广东主要森林类型林分生物量和碳储量模型研建

Stand-Level Models of Biomass and Carbon Stock for Major Forest Types in Guangdong

【目的】构建广东省主要森林类型林分生物量和碳储量模型,为省内储量数据的本底摸查、省级与县市级储量数据的有效衔接提供模型支撑;分析树种结构和气候条件对模型的影响和作用机制,为更精细的碳汇监测及森林质量提升提供理论指导。【方法】以广东省12种主要森林类型为研究对象,基于2007、2012和2017年3期森林资源连续清查数据,采用非线性误差变量联立方程组构建各森林类型与蓄积量兼容的地上和地下生物量、地上和地下碳储量模型。以哑变量形式区分树种结构,以再参数化方法建立气候敏感的林分生物量和碳储量模型,评价模型拟合结果,分析气候变量对林分生物量和碳储量的影响。【结果】研究得到各森林类型的蓄积量、地上和地下生物量模型以及地上R^(2)_(a)和地下林分平均含碳系数。(1)基于胸高断面积和平均树高的基础模型调整决定系数()为0.947~0.997,总相对误差(TRE)和平均系统误差(MSE)分别在±1.54%和±2.48%范围,均不超±3%。平均预估误差(MPE)为0.30%~3.61%,仅栎树林、相思林部分模型略超3%。平均百分标准误差(MPSE)为3.30%~13.39%,均不超15%。(2)基于胸高断面积的简化模R^(2)_(a)型为0.876~0.996,除相思林地下生物量模型拟合效果较差外,其余模型的TRE和MSE分别在±3.19%和±2.74%范围,R^(2)_(a)MPE为0.36%~4.70%,MPSE为4.18%~15.61%。基于平均胸径和林分密度的补充模型为0.775~0.977,多数在0.9以上,除相思林部分模型拟合效果较差外,其余模型的TRE和MSE分别在±2.28%和±1.83%范围,MPE为1.12%~6.24%,R^(2)_(a)MPSE为5.91%~17.44%。(3)区分树种结构的林分模型为0.960~0.997,TRE和MSE分别在±1.61%和±2.33%范围,MPE为0.30%~3.41%,MPSE为2.67%~12.92%,多数模型显著优于基础模型。(4)建立8种森林类型气候敏感的林分生R^(2)_(a)物量和碳储量模型,为0.947~0.998,TRE和MSE分别在±1.86%和±1.96%范围,MPE为0.29%~2.65%,MPSE为3.18%~13.29%,多数模型较基础模型得到显著改进。生物量大多情况下与温度呈负相关,与蒸散量呈负相关或与降水量呈正相关。【结论】所建模型具有较好拟合效果和较高预估精度,实际应用时可根据数据详略和估算范围选择合适模型。温度过高、蒸散过多或降水不足是限制广东省森林生物量和碳储量增长的主要因素。

【Objective】The aim of this study is to develop stand-level biomass and carbon stock models for major forest types in Guangdong Province,so as to facilitate the estimation of the biomass and carbon stock in the province,and realize the effective connection of the provincial with municipal-and county-level data.In addition,this study also analyzes the impact of tree species structure and climate on models and its impact mechanism,providing theoretical guidance for more detailed carbon sink monitoring and forest quality improvement.【Method】Taking 12 major forest types in Guangdong Province as research object,and using the national forest inventory in 2007,2012 and 2017 in Guangdong Province,the models of above-ground biomass,below-ground biomass,above-ground carbon stock and below-ground carbon stock compatible with the volume for each forest type were established by using the nonlinear error-in-variable simultaneous equation approach.The influence of tree species structure was introduced into each model in the form of dummy variables,and the climate-sensitive stand biomass and carbon stock models were established by the method of reparameterization.Finally,the model fitting results were evaluated,and the impact of climate variables on stand biomass and carbon stock was also analyzed.【Result】The models of volume,above-and below-ground biomass,and the mean carbon content coefficient of above-and below-ground biomass were obtained.(1)The adjusted coefficients of determination()of basic models based on basal area and mean height were between 0.947−0.997.The total relative errors(TRE)and the mean systematic errors(MSE)were within±1.54%and±2.48%,respectively,which were not more than±3%.The mean prediction errors(MPE)were between 0.30%−3.61%,and only some of models of Quercus spp.forest and Acacia spp.forest were slightly more than 3%.The mean percent standard errors(MPSE)were between 3.30%−13.39%,which were not more than 15%.(2)The of simplified models based on basal area were between 0.876−0.996.Except for the poor fitting effect of below-ground biomass model of Acacia spp.forest,the TRE and MSE of other models were within±3.19%and±2.74%,respectively,with MPE of 0.36%−4.70%and MPSE of 4.18%−15.61%.The of supplementary models based on mean DBH and stand density were between 0.775−0.977,most of which were above 0.9.Except for the poor fitting effect of some models of Acacia spp.forest,the TRE and MSE of other models were within±2.28%and±1.83%respectively,with MPE of 1.12%−6.24%and MPSE of 5.91%−17.44%.(3)The of stand-level models for distinguishing tree species structure were between 0.960−0.997.TRE and MSE were within±1.61%and±2.33%respectively,MPE were between 0.30%−3.41%,and MPSE were between 2.67%−12.92%.In addition,most models were significantly better than the basic models.(4)The climate-sensitive stand-level biomass and carbon stock models for 8 forest types were constructed,which were between 0.947−0.998.TRE and MSE were within±1.86%and±1.96%respectively,MPE were between 0.29%−2.65%,and MPSE were between 3.18%−13.29%.Most models were significantly improved compared with the basic models.In most cases,biomass is negatively correlated with temperature index,and negatively correlated with evapotranspiration or positively correlated with precipitation.【Conclusion】The models constructed in this study have good fitting effect and high prediction accuracy.In practical application,appropriate models can be selected according to the availability of data and the estimation range.High temperature,excessive evapotranspiration,or insufficient precipitation are the main factors limiting the growth of forest biomass and carbon stock in Guangdong.