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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. [Copyright &y& Elsevier]

Abstract: 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. [Copyright &y& Elsevier]

Abstract: In this paper, we use compromise programming to solve a multiple-objective land use and forest management planning model. Long- and short- (‘fast’) term carbon uptake, maintenance of structural diversity, and economic (net returns to forestry and agriculture) objectives are simultaneously achieved by minimizing the distance between current objective values and the ideal ones. Two distance metrics are used, representing a risk neutral and highly risk-averse decision maker. An application of the model to public forestland and adjacent private agricultural lands in the (boreal) Peace River region of northeastern British Columbia indicates that both short- and long-term carbon uptake, and maintenance of structural diversity, can be achieved only at the high financial costs. Contrary to earlier studies, we also find conflict between both short- and long-term carbon uptakes and maintenance of landscape structural diversity. Targeting short-term carbon uptake results in the greatest deviation from desired structural diversity, although the deviation is somewhat smaller with respect to the long-term carbon uptake goal. Further, risk neutral and risk-averse decision makers will employ significantly different land use and forest management strategies. Finally, the ‘balanced’ strategy (which underachieves attainment of the ‘ideal’ by the same degree for all objectives) attains diversity targets quite closely, but significantly underachieves economic and carbon objectives. Maximization of the weighted sum of objective deviations results in an ‘average’ strategy that performs much better in attaining carbon objectives, but diversity is sacrificed. [Copyright &y& Elsevier]