Last updated | August 21, 2025

Indicator information

Name

Forest Cover 2000 and Forest Cover Change statistics 

Unit

Forest change statistics are expressed as the trend in the percent of the land covered by forests, as well as the total forest area (km²) gained or lost when compared to the reference year 2000. 

Area of interest

The forest cover for the year 2000 and the forest change statistics are computed and distributed through REST services at country, terrestrial ecoregion and protected area level and are available in KCBD - Global Biodiversity Data Viewer (GBDV) at country level.

Related targets

	Sustainable Development Goal 15 on life on land
	Global Biodiversity Framework Target 2

Policy question

How well are forests preserved in a given area? Forests are one of the most important terrestrial habitats and a carbon sink that needs to be conserved to fulfil biodiversity conservation and climate change mitigation targets. By informing of forest cover trends, and their spatial distribution, it is possible to highlight countries, ecoregions or specific protected areas with worrying forest loss trends, as well as others where forest cover is well maintained or even increases through time either naturally or through forestation.

Use and interpretation

Forests concentrate a large portion of terrestrial biological diversity and are one of the main storages of carbon, which is accumulated in the forest biomass, in the dead organic matter and in the forest soils. Halting or reducing forest loss is required to ensure the conservation of many species that depend on forest habitats and ecosystems. On the other hand, a sustainable use and management of forests, which obtains wood and non-wood products for human use while ensuring forest persistence, is crucial to support, in many areas, the livelihoods of rural communities, which would otherwise get impoverished by a depleted forest cover. Avoiding forest loss, or increasing the area covered by forests where necessary, is also needed to preserve or restore many other important ecosystem services like water regulation, erosion control, pollution control or recreation. Forests are in risk in many areas due to a variety of pressures, most notably agricultural expansion but also extractive activities (such as mining), urbanization, infrastructure development or wildfires, among others.

Key caveats

The forest change statistics included in the KCBD Global Biodiversity Data Viewer are based on global products derived from earth observation, which may have varying classification accuracies and unavoidable uncertainties in the classification of forests in different areas (see e.g. Gross et al., 2017; and section on methodology).

The period considered for assessing the forest trends computed for the period 2001–2023 and defines forest based on tree cover. This means that this product considers as forest loss the temporarily unstocked areas, and that trees in agricultural lands may be classified as forests.

The forest data sources and classifications do not differentiate natural forest from plantations, nor natural regeneration or expansion from that due to planting or deliberate seeding. Forest habitat quality, for particular taxa or for forest biodiversity in general, is not discriminated in these sources, which would need to be considered in more detailed assessment that may focus on individual species or groups of species of particular conservation interest.

Indicator status

Operational indicator published by Hansen et al. (2013) derived here from GIS analyses applied at country, ecoregion and protected area level. 

Available data and resources

Data

Datasets with forest gains and losses compared to the reference year 2000 are obtained from a global remote sensing product indicating loss for the period 2000-2023 and gain for the period 2000-2012. Trends in forest loss and gain are available at the country and ecoregion level as well as for each protected area at least as large as 1 km².

Update frequency

Planned annually.

Code

The procedure for the computation of the indicator, which currently involves the use of a wide range of software to handle the different steps, is documented in Juffe Bignoli et al. (2024).

Methodology

The forest cover for the reference year 2000 and change statistics (gain 2000-2012, and loss 2000-2023) were produced at a spatial resolution of 30 m by the analyses of remote sensing images acquired by Landsat satellites, as described in Hansen et al. (2013). These analyses have been updated, including improvements to the methodology, to assess forest trends up to 2023. In these analyses, forest refers to tree cover, defined as all vegetation taller than 5 m in height, which may include some tree crops under agricultural land use. Although these maps allow differentiating forests, and their trends, for different percentages of tree canopy cover, the gains and losses are reported here for 30% or more of canopy cover. Forest loss is defined as a stand-replacement disturbance or the complete removal of tree cover canopy at the 30 m Landsat pixel scale. Forest gain is defined as the inverse of loss, or the establishment of tree canopy from a non-forest state. The authors evaluated the accuracy of the forest change maps and reported the classification errors to be of about 12% of for the forest losses and of about 25% for the forest gains. These error rates are global averages, and may vary significantly in particular biomes or focal areas.

Some additional information on the accuracy of these maps is provided at http://blog.globalforestwatch.org/data/how-accurate-is-accurate-enough-examining-the-glad-global-tree-cover-change-data-part-1.html

For the computation of forest trends, the forest change maps by Hansen et al. (2013), updated for the period 2001–2023, are overlaid with country and ecoregions boundaries as well as with all protected areas provided by the UNEP-WCMC and IUCN (2025). This analysis excludes UNESCO Biosphere Reserves as well as protected areas recorded only as points. 

Input datasets

References

Dinerstein et al. (2017), An Ecoregion-Based Approach to Protecting Half the Terrestrial Realm, BioScience, Volume 67, Issue 6, June 2017, Pages 534–545, https://doi.org/10.1093/biosci/bix014

Gross, D., et al. (2017) Uncertainties in tree cover maps of Sub-Saharan Africa and their implications for measuring progress towards CBD Aichi Targets. Remote Sensing in Ecology and Conservation. http://dx.doi.org/10.1002/rse2.52

Hansen, M. C., et al. (2013). High-resolution global maps of 21st-century forest cover change. Science, 342: 850–853. http://dx.doi.org/10.1126/science.1244693

Juffe-Bignoli et al. (2024) Delivering Systematic and Repeatable Area-Based Conservation Assessments: From Global to Local Scales. Land 2024, 13, 1506. https://doi.org/10.3390/land13091506

Lázaro, C., Mandrici, A., Delli, G., Caudullo, G., Bourgoin, C. et al., Challenges in integrating global environmental data with GISCO administrative layers – A GIS perspective, Publications Office of the European Union, 2025. https://dx.doi.org/10.2760/8183010

UNEP-WCMC & IUCN (2025). Protected Planet: The World Database on Protected Areas (WDPA) [On-line], [January/2025], Cambridge, UK: UNEP-WCMC and IUCN. www.protectedplanet.net