# Monitoring 30 m

Kanop — version 4.0

## 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).

## Summary

- **ID:** 938c211c-a806-4dde-82ee-6548a9eec439
- **Type:** Raster
- **CRS:** EPSG:4326
- **Spatial coverage:** Global
- **Spatial resolution:** 30 m
- **Temporal coverage:** 2013+
- **Temporal resolution:** Annual

## 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
- **Type:** float32
- **Units:** %
- **NoData:** NaN
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
- **Type:** float32
- **Units:** %
- **NoData:** NaN
95% confidence interval for canopy cover variable.

### canopy_height
- **Type:** float32
- **Units:** m
- **NoData:** NaN
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.

### canopy_height_uncertainty
- **Type:** float32
- **Units:** m
- **NoData:** NaN
95% confidence interval for canopy height variable.

### forest_cover
- **Type:** float32
- **NoData:** NaN
Indicates whether a pixel is forest.

### forest_cover_probability
- **Type:** float32
- **Units:** %
- **NoData:** NaN
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
- **Type:** float32
- **Units:** Mg/ha
- **NoData:** NaN
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.

### living_aboveground_biomass_uncertainty
- **Type:** float32
- **Units:** Mg/ha
- **NoData:** NaN
95% confidence interval for living aboveground biomass variable.

### living_belowground_biomass
- **Type:** float32
- **Units:** Mg/ha
- **NoData:** NaN
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.

### living_belowground_biomass_uncertainty
- **Type:** float32
- **Units:** Mg/ha
- **NoData:** NaN
95% confidence interval for living belowground biomass variable.

### living_biomass
- **Type:** float32
- **Units:** Mg/ha
- **NoData:** NaN
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
- **Type:** float32
- **Units:** Mg/ha
- **NoData:** NaN
95% confidence interval for living biomass variable.

### living_biomass_carbon_stock
- **Type:** float32
- **Units:** Mg C/ha
- **NoData:** NaN
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.

### living_biomass_carbon_stock_uncertainty
- **Type:** float32
- **Units:** Mg C/ha
- **NoData:** NaN
95% confidence interval for living biomass carbon stock variable.

### living_biomass_co2_eq
- **Type:** float32
- **Units:** Mg CO₂ eq/ha
- **NoData:** NaN
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
- **Type:** float32
- **Units:** Mg CO₂ eq/ha
- **NoData:** NaN
95% confidence interval for living biomass CO2 equivalent variable.

### tree_height
- **Type:** float32
- **Units:** m
- **NoData:** NaN
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.

### tree_height_uncertainty
- **Type:** float32
- **Units:** m
- **NoData:** NaN
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 |