Estimating carbon

Status Quo: Field crews measure individual trees by hand to inventory carbon stocks over a small sample of a project’s area. This process costs tens to hundreds of thousands of dollars and can take months—or even years.

Pachama’s Approach: We are building machine-learning models that integrate satellite data, field plots, and 3D airborne lidar imaging to map forest carbon over entire regions, and eventually, anywhere in the world. This satellite-based carbon mapping unlocks a more scalable, cost-efficient future for crediting.

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Assessing the baseline

Status Quo: In forest carbon markets, a baseline is a business-as-usual scenario used to estimate expected emissions without the incentive of carbon financing. Today, credit issuance relies on historical data and a complex set of subjective factors to set baselines.

Pachama’s Approach: Using machine learning models, we match each satellite pixel in a project to a ‘control’ pixel outside the project with similar attributes. We then use satellite data to observe forest loss in the control pixels compared to the pixels in the project area.

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Track and share progress over time

Pachama’s suite of in-the-sky and on-the-ground technology, paired with machine learning, enables you to monitor progress and identify potential risks sooner.