Researchers awarded Forest Service grant to advance forest monitoring science
With support from the U.S. Forest Service, Virginia Tech researchers are developing next-generation tools to track how forests recover, grow, and adapt over time.
Researchers from the College of Natural Resources and Environment have received a new grant from the U.S. Forest Service Southern Research Station to advance forest monitoring science through innovative uses of remote sensing technologies. The project aims to improve how scientists measure forest recovery and growth across the Southeast.
The research is led by Professor Val Thomas, with co-principal investigator Professor Randolph Wynne, both from the Department of Forest Resources and Environmental Conservation, in collaboration with Todd Schroeder of the U.S. Forest Service. The joint venture agreement supports a two-year project titled Exploring Forest Growth with Multi-date LiDAR, 3D NAIP Point Clouds, and Spectral Trajectories.
“Remotely-sensed changes in canopy vertical structure, coupled with higher temporal resolution changes in canopy spectral reflectance, have strong potential to improve forest science and management at a range of scales,” Wynn said.
The study will combine repeat collections of airborne LiDAR and photogrammetric point clouds from the National Agriculture Imagery Program with spectral data to measure forest growth and change over time. By aligning these datasets, researchers aim to overcome long-standing challenges in detecting how forests respond to logging, storms, or fire.
The $142,000 award provides funding to Virginia Tech, with additional Forest Service contributions of staff expertise and data resources. The investment will support a doctoral student, research assistance, and the development of new modeling approaches for mapping changes in forest structure at unprecedented resolution.
The project has four primary goals:
- Refine methods to distinguish between stand-replacing disturbances and gradual regrowth for more accurate forest condition assessments.
- Characterize drivers of forest growth across environmental gradients, including climate, soils, and management factors.
- Validate techniques by linking remote sensing observations to the U.S. Forest Service’s Forest Inventory and Analysis (FIA) network, ensuring results are robust and operationally useful.
- Generate high-resolution models and maps to inform both scientific understanding and practical forest management decisions.
Results will be shared through conference presentations, peer-reviewed publications, and student theses.
