摘要
为了探究杉木Cunninghamia lanceolata的碳储量生长规律,科学指导碳汇导向型森林的经营,最大化实现杉木人工林的碳汇服务功能和价值,研究基于2019年广东省二类调查的杉木人工林样地数据,通过条件筛选和异常值处理筛选出建模数据17680条,以林龄为解释变量,碳储量为目标变量,采用加权非线性回归方法求解模型参数,在Logistics、Mitscherlich、Gompertz和Schumacher 4种基础模型中筛选最优基础模型,在最优基础模型中引入以杉木适生区分布区域为特征的哑变量,在不同参数引入哑变量以确定最佳的引入参数位置,建立可兼容不同区域的杉木人工林碳储量生长模型。结果表明,在4种候选理论生长模型中,Schumacher模型的拟合效果最佳,各项评价指标在候选模型中均为最优,确定系数为0.8143,标准误差为8.5261,平均百分比标准误差为19.03%,平均百分比误差为4.66%。在参数b中引入哑变量效果较好,模型的评价指标得到显著优化,最终模型的确定系数为0.8576,标准误差为7.5769,平均百分比标准误差为14.63%,平均百分比误差为4.14%,说明引入哑变量后有效提高模型的拟合效果和稳定性,包含哑变量的碳储量生长模型可用于预测广东省不同区域杉木人工林碳储量。
To explore the growth patterns of carbon storage in Cunninghamia lanceolata and scientifically guide carbon-sink-oriented forest management practices,aiming to maximize carbon sequestration services and value of C.lanceolata plantations,this study utilized data from the 2019 second-class forest inventory of C.lanceolata plantations in Guangdong Province.After conditional screening and outlier removal,17680 modeling data entries were selected.Using stand age as the explanatory variable and carbon storage as the response variable,model parameters were solved for using weighted nonlinear regression methods.The optimal basic model was selected from four candidates:Logistic,Mitscherlich,Gompertz,and Schumacher.Dummy varia-bles representing suitable growth regions of C.lanceolata were introduced into the optimal basic model.The optimal parameter position for incorporating dummy variables was identified to establish a region-compatible carbon storage growth model.The results showed that among the four candidate theoretical growth models,the Schumacher model exhibited the best fit with an R2 of 0.8143,standard error(SE)of 8.5261,mean percentage standard error(MPSE)of 19.03%,and mean percentage error(MPE)of 4.66%.After introducing dummy variables at parameter b,the model′s performance significantly improved:R2 increased to 0.8576,SE decreased to 7.5769,MPSE decreased to 14.63%,and MPE decreased to 4.14%.This demonstrates that dummy variable incorporation effectively enhanced the model′s fitting accuracy and stability.The final carbon storage growth model with dummy variables can be used to predict C.lanceolata plantations across different regions of Guangdong Province.
作者
古洛炜
何雨恒
黄焕强
冯铭淳
林娜
陈世清
GU Luowei;HE Yuheng;HUANG Huanqiang;FENG Mingchun;LIN Na;CHEN Shiqing(South China Agricultural University,College of Forestry and Landscape Architecture,Guangzhou,Guangdong 510642,China)
出处
《林业与环境科学》
2025年第4期35-41,共7页
Forestry and Environmental Science
基金
广东省林业科技创新专项资金项目(2023KJCX001)。
关键词
碳储量
生长模型
哑变量
杉木人工林
carbon storage
growth model
dummy variable
Cunninghamia lanceolata plantation
