Quantifying carbon storage and water fluxes in Shenandoah National Park using machine learning and novel remote sensing data

This project aims to advance understanding of the potential impacts of climate change on ecosystem services provided by forests, including carbon sequestration and water filtration.

Using emerging machine learning techniques and high-resolution data from two recently-launched NASA Earth observation missions, researchers Linnea Saby and Jacob Malcomb will examine spatial and temporal patterns of streamflow, evapotranspiration, and forest biomass across Shenandoah National Park under varying climate conditions. Machine learning models will be trained using data from long-term, UVA-led hydrologic monitoring efforts in Shenandoah.

The resulting models will provide environmental planners with data-driven tools to predict, and manage for, forest ecosystem resilience to climate change.

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