LiDAR stem metrics for tree list models

One important limitation of existing growth and yield models is their lack of flexibility in terms of stand structure and composition. Models such as Forest Vegetation Simulator (FVS) platform or Open Stand Model (OSM) which have been calibrated for the Acadian forest, simulate the growth of tree lists representing the distributions of species and stem size of individual trees in the stand allowing more complex stand representations. Using high-density lidar data, an MSc project will have for objective to extract lidar stem-level metrics for input into tree-list models to obtain refined predictions of growth in complex, diverse forests. Using this approach, EFI-informed predictions of forest growth will be made in complex stands of the NS Hub. Outcome (OB.4b): Transferable workflows to derive lidar stem distribution metrics to better inform tree-list growth models.

Recruiting, MSc at University of New Brunswick
Main Partner: Department of Lands and Forestry (Nova Scotia)
Professor Loïc D'Orangeville