From points to numbers: a database-driven approach to convert terrestrial LiDAR point clouds to tree volumes
A set of tools are described for optimal allocation of wood fibre at an operational planning level. These were applied to a case study in Ireland. Allocation was based on optimising net value recovery (delivered price minus harvesting and transportation costs) while meeting market demands and operational constraints (mainly crew capability and productivity limits). Two new models were developed to predict harvesting costs and transportation costs for Irish forest conditions. A new model was developed to link Sitka spruce biomass expansion factors to optimal log-making algorithms so that log and bio-energy product yields could be estimated for individual harvest areas. An existing operational allocation model based on a tabu search heuristic procedure was used. The case study included 16 forest harvest areas and 12 processing plants (saw logs, pallet logs, stakes, pulp, bio-energy slash bundles, etc.). New terrestrial lidar scanning procedures were used to obtain representative stem profiles from over 4,000 trees for the 16 forests. We demonstrated that optimal allocation of bio-energy and log products, while complex, can be achieved through the use of appropriate management tools.