In forest management planning, the dynamic treatment unit (DTU) approach has become an increasingly relevant alternative to the traditional planning approach using fixed stands, due to improved remote sensing techniques and optimization procedures, with the potential for the higher goal fulfillment of forest activities. For the DTU approach, the traditional concept of fixed stands is disregarded, and forest data are kept in units with a high spatial resolution. Forest operations are planned by clustering cells to form treatment units for harvest operations. This paper presents a new model with an exact optimization technique for forming DTUs in forest planning. In comparison with most previous models, this model aims for increased flexibility by modelling the spatial dimension according to cell proximity rather than immediate adjacency. The model is evaluated using a case study with harvest flow constraints for a forest estate in southern Sweden, represented by 3587 cells. The parameter settings differed between cases, resulting in varying degrees of clustered DTUs, which caused relative net present value losses of up to 4.3%. The case without clustering had the lowest net present value when considering entry costs. The solution times varied between 2.2 s and 42 min 6 s and grew rapidly with increasing problem size.

We present a model for conducting dynamic treatment unit (DTU) forest planning using a heuristic cellular automata (CA) approach. The clustering of DTUs is driven by entry costs associated with treatments, thus we directly model the economic incentive to cluster. The model is based on the work presented in the literature but enhanced by adding a third phase to the CA algorithm where DTUs are mapped in high detail. The model allows separate but nearby forest areas to be included in the same DTU and shares the entry cost if they are within a defined distance. The model is applied to a typical long-term forest planning problem for a 1 182 ha landscape in northern Sweden, represented by 4 218 microsegments with an average size of 0.28 ha. The added phase increased the utility by 1.5–32.2%. The model produced consistent solutions—more than half of all microsegments were managed with the same treatment program in 95% of all solutions when multiple solutions were found.