摘要
使用Voronoi法确定对象木与相邻木存在边缘校正不准确和阈值大小不确定等问题,不利于林分空间结构量化描述。针对这些问题,本研究设定所有距离样地边界线小于0.5 m且不完整的Voronoi单元中的林木为非对象木,并引进对象木与相邻木的距离调控阈值Rmax,修正了Voronoi空间单元在边缘效应处理上存在的对象木和相邻木选取不合理问题。基于修正的Voronoi空间单元,构建了多目标生态采伐方案,并通过蚁群算法进行智能优化。结果表明,调控前20个样地中4个林分空间结构状况相对较差的样地,通过模拟采伐调控后,适应度函数分别提升0.72,0.92,0.93,0.86,证明了该方案对于优化林分空间结构具有较好的效果,可为森林经营决策提供支持工具。
Using the Voronoi method to determine the target tree and neighbourhood tree has the problems of inaccurate edge correction and uncertainty of threshold size,which are not conducive to the quantitative description of the spatial structure of forest stand.To address these problems,this study set all the forest trees in Voronoi units that are less than 0.5 m from the sample plot boundary line and incomplete as non-objective trees,and introduced a regulation threshold Rmax for the distance between the target tree and neighbourhood tree to correct the problem of unreasonable selection of target tree and neighbourhood tree in the processing of edge effects in Voronoi spatial units.Based on the modified Voronoi spatial unit,a multi-objective ecological harvesting scheme was constructed and intelligently optimized by an ant colony algorithm.The results showed that the fitness functions of the four sample plots,M4,M8,M14 and M17,which had relatively poor stand spatial structure conditions among the 20 sample plots before regulation,were improved by 0.72,0.92,0.93 and 0.86,respectively,after regulation by simulated harvesting,which proved that the scheme has a better effect on optimizing the spatial structure of forest stand,and can provide a supportive tool for forest management decisions.
作者
刘鑫
黄浪
卿东升
李建军
LIU Xin;HUANG Lang;QING Dongsheng;LI Jianjun(Hunan Applied Technology University,Changde,Hunan 415100,China;Central South University of Forestry and Technology,Changsha 410004,China)
出处
《林业资源管理》
北大核心
2023年第4期27-35,共9页
Forest Resources Management
基金
湖南省自科基金面上项目“洞庭湖森林结构与生态系统服务尺度关联及优化模型”(2022JJ31000)
教育部产学合作协同育人项目“基于Hadoop的数据科学与大数据技术专业实训平台建设与应用”(220905073260522)
基于数据安全的大数据实践基地建设(220601020235643)。
