Last updated | August 21, 2025

Indicator information

Name

Above-Ground Carbon Indicator (AGCI)

Unit

The above-ground carbon (AGC) is expressed in Mg (megagrams or tonnes) of carbon per ha. It corresponds to the carbon fraction of the oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees, excluding stump and roots, as estimated by the CCI BIOMASS project, aimed to a) generate annual global estimates of AGB for several epochs between 2005 and 2022, and b) quantify AGB changes between epochs.

Area of interest

The AGCI has been calculated and distributed through REST services at country, terrestrial ecoregion and protected area level and is available in KCBD - Global Biodiversity Data Viewer (GBDV) at country level.

Related targets

	Sustainable Development Goal 13 on climate action
	Sustainable Development Goal 15 on life on land
	Global Biodiversity Framework Target 8

Policy question

There are two main policy questions to which AGCI is relevant:

• How do protected areas contribute, through the conservation of vegetation resources, to the health and productivity of the ecosystems and to the sustainability of the local communities that depend on these resources? The persistent loss of AGC can indicate a degradation of the forest vegetation canopy, and can happen through unsustainable management practices and through detrimental land use and land cover changes.

• How do protected areas contribute to carbon storage and hence to offset the impacts of fossil fuel emissions and to climate change mitigation? Forests represent one of the largest terrestrial organic carbon reservoirs, and significantly contribute to the regulation of the global carbon cycle. Changes in land use and land cover can cause AGC decreases and related carbon emissions, which are one of the largest sources of human-caused carbon emissions to the atmosphere. Protected areas may contribute to biomass and carbon retention and hence to the reduction of net emissions of greenhouse gasses responsible for climate change.

Use and interpretation

Tree carbon stocks are relevant for quantifying terrestrial carbon storage and carbon sinks as well as for estimating potential emissions and removals from land cover changes (deforestation, reforestation, afforestation) and from biotic (pests, diseases) and abiotic (e.g. forest fires, windstorms) disturbances. Forests in particular have a key role in the global carbon cycle and are considered large and persistent carbon sinks thanks to the CO₂ fixed by photosynthesis into organic matter, such as wood. Therefore, spatially explicit data and assessments of forest biomass and carbon are of paramount importance for the design and implementation of effective sustainable forest management options and forest related policies.

The AGCI provides useful information about the tree carbon stocks and condition in protected areas, which can contribute to identify potentially degraded areas, evaluate the conservation performance of protected areas, set restoration targets, and assess the contribution of protected areas to reduce net global carbon emissions.

In the assessment of AGCI, water bodies, urban areas, permanent snow/ice and bare area land cover classes mapped by the Climate Change Initiative – Land Cover map (ESA (2024)) have been masked out for preventing distortions and potentially biased estimates that unvegetated areas, or areas with very low canopy cover, can cause in the assessment (Quegan et al. 2017).

Key caveats

The AGCI was obtained by converting biomass to carbon using a conversion factor of 0.5 applied to the above-ground biomass (AGB) data provided by the global terrestrial biomass map derived from Earth Observation data in the framework of the CCI BIOMASS project, funded by the European Space Agency (ESA). The AGB estimates in the CCI BIOMASS were derived from images acquired by SAR C-band (Envisat ASAR for 2010 and Sentinel-1 starting with 2017) and L-band (ALOS-1 PALSAR-1 for 2010 and ALOS-2 PALSAR-2 starting with 2017) and auxiliary datasets using multiple estimation procedures.

Overall, the CCI BIOMASS approach seems to be performing appropriately in estimating AGB in all biomes, as assessed over a significant set of locations with independent in situ reference data (Rozendaal et al., 2017). The validation confirmed the quality of the AGB estimates and indicated reliability even in the wet tropics, which was initially considered to be beyond the capability of the EO datasets and algorithms available. The estimates, however, are not free from errors (local biases and substantial uncertainties), primarily in regions where the remote sensing data available had limited capability to resolve forest structures or in areas not sufficiently characterized in terms of wood density and biomass expansion factors. For all biomes, AGB predictions strongly agreed with the observations in the lower biomass range. In the temperate and subtropical zone, AGB was underestimated for reference values ≥ 150 Mg/ha, while in the tropics and in the boreal ecozone the agreement between predicted and observed AGB was remarkable. We refer to the CCI BIOMASS Product User guide v. 5 (Santoro et al., 2024) about the validation data and protocol and to Quegan et al., 2017 and Rozendaal et al., 2017 for a detailed discussion about the main strengths and limitations of the product.

Trees are the main stock of terrestrial vegetation biomass and carbon but, in certain biomes, other vegetation types such as shrubs or herbaceous plants can provide also significant contributions to AGB, which are not considered by the AGCI.

AGB estimates were generated in the CCI BIOMASS project for each point on Earth for which EO data were available. However, areas with no or very low canopy cover (typically, water, urban, permanent snow and ice and bare soil) have been masked out and are not considered in the assessment.

The biomass to carbon conversion factor of 0.5 here used is a good approximation of the typical carbon content in the biomass of terrestrial vegetation, and is consistent with the Good Practice Guidance in LULUCF by the IPCC (2003) and with other related assessments (Baccini et al., 2017; Zarin et al., 2016; Achard et al., 2014; Baccini et al., 2012; Saatchi et al., 2011; Gallaun et al., 2010). There is however some variation of this biomass to carbon conversion factor for different tree species, different components of a tree or a stand and age of the stand, which may be accounted for in more detailed assessments (Ruesch and Gibbs, 2008; Thurner et al., 2014).

Because the AGCI is computed within the boundaries for each protected area, results will be affected by the accuracy of the available protected area boundaries.

Indicator status

The latest version of the Above Ground Biomass map, developed by ESA's CCI BIOMASS project, is publicly available for download (Santoro, M.; Cartus, O. (2025)) and is described in detail in the relevant CCI BIOMASS Product user Guide v.5 (Santoro et al., 2024). The assessment of the AGCI in protected areas, countries and ecoregions is not yet published.

Available data and resources

Data

AGCI values are provided at country level on the  KCBD Global Biodiversity Data Viewer  

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 AGCI is based on the information provided by the global terrestrial biomass map developed by the CCI BIOMASS project, which estimates, with a spatial resolution of 100 m and for the reference year 2022, the amount of AGB (Mg/ha) considering the oven-dry weight woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots. The AGB values have been here converted to carbon content (AGCI) using the conversion factor of 0.5 (Mg C / Mg dry matter), which is consistent with the approach in the Good Practice Guidance in LULUCF by the IPCC (2003) and within the range of values (0.47 - 0.51) used in the related literature (Ruesch and Gibbs, 2008; Thurner et al., 2014).

The CCI BIOMASS map has been developed through a synergistic mapping approach and multiple estimation procedures combining SAR C-band (Envisat ASAR for 2010 and Sentinel-1 starting with 2017) and L-band (ALOS-1 PALSAR-1 for 2010 and ALOS-2 PALSAR-2 starting with 2017) datasets, further supported by auxiliary products derived from earth observation (land cover, land surface temperature etc.) and in situ information. The earth observation data were used to estimate the Growing Stock Volume (GSV) of trees. GSV accounts for the volume of all living trees with more than 10 cm in diameter at breast height measured over bark from ground or stump height to a top stem diameter of 0 cm and excludes smaller branches, twigs, foliage, flowers, seeds, stump and roots (definition of FAO). Then, AGB was obtained from GSV with a set of biomass expansion and conversion factors, derived from ground estimates of wood density and stem-to-total biomass expansion factors. See the Algorithm Theoretical Basis document, v. 5.0 for a detailed description of the algorithms and methods used in the production of the CCI BIOMASS map.

The AGB map data, with a spatial resolution of 100 m, have been submitted to the standard procedure for continuous raster datasets described in details Juffe Bignoli et al. (2024) in order to derive the minimum, maximum, mean of AGCI (as density, in Mg C/ha) and the total AGC stored (Mg) within each country, terrestrial ecoregion and protected area. UNESCO Biosphere Reserves were discarded as well as protected areas with known areas but undefined boundaries.

Input datasets

References

Achard, F., Beuchle, R., Mayaux, P., Stibig, H. J., Bodart, C., Brink, A., ... & Lupi, A. (2014). Determination of tropical deforestation rates and related carbon losses from 1990 to 2010. Global change biology, 20(8): 2540-2554. https://doi.org/10.1111/gcb.12605

Baccini, A. G. S. J., Goetz, S. J., Walker, W. S., Laporte, N. T., Sun, M., Sulla-Menashe, D., ... & Samanta, S. (2012). Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nature climate change, 2(3): 182. https://doi.org/10.1038/nclimate1354

Baccini, A., Walker, W., Carvalho, L., Farina, M., Sulla-Menashe, D., & Houghton, R. A. (2017). Tropical forests are a net carbon source based on aboveground measurements of gain and loss. Science, 358(6360): 230-234. DOI: 10.1126/science.aam5962

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

ESA (2024). ESA Land Cover CCI – Product User Guide and Specification. CDR and ICDR Sentinel-3 Land Cover (v2.1.1) – v2.1.1. Available online at https://cds.climate.copernicus.eu/datasets/satellite-land-cover?tab=documentation

Gallaun, H., Zanchi, G., Nabuurs, G. J., Hengeveld, G., Schardt, M., & Verkerk, P. J. (2010). EU-wide maps of growing stock and above-ground biomass in forests based on remote sensing and field measurements. Forest Ecology and Management, 260(3): 252-261. https://doi.org/10.1016/j.foreco.2009.10.011

IPCC. (2013). Good Practice Guidance for Land Use, Land-Use Change and Forestry. Intergovernmental Panel on Climate Change. IPCC National Greenhouse Gas Inventories Programme.

https://www.ipcc-nggip.iges.or.jp/public/gpglulucf/gpglulucf_files/GPG_LULUCF_FULL.pdf

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

Quegan, S., Rauste, Y., Bouvet, A., Carreiras, J., Cartus, O., Carvalhais, N., LeToan, T., Mermoz, S., Santoro, M. (2017). D6 – Global biomass map algorithm theoretical basis document. Prepared by the GlobBiomass project for the European Space Agency (ESA-ESRIN) in response to ESRIN/Contract No. 4000113100/14/I_NB. Available at http://globbiomass.org/products/global-mapping/

Rozendaal, D. M. A., M. Santoro, D. Schepaschenko, V. Avitabile and M. Herold (2017). DUE GlobBiomass D17 Validation Report, European Space Agency (ESA-ESRIN): 26. Available at:

https://www.dropbox.com/scl/fi/i4mea6u1zqwg5wgf2sfq0/GlobBiomass_D_17_Validation_Report_V06.pdf?rlkey=ha2n1tha17fn4cojqipl5yy0q&e=1&st=v493rkq1&dl=0

Ruesch, A., and Holly K. Gibbs. 2008. New IPCC Tier-1 Global Biomass Carbon Map For the Year 2000. Available online from the Carbon Dioxide Information Analysis Center [http://cdiac.ess-dive.lbl.gov], Oak Ridge National Laboratory, Oak Ridge, Tennessee.

Saatchi, S. S., Harris, N. L., Brown, S., Lefsky, M., Mitchard, E. T., Salas, W., ... & Petrova, S. (2011). Benchmark map of forest carbon stocks in tropical regions across three continents. Proceedings of the National Academy of Sciences, 108(24): 9899-9904. https://doi.org/10.1073/pnas.1019576108

Santoro, M.; Cartus, O. (2024): CCI BIOMASS Product User Guide v5. Aberystwyth University and GAMMA Remote Sensing, 2024.   https://climate.esa.int/media/documents/D4.3_CCI_PUG_V5.0_20240617.pdf

Santoro, M.; Cartus, O. (2025): ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest above-ground biomass for the years 2007, 2010, 2015, 2016, 2017, 2018, 2019, 2020, 2021 and 2022, v6.0. NERC EDS Centre for Environmental Data Analysis, 17 April 2025. doi:10.5285/95913ffb6467447ca72c4e9d8cf30501. https://dx.doi.org/10.5285/95913ffb6467447ca72c4e9d8cf30501

Thurner, M., Beer, C., Santoro, M., Carvalhais, N., Wutzler, T., Schepaschenko, D., ... & Schmullius, C. (2014). Carbon stock and density of northern boreal and temperate forests. Global Ecology and Biogeography, 23(3): 297-310. https://doi.org/10.1111/geb.12125

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

Zarin, D. J., Harris, N. L., Baccini, A., Aksenov, D., Hansen, M. C., Azevedo‐Ramos, C., ... & Allegretti, A. (2016). Can carbon emissions from tropical deforestation drop by 50% in 5 years? Global change biology, 22(4): 1336-1347. https://doi.org/10.1111/gcb.13153