Partitioning biomass in Canadian boreal forests with fine-scale structure characterization from terrestrial laser scanners

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Precise tree-level estimates of biomass partitioning are fundamental to compiling forest resource inventories, carbon accounting or wood products. Without certainty in biomass inventories, it becomes difficult to make capital investments in bioeconomy projects and limits forest sector potential. As of today, several provinces and territories rely on traditional forest inventory for spatializing biomass estimates. This project aims at producing high value information from terrestrial lidar (TLS) data of two types: 1) wood and leaf biomass portioning, and 2) forest fire combustible estimation. It is thus guided by two research questions: 1) Can TLS be used to replace current destructive measurements to provide species-specific models of biomass partitioning (stem, branch, and leaf) while retaining an accuracy requirement of 15%? And 2) Can broader and better fine-scale structure characterization support the next generation fire behavior products to better characterize and map fuel types, which in turn would allow better predictions of fire behavior and risk at national scale? The principal objective is to implement a suite of validated algorithms into an operational, open-access, software pipeline with systematic output controls (human-assisted retroaction) to facilitate the processing of TLS data for characterizing forest structure and estimate tree biomass partitioning. The pipeline will implement four main algorithms to process TLS point clouds of forest plots: (1) segmentation of individual tree crown, (2) classification of wood vs. leaf material, (3) characterization of the stem form and branching structure, and (4) estimation of the leaf area index (LAI) of trees. The collaborative, inter-disciplinary research project presented here will address knowledge gaps and generate significant outcomes for the Canadian forest sector, including: a) improved biomass supply access, security and competitiveness, b) an improved anticipation and response to forest wildfire risks, and c) training the future workforce, with an emphasis on diversity.