Jovian Kan

Remote Sensing of the Impact of Mega-fires on Snow

Mountain snow is a virtual reservoir that stores water during the winter and releases it when it is most needed at the beginning of the agricultural growing season. Snowpack water storage, measured as snow water equivalent (SWE), is therefore important to track and predict. Most numerical models that try to predict snow use maps of forests from previous years, but major disturbances, such those caused by fire, make these older versions unreliable for the future. This project aims to improve our ability to observe and predict SWE in areas where there has recently been a major wildfire.

This summer I will work with high-resolution maps of trees and snow created from Light Detection and Ranging (Lidar) cameras on specialized aircraft to research on questions around Lidar snow depth observations and density modeling. Specifically, I will try to explore the spreading patterns of fire on the snow cover and how this disturbance changes the local water hydrology. After analyzing that, we could make predictions on the change of the water hydrology, which will bring solutions to the surrounding agriculture systems. I am also interested in the topic of the spatial distribution of snow in areas with different densities of forest cover, slope, aspect, and elevation. I believe this topic can also help my main research topic of the relationship between mega-fires and snow cover.

Message to Sponsor

SURF-SMART is an amazing opportunity for me. It provides me with a valuable experience to conduct research as an environmental scientist. This experience will encourage me to make more innovations in the field of remote sensing in the future. Thanks again for the opportunity.
  • Major: Environmental Sciences, Environmental Economics and Policy
  • Sponsor: Johnson Fund
  • Mentor: Marianne Cowherd