Background:Modern remote sensing methods enable the prediction of tree-level forest resource data.However,the benefits of using tree-level data in forest or harvest planning is not clear given a relative paucity of research.In particular,there is a need for tree-level methods that simultaneously account for the spatial distribution of trees and other objectives.In this study,we developed a spatial tree selection method that considers tree-level(relative value increment),neighborhood related(proximity of cut trees)and global objectives(total harvest).Methods:We partitioned the whole surface area of the stand to trees,with the assumption that a large tree occupies a larger area than a small tree.This was implemented using a power diagram.We also utilized spatially explicit tree-level growth models that accounted for competition by neighboring trees.Optimization was conducted with a variant of cellular automata.The proposed method was tested in stone pine(Pinus pinea L.)stands in Spain where we implemented basic individual tree detection with airborne laser scanning data.Results:We showed how to mimic four different spatial distributions of cut trees using alternative weightings of objective variables.The Non-spatial selection did not aim at a particular spatial layout,the Single-tree selection dispersed the trees to be cut,and the Tree group and Clearcut selections clustered harvested trees at different magnitudes.Conclusions:The proposed method can be used to control the spatial layout of trees while extracting trees that are the most economically mature.
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