Purdue University’s Jingjing Liang has received a two-year, $870,000 grant from the World Resources Institute to map global forest carbon accumulation rates. “To accurately capture the carbon accumulation rates of forested ecosystems across the world has always been a challenging task, mostly because doing so requires lots of ground-sourced data, and currently such data are very limited to the scientific community”. “Even the most advanced satellite sensor can’t capture this reliably on its own, especially in older forests where the signal saturates. A forest stops getting taller long before it stops accumulating carbon.” Liang is developing an artificial intelligence model that will combine information collected about billions of trees measured on-site with satellite and other geospatial data to map local forest growth rates throughout the global forest range. “This will be the first AI-based forest growth model deployed at a global scale,” he said. Beyond accurately quantifying carbon dynamics, Liang’s AI-based forest growth model will also capture the dynamics of forest biodiversity and timber quality.