Above Ground Carbon (Low resolution)

Provides medium-resolution data on carbon stored in trees, crucial for verifying carbon credits and tracking the impacts of deforestation, afforestation and natural regeneration at a landscape level.

availability

On Demand
Now

indicator tier

Silver

unit

Tonnes of Carbon

spatial resolution

100m

measurement frequency

One-off

measurement level

Plot

historic data availability

2018-2022

Forescast data availability

N/A

applicable crop types

Tree Crops

applicable land type

Forestry
Conservation
Perennial Cropland
Annual Cropland

compliance frameworks

CSRD (ESRS E1), CSRD (ESRS E4), Nature Positive Initiative (NPI), SBTN, SBTi, TNFD

description

Measures the amount of carbon stored in live woody vegetation in large forest areas. This medium-resolution dataset is useful for cost-efficient monitoring of landscape-level forest carbon stocks. It offers a scalable solution to track carbon stocks over time without the need for extensive fieldwork.

methodology

Above-ground carbon stocks are derived by applying IPCC conversion rate on above ground biomass. The above ground biomass is quantified using a combination of multiple satellite radar technologies (Sentinel-1, Envisat ASAR, ALOS-1/2). The model is calibrated with spaceborne LiDAR data from GEDI and ICESat-2 missions. This produces annual, global maps of above-ground biomass at 100m spatial resolution under the European Space Agency's (ESA) Climate Change Initiative.

validation

Validation of this indicator involves several integrated methods: (1) Direct comparison of satellite-derived maps against independent reference data from forest plots and LiDAR campaigns, ensuring full independence by excluding validation data from the map calibration process; (2) A tiered evaluation system that categorizes reference data by plot size—ranging from Tier 1 (small National Forest Inventory plots ≤ 0.6 ha) to Tier 3 (high-quality "super-plots" ≥ 6 ha)—to account for varying levels of measurement accuracy and spatial representation; (3) Error modeling and mitigation to address discrepancies caused by "support" differences (the size gap between a map pixel and a ground plot), temporal mismatches between measurement dates and map epochs, and GNSS positioning inaccuracies; (4) Standardized protocols following CEOS (Committee on Earth Observation Satellites) guidelines to minimize systematic bias in reference data; and (5) Map inter-comparison, which evaluates the consistency of deviations across different product versions (e.g., Version 5 vs. Version 6) and historical epochs.
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