A co-evolutionary cellular automata for the integration of spatial and temporal scales in forest management planning

The scope of forest management has broadened to encompass ever more values and services. Designing decision support tools to provide for them involves incorporating a number of spatial and dynamic processes. This thesis presents a case for more holistic numerical planning tools which can handle spatial objectives and inter-temporal trade-offs.

A novel algorithm based on cellular automata (CA) is designed to address forest planning objectives that are both spatial and temporal and subject to global constraints. In this decentralized framework, the landscape management goals are achieved through a co-evolutionary decision process between interdependent stands. The problem considered is maximization of cumulative harvest volume and amount of clustered old forest subject to stable flow and minimum old growth retention. Applied to a small test area, the model demonstrates short computation time and shows sensitivity to both local constraints and global goals and constraints.

The implementation requirements of forest planning models are an issue that affects both the efficacy and the efficiency of planning tools. It is argued that object-oriented implementations could efficiently integrate the spatial and temporal data required by the various processes underpinning forest planning tools. An object-oriented design for the previously developed CA-based algorithm proves capable of considering spatial relationships with consistent allocation of clustered old growth areas. The object orientation permits a fast computation of both local and global limitations on local decision making and speedy modification of the problem definition (local and global requirements or spatial resolution).

Finally, the CA-based planning approach is used on a large scale planning problem to investigate different policy scenarios. The problem under investigation is the impact on volume flow and net present value of introducing intensive forest management (IFM) and clustering harvest activities. The main trade-off in this study was found to be between volume and net present value. In this context, IFM is used to meet the harvest targets from a smaller land base but at increased costs. Spatially clustering harvest activities, however, greatly increases the output net present value of a plan.

Keywords. cellular automata; clustering; decentralized planning; decision support tools; evolutionary algorithm; evolutionary game; forest management; geographic information systems; intensive forest management; multiple scales; object-oriented design; old growth forest; self-organization; spatial forest planning; strategic planning; sustainable forest management.