基于连清数据的广东杉木人工林生物量模型构建  

Construction of biomass models of Cunninghamia lanceolata plantation based on the continuous forest inventory in Guangdong

作　　者：郭泽鑫 曹聪 刘萍 GUO Zexin;CAO Cong;LIU Pin(College of Forestry and Landscape Architecture,South China Agricultural University,Guangzhou 510642,Guangdong,China)

机构地区：[1]华南农业大学林学与风景园林学院,广东广州510642

出　　处：《中南林业科技大学学报》2022年第8期78-89,共12页Journal of Central South University of Forestry & Technology

基　　金：广东省林业科技创新重点项目(2021KJCX009)。

摘　　要：【目的】构建适宜于广东杉木人工林生物量模型,精准估测广东杉木人工林生物量和碳储量,对提高广东杉木人工林森林经营成效与森林碳汇效益评价具有重要指导意义。【方法】基于2007、2012、2017年广东省森林资源连续清查杉木人工林固定样地数据,采用非线性度量误差联立方程组和哑变量建模方法,构建基于林分因子和材积源的杉木人工林林分生物量模型,比较检验各模型拟合效果,提出适宜于广东杉木人工林林分生物量预测模型。【结果】1)构建基于林分因子和材积源的一元、二元常规模型及含龄组哑变量模型8套,共16个地上、地下生物量模型,决定系数R^(2)均大于0.94,总相对误差TRE以及平均系统误差MSE均保持在±3%范围内,平均预估精度均在97.90%以上。2)材积源生物量模型拟合效果优于基于林分因子的生物量模型,引入平均树高二元模型较一元模型提升不显著,含龄组因子哑变量模型优于常规模型。3)材积源一元哑变量模型精度高,实用性强,为本研究推荐模型。【结论】杉木林分生物量受立地质量、林分密度、林龄、树种组成等多重因素影响,构建模型时需综合考虑林分生长影响因素,林分胸高断面积、蓄积量等都是重要的预测变量。生物量与蓄积量高度相关,材积源模型具有更高精度。杉木林分生物量模型受林龄影响显著,需区分建模。本研究所建模型具有较高的预估精度,适用于广东省杉木人工纯林或相对纯林,可在实践中推广。【Objective】The aim of this study was to construct a biomass model for the Cunninghamia lanceolata plantation in Guangdong, to accurately estimate the biomass and carbon storage of the plantation, which has important guiding significance for improving the forest management effect and forest carbon sink benefit evaluation of the Cunninghamia lanceolata plantation in Guangdong.【Method】Based on the continuous inventory data of the sample plots in the Cunninghamia lanceolata plantation of Guangdong Province in 2007, 2012 and 2017, the stand biomass models of the Cunninghamia lanceolata plantation were constructed based on stand factors and volume by using the nonlinear error-in-variable simultaneous equation method and dummy variable modeling method. The fitting effects of each model were compared and tested, and a stand biomass prediction model system suitable for the Cunninghamia lanceolata plantation in Guangdong was proposed.【Result】1) Eight sets of univariate and binary conventional models and dummy variable models with age groups were constructed based on stand factors and volume, including 16 aboveground and underground biomass models. The model fitting effects were satisfactory. The coefficients of determination(R^(2)) were greater than 0.94,the total relative errors(TRE) and the mean systematic errors(MSE) were within ±3%, and the mean prediction accuracy was above 97.90%. 2) The fitting effects of the biomass models based on volume were better than the biomass models based on stand factors. In comparison with the univariate models, the binary models were not significantly improved via the introduction of average tree height, and the dummy variable models with age groups were better than the conventional models. 3) The univariate dummy variable model based on volume had high accuracy and strong practicability which was the recommended model in this study.【Conclusion】The biomass of Cunninghamia lanceolata stands is affected by multiple factors such as site quality, density, forest age and tree

关 键 词：森林生物量 非线性度量误差模型 森林资源连续清查 杉木 

分 类 号：S757[农业科学—森林经理学]