文献类型：期刊文献

中文题名：经营的马尾松森林类型发育演替阶段量化指标研究

英文题名：Quantitative indices to identify succession stages of managed Pinus massoniana forest

作者：李婷婷[1] 陆元昌[1] 张显强[2] 王霞[1,3] 庞丽峰[1] 刘宪钊[1,3] 姜俊[1]

机构：[1]中国林业科学研究院资源信息研究所;[2]中国林业科学研究院热带林业实验中心;[3]北京林业大学林学院

年份：2014

卷号：36

期号：3

起止页码：9-17

中文期刊名：北京林业大学学报

外文期刊名：Journal of Beijing Forestry University

收录：CSTPCD;;北大核心:【北大核心2011】；CSCD:【CSCD2013_2014】；

基金：中央级公益性科研院所基本科研业务费专项(CAFYBB2012013);中国林业科学研究院资源信息研究所项目(IFRIT201101)

语种：中文

中文关键词：林分发育演替阶段;逐步判别;Fisher判别;Bayes判别;马尾松林

外文关键词：forest succession stage; stepwise discriminant; Fisher discriminant; Bayes discriminant; Pinus massoniana forest

分类号：S757.1

摘要：经营条件下的森林发育是自然演替和人为干扰共同作用的渐变过程,不同森林发育演替阶段具有不同的林分特征,对森林经营具有导向作用,好的经营处理能够加快森林生长发展进程;反之,则效果甚微或使森林发展发生倒退,因此了解经营林分所处的阶段,对提高森林经营水平起着事半功倍的作用。本文把经营条件下的森林发育演替阶段细分为森林建群、竞争生长、质量选择、近自然林和恒续林5个阶段。利用逐步判别分析(Wilks’Lambda)对初选的13个林分指标进行筛选,结果表明:马尾松年龄、平均胸径、大树蓄积比、林分胸高断面积、胸径变异系数5个指标能够较好地区分森林发育演替阶段。利用这5个指标建立Fisher判别式与Bayes判别式,结果表明:2种判别方法的正判率完全一致,训练样本正判率为94%,验证样本正判率为89%。除此之外,本研究还发现大树蓄积比是划分森林发育演替阶段的最好指标。
Forest growth under management is a gradual process, which is resulted from interaction of nature and man disturbance. Different forest succession stages have specific attributes, playing a guiding role in forest management. Appropriate management can positively affect forest growth, otherwise, the effect is little or even negative. Consequently, identifying forest succession stage has significant influence on improving forest management level. Five forest stages, i. e. forest establishment stage, competition differentiation stage, selection stage, close-to-nature sand naturalness permanent forest stage, were presented based on previous researches. Stepwise discriminant （Wilks＇ Lambda） was used to select important indices among initial 13 stand indices. Age, average DBH of dominate tree species, ratio of tree volume to total volume, stand basal area, and DBH variable coefficient were selected to classify forest stage. Fisher discriminant function and Bayes discriminant function were established referring to the five indices mentioned above, the classification results were 94% of training samples correctly classified, 89% of verification samples correctly classified, and ratio of tree volume to total volume is the best index to identify forest stage.