We gather a wide variety of data using satellite imagery, an extensive network of field plots, and other cutting-edge remote sensing methods. This broad approach helps us identify key forest attributes like forest coverage, tree density, height, and health. Moreover, we are keeping pace with the newest advancements in LiDAR and MRV technologies to deliver even more precise estimates.

To predict carbon sequestration, we combine the power of machine learning algorithms with historical data to establish dynamic baselines. This process involves matching data points from the surrounding area to each specific point within the project boundary, taking into account factors such as topography or forest structure. Also, our approach evolves with real-world changes. Instead of relying on static baselines, we continually update our baseline on an annual basis to accurately reflect actual forest changes outside of carbon projects and accommodate shifts in surrounding land use.

By integrating additional machine learning models and allometric equations, we then achieve more accurate estimations of carbon storage. The advantage of these carbon models lies in their applicability over large regions, thus making cumbersome and costly on-site manual measurements obsolete.

Our blockchain technology is the answer to the common challenge of transparency in REDD+ projects. By publishing all relevant data on our decentralized platform, we create a secure and immutable data trail. This approach provides clients with utmost transparency over their sustainability efforts, thereby protecting their company reputation.