Modeling Vegetation Cover at Sub-Pixel Scales for the Next Generation of Earth Remote Sensing Satellites

[Grigory Bronevetsky]

Modeling Talks

Modeling Vegetation Cover at Sub-Pixel Scales for the Next Generation of Earth Remote Sensing Satellites Philip Dennison, University of Utah Tues, Feb 25, 2025 | 9am PT Meet | Youtube Stream Hi all, The presentation will be via Meet and all questions will be addressed there. If you cannot attend live, the event will be recorded and can be found afterward at https://sites.google.com/modelingtalks.org/entry/modeling-vegetation-cover-at-sub-pixel-scales-for-the-next-generation More information on previous and future talks: https://sites.google.com/modelingtalks.org/entry/home Abstract: Earth’s satellite observation record now exceeds 50 years, and supports a vast range of science and applications. Multidecadal programs like Landsat have relied on a limited number of spectral bands that do not fully capture information about vegetation cover. In particular, past satellite missions have lacked the ability to accurately distinguish non-photosynthesizing vegetation (NPV), which includes plant litter, senesced leaves, and crop residues. NPV is an important indicator of ecosystem disturbance, agricultural resilience, drought severity, and wildfire danger, making accurate mapping of NPV as a component of vegetation cover a high priority for future satellite missions. This talk will describe how machine learning is being used to demonstrate the capabilities of upcoming Landsat Next and Surface Biology Geology satellite missions. These innovative missions will launch close to the end of the decade and should revolutionize global mapping of vegetation cover.   Bio: Phil Dennison is a professor and director of the School of Environment, Society, and Sustainability at the University of Utah. His past work includes remote sensing of ecosystem disturbance, including drought and insect attack; remote sensing of fuels, active fire, and vegetation recovery following wildfire; remote sensing and GIS applications for improving firefighter safety; detection and mapping of point source greenhouse gas emissions; and spectral mixture modeling and estimation of fractional cover. He is a member of NASA’s FireSense Implementation Team, which is working to mature remote sensing technologies to address operational fire management needs.