Separating natural forests from other tree cover with AI for deforestation-free supply chains

Forests are vital for our planet as they regulate rainfall, mitigate floods, store and sequester carbon, and help sustain the majority of the planet’s land-based species. Despite their importance, deforestation continues at an alarming rate. A key challenge in conservation efforts is differentiating centuries-old natural ecosystems from newly planted forests or tree crop plantations with satellite data. Most existing maps simply show "tree cover," a basic measure of any woody vegetation, leading to an "apples-to-oranges" comparison. This conflates the harvesting of a short-term plantation with the permanent loss of an irreplaceable, biodiversity-rich natural forest.

The need for this distinction is more important than ever due to new global regulations, like the European Union Regulation on Deforestation-free Products (EUDR). This regulation mandates that products like coffee, cocoa, rubber, timber, and palm oil sold in the EU cannot come from land that was deforested or degraded after December 31, 2020, with the goal of protecting natural forests, like primary and naturally regenerating forests. This policy creates a need for a reliable, high-resolution, and globally-consistent map of natural forests as they existed in 2020. The protection of these forests is also a central pillar for COP30, which recognizes their crucial role in climate stability and human well-being.

How AI can separate the forest from the trees

What's next: A new vision for forest understanding

To advance this effort, we’re developing a new multi-year series of global forest type maps, powered by next-generation AI models. These maps will categorize the world's land into six distinct types: Primary Forest, Naturally Regenerating Forest, Planted Forest, Plantation Forest, Tree Crops, and Other Land Cover. We expect to release these comprehensive maps in 2026.