作 者：
何潇;雷渊才;薛春泉;徐期瑚;李海奎;曹磊
单 位：
中国林业科学研究院资源信息研究所;广东省林业调查规划院
关键词：
含碳率;生物量模型;碳密度;不确定性量化;Monte Carlo模拟
摘 要：
【目的】基于实测的广东省木荷地上和地下生物量数据及加权平均含碳率,建立单木地上、地下生物量模型,获得区域尺度木荷碳密度及其估计误差,为其他树种的区域尺度碳汇估计提供参考。【方法】参考广东省木荷分布数据,选择并伐倒90株木荷测定地上部分的含碳率和生物量,并从中抽取40株木荷测定地下部分的含碳率和生物量。分地上、地下部分构建生物量随胸径变化的异速模型,利用非线性回归拟合模型参数。基于广东省第八次森林资源连续清查数据,使用Monte Carlo模拟法分地上、地下部分估计区域尺度上木荷的碳密度。采用决定系数、均方根误差和平均预估误差评价单木生物量模型拟合效果,通过均方根误差和相对均方根误差度量区域碳密度估测的不确定性。【结果】广东省木荷地上部分含碳率为0554 9,地下部分含碳率为0548 7;建立的单木地上和地下生物量模型的决定系数分别为0909 8和0793 1,表明木荷单木生物量模型具有良好的拟合优度和预估精度;广东省第八次森林资源清查时的木荷地上碳密度为580±044 t·hm~(-2),不确定性占比762%,地下碳密度为173±017 t·hm~(-2),不确定性占比976%,总碳密度为753±054 t·hm~(-2),不确定性占比723%。【结论】广东省木荷地上和地下部分含碳率均大于南方地区的平均水平,有明显的地域特征。使用Monte Carlo方法可得到稳定可靠的区域尺度的碳密度,并可量化广东省木荷碳密度估计的不确定性。
译 名：
Carbon Density Uncertainty Estimates for Schima superba in Guangdong Province
作 者：
He Xiao;Lei Yuancai;Xue Chunquan;Xu Qihu;Li Haikui;Cao Lei;Research Institute of Forest Resource Information Techniques,CAF;Guangdong Institute of Forestry Inventory and Planning;
关键词：
carbon content;;biomass model;;carbon density;;uncertainty quantification;;Monte Carlo simulation
摘 要：
【Objective】 Based on actual measurement biomass data and weighted average carbon content of the aboveground and below-ground components ofSchima superbain Guangdong Province, this study established the above-ground and below-ground biomass models for individual tree. The carbon density and its uncertainty forS. superbawere estimated at a regional-scale, which could provide a reference for the estimation of tree species carbon sinks at other regional-scale.【Method】 According to the inventory data of distribution ofS. superbain Guangdong Province, the number of 90 trees of S. superbawere cut down, the carbon content and biomass of the above-ground part were measured and the number of 40 trees among were selected to measure the carbon content and biomass of the below-ground part. The above-ground and below-ground biomass relative growth models were constructed respectively based on diameter at breast height(DBH). The model parameters were obtained by non-linear regression. Based on the 8 thNational Forest Inventory data of Guangdong Province, Monte Carlo method was used to simulate the process of estimating the carbon density ofS. superbacomponents at the regional-scale. Used R-square, root-mean-square error and mean predicted error to evaluate the fitting individual tree biomass model effect. Regional-scale carbon density uncertainty were calculated by root-mean-square error and relative-root-mean-square error.【Result】 1) The above-ground carbon content is 0554 9 and the below-ground carbon content is 0548 7 forS. superbain Guangdong Province;2) The individual tree above-ground biomass model' s R~2 is0909 8 and the below-ground biomass model's R~2 is 0 793 1, which showed that the biomass models fitted well and predicted accurately;3) In the 8 thNational Forest Inventory in Guangdong Province, the above-ground carbon density ofS.superbawas 5 80± 0 44 t·hm~(-2), uncertainty was 7 62%; the below-ground carbon density was 1 73 ± 0 17 t·hm~(-2),uncertainty was 976%; the total carbon density was 753±054 t·hm~(-2), uncertainty was 7 23%. 【 Conclusion】 The above-ground part and the below-ground part carbon content ofS. superbain Guangdong Province is higher than the average level in southern China, and it obviously has regional characteristics. The stable and reliable regional-scale estimates of carbon density and their uncertainty could be obtained by using Monte Carlo method.
