Forest Carbon Monitoring
Description
This dataset quantifies the aboveground live carbon density (Mg C/ha), canopy cover (%), and canopy height (m) of plants at 3.5 m spatial resolution. Data has global coverage and quarterly temporal resolution, available from 2021 onwards. Data includes pixel-level variable uncertainties (90% prediction intervals).
4d8bd6ba…Usage notes
Each quarter (Dec-Mar, Mar-Jun, Jun-Sep, Sep-Dec) runs from the 21st to the 20th day of start and end months and is delivered on the day after quarter end. Spatial coverage is limited to land surfaces between 76°N and 56°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.
Variables
aboveground_live_carbon_density
Description
The density of carbon stored in live woody vegetation on a per hectare basis. 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. 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 of 0% are set to 0 Mg C/ha.
aboveground_live_carbon_density_lower_bound
Description
The 90% prediction interval lower bound for the aboveground live carbon density variable.
aboveground_live_carbon_density_upper_bound
Description
The 90% prediction interval upper bound for the aboveground live carbon density variable.
canopy_cover
Description
The relative cover of trees. 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
Trees are defined as vegetation with canopy height > 5 m. Buildings, bridges, and other non-vegetation classes are excluded. Values < 10% are set to 0%. Pixels with canopy cover of 0% at the start and end of the full temporal coverage are set to 0% throughout.
canopy_cover_lower_bound
Description
The 90% prediction interval lower bound for the canopy cover variable.
canopy_cover_upper_bound
Description
The 90% prediction interval upper bound for the canopy cover variable.
canopy_height
Description
The average height (m) of trees. 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
Trees are defined as vegetation with canopy height > 5 m. Pixel values are averages of higher resolution point clouds, so modelled height values are shorter than the tallest individual tree in a pixel. Buildings, bridges, and other non-vegetation classes are excluded. Square spatial artefacts can result in canopy height appearing up to 2 m higher in a pixel relative to surrounding pixels, but remain within prediction intervals. Pixels with a canopy height difference of < 3 m between the start and end of the full temporal coverage are set to the median value. Canopy height for pixel values with canopy cover of 0% is set to 0 m.
canopy_height_lower_bound
Description
The 90% prediction interval lower bound for the canopy height variable.
canopy_height_upper_bound
Description
The 90% prediction interval upper bound for the canopy height variable.
Pricing
This dataset adds a new time point quarterly. A data request returns the full archive up to the most recent time point and creates a one-year subscription to future time points. Data acquisition costs are per hectare:
| Volume | Price |
|---|---|
| Any | $0.25 / ha |
Resources
Provider
Planet
www.planet.comPlanet is an Earth imaging company based in San Francisco (US). Planet aims to image the Earth daily and support change analysis. Planet offers several products, ranging from satellite imaging to derived scientific data.