Forest inventory is increasingly producing infor-mation on the locations and sizes of individual trees.This information can be acquired by airborne or terrestrial laser scanning or analyzing photogrammetric data.Howev...Forest inventory is increasingly producing infor-mation on the locations and sizes of individual trees.This information can be acquired by airborne or terrestrial laser scanning or analyzing photogrammetric data.However,all trees are seldom detected,especially in young,dense,or multi-layered stands.On the other hand,the complete size distributions of trees can be predicted with various methods,for instance,kNN data imputation in an area-based LiDAR inventory,predicting the parameters of a distribution func-tion from remote sensing data,field sampling,or using his-togram matching and calibration methods.The predicted distribution can be used to estimate the number and sizes of the non-detected trees.The study’s objective was to develop a method for forest planning that efficiently uses the avail-able tree-level data in management optimization.The study developed a two-stage hierarchical method for tree-level management optimization for cases where only part of the trees is detected or measured individually.Cutting years and harvest rate curves for the non-detected trees are optimized at the higher level,and the cutting events of the detected trees are optimized at the lower level.The study used differ-ential evolution at the higher level and simulated annealing at the lower level.The method was tested and demonstrated in even-aged Larix olgensis plantations in the Heilongjiang province of China.The optimizations showed that optimiz-ing the harvest decisions at the tree level improves the profit-ability of management compared to optimizations in which only the dependence of thinning intensity on tree diameter is optimized.The approach demonstrated in this study pro-vides feasible options for tree-level forest planning based on LiDAR inventories.The method is immediately applicable to forestry practice,especially in plantations.展开更多
基金supported by the Natural Science Foundation of China (U21A20244 and 32071758)funding provided by University of Eastern Finland (including Kuopio University Hospital)
文摘Forest inventory is increasingly producing infor-mation on the locations and sizes of individual trees.This information can be acquired by airborne or terrestrial laser scanning or analyzing photogrammetric data.However,all trees are seldom detected,especially in young,dense,or multi-layered stands.On the other hand,the complete size distributions of trees can be predicted with various methods,for instance,kNN data imputation in an area-based LiDAR inventory,predicting the parameters of a distribution func-tion from remote sensing data,field sampling,or using his-togram matching and calibration methods.The predicted distribution can be used to estimate the number and sizes of the non-detected trees.The study’s objective was to develop a method for forest planning that efficiently uses the avail-able tree-level data in management optimization.The study developed a two-stage hierarchical method for tree-level management optimization for cases where only part of the trees is detected or measured individually.Cutting years and harvest rate curves for the non-detected trees are optimized at the higher level,and the cutting events of the detected trees are optimized at the lower level.The study used differ-ential evolution at the higher level and simulated annealing at the lower level.The method was tested and demonstrated in even-aged Larix olgensis plantations in the Heilongjiang province of China.The optimizations showed that optimiz-ing the harvest decisions at the tree level improves the profit-ability of management compared to optimizations in which only the dependence of thinning intensity on tree diameter is optimized.The approach demonstrated in this study pro-vides feasible options for tree-level forest planning based on LiDAR inventories.The method is immediately applicable to forestry practice,especially in plantations.
