Presentation by Mallory Barnes, Assistant Professor, School of Public and Environmental Affairs, Indiana University

Assessment and prediction of the impacts of climate change on the environment and human societies requires multi-scale understanding of interactions between biogeochemical, hydrologic, and atmospheric cycles. For many years, measures of greenness from spaceborne satellites were the only way to consistently observe the terrestrial biosphere. Recent and emerging advances in remote sensing technologies and data science present a timely opportunity to explore new aspects of vegetation-climate interactions. In this talk, I highlight two plant processes that impact climate: carbon uptake and transpiration. I will first show how machine learning techniques can be used to globally upscale carbon uptake measurements from eddy covariance towers in highly heterogenous drylands. Next, I will show that the biophysical impacts of reforestation in the Eastern United States reduced anthropogenic warming in the Eastern United States. I will end by highlighting future opportunities for emerging technologies to overcome key knowledge gaps in vegetation-climate interactions.