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    黄金金, 刘晓彤, 张逸如, 李海奎. 广东省含参数分级的阔叶林分生物量生长模型[J]. 北京林业大学学报, 2022, 44(5): 19-33. DOI: 10.12171/j.1000-1522.20210403
    引用本文: 黄金金, 刘晓彤, 张逸如, 李海奎. 广东省含参数分级的阔叶林分生物量生长模型[J]. 北京林业大学学报, 2022, 44(5): 19-33. DOI: 10.12171/j.1000-1522.20210403
    Huang Jinjin, Liu Xiaotong, Zhang Yiru, Li Haikui. Stand biomass growth model of broadleaved forest with parameter classification in Guangdong Province of southern China[J]. Journal of Beijing Forestry University, 2022, 44(5): 19-33. DOI: 10.12171/j.1000-1522.20210403
    Citation: Huang Jinjin, Liu Xiaotong, Zhang Yiru, Li Haikui. Stand biomass growth model of broadleaved forest with parameter classification in Guangdong Province of southern China[J]. Journal of Beijing Forestry University, 2022, 44(5): 19-33. DOI: 10.12171/j.1000-1522.20210403

    广东省含参数分级的阔叶林分生物量生长模型

    Stand biomass growth model of broadleaved forest with parameter classification in Guangdong Province of southern China

    • 摘要:
        目的  建立区域尺度林分生物量生长模型,为预测未来某一时段广东省天然阔叶林生物量和碳储量提供方法学支持。
        方法  基于广东省1997—2017年5期森林清查数据,选择栎类、木荷和其他软阔类等6个阔叶树种为优势树种的203个天然林样地,以参数分级反映立地质量差异,以竞争指数表示密度影响,以分步建模(一元非线性回归法)和联合建模(非线性联立方程组法)区别建模方式,采用理论生长方程构建胸径生长模型估计林龄进而构建多种林分生物量生长模型,以决定系数和平均预估误差等4个指标评价模型拟合优度;对拟合优度较高的模型,以2002—2017年4期连清的183块样地为检验样本,用总相对误差来验证其应用效果。
        结果  对比模型拟合效果和区域尺度及样地水平上的估计精度,以探究林分密度、不同参数分级、分级方法和建模方法共4项影响因素对生物量生长模型的效果,结果表明:非线性联立方程组法优于分步建模法;与生长速度有关的参数b分级模型优于与生长潜力有关的参数a分级模型;考虑林木竞争和分级方程中加入竞争指数对优化模型性能影响不大。参数b分级、自变量和分级方程皆不含竞争指数的联合模型(模型10)为最优模型,其生物量生长模型确定系数R2为0.970 1;预测4期生物量时,估计效果较好,后期估计误差明显低于前期,如采用模型10预估栎类2002—2017年区域尺度生物量时,4期的估计误差分别为6.22%、15.27%、4.80%、−1.84%。
        结论  以Richards理论生长方程为基础构建林分生物量生长模型来估测区域尺度生物量是一种可行的方法,为评估未来某一时段区域尺度森林生态系统的固碳能力提供依据,也为其他区域的林分生物量生长模型研建提供参考。

       

      Abstract:
        Objective  A regional-scale stand biomass growth model was established to provide methodological support for predicting the biomass and carbon storage of natural broadleaved forests in Guangdong Province of southern China in the future.
        Method  Based on the five forest inventory data of Guangdong Province from 1997 to 2017, 203 natural forest sample plots with six broadleaved tree species such as Quercus spp., Schima superba and other soft broadleaved species as dominant tree species were selected. The site quality difference was reflected by parameter classification, the density effect was expressed by competition index, and the modeling method was distinguished by step-by-step modeling (univariate nonlinear regression method) and joint modeling (nonlinear simultaneous equations method). The DBH growth model, constructed by the theoretical growth equation, was used to estimate the stand age, and then various stand biomass growth models were constructed. The goodness of fit of the model was evaluated by four indexes such as determination coefficient and average prediction error. For the model with high goodness of fit, 183 sample plots by continuously inventory in four periods from 2002 to 2017 were taken as test samples, and the total relative error was used to verify its application effect.
        Result  To compare the fitting effect and the estimation accuracy at regional scale and sample plot level for exploring the influence of four factors including stand density, different parameter classification, classification method and modeling method on the biomass growth model, it was found that nonlinear simultaneous equation was better than step-by-step modeling; the classification of model parameter b related to growth rate was better than that of model parameter a related to growth potential; considering the stand density and adding competition index to the hierarchical equation had little effect on optimizing model performance. Based on the classification of parameter b, the joint model without competition index in independent variable and the hierarchical equation was the optimal model, i.e. Model 10. The determination coefficient of the biomass growth model was 0.970 1. When Model 10 was used to predict the biomass of four periods, the prediction effect was good. But the estimation error in the later stage was significantly lower than that in the earlier stage. For example, when Model 10 was used to estimate the biomass of Quercus spp. at regional scale from 2002 to 2017, the estimation errors of four periods were 6.22%, 15.27%, 4.80% and −1.84%, respectively.
        Conclusion  It is a feasible method to establish stand biomass growth model based on the Richards growth equation to estimate regional-scale biomass, which not only provides a basis for evaluating the carbon sink capacity of forest ecosystem at regional scale in a certain period in the future, but also provides a reference for the construction of stand biomass growth model in other regions.

       

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