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    刘子洋, 强波, 张浩, 符利勇, 郭晋平. 气候和立地等级对落叶松林分生物量估计的影响研究[J]. 北京林业大学学报. DOI: 10.12171/j.1000-1522.20240071
    引用本文: 刘子洋, 强波, 张浩, 符利勇, 郭晋平. 气候和立地等级对落叶松林分生物量估计的影响研究[J]. 北京林业大学学报. DOI: 10.12171/j.1000-1522.20240071
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    Liu Ziyang, Qiang Bo, Zhang Hao, Fu Liyong, Guo Jinping. Climate and site class impact on biomass estimation in Larix gmelinii forest stands[J]. Journal of Beijing Forestry University. DOI: 10.12171/j.1000-1522.20240071
    Citation: Liu Ziyang, Qiang Bo, Zhang Hao, Fu Liyong, Guo Jinping. Climate and site class impact on biomass estimation in Larix gmelinii forest stands[J]. Journal of Beijing Forestry University. DOI: 10.12171/j.1000-1522.20240071
    shu

    气候和立地等级对落叶松林分生物量估计的影响研究

    Climate and site class impact on biomass estimation in Larix gmelinii forest stands

      摘要
    • 摘要:
      目的 环境和气候对林分生物量的影响不容忽视。在林分生长模型的构建中，常常假定气候不变，或仅考虑单一环境因子的影响，这不利于分析环境整体对林分生物量生长的影响。构建含立地等级与气候因子的落叶松生物量模型，分析同一林分中环境与气候共同对生物量估计的影响，为森林经营和决策提供理论依据。
      方法 基于吉林省2004、2009及2014年落叶松人工林固定样地数据，采用通过World Clim获取的1950—2000年的平均气候因子，选用Richards模型作为基础模型。选用与生物量显著相关的地形因子合并的立地单元划分立地等级，并将立地等级作为哑变量，建立含立地等级和气候的落叶松林分生物量模型，并分析气候和立地等级对林分生物量的影响。
      结果 (1)建立含立地等级和气候的落叶松生物量模型，新的模型拟合精度由0.937提升为0.961。(2)林分因子对林分生物量的独立解释率为93.7%，立地等级的独立解释度为2.4%，而气候因子仅为0.3%。(3)温度和降水共同影响林分生物量，最干旱季温度升高将降低林分生物量最大值，而最冷季降水增加可促进林分生物量生长速度。
      结论 立地等级对落叶松林分生物量估算的影响大于气候，建立的含立地等级和气候的落叶松生长收获预估模型揭示了气候因子和立地等级对落叶松生物量生长的影响，这为林分适宜性经营和森林精准增汇提供科学指导。

       

      Abstract:
      Objective The impact of environment and climate on forest stand biomass cannot be overlooked. However, previous models of forest stand growth generally assumed a constant climate or only considered the influence of a single environmental factor on biomass estimation, which is not conducive to analyzing the overall impact of the environment on forest stand biomass growth. Constructing a larch biomass model that includes site class and climate factors, and analyzing the combined effects of environment and climate on the biomass model within the same forest stand, provides a theoretical basis for forest management and decision-making.
      Method Based on the data from fixed sample plots in Jilin Province’s larch plantations from 2004, 2009, and 2014, and average climate factors from 1950−2000 obtained through World Clim, the Richards model was selected as the base model. Topographic factors significantly correlated with biomass were classified and integrated into the site class used as a dummy variable to establish a larch stand biomass model that includes both site quality and climate factors. The impacts of climate and site quality on stand biomass were also analyzed.
      Result A new larch biomass model incorporating site class and climate improved the model fitting accuracy from 0.937 to 0.961. (2) Stand factors independently explained 93.7% of the variance in stand biomass, while site class accounted for 2.4%, and climate factors only 0.3%. (3) Temperature and precipitation jointly affect stand biomass. Higher temperatures in the driest season can reduce the maximum biomass of the stand, while increased precipitation in the coldest season can accelerate the growth rate of stand biomass.
      Conclusion Site quality has a greater impact on the estimation of larch stand biomass than climate does. The established larch growth and yield prediction model, which incorporates both site class and climate factors, reveals the effects of these factors on larch biomass growth. This can provide scientific guidance for suitable forest stand management and precise carbon sequestration in forestry.

       

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