Current research
My research focuses on aquatic carbon fluxes, bridging field and satellite measurements, Arctic environmental science and climate change, and geospatial data science/machine learning
Atmospheric methane
Methane is now understood to have caused 65% as much anthropogenic warming as carbon dioxide. As part of the MethaneSAT science team, I have pivoted to studying methane from an atmospheric transport perspective. I contribute to a geostatistical inverse model (GIM), working alongside scientists and software engineers. Our ambitious goal is to develop a statistical inverse model of gridded methane emissions that can run operationally, driven by known wind fields and observations of methane concentration. The GIM will be the MethaneSAT product linking atmospheric enhancements to diffuse sources of emissions, such as oil fields. Photo: Pexels/Tom Fournier
Prior to the MethaneSAT launch, my team processed hundreds of flight hours of data from MethaneAIR, an airborne prototype of MethaneSAT. Both sensors are infrared spectrometers sensitive to light absorption by methane, carbon dioxide, oxygen and water.
Aquatic carbon fluxes
1. Kyzivat, E. D., & Smith, L. C. (2023). A Closer Look at the Effects of Lake Area, Aquatic Vegetation, and Double-Counted Wetlands on Pan-Arctic Lake Methane Emissions Estimates. Geophysical Research Letters, 50(24), e2023GL104825. https://doi.org/10.1029/2023GL104825
Kyzivat, E.D., Smith, L.C., Garcia-Tigreros, F., Huang, C., Wang, C., Langhorst, T., Fayne, J.V., Harlan, M.E., Ishitsuka, Y., Feng, D., Dolan, W., Pitcher, L.H, Wickland, K.P., Dornblaser, M.M., Striegl, R.G., Pavelsky, T.M., Butman, D.E., and Gleason, C.J. (2022). The Importance of Lake Emergent Aquatic Vegetation for Estimating Arctic-Boreal Methane Emissions. Journal of Geophysical Research: Biogeosciences, 127, e2021JG006635. https://doi.org/10.1029/2021JG006635
Lake and wetland mapping
Watch a video of me explaining my research on Arctic wetlands at a Brown Graduate School TED-style talk.
Kyzivat, E.D., L. C. Smith, L.H Pitcher, J.V. Fayne, S.W. Cooley, M.G. Cooper, S.N. Topp, T. Langhorst, M. Harlan, C. Horvat, C. J. Gleason, T. M. Pavelsky (2019). A high-resolution airborne color-infrared camera water mask for the NASA ABoVE campaign. Remote Sensing 11. https://doi.org/10.3390/rs11182163
Geospatial data science / machine learning
The high-resolution satellite images above are generated from low-resolution originals using a neural network.
The first paper on this project is available from the Canadian Journal of Remote Sensing, complete with a French abstract for all the francophones out there.
Kyzivat, E.D. and Smith, L.C. (2023). Contemporary and historical detection of small lakes using super resolution Landsat imagery: Promise and peril. GIScience & Remote Sensing, 60:1. https://doi.org/10.1080/15481603.2023.2207288
Lezine, E.M., Kyzivat, E.D., Smith, L.C. (2021). Super-resolution surface water mapping on the Canadian Shield using Planet CubeSat images and a Generative Adversarial Network. Canadian Journal of Remote Sensing 47:2, 261-275. https://doi.org/10.1080/07038992.2021.1924646
Super resolution demo
Pan around this map and adjust the slider to view native Landsat Imagery (30 m resolution) and an AI-generated counterpart at 3 m resolution