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Dataset

 

ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest above-ground biomass for the years 2010, 2017 and 2018, v2

Update Frequency: Not Planned
Latest Data Update: 2021-01-28
Status: Completed
Online Status: ONLINE
Publication State: Citable
Publication Date: 2021-03-16
DOI Publication Date: 2021-03-17
Download Stats: last 12 months
Dataset Size: 1.71K Files | 114GB
  Citations: 28
This dataset has been superseded. See Latest Version here
Abstract

This dataset comprises estimates of forest above-ground biomass for the years 2010, 2017 and 2018. They are derived from a combination of Earth observation data, depending on the year, from the Copernicus Sentinel-1 mission, Envisat’s ASAR instrument and JAXA’s Advanced Land Observing Satellite (ALOS-1 and ALOS-2), along with additional information from Earth observation sources. The data has been produced as part of the European Space Agency's (ESA's) Climate Change Initiative (CCI) programme by the Biomass CCI team.

The data products consist of two (2) global layers that include estimates of:
1) above ground biomass (AGB, unit: tons/ha i.e., Mg/ha) (raster dataset). This 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
2) per-pixel estimates of above-ground biomass uncertainty expressed as the standard deviation in Mg/ha (raster dataset)

This release of the data is version 2, with data provided in both netcdf and geotiff format. The quantification of AGB changes by taking the difference of two maps is strongly discouraged due to local biases and uncertainties. Version 3 maps will ensure a more realistic representation of AGB changes.

Citable as:  Santoro, M.; Cartus, O. (2021): ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest above-ground biomass for the years 2010, 2017 and 2018, v2. Centre for Environmental Data Analysis, 17 March 2021. doi:10.5285/84403d09cef3485883158f4df2989b0c. https://dx.doi.org/10.5285/84403d09cef3485883158f4df2989b0c
Abbreviation: Not defined
Keywords: ESA, CCI, Biomass

Details

Previous Info:
No news update for this record
Previously used record identifiers:
No related previous identifiers.
Access rules:
Public data: access to these data is available to both registered and non-registered users.
Use of these data is covered by the following licence(s):
https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_biomass_terms_and_conditions_v2.pdf
When using these data you must cite them correctly using the citation given on the CEDA Data Catalogue record.
Data lineage:

Data was created by the Biomass CCI team.
Data Quality:
For information on the quality of the data see the documentation and website for the Biomass CCI
File Format:
Data are in NetCDF and GeoTiff formats

Related Documents

 ESA CCI Biomass project website
 Product User Guide
 Algorithm Theoretical Basis Document
 Project documentation for Biomass CCI

Citations: 28

The following citations have been automatically harvested from external sources associated with this resource where DOI tracking is possible. As such some citations may be missing from this list whilst others may not be accurate. Please contact the helpdesk to raise any issues to help refine these citation trackings.

Aleixo, I. (2022) Comment on bg-2022-87. https://doi.org/10.5194/bg-2022-87-rc1 https://doi.org/10.5194/bg-2022-87-rc1
Amuyou, U.A., Wang, Y., Ebuta, B.F., Iheaturu, C.J. & Antonarakis, A.S. (2022) Quantification of Above-Ground Biomass over the Cross-River State, Nigeria, Using Sentinel-2 Data. Remote Sensing 14, 5741. https://doi.org/10.3390/rs14225741 https://doi.org/10.3390/rs14225741
Bastos, A., Ciais, P., Sitch, S., et al. (2022) On the use of Earth Observation to support estimates of national greenhouse gas emissions and sinks for the Global stocktake process: lessons learned from ESA-CCI RECCAP2. Carbon Balance and Management 17. https://doi.org/10.1186/s13021-022-00214-w https://doi.org/10.1186/s13021-022-00214-w
Clarke, H., Nolan, R.H., De Dios, V.R., Bradstock, R., Griebel, A., Khanal, S. & Boer, M.M. (2022) Forest fire threatens global carbon sinks and population centres under rising atmospheric water demand. Nature Communications 13. https://doi.org/10.1038/s41467-022-34966-3 https://doi.org/10.1038/s41467-022-34966-3
Dalagnol, R., Galvão, L.S., Wagner, F.H., de Moura, Y.M., Gonçalves, N., Wang, Y., Lyapustin, A., Yang, Y., Saatchi, S. & Aragão, L.E.O.C. (2023) AnisoVeg: anisotropy and nadir-normalized MODIS multi-angle implementation atmospheric correction (MAIAC) datasets for satellite vegetation studies in South America. Earth System Science Data 15, 345–358. https://doi.org/10.5194/essd-15-345-2023 https://doi.org/10.5194/essd-15-345-2023
El Masri, B. & Xiao, J. (2024) Comparison of Global Aboveground Biomass Estimates From Satellite Observations and Dynamic Global Vegetation Models. Journal of Geophysical Research: Biogeosciences 130. https://doi.org/10.1029/2024jg008305 https://doi.org/10.1029/2024jg008305
error occurred https://doi.org/10.1038/s43247-023-00893-4
error occurred https://doi.org/10.1111/gcb.16513
error occurred https://doi.org/10.1002/rse2.328
error occurred https://doi.org/10.1088/1748-9326/acdf03
error occurred https://doi.org/10.5194/essd-2022-166
error occurred https://doi.org/10.4236/sgre.2022.132002
error occurred https://doi.org/10.1038/s43247-022-00383-z
error occurred https://doi.org/10.1029/2023gb007969
Fiel, L.G., Aviz, M.D., Sousa, M.C., Rayol, B.P., Lima, A.M., Souza, E.B. & Anjos, L.J.S. (2023) Evaluating the ability of agroforestry systems to mimic forests with remote sensing data in the Amazon. https://doi.org/10.21203/rs.3.rs-3045020/v1 https://doi.org/10.21203/rs.3.rs-3045020/v1
Hegglin, M.I., Bastos, A., Bovensmann, H., et al. (2022) Space-based Earth observation in support of the UNFCCC Paris Agreement. Frontiers in Environmental Science 10. https://doi.org/10.3389/fenvs.2022.941490 https://doi.org/10.3389/fenvs.2022.941490
Heinrich, V.H.A., Vancutsem, C., Dalagnol, R., et al. (2023) The carbon sink of secondary and degraded humid tropical forests. Nature 615, 436–442. https://doi.org/10.1038/s41586-022-05679-w https://doi.org/10.1038/s41586-022-05679-w
Lewis, K., Barros, F. de V., Moonlight, P.W., Hill, T.C., Oliveira, R.S., Schmidt, I.B., Sampaio, A.B., Pennington, R.T. & Rowland, L. (2022) Identifying hotspots for ecosystem restoration across heterogeneous tropical savannah-dominated regions. Philosophical Transactions of the Royal Society B: Biological Sciences 378. https://doi.org/10.1098/rstb.2021.0075 https://doi.org/10.1098/rstb.2021.0075
Lippe, M., Rummel, L. & Günter, S. (2022) Simulating land use and land cover change under contrasting levels of policy enforcement and its spatially-explicit impact on tropical forest landscapes in Ecuador. Land Use Policy 119, 106207. https://doi.org/10.1016/j.landusepol.2022.106207 https://doi.org/10.1016/j.landusepol.2022.106207
Mugabowindekwe, M., Brandt, M., Chave, J., et al. (2022) Nation-wide mapping of tree-level aboveground carbon stocks in Rwanda. Nature Climate Change 13, 91–97. https://doi.org/10.1038/s41558-022-01544-w https://doi.org/10.1038/s41558-022-01544-w
Mugabowindekwe, M., Brandt, M., Chave, J., et al. (2022) Nation-wide mapping of tree level carbon stocks in Rwanda. https://doi.org/10.21203/rs.3.rs-1536453/v1 https://doi.org/10.21203/rs.3.rs-1536453/v1
Power, D., Rico-Ramirez, M.A., Desilets, S., Desilets, D. & Rosolem, R. (2021) Cosmic-Ray neutron Sensor PYthon tool (crspy 1.2.1): an open-source tool for the processing of cosmic-ray neutron and soil moisture data. Geoscientific Model Development 14, 7287–7307. https://doi.org/10.5194/gmd-14-7287-2021 https://doi.org/10.5194/gmd-14-7287-2021
Ruiz Ramos, J. (2023) Continuous Monitoring of Environmental Disturbances by Cumulative Sums of Dense SAR Satellite Timeseries. The Open University. https://doi.org/10.21954/OU.RO.00015D6B https://doi.org/10.21954/ou.ro.00015d6b
Smallman, T. (2021) Reply on RC1. https://doi.org/10.5194/esd-2021-17-ac1 https://doi.org/10.5194/esd-2021-17-ac1
Smallman, T. (2021) Reply on RC2. https://doi.org/10.5194/esd-2021-17-ac2 https://doi.org/10.5194/esd-2021-17-ac2
Sun, S., Wang, Yafei, Song, Z., et al. (2021) Modelling Aboveground Biomass Carbon Stock of the Bohai Rim Coastal Wetlands by Integrating Remote Sensing, Terrain, and Climate Data. Remote Sensing 13, 4321. https://doi.org/10.3390/rs13214321 https://doi.org/10.3390/rs13214321
Tong, X., Brandt, M., Yue, Y., et al. (2023) Reforestation policies around 2000 in southern China led to forest densification and expansion in the 2010s. Communications Earth & Environment 4. https://doi.org/10.1038/s43247-023-00923-1 https://doi.org/10.1038/s43247-023-00923-1
Wingate, V.R., Akinyemi, F.O. & Speranza, C.I. (2023) Archetypes of remnant West African forest patches, their main characteristics and geographical distribution. Applied Geography 158, 103024. https://doi.org/10.1016/j.apgeog.2023.103024 https://doi.org/10.1016/j.apgeog.2023.103024

Process overview

This dataset was generated by a combination of instruments deployed on platforms and computations as detailed below.

Instrument/Platform pairings

Sentinel 1 Synthetic Aperture Radar (SAR) Deployed on: Sentinel 1A
PALSAR-2 Deployed on: ALOS-2

Computation Element: 1

Title GlobBiomass retrieval algorithm used in the production of the Biomass CCI global dataset of forest above ground biomass for the year 2017.
Abstract The GlobBiomass retrieval algorithm is used to derive estimates of forest above-ground biomass (AGB) from satellite data for the year 2017 as part of the Biomass CCI project. First, per-pixel estimates of growing stock volume (GSV, unit: m3/ha) were obtained from spaceborne SAR images acquired in 2017 (ALOS-2 PALSAR-2, Sentinel-1) with the BIOMASAR algorithm, adapted specifically to ALOS-2 PALSAR-2 and Sentinel-1 data. Individual per-pixel GSV estimates were then combined with a set of merging rules to form the final per-pixel estimate of GSV. The retrieval was supported by LiDAR (ICESAT) data and auxiliary datasets . AGB was then obtained from GSV with a set of Biomass Expansion and Conversion Factors (BCEF) following approaches to extend on ground estimates of wood density and stem-to-total biomass expansion factors to obtain a global raster dataset. See the Algorithm Theoretical Basis Document for details on the EO datasets, the biomass retrieval algorithms and the estimation of the BCEF (see http://cci.esa.int/biomass)
Input Description None
Output Description None
Software Reference None
Output Description

None
  • units: Mg/ha
  • long_name: Above-ground biomass
  • var_id: agb
  • long_name: Above-ground biomass standard_error
  • units: Mg/ha
  • var_id: agb_se
  • var_id: crs
  • long_name: CRS definition
  • var_id: lat_bnds
  • var_id: lon_bnds
  • var_id: time_bnds

Co-ordinate Variables

  • units: degrees_north
  • standard_name: latitude
  • var_id: lat
  • long_name: WGS84 latitude coordinate
  • units: degrees_east
  • standard_name: longitude
  • var_id: lon
  • long_name: WGS84 longitude coordinate
  • standard_name: time
  • var_id: time
  • long_name: single-year period
Coverage
Temporal Range
Start time:
2010-01-01T00:00:00
End time:
2020-12-31T23:59:59
Geographic Extent

 
90.0000°
  
-180.0000°
  
180.0000°
 
-90.0000°
  
Related parties
Authors (2)
Publishers (1)