# Forest Carbon Monitoring

Planet — version 1.0

## Description

This dataset quantifies aboveground live carbon density (Mg C/ha), canopy cover (%), and canopy height (m) of vegetation at 3.5 m spatial resolution. Data has global coverage and quarterly temporal resolution, available from 2021 onwards. Data includes pixel-level variable uncertainties (90% prediction intervals).

## Summary

- **ID:** 4d8bd6ba-b751-4c46-8e70-5000e6d8b212
- **Type:** Raster
- **CRS:** EPSG:4326
- **Spatial coverage:** Global
- **Spatial resolution:** 3.5 m
- **Temporal coverage:** 2021+
- **Temporal resolution:** Quarterly

## Usage notes

Each quarter (Dec-Mar, Mar-Jun, Jun-Sep, Sep-Dec) runs from the 21st to the 20th day of start and end months and is delivered on the day after quarter end. Spatial coverage is limited to land surfaces between 76°N and 56°S. In urban areas, double-bounce radar backscatter among buildings may result in over-predictions. Circular spatial artefacts can lead to temporal differences between adjacent pixels. Models are linearly downscaled from the 30 m Diligence dataset models, so variables are consistent among the two datasets. Original 30 m aboveground live carbon density predictions are not included.

## Variables

### aboveground_live_carbon_density
- **Type:** int16
- **Units:** Mg C/ha
- **NoData:** 32767
Density of carbon stored in live woody vegetation on a per hectare basis. Data is generated using LightGBM regression models trained to predict GEDI L4A aboveground biomass carbon from modelled canopy height, modelled canopy cover, elevation, and location.

### aboveground_live_carbon_density_lower_bound
- **Type:** int16
- **Units:** Mg C/ha
- **NoData:** 32767
90% prediction interval lower bound for aboveground live carbon density variable.

### aboveground_live_carbon_density_upper_bound
- **Type:** int16
- **Units:** Mg C/ha
- **NoData:** 32767
90% prediction interval upper bound for aboveground live carbon density variable.

### canopy_cover
- **Type:** uint8
- **Units:** %
- **NoData:** 255
Relative cover of trees. Data is generated using U-Net image regression models trained to predict > 9 million km² of airborne LiDAR data from a combination of spaceborne remote sensing datasets.

### canopy_cover_lower_bound
- **Type:** uint8
- **Units:** %
- **NoData:** 255
90% prediction interval lower bound for canopy cover variable.

### canopy_cover_upper_bound
- **Type:** uint8
- **Units:** %
- **NoData:** 255
90% prediction interval upper bound for canopy cover variable.

### canopy_height
- **Type:** uint8
- **Units:** m
- **NoData:** 255
Average height (m) of trees. Data is generated using U-Net image regression models trained to predict > 9 million km² of airborne LiDAR data from a combination of spaceborne remote sensing datasets.

### canopy_height_lower_bound
- **Type:** uint8
- **Units:** m
- **NoData:** 255
90% prediction interval lower bound for canopy height variable.

### canopy_height_upper_bound
- **Type:** uint8
- **Units:** m
- **NoData:** 255
90% prediction interval upper bound for canopy height variable.

## Pricing

This dataset adds a new time point quarterly. A new subscription returns the full archive up to the most recent time point and creates a one-year subscription to future time points. Data acquisition costs are per hectare:

| Volume | Price |
| --- | --- |
| Any | $0.25 / ha |