Background:Mod 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 airbe laser scanning data. Results:We showed how to mimic four different spatial distributions of cut trees using altative 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.