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应用生态学报 ›› 2020, Vol. 31 ›› Issue (4): 1113-1120.doi: 10.13287/j.1001-9332.202004.007

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人工长白落叶松树干边材、心材和树皮密度预测模型

彭雨欣1, 李凤日1, 刘福2, 董利虎1*   

  1. 1东北林业大学林学院, 森林生态系统可持续经营教育部重点实验室, 哈尔滨 150040;
    2国家林业和草原局大兴安岭调查规划设计院, 黑龙江加格达奇 165000
  • 收稿日期:2019-11-28 出版日期:2020-04-20 发布日期:2020-04-20
  • 通讯作者: *E-mail: donglihu2006@163.com
  • 作者简介:彭雨欣, 女, 1994年生, 硕士研究生。主要从事木材密度研究。E-mail: pengyx2019@163.com
  • 基金资助:
    国家重点研发计划项目(2017YFD0600402)、黑龙江省科学技术项目(GX18B041)和黑龙江头雁创新团队计划项目(森林资源高效培育技术研发团队)资助

Prediction models of sapwood density, heartwood density, and bark density in Larix olgensis plantation

PENG Yu-xin1, LI Feng-ri1, LIU Fu2, DONG Li-hu1*   

  1. 1Ministry of Education Key Laboratory of Sustainable Forest Ecosystem Management, School of Forestry, Northeast Forestry University, Harbin 150040, China;
    2Daxinganling Survey Planning and Design Institute, National Forestry and Grassland Bureau, Jiagedaqi 165000, Heilongjiang, China.
  • Received:2019-11-28 Online:2020-04-20 Published:2020-04-20
  • Contact: *E-mail: donglihu2006@163.com
  • Supported by:
    This work was supported by the National Key R&D Program of China (2017YFD0600402), the Science and Technology Project of Heilongjiang Province (GX18B041), and the Heilongjiang Touyan Innovation Team Program (Technology Development Team for High-efficient Silviculture of Forest Resources).

摘要: 基于黑龙江省林口林业局、东京城林业局和东北林业大学帽儿山实验林场的35株人工长白落叶松的解析样木数据,构建长白落叶松的边材、心材和树皮密度的Beta回归模型,采用赤池信息准则、决定系数、平均绝对偏差、均方根误差和似然比检验对模型的拟合优度进行比较评价,进而选取边材、心材和树皮密度的最优模型,最后采用刀切法对选择出的最优模型进行检验,评价模型预测能力。结果表明: 边材、心材和树皮密度的最优模型的自变量不完全相同,其中,边材密度与树木年龄、树高、相对高度和相对高度的平方关系较好,而心材密度最优模型的自变量为年生长量、相对高度和相对高度的平方,树皮密度最优模型的自变量为树木年龄、年生长量、相对高度和相对高度的平方。对最优模型分析可知,从树干基部到树梢,边材密度逐渐减小,心材密度先减小后增加,树皮密度先增加后减小。本研究所建立的Beta回归模型可以预估该研究区域的人工林内长白落叶松的边材、心材和树皮任意位置的木材密度,为树干平均密度和生物量的研究奠定基础。

Abstract: In this study, the Beta regression models of sapwood, heartwood, and bark density of Larix olgensis were constructed. A total of 35 trees were destructively sampled from plantations in three different sites, Linkou Forestry Bureau of Heilongjiang Province, Dongjingcheng Forestry Bureau, and Maoershan Experimental Forest Farm of Northeast Forestry University. AIC, R2, BIAS, RMSE and LRT were used as the goodness-of-fit statistics to compare and select the most optimal models for sapwood, heartwood, and bark density. The jackknife resampling technique was used to verify and evaluate the developed models. The results showed that the independent variables of the optimal sapwood, heartwood, and bark density model were not identical. Sapwood density had a good relationship with tree age, tree height, relative height, and the square of relative height. The independent variables of the optimal heartwood density model were annual growth, relative height, and the square of relative height. The independent variables of the optimal bark density model were tree age, annual growth, relative height, and the square of relative height. The analysis of the optimal model showed that from the base to the tip of the trunk, sapwood density decreased gradually, heartwood density initially decreased and then increased regularly, bark density initially increased and then decreased gradually. The established Beta regression models could predict sapwood, heartwood, and bark density of L. olgensis at any position in the research area and be an essential basis for the study of trunk average density and biomass.