A learning heuristic for integrating spatial and temporal detail in forest planning

Abstract

We present a learning heuristic using dynamic programming (DP) formulations to address both spatial and temporal detail in multiobjective forest management planning. The problem is decomposed into smaller problems to avoid the curse of dimensionality associated with DP. The heuristic learns from multiple decomposed problem formulations to identify stands assigned the same management option regardless of formulation. Consistently managed stands are recognized, and the problem is eventually distilled to the most difficult to solve portions of the forest. The heuristic is demonstrated on a forest management problem with short‐lived core area wildlife habitat, where temporal detail is important. The problem is large due to the number of stand‐level management timing options and associated interactions with the management timings of nearby stands. Results show solution improvement along with substantial time savings over previously used heuristics. The learning heuristic enables analysis of large problems that emerge with high levels of spatial and temporal detail. Recommendations for Resource Managers: Adding spatial and temporal detail to forest planning models can improve the utility of the solution for some applications such as wildlife habitat management. Decomposing a large optimization problem into linked, smaller subproblems can speed the solution search time. Learning from problem decomposition solutions to formulate subsequent search attempts can improve the quality of a solution.

An Updated Review of Spatial Forest Planning: Approaches, Techniques, Challenges, and Future Directions

“…In addition to well-known metaheuristics like SA, TS and GA, other heuristics are occasionally applied in spatial forest modeling. For instance, Wei and Hoganson [67] and later Henderson and Hoganson [68] developed a learning-based dynamic programming solution heuristic to address spatial forest management problems efficiently. Their approach decomposes the problem into smaller sub-problems, using windowing techniques to avoid dimensionality issues and accelerate solution search times.…”