Screening

Kanop — version 2.0.2 (2024-03)

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.

Dataset ID
f79af205
Category
Plant biomass
Type
Raster
CRS
EPSG:4326
Spatial coverage
Global
Spatial resolution
25 m
Temporal coverage
2013+
Temporal resolution
Annual

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.

Variables

canopy_cover

NoDataNaNUnits%

Description

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

NoDataNaNUnitsm

Description

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

NoDataNaN

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%). Forest definition can be replaced by user specified thresholds.

IndexName
0Non-forest
1Forest

living_aboveground_biomass

NoDataNaNUnitsMg/ha

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 > 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.

living_belowground_biomass

NoDataNaNUnitsMg/ha

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_biomass

NoDataNaNUnitsMg/ha

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_carbon_stock

NoDataNaNUnitsMg C/ha

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_co2_eq

NoDataNaNUnitsMg CO₂ eq/ha

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.

tree_height

NoDataNaNUnitsm

Description

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 new subscription returns the full archive up to the most recent time point. Data acquisition costs are per hectare for the full archive:

VolumePrice
Any$0.10 / ha

Resources

Provider

Kanop

www.kanop.io

Kanop 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.