缺失数据插补方法及其参数估计窗口大小对毛竹林CO2通量估算的影响

doi:10.11707/j.1001-7488.20150918

林业科学 ›› 2015, Vol. 51 ›› Issue (9): 141-149.doi: 10.11707/j.1001-7488.20150918

缺失数据插补方法及其参数估计窗口大小对毛竹林CO₂通量估算的影响

徐小军, 周国模, 杜华强, 施拥军, 周宇峰

浙江农林大学环境与资源学院浙江省森林生态系统碳循环与固碳减排重点实验室临安 311300

收稿日期:2014-09-04 修回日期:2015-08-28 出版日期:2015-09-25 发布日期:2015-10-16
通讯作者: 周国模
基金资助:
国家自然科学基金项目(31370637)、国家林业局948(2013-4-71)、浙江省教育厅项目(Y201432350)、浙江省基金杰出青年科学基金项目(LR14C160001)、浙江农林大学科研发展基金人才启动项目(2014FR025)、浙江省本科院校中青年学科带头人学术攀登项目(pd2013239)资助。

Effects of Interpolation and Window Sizes in Phyllostachys edulis forest for Parameter Estimation on Calculation of CO₂Flux

Xu Xiaojun, Zhou Guomo, Du Huaqiang, Shi Yongjun, Zhou Yufeng

School of Environment and Resource, Zhejiang A&F University Zhejiang Provincial Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration Lin'an 311300

Received:2014-09-04 Revised:2015-08-28 Online:2015-09-25 Published:2015-10-16

摘要/Abstract

摘要：

[目的] 通量塔观测的CO₂通量数据具有较高的缺失比率,给通量塔数据的应用带来困难,因此,需要建立合理的插补方法对缺失数据进行插补,获取完整和可靠的CO₂通量数据。本研究分析了不同参数估计窗口大小条件下基于夜间数据(NB)和基于白天数据(DB)的插补方法对CO₂通量估算的影响,从而为选择最优的插补方法提供参考依据。[方法] 以2011年毛竹林生态系统碳通量塔观测的净生态系统交换量、温度以及光合有效辐射为基础数据,给定不同的参数估计窗口大小,采用NB和DB插补方法对CO₂通量缺失数据进行插补,以实测数据评价参数估计窗口大小对CO₂通量估算的影响。[结果] 参数估计窗口影响插补的波动程度随窗口增大而降低,窗口过大时结果不能体现局部变化,过小时结果出现异常,最优值与观测数据缺失量密切相关; 在本研究案例情况下,对于NB方法,生态系统总呼吸速率Re插补的最优移动窗口和参数估计窗口分别为15天和90天,总初级生产力GPP插补的最优移动窗口和参数估计窗口分别为2天和4天; 而对于DB方法,最优移动窗口和参数估计窗口分别为2天和60天; NB方法估算的年尺度GPP和Re分别高出DB方法的13.8%和26.8%,净生态系统交换量低于DB方法的32.2%; NB和DB方法得到的白天净生态系统交换量非常接近,但两者的Re分量具有较大差异。[结论] 缺失数据比率对参数估计窗口大小的选择具有重要影响。通量塔缺失数据插补时,综合考虑数据缺失比率和下垫面的碳通量季节变化特征,选择合适的插补方法及其参数估计窗口对提高CO₂通量估算准确性具有帮助。

关键词: 缺失数据, 插补方法, 窗口大小, 通量塔, 毛竹林, CO₂通量

Abstract:

[Objective] Due to high ratio of missing CO₂ flux data, a suitable interpolation method is necessary to collect continuous and reliable CO₂ flux data. The objective of this study is to analyze effect of interpolation (nighttime data-based method (NB) and daytime data-based method (DB)) with different window sizes for fitting parameters on CO₂ flux data estimation, which provides a basis for selecting suitable interpolation method. [Method] Based on the data of net ecosystem exchange (NEE), temperature and photosynthetically active radiation obtained from the carbon flux tower for moso bamboo forest ecosystem in 2011, interpolation (NB and DB) with different time window sizes for fitting parameters were used to estimate missing data through interpolation. Then, the estimated data from interpolation was compared with the observed data. [Results] Time window size for fitting parameter has an effect on the fluctuation of CO₂ flux. Fluctuation of CO₂ flux decreases as time window size increases. If the time window size is too large, the result can not reflect the local specific variation in CO₂ flux, and if the time window size is too small, it can get abnormal CO₂ flux. The optimal time window size is closely related to the amount of missing data. As to this case study, for NB method, the 15-day moving window size and 90-day window size for fitting parameters are suitable to interpolate ecosystem respiration (Re). The 2-day moving window size and 4-day window size for fitting parameters are suitable to interpolate gross primary production (GPP). For DB method, the 2-day moving window size and 60-day window size for fitting parameters are optimal. Annual GPP and Re from NB method are 13.8% and 26.8% greater than those from DB method, respectively. While NEE from NB method is 32.2% lower than that from DB method. Daytime NEE from NB and DB methods are very similar, but there is great difference in Re between NB and DB methods. [Conclusion] The proportion of missing data has important effect on determining time window sizes for fitting parameters. Taken the proportion of missing data and the feature of seasonal variation in CO₂ flux into account, selecting suitable interpolation method and window size for fitting parameters is helpful to increase the accuracy of CO₂ flux estimation.

Key words: missing data, gap-filling method, window size, flux tower, Phyllostachys edulis forest, CO₂ flux

中图分类号:

S718

徐小军, 周国模, 杜华强, 施拥军, 周宇峰. 缺失数据插补方法及其参数估计窗口大小对毛竹林CO₂通量估算的影响[J]. 林业科学, 2015, 51(9): 141-149.

Xu Xiaojun, Zhou Guomo, Du Huaqiang, Shi Yongjun, Zhou Yufeng. Effects of Interpolation and Window Sizes in Phyllostachys edulis forest for Parameter Estimation on Calculation of CO₂Flux[J]. Scientia Silvae Sinicae, 2015, 51(9): 141-149.

参考文献

黄昆,王绍强,王辉民,等. 2013.中亚热带人工针叶林生态系统碳通量拆分差异分析. 生态学报, 33(17):5252-5265.
(Huang K, Wang S Q, Wang H M, et al. 2013. An analysis of carbon flux partition differences of a mid-subtropical planted coniferous forest in southeastern China. Acta Ecologica Sinica, 33(17):
5252-5265.[in chinese])
刘敏, 何洪林, 于贵瑞, 等. 2010. 数据处理方法不确定性对 CO₂ 通量组分估算的影响. 应用生态学报, 21(9):2389-2396.
(Liu M, He H L, Yu G R, et al. 2010. Impacts of uncertainty in data processing on estimation of CO₂ flux components. Chinese Journal of Applied Ecology, 21(9):2389-2396.[in chinese])
孙成, 江洪, 周国模, 等. 2013. 我国亚热带毛竹林CO₂通量的变异特征. 应用生态学报,24(10):2717-2724.
(Sun C, Jiang H, Zhou G M, et al. 2013. Variation characteristics of CO₂ flux in Phyllostachys edulis forest ecosystem in subtropical region of China. Chinese Journal of Applied Ecology, 24(10):
2717-2724.[in chinese])
谢莉,陈三雄,彭庭国,等. 2012. 浙江安吉主要植被类型土壤水库库容特性研究.亚热带水土保持,24(3):14-18.
(Xie L, Chen S X, Peng T G, et al. 2012. Study on the properties of soil reservoir in the main vegetation types of Anji county of Zhejiang Province. Subtropical Soil and Water Conservation, 24(3):14-18.[in chinese])
于贵瑞, 伏玉玲, 孙晓敏, 等. 2006a.中国陆地生态系统通量观测研究网络 (ChinaFLUX) 的研究进展及其发展思路. 中国科学: D 辑, 36(A01): 1-21.
(Yu G R, Fu Y L, Sun X M, et al. 2006a. Progresses and prospects of Chinese terrestrial ecosystem flux observation and research network (ChinaFLUX). Science in China Ser:D Earth Sciences, 36(A01): 1-21.[in chinese])
于贵瑞, 孙晓敏. 2006b. 陆地生态系统通量观测的原理与方法. 北京:高等教育出版社.
(Yu G R, Sun X M, 2006b. Principles of flux measurement in terrestrial ecosystems. Beijing: Higher Education Press.[in chinese])
Beer C, Reichstein M, Tomelleri E, et al. 2010. Terrestrial gross carbon dioxide uptake: global distribution and covariation with climate. Science, 329(5993): 834-838.
Desai A R, Richardson A D, Moffat A M, et al. 2008. Cross-site evaluation of eddy covariance GPP and RE decomposition techniques. Agricultural and Forest Meteorology, 148(6): 821-838.
Falge E, Baldocchi D, Olson R, et al. 2001. Gap filling strategies for defensible annual sums of net ecosystem exchange. Agricultural and Forest Meteorology, 107(1): 43-69.
Hollinger D Y, Aber J, Dail B, et al. 2004. Spatial and temporal variability in forest-atmosphere CO₂ exchange. Global Change Biology, 10(10): 1689-1706.
Janssens I A, Freibauer A, Ciais P, et al. 2003. Europe’s terrestrial biosphere absorbs 7% to 12% of European anthropogenic CO₂ emissions. Science, 300(5625): 1538-1542.
Lasslop G, Reichstein M, Papale D, et al. 2010. Separation of net ecosystem exchange into assimilation and respiration using a light response curve approach: critical issues and global evaluation. Global Change Biology, 16(1):187-208.
Li Z Q, Yu G R, Wen X F, et al. 2005. Energy balance closure at ChinaFLUX sites. Science in China Ser. D Earth Sciences, 48(Supp II): 51-62.
Lloyd J, Taylor J A. 1994. On the temperature dependence of soil respiration. Functional Ecolgy, 8(3):315-323.
Moffat A M, Papale D, Reichstein M, et al. 2007. Comprehensive comparison of gap-filling techniques for eddy covariance net carbon fluxes. Agricultural and Forest Meteorology, 147(3): 209-232.
Noormets A, Chen J, Crow T R. 2007. Age-dependent changes in ecosystem carbon fluxes in managed forests in northern Wisconsin, USA. Ecosystems, 10(2): 187-203.
Reichstein M, Falge E, Baldocchi D, et al. 2005. On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm. Global Change Biology, 11(9): 1424-1439.
Richardson A D, Braswell B H, Hollinger D Y, et al. 2006. Comparing simple respiration models for eddy flux and dynamic chamber data. Agricultural and Forest Meteorology, 141(2): 219-234.
Running S W. 2008. Ecosystem disturbance, carbon, and climate. Science, 321(5889): 652-653.
Zhao M, Running S W. 2011. Response to comments on “Drought-induced reduction in global terrestrial net primary production from 2000 through 2009”. Science, 333(6046): 1093-1093.

[1]	陈亮, 周国模, 杜华强, 刘玉莉, 毛方杰, 徐小军, 李雪建, 崔璐, 李阳光, 朱迪恩. 基于随机森林模型的毛竹林CO₂通量模拟及其影响因子[J]. 林业科学, 2018, 54(8): 1-12.
[2]	杜满义, 封焕英, 范少辉, 苏文会, 毛超, 唐晓鹿, 刘广路. 闽西毛竹林不同施肥处理下土壤有机碳含量垂直分布与季节动态[J]. 林业科学, 2017, 53(3): 12-20.
[3]	李翀, 周国模, 施拥军, 周宇峰, 徐林, 范叶青, 沈振明, 李少虹, 吕玉龙. 不同经营措施对毛竹林生态系统净碳汇能力的影响[J]. 林业科学, 2017, 53(2): 1-9.
[4]	高国龙, 杜华强, 韩凝, 徐小军, 孙少波, 李雪建. 基于特征优选的面向对象毛竹林分布信息提取[J]. 林业科学, 2016, 52(9): 77-85.
[5]	王茜, 王成, 王艳英. 毛竹林森林浴对小白鼠自发行为的影响[J]. 林业科学, 2015, 51(5): 78-86.
[6]	汤孟平, 沈利芬, 赵赛赛, 仇建习, 徐文兵, 庞春梅, 赵明水, 梅爱君. 基于谱分析的天目山毛竹林生长周期[J]. 林业科学, 2014, 50(9): 184-188.
[7]	郭宝华, 刘广路, 范少辉, 杜满义, 苏文会. 不同生产力水平毛竹林碳氮磷的分布格局和计量特征[J]. 林业科学, 2014, 50(6): 1-9.
[8]	徐小军, 周国模, 杜华强, 周宇峰, 胡军国, 陆国富. 样本分层对毛竹林地上部分碳储量估算精度的影响[J]. 林业科学, 2013, 49(6): 18-24.
[9]	孙晓艳, 杜华强, 韩凝, 葛宏立, 谷成燕. 面向对象多尺度分割的SPOT5影像毛竹林专题信息提取[J]. 林业科学, 2013, 49(10): 80-87.
[10]	汤孟平;徐文兵;陈永刚;娄明华;仇建习;庞春梅;赵明水. 毛竹林空间结构优化调控模型[J]. , 2013, 49(1): 120-125.
[11]	赵洋毅;王玉杰;王云琦;刘楠;刘敏;吴云;陈林. 基于修正的Gash模型模拟缙云山毛竹林降雨截留[J]. 林业科学, 2011, 47(9): 15-20.
[12]	汤孟平;徐文兵;陈永刚;邓英英;赵明水. 天目山近自然毛竹林空间结构与生物量的关系[J]. 林业科学, 2011, 47(8): 1-6.
[13]	孙棣棣;徐秋芳;田甜;刘卜榕. 不同栽培历史毛竹林土壤微生物生物量及群落组成变化[J]. 林业科学, 2011, 47(7): 181-186.
[14]	赵仲辉;张利平;康文星;田大伦;项文化;闫文德;彭长辉;. 湖南会同杉木人工林生态系统CO₂通量特征[J]. 林业科学, 2011, 47(11): 6-12.
[15]	肖复明范少辉汪思龙官凤英于小军申正其. 毛竹、杉木人工林生态系统碳平衡估算[J]. 林业科学, 2010, 46(11): 59-65.

缺失数据插补方法及其参数估计窗口大小对毛竹林CO₂通量估算的影响

Effects of Interpolation and Window Sizes in Phyllostachys edulis forest for Parameter Estimation on Calculation of CO₂Flux

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

作者中心

期刊获奖

引证指标

联系方式