胡靖扬, 贾宝军, 林宽, 冯倩男, 刘常富, 于立忠. 辽东山区长白落叶松枝叶生物量模型建立与评估[J]. 西南林业大学学报, 2016, 36(5): 52-57. DOI: 10.11929/j.issn.2095-1914.2016.05.009
引用本文: 胡靖扬, 贾宝军, 林宽, 冯倩男, 刘常富, 于立忠. 辽东山区长白落叶松枝叶生物量模型建立与评估[J]. 西南林业大学学报, 2016, 36(5): 52-57. DOI: 10.11929/j.issn.2095-1914.2016.05.009
Hu Jingyang1, Jia Baojun1, Lin Kuan1, Feng Qiannan1, Liu Changfu1, 2. Establishment and Evaluation of Biomass Models of Branches and Leaves for Larix olgensis Plantation in Montane Region of Eastern Liaoning Province[J]. Journal of Southwest Forestry University, 2016, 36(5): 52-57. DOI: 10.11929/j.issn.2095-1914.2016.05.009
Citation: Hu Jingyang1, Jia Baojun1, Lin Kuan1, Feng Qiannan1, Liu Changfu1, 2. Establishment and Evaluation of Biomass Models of Branches and Leaves for Larix olgensis Plantation in Montane Region of Eastern Liaoning Province[J]. Journal of Southwest Forestry University, 2016, 36(5): 52-57. DOI: 10.11929/j.issn.2095-1914.2016.05.009

辽东山区长白落叶松枝叶生物量模型建立与评估

Establishment and Evaluation of Biomass Models of Branches and Leaves for Larix olgensis Plantation in Montane Region of Eastern Liaoning Province

  • 摘要: 以辽东山区林龄为50年生的不同密度长白落叶松人工林为研究对象,枝条为单位,获取了枝基径 (d)、枝长 (L) 与枝叶生物量 (W) 的相关关系来建立生物量模型,并将枝条材积 (V) 引入CAR模型。结果表明:引入枝条材积建立生物量模型获得了较常规CAR模型效果更好,预估精度和拟合效果均有明显提高,其中枝叶生物量Wbl=1915682d-0315V的决定系数 (R2) 提高至0983,预估精度提高了273%;枝生物量Wb=1793800L-0208V的决定系数提高至0994,预估精度提高了915%;叶生物量Wl=3387837 (d2L)-0427V的决定系数提高至0701,预估精度提高了161%。

     

    Abstract: 50yearold Larix olgensis plantation in montane region of eastern Liaoning Province was selected with different density, branches were effectively collected. The correlations among biomass of these branches and leaves, basal branch diameter and branch length were analyzed respectively to establish model. The CAR model was introduced into the branches volume. The results showed that the biomass models, with the branches volume as parameters, are better than the traditional CAR model. The determination coefficients (R2) of total biomass model of branch and leaf (Wbl=1915682d-0315V) was up to 0983 and forecast accuracy increased by 273%. R2 of branch biomass model (Wb=1793800L-0208V) was up to 0994 and forecast accuracy increased by 915%. R2 of leaf biomass model (Wl=3387837 (d2L)-0427V) was up to 0701 and forecast accuracy increased by 161%.

     

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