Plants exhibit diverse leaf traits, but only a few viable combinations support survival in today’s changing climate. To predict ecosystem functioning—how carbon, water, and energy move between plants and the atmosphere—it is crucial to understand both leaf traits and their arrangement within canopies. Leaves interact with one another in complex ways, largely driven by their position and orientation. This project uses new 3D mapping technologies to measure every leaf’s angle and position in forests across eight eastern U.S. NEON sites, enhancing our understanding of how plant traits affect growth and resource use.
The research bridges leaf-level traits and ecosystem-scale processes, asking: To what extent do canopy structure and leaf traits converge at the ecosystem scale? Researchers will map canopy traits—like leaf angle, area, and clumping—using algorithms calibrated with NEON hyperspectral and Terrestrial LiDAR data. The project will also explore how canopy traits affect photosynthetic optimization across vertical layers in three forest sites, revealing how trees manage light and water competition.
Broader impacts include creating virtual reality forests to improve accessibility and outreach, developing classroom modules for local schools, and supporting remote sensing education at the University of Virginia. The dataset produced—canopy trait maps, vertical profiles, and structural metrics—will offer long-term value to the forest ecology community.