Research Focus
The undergraduate fellow will participate in a NASA funded project to better understand the impact of tundra and boreal wildfires on permafrost across different environmental gradients in the Alaskan Arctic and Subarctic. This project will primarily revolve around the processing, analysis, and interpretation of Interferometric Synthetic Aperture Radar (InSAR), which can resolve centimetric-scale ground motion associated with seasonal freeze/thaw of the active layer and postfire permafrost degradation/reaggradation. The fellow will perform a comparative analysis across three wildfire complexes chosen for their diversity in local and regional environmental characteristics (e.g., vegetation cover, mean annual air temperature, permafrost extent). The fellow will generate time series of ground displacement, from which estimates of postfire permafrost response can be inferred. The fellow will also explore the integration of complementary remotely sensed datasets (e.g., multispectral, lidar) and the use of machine learning techniques.
Skills, Techniques, Methods
- Remote Sensing
- Electromagnetic Geophysics
- Geospatial Data Science
- Machine Learning
Research Conditions
The research will be conducted primarily in person in the Radar Lab in Rudolph Hall. It will involve software development, analysis of remotely sensed imagery, climate reanalysis data, and in situ geophysical data.
Team Structure and Opportunities
The undergraduate fellow will work closely with Roger Michaelides and a postdoctoral research associate mentor within the Radar Lab. The fellow will participate in weekly group meetings, and regular meetings with external collaborators involved on the project.
Requirements
Python Programming, basic statistics, prior experience working with remotely sensed image data is helpful.
