Description:

Wildfires are growing in frequency and magnitude, and result in great cost to economies, infrastructure, ecosystems, and human health. Wildfires can also drastically alter landscapes, leading to hotter and drier conditions with implications on vegetation succession, water budgets, streamflow and water quality. The project will apply a wide range of hydrology, thermal, surface reflectance, and vegetation satellite data (evapotranspiration, evaporative stress index, precipitation, land surface temperature, vegetation productivity, burn severity from ECOSTRESS, EMIT, MODIS, Landat, Sentinel-2, GPM) to improve wildfire prediction and forecasting, as well as identify impacts of wildfires on vegetation and water budgets. Work includes use of machine learning to develop models of wildfire severity and occurrence, and produce maps of fire risk from a range of satellite data sets. Work will be carried out closely with JPL scientists who are part of the project from the Water and Ecosystems Group and the Carbon Cycle group, and stakeholders. 

Field of Science:

• Earth Science

Advisors: