Optimization of land use in afforestation areas using evolutionary self-organization

This article presents a cellular automaton approach to the optimization of land use planning in afforestation areas. The case study deals with 110 ha of recently afforested land in Denmark. A modified two-dimensional automaton containing 2,345 active cells is used for optimizing the land use setup in this area. Four land use alternatives are considered: pasture, beech (Fagus sylvatica L.), Norway spruce (Picea abies [L.] Karst.), and oak (Quercus robur L.). The objective is to maximize a weighted sum of soil expectation value, scale-dependent costs, recreational value, and the value of structural variation. It is demonstrated that for this sort of problem, appropriately modified cellular automata may yield acceptable solutions within a comparatively low number of iterations. Furthermore, for a simplified hypothetical problem, it is shown that the cellular automaton approach was able to identify optimal solutions within reasonable time.