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    基于广义代数差分法的长白落叶松人工林地位指数模型

    Site index model for Larix olgensis plantation based on generalized algebraic difference approach derivation

    • 摘要:
      目的森林立地质量评价是森林经营的基础工作,是估计林分生长量和收获量、评价森林生产潜力及确定合理经营措施的重要依据。本文基于60株优势木和亚优势木解析木数据,采用广义代数差分法(GADA)构建了更加灵活的异形地位指数模型,为黑龙江省长白落叶松人工林立地质量的精准评价提供了依据。
      方法选择修正Weibull、Korf和Richards生长方程为基础方程,使用GADA法推导出6个差分地位指数模型,基于1994—2017年在黑龙江省调查的解析木数据,利用非线性最小二乘法进行拟合。结合拟合样本和检验样本计算R2、均方根误差(RMSE)、模拟效率和平均绝对误差等4项指标检验模型的拟合效果与预测能力,初步筛选出较优模型。通过分析比较它们的残差图、地位指数曲线簇进一步筛选出最优模型。将最优模型和使用代数差分法(ADA)推导的模型绘制地位指数曲线簇,并对地位指数为12 ~ 22 m曲线的参数、年生长量达到最大值的时间(拐点)和数值进行分析比较。
      结果基于Richards方程h = a\left( 1 - \rme^ - bt \right)^c,设定自由参数为a = \rme^X_0,c = c_2/X_0,X_0 = \dfrac12\left \ln h_1 + \sqrt \ln h_1^2 - 4c_2\ln \left( 1 - \rme^ - bt_1 \right) \right 的差分地位指数模型被选为最优模型。其参数的拟合结果为b = 0.046 8,c2 = 4.675 4,R2为0.987 4,RMSE 为0.749 1,平均绝对误差为0.904 0,模拟效率为97.04%。相比于使用ADA法推导的模型,采用GADA法推导的最优模型能更好地预测优势木树高生长过程。
      结论在推导地位指数模型时,根据GADA法,指定多个参数为自由参数所推导出的差分模型不仅具有良好的拟合效果,也能同时符合多条水平渐近线与曲线多形性的性质,而ADA法只能满足其中一个条件。最优模型的拟合结果表明,随着地位指数的提升,优势木树高生长曲线的渐近最大值(参数)逐渐增大,连年生长量达到最大值(拐点)的时间越早。这说明立地条件越好长白落叶松人工林优势木树高年生长量和最大值越大,且年生长量更早达到最大值。

       

      Abstract:
      Objective Forest site quality assessment is fundamental to forest management and important for estimating forest growth and yields, evaluating forest potential productivity, and making suitable silviculture practices. In this study, the generalized algebraic difference approach (GADA) method was used to develop the more flexible polymorphic site index model based on 60 stem analysis data of dominant and co-dominant trees. The model will provide basic reference for evaluation of the site quality for Larix olgensis plantation in Heilongjiang Province of northeastern China.
      Method By selecting the growth equations of modification of the Weibull equation, Korf equation and Richards function, 6 difference site index models were developed by GADA method based on the stem analysis data collected from 1994 to 2017 in Heilongjiang Province. The parameters of model were fitted with nonlinear least square method. Combined with fitting data and validation data set, the model was preliminarily selected by four indexes, i.e. R2, root mean square error (RMSE), modelling efficiency, and average absolute error. The optimal models were further screened by residual plots and site index curve clusters. The optimal model and the model developed from ADA method by the same basic equation were compared and evaluated through site index curve cluster and parameters, ages when annual growth reaching the maximum value (inflection) and the values.
      Result The difference model based on the Richards equation h = a\left( 1 - \rme^ - bt \right)^c with free parameters a = \rme^X_0, c = c_2/X_0, X_0 = \dfrac12\left \ln h_1 + \sqrt \ln h_1^2 - 4c_2\ln \left( 1 - \rme^ - bt_1 \right) \right was selected as the optimal model. The results of its parameter estimations were b = 0.046 8 and c2 = 4.675 4, respectively. The goodness of fit and validation indicators of model were as follows: R2 was 0.987 4, RMSE was 0.749 1, MAE was 0.904 0, and EF was 97.04%. Compared with the model developed by ADA method, the optimal model derived by GADA method can better predict the growth process of dominant trees.
      Conclusion In the derivation of the status index model, according to the GADA method, the difference model derived from specifying multiple parameters as free parameters has not only good fitting effect, but also can conform to the properties of multiple asymptotic lines and curve polymorphism at the same time, while the ADA method can only satisfy one of them at the same time. According to the fitting results of the optimal model, the asymptotic value of the high growth curve for the dominant tree increases gradually with the increase of the site index, and the time of inflection position occurs earlier. This shows that the Larix olgensis plantation with better site conditions, the growth rate and maximum value of the dominant tree height increase, and the maximum value of height growth rate occurs earlier.

       

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