Using a heuristic programming method for incorporating wildlife habitat constraints into spatial harvest scheduling on the Elliott State Forest Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/w0892d500

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  • The Elliott State Forest, located in the Coast Range of Oregon, is currently revising their Habitat Conservation Plan (HCP). Many of the constraints in the HCP are spatial, requiring identification of specific parcels in order to limit activity along habitat reserves, limit harvest opening size, and to coordinate activities within harvest units. To model the Elliott, the state forest planning team divided the 93,000 acres into 17 management basins, 576 forest strata, 1900 logging settings, and 57,700 parcels. There are 63 prescriptions per forest strata with 20 alternative final harvest ages. Due to the large number of integer variables necessary to represent the decision variables in the harvest scheduling model, a heuristic modeling technique, simulated annealing, was used to solve the 400,000 decision variable problem for a 30-period, 150-year planning horizon. The modeling framework presented here allows for the evaluation of forest management alternatives by spatially quantifying timber harvest, revenue stream, and habitat outputs through time while tracking both stand- and landscape-level attributes. It provides spatially explicit features and measurable trade-offs for a problem that could not otherwise be accurately modeled using traditional forest planning techniques. A feasible timber harvest schedule that meets all habitat constraints and policy mandates was produced, estimating an NPV of $260 million for the Elliott State Forest under the current HCP alternative. Statistical inference involving extreme value theory can be used to obtain an estimate of the global optimum to a heuristic solution. This method was evaluated for use in this planning problem and found to be unreliable as a means for model validation. The limitations of heuristic validation through extreme value theory are highlighted, and suggestions for future research in this area are made.
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