Understanding carbon, biodiversity, and climate interactions
Explore Our WorkThe Global Ecology Lab conducts research on the structure, function and dynamics of ecological systems, and their interactions in the Earth system. Our primary approach is to combine data and mathematics to develop models for quantifying patterns and predicting future dynamics. Current research is focused on the development and application of models to address issues such as: terrestrial carbon balance, the sustainability of land-use practices, the effects of disturbances on ecosystem structure and function, and interactions between the biosphere, hydrosphere, and atmosphere.
Global Carbon Modeling and Simulation
Modeling changes in land use over time
Mapping, modelling, and monitoring changes to the world's forest carbon.
Shuo Xu is the first author on a paper titled "Knowledge-Guided Learning for Global Carbon Flux Prediction: Integrating High-Level Remote Sensing with Bottom-Up Physical Modeling", that will be presented in at the KDD Conference in South Korea on August 10th. Zhihao Wang, Yiqun Xie, Ruohan Li, Lei Ma and George Hurtt are coauthors.
Lei Ma and George Hurtt have led a a paper titled "Widespread carbon accumulation in mature forests remains underestimated by satellite observations", published in Nature Ecology and Evolution!
Janna Chapman and George Hurtt led the Summer 2026 High School Internship Program with 12 interns!
George Hurtt and Lei Ma are coauthors on a new publication titled "A quantitative evaluation of forest aboveground biomass density map products in Oregon, USA" in Environmental Research Letters!
Data Challenge for the 2026 IEEE International Conference on Big Data (IEEE BigData 2026). Data Challenges are analytics and programming contests based on datasets released by the organizations sponsoring the competitions. These contests will run for several months, with winners announced in the IEEE BigData 2026 in December 14-17, 2026 at Phoenix, AZ.
A collaboration with the UMD's Global Ecology Lab will apply high-resolution satellite data and advanced modeling to improve Rhode Island's forest carbon estimates.