欢迎访问《应用生态学报》官方网站,今天是 分享到:

应用生态学报

• 研究报告 • 上一篇    下一篇

大兴安岭天然落叶松林单木健康评价

朱宇1,刘兆刚1**,金光泽2   

  1. (1东北林业大学林学院, 哈尔滨 150040; 2东北林业大学生态研究中心, 哈尔滨 150040)
  • 出版日期:2013-05-18 发布日期:2013-05-18

Health assessment of individual trees in natural Larix gmelinii forest in Great Xing’an Mountains of China.

ZHU Yu1, LIU Zhao-gang1, JIN Guang-ze2   

  1. (1School of Forestry, Northeast Forestry University, Harbin 150040, China; 2Center for Ecological Research, Northeast Forestry University, Harbin 150040, China)
  • Online:2013-05-18 Published:2013-05-18

摘要:

单木健康评价结果融入传统小班(或林分)尺度健康评价中,可提高小班(或林分)尺度评价准确性,实现单木尺度与小班(或林分)尺度间的耦合过程,可为实现森林的健康经营提供理论依据.本文以大兴安岭林区天然落叶松林为对象,建立了包括根部状态、树冠落叶程度、树冠透视度、树冠重叠程度、树冠枯梢比重、活冠层比重、树冠偏斜程度和垂直竞争指数的单木健康评价指标体系,根据主成分分析法进行去相关性处理,并使用熵值法赋予指标权重,利用模糊综合评价法对天然落叶松林进行单木健康评价.在评价结果的基础上,采用判别分析法建立单木健康预测模型.结果表明: 研究区内处于亚健康等级的林木最多,占总株数的36.7%;健康林木最少,仅占总株数的12.9%;不健康林木比例超过健康林木,达到21.1%;单木健康预测模型预测正确率为86.3%.应采取合理有效的经营措施提高林木的健康等级.
 

Abstract: To integrate the health assessment results of individual trees into the health assessment of subcompartment (or stand) scale could improve the accuracy of subcompartment (or stand) scale health assessment, and realize the coupling process between the individual tree scale and the subcompartment (or stand) scale, providing a theoretical basis for the realization of forest health management. Taking the natural Larix gmelinii forest in Great Xing’an Mountains as the object, a health assessment indicators system of individual trees was established, which included root state, canopy defoliation degree, crown transparency, crown overlap, crown dieback ratio, live crown ratio, crown skewness, and vertical competition index. The principal component analysis (PCA) was employed to eliminate the correlations, the entropy value method was adopted to confirm the weight of each indicator, and the health status of individual L. gmelinii was assessed by fuzzy synthetic evaluation (FSE) method. Based on the health assessment results, a prediction model of the individual tree health was established by discriminant analysis (DA) method. The results showed that the trees in subhealthy gradation were up to 36.7%, and those in healthy gradation only reached 12.9%. The proportion of the trees in unhealthy gradation exceeded that of the trees in healthy gradation, occupying 21.1% of the total. The prediction accuracy of the established model was 86.3%. More rational and effective management measures should be taken to improve the tree health grade.