Open access publications in the SLU publication database

Abstract

This thesis deals with approaches that can be used to consider spatial relationships in long term forest planning. In the thesis the optimization approach is used, i.e. variables in some way describing the spatial relationships are processed by the solution algorithm, and the spatial layout of harvest activities or habitats is generated by the optimization. In the first part of the thesis, the core area was tested as a criterion for forming contiguous areas of old forest while maximizing the net present value of future forest management. The core area concept was applied in different case studies both to a simulated forest landscape and an actual one in northern Sweden. Because of the non-linear characteristics in the model formulations the problems were solved with a heuristic method, in this case simulated annealing. To increase the scope of the models to include the forest-wide constraints normally encountered in forest planning, a new approach that integrates linear programming with simulated annealing was also tested. The second part of the thesis deals with the aggregation of the harvests in time and space. A new criterion, based on the clustered volume of timber to be harvested, was developed to obtain aggregation of harvested areas. The criterion was in a case study applied to a landscape where high levels of consideration are also paid to biodiversity and recreation. Finally, a new model was developed that could be solved with exact solution techniques for clustering the harvest and the areas set aside as reserves. In contrast to the other models this was applied to a landscape where the decision units were pixels (20*20meters) instead of stands. The results show that it is possible to include considerations of spatial relationships in long-term forest planning, also when the problems are of a size found in real-world situations. For problems where the forest-wide constraints are few and only relate to the spatial aspects it seems that heuristics alone is adequate. When more forest-wide constraints are added to the problem, a suitable approach could be to combine two solution techniques into one integrated solution procedure. The experiments with exact solution techniques suggest that, at least when pixels are used as the primary decision unit, also relatively large problems can be solved exactly if proper formulations can be found. Finally, the results indicate that using effective methods for solving spatial problems will reduce the cost connected to taking spatial consideration