HOW IT WORKS
How we measure forests
Gather Satellite
ObservationsPachama trains machine learning models using satellite imagery, a vast network of field plots, LiDAR imaging, and other remote sensing data to identify key forest characteristics that are used to estimate carbon.
Assess the Baseline
We are developing machine learning models for business-as-usual emissions of carbon projects. We do this by matching pixels within the surrounding region to each and every pixel within the project boundary, based on attributes such as distance to roads, topography, and forest structure. Unlike status quo static baselines, Pachama’s dynamic baseline is updated annually to observe what actually happened in forests without carbon projects, and capture shifts in background land use that are impossible to predict due to elections or commodity prices.
Gather Satellite
ObservationsPachama trains machine learning models using satellite imagery, a vast network of field plots, LiDAR imaging, and other remote sensing data to identify key forest characteristics that are used to estimate carbon.
Assess the Baseline
We are developing machine learning models for business-as-usual emissions of carbon projects. We do this by matching pixels within the surrounding region to each and every pixel within the project boundary, based on attributes such as distance to roads, topography, and forest structure. Unlike status quo static baselines, Pachama’s dynamic baseline is updated annually to observe what actually happened in forests without carbon projects, and capture shifts in background land use that are impossible to predict due to elections or commodity prices.