Global forest mapping moves past binary canopy cover

Satellite monitoring often flattens ecology into a binary measure: whether an area has tree canopy or bare ground. That distinction counts an industrial timber plantation and a primary rainforest as the same land cover as long as both stay green. Google DeepMind's AlphaEarth Foundations attempts to separate them by categorizing every 10-meter patch of land on Earth into distinct forest types.

At a 10-meter scale, classifying forest composition globally requires resolving structural differences rather than just measuring reflected light. The model has to handle seasonal shifts and cloud cover across biomes without misreading different species mixes. If those predictions hold up against local ground surveys, the resulting map provides a clearer baseline than broad canopy percentages.

For conservation and carbon tracking, forest composition matters because canopy density alone hides habitat degradation and rotational harvesting. A replanted monoculture can show complete canopy recovery while lacking the biodiversity or carbon stability of mature woodland. Moving from visual greenness to granular forest taxonomy gives analytical systems data that reflects the actual composition of the land.