Data and Methods Old

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:

• Forest change
• Forest cover
• Commodities 
• Management
• Biodiversity
• Carbon
• Water
• Social 
• Other 

Tree cover loss.Hansen et al. 2013, https://doi.org/10.1126/science.aar3629 . 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 2011–22 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 2011–20 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.Curtis et al. 2018, https://doi.org/10.1126/science.aau3445 .  This data set shows the dominant driver of tree cover loss from 2001 to 2022 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 2022.

Tree cover gain.Potapov et al. 2022, https://doi.org/10.3389/frsen.2022.856903 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.Potapov et al. 2019, https://doi.org/10.1016/j.rse.2019.111278 . 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.Potapov et al. 2022, https://doi.org/10.3389/frsen.2022.856903 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.Harris et al. 2017, https://doi.org/10.1088/1748-9326/aa5a2f . The emerging hot spots data set identifies the most significant clusters of primary humid tropical forest loss between 2002 and 2022 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 2022, 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. 

Global cocoa, coffee, soy.Wood-Sichra et al. 2016, http://ebrary.ifpri.org/utils/getfile/collection/p15738coll2/id/133503/filename/133714 . 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.IIASA and FAO 2012, http://www.fao.org/fileadmin/user_upload/gaez/docs/GAEZ_Model_Documentation.pdf . 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.Balk et al. 2006, https://doi.org/10.1016/S0065-308X(05)62004-0 .   

Global pasture.Ramankutty et al. 2008, https://doi.org/10.1029/2007GB002952 . 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.See LAPIG, https://www.lapig.iesa.ufg.br/lapig/ . 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.Peng et al., forthcoming. 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.Harris et al. 2019, https://files.wri.org/s3fs-public/spatial-database-planted-trees.pdf . 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. 

Biodiversity intactness.Hill et al. 2019, https://doi.org/10.3389/ffgc.2019.00070 . 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.Hudson et al. 2017, https://doi.org/10.1002/ece3.2579 . 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.Hoskins et al. 2016, https://doi.org/10.1002/ece3.2104 . 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.Hoskins et al. 2016, https://doi.org/10.1002/ece3.2104 . 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.Hill et al. 2019, https://doi.org/10.3389/ffgc.2019.00070 . 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.See BirdLife International, http://www.keybiodiversityareas.org . Key Biodiversity Areas (KBAs) are “sites contributing significantly to the global persistence of biodiversity.” The Global Standard for the Identification of Key Biodiversity AreasIUCN 2016, https://portals.iucn.org/library/sites/library/files/documents/2016-048.pdf . 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.See the Alliance for Zero Extinction, https://zeroextinction.org/ . 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, orangutan, and tiger ranges to assess tree cover loss in their habitat ranges, which were mapped in 2008, 2017, and 2014, 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. 

Tiger Conservation Landscapes.Dinerstein et al. 2007, https://doi.org/10.1641/B570608 . This data set shows the locations of tiger habitat as of the year 2007. Tiger conservation landscapes are large blocks of contiguous or connected area of suitable tiger habitat that that can support at least five adult tigers and where tiger presence has been confirmed in the past 10 years. The data set was created by mapping tiger distribution, determined by land cover type, forest extent, and prey base, against a human influence index. Areas of high human influence that overlapped with suitable habitat were not considered tiger habitat. 

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.

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. 

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.

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 18 indicators. The next section outlines each statistic produced by GFR authors, along with the data set and method summary used to generate each calculation.  

Indicators of Forest Change 

• Forest Extent 
• Forest Loss 
• Primary Forest Loss 
• Deforestation Linked to Agriculture 
• Forest Gain 
• Trees outside Forests 

Indicators of Forest Condition 

• Forest Degradation 
• Forest Recovery 

Indicators of Forest Designation 

• Protected Forests 
• Production Forests 

Indicators of Biodiversity and Ecological Services 

• Biodiversity Conservation 
• Forest Carbon Stocks 
• Greenhouse Gas Fluxes from Forests 
• Soil Stability and Water Regulation 

Indicators of Social and Governance Issues  

• Indigenous and Community Forests  
• At-Risk Populations 
• Forestland-Related Conflict  

This indicator aims to monitor the total area of forest that is lost or removed globally each year. Due to data limitations, the indicator currently measures tree cover loss as a best-available proxy for forest loss. Tree cover loss includes forest loss as well as loss of industrial tree plantations and agricultural tree crops, which are not typically defined as forests. The statistics reported in this indicator capture “gross” tree cover loss—that is, total loss irrespective of any tree cover gain that may have occurred in that same year. 

This indicator estimates the role of specific agricultural commodities in agriculture-linked deforestation, namely cattle, oil palm, soy, cocoa, rubber, coffee, and wood fiber.   

Maps of croplands and tree plantations make it possible to identify the specific commodities that now occupy deforested land, although the spatial resolution and geographic scope of these maps are variable. High- or medium-resolution maps exist for oil palm globally, soy in South America, pasture in Brazil, and wood fiber and rubber for select countries, allowing for more precise analyses of where these commodities currently exist. For other commodities and geographies not covered by detailed data, global 10-kilometer data are used in the analysis instead.   

To produce these calculations, the commodity extent maps are overlaid with tree cover loss data to estimate the extent of global deforestation that is spatially correlated with these commodities—that is, where recently mapped cropland or plantations occur on lands that have experienced deforestation since 2001. Results can also be further disaggregated and mapped at the second administrative level.  

This analysis cannot determine whether areas were deforested for the purpose of growing the specific commodity or for other reasons (e.g., harvesting wood products). These data also do not measure possible leakage effects of these commodities on deforestation—for example, where expansion of a commodity displaces other forms of farming that contribute to deforestation elsewhere.  As such, this indicator refers to forests that are “replaced by” specific commodities rather than deforestation “driven by” specific commodities.     

The data sets and methods used rely on a number of assumptions and have a number of associated caveats, which are further described in Goldman et al. (2020).Goldman et al. 2020, https://www.wri.org/publication/estimating-role-seven-commodities-agriculture-linked-deforestation .  No independently derived statistics were calculated by Global Forest Review authors in this indicator, however, a comparison between forest area replaced by oil palm, as calculated in Goldman et al. (2020), and palm oil price was included to demonstrate the sensitivity of forest area replaced by oil palm to changes in price. Forest area represents the year in which future oil palm plantations were deforested, and price dataSee World Bank Commodity Markets, https://www.worldbank.org/en/research/commodity-markets . is displayed using a one-year lag to easily show the relationship between price and forest area replaced by oil palm. 

The Trees outside Forests Indicator aims to monitor trees that are growing outside forests on farms, in orchards and tree plantations, in cities, along roads, and across other nonforest landscapes. Such trees provide critical products such as fertilizer, fruit, or firewood for local people and are important for ecosystem services, including erosion control and water retention, which make farms more productive. They also provide clean air, offset heat islands, lower energy bills, and provide habitat for wildlife in cities. Spatial data on trees outside forests are not consistently available, so this indicator currently relies on other (nonspatially explicit) estimates as well as case studies from countries where spatial data is available. No independently derived statistics were calculated by Global Forest Review authors in this section. 

The Forest Recovery Indicator aims to monitor the area of forest that has been previously degraded (but not completely deforested) and are now regrowing, recovering, and regaining ecological and economic functions, biodiversity, and/or carbon levels. There are currently no global-scale geospatial data sets that allow measurement of any aspects (structure, ecosystem, biodiverse, carbon) of forest recovery directly or by proxy. This indicator will be updated when data become available (see the Limitations section).  

The Production Forests Indicator aims to monitor the extent and location of forests (natural, seminatural, and planted) and tree plantations designated primarily for production of forest products such as timber, pulp, and fuelwood. The Food and Agriculture Organization of the United Nations collates national and global statistics every five years on the extent of production forests, but maps showing the specific locations of these forests are not available with consistent global coverage. This indicator reviews several spatial data sets offering partial coverage of this topic.  

Due to a lack of comprehensive spatial data showing the location of production forests worldwide, this indicator cites nongeospatial statistical data from the Global Forest Resources Assessment 2020 to provide estimates of the global extent of production forests. 

To approximate the location of production forests, the indicator uses the tree cover loss by dominant driver data set produced by Curtis et al. (2018),Curtis et al. 2018, https://doi.org/10.1126/science.aau3445 .  which identifies areas in which large-scale forestry operations are the dominant cause of tree cover loss. The Curtis et al. model analyzes 30-by-30-meter pixel tree cover change data and other data inputs to identify the dominant driver of tree cover loss within a 10-by-10-kilometer grid cell. Cyclical patterns of tree cover loss and gain in the same grid call are considered a likely indicator of forestry activities, in which wood harvesting is followed by forest regeneration and/or tree planting. Due to the spatial resolution of the model and underlying tree cover change data, the map is most likely to capture tree cover in which industrial roundwood is being harvested at large scales, whether through rotational harvesting of plantations, stand-level clear-cuts of natural or seminatural forests, or mechanized selective logging. Nonindustrial or highly selective forms of harvesting are less likely to be visible within 30-meter resolution satellite imagery and are therefore less likely to be reflected in the model results. This could include collection of fuelwood for subsistence purposes, some forms of thinning to reduce fire risk, or highly selective felling of individual trees (e.g., canoe trees) where the surrounding forest is left intact. 

To estimate the location of tree plantations, the indicator uses data from the Spatial Database of Planted Trees by Harris et al. (2019). This database aggregates maps of tree plantations and agricultural tree crops from a variety of sources and time periods and lacks data from a few important roundwood-producing countries, including Canada and Russia. 

This indicator aims to monitor the amount of carbon stored in forests worldwide. Forests store carbon in their above- and belowground live biomass, dead wood and litter, and soils.  

This indicator aims to monitor the extent of tree cover globally that is critical for soil stability and water regulation as a proxy for measuring changes in these ecological functions directly. The indicator considers three categories of forest that are important for soil stability and water regulation. These three categories are not mutually exclusive (i.e., the same forest area may be included in more than one category) and are therefore estimated individually rather than summed.

• Forests in areas with high erosion risk, determined by an area’s precipitation, elevation, slope, soil properties, and land cover 
• Forests within watersheds that supply water to the world’s urban areas
• Mangrove forests

The At-Risk Populations Indicator measures the number of people who are potentially vulnerable to losing sources of food and other forest-related income due to high rates of deforestation affecting their local forests. Estimates are derived by combining population density maps with maps of primary forest loss hot spots and their five-kilometer buffer. This buffer distance was chosen to estimate a reasonable walking distance to reach forests.

This indicator provides a general estimate of at-risk populations, but some populations in these areas will not be at risk. There are also some populations outside these areas who are at risk, but they are not included in this estimate. No error estimates are provided.

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 are likely to deliver the first annualized global tree cover gain data set by 2020.
• 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.