Description
This dataset quantifies the aboveground biomass carbon (Mg C/ha), canopy cover (%), and canopy height (m) of terrestrial plants at 3.5 m spatial resolution. Data has global coverage and quarterly temporal resolution (March, June, September, December), available from 2021 onwards. For each pixel, data includes variable uncertainties (90% prediction intervals).
Usage notes
Spatial coverage is land surfaces between 75°N and 60°S. In urban areas, double-bounce radar backscatter among buildings may result in over-predictions. Circular spatial artefacts can lead to temporal differences between adjacent pixels. Models are linearly downscaled from the 30 m Diligence dataset models, so variables are consistent among the two datasets. Original 30 m aboveground live carbon density predictions are not included. Each quarter (Dec-Mar, Mar-Jun, Jun-Sep, Sep-Dec) comprises data from the 21st and 20th day of respective months, and is delivered on the 21st of the quarter end month. Areas of interest (AOIs) must have < 1,500 vertices and be < 8,000 km².
Licence
Commercial
Version
1.0
Release date
October 2024
CRS
EPSG:4326
Update schedule
2+ weeks after quarter end
Provider cost
$0.25/ha
Description
Aboveground live carbon density quantifies the density of carbon (Mg C/ha) stored in live woody vegetation in each pixel on a per hectare basis. The data is generated using LightGBM regression models trained to predict GEDI L4A aboveground biomass carbon from modelled canopy height, modelled canopy cover, elevation, and location.
Usage notes
Calculations are restricted to live woody aboveground biomass, so exclude herbaceous plants, non-woody tissues, and deadwood or litter. A relative carbon content of 0.476 was used to convert GEDI L4A biomass data from total biomass density to biomass carbon density. Pixels with canopy cover values of 0% are set to 0 Mg C/ha.
Variable category
Aboveground biomass carbon
Variable type
Continuous
Description
Canopy cover quantifies the relative cover (%) of trees (i.e. canopy height > 5 m) in each pixel. The data is generated using U-Net image regression models trained to predict > 9 million km² of airborne LiDAR data from a combination of spaceborne remote sensing datasets.
Usage notes
Calculations are not technically restricted to trees. Buildings, bridges, and other non-vegetation classes are excluded. Canopy cover values < 10% are set to 0%. Pixels with canopy cover of 0% at the start and end of the full temporal extent are set to 0% throughout.
Variable category
Canopy cover
Variable type
Continuous