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Harvard Forest Research Project 2024
- Title: Quantifying Seasonal Change in Forest Structure with high-resolution UAV Lidar
- Principal investigator: Andrew Davies (andrew_davies@g.harvard.edu)
- Institution: Harvard University
- Primary contact: Evan Hockridge (evanhockridge@g.harvard.edu)
- Team members: Lucrecia Aguilar
Katherine Angier
Tracy Barbaro
Peter Boucher
Ella Bradford
Andrew Davies
Evan Hockridge
Joseph Hupy
Catherine Ressijac
Jenia Singh - Abstract:
Recent advances in forest remote sensing have shown the great potential of airborne light detection and ranging (lidar) for measuring crucial aspects of forest structure, such as canopy height and aboveground biomass (AGB), in high resolution and across large extents. However, the majority of studies rely on lidar acquisitions from the leaf-on season, when conditions are more favorable for remote sensing and field acquisitions. This temporal bias in sampling is a missed opportunity to study the phenology of forest structure and to develop more robust methods of AGB estimation with lidar data. By comparing leaf-on and leaf-off airborne lidar data within a mixed temperate New England forest, we can separate the woody and leafy components of structure across a large area (~100 ha), opening up a myriad of opportunities for studying phenological cycles of forest structure and function across the wider region.
The phenology of forest structure is particularly critical to understand in mixed-temperature forests in New England, where climate change is rapidly altering the length and intensity of seasons. By analyzing seasonal changes in forest structure with high-resolution lidar data at Harvard Forest, we will improve models of forest AGB and produce a baseline dataset for evaluating the changing dynamics of phenological cycles within the wider New England region.
In addition, comparisons of leaf-on and leaf-off forest structure can help contextualize the structural impacts of forest disturbances, such as the impacts of the invasive hemlock wooly adelgid (Adelges tsugae). We explore the potential for using a time series of UAV lidar data to characterize and compare structural changes brought about both by seasonal changes and disturbances.
