Forest planning using co-evolutionary cellular automata

The spatial distribution of forest management activities has become increasingly important with, most notably, rising concerns for biodiversity. Addressing both timber production and non-timber goals requires planning tools that support spatially explicit decision-making. The paper examines the capability of a co-evolutionary cellular automata (CA) approach to address forest planning objectives that are both spatial and temporal with global constraints. In this decentralized self-organizing planning framework, each forest stand and its associated management treatment over the planning horizon is represented as a cellular automaton. The landscape management goals are achieved through a co-evolutionary decision process between interdependent stands. A novel, computationally efficient CA algorithm for asynchronous updating of stand states is developed. The specific problem considered in the paper is maximization of cumulative harvest volume and amount of clustered late-seral forest. The global constraints considered are stable harvest flow and minimum amount of late-seral stands in each period of the planning horizon. Applied to a test area from the Northeastern forest region of Ontario, Canada, the model demonstrates short computation time and consistent results from multiple runs. It also compares favorably with outputs from a simulated annealing search. The CA-based algorithm developed in the paper successfully identifies sustainable forest outputs over the planning horizon. It shows sensitivity to both local constraints, strategic goals and strategic constraints and generates spatially explicit forest plans.