use of cellular automaton approach in forest planning

• Published in: Canadian journal of forest research Vol. 37; no. 11; pp. 2188 - 2200
• Main Authors: Heinonen, T, Pukkala, T
• Format: Journal Article
• Language: English
• Published: Ottawa, ON National Research Council of Canada 01.11.2007; NRC Research Press; Canadian Science Publishing NRC Research Press
• Subjects: algorithms; Biological and medical sciences; Cellular automata; equations; Forest management; forest stands; Forestry; Forests; Fundamental and applied biological sciences. Psychology; heuristics; Nuclear polyhedrosis virus; Objective function; optimization; Optimization techniques; planning; problem solving; raster data; Robots; spatial data; stand management; Technology application; California; Finland; Forestry
• ISSN: 0045-5067; 1208-6037
• DOI: 10.1139/X07-073

This study presents an optimization method based on cellular automaton (CA) for solving spatial forest planning problems. The CA maximizes stand-level and neighbourhood objectives locally, i.e., separately for different stands or raster cells. Global objectives are dealt with by adding a global part to the objective function and gradually increasing its weight until the global targets are met to a required degree. The method was tested in an area that consisted of 2500 (50 x 50) hexagons 1 ha in size. The CA was used with both parallel and sequential state-updating rules. The method was compared with linear programming (LP) in four nonspatial forest planning problems where net present value (NPV) was maximized subject to harvest constraints. The CA solutions were within 99.6% of the LP solutions in three problems and 97.9% in the fourth problem. The CA was compared with simulated annealing (SA) in three spatial problems where a multiobjective utility function was maximized subject to periodical harvest and ending volume constraints. The nonspatial goal was the NPV and the spatial goals were old forest and cutting area aggregation as well as dispersion of regeneration cuttings. The CA produced higher objective function values than SA in all problems. Especially, the spatial objective variables were better in the CA solutions, whereas differences in NPV were small. There were no major differences in the performance of the parallel and sequential cell state-updating modes of the CA.