Biomass

The Biomass project currently provides global maps of above-ground biomass for eight epochs (2010 then every year 2015 - 2021), and change products between years with associated quality flag maps.

About

Humans have exploited forest biomass as a material and energy source for millennia, but population growth and increasing demand for resources have diminished the extent and condition of forests, including the amount of carbon they store and exchange with the atmosphere.

The Global Climate Observing System considers above-ground biomass (AGB) an Essential Climate Variable due to its functions as both a source of atmospheric CO2 (and other greenhouse gases) when forest is lost under Land Use Change or by degradation, and as a sink for CO2 due to forest growth.

Information on forest biomass can also play a much wider role in understanding and predicting climate, for example in model initialisation, model testing, estimation of carbon turnover, inferring the forest disturbance regime, and data assimilation in carbon cycle and climate models.

Objective

The primary science objective of ESA’s Climate Change Initiative (CCI) Biomass project was to provide global maps of above-ground biomass (Mg ha-1) for three epochs (2010, 2017 and 2018), with these being capable of supporting quantification of biomass change. For Phase 2 of the project annual mapping will continue creating a time series from 2015/16 - 2022, plus a 2005/7 epoch.

The mapping is at 100 m grid spacing with a target relative error of less than 20 per cent where AGB exceeds 50 Mg ha-1. Although this resolution is finer than required for current climate modelling, it will allow more refined information to be inferred (e.g. forest age structure and the disturbance regime) that is relevant for climate and has the potential to be exploited by carbon cycle and climate models as they develop.

About the project

To generate maps of biomass and biomass change, maximum use will be made of spatial data from past, current and future Earth observation missions. The products rely on ESA's C-band (Sentinel 1A & B) and JAXA's L-band Synthetic Aperture Radar (ALOS-2 PALSAR-2) with additional information from space borne LIDAR (e.g. NASA's Global Ecosystem Dynamics Investigation Lidar GEDI). The combination of these sensor types will allow information on the amounts of foliage and woody plant material and their distribution to be retrieved, both spatially and vertically, and over time.

The mapping will be achieved using algorithms developed within a globally consistent biomass retrieval framework and builds on the experience gained during ESA’s GlobBiomass Project. The quality of the maps will be extensively verified through the use of existing and new ground and airborne data sets. The resulting global biomass data sets will provide the carbon cycle and climate science community with the capacity to integrate global biomass layers into their models.

The major technical objective is to build on pre-existing methods for estimating biomass from satellite data (notably those developed during the GlobBiomass project), identify the weaknesses in these methods (arising from algorithmic, sensor or data issues) and address these issues using methodological advances that are applicable globally and consider different biomes. The project will also make significant advances in understanding how biomass data are used in carbon cycle and climate modelling, for example in model initialisation, model testing, carbon turnover time modelling and data assimilation.

Data

The primary science objective ESA’s Climate Change Initiative Biomass project (Biomass_cci) is to provide global maps of above-ground biomass (Mg ha-1) for multiple epochs (2005/2006, 2010, 2015/6 and annually from 2018 to 2022), with these being capable of supporting quantification of biomass change.

All datasets are available via the CCI Open Data Portal

The CCI BIOMASS project currently delivers spatially explicit estimates of AGB for five epochs and related standard deviations (SDs) as separate map products, plus AGB change on an annual and decadal basis. The AGB product consists of global datasets with estimates of AGB (unit: tons/ha = Mg/ha). AGB is defined as the mass, expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots. The AGB SD product is a separate data layer providing per-pixel SD of the AGB estimates in Mg/ha.

The data products currently provided by the project (year 4, Version 4.0) consist of five maps of AGB and AGB SD based on Earth Observation data acquired in 2010, 2017, 2018, 2019 and 2020, respectively. The spatial resolution of the map products is 100 m. AGB change is expressed as the difference between AGB maps for either two consecutive years (e.g. 2020-2019) or for a decade (2020-2010), together with an estimate of their SD and a quality flag map, which details the reliability of the AGB change estimate.

INTERACTIVE GLOBE

Explore the ESA CCI Above-Ground Biomass dataset (version 3)

Key Documents

Documentnaam	Versie	Datum van publicatie
Product User Guide	5.0	17 juni 2024
System_Specification_Document	5.0	3 juni 2024
Algortihm_Theoretical_Basis_Document	5.0	30 november 2023
End_to_End_ECV_Uncertainty_Budget	5.0	30 november 2023
Algorithm_Development_Plan	5.0	12 december 2023
Product_Validation_Plan	4.0	7 februari 2023
User_Requirement_Document	3.0	14 november 2022
Product_Specification_Document	3.0	4 september 2022
Product Fact Sheet v5	5.0	19 maart 2024

Documentnaam	Versie	Datum van publicatie
Product User Guide	3.0	5 juli 2021
Algorithm Theoretical Basis Document	3.0	14 juni 2021
User Requirements Document	2.0	6 juni 2021
Product Specification Document	3.0	20 november 2021
Product Regional Assessment and Intercomparison Report	2.0	17 december 2020
End to End ECV Uncertainty Budget	3.0	14 juni 2020
Algorithm Development Plan	3.0	16 juni 2021
Product Validation Plan	3.0	25 januari 2021
Product Validation and Intercomprison Report	3.0	29 juli 2021
Climate Assessment Report	3.0	1 september 2020

Team

The ESA CCI Biomass team consists of leading scientists from 12 organisations in Europe. The project is led and managed by Professor Richard Lucas (University of Aberystwyth) and the Science Lead is Professor Shaun Quegan (Sheffield University).

Richard Lucas (Project Manager) and Heather Friendship-Kay (Aberystwyth Univ., UK)
Alexandre Bouvet (Centre d'Etudes Spatiales de la Biosphère, France)
Jérome Chave (Evolution et Diversité Biologique, France)
Maurizio Santoro, Oliver Cartus and Andreas Wiesmann (Gamma Remote Sensing, Switzerland)
Dmitry Schepaschenko (International Institute for Applied Systems Analysis, Austria)
Christiane Schmullius and Carsten Pathe (Earth Observation Services Jena, Germany)
Philippe Ciais, (Laboratoire des Sciences du Climat et de l'Environnement, France)
Oliver Phillips (University of Leeds, UK)
Heiko Baltzer, Nezha Acil (University of Leicester, UK)
Shaun Quegan (University of Sheffield, UK)
Martin Herold (GFZ Helmholtz Centre Potsdam, Netherlands)

1st User Workshop - Phase II

Biomass_cci workshops

User Workshop | 28-30 June 2022 | Virtual

The first CCI Biomass User Workshop to discuss biomass and biomass change datasets with a consolidation of users requirements will have three sessions 1500 to 1700 CET:

28^th June - Session 1 - Mapping Global Biomass
29^th June - Session 2 - Needs from national users
30^th June - Session 3 - Biomass change and climate users

Agenda

View the meeting agenda

Meeting documents, presentations and posters

View meeting related documentation, presentations posters and session recordings

Publications

Santoro M., Cartus, O., Quegan, S., Kay H., Lucas, R. M., Araza, A., Herold, M., Labrière, N., Chave, J., Rosenqvist, Å., Tadono, T., Kobayashi, K., Kellndorfer, J., Avitabile, V., Brown, H., Carreiras, J., Campbell, M. J., Cavlovic, J., Conceição Bispo, P., Gilani, H., Khan, M. L., Kumar, A., Lewis, S. L., Jingjing Liang, J., Mitchard, E. T. A., Pacheco-Pascagaza, A. M., Phillips, O. L., Ryan, C. M., Saikia, P., Schepaschenko, D., Sukhdeo, H., Verbeeck, H., Vieilledent, G., Wijaya, A., Willcock, S. and Seifert, F.M.. Design and performance of the Climate Chnage Initiative Biomass gobal retrieval algorithm. Science of Remote Sensing. 2024. https://doi.org/10.1016/j.srs.2024.100169
Chen, N., Tsendbazar, N. E., Requena Suarez, D., Silva-Junior, C. H. L., Verbesselt, J., Herold, M.. Revealing the spatial variation in biomass uptake rates of Brazil’s secondary forests.. ISPRS Journal of Photogrammetry and Remote Sensing. 2024. https://doi.org/10.1016/j.isprsjprs.2023.12.013
Hunka, N., Santoro, M., Armston. J. et al.. On the NASA GEDI and ESA CCI biomass maps: aligning for uptake in the UNFCCC global stocktake.. Environmental Research Letters. 203. https://doi.org/10.1088/1748-9326/ad0b60
Dalagnol, R., Wagner, F., H., Galvão, L., S. et al.. Mapping tropical forest degradation with deep learning and Planet NICFI data.. Remote Sensing of Environment.. 2023. https://doi.org/10.1016/j.rse.2023.113798
Mo, L., Zohner, C.M., Reich, P.B. et al.. Integrated global assessment of the natural forest carbon potential.. Nature. 2023. https://doi.org/10.1038/s41586-023-06723-z
Araza, A., de Bruin, S., Hein, L. and Herold, M.. Spatial predictions and uncertainties of forest carbon fluxes for carbon accounting.. Scientific Reports. 2023. https://doi.org/10.1038/s41598-023-38935-8
Yang, H., Ciais, P., Frappart, F., et al.. Global increase in biomass carbon stock dominated by growth of northern young forests over past decade.. Nature Geoscience. 2023. https://doi.org/10.1038/s41561-023-01274-4
Araza, A., Herold, M., de Bruin, S., et al.. Past decade above-ground biomass change comparisons from four multi-temporal global maps. International Journal of Applied Earth Observation and Geoinformation. 2023. https://doi.org/10.1016/j.jag.2023.103274
Chen, N., Tsendbazar, N.E., Requena Suarez, D., Verbesselt, J. and Herold, M.. Characterizing aboveground biomass and tree cover of regrowing forests in Brazil using multi-source remote sensing data. Remote Sensing in Ecology and Conservation. 2023. https://doi.org/10.1002/rse2.328
Ochiai, O., Poulter, B., Seifert, F.M. et al.. Towards a roadmap for space-based observations of the land sector for the UNFCCC Global Stocktake.. iScience. 2023. http://dx.doi.org/10.1016/j.isci.2023.106489
Labrière, N., Davies, S. J., Disney, M. I., Duncanson, L. I., Herold, M., Lewis, S. L., Phillips, O. L., Quegan, S., Saatchi, S. S., Schepaschenko, D. G., Scipal, K., Sist, P., & Chave, J.. Toward a forest biomass reference measurement system for remote sensing applications. Global Change Biology. 2023. https://doi.org/10.1111/gcb.16497
Bennett, A. C., Rodrigues de Sousa, T., Monteagudo-Mendoza, A., Esquivel-Muelbert, A., Morandi, P. S., Coelho de Souza, F., ... & Phillips, O. L.. Sensitivity of South American tropical forests to an extreme climate anomaly. Nature Climate Change. 2023.
Viana Santos, H. K., Borges De Lima, R., Figueiredo De Souza, R. L., Cardoso, D., Moonlight, P. W., Teixeira Silva, T., ... & Phillips, O. L.. Spatial distribution of aboveground biomass stock in tropical dry forest in Brazil. iForest-Biogeosciences and Forestry. 2023.
Málaga, N., De Bruin, S., McRoberts, R.E., Arana Olivos, A., de la Cruz Paiva, R., Durán Montesinos, P., Requena Suarez, D. and Herold, M.. Precision of subnational forest AGB estimates within the Peruvian Amazonia using a global biomass map.. International Journal of Applied Earth Observation and Geoinformation.. 2022. http://dx.doi.org/10.1016/j.jag.2022.103102
Fan, L., Wigneron, J.P., Ciais, P. et al.. Siberian carbon sink reduced by forest disturbances.. Nature Geoscience. 2022. http://dx.doi.org/10.1038/s41561-022-01087-x
Tao, S., Chave, J., Frison, P.L. and Saatchi, S.. Increasing and widespread vulnerability of intact tropical rainforests to repeated droughts.. PNAS. 2022. http://dx.doi.org/10.1073/pnas.2116626119
Liang, J., Gamarra, J.G.P., Picard, N. et al.. Co-limitation towards lower latitudes shapes global forest diversity gradients.. Nature Ecology and Evolution.. 2022. http://dx.doi.org/10.1038/s41559-022-01831-x
Santoro, M., Cartus, O., Wegmüller, U., Besnard, S., Carvalhais, N., Araza, A., Herold, M., Liang, J., Cavlovic, J., Engdahl, M.E.. Global estimation of above-ground biomass from spaceborne C-band scatterometer observations aided by LiDAR metrics of vegetation structure.. Remote Sensing of Environment. 2022. https://doi.org/10.1016/j.rse.2022.113114
Yang, H., Ciais, P., Wigneron, J.P., Chave, J., Cartus, O., Chen, X., Fan, L., Green, J.K., Huang, Y., Joetzjer, E. and Kay, H.. Climatic and biotic factors influencing regional declines and recovery of tropical forest biomass from the 2015/16 El Niño.. PNAS. 2022. https://doi.org/10.1073/pnas.2101388119
Araza, A., de Bruin, S., Herold, M., Quegan, S., Labriere, N., Rodriguez-Veiga, P., Avitabile, V., Santoro, M., Mitchard, E.T.A., Ryan, C.M., Phillips, O.L., Willcock, S., Verbeeck, H., Carreiras, J., Hein, L. et al.. A comprehensive framework for assessing the accuracy and uncertainty of global above-ground biomass maps. Remote Sensing of Environment. 2022. https://doi.org/10.1016/j.rse.2022.112917.
Adzhar, R., Kelley, D. I., Dong, N., George, C., Torello Raventos, M., Veenendaal, E., ... & Gerard, F.. MODIS Vegetation Continuous Fields tree cover needs calibrating in tropical savannas.. Biogeosciences. 2022.
de Lima, R. A., Phillips, O. L., Duque, A., Tello, J. S., Davies, S. J., de Oliveira, A. A., ... & Vásquez, R.. Making forest data fair and open.. Nature Ecology & Evolution. 2022.
Sousa, T. R., Schietti, J., Ribeiro, I. O., Emílio, T., Fernández, R. H., Ter Steege, H., ... & Costa, F. R.. Water table depth modulates productivity and biomass across Amazonian forests.. Global Ecology and Biogeography. 2022.
Kay, H., Santoro, M., Cartus, O., Bunting, P. and Lucas, R.. Exploring the relationship between forest canopy height and canopy density from Spaceborne LiDAR observations. Remote Sensing. 2021. https://doi.org/10.3390/rs13244961
Duncanson, L., Armston, J., Disney, M. et al.. Good Practices for Satellite-Derived and Product Validation: Land Product Validation Subgroup (WGCV/CEOS).. 2021. https://doi.org/10.5067/doc/ceoswgcv/lpv/agb.001
Santoro, M., Cartus, O. and Fransson, J.E.. Integration of allometric equations in the water cloud model towards an improved retrieval of forest stem volume with L-band SAR data in Sweden.. Remote Sensing of Environment. 2021. https://doi.org/10.1016/j.rse.2020.112235
ForestPlots.net, Blundo, C., Carilla, J., Grau, R., Malizia, A., Malizia, L., Osinaga-Acosta, O., Bird, M., et al.. Taking the pulse of Earth’s tropical forests using networks of highly distributed plots.. Biological Conservation. 2021. https://doi.org/10.1016/j.biocon.2020.108849
Rodríguez-Veiga, P., Carreiras, J., Smallman, T.L., Exbrayat, J.-F., Ndambiri, J., Mutwiri, F., Nyasaka, D., Quegan, S., Williams, M. and Balzter, H. Carbon Stocks and Fluxes in Kenyan Forests and Wooded Grasslands Derived from Earth Observation and Model-Data Fusion.. Remote Sensing. 2020. https://doi.org/10.3390/rs12152380
Schepaschenko, D., Chave, J., Phillips, O.L., Lewis, S.L., Davies, S.J., Réjou-Méchain, M., Sist, P., Scipal, K., Perger, C., Herault, B. and Labrière, N.. The Forest Observation System, building a global reference dataset for remote sensing of forest biomass.. Scientific Data. 2019. https://doi.org/10.1038/s41597-019-0196-1
Herold, M., Carter, S., Avitabile, V., Espejo, A.B., Jonckheere, I., Lucas, R., McRoberts, R.E., Næsset, E., Nightingale, J., Petersen, R. and Reiche, J.. The role and need for space-based forest biomass-related measurements in environmental management and policy.. Surveys in Geophysics.. 2019. https://doi.org/10.1007/s10712-019-09510-6
Berzaghi, F., Longo, M., Ciais, P., Blake, S., Bretagnolle, F., Vieira, S., Scaranello, M., Scarascia-Mugnozza, G. and Doughty, C.E.. Carbon stocks in central African forests enhanced by elephant disturbance.. Nature Geoscience. 2019. https://www.nature.com/articles/s41561-019-0395-6
Fischer, F. J., Maréchaux, I., & Chave, J.. Improving plant allometry by fusing forest models and remote sensing. New Phytologist.. 2019. https://doi.org/10.1111/nph.15810
Chave, J., Davies, S. J., Phillips, O. L., Lewis, S. L., Sist, P., Schepaschenko, D., ... & Duncanson, L.. Ground data are essential for biomass remote sensing missions. Surveys in Geophysics. 2019. https://doi.org/10.1007/s10712-019-09528-w
Quegan, S., Le Toan, T., Chave, J. et al.. The European Space Agency BIOMASS mission: Measuring forest above-ground biomass from space. Remote Sensing of Environment. 2019. https://doi.org/10.1016/j.rse.2019.03.032
Tebaldini, S., Minh, D. H. T., d’Alessandro, M. M., Villard, L., Le Toan, T., & Chave, J.. The Status of Technologies to Measure Forest Biomass and Structural Properties: State of the Art in SAR Tomography of Tropical Forests. Surveys in Geophysics. 2019. https://doi.org/10.1007/s10712-019-09532-0
Duncanson, L., Armston, J., Disney, M. et al.. The importance of consistent global forest aboveground biomass product validation. Surveys in Geophysics. 2019. https://doi.org/10.1007/s10712-019-09538-8
Schepaschenko, D., See, L., Lesiv, M., Bastin, J.F., Mollicone, D., Tsendbazar, N.E., Bastin, L., McCallum, I., Bayas, J.C.L., Baklanov, A. and Perger, C. Recent Advances in Forest Observation with Visual Interpretation of Very High-Resolution Imagery. Surveys in Geophysics. 2019. https://doi.org/10.1007/s10712-019-09533-z
Phillips, O.L., Sullivan, M.J.P., Baker, T.R., Monteagudo Mendoza, A., Nunez Vargas, P., Vasquez, R.. Species Matter: Wood density Influences Tropical Forest Biomass at Multiple Scales.. Surveys in Geophysics.. 2019. https://doi.org/10.1007/s10712-019-09540-0
Schepaschenko, D., Moltchanova, E., Shvidenko, A., Blyshchyk, V., Dmitriev, E., Martynenko, O., See, L. and Kraxner, F.. Improved estimates of biomass expansion factors for Russian forests. Forests. 2018. https://doi.org/10.3390/f9060312