This layer quantifies the impact humans have had on the intactness of species communities. Anthropogenic pressures such as land use conversion have caused dramatic changes to the composition of species communities and this layer illustrates these changes by focusing on the impact of forest change on biodiversity intactness. The maximum value indicates no human impact, while lower values indicate that intactness has been reduced. The PREDICTS database comprises over 3 million records of geographically and taxonomically representative data of land use impacts to local biodiversity (Hudson et al. 2017). A subset of the PREDICTS database, including data pertaining to forested biomes only, is employed to model the impacts of land use change and human population density on the intactness of local species communities. To produce the land use map, all forested biomes are selected and each 30 x 30 m pixel within the biome is assigned a land use category based upon inputs from the GFW forest change database and a downscaled land use map (Hoskins et al 2016). The modelled results of biodiversity intactness derived from the PREDICTS database are projected onto the land use and human population density maps, and the final product is aggregated to match the resolution of the downscaled land use map (Hoskins et al 2016). The final output models the impacts of forest change on local biodiversity intactness within forested biomes.

About the layer

The metric assumes that the biodiversity found in a perfectly intact site is equivalent to the biodiversity that would be present without human interference

Human impacts on biodiversity intactness are quantified through models that extrapolate results from site-specific studies across large areas and there is always a degree of uncertainty in such extrapolations

Plantation forests were not distinguished from natural forests in this analysis, and as such, plantation areas may be counted as intact biodiversity areas.

Cautions

Hill, S. L., Arnell, A., Maney, C., Butchart, S. H., Hilton-Taylor, C., Ciciarelli, C., ... & Burgess, N. D. (2019). Measuring forest biodiversity status and changes globally. Frontiers in Forests and Global Change, 2, 70. 

Source

Use the following credit when this data is displayed:
UNEP-WCMC and Natural History Museum. “Biodiversity Intactness.” Accessed from LandMark on (date). 



Use the following credit when this data is cited:
Hill, S. L., Arnell, A., Maney, C., Butchart, S. H., Hilton-Taylor, C., Ciciarelli, C., ... & Burgess, N. D. (2019). Measuring forest biodiversity status and changes globally. Frontiers in Forests and Global Change, 2, 70. https://doi.org/10.3389/ffgc.2019.00070

Citation

License

Biodiversity Intactness

About the data

This data displays the net change in forest ecosystem carbon, calculated as the difference between forest greenhouse gas emissions (i.e., carbon emissions) from deforestation and forest greenhouse gas removals (i.e., carbon sequestration) from forest growth. Greenhouse gas emissions and removals are measured in terms of carbon dioxide (CO2) equivalent.

Data are the product of modeling and thus have an inherent degree of error and uncertainty. Users are strongly encouraged to read and fully comprehend the metadata and other available documentation prior to data use.

Net flux reflects the total over the model period of 2001-2024, not an annual time series from which a trend can be derived. Thus, values must be divided by 23 to calculate average annual net flux.

Uncertainty is higher in gross removals than emissions, particularly driven by uncertainty in removal factors. These uncertainties are propagated to the uncertainty in net flux.

Values are applicable to forest areas (canopy cover >30 percent and >5 m height). See Harris et al. (2021) for further information on the forest definition used in the analysis.

Emissions reflect stand-replacing disturbances as observed in Landsat satellite imagery and do not include emissions from unobserved forest degradation.

Activity data used as the basis of the estimates contain temporal inconsistencies:

Removals data contain temporal inconsistencies because tree cover gain represents a cumulative total from 2000-2020, rather than annual gains as estimated through 2023.

Improvements in the detection of tree cover loss due to the incorporation of new satellite data and methodology changes between 2011 and 2015 may result in higher estimates of emissions in recent years compared to earlier years. Refer here for additional information.

Large jumps in net flux along some boundary are due to the use of ecozone-specific removal factors. The changes in net flux occur at ecozone boundaries, where different removal factors are applied on each side.

This dataset has been updated since its original publication. See Overview for more information.

Harris, N.L., D.A. Gibbs, A. Baccini, R.A. Birdsey, S. de Bruin, M. Farina, L. Fatoyinbo, M.C. Hansen, M. Herold, R.A. Houghton, P.V. Potapov, D. Requena Suarez, R.M. Roman-Cuesta, S.S. Saatchi, C.M. Slay, S.A. Turubanova, A. Tyukavina. 2021. Global maps of twenty-first century forest carbon fluxes. Nature Climate Change. 

https://doi.org/10.1038/s41558-020-00976-6

Harris et al. (2021). "Global maps of 21st century forest carbon fluxes." Accessed on (date) from LandMark 

Forest Greenhouse Gas Net Flux

This layer shows the boundaries of lands that are held or used by 

. When boundaries are not available, point data indicate the location of the lands. LandMark classifies the data into a standard global typology based on Identity (Indigenous or Community), Government Recognition Status, and Documentation Status. For more information, see Methods for Community level data.

About this layer

Platform may be cited as: LandMark. 2025. LandMark: The Global Platform of Indigenous and Community Lands. Available at: 

. To cite data sources for individual countries or other areas, see the Data Providers page or attribute box associated with each spatial entity.

Local Community Lands

When boundaries are not available, point data indicate the location of the lands. LandMark classifies the data into a standard global typology based on Identity (Indigenous or Community), Government Recognition Status, and Documentation Status. For more information, see Methods for Community Level Data.

Indigenous Peoples’ Lands and Territories

The percentage of the world's Indigenous and community lands and natural resources mapped on LandMark was calculated using the polygon data for Indigenous Peoples' Lands and Territories and Local Community Lands; Indigenous and Community Natural Resource Rights; and Indicative Areas of Indigenous and Community Land Rights, as a proportion of total global land area (excluding Antarctica) sourced from 

 version 4.1. The estimated 50% or more of total global land area held or used by Indigenous Peoples and communities is sourced from 

Oxfam, International Land Coalition, Rights and Resources Initiative. 2016. Common Ground. Securing Land Rights and Safeguarding the Earth. Oxford: Oxfam.

Percentage of Indigenous and community lands  and natural resources mapped on LandMark

Global Land Coverage

About the calculation

The data layer represents areas where Indigenous Peoples' land and territories and Local Communities' lands likely exist, but data on the precise delimitation, recognition and/or documentation status of the lands is not available at this time. In all cases, these are areas where Indigenous Peoples’ and Local Communities’ are known to hold or use lands, but available attribute or locational information is insufficient for their inclusion in the data layers on Indigenous and Community Lands or Indigenous and Community Natural Resource Rights. The purpose of this data layer is to promote awareness of the extent of Indigenous Peoples’ and local communities’ lands even in the absence of detailed demarcation data. For more information on how indicative areas of indigenous and community lands are classified and displayed on LandMark, see Methods for Community-Level Data.

Indicative Areas of Indigenous and Community Land Rights

This layer shows where Indigenous Peoples and/or local communities have rights to forests to harvest various products, rights to wildlife for hunting and fishing, or rights to pasture for herding and grazing. The areas in the data layer are categorized by the type of natural resource right. For more information, see Methods for Community Level Data.

Platform may be cited as: LandMark. 2024. LandMark: The Global Platform of Indigenous and Community Lands. Available at: 

Indigenous and Community Natural Resource Rights

This layer shows the estimated indigenous population per country. The data are divided into two categories for visualization on the global map: indigenous population as a percentage of the country total population, and indigenous population counts

Adapted from Dubertret, F. (2020). Recension des peuples autochtones du monde par pays. [Dataset] based on Annex 1 of "Rendre explicite une présence invisible: Vers un atlas mondial des territoires autochtones". PhD thesis. IHEAL-CREDA, Université Sorbonne Nouvelle. 446 p. Available (in French) at: 

https://tel.archives-ouvertes.fr/tel-03426280

Indigenous Population per Country

 (IFL) 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. To map IFL areas, 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. These were then mapped from Landsat satellite imagery for the year 2000.

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

This data can be used to assess forest intactness, alteration, and degradation at global and regional scales.

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 in proximity to 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.

Potapov, P., M. C. Hansen, L. Laestadius, S. Turubanova, A. Yaroshenko, C. Thies, W. Smith, I. Zhuravleva, A. Komarova, S. Minnemeyer, and E. Esipova. 2017. "The last frontiers of wilderness: Tracking loss of intact forest landscapes from 2000 to 2013." Science Advances 3: e1600821.

Greenpeace, University of Maryland, World Resources Institute and Transparent World. “Intact Forest Landscapes. 2000/2013/2016/2020” Accessed through Landmark on (date) 

Intact Forest Landscapes

Full details of each KBA, including information on their biodiversity importance, can be viewed at http://www.keybiodiversityareas.org. The criteria by which KBAs are identified are described in the Global Standard for the Identification of Key Biodiversity Areas (IUCN 2016). Sites qualify as global KBAs if they meet one or more of 11 criteria, clustered into five categories: threatened biodiversity; geographically restricted biodiversity; ecological integrity; biological processes; and, irreplaceability. The KBA criteria can be applied to species and ecosystems in terrestrial, inland water and marine environments. Although not all KBA criteria may be relevant to all elements of biodiversity, the thresholds associated with each of the criteria may be applied across all taxonomic groups (other than micro-organisms) and ecosystems. The KBA identification process is a highly inclusive, consultative and bottom-up exercise. Although anyone with appropriate scientific data may propose a site to qualify as a KBA, consultation with stakeholders at the national level (both non-governmental and governmental organizations) is required during the proposal process. The KBA Partnership brings together 13 of the world’s largest international conservation organisations to map, monitor and conserve the most important places for life on earth. For further information, visit 

Note that KBAs have not yet been identified comprehensively in every country for all species groups and biodiversity features. See further details at 

BirdLife International (2020). Digital boundaries of Key Biodiversity Areas from the World Database of Key Biodiversity Areas. Developed by the KBA Partnership: BirdLife International, International Union for the Conservation of Nature, American Bird Conservancy, Amphibian Survival Alliance, Conservation International, Critical Ecosystem Partnership Fund, Global Environment Facility, Global Wildlife Conservation, NatureServe, Rainforest Trust, Royal Society for the Protection of Birds, Wildlife Conservation Society and World Wildlife Fund. June 2021 Version. Available at http://keybiodiversityareas.org/kba-data/request.

Non-commercial use only. Full terms of use available at http://www.keybiodiversityareas.org/info/dataterms. Commercial access is available at 

Licence

Key Biodiversity Areas

This data set shows the global distribution of land cover in 2015. This version (2.07) was created as part of the Climate Change Initiative (CCI), an initiative of the European Space Agency to create long-term, consistent, global data for the purposes of climate modelling. The CCI Land Cover project delivers consistent global land cover maps at 300 m spatial resolution on an annual basis from 1992 to 2015. The Global Forest Watch platform only displays the 2015 land cover data.

To ensure consistency from year to year, land cover maps for each year are derived from a single baseline land cover map. The baseline map was created using the full record of MERIS images from 2003 to 2012, using unsupervised classification as well as a machine learning algorithm over multiple years of imagery. Changes are then detected between individual years at 1 km resolution, using AVHRR data from 1992 to 1999, SPOT-VGT data from 1999 to 2013, and PROVA-V data from 2014 and 2015. Changes must be consistent for two consecutive years in order to be counted, with the exception of forest changes in 2014 and 2015 which are assumed to be well detected. The 1 km changes are then combined with the baseline land cover map and delineated to 300 meters for 2004 onward (when MERIS and PROVA-V data are available).

The resulting data have a total of 22 global land cover classes. For the sake of better visualization, Global Forest Watch shows only a set of simplified classes, based on the IPCC (agriculture, forest, grassland, wetland, settlement, shrubland, sparse vegetation, bare area, water, and permanent ice and snow). 

The full set of classes as well as annual land cover maps back to 1992 are available on the 

 is available from the CCI. In general, land cover classes such as rainfed and irrigated croplands, broadleaved evergreen forest, urban areas, bare areas, water bodies and permanent snow are found quite accurately mapped. On the other hand, classes such as lichens and mosses, sparse vegetation and flooded forest with fresh water can be affected by errors.

Data quality varies by region, particularly as related to the coverage of MERIS imagery for creation of the baseline map. Areas with less coverage include the western part of the Amazon basin, Chile and the southern part of Argentina, the western part of Congo basin as well as the gulf of Guinea, the eastern part of Russia, and the eastern coast of China and Indonesia.

ESA Climate Change Initiative, Land Cover - led by UCLouvain. “2015 global land cover.” Accessed through LandMark on (date). www.landmarkmap.org.

Land Cover

Displays boundaries of forested areas allocated by governments to companies for harvesting timber and other wood products, as compiled by Global Forest Watch at https://www.globalforestwatch.org/. Currently available for Cameroon, Canada, Central African Republic, Democratic Republic of the Congo (DRC), Equatorial Guinea, Gabon, Indonesia, Liberia, and Republic of the Congo.

“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 displays managed forest concessions as a single layer assembled by aggregating data for multiple countries. The data may come from government agencies, NGOs, or other organizations and varies by date and data sources. For more information on concession data for each country please visit the Global Forest Watch 

This layer is a compilation of concession data from various countries and sources. The quality of these data can vary depending on the source. This layer may not be comprehensive of all existing concessions in a country, and the location of certain concessions can be inaccurate.

Compiled from multiple sources including: 

 Ministry of Water and Forests, Hunting, and Fishing & WRI

 Ministry of Environment, Nature Conservation, and Tourism & WRI

 Ministry of Agriculture and Forests & WRI

 Ministry of Forest Economy, Water, Fisheries, and Aquaculture & WRI

 Earthsight Investigations & Global Witness

Managed forest concessions as compiled by Global Forest Watch. Accessed through LandMark on (date) from www.landmarkmap.org.

Logging Concessions

About data

The State of the World's Rivers is an interactive web database that illustrates data on ecological health in the world’s 50 major river basins. Indicators of ecosystem health are grouped into the categories of river fragmentation, biodiversity, and water quality. The database was created and published by International Rivers in 2014.

The Dam Hotspots data contains over 5,000 dam locations determined by latitude and longitude coordinates. These locations were confined to the world’s 50 major river basins. The data set comes from multiple sources, and was corrected for location errors by International Rivers. The “project status”—a moving target—was determined by acquiring official government data, as well as through primary research from Berkeley and five International Rivers’ regional offices.
* Operational: Already existing dams.
* Under construction: Dams which are currently being constructed.
* Planned: Dams whose studies or licensing have been completed, but construction has yet to begin.
* Inventoried: Dams whose potential site has been selected, but neither studies nor licensing have occurred.
* Suspended: Dams which have been temporarily or permanently suspended, deactivated, cancelled, or revoked.
* Unknown: No data are currently available.

Dams data was compiled by International Rivers from various sources, including: the Global Reservoir and Dam (GRanD) Database, the Consultative Group on International Agricultural Research (CGIAR) Challenge Program on Water and Food - Mekong (for Mekong basin dams only), the United States National Inventory of Dams (NID), other government dam inventories, and original data collection by International Rivers.

International Rivers. "Dam hotspots". Available at http://tryse.net/googleearth/irivers-dev3/. Accessed through LandMark on (date). 

Major dams

Displays boundaries of areas allocated by governments to companies for extraction of minerals as compiled by Global Forest Watch at https://www.globalforestwatch.org/. Currently available for Cameroon, Cambodia, Canada, Colombia, Republic of the Congo, Gabon, Democratic Republic of the Congo (DRC), Peru, Brazil, Suriname, and Mexico. 

“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 displays mining concessions as a single layer assembled by aggregating concession data for multiple countries. The data may come from government agencies, NGOs, or other organizations and varies by date and data sources. For more information on concession data for each country please visit the Global Forest Watch 

This layer is a compilation of concession data from various countries and sources. The quality of these data can vary depending on the source. This layer may not include all existing concessions in a country, and the location of certain concessions can be inaccurate.

Compiled from a variety of sources, including:

 National Department of Mineral Protection

 Ministry of Mines and Technological Development

 Tierra Minada; Agencia Nacional de Minería de Colombia

 Democratic Republic of the Congo Ministry of Mines Mining Registry (CAMI)

 Republic of the Congo Ministry of Mines and Geology & WRI

Mining concessions as compiled by Global Forest Watch. Accessed through LandMark on (date) from www.landmarkmap.org.

Mining Concessions

This layer show the estimated area of land held or used by Indigenous Peoples and local communities per country as a percentage of the country’s total land area. The data are divided into three categories, where available: acknowledged by government (i.e., lands recognized by the State), not acknowledged by government (i.e., lands held or used but not recognized by the State), and total (i.e., sum of all lands held or used, independent of recognition status). For more information, see Methods for National Level Data.

Compiled by F. Dubertret and L. Alden Wily. 2024. Percent of Country Held by Indigenous Peoples and Local Communities. Data file from LandMark: The Global Platform of Indigenous and Community Lands. Available at: 

Percent of Country Held by Indigenous Peoples and Communities

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 World Database on Protected Areas (WDPA). The WDPA is a joint initiative of the IUCN and UNEP-WCMC to compile spatially referenced information about protected areas.

IUCN Management Categories

Not all protected areas receive the same degree of protection. While some have strict guidelines designed to preserve intact ecosystems, others allow for sustainable land use, often including limited resource extraction. In addition, not all countries use the same terminology when designating a protected area. Accordingly, the International Union for Conservation of Nature defined universal management categories that stipulate the level of protection for most protected areas.

As you click through protected areas in this layer, note the “legal designation” and the explanations below to better understand the degree to which an area is protected.

Ia. Strict Nature Reserves. Protected areas designed to preserve biodiversity and all geological features. Limited human use (e.g., scientific study, education) is allowed and carefully monitored. Strict Nature Reserves are often used to understand the impact of indirect human disturbance (e.g., burning fossil fuels) because of the area’s high level of preservation. Other common designations: Biological Reserve, Botanical Reserve

Ib. Wilderness Areas. Protected areas managed to preserve ecosystem processes with limited human use. Wilderness Areas cannot contain modern infrastructure (e.g., a visitor’s center), but they allow for local indigenous groups to maintain subsistence lifestyles. These areas are often established to restore disturbed environments. Other common designations: Wilderness Reserve, Wildlife Area

II. National Parks. Protected areas designed to preserve large-scale ecosystems and support human visitation. With conservation as a priority, these areas allow infrastructure and contribute to the local economy by providing opportunities for environmental educational and recreation. Other common designations: State Park, Class A Park, Park Reserve, Provincial Park

III. National Monuments or Features. Areas established to protect a specific natural feature (e.g., cave, grove) or human-made monument with significant historical, spiritual, or environmental importance and the immediate surroundings. Accordingly, Natural Monuments or Features are typically smaller in area and have high human impact resulting from visitor traffic. Other common designations: Natural Features Reserve, Nature Monument, Botanical Garden

IV. Habitat and Species Management Areas. Areas designed to conserve specific wildlife populations and/or habitats. Habitat and Species Management Areas often exist within a larger ecosystem or protected area and are carefully managed (e.g., through hunting abatement or habitat restoration) to conserve a target species or habitat. Other common designations: National Wildlife Refuge, State Wildlife Management Area, Faunal Reserve, Zakaznik (Russia), Provincial Reserve, Wildlife Sanctuary

V. Protected Landscapes and Seascapes. Protected areas with ecological, biological, or cultural importance that have been shaped by human use of the landscape. Protected landscapes and seascapes typically cover entire bodies of land or ocean and allow for a number of for-profit activities (e.g., ecotourism) in accordance with the region’s management plan. Other common designations: National Forest, State Natural Area, Environmental Protection Area, Protected Area, Quasi National Park (Japan), Nature Reserve, State Natural Area

VI. Protected Areas with Sustainable Use of Natural Resources. Areas designed to manage natural resources and uphold the livelihoods of surrounding communities. These regions have a low level of human occupation, small-scale developments (i.e., not industrial), and part of the landscape in its natural condition. Other common designations: Wildlife Reserve, Biosphere Reserve, Forest Reserve, Protective Zone, National Forest, Natural and National Reserves, Reserve, Multiple Use Reserve, Municipal Reserve

UNESCO-MAP Biosphere Reserves: areas under UNESCO’s Man and the Biosphere Programme designated to “promote sustainable development based on local community efforts and sound science.”

World Heritage Sites: areas considered to have “outstanding universal value” and meet at least one of ten criteria, as described here.

Ramsar Sites—Wetlands of International Importance: wetlands that hold significant value designated under the Ramsar Convention on Wetlands.

Boundaries come from a variety of sources, with varying accuracy, up-to-dateness, and resolution. Data for some countries may be imprecise, miss some protected areas, or include boundaries that have since been cancelled.

Protected area designations, such as “National Park,” can be applied differently in different countries and may be associated with different IUCN categories. Only areas that meet the IUCN definition of protected areas are collected by WDPA, and thus the data may differ from national-level data on protected areas.

Protected areas with no boundary data are displayed as boxes which represent the reported protected area size. The box is centered around a single point location and the borders do not indicate the real boundary of the protected area.

The World Database on Protected Areas, which compiles protected area data from governments, NGOs, and international secretariats

IUCN and UNEP-WCMC (2022), The World Database on Protected Areas (WDPA) [On-line], Cambridge, UK: UNEP-WCMC. Available at: www.protectedplanet.net. Accessed through Landmark. 

Protected Areas

test

Test

This is a global, wall-to-wall map of aboveground biomass (AGB) at approximately 30-meter resolution. This data product expands on the methodology presented in Baccini et al. (2012) to generate a global map of aboveground live woody biomass (AGB) density (megagrams biomass per ha) at 0.00025-degree (approximately 30-meter) resolution for the year 2000. Aboveground biomass was estimated using a multi-step process of calculating AGB at more than seven hundred thousand points with LiDAR with regional allometric equations, then using those to train a wall-to-wall model based on Landsat imagery. Pixels without tree canopy were assigned a biomass density of 0 Mg/ha. Additional information on the creation of this map can be found in Harris et al. 2021.


Aboveground biomass is available for download in two different units: 1) megagrams AGB per hectare, and 2) megagrams AGB per pixel. The first is appropriate for visualizing (mapping) AGB and estimating average AGB density in an area of interest because it represents the density of AGB per hectare. The second is appropriate for calculating the total aboveground biomass stock in an area of interest because the values of the pixels in the AOI can be summed to obtain the total AGB stock in that area. The values in the latter were calculated by adjusting the AGB per hectare by the size of each pixel, which varies by latitude.


Data are the product of modeling and thus have an inherent degree of error and uncertainty. Users are strongly encouraged to read and fully comprehend the metadata and other available documentation prior to data use.

The biomass density value individual pixels have large uncertainty. Values in individual pixels are expected to differ from biomass estimates in field-measured plots.

Aboveground biomass may be overestimated at low values and underestimated at high values.

Harris et al. (2021). Global maps of 21st century forest carbon fluxes. Accessed on (date) from LandMark.

Tree Biomass Carbon Density

This data set, a collaboration between the 

 (Global Land Analysis & Discovery) lab at the University of Maryland, Google, USGS, and NASA, displays tree cover over all global land (except for Antarctica and a number of Arctic islands) for the years 2000 and 2010 at 30 × 30 meter resolution. “Percent tree cover” is defined as the density of tree canopy coverage of the land surface and is color-coded by density bracket (see legend).

Data in this layer were generated using multispectral satellite imagery from the 

 thematic mapper plus (ETM+) 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.

The tree cover canopy density of the displayed data varies according to the selection - use the legend on the map to change the minimum tree cover canopy density threshold.

For the purpose of this study, “tree cover” was defined as all vegetation taller than 5 meters in height. “Tree cover” is the biophysical presence of trees and may take the form of natural forests or plantations existing over a range of canopy densities.

Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend. 2013. “High-Resolution Global Maps of 21st-Century Forest Cover Change.” Science 342 (15 November): 850–53. Data available from: 

https://glad.umd.edu/dataset/global-2010-tree-cover-30-m

Use the following credit when these data are displayed:
Source: Hansen/UMD/Google/USGS/NASA, accessed through Global Forest Watch

Use the following credit when these data are cited:
Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend. 2013. “High-Resolution Global Maps of 21st-Century Forest Cover Change.” Science 342 (15 November): 850–53. Data available on-line from:https://glad.umd.edu/dataset/global-2010-tree-cover-30-m. Accessed through Global Forest Watch on 19/07/2024. www.globalforestwatch.org. 

Tree Cover

 (Global Land Analysis & Discovery) lab at the University of Maryland measures areas of tree cover gain from the year 2000 to 2020 across the globe at 30 × 30 meter resolution, displayed as a 20-year cumulative layer. Tree cover gain was determined using tree height information from the years 2000 and 2020. Tree height was modeled by the integration of the Global Ecosystem Dynamics Investigation (GEDI) lidar forest structure measurements and Landsat analysis-ready data time-series. The NASA GEDI is a spaceborne lidar instrument operating onboard the International Space Station since April 2019. It provides point-based measurements of vegetation structure, including forest canopy height at latitudes between 52°N and 52°S globally. Gain was identified where pixels had tree height ≥5 m in 2020 and tree height <5 m in 2000.

Tree cover gain may indicate a number of potential activities, including natural forest growth, the tree crop rotation cycle, or tree plantation management.

When zoomed out (< zoom level 12), pixels of gain are shaded according to the density of gain at the 30 x 30 meter scale. Pixels with darker shading represent areas with a higher concentration of tree cover gain, whereas pixels with lighter shading indicate a lower concentration of tree cover gain. There is no variation in pixel shading when the data is at full resolution (≥ zoom level 12).

In this data set, “tree cover” is defined as woody vegetation with the height of 5 m and taller, and may take the form of natural woodlands, forests, or tree plantations across a range of canopy densities. Tree cover gain does not equate directly to restoration, afforestation or reforestation.

Due to variation in research methodology and date of content, tree cover, gain, and annual loss data sets cannot be compared accurately against each other. Accordingly, “net” cannot be calculated by subtracting figures for tree cover gain from the annual tree cover loss data set. Instead, the net tree cover change layer should be used, which was calculated exclusively from tree height data.

Integrated use of other products such as canopy cover density data also available on GFW should be performed with caution.

The authors evaluated the accuracy of the product, and the overall accuracy was found to be 99.3%, commission error (false positives) of 28.6%, and omission error (false negatives) of 42.2%. The accuracy does vary by biome and thus may be higher or lower in any particular location. As the omission error is higher than the commission error, this indicates that the product provides conservative estimates of forest dynamics.

There was confusion between forest enhancement (existing forest height increase) and forest gain (establishment of forests within the year 2000 non-forest land), and this was more prominent in boreal forest areas where forest height in the year 2000 was difficult to determine.

Potapov, P., Hansen, M.C., Pickens, A., Hernandez-Serna, A., Tyukavina, A., Turubanova, S., Zalles, V., Li, X., Khan, A., Stolle, F., Harris, N., Song, X-P., Baggett, A., Kommareddy, I., and Kommareddy, A. 2022. The Global 2000-2020 Land Cover and Land Use Change Dataset Derived From the Landsat Archive: First Results. Frontiers in Remote Sensing, 13, April 2022. 

https://doi.org/10.3389/frsen.2022.856903

Use the following credit when this data is displayed:


Accessed through Landmark on (date). www.landmarkmap.org.

Use the following credit when this data is cited:


Potapov, P., Hansen, M.C., Pickens, A., Hernandez-Serna, A., Tyukavina, A., Turubanova, S., Zalles, V., Li, X., Khan, A., Stolle, F., Harris, N., Song, X-P., Baggett, A., Kommareddy, I., and Kommareddy, A. 2022. The Global 2000-2020 Land Cover and Land Use Change Dataset Derived From the Landsat Archive: First Results. Frontiers in Remote Sensing, 13, April 2022. 

Tree Cover Gain

Identifies areas of gross tree cover loss during the period 2000-2024, defined as a stand-replacement disturbance, or a change from a forest to non-forest state. 

"In this data set, “tree cover” is defined as all vegetation greater than 5 meters in height, and may take the form of natural forests or plantations across a range of canopy densities. “Loss” indicates the removal or mortality of tree cover and can be due to a variety of factors, including mechanical harvesting, fire, disease, or storm damage. As such, “loss” does not equate to deforestation.

Due to variation in research methodology and date of content, tree cover, loss, and 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.

. Comparisons between the original 2001-2010 data and the 2011-2022 update should be performed with caution.

The authors evaluated the overall prevalence of false positives (commission errors) in this data at 13%, and the prevalence of false negatives (omission errors) at 12%, 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. The authors are 75 percent confident that the loss occurred within the stated year, and 97 percent confident that it occurred within a year before or after. Users of the data can smooth out such uncertainty by examining the average over multiple years. Read our 

 on the accuracy of this data for more information."

Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend. 2013. “High-Resolution Global Maps of 21st-Century Forest Cover Change.” Science 342 (15 November): 850–53. Data available from: earthenginepartners.appspot.com/science-2013-global-forest.

"Use the following credit when these data are displayed:
Source: Hansen/UMD/Google/USGS/NASA, accessed through LandMark

Use the following credit when these data are cited:
Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend. 2013. “High-Resolution Global Maps of 21st-Century Forest Cover Change.” Science 342 (15 November): 850–53. Data available on-line from:http://earthenginepartners.appspot.com/science-2013-global-forest. Accessed through LandMark on (date). www.landmarkmap.org 

Tree Cover Loss

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