摘要
为了确定人工林胡桃楸的成熟期,以其年轮间的径向解剖性质为研究对象,采用有序聚类最优分割法、主成分聚类法、BP神经网络和支持向量机(SVM)4种分类方法分别进行界定。研究结果表明:可以采用主成分聚类、BP神经网络与支持向量机分类方法界定人工林胡桃楸的生长期,其分类结果与采用有序聚类最优分割法界定的年限差值为0~3 a;人工林胡桃楸的过渡期与成熟期可以进行有效界定,但对幼龄期与过渡期的划分结果很难确定为一个精准的数值。通过比较分析,支持向量机为界定人工林胡桃楸幼龄期与成熟期的最佳方法。
Juglans mandshurica Maxim.plantation radical anatomical characteristics between rings were analyzed in order to confirm the mature period.Sequential clustering optimal segmentation,principal component clustering,BP neural network and support vector machine(SVM) methods were respectively used for the demarcation.Results indicated that principal component clustering,BP neural network and SVM methods could determine the growth period of Juglans mandshurica Maxim.plantation,which had 0 to 3 years distinction to the sequential clustering optimal segmentation method.Mature period and transitional period of Juglans mandshurica Maxim.plantation could be determined efficiently,while juvenile period and transitional which could not demarcated on a specific year.Through the comparative analysis of these above methods,SVM was a preferred method for juvenile and mature period demarcation of Juglans mandshurica Maxim.plantation.
出处
《南京林业大学学报(自然科学版)》
CAS
CSCD
北大核心
2013年第3期103-109,共7页
Journal of Nanjing Forestry University:Natural Sciences Edition
基金
黑龙江省重点攻关课题(GA09B202-07)
关键词
胡桃楸
成熟期
支持向量机
BP神经网络
主成分聚类
有序聚类
Juglans mandshurica Maxim.
mature period
support vector machine(SVM)
BP neural network
principal component clustering
sequential cluster