” The Global Standard for the Identification of Key Biodiversity Areas  sets out globally agreed-upon criteria for the identification of KBAs worldwide. Sites qualify as global KBAs if they meet one or more of 11 criteria, clustered into five categories: threatened biodiversity, geographically restricted biodiversity, ecological integrity, biological processes, and irreplaceability. The KBA criteria can be applied to species and ecosystems in terrestrial, inland water, and marine environments. Although not all KBA criteria may be relevant to all elements of biodiversity, the thresholds associated with each of the criteria may be applied across all taxonomic groups (other than microorganisms) and ecosystems.

The KBA identification process is a highly inclusive, consultative, and bottom-up exercise. Although anyone with appropriate scientific data may propose a site to qualify as a KBA, consultation with stakeholders at the national level (both nongovernmental and governmental organizations) is required during the proposal process.

Over 15,000 KBAs have been identified to date, including Important Bird and Biodiversity Areas, Alliance for Zero Extinction sites, and KBAs identified through hot spot ecosystem profiles supported by the Critical Ecosystem Partnership Fund.

Alliance for Zero Extinction.  A subset of KBAs, this data set shows 587 sites for 920 species of mammals, birds, amphibians, reptiles, conifers, and reef-building corals. The species found within these sites have extremely small global ranges and populations; any change to habitat within a site may lead to the extinction of a species in the wild. To meet Alliance for Zero Extinction site status, a site must

  • contain at least one “Endangered” or “Critically Endangered” species;
  • be the sole area where an Endangered or Critically Endangered species occurs;
  • contain greater than 95 percent of either the known resident population of the species or 95 percent of the known population of one life history segment (e.g., breeding or wintering) of the species; and
  • have a definable boundary (e.g., species range, extent of contiguous habitat, etc.).

IUCN Red List of Threatened Species. This data set contains distribution information on species assessed for the IUCN Red List of Threatened Species. The maps are developed as part of a comprehensive assessment of global biodiversity to highlight taxa threatened with extinction and thereby promote their conservation. The IUCN Red List contains global assessments for 105,732 species, with more than 75 percent of these having spatial data. The Global Forest Review (GFR) uses the Asian elephant, Bornean orangutan, Sumatran orangutan and tiger ranges to assess tree cover loss in their habitat ranges, which were mapped in 2020, 2016, 2017 and 2022 respectively. These three species represent endangered, iconic animals of Southeast Asia. Future editions of the GFR will likely include additional iconic species from South America and Africa. 

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Data SetSourceSpatial ResolutionTemporal ResolutionYears of CoverageSpatial Coverage
Aboveground biomass densityZarin and Woods Hole Research Center30-meter1 year2000Global
Gross emissions, gross removals, and net forest GHG fluxHarris et al. (2021)30-meter23 years2001–23Global

 

 

Aboveground biomass density.  This data set expands on the methodology presented in Baccini et al. (2012)  to generate a global map of aboveground live woody biomass density at 30-meter resolution for the year 2000. Aboveground biomass (AGB) was estimated for more than 700,000 quality-filtered Geoscience Laser Altimeter System (GLAS) lidar observations using allometric equations that estimate AGB based on lidar-derived canopy metrics. The global set of GLAS AGB estimates was used to train random forest models that predict AGB based on spatially continuous data. The predictor data sets include Landsat 7 Enhanced Thematic Mapper Plus top-of-atmosphere reflectance and tree canopy cover from the Global Forest Change data set, Version 1.2;  one arc-second Shuttle Radar Topography Mission, Version 3, elevation;  GTOPO30 elevation from the U.S. Geological Survey (for latitudes greater than 60° north); and WorldClim climate data.  The predictor pixel values were extracted and aggregated for each GLAS footprint to link the GLAS AGB estimates with the predictor data. A random forest model was trained for each of six continental-scale regions: the Afrotropic, Australia, Nearctic, Neotropic, Palearctic, and Tropical Asia regions.  

Gross emissions, gross removals, and net forest greenhouse gas (GHG) flux.  This data set includes estimates for gross GHG emissions, gross carbon removals, and net GHG flux at 30-meter resolution and is derived from a model that combined ground measurements and satellite observations with national GHG inventory methods from the Intergovernmental Panel on Climate Change (IPCC).

Emissions include all carbon pools and multiple greenhouse gases (CO2, CH4, N2O). The CO2e emitted from each pixel is based on maps of carbon densities in 2000 (with adjustment for carbon accumulated between 2000 and the year of disturbance), drivers of tree cover loss, forest type, and burned areas. All emissions are assumed to occur in the year of disturbance (committed emissions). Removals in standing and regrowing forests include the accumulation of carbon in both aboveground and belowground live tree biomass, while ignoring accumulation in dead wood, litter and soil organic carbon due to lack of data. Carbon removed by trees in each pixel is based on maps of forest type, ecozone, forest age, and number of years of forest growth. Net forest GHG flux represents the difference between GHG emissions and carbon removals. Forest is defined as woody vegetation with a height of at least 5 meters and a canopy density of at least 30 percent at 30-meter resolution.

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Data Set Source Spatial Resolution Temporal Resolution Years of Coverage Spatial Coverage
Erosion risk Qin et al. (2016) 10-kilometer 1 year 2015 Global
Urban watersheds The Nature Conservancy Vector 1 year Unknown Global

Erosion risk.Qin et al. 2016, https://www.wri.org/publication/gfw-water-metadata . This data set maps the risk of erosion around the world. Erosion and sedimentation by water involves the process of detachment, transport, and deposition of soil particles, driven by forces from raindrops and water flowing over the land surface. The Revised Universal Soil Loss Equation (RUSLE), which predicts annual soil loss from rainfall and runoff, is the most common model used at large spatial extents due to its relatively simple structure and empirical basis. The model takes into account rainfall erosivity, topography, soil erodibility, land cover and management, and conservation practices. Because the RUSLE model was developed based on agricultural plot scale and parameterized for environmental conditions in the United States, modifications of the methods and data inputs were necessary to make the equation applicable to the globe. Conservation practices and topography information were not included in this model to calculate global erosion potential due to data limitations and their relatively minor contribution to the variation in soil erosion at the continental to global scale compared to other factors. The result of the global model was categorized into five quantiles, corresponding to low to high erosion risks. 

Urban watersheds.McDonald and Shemie 2014, http://water.nature.org/waterblueprint/#/intro=true . Urban watershed boundaries for 530 cities, mapped as part of the Urban Water Blueprint project, including 33 megacities with more than 10 million people. According to United Nations population data for 2018, there were 33 cities with a population greater than 10 million people in 2018. Due to the availability of watershed boundary data, 32 of these cities are included in the Global Forest Review

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Data SetSourceSpatial ResolutionTemporal ResolutionYears of CoverageSpatial Coverage
LandMarkGlobal Platform of Indigenous and Community LandsVector1 year2021Select countries
PopulationGlobal Human Settlement Layer250-meter1 year2015Global
ConflictGlobal WitnessVectorAnnual2012-18Select countries
UN Subregionsthematicmapping.org and UNSD — MethodologyVectorn/aUnknownGlobal

LandMark.  This data set depicts collectively held and used lands worldwide. It consolidates the numerous ongoing local, national, and regional efforts to map and document indigenous and community lands within a single global data set. The data set distinguishes indigenous lands from other community lands in part because various international human rights instruments specifically grant Indigenous Peoples a range of rights, including rights to their land and natural resources. LandMark uses the best-quality data available from reputable organizations and recognized experts, but it does not endorse or verify the accuracy of any data set. 

Population.  The Global Human Settlement Layer (GHSL) Population Grid depicts the distribution and density of population, expressed as the number of people per cell, for 2015. Whereas the Global Forest Review only uses 2015 data, the GHSL is a multitemporal population data set that employs new spatial data mining technologies. These methods enable the automatic processing and extraction of analytics and knowledge from different data sets: global, fine-scale satellite image data streams; census data; and crowd sources or volunteered geographic information sources. 

To produce this population density and distribution data set, researchers mapped global built-up areas, which are defined as all aboveground constructions intended for human or animal sheltering or to produce economic goods. The locations of these built-up areas were established using Landsat imagery analysis. An additional source used to compile this data set was the Gridded Population of the World (GPW) data set assembled by Columbia University’s Center for International Earth Science Information Network. The GPW data set consists of census population data and bolstered the built-up areas data by enabling researchers to estimate residential population. To present this data as grid cells, GPW data was disaggregated from census or administrative units. 

Overall, the GHSL data set is an accurate and high-resolution estimate of global population. Known issues with this data include the insufficient availability of global test sets with the right scale, time period, and reliability to validate and improve the GHSL. Another known challenge is the lack of remote sensing studies that compare the use of different sensors to detect human settlements.  

Conflict.  Global Witness compiles location data documenting the killing and enforced disappearances of land and environmental defenders. This global data set uses credible, published, and current online media reports to identify and report the location of killings. If the exact location is unknown, the location of the media report is used instead, which is typically the closest urban area. Some regions of the world, particularly rural areas, may have underreported numbers due to limited media reports.

UN Subregions.  Composition of geographic regions used by the UN Statistics Division. For the Global Forest Review, it is used as a set of boundaries in which trees outside forests are quantified.

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Data Set Source Spatial Resolution Temporal Resolution Years of Coverage Spatial Coverage
Ecozones FAO (2012) Vector 1 year 2010 Global
Peatlands Indonesia Ministry of Agriculture Vector 1 year 2012 Indonesia
Indonesian forest moratorium Indonesia Ministry of Forestry and Environment Vector 1 year 2019 Indonesia
Rural complex Molinario et al. (2015) 30-meter 4 years 2000, 2005, 2010, 2015 Democratic Republic of the Congo
Countries Database of Global Administrative Areas (GADM) Vector n/a 2019 Global
ESA/CCI Land cover Defourny et al. In preparation, 2023 300-meter 1 year 2020 Global
SBTN Natural Lands Mazur et al. 2023 30-meter 1 year 2020 Global

Ecozones.FAO 2012, http://www.fao.org/3/ap861e/ap861e00.pdf . This data set depicts major ecozones, including boreal, temperate, tropical, and subtropical regions. 

Peatlands.For more information about peatlands, see Global Forest Watch, https://gfw.global/37Pfnpw . This data set shows peatlands in Indonesia greater than five meters in depth. 

Indonesian forest moratorium.For more information about the moratorium, see Global Forest Watch, https://gfw.global/3oxnjBJ . Data set indicating the area of Indonesia’s moratorium against new forest concessions, designed to protect Indonesia’s peatlands and primary natural forests from future development. In May 2011, the Ministry of Environment and Forestry put into effect a two-year moratorium on the designation of new forest concessions in primary natural forests and peatlands. This moratorium is designed to allow time for the government to develop improved processes for land-use planning, strengthen information systems, and build institutions to achieve Indonesia’s low-emission development goals. The moratorium, made permanent in 2019, is part of Indonesia’s pledge to curtail forest clearing in a US$1 billion deal with the Norwegian government. 

Rural complex.Molinario et al. 2015, https://doi.org/10.1088/1748-9326/10/9/094009 . This Democratic Republic of the Congo (DRC) land-use and land cover data set depicts core forest, forest fragmentation, and the rural complex, a land-use mosaic of roads, villages, active and fallow fields, and secondary forest that we use as a proxy for shifting cultivation in the DRC. This is separate from shifting cultivation identified in the drivers of deforestation data set and is only used in the DRC-specific analysis from the Forest Extent Indicator.  

The data set was created by characterizing forest clearing using spatial models in a geographical information system, applying morphological image processing to the Central African Forests Remotely Assessed (Forets d'Afrique Central Evaluee par Teledetection; FACET) product. This process allowed for the creation of maps for 2000, 2005, 2010, and 2015, classifying the rural complex and previously homogenous primary forest into separate patch, edge, perforated, fragmented, and core forest subtypes. 

Countries.See GADM, https://gadm.org/ .  This data set shows political boundaries, including country, provincial, and jurisdictional administrative units. 

ESA CCI Land Cover.See ESA CCI, http://maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf This data set maps annual global land cover at 300-meter resolution for the year 2020. The annual maps are derived from a baseline land cover map based on the Medium Resolution Imaging Spectroradiometer (MERIS) Full Resolution and Reduced Resolution archive from 2003 to 2012. Land cover changes are detected based on the Very High Resolution Radiometer (VHRR) time series from 1992 to 1999, SPOT-Vegetation time series between 1999 and 2013 and PROBA-Vegetation for years 2013, 2014 and 2015. The baseline land cover map is then backdated and updated to produce annual maps. For the Global Forest Review, it is used to delineate urban, grassland and agricultural land, on which trees outside forests are quantified.

SBTN Natural Lands.Mazur et al. 2023, https://sciencebasedtargetsnetwork.org/wp-content/uploads/2023/05/Technical-Guidance-2023-Step3-Land-v0.3-Natural-Lands-Map.pdf This data maps natural and non-natural lands for the year 2020 at 30-meter resolution based on Accountability Framework Initiative (AFi) definitions and operational guidance. This data was created by WRI in collaboration with WWF and Systemiq as part of the Science Based Target Network‘s guidance for setting land science-based targets. The Natural Lands map was created by combining the best available global spatial data on land cover and land use into a single, harmonized map using a series of overlays and decision rules. Both global and regional/local data was used, and where available, regional/local data was given priority. The global binary map was independently validated by the International Institute for Applied Systems Analysis (IIASA) using a random sample of 4,730 points and has an overall accuracy of 91.6 percent. Individual class accuracies show that the map misclassifies 6 percent of the natural points as non-natural, and 18 percent of the non-natural points as natural. Limitations include definitional, temporal and resolution inconsistencies due to the combination of data from various sources. Due to a lack of global remote sensing data on pasturelands, gridded livestock densities from the UN Food and Agriculture Organization (FAO) were used as a proxy for non-natural short vegetation.

 
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Methodology

All analyses in the Global Forest Review draw heavily on per-pixel geodesic area calculations for accurate global area estimations of forests. This means that the precise geodesic area of each 30-meter (m) pixel across the globe is calculated and then summed for each year of loss and unique area of interest, such as countries or protected areas. Due to distortions from projecting the three-dimensional surface of the earth onto a flat surface, the area of a 30 m pixel can vary from roughly 900 m2 at the equator to roughly 200 m2 at the poles. These area differences are accounted for when using geodesic area calculations. Unless otherwise specified, all calculations are run at a 30 percent tree canopy density threshold as of the year 2000.

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Calculations

  • Area calculation: Sum the geodesic area of all pixels within an area of interest.
  • Extent calculation: Sum the geodesic area of all pixels within the tree cover extent raster data set. 
  • Tree cover loss calculation: Sum the geodesic area of all tree cover loss pixels within an area of interest (AOI; e.g., country boundaries or protected areas).
  • Rate of loss calculation: Loss area in current year minus loss area in past year divided by loss area in past year. Only countries with at least 100,000 hectares of tree cover in the year 2000 were included.
  • Percent of loss calculation: Divide loss of current year by earlier forest extent area.
  • Carbon storage calculation: The aboveground biomass density data set is formatted as biomass per hectare. To convert values to carbon per pixel, each biomass pixel is multiplied by the geodesic area (in hectares) of that pixel to get biomass per pixel, and then divided by 0.47 to convert biomass to carbon. Finally, sum the aboveground biomass pixel values that overlap with the tree cover extent raster data set.
  • Gross emissions, gross removals, and net forest greenhouse gas (GHG) flux calculation: Gross emissions are estimated annually, while removals and net flux reflect the total over the period of 2001-2023 and are divided by 23 to calculate the average annual gross removals and average annual net flux. To calculate gross emissions or gross removals over specific areas, we convert emissions/removals per hectare to emissions/removals per pixel by multiplying emissions/removals (in CO2e) by the geodesic area of each pixel (in hectares), and then summing within the area of interest. Net flux is calculated by subtracting average annual gross removals from average annual gross emissions in each modeled pixel.
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Indicators Overview

Using the above data sets and methodologies, the Global Forest Review (GFR) assesses the state of the world’s forests and provides insight into how they are changing year to year based on 17 indicators. The next section outlines each statistic produced by GFR authors, along with the data set and method summary used to generate each calculation.  

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Forest Extent

The Forest Extent Indicator aims to monitor the total area of forest worldwide, including unmanaged natural forests and managed natural forests. The indicator currently measures tree cover extent in the year 2020 as a best-available proxy for forest. Tree cover extent includes unmanaged and managed natural and planted forests, as well as agricultural tree crops, which are not typically considered forests.

Statistic Data Set Method
In 2020, the world had 4.02 billion hectares of tree cover, covering 30 percent of land on Earth.  Tree cover extent; countries Extent calculation on 2020 tree cover extent; area calculation on countries
Russia, Brazil, Canada, the United States and China … have the highest total area of tree cover. Tree cover extent; countries Extent calculation on 2020 tree cover extent by country
… more than 90 percent of the land has tree cover [in] Equatorial Guinea, French Guiana, Gabon, Liberia, the Solomon Islands, Suriname and Vanuatu. Tree cover extent; countries Extent calculation on 2020 tree cover extent by country; area calculation on countries
Tropical and subtropical forests … account for 61 percent of 2020 global tree cover by area. Ecozones; tree cover extent Extent calculation on 2020 tree cover extent by ecozone
Boreal forests … make up 24 percent of global tree cover. Ecozones; tree cover extent Extent calculation on 2020 tree cover extent by ecozone
Temperate forests … account for about 15 percent of global tree cover. Ecozones; tree cover extent Extent calculation on 2020 tree cover extent by ecozone
Primary forests account for roughly 50 percent of all forests in the tropics (1.03 billion hectares). Ecozones; primary forests Extent calculation on primary forest divided by extent calculation on 2010 tree cover extent by ecozone
The world lost 101 million hectares (Mha) of tree cover between 2000 and 2020. Tree cover change Sum of extent calculation on 20 years tree cover loss and gain
The tropics and subtropics lost 92 Mha and the boreal lost 14 Mha of tree cover, while temperate forests gained 4.5 Mha. Ecozones; tree cover change Sum of extent calculation on 20 years tree cover loss and gain by ecozone
During this 20-year period, Brazil had the highest net loss of tree cover by area, more than three times the next highest country. Tree cover change; countries Sum of extent calculation on 20 years tree cover loss and gain by country
Cambodia, Paraguay and Uganda experienced the highest percentage of net tree cover loss, losing over 23% of their tree cover between 2000 and 2020. Tree cover change; countries Sum of extent calculation on 20 years tree cover loss and gain by country, divided by 2000 tree cover height extent
… 36 countries experienced net tree cover gain, including China, India, Uruguay, Belarus, Ukraine and Poland. Tree cover change; countries Sum of extent calculation on 20 years tree cover loss and gain by country
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Limitations

The Global Forest Change data offer an annual view of the world’s forests at locally relevant scales using globally consistent criteria. However, they also have key limitations: 

  • Not all tree cover is a forest. Satellite data are effective for monitoring changes in tree cover, but forests are typically defined as a combination of tree cover and land use. For example, agricultural tree cover, such as an oil palm plantation, is not usually considered to be forest. As such, satellite-based monitoring systems may overestimate forest area unless combined with additional land-use data sets. No land-use data set currently exists at an adequate resolution or update frequency to enable this analysis at global scale. 
  • Not all tree cover loss is deforestation. Defined as permanent conversion of forested land to other land uses, deforestation can only be identified at the moment trees are removed if it is known how the land will be used afterward. In the absence of a global data set on land use, it is not possible to accurately classify tree cover loss as permanent (i.e., deforestation) or temporary (e.g., where it is associated with wildfire, timber harvesting rotations, or shifting cultivation) at the time it occurs. However, new models analyzing spatial and temporal trends in tree cover loss are enabling better insights into the drivers of loss.   
  • Tree cover is a one-dimensional measure of a forest. Many qualities of a forest cannot be measured as a function of tree cover and are difficult, if not impossible, to detect from space using existing technologies. Forests that are vastly different in terms of form and function—such as an intact primary forest and a planted forest managed for timber production—are nearly indistinguishable in satellite imagery based on tree cover. Detecting forest degradation through remote sensing is also challenging because degradation often entails small changes occurring beneath the forest canopy.
  • Tree cover gain is more difficult to measure than loss. Whereas tree cover loss is distinctly visible at a specific moment in time, tree cover gain is a gradual process and is thus more difficult to discern from one satellite image to the next. Annual reporting of tree cover loss has not been matched by annual reporting of tree cover gain, resulting in an unbalanced view of global forest change dynamics. Ongoing improvements in detection methodologies will provide annualized global tree cover gain data in the coming years.
  • Tree cover loss and gain do not equal net forest. Due to variation in research methodology and date of content, tree cover, tree cover loss, and tree cover gain data sets cannot be compared accurately against each other. Accordingly, “net” loss cannot be calculated by subtracting figures for tree cover gain from tree cover loss, and current (post-2000) tree cover cannot be determined by subtracting figures for annual tree cover loss from year 2000 tree cover.
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{"Glossary":{"51":{"name":"agricultural tree crops","description":"Trees cultivated for their food, cultural, or economic values. These include oil palm, rubber, cocoa, cashew, mango, oranges (citrus), plantain, banana, and coconut.\r\n"},"141":{"name":"agroforestry","description":"A diversified set of agricultural or agropastoral production systems that integrate trees in the agricultural landscape.\r\n"},"101":{"name":"albedo","description":"The ability of surfaces to reflect sunlight.\u0026nbsp;Light-colored surfaces return a large part of the sunrays back to the atmosphere (high albedo). Dark surfaces absorb the rays from the sun (low albedo).\r\n"},"94":{"name":"biodiversity intactness","description":"The proportion and abundance of a location\u0027s original forest community (number of species and individuals) that remain.\u0026nbsp;\r\n"},"95":{"name":"biodiversity significance","description":"The importance of an area for the persistence of forest-dependent species based on range rarity.\r\n"},"142":{"name":"boundary plantings","description":"Trees planted along boundaries or property lines to mark them well.\r\n"},"98":{"name":"carbon dioxide equivalent (CO2e)","description":"Carbon dioxide equivalent (CO2e) is a measure used to aggregate emissions from various greenhouse gases (GHGs) on the basis of their 100-year global warming potentials by equating non-CO2 GHGs to the equivalent amount of CO2.\r\n"},"99":{"name":"CO2e","description":"Carbon dioxide equivalent (CO2e) is a measure used to aggregate emissions from various greenhouse gases (GHGs) on the basis of their 100-year global warming potentials by equating non-CO2 GHGs to the equivalent amount of CO2.\r\n"},"1":{"name":"deforestation","description":"The change from forest to another land cover or land use, such as forest to plantation or forest to urban area.\r\n"},"77":{"name":"deforested","description":"The change from forest to another land cover or land use, such as forest to plantation or forest to urban area.\r\n"},"76":{"name":"degradation","description":"The reduction in a forest\u2019s ability to perform ecosystem services, such as carbon storage and water regulation, due to natural and anthropogenic changes.\r\n"},"75":{"name":"degraded","description":"The reduction in a forest\u2019s ability to perform ecosystem services, such as carbon storage and water regulation, due to natural and anthropogenic changes.\r\n"},"79":{"name":"disturbances","description":"A discrete event that changes the structure of a forest ecosystem.\r\n"},"68":{"name":"disturbed","description":"A discrete event that changes the structure of a forest ecosystem.\r\n"},"65":{"name":"driver of tree cover loss","description":"The direct cause of forest disturbance.\r\n"},"70":{"name":"drivers of loss","description":"The direct cause of forest disturbance.\r\n"},"81":{"name":"drivers of tree cover loss","description":"The direct cause of forest disturbance.\r\n"},"102":{"name":"evapotranspiration","description":"When solar energy hitting a forest converts liquid water into water vapor (carrying energy as latent heat) through evaporation and transpiration.\r\n"},"2":{"name":"forest","description":"Forests include tree cover greater than 30 percent tree canopy density and greater than 5 meters in height as mapped at a 30-meter Landsat pixel scale.\r\n"},"3":{"name":"forest concession","description":"A legal agreement allowing an entity the right to manage a public forest for production purposes.\r\n"},"90":{"name":"forest concessions","description":"A legal agreement allowing an entity the right to manage a public forest for production purposes.\r\n"},"53":{"name":"forest degradation","description":"The reduction in a forest\u2019s ability to perform ecosystem services, such as carbon storage and water regulation, due to natural and anthropogenic changes.\r\n"},"54":{"name":"forest disturbance","description":"A discrete event that changes the structure of a forest ecosystem.\r\n"},"100":{"name":"forest disturbances","description":"A discrete event that changes the structure of a forest ecosystem.\r\n"},"5":{"name":"forest fragmentation","description":"The breaking of large, contiguous forests into smaller pieces, with other land cover types interspersed.\r\n"},"6":{"name":"forest management plan","description":"A plan that documents the stewardship and use of forests and other wooded land to meet environmental, economic, social, and cultural objectives. Such plans are typically implemented by companies in forest concessions.\r\n"},"62":{"name":"forests","description":"Forests include tree cover greater than 30 percent tree canopy density and greater than 5 meters in height as mapped at a 30-meter Landsat pixel scale.\r\n"},"69":{"name":"fragmentation","description":"The breaking of large, contiguous forests into smaller pieces, with other land cover types interspersed.\r\n"},"80":{"name":"fragmented","description":"The breaking of large, contiguous forests into smaller pieces, with other land cover types interspersed.\r\n"},"74":{"name":"gain","description":"The establishment of tree canopy in an area that previously had no tree cover. Tree cover gain may indicate a number of potential activities, including natural forest growth or the crop rotation cycle of tree plantations.\r\n"},"143":{"name":"global land squeeze","description":"Pressure on finite land resources to produce food, feed and fuel for a growing human population while also sustaining biodiversity and providing ecosystem services.\r\n"},"7":{"name":"hectare","description":"One hectare equals 100 square meters, 2.47 acres, or 0.01 square kilometers and is about the size of a rugby field. A football pitch is slightly smaller than a hectare (pitches are between 0.62 and 0.82 hectares).\r\n"},"66":{"name":"hectares","description":"One hectare equals 100 square meters, 2.47 acres, or 0.01 square kilometers and is about the size of a rugby field. A football pitch is slightly smaller than a hectare (pitches are between 0.62 and 0.82 hectares).\r\n"},"67":{"name":"intact","description":"A forest that contains no signs of human activity or habitat fragmentation as determined by remote sensing images and is large enough to maintain all native biological biodiversity.\r\n"},"78":{"name":"intact forest","description":"A forest that contains no signs of human activity or habitat fragmentation as determined by remote sensing images and is large enough to maintain all native biological biodiversity.\r\n"},"8":{"name":"intact forests","description":"A forest that contains no signs of human activity or habitat fragmentation as determined by remote sensing images and is large enough to maintain all native biological biodiversity.\r\n"},"55":{"name":"land and environmental defenders","description":"People who peacefully promote and protect rights related to land and\/or the environment.\r\n"},"9":{"name":"loss driver","description":"The direct cause of forest disturbance.\r\n"},"10":{"name":"low tree canopy density","description":"Less than 30 percent tree canopy density.\r\n"},"84":{"name":"managed forest concession","description":"Areas where governments have given rights to private companies to harvest timber and other wood products from natural forests on public lands.\r\n"},"83":{"name":"managed forest concession maps for nine countries","description":"Cameroon, Canada, Central African Republic, Democratic Republic of the Congo, Equatorial Guinea, Gabon, Indonesia, Liberia, and the Republic of the Congo\r\n"},"104":{"name":"managed natural forests","description":"Naturally regenerated forests with signs of management, including logging, clear cuts, etc.\r\n"},"91":{"name":"megacities","description":"A city with more than 10 million people.\r\n"},"57":{"name":"megacity","description":"A city with more than 10 million people."},"56":{"name":"mosaic restoration","description":"Restoration that integrates trees into mixed-use landscapes, such as agricultural lands and settlements, where trees can support people through improved water quality, increased soil fertility, and other ecosystem services. This type of restoration is more likely in deforested or degraded forest landscapes with moderate population density (10\u2013100 people per square kilometer). "},"86":{"name":"natural","description":"A forest that is grown without human intervention.\r\n"},"12":{"name":"natural forest","description":"A forest that is grown without human intervention.\r\n"},"63":{"name":"natural forests","description":"A forest that is grown without human intervention.\r\n"},"144":{"name":"open canopy systems","description":"Individual tree crowns that do not overlap to form a continuous canopy layer.\r\n"},"82":{"name":"persistent gain","description":"Forests that have experienced one gain event from 2001 to 2016.\r\n"},"13":{"name":"persistent loss and gain","description":"Forests that have experienced one loss or one gain event from 2001 to 2016."},"97":{"name":"plantation","description":"An area in which trees have been planted, generally for commercial purposes.\u0026nbsp;\r\n"},"93":{"name":"plantations","description":"An area in which trees have been planted, generally for commercial purposes.\u0026nbsp;\r\n"},"88":{"name":"planted","description":"A forest composed of trees that have been deliberately planted and\/or seeded by humans.\r\n"},"14":{"name":"planted forest","description":"A forest composed of trees that have been deliberately planted and\/or seeded by humans.\r\n"},"73":{"name":"planted forests","description":"A forest composed of trees that have been deliberately planted and\/or seeded by humans.\r\n"},"15":{"name":"primary forest","description":"Old-growth forests that are typically high in carbon stock and rich in biodiversity. The GFR uses a humid tropical primary rainforest data set, representing forests in the humid tropics that have not been cleared in recent years.\r\n"},"64":{"name":"primary forests","description":"Old-growth forests that are typically high in carbon stock and rich in biodiversity. The GFR uses a humid tropical primary rainforest data set, representing forests in the humid tropics that have not been cleared in recent years.\r\n"},"58":{"name":"production forest","description":"A forest where the primary management objective is to produce timber, pulp, fuelwood, and\/or nonwood forest products."},"89":{"name":"production forests","description":"A forest where the primary management objective is to produce timber, pulp, fuelwood, and\/or nonwood forest products.\r\n"},"87":{"name":"seminatural","description":"A managed forest modified by humans, which can have a different species composition from surrounding natural forests.\r\n"},"59":{"name":"seminatural forests","description":"A managed forest modified by humans, which can have a different species composition from surrounding natural forests. "},"96":{"name":"shifting agriculture","description":"Temporary loss or permanent deforestation due to small- and medium-scale agriculture.\r\n"},"103":{"name":"surface roughness","description":"Surface roughness of forests creates\u0026nbsp;turbulence that slows near-surface winds and cools the land as it lifts heat from low-albedo leaves and moisture from evapotranspiration high into the atmosphere and slows otherwise-drying winds. \r\n"},"17":{"name":"tree cover","description":"All vegetation greater than five meters in height and may take the form of natural forests or plantations across a range of canopy densities. Unless otherwise specified, the GFR uses greater than 30 percent tree canopy density for calculations.\r\n"},"71":{"name":"tree cover canopy density is low","description":"Less than 30 percent tree canopy density.\r\n"},"60":{"name":"tree cover gain","description":"The establishment of tree canopy in an area that previously had no tree cover. Tree cover gain may indicate a number of potential activities, including natural forest growth or the crop rotation cycle of tree plantations.\u0026nbsp;As such, tree cover gain does not equate to restoration.\r\n"},"18":{"name":"tree cover loss","description":"The removal or mortality of tree cover, which can be due to a variety of factors, including mechanical harvesting, fire, disease, or storm damage. As such, loss does not equate to deforestation.\r\n"},"19":{"name":"tree plantation","description":"An agricultural plantation of fast-growing tree species on short rotations for the production of timber, pulp, or fruit.\r\n"},"72":{"name":"tree plantations","description":"An agricultural plantation of fast-growing tree species on short rotations for the production of timber, pulp, or fruit.\r\n"},"85":{"name":"trees outside forests","description":"Trees found in urban areas, alongside roads, or within agricultural land\u0026nbsp;are often referred to as Trees Outside Forests (TOF).\u202f\r\n"},"105":{"name":"unmanaged natural forests","description":"Naturally regenerated forests without any signs of management, including primary forest.\r\n"}}}