Biomass Atlas
Description
This dataset quantifies aboveground biomass density (Mg/ha) and forest canopy height (m) at 30 m spatial resolution, with global coverage annually from 2000 onwards. Pixel-level residual uncertainty is provided for each variable (± standard error).
db67c6ad…Usage notes
New time points are delivered in Q1 for the previous year. Spatial coverage is limited to land surfaces between 81.8ºN and 81.8ºS, excluding ice sheets. The dataset uses seven region-specific canopy height models and twenty biome-specific biomass models.
Variables
aboveground_biomass_density
Description
The aboveground biomass density of dry matter in live woody vegetation on a per hectare basis. Data is generated using biome-specific non-linear regression models calibrated on allometric-free multi-scale LiDAR or GEDI L4A allometric-derived biomass data to convert canopy height values into aboveground biomass.
Usage notes
Calculations are designed for areas with live woody aboveground biomass. Models are calibrated on multi-scale LiDAR data where available and GEDI L4A biomass data otherwise (see aboveground biomass density multiscale lidar variable). Biome-specific models may result in artefacts for AOIs that span biome borders. Values lying outside first observation and last observation variable years are filled with values of the fitted model from first and last valid observation years, respectively.
aboveground_biomass_density_standard_error
Description
The standard error for the aboveground biomass density variable.
canopy_height
Description
The canopy height of the 95th percentile of woody vegetation. Data is generated using region-specific machine learning models that predict GEDI canopy height data from a range of spaceborne remote sensing datasets.
Usage notes
Canopy heights < 3 m are unreliable due to minimum detection limits of the GEDI instrument. Region-specific models may result in artefacts for AOIs that span borders. Values lying outside first observation and last observation variable years are filled with values of the fitted model from first and last valid observation years, respectively.
canopy_height_standard_error
Description
The standard error for the canopy height variable.
aboveground_biomass_density_multiscale_lidar
Description
Indicates whether multi-scale LiDAR (MSL) data or GEDI L4A data was used to calibrate aboveground biomass density models.
Usage notes
Multi-scale LiDAR data is not yet available for all regions/biomes but coverage increases as new data becomes available from ongoing field campaigns.
| Index | Name |
|---|---|
| 0 | GEDI |
| 1 | MSL |
first_observation
Description
Offset in years from the year 2000 to first valid observation used in models.
Usage notes
A value of 0 indicates first valid observation in 2000, with increasing integers representing subsequent years. For pixels where first valid observation occurs after 2000, canopy height and aboveground biomass density values for earlier years are backfilled from the first valid observation year using the fitted model. Pixels with large first observation values may have limited temporal coverage for model fitting.
last_observation
Description
Offset in years from the year 2000 to last valid observation used in models.
Usage notes
A value of 24 indicates last valid observation in 2024, with decreasing integers representing previous years. For pixels where last valid observation occurs before the most recent year, canopy height and aboveground biomass density values for later years are forward-filled from the last valid observation year using the fitted model. Pixels with small last observation values may have limited temporal coverage for model fitting.
Pricing
This dataset adds a new time point annually. A data request returns the full archive up to the most recent time point. Data acquisition costs are per hectare for the full archive:
| Volume | Price |
|---|---|
| GEDI-derived | $0.07 / ha |
| MSL-derived | $0.12 / ha |
Resources
Provider
Sylvera
www.sylvera.comSylvera is a Carbon Data Platform based in London (UK). Sylvera's goal is to incentivise investments in real climate action by providing carbon ratings, pre-issuance assessments, geospatial products, and market intelligence.