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

Aquatic carbon fluxes

The importance of Lake Littoral Zones to Arctic-Boreal Methane Emissions (B35G-1503): Accounting for littoral zones can increase lake methane emissions estimates by 12%

Poster on estimating methane emissions from airborne remote sensing

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

A Closer Look at the Effects of Lake Area, Aquatic Vegetation, and Double‐Counted Wetlands on Pan‐Arctic Lake Methane Emissions Estimates High-resolution lake maps are scaled to the pan-Arctic, showing small lakes <0.1 km2 comprise less area (12% of total) than thought Double counting between lake aquatic vegetation and wetlands is...

Read open-access paper at Geophysical Research Letters

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

Generated super resolution (SR) image of surface water

animation of native resolution images of lake shorelines and islands being transformed to higher resolution

The high-resolution satellite images above are generated from low-resolution originals using a neural network.

Geospatial data science paper: "Super-Resolution Surface Water Mapping on the Canadian Shield Using Planet CubeSat Images and a Generative Adversarial Network Cartographie des eaux de surface à très haute résolution sur le Bouclier canadien à l’aide d’images Planet CubeSat et d’un réseau antagoniste génératif" by Ekaterina M. D. Lezine, Ethan D. Kyzivat & Laurence C. Smith

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