Clustering of harvest activities in multi-objective long-term forest planning

Models used in long-term forest planning were generally, until recently, non-spatial. The locations of harvest activities were considered first in lower, more short-term steps of the planning hierarchy. However, now that issues related to biodiversity, recreation and road planning have to be considered, this is no longer a viable option. The spatial arrangement of harvest activities affects parameters such as the proportion of undisturbed interior forest and the sites of new roads. Thus, in long-term planning the spatial location of harvesting operations needs to be taken into consideration. However, including spatiality in long-term planning complicates the planning problems, and requires the development of new methods and approaches. This study presents a new approach for clustering harvest activities in time and space in long-term forest planning. The planning problem essentially consists of maximizing the weighted sum of the net present value of future forest management and the clustered volume of timber to be harvested. This objective is subject to the restriction that a certain volume should be harvested each period. Since the spatial dimension leads to a problem that is difficult to solve with ordinary optimization techniques, the ensuing problem is solved with a heuristic technique called simulated annealing. In a case study the suggested approach is applied to a landscape consisting of 2600 stands in southern Sweden. The results indicate that the model is effective for clustering the harvest and that it is possible to aggregate the harvest with only a small sacrifice of the net present value.

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