首页|Optimizing neighborhood-based stand spatial structure: Four cases of boreal forests
Optimizing neighborhood-based stand spatial structure: Four cases of boreal forests
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NSTL
Elsevier
Over the past fifty years, societies have placed increasing demands on forests, and their use has shifted gradually from wood production to the diversified benefits and functions of ecosystem services. The effects of neighborhood-based structural characteristics on regulating growth and promoting sustainability have therefore drawn much attention. However, direction for managing natural mixed forests using neighborhood-based indexes are still not clear. Thus, a tree-level harvest planning tool that considers four neighborhood-based structural indexes (species mingling, diametric differentiation, horizontal spatial pattern and crowdedness of trees) while concurrently recognizing other operational constraints, was developed using simulated annealing algorithm. The approach was applied to four 1-ha mapped stands in northeast China, namely a natural larch forest (NLF), a natural birch forest (NBF), a natural secondary forest (SEF), and a Korean pine broad-leaved forest (KBF). The results indicated that the tree-level harvest optimization tool improved the objective function values by approximately 78.33% of NLF, and 134.96% of NBF, and 156.70% of SEF and 252.95%, respectively. The optimal harvest intensities for partial cutting activities varied from 22.16% (SEF) to 26.07% (NBF) of the standing volume. In evaluating the four neighborhood-based structural indexes, both species mingling and crowdedness have the highest priority to be adjusted in structure-based forest management. Our results demonstrated that that the commonly used neighborhood-based structural indexes could be employed to control the spatial layout of potential harvest trees, in turn may be conducive to regulate the growth and stability of forests.
Stand structureBoreal forestsSpecies minglingCombinatorial optimizationNeighborhood-based indicatorMANAGEMENTOPTIMIZATIONINDEXESTREES
NSTL
Elsevier
