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林业资源管理 ›› 2018, Vol. 0 ›› Issue (6): 146-149.doi: 10.13466/j.cnki.lyzygl.2018.06.023

• 技术应用 • 上一篇    

太行山区栓皮栎天然次生林树高模型构建

陈晨1(), 刘光武1, 申洁梅2, 高福玲2   

  1. 1.河南林业职业学院,河南 洛阳 471002
    2.河南省经济林和林木种苗工作站,郑州 450003
  • 收稿日期:2018-09-30 修回日期:2018-12-04 出版日期:2018-12-28 发布日期:2020-09-27
  • 作者简介:陈晨(1980-),女,河南西平人,讲师.硕士,主要研究方向:森林资源与经营管理。Email: 1005147274@qq.com
  • 基金资助:
    河南省2017年科技攻关计划项目(172102110239)

Establishment of Height Model for Natural Secondary Quercus variabilis Forest in Taihang Mountains

CHEN Chen1(), LIU Guangwu1, SHEN Jiemei2, GAO Fuling2   

  1. 1. Henan Forestry Vocationan College,Luoyang,Henan 471002,China
    2. Henan Workstation of Non-timber Forestry and Seedling,Zhengzhou 450003,China
  • Received:2018-09-30 Revised:2018-12-04 Online:2018-12-28 Published:2020-09-27

摘要:

以太行山区栓皮栎天然次生林为研究对象,以65块标准地及40株解析木为数据源,构建了优势木平均高神经网络模型和传统的函数曲线模型。结果表明:太行山栓皮栎天然次生林适宜的人工神经网络模型结构为1∶2∶1。与传统函数曲线模型相比,人工神经网络模型具有不依赖现存数学函数,拟合精度高等优点,更适合用来建立林分生长模型。

关键词: 栓皮栎, 天然次生林, 树高模型, 神经网络, 太行山区, 生长模型

Abstract:

We investigated sixty-five plots and forty analytic dominant individuals in the natural secondary Quercus variabilis forest in Taihang Mountains to establish the growth model of the dominant tree average height based on ANN and the existing function.The result showed that the optimum model structure of natural secondary Quercus variabilis forest in Taihang Mountains was 1∶2∶1.ANN didn’t depend on existing function,which was better than existing function in the fitting effect and was more suitable for building the growth model.

Key words: Quercus variabilis, natural secondary forest, height model, ANN, Taihang Mountains, growth model

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