# Monitoring 25 m (discontinued)

Kanop — version 2.0.2

## 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.

## Summary

- **ID:** 084fa670-828b-4749-bbc3-77ad9123ad8e
- **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. Post calibration allows tuning of model predictions from user field measurements.

## Variables

### canopy_cover
- **Type:** float64
- **Units:** %
- **NoData:** NaN
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
- **Type:** float64
- **Units:** m
- **NoData:** NaN
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.

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

### living_aboveground_biomass
- **Type:** float64
- **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 > 60 million ha of airborne and spaceborne LiDAR data from a combination of spaceborne remote sensing datasets.

### living_belowground_biomass
- **Type:** float64
- **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_biomass
- **Type:** float64
- **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_carbon_stock
- **Type:** float64
- **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_co2_eq
- **Type:** float64
- **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.

### tree_height
- **Type:** float64
- **Units:** m
- **NoData:** NaN
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.

## 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 |