Monitoring 30 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 30 m spatial resolution, with global coverage annually from 2013 onwards. Data includes pixel-level variable uncertainties (95% confidence intervals).
938c211c…Usage notes
New time points are delivered annually. Spatial coverage is limited to land surfaces. Acquisition windows are constrained to phenologically active months identified from MODIS NDVI composites. Adjacent pixels are not numerically independent, in that models predict a pixel value taking into account input data from surrounding pixels.
Variables
canopy_cover
Description
Relative cover of trees. Data is generated using a deep neural network trained to predict ~125 million ha of airborne and spaceborne LiDAR data from a combination of spaceborne remote sensing datasets.
canopy_cover_uncertainty
Description
95% confidence interval for canopy cover variable.
canopy_height
Description
Mean height of the tree canopy. Data is generated using a deep neural network trained to predict ~125 million ha of airborne and spaceborne LiDAR data from a combination of spaceborne remote sensing datasets.
Usage notes
Canopy height is constrained to be equal to or lower than tree height variable.
canopy_height_uncertainty
Description
95% confidence interval for canopy height variable.
forest_cover
Description
Indicates whether a pixel is forest.
Usage notes
Forest is defined using the FAO definition of forest (surface area > 0.5 ha, tree height > 5 m, canopy cover > 10%).
| Index | Name |
|---|---|
| 0 | Non-forest |
| 1 | Forest |
forest_cover_probability
Description
Probability of a pixel being classified as forest. Derived from Monte Carlo simulation using the joint distribution of canopy cover and tree height variables.
living_aboveground_biomass
Description
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 ~125 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.
living_aboveground_biomass_uncertainty
Description
95% confidence interval for living aboveground biomass variable.
living_belowground_biomass
Description
Total biomass stock of dry matter in belowground biomass on a per hectare basis. Data is calculated from living aboveground biomass variable using biome-specific allometric equations.
Usage notes
Biome-specific allometric equations are taken from Verra methodology VMD0001.
living_belowground_biomass_uncertainty
Description
95% confidence interval for living belowground biomass variable.
living_biomass
Description
Total biomass stock of dry matter in live vegetation on a per hectare basis. Sum of living aboveground biomass and living belowground biomass variables.
living_biomass_uncertainty
Description
95% confidence interval for living biomass variable.
living_biomass_carbon_stock
Description
Total biomass carbon stock on a per hectare basis. Data is calculated by multiplying living biomass variable by the relative contribution of carbon to total biomass.
Usage notes
Biome-specific allometric equations are taken from Verra methodology VMD0001.
living_biomass_carbon_stock_uncertainty
Description
95% confidence interval for living biomass carbon stock variable.
living_biomass_co2_eq
Description
Total biomass carbon stock in carbon dioxide equivalent. Data is calculated by multiplying living biomass carbon stock variable by the relative molar mass of carbon dioxide to carbon.
living_biomass_co2_eq_uncertainty
Description
95% confidence interval for living biomass CO2 equivalent variable.
tree_height
Description
Maximum height of trees. Data is generated using a deep neural network trained to predict ~125 million ha of airborne and spaceborne LiDAR data from a combination of spaceborne remote sensing datasets.
Usage notes
Tree height is constrained to be equal to or higher than canopy height variable.
tree_height_uncertainty
Description
95% confidence interval for tree height variable.
Pricing
This dataset adds a new time point annually. A new subscription 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.