Trees are essential to ecosystems. They store carbon, reduce erosion, and serve as habitat for other species. The factors influencing trees, and the spatial scales at which they are managed, range from an individual tree to entire continents. Since there are approximately three trillion trees in the world collecting data on every tree over large areas is impossible using traditional methods. Therefore, it is necessary to use new technology to measure and describe individual trees over large geographic areas. This research will address this fundamental challenge by combining high resolution remote sensing data with field data on trees. Together, the remote sensing and field data will be used to understand what influences the number of trees, their size, where different species occur, and how this changes from spatial scales of local parks to the entire United States. This project will also make it easier for other scientists to study trees over large areas by developing software, producing data products, and providing training and collaboration opportunities for working with these novel datasets. This will help drive rapid advances in the cross-scale understanding of tree ecology with broad applications in forestry, management, and fundamental scientific understanding.

This project combines National Ecological Observatory Network (NEON) data from airborne remote sensing and field data collection. These data will be used to develop machine learning based approaches to identify, measure, and characterize to species all of the canopy trees located within each forested NEON site. This will yield data on approximately 50 million individual trees at about 40 sites across the United States. These data from NEON will be combined with data from the US Forest Service Forest Inventory and Analysis Project, which samples millions of trees at over 100,000 locations across the United States. These combined data will be used to develop joint models of the distribution, abundance, and structural traits of trees, that explicitly incorporate the concept of scale. These models will be used to understand how the processes influencing tree distribution and traits change across scales by comparing the importance of different factors at scales ranging from a few meters, where individual trees directly interact, to the entire United States, where large gradients in climate and land use are important. This research will address three broad questions in ecology: 1) what processes govern species distribution and abundance at different scales and how do they interact? 2) how are landscape and regional process of species coexistence connected to local biodiversity? 3) how do changes in the processes influencing tree traits across scales impact estimates of biomass and carbon storage?

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

National Science Foundation (NSF)
Division of Environmental Biology (DEB)
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Matthew Kane
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University of Florida
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