An object-oriented cellular automata model for forest planning problems

Various difficulties are encountered when integrating spatial and temporal goals of forest planning with dynamics of natural processes. Some of these difficulties stem from the difference between the data and models involved in the various processes to be integrated. The focus of this study is the development of a decentralized spatial decision support tool for forest management planning based on cellular automata (CA) modeling. An innovation of this model is that beyond spatially allocating/simulating management activities, the CA rules and state space are modified to allow cells to co-evolve until a plan for all periods of the planning horizon has been achieved. The object-oriented implementation of the CA model in a geographic information system (GIS) is presented as a decentralized bottom-up forest planning approach. Moreover, a sensitivity analysis of the model outcomes to spatial resolution and objective weights is performed. This planning tool effectively addresses local spatial constraints (limitation on the type of management depending on location), global spatial objectives (spatial clustering of old growth conservation areas), global aspatial objectives (timber harvest) and global constraints (stable flow and minimum old growth conservation). The capability of this approach to consider spatial relationships in the strategic planning process is illustrated by a spatially consistent allocation of clustered old growth areas. The object orientation of the implementation permits a fast computation of both local and global limitations on local decision making. This framework allows speedy modification of either local or global requirements and is highly portable to other complex planning problems.