Strategic forestry planning aims at optimising a set of objectives within a set of constraints. Incorporating market dynamics into the optimisation process increases the complexity of formulating the strategic planning problem by including details at the tree level (such as timber, chips, or chips). In addition, the continuous adjustment of planning objectives to market changes requires the rapid and accurate identification of products that can be obtained from the forest. Technological advances in remote sensing, in particular LIDAR, reduce the time to accurately acquire the information needed to identify the products that can be obtained from a tree. The present research aims to identify tree-level products based on LIDAR that will provide the information needed to obtain the optimal solution to the strategic planning problem. LIDAR will be used to determine different tree attributes, (height, crown diameter, crown length and crown asymmetry), which will be used to delineate the outputs that can be obtained from each tree. Models describing either tree in terms of product allocation will be developed by fitting the contour curve to the attributes estimated from the LIDAR data. The optimal allocation of products will be determined at the forest level using several planning algorithms, including linear programming, controlled cooling, and first-order descent constrained to the perfect packing theorem. The present research will provide the information needed by forest operators in Romania for adjusting planning strategies and techniques to market conditions.