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杉木人工林林分断面积生长模型的贝叶斯法估计

Application of Bayesian Method in Stand Basal Area Prediction of Chinese Fir Plantation

  • 摘要: 以江西杉木人工林为例,以Korf型、Richards型和Hossfeld型3种模型为基础,通过广义代数差分法(GADA)分别建立杉木林分断面积生长模型。结果表明:以Richards型为基础的杉木林分断面积预测精度最高,以Richards型模型为最优模型,分别基于贝叶斯法和传统法(非线性最小二乘法)估计杉木林分断面积生长模型。研究发现,利用贝叶斯法估计杉木林分断面积生长模型,预测精度相当且预测值的可靠性比传统法好。

     

    Abstract: Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.), an endemic tree species in China's subtropical area, is one of the most important fast-growing tree species for timber production in southern China. Based on the periodic data of the Chinese fir in Jiangxi province, three stand basal area models (Korf-based model, Richards-based model, and Hossfeld-based model) were developed using generalized algebraic difference approach (GADA). The results showed that Richards-based model was the best for modeling the stand basal area of Chinese fir in the study. Additionally, Bayesian method and Classical method (nonlinear least squares method) were used to estimate the Richards-based model. Although the precision of Bayesian method was nearly equal to that of the classical method, the model reliability using Bayesian method was better than using classical method.

     

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