欢迎访问林业资源管理

林业资源管理 ›› 2020, Vol. 0 ›› Issue (5): 82-88.doi: 10.13466/j.cnki.lyzygl.2020.05.013

• 科学研究 • 上一篇    下一篇

湖南杉木人工林单木干形特征及影响因子研究

许冰冰1(), 边更战2, 易烜3, 朱光玉1, 齐战涛1, 吕勇1()   

  1. 1.中南林业科技大学,长沙 410004
    2.中部林业产权交易服务中心,长沙 410007
    3.湖南省青羊湖国有林场,长沙 410600
  • 收稿日期:2020-07-02 修回日期:2020-10-14 出版日期:2020-10-28 发布日期:2020-11-30
  • 通讯作者: 吕勇
  • 作者简介:许冰冰(1994-),男,福建龙岩人,在读硕士,研究方向:林学。Email: 747008864@qq.com
  • 基金资助:
    国家林业和草原局青年拔尖人才项目“杉木人工林立地生产力精准评价研究”(2019132605);国家自然科学基金“亚热带栎类天然混交林立地质量评价与生长预估”(31570631)

Study on the Characteristics and Influencing Factors of the Single Tree Stem Form of Cunninghamia lanceolata Plantation in Hunan Province

XU Bingbing1(), BIAN Gengzhan2, YI Xuan3, ZHU Guangyu1, QI Zhantao1, LV Yong1()   

  1. 1. Central South University of Forestry and Technology,Changsha 410004,China
    2. Central Trading Serive Center of Forestry Property Right,Changsha 410007,China
    3. Qingyanghu state owned Forest Farm,Hunan Changsha 410600,China
  • Received:2020-07-02 Revised:2020-10-14 Online:2020-10-28 Published:2020-11-30
  • Contact: LV Yong

摘要:

以湖南杉木人工林单木为研究对象,采用变异系数法、单因素方差分析法、Pearson相关分析、多元线性回归分析法研究湖南杉木人工林单木形数的变化特征及其影响因子,研究结果表明:1)通过变异系数法得到杉木人工林单木实验形数的变异系数(0.112 7)小于胸高形数的变异系数(0.204 0),表明实验形数具有较好的稳定性。2)用单因素方差分析法研究不同树高级和胸径级的形数变化规律,结果表明杉木人工林单木,胸高形数在不同胸径级具有显著性差异(sig<0.05),二者呈负相关;实验形数在不同树高级具有显著性差异(sig<0.05),二者呈正相关。3)Pearson相关分析结果显示,胸高形数和实验形数的影响因子存在一定的差异。胸高形数与坡度呈极显著正相关(p<0.01),与坡位呈显著正相关(p<0.05),与海拔、林分年龄和胸径(D)呈极显著负相关(p<0.01)。实验形数与海拔呈极显著负相关(p<0.01),与林分密度和林分年龄呈显著负相关(p<0.05),与坡度和树高(H)呈极显著正相关(p<0.01),并与坡位、干湿度呈显著正相关(p<0.05)。 4)多元线性回归结果表明,两种形数模型的拟合效果均显著(sig<0.05),胸高形数模型拟合调整后的R2为0.417,实验形数模型拟合调整后的R2为0.495,即回归方程可解释各自变异的41.7%和49.5%。

关键词: 杉木人工林, 胸高形数, 实验形数, 立地因子, 相关性分析

Abstract:

With the research object of single tree of Cunninghamia lanceolata plantation in Hunan Province,the variation characteristics and influencing factors of that were studied by using the coefficient of variation method,one-way analysis of variance,pearson correlation analysis and multiple linear regression analysis.Research indicates:1) Through the variation coefficient method,the coefficient of variation of the experimental form factor of the single tree in Cunninghamia lanceolata plantation (0.112 7) was smaller than the coefficient of variation of the breast-height form factor (0.204 0),indicating that the experimental form factor was of good stability.2) one-way analysis of variance was used to study the form factor growth rhythm of different tree height levels and diameter at breast height (DBH) levels.The results showed that the breast-height form factor of single tree in Cunninghamia lanceolata plantation had significant differences at different DBH levels (sig<0.05),and they were negatively correlated;The experimental form factor were significantly different at different tree height levels (sig<0.05),and they were positively correlated.3) Pearson correlation analysis showed that there were some differences in the influencing factors between the breast-height form factor and the experimental form factor.The breast-height form factor was of extremely significant positive correlation with slope (p<0.01),was of significant positive correlation with slope position (p<0.05),and was of extremely significant negative correlation with altitude,stand age and DBH (p<0.01).The experimental form factor was of extremely significant negative correlation with altitude (p<0.01),and was of significant negative correlation with stand density and stand age (p<0.05),but was of extremely significant positive correlation with slope and tree height (p<0.01),and was of significant positive correlation with slope position and dry humidity (p<0.05).4) The results of multiple linear regression showed that the fitting effects of the two form factor models were significant (sig<0.05),the adjusted R 2 of the breast-height form factor model was 0.417,and the experimental form factor model adjusted R 2 was 0.495,indicating the regression equation can explaine 41.7% and 49.5% of the respective variation.

Key words: Cunninghamia lanceolata plantation, breast-height form factor, experimental form factor, site factors, correlation analysis

中图分类号: