Data and Methods

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.

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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Summary of main data sets analyzed for the 2020 edition of the Global Forest Review

Type Data Set Source Spatial Resolution Temporal Resolution Years of Coverage Spatial Coverage
Forest change Tree cover loss Hansen et al. (2013) 30-meter Annual 2001-19 Global
Tree cover loss by dominant driver Curtis et al. (2018) 10-kilometer Annual 2001-19 Global
Tree cover gain Hansen et al. (2013) 30-meter 12 years 2001-12 Global 
Lower Mekong height and canopy Potapov et al. (2019) 30-meter Annual 2001-17 Lower Mekong
Tree cover change Song et al. (2018) 5-kilometer 34 years 1982–2016 Global
Hot spots of primary forest loss Harris et al. (2017) Vector 17 years 2002–19 Tropics

Forest cover

 

Tree cover extent Hansen et al. (2013) 30-meter 1 year 2000, 2010 Global
Primary forest Turubanova et al. (2018) 30-meter 1 year 2001 Tropics
Intact forest landscapes Potapov et al. (2017) Vector 3 years 2000, 2013, 2016 Global
Tree plantations Harris et al. (2019) Vector 1 year 2015 Global
Mangroves Bunting et al. (2018) Vector 7 years 1996, 2007, 2008, 2009, 2010, 2015, 2016 Global
Commodities Global cocoa, coffee, soy MapSPAM 10-kilometer 1 year 2010 Global
Global pasture Ramankutty et al. (2008) 10-kilometer 1 year 2000 Global
Brazilian pasture Laboratório de Processamento de Imagens e Geoprocessamento (LAPIG) 30-meter 1 year 2018 Brazil
South America Soy Song et al. (forthcoming) 30-meter Annual 2001-18 South America
Oil palm, rubber, wood fiber Harris et al. (2019) Vector 1 year 2015 Select countries
Management Protected areas World Database on Protected Areas Vector 1 year Varies, see below Select countries
Logging concessions Varies, see below Vector 1 year Varies, see below Select countries
Wood fiber concessions Varies, see below Vector 1 year Varies, see below Select countries
Oil palm concessions Varies, see below Vector 1 year Varies, see below Select countries
Roundtable on Sustainable Palm Oil (RSPO) concessions RSPO member companies Vector 1 year Varies, see below Select countries
Mining concessions Varies, see below Vector 1 year Varies, see below Select countries
Oil and gas concessions Varies, see below Vector 1 year Varies, see below Select countries
Carbon Aboveground biomass density Zarin and Woods Hole Research Center 30-meter 1 year 2000 Global
Water Erosion risk Qin et al. (2016) 10-kilometer 1 year 2015 Global
Urban watersheds The Nature Conservancy Vector 1 year Unknown Global
Social LandMark Global Platform of Indigenous and Community Lands Vector 1 year 2019 Select countries
Population Global Human Settlement Layer 250-meter 1 year 2015 Global
Conflict Global Witness Vector Annual 2012-18 Select countries
Other 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
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Forest Change

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 2011–19 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.

This data set has been updated seven times since its creation and now includes loss data up to 2019 (Version 1.7). The analysis method has been modified in numerous ways, including new data for the target year, reprocessed data for previous years (2011 and 2012 for the Version 1.1 update, 2012 and 2013 for the Version 1.2 update, and 2014 for the Version 1.3 update), and improved modeling and calibration. These modifications improve change detection for 2011–19, including better detection of boreal loss due to fire, smallholder rotation agriculture in tropical forests, selective logging, and short-cycle plantations. Eventually, a future “Version 2.0” will include reprocessing for 2000–10 data, but in the meantime, comparisons between the original 2001–10 data and the 2011–19 update should be performed with caution. Although we report on annual trends since 2001, we are more confident in the later part of the time series.

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 2019 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 2019.

Tree cover gain.Hansen et al. 2013, https://doi.org/10.1126/science.aar3629 . This data set measures areas of tree cover gain across all global land at 30 m resolution, displayed as a 12-year cumulative total. The data were generated using multispectral satellite imagery from the Landsat 7 ETM+ sensor. Over 600,000 Landsat 7 images were compiled and analyzed using Google Earth Engine, a cloud platform for Earth observation and data analysis. The clear land surface observations (30-by-30 m pixels) in the satellite images were assembled, and a supervised learning algorithm was then applied to identify per-pixel tree cover gain.

Tree cover gain is defined as the establishment of tree canopy at the Landsat pixel scale in an area that previously had no tree cover but regrew to greater than 50 percent tree canopy cover density. Tree cover gain may indicate a number of potential activities, including natural forest growth or the crop 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.Song et al. 2018, https://doi.org/10.1038/s41586-018-0411-9 . This data set analyzes over 35 years of satellite data to provide a comprehensive record of global land change dynamics during the period 1982–2016, including tree canopy cover. To generate this data set, monthly Advanced Very High Resolution Radiometer composites at 5 km resolution were created and converted into adjusted annual penological metrics. These were then used as input into a supervised regression tree model to generate annual tree canopy cover and trend analyses. The data set shows net percent tree canopy change over the 35-year period. 

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 2019 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 2019, 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. 

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Forest Cover

Tree cover extent.Hansen et al. 2013, https://doi.org/10.1126/science.aar3629 . Tree cover is defined as all vegetation greater than 5 meters (m) in height, and it may take the form of natural forests or plantations across a range of canopy densities. The tree cover extent data set covers all global land for the years 2000 and 2010 at 30 m resolution. 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, unless otherwise noted.

Primary forest.Turubanova et al. 2018, https://doi.org/10.1088/1748-9326/aacd1c . 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.

Intact forest landscapes.Potapov et al. 2017, https://doi.org/10.1126/sciadv.1600821 . 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)Hansen et al. 2013, https://doi.org/10.1126/science.aar3629 . 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 2016. 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.Harris et al. 2019, https://files.wri.org/s3fs-public/spatial-database-planted-trees.pdf . 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.Bunting et al. 2018, https://doi.org/10.3390/rs10101669 . This data set (version 2.0) depicts the global extent of mangrove forests for the year 2010, derived by random forest classification of a combination of L-band radar (Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar, or ALOS PALSAR) and optical (Landsat 5, 7) satellite data. All satellite data and software used to derive the Global Mangrove Watch mangrove maps are available in the public domain.

Approximately 15,000 Landsat scenes and 1,500 ALOS PALSAR (one-by-one degree) mosaic tiles were used to create optical and radar image composites covering the coastlines along the tropical and subtropical coastlines in the Americas, Africa, Asia, and Oceania.

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. Training for the habitat mask and classification of the 2010 mangrove mask was based on randomly sampling 38 million points using the mangrove masks (for the year 2000) of Giri et al. (2011)Giri et al. 2011, https://doi.org/10.1111/j.1466-8238.2010.00584.x . and Spalding et al. (2010)Spalding et al. 2010, https://www.routledge.com/World-Atlas-of-Mangroves/Spalding-Kainuma-Collins/p/book/9781844076574 . and the water occurrence layer defined by Pekel et al. (2016). Pekel et al. 2016, https://doi.org/10.1038/nature20584 . 

The Landsat 7 scan-line error affects the classification in certain areas, resulting in striping artifacts in the data. Classification accuracy was assessed with over 53,800 randomly sampled points across 20 randomly selected regions. Overall accuracy was 95.25 percent, and the user’s and producer’s accuracies for the mangrove class were estimated at 97.5 percent and 94.0 percent, respectively. Factors such as satellite data availability (due to clouds, cloud shadows, and Landsat 7 scan-line error), mangrove species composition, and level of degradation can all lead to local variations in accuracy. The mangrove seaward border is generally also more accurately defined than the landward side, where distinction between mangrove and certain terrestrial vegetation species can be unclear.

Areas known to be missing in this version (2.0) of the data set include Bermuda (United Kingdom); Europa Island and the Wallis and Futuna Islands (France); Fiji, east of longitude 180° east; Guam and Saipan (United States); Kiribati; Maldives; and Peru, south of latitude 4° south.

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Commodities

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. 

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Management

Protected areas.See the World Database on Protected Areas, www.protectedplanet.net .  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 November 2019 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 

Country Source Date
Cameroon Ministry of Forestry and Wildlife and World Resources Institute (WRI)  Unknown
Canada Global Forest Watch Canada  2016
Central African Republic Ministry of Water, Forests, Hunting, and Fishing and WRI  Unknown
Democratic Republic of the Congo Ministry of the Environment, Nature Conservation, and Tourism and WRI  Unknown
Equatorial Guinea Ministry of Agriculture and Forests and WRI  2013
Gabon Ministry of Forest Economy, Water, Fisheries, and Aquaculture and WRI  Unknown
Indonesia Ministry of Environment and Forestry  2018
Liberia Global Witness  2016
Sarawak, Malaysia Earthsight and Global Witness  2010
Republic of the Congo Ministry of Forest Economy and Sustainable Development and WRI 2013

 

Wood fiber concessions. Wood fiber concession refers to an area allocated by a government or other body for establishing fast-growing tree plantations for the production of timber and wood pulp for paper and paper products. This data set is assembled by aggregating data for multiple countries. Source and date information can be found in the table below. 

Wood fiber concession data sources and dates 

Country Source Date
Indonesia Ministry of Environment and Forestry, Asia Pulp & Paper, and Asia Pacific Resources International Limited   2018
Republic of the Congo Ministry of Agriculture and WRI  Unknown
Sarawak and Sabah, Malaysia Earthsight and Global Witness  2011

 

Oil palm concessions. Oil palm concession refers to an area allocated by a government or other body for industrial-scale oil palm plantations. This data set is assembled by aggregating data for multiple countries. Source and date information can be found in the table below. 

Oil palm concession data sources and dates 

Country Source Date
Cameroon WRI Unknown
Indonesia Ministry of Environment and Forestry  2012
Liberia AidData 2016
Sarawak, Malaysia Sarawak Dayak Iban Association (SADIA), Aidenvironment, and Earthsight   2010
Republic of the Congo Ministry of Agriculture and WRI 2013

 

Roundtable on Sustainable Palm Oil (RSPO) concessions. This data set maps the concession boundaries of RSPO member companies through December 2017, including both certified and noncertified concessions, as well as concessions where the certification status is unknown. The concession boundaries were provided to the RSPO by member companies. 

Mining concessions. Mining concession refers to an area allocated by a government or other body for the extraction of minerals. The terminology for these areas varies from country to country. Concession is used as a general term for licenses, permits, or other contracts that confer rights to private companies to manage and extract minerals from public lands; terminology varies at the national level, however, and includes mineral or mining 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. 

Mining concession data sources and dates 

Country Source Date
Brazil National Department of Mineral Production 2019
Cameroon Ministry of Mines, Industry and Technological Development  Unknown
Cambodia Open Development Cambodia 2014
Canada Global Forest Watch Canada  2016
Colombia Tierra Minanda; Agencia Nacional de Minería de Colombia 2008
Democratic Republic of the Congo Ministry of Mines Mining Registry Unknown
Gabon Ministry of Mines, Petroleum, and Hydrocarbons and WRI  Unknown
Mexico Secretaría de Economía  2015
Peru Instituto Geológico Minero y Metalúrgico (INGEMMET) 2015
Republic of the Congo Ministry of Mines and Geology and WRI Unknown

 

Oil and gas concessions. Oil and gas concession refers to an area allocated by the government to companies who explore for and produce oil, natural gas, and other hydrocarbons. The terminology for these areas varies from country to country. Concession is used as a general term for licenses, permits, or other contracts that confer rights to private companies to manage and extract oil and natural gas from public lands; terminology varies at the national level, however, and includes 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. 

Oil and gas concession data sources and dates 

Country Source Date
Argentina Ministry of Energy and Mining 2015
Brazil National Agency of Petroleum, Natural Gas and Biofuels  2017
Colombia National Agency of Hydrocarbons  2017
Ecuador Ministry of Non-renewable Natural Resources  2013
Peru Petróleos del Perú S.A.  2018
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Carbon

Aboveground biomass density.Harris et al. 2020. "Global Maps of 21st Century Forest Carbon Fluxes". In press at Nature Climate Change.  This data set expands on the methodology presented in Baccini et al. (2012)Baccini et al. 2012, https://doi.org/10.1038/nclimate1354 . to generate a global map of aboveground live woody biomass density at 30-meter (m) 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;Hansen et al. 2013, https://doi.org/10.1126/science.1244693 . one arc-second Shuttle Radar Topography Mission, Version 3, elevation;Farr et al. 2007, https://doi.org/10.1029/2005RG000183 . GTOPO30 elevation from the U.S. Geological Survey (for latitudes greater than 60° north); and WorldClim climate data.Hijmans et al. 2005, https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/joc.1276 .  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. regions.  

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Water

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.

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Social

LandMark.LandMark 2019, http://www.landmarkmap.org/data/ . 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.European Commission and Columbia University 2015, http://data.europa.eu/89h/jrc-ghsl-ghs_pop_gpw4_globe_r2015a . 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 2019a, https://www.globalwitness.org/en/campaigns/environmental-activists/enemies-state/ . 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.

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Other

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 two-year moratorium, renewed again in May 2015, 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. 

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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 0.5 to convert biomass to carbon. Finally, sum the aboveground biomass pixel values that overlap with the tree cover extent raster data set.
  • Carbon emissions 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 0.5 to convert biomass to carbon. Finally, sum the aboveground biomass pixel values that overlap with the tree cover loss data set. All of the aboveground biomass is considered to be “committed” emissions to the atmosphere upon clearing. Emissions are “gross” rather than “net” estimates, meaning that information about the fate of land after clearing, and its associated carbon value, is not incorporated. Emissions associated with other carbon pools, such as soil carbon, are not included.
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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 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 

Indicators of Forest Condition 

Indicators of Forest Designation 

Indicators of Biodiversity and Ecological Services 

Indicators of Social and Governance Issues  

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Forest Extent

The Forest Extent Indicator aims to monitor the total area of forest worldwide, including natural and seminatural forests. This indicator also attempts to distinguish primary forests due to their disproportionate importance for biodiversity conservation and carbon storage. The global tree cover extent data set from 2010 forms the basis for all statistics in this indicator, and it includes all woody vegetation with a height of at least 5 meters (m) and a canopy density greater than 30 percent at 30 m resolution. A key limitation of this data set is that it includes tree plantations (such as an oil palm plantation or apple orchard), which would not be considered a “forest” by most definitions. 

Statistic Data Set Method
In 2010, the world had 3,929 million hectares (Mha) of tree cover covering 30 percent of land on Earth.  Tree cover extent  Extent calculation on 2010 tree cover extent 
In the tropics, mapped tree plantations account for roughly 2 percent of all tree cover; the remaining 98 percent of tree cover can be assumed to be natural or seminatural forest.  Tree plantations  Extent calculation on tree plantations; includes all area within the tree plantation data set, divided by tree cover extent in the tropics  
Tropical and subtropical forests account for 58 percent of 2010 tree cover by area. Boreal forests make up 27 percent of tree cover. Temperate forests account for about 15 percent of tree cover.  Ecozones; tree cover extent   Extent calculation on 2010 tree cover extent by ecozone 
Primary forests account for roughly 50 percent of all forests in the tropics (1,030 Mha). Brazil, the Democratic Republic of the Congo, and Indonesia have the most primary forest in absolute terms, whereas French Guiana, Gabon, and Suriname have the highest proportion of their total land area covered by primary forest.  Ecozones; primary forest  Extent calculation on primary forest divided by extent calculation on 2010 tree cover extent by ecozone 
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Forest Loss

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. 

Tree cover loss calculation in shifting agriculture overlapping primary forest, commodity-driven deforestation, and urbanization driver categories 

Statistic Data Set Method
The world has lost 386 million hectares (Mha) of tree cover since the turn of the century, equivalent to about 10 percent of global tree cover in 2001.  Tree cover loss; tree cover extent  Tree cover loss calculation in 2000 extent 
Tree cover loss has been rising in recent history, from 13.4 Mha of tree cover loss in 2001 to 24.2 Mha in 2019.  Tree cover loss  Tree cover loss calculation 
Forestry is associated with 115 Mha of tree cover loss. . . Commodity-driven deforestation is associated with 98 Mha . . . Wildlife is associated with 86 Mha. . . Shifting agriculture is associated with 80 Mha . . . Urbanization is associated with 3 Mha. Tree cover loss; tree cover loss by dominant driver  Tree cover loss in all drivers of tree cover loss categories 
Roughly one-third of tree cover loss since 2000 was likely to be deforestation, characterized by permanent loss of forest.  Tree cover loss; tree cover loss by dominant driver  Tree cover loss calculation in shifting agriculture overlapping primary forest, commodity-driven deforestation, and urbanization driver categories 
The remaining two-thirds of tree cover loss was likely more temporary in nature.  Tree cover loss; tree cover loss by dominant driver  Tree cover loss calculation in wildfire, forestry, and nonprimary shifting agriculture driver classes 
Though only half (192 Mha) of global tree cover loss this century occurred in the tropical ecozones, the tropics accounted for more than 95 percent of all global deforestation.  Tree cover loss; tree cover loss by dominant driver; ecozones  Tree cover loss calculation in tropical ecozones and shifting agriculture overlapping primary forest, commodity-driven deforestation, and urbanization driver categories 
The annual rate of tropical tree cover loss nearly doubled from 6.7 Mha in 2001 to 11.9 Mha in 2019. Tree cover loss; ecozones  Tree cover loss calculation in tropical ecozones 
Although Brazil and Indonesia have experienced the largest area of tree cover loss this century, these two countries have become less dominant as the rate of tree cover loss in all other tropical countries combined has increased by 154 percent since 2001.  Countries; tree cover loss  Tree cover loss and rate of loss calculation in countries 
Temperate and boreal forests have experienced 148 Mha of gross tree cover loss since the turn of the century. . .  Tree cover loss; ecozones  Tree cover loss calculation in temperate and boreal ecozones 
. . . with almost 98 percent related to temporary factors (forestry and wildfire).  Tree cover loss; tree cover loss by dominant driver  Tree cover loss calculation in forestry and wildfire driver categories 
Canada, Russia, and the United States together make up 94 percent of all tree cover loss related to wildfire and 54 percent of all loss related to forestry.  Countries; tree cover loss; tree cover loss by dominant driver  Tree cover loss calculation in countries and forestry and wildfire driver categories 
The United States lost more than 2.8 Mha of tree cover to urbanization between 2001 and 2019, about 25 times more than Canada.  Countries; tree cover loss; tree cover loss by dominant driver  Tree cover loss calculation in countries and urbanization driver category 
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Primary Forest Loss

This indicator aims to monitor the total area of primary forest lost or removed globally each year. Primary forests are mature natural forests that have not been disturbed in recent history. Primary forests have only been mapped comprehensively in the humid tropics. Therefore, this indicator uses maps of intact forest landscapes as a proxy for primary forest loss in the temperate and boreal ecozones. Intact forest landscapes are a subset of primary forests. To be considered intact, a forest must be both undisturbed by human activity and larger than 50,000 hectares

Statistic Data Set Method
The humid tropics have lost 60 million hectares (Mha) of primary forests since the turn of the century, representing 5.9 percent of their extent in 2001.  Tree cover loss; primary forest  Tree cover loss and percent loss calculation in primary forest 
Just three countries—Brazil, the Democratic Republic of the Congo, and Indonesia—accounted for 65 percent of this loss.  Countries; tree cover loss; tree cover loss by dominant driver  Tree cover loss calculation in primary forest and tree cover loss by dominant driver 
Roughly two-thirds of this loss was related to conversion for commodity production (industrial-scale agriculture, mining, oil and gas, etc.), with an additional one-third related to shifting agriculture Tree cover loss; primary forest; tree cover loss by dominant driver  Tree cover loss calculation in primary forest and tree cover loss by dominant driver 
Russia and Canada experienced the highest levels of tree cover loss in intact forest landscapes in temperate and boreal ecozones, primarily due to logging and fire. Russia lost 6 Mha of tree cover in intact forests between 2000 and 2016, and Canada lost 4 Mha.  Countries; tree cover loss; ecozones; intact forest landscapes  Tree cover loss calculation in primary forest and intact forest landscapes 

 

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Deforestation Linked to Agriculture

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. 

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Forest Gain

This indicator aims to monitor the total land area that has transitioned from an unforested to forested state each year.  

Due to data limitations, the indicator currently measures tree cover gain as a best-available proxy for forest gain. Tree cover gain includes forest gain as well as gain of industrial tree plantations and agricultural tree crops, which are not typically defined as forest. An area is defined as experiencing tree cover gain when an increase in tree cover to at least 50 percent canopy cover has occurred (measured at 30-meter resolution in satellite imagery). The statistics reported in this indicator capture “gross” tree cover gain—that is, total gain irrespective of any tree cover loss that may have occurred during that same year (see the Forest Loss Indicator). 

Statistic Data Set Method
The world experienced 80.6 million hectares (Mha) of tree cover gain between 2001 and 2012.  Tree cover gain  Tree cover gain calculation 
More than half (55 percent) of tree cover gain between 2001 and 2012 occurred in tropical and subtropical ecozones.  Tree cover gain; ecozones  tropical and subtropical ecozones 
Around a fifth of all gain (21 percent) in this part of the world occurred in existing tree plantations and presumably does not reflect regeneration of natural forests.  Tree cover gain; tree plantations  Tree cover gain calculation in tree plantations 
The remaining 45 percent of tree cover gain between 2001 and 2012 occurred in temperate and boreal ecozones.  Tree cover gain; ecozones  Tree cover gain calculation in boreal and temperate ecozones 
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Trees outside Forests

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. 

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Forest Degradation

This indicator aims to monitor the area of forest worldwide that has experienced degradation due to natural or human causes. Because it is not currently possible to measure forest degradation directly at a global scale, this indicator uses disturbed forest area that has not necessarily been completely cleared as a proxy for where degradation may have occurred, acknowledging that not all forest disturbance results in forest degradation.  

The indicator measures disturbed areas using three approaches that are currently possible using medium-resolution satellite imagery. The three measures are not mutually exclusive (i.e., areas of different kinds of disturbance often overlap) and they are therefore estimated individually rather than summed: 

  • Forest area experiencing a partial (more than 20 percent and less than 90 percent) loss of tree canopy cover (as measured at 250-meter resolution via satellite imagery) 
  • Tree cover extent experiencing tree cover loss due to fire 
  • Intact forest landscapes (defined as forests that are undisturbed by human activity and larger than 50,000 hectares) that can no longer be considered intact due to evidence of human disturbance 
Statistic Data Set Method
Worldwide, 86 million hectares (Mha) of tree cover loss were associated with fire between 2001 and 2019, affecting 2.1 percent of global tree cover area.  Tree cover loss; tree cover loss by dominant driver  Tree cover loss calculation in wildfire drivers of tree cover loss category 
Worldwide, 120 Mha of forest area that were considered intact in 2000 could no longer be considered intact in 2016, corresponding to a reduction of approximately 7.5 Mha of intact forest per year. . .    Intact forest landscapes (IFLs)  Subtract extent calculation in 2016 IFLs from 2000 IFLs 
. . . and a total reduction of 9 percent of intact forest area.  IFLs Subtract extent calculation in 2016 from 2000 IFLs and divide by extent calculation in 2000 IFLs 
Russia experienced the largest reduction of intact forest area (29 Mha), primarily due to fire.  Countries; IFLs  Subtract extent calculation in 2016 IFLs from 2000 IFLs by country 
Romania saw the largest percentage decline, with its last remaining tract of intact forest fragmented by new transport infrastructure.  Countries; IFLs  Subtract extent calculation in 2016 IFLs from 2000 IFLs by country, divided by 2000 IFLs 
Paraguay also experienced a notable decline, with an 80 percent decrease in forest area that could be considered intact between 2000 and 2016 due to the clearing of the Chaco for cattle ranching.  Countries; IFLs  Subtract extent calculation in 2016 IFLs from 2000 IFLs by country, divided by 2000 IFLs 
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Forest Recovery

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).  

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Protected Forests

This indicator aims to monitor the extent of forests that are legally protected by governments for conservation or other reasons, including amenity and cultural values. It draws on the United Nations Environment Programme’s World Database on Protected Areas (WDPA), which aggregates and publishes national maps of protected areas worldwide, coupled with 30-meter resolution maps of global tree cover extent. Critically, having legal protection status does not always indicate that active protection is occurring.  

Statistic Data Set Method
Approximately 20 percent of global forest area is currently under some form of legal protection.  Tree cover extent; protected areas  Extent calculation in 2010 tree cover extent and protected areas 
Existing protected areas cover 36 percent of tropical primary forest and 28 percent of global intact forest landscapes.  Primary forest; intact forest landscapes (IFLs); protected areas  Extent calculation in primary forest, IFLs, and protected areas 
In 2019, protected areas experienced 3.4 million hectares (Mha) of tree cover loss, including 0.62 Mha of primary forests and 0.69 Mha of intact forests.  Tree cover loss; primary forest; IFLs  Tree cover loss calculation in primary forest, IFLs, and protected areas 
A total of 40.1 Mha of tree cover loss has occurred within protected areas since 2001, and a total of 4.7 Mha of tree cover gain also occurred in these areas between 2001 and 2012.    Tree cover loss; tree cover gain; protected areas  Tree cover loss and gain calculation in protected areas 
In strict nature reserves, wilderness areas, and national parks (a subset of all protected areas that often have the most importance for biodiversity conservation), tree cover loss has increased since 2001 by an average of 18 percent per year, and total tree cover extent has shrunk by 3.9 percent.  Tree cover loss; tree cover extent; protected areas  Tree cover loss and extent calculation in Category I and II protected areas 
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Production Forests

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. 

Statistic Data Set Method
In these nine countries, managed forest concessions cover 26 percent of the total forest area, including 13 percent of intact forest landscapes. Managed forest concessions; intact forest landscapes; tree cover extent Tree cover extent calculation in intact forest landscapes, managed forest concessions, and countries
…which are estimated to cover roughly 75 percent (173 million hectares) of planted forests worldwide. Tree plantations Area calculation in tree plantations
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Forest Carbon Stocks

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.  

Statistic Data Set Method
Tropical rainforests account for only 30 percent of global tree cover but contain 50 percent of the world’s carbon stored in trees. Tree cover extent; aboveground biomass density; ecozones Carbon storage calculation in tropical ecozones
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CO2 Fluxes from Forests

This indicator aims to monitor the amount of carbon dioxide (CO2) released into or absorbed from the atmosphere. As forests grow, they reduce the level of CO2 in the atmosphere by absorbing CO2 through photosynthesis and storing the carbon in vegetation and soil. When trees are cut, cleared, or burned and forest soils are tilled or drained for agriculture, CO2 is released back into the atmosphere. The net sum of CO2 that is released (emissions) versus absorbed (removals) is the “flux.” 


At this time, Global Forest Watch monitors CO2 emissions related to annual tree cover loss in tropical forests using spatially explicit data. More comprehensive estimates of global forest carbon fluxes based on spatial data are currently not available due to lack of data and more complex processes influencing carbon dynamics in temperate and boreal forests (e.g., forest management, fire), although other estimates have been produced using nongeospatial approaches (see the Limitations section). 

Statistic Data Set Method
It is now possible to map annual gross emissions from tropical tree cover loss. These emissions have increased significantly since the turn of the century, from 2.4 gigatons (Gt) of CO2 in 2001 to 4.0 Gt of CO2 in 2019. Tree cover loss; aboveground biomass density; ecozones Carbon emissions calculation in tropical ecozones
Emissions from tree cover loss within tropical primary forests . . . have also been on the rise, more than tripling since 2001. Tree cover loss; aboveground biomass density; primary forest Carbon emissions calculation in primary forest
Brazil, the Democratic Republic of the Congo, and Indonesia were responsible for two-thirds of total CO2 emissions from tropical primary forest loss between 2001 and 2019. Countries; tree cover loss; aboveground biomass density; primary forest Carbon emissions calculation in primary forest, by country
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Soil Stability and Water Regulation

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
Statistic Data Set Method
As of 2010, . . . 47 percent of the world’s tree cover was located in areas defined as being at medium to high risk of erosion. Tree cover extent; erosion risk Tree cover extent calculation in 2010 tree cover extent and high erosion risk
In 2019, 10.6 million hectares (Mha) of tree cover loss (44 percent of total global tree cover loss) occurred in areas of medium to high erosion risk, a 1 percent increase from 2018. Tree cover loss; erosion risk Tree cover loss and rate of loss calculation in high erosion risk
Australia, Brazil, and Indonesia accounted for 30 percent of global tree cover loss in such areas in 2019. Countries; tree cover loss; erosion risk Tree cover loss calculation in high erosion risk, by country
Since 2001, 90 Mha of tree cover have been lost in watersheds that supply the world’s urban areas—representing an 8.6 percent reduction in tree cover in these watersheds since 2000. Tree cover loss; tree cover extent; watershedsMcDonald and Shemie 2014, http://water.nature.org/waterblueprint/#/intro=true . Tree cover loss calculation in urban watersheds 
The Beaumont, Monroe, Port Arthur, and Wilmington watersheds in the United States; the Belém watershed in Brazil; and the Concepción watershed in Chile have lost 30 percent or more of their tree cover since 2000. Tree cover loss; tree cover extent; watershedsMcDonald and Shemie 2014, http://water.nature.org/waterblueprint/#/intro=true . Tree cover loss calculation in urban watersheds
Since 2016, 124 Mha of tree cover loss has occurred within mangrove forests. Over 80 percent of this loss has occurred in Brazil, Cuba, Indonesia, Malaysia, and the United States. Countries; tree cover loss; mangroves Tree cover loss calculation in 2016 mangroves, by country
As of 2016, coastal mangrove forests covered 13.3 Mha across 106 countries, representing a tiny fraction of global tree cover—just 0.3 percent. Tree cover extent; mangroves Area extent calculation in mangroves; total mangrove area was included in estimation and divided by 2010 tree cover extent
One-fifth of coastal mangroves are located in Indonesia, and another quarter are located in Australia, Brazil, Mexico, and Nigeria. Countries; mangroves Area extent calculation in mangroves, by country
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Indigenous and Community Forests

The Indigenous and Community Forests Indicator measures changes in the extent of forestlands that are both legally titled and customarily held by Indigenous Peoples and other forest-dependent local communities and also changes to environmental assets contained on those lands. Communities have occupied forested regions—from the tropical forests of the Amazon Basin to the boreal forests of Canada—for thousands of years and are important stewards of a significant portion of the world’s forests. Spatial data on the location and boundaries of community land are limited, so this indicator also draws on other (nonspatial) statistics from peer-reviewed literature. 

Statistic Data Set Method
The LandMark data set includes approximately 136,000 indigenous and community land maps and indicative areas, representing about 12 percent of the world’s land.  LandMark Area extent calculation in LandMark
These areas contain approximately 16 percent of the world’s intact forest landscapes (IFLs) and 293 gigatons of carbon. LandMark; IFLs; carbon density Area extent and carbon storage calculation in LandMark and IFLs
Between 2013 and 2018, the percentage of tree cover loss within indigenous and community lands in Brazil and Peru was much higher outside of indigenous and community lands than within. Tree cover loss; LandMark; countries Tree cover loss calculation in LandMark and countries
When comparing tree cover loss across all indigenous and community lands between two time periods (three-year averages between 2013–15 and 2016–18), tree cover loss was 1.6 times higher in indigenous and community lands that were not acknowledged by government versus those that were. Tree cover loss; LandMark Tree cover loss calculation in LandMark
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At-Risk Populations

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.

Statistic Data Set Method
Approximately 136 million people currently live within five kilometers of an emerging hot spot of forest loss. Hot spots of primary forest loss; population Sum population within hot spots
The Democratic Republic of the Congo, India, Cameroon, Vietnam and Brazil have the highest number of people living within deforestation hot spots, whereas French Guiana, Gabon, the Solomon Islands, Equatorial Guinea, and the Central African Republic have the highest proportion of their total population close to these hot spots. Hot spots of primary forest loss; population Sum population within hot spots, by country
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Forestland-Related Conflict

The Forestland-Related Conflict Indicator measures the amount of social conflict and violence stemming from decisions about the use of forestlands and resources. As competition for land and natural resources expands and Indigenous Peoples and local communities act to protect forests with high social and cultural values, conflicts over land and natural resources are rising in number—and these conflicts are often violent. Spatial data showing the location and nature of conflict are currently limited. No independently derived statistics were calculated by Global Forest Review authors in this section.

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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 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.
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Definitions

Definitions for basic forest-related concepts and terms are often varied and controversial. For consistency and clarify, the Global Forest Review (GFR) uses the following definitions across the report:

agricultural tree crop: Trees cultivated for their food, cultural, or economic values. These include oil palm, rubber, cocoa, cashew, mango, oranges (citrus), plantain, banana, and coconut.

biodiversity intactness: The proportion and abundance of a location's original forest community (number of species and individuals) that remain.

biodiversity significance: The importance of an area for the persistence of forest-dependent species based on range rarity.

deforestation: The change from forest to another land cover or land use, such as forest to plantation or forest to urban area.  

forest: 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. 

forest concession: A legal agreement allowing an entity the right to manage a public forest for production purposes.

forest degradation: The reduction in a forest’s ability to perform ecosystem services, such as carbon storage and water regulation, due to natural and anthropogenic changes. 

forest disturbance: A discrete event that changes the structure of a forest ecosystem.

forest fragmentation: The breaking of large, contiguous forests into smaller pieces, with other land cover types interspersed. 

forest management plan: 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. 

hectare: 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). 

intact forest: 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.  

land and environmental defenders:  People who peacefully promote and protect rights related to land and/or the environment.

loss driver: The direct cause of forest disturbance.

low tree canopy density: Less than 30 percent tree canopy density.

managed forest concession: Areas where governments have given rights to private companies to harvest timber and other wood products from natural forests on public lands.

mosaic restoration: 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 (10–100 people per square kilometer). 

natural forest: A forest that is grown without human intervention. 

persistent loss and gain: Forests that have experienced one loss or one gain event from 2001 to 2016. 

plantations: An area in which trees have been planted, generally for commercial purposes. 

planted forest: A forest composed of trees that have been deliberately planted and/or seeded by humans. 

primary forest: 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.  

production forest: A forest where the primary management objective is to produce timber, pulp, fuelwood, and/or nonwood forest products.

seminatural forest: A managed forest modified by humans, which can have a different species composition from surrounding natural forests. 

shifting agriculture: Temporary loss or permanent deforestation due to small- and medium-scale agriculture. 

tree cover: 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. 

tree cover gain: 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.

tree cover loss: 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.  

trees outside forests: Trees found in urban areas, alongside roads, or within agricultural land are often referred to as Trees Outside Forests (TOF).

tree plantation: An agricultural plantation of fast-growing tree species on short rotations for the production of timber, pulp, or fruit. 

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These include oil palm, rubber, cocoa, cashew, mango, oranges (citrus), plantain, banana, and coconut.\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"},"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"},"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"},"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. 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"},"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"},"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."},"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. 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