Blocking and Scheduling (TFBS) approach that can assist in solving complex forest landscape transformation problems. TFBS blocks and schedules forest treatments according to the requirements of transforming forest landscapes to desired states and projected forest stand dynamics. Timber flows are the results of the landscape transformations. The forest treatment schedules produced by TFBS not only sustain a wide range of non-timber resources but also maximize and maintain timber flows. TFBS can facilitate the forest management transition from timber harvesting regulation-based planning to desired state-oriented forest planning.; A desired state of a forest landscape is a state where all the resource layers on the landscape are in their desired states. The polygons created from overlaying multiple resource layers form the basic units for building the cutblocks. These dynamic cut blocks are combined over time to create patches and desired landscape structures. Age structures and patch size distributions are used as common indicators for all nontimber resources. Each resource layer is assigned one or more age structures and patch size distributions according to the management objectives.; To achieve the objectives of this research, a tool, Forest Simulation Optimization System (FSOS) was developed and tested on a simplified 400-polygon (10 ha per polygon) grid data set as well as a complicated 80,000 ha (18,000 polygons) Tree Farm in the Slocan Valley. The results spatially and temporally demonstrated the processes required in building blocks and patches, transforming forest landscapes to desired states and sustaining the desired states. FSOS is also compared to a timestep simulation model, ATLAS.; The results show that TFBS can produce strategies to transform forest landscapes to the same desired states with different initial states and different natural disturbance rates and patterns. TFBS simultaneously blocks and schedules the whole landscape for the entire planning horizon and the impacts of treatments on future landscape states are considered. Adaptive strategies are modified accordingly.; It was found that Simulated Annealing (SA) was an efficient algorithm for TFBS problems. No guarantee of optimality can be assured; however, SA can find good solutions within a reasonable time for complex problems. This is difficult or even impossible with directed search methods such as mixed integer programming.
展开▼
