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林业科学 ›› 2019, Vol. 55 ›› Issue (11): 163-171.doi: 10.11707/j.1001-7488.20191118

• 研究简报 • 上一篇    下一篇

广东省木荷碳密度及其不确定性估计

何潇1,雷渊才1,薛春泉2,徐期瑚2,李海奎1,*,曹磊1   

  1. 1. 中国林业科学研究院资源信息研究所 北京 100091
    2. 广东省林业调查规划院 广州 510520
  • 收稿日期:2019-02-20 出版日期:2019-11-25 发布日期:2019-12-21
  • 通讯作者: 李海奎
  • 基金资助:
    国家自然科学基金项目(31770676);广东省林业科技专项(2015-02);广东省林业科技创新平台建设项目(2016CXPT03)

Carbon Density Uncertainty Estimates for Schima superba in Guangdong Province

Xiao He1,Yuancai Lei1,Chunquan Xue2,Qihu Xu2,Haikui Li1,*,Lei Cao1   

  1. 1. Research Institute of Forest Resource Information Techniques, CAF Beijing 100091
    2. Guangdong Institute of Forestry Inventory and Planning Guangzhou 510520
  • Received:2019-02-20 Online:2019-11-25 Published:2019-12-21
  • Contact: Haikui Li
  • Supported by:
    国家自然科学基金项目(31770676);广东省林业科技专项(2015-02);广东省林业科技创新平台建设项目(2016CXPT03)

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0.554 9,地下部分含碳率为0.548 7;建立的单木地上和地下生物量模型的决定系数分别为0.909 8和0.793 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5.80±0.44 t·hm-2,不确定性占比7.62%,地下碳密度为1.73±0.17 t·hm-2,不确定性占比9.76%,总碳密度为7.53±0.54 t·hm-2,不确定性占比7.23%。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。

关键词: 含碳率, 生物量模型, 碳密度, 不确定性量化, Monte Carlo模拟

Abstract:

Objective: Based on actual measurement biomass data and weighted average carbon content of the above-ground and below-ground components of Schima superba in Guangdong Province, this study established the above-ground and below-ground biomass models for individual tree. The carbon density and its uncertainty for S. superba were 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 of S. superba in Guangdong Province, the number of 90 trees of S. superba were 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 8th National Forest Inventory data of Guangdong Province, Monte Carlo method was used to simulate the process of estimating the carbon density of S. superba components 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 0.554 9 and the below-ground carbon content is 0.548 7 for S. superba in Guangdong Province; 2) The individual tree above-ground biomass model's R2 is 0.909 8 and the below-ground biomass model's R2 is 0.793 1, which showed that the biomass models fitted well and predicted accurately; 3) In the 8th National Forest Inventory in Guangdong Province, the above-ground carbon density of S. superba was 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 9.76%; the total carbon density was 7.53±0.54 t·hm-2, uncertainty was 7.23%. Conclusion: The above-ground part and the below-ground part carbon content of S. superba in 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.

Key words: carbon content, biomass model, carbon density, uncertainty quantification, Monte Carlo simulation

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