Monitoring 25 m
Description
This dataset quantifies canopy cover (%), canopy height (m), forest cover, living aboveground biomass (Mg/ha), living belowground biomass (Mg/ha), living biomass (Mg/ha), living biomass carbon stock (Mg C/ha), living biomass CO₂ equivalent (Mg CO₂ eq/ha), and tree height (m) at 25 m spatial resolution, with global coverage annually from 2013 onwards.
084fa670…Usage notes
New time points are delivered in February for the previous year. Spatial coverage is limited to land surfaces. Filtering of input data for months with the fewest wet days may result in models being trained on different months between years. Adjacent pixels are not numerically independent, in that models predict a pixel value taking into account inputs data from surrounding pixels. Post calibration allows tuning of model predictions from user field measurements.
Variables
canopy_cover
Description
The relative cover of trees. Data is generated using a deep neural network trained to predict > 60 million ha of airborne and spaceborne LiDAR data from a combination of spaceborne remote sensing datasets.
canopy_height
Description
The mean height of the tree canopy. Data is generated using a deep neural network trained to predict > 60 million ha of airborne and spaceborne LiDAR data from a combination of spaceborne remote sensing datasets.
Usage notes
Pixels with canopy cover < 10% or living aboveground biomass of < 50 Mg/ha are adjusted downwards using a relative correction factor. Pixels with living aboveground biomass values of 0 Mg/ha are set to 0 m.
forest_cover
Description
Indicates whether a pixel is forest.
Usage notes
Forest is defined using the FAO definition of forest (i.e. surface area > 0.5 ha, tree height > 5 m, canopy cover > 10%). Forest definition can be replaced by user specified thresholds.
| Index | Name |
|---|---|
| 0 | Non-forest |
| 1 | Forest |
living_aboveground_biomass
Description
The total biomass stock of dry matter in aboveground live vegetation on a per hectare basis. Data is generated using a deep neural network trained to predict > 60 million ha of airborne and spaceborne LiDAR data from a combination of spaceborne remote sensing datasets.
Usage notes
Calculations are restricted to living aboveground biomass. Biome-specific allometric equations are taken from Verra methodology VMD0001 but can be replaced by user specified allometric models.
living_belowground_biomass
Description
The total biomass stock of dry matter in belowground biomass on a per hectare basis. Data is calculated from the living aboveground biomass variable using biome-specific allometric equations.
Usage notes
Biome-specific allometric equations are taken from Verra methodology VMD0001 but can be replaced by user specified allometric models.
living_biomass
Description
The total biomass stock of dry matter in live vegetation on a per hectare basis. The sum of living aboveground biomass and living belowground biomass variables.
living_biomass_carbon_stock
Description
The total biomass carbon stock on a per hectare basis. Data is calculated by multiplying the living biomass variable by the relative contribution of carbon to total biomass.
Usage notes
Biome-specific allometric equations are taken from Verra methodology VMD0001 but can be replaced by user specified allometric models.
living_biomass_co2_eq
Description
The total biomass carbon stock in a carbon dioxide equivalent. Data is calculated by multiplying the living biomass carbon stock variable by the relative molar mass of carbon dioxide to carbon.
tree_height
Description
The maximum height of trees. Data is generated using a deep neural network trained to predict > 60 million ha of airborne and spaceborne LiDAR data from a combination of spaceborne remote sensing datasets.
Usage notes
Pixels with canopy cover < 10% or living aboveground biomass of < 50 Mg/ha are adjusted downwards using a relative correction factor. Pixels with living aboveground biomass of 0 Mg/ha are set to 0 m.
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 |
|---|---|
| Any | $0.10 / ha |
Resources
Provider
Kanop
www.kanop.ioKanop is a nature data analytics company based in Paris (France). Kanop aims to use satellite imagery and AI to provide data to inform carbon markets, supply chains, and other nature disclosures. Kanop offers products for nature-based solutions, Scope 3 reporting, and EUDR compliance.