摘要
【目的】探索在森林抚育间伐任务目标控制下,基于空间分析和遗传算法的森林抚育间伐小班智能选择方法,为以小班为单位的作业方案编制等后续森林经营活动提供决策支持。【方法】以内蒙古赤峰市桦木沟林场为例,根据抚育间伐任务和经营者指定的基本属性条件,基于空间查询或点缓冲区分析方式选择出空间分布连续的小班作为初始小班集合,设计环带控制算法(ACA)动态计算点缓冲区的初始半径和步长。以迫切度、难易度和立地因子构建目标条件函数,用目标条件值衡量小班与任务目标的符合程度,并以目标条件值最大为目标条件、任务面积为限制条件建立抚育间伐小班智能选择的数学模型,使用改进的遗传算法(IGSEGA)进行求解,从而对初始小班集合内的小班进行优选,得到最符合任务目标的小班集合。【结果】研究区内指定任务面积300 hm^2、上限值5%和其他条件,基因交叉率0.6,变异率0.3,变长系数3,迭代次数100,目标函数各参数根据任务要求进行设置。ACA算法计算得到初始半径1 407 m,且只需1次半径扩增即可构造出初始小班集合,普通点缓冲区分析由于初始半径和步长的不确定性,分析效率明显低于ACA算法。以林场场部为中心点选择时得到小班40个,所选小班皆为符合基本条件且最接近目标条件值的小班,另外2个测试点得到的结果同样说明算法的智能性和有效性。由于IGSEGA算法构建的初始个体适应值已较为接近最优解,经14~15次迭代即可求解出任务目标小班集合,求解效率优于标准遗传算法(SGA)。【结论】提出森林抚育间伐小班智能选择概念,以迫切度、难易度和立地因子构建目标条件函数,同时构建小班选择的数学模型并采用IGSEGA算法进行求解。设计ACA算法优化点缓冲区分析,可提高空间分析效率,改进贪婪策略对遗传算法的编码方式及对应的遗传算子
[ Objective] This study investigated the intelligent selection method of subcompartments based on spatial analysis and genetic algorithm （SGA） in order to provide decision support for formulating forest management plan, conducting under the thinning target control. [ Method] Huamugou forest farm, in Chifeng City, Inner Mongolia, was selected as research area to simulate intelligent selection. According to the basic condition of thinning target and operator, the initial small class collection was chosen from continuous distribution of tiny space by spatial query or point buffer analysis. Initial radius and step of point buffer analysis were calculated dynamically by annulus control algorithm（ACA）. Urgency indicator, difficulty indicator and site indicator constituted the objective condition formula（ OCF）, whose value measured the coincidence level of task object. The mathematical model was built by maximum value of OCF and task area. The solution could be obtained by improved genetic algorithm（ IGSEGA）, which selected the best subcompartments from the initial small class collection, and obtained the most optimal small class collection. [ Result ] The parameters of OCF were set with task requirement. In research area, the task area was 300 hm^2 , upper limit as 5% and other conditions. The parameters of GA were as following： gene crossover probability as 0.6, gene variation rate as 0.3, gene variable-length coefficient as 3, iterations as 100. The initial radius as 1 407 m was acquired by ACA, and the radius of expansion was only one time to construct the initial small class collection ACA because of the uncertainty of initial radius and steps Analytical efficiency of general point buffer was lower than The initial subcompartment collection could be generated through 14 to 15 iterations because the initial adaptive value was close to the optimal solution by IGSEGA, and the efficiency of solving was higher than the ordinary SGA. The center point of forestry station, 40 subcompartments were obtaine
出处
《林业科学》
EI
CAS
CSCD
北大核心
2017年第9期63-72,共10页
Scientia Silvae Sinicae
基金
"十三五"国家重点研发计划课题(2017YFD0600906)
"十二五"国家高技术研究发展计划(863计划)课题(2012AA102003)
关键词
抚育间伐
小班选择
贪婪策略
遗传算法
小班智能选择算法
forest thinning
subcompartments selection
greedy strategy
genetic algorithm
subcompartmentsintelligent selection algorithm
