Improving forest structure mapping and regeneration prediction with multi-scale lidar observations

To make informed decisions, land managers require knowledge about the state of the ecosystems present. Vegetation structure is a key indicator of the state of forested systems; it influences habitat suitability, water quality and runoff, microclimate, and informs wildfire-related characteristics such as fuel loads, burn severity, and post-fire regeneration.  Field data used to derive vegetation structure are limited in spatial and temporal extent. Alternatively, forest growth simulation models estimate vegetation structure, but do not capture all factors influencing vegetation growth.  Assessment of vegetation structure can be improved by using observations to derive maps which can be used to calibrate modeled forest structure, leading to information assets that enable better monitoring of structure dynamics, especially when linked with landcover mapping efforts.  This project leverages existing data to generate new geospatial products and to validate an existing model, providing stakeholders with the tools needed to map, monitor, and predict vegetation structure dynamics.



Principal Investigator : Birgit Peterson

Co-Investigator : Kurtis Nelson, Paul B May

Cooperator/Partner : Daniel L Swanson, Mark Hendrix