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地球科学进展  2014, Vol. 29 Issue (6): 723-733    DOI: 10.11867/j.issn.1001-8166.2014.06.0723
研究论文     
基于时间序列建模的城市热岛时间尺度成分分离方法与应用
权凌, 周纪*, 李明松, 代冯楠, 李国全
电子科技大学资源与环境学院, 四川 成都 611731
A Method for Separating Temporal Components of the Urban Heat Island Based on Time Series Modeling and Its Application
Quan Ling, Zhou Ji, Li Mingsong, Dai Fengnan, Li Guoquan
School of Resourcesand Environment, University of Electronic Science and Technology of China,Chengdu611731, China
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摘要:

城市热岛效应是全球与区域气候变化研究中的焦点问题。基于2001—2012年较长时间序列的北京市MODIS地表温度产品及相关NDVI和反射率产品,给出地表温度时间序列构建方法。基于站点气象观测资料进行的精度验证表明地表温度时间序列构建方法可行,并最终给出城市热岛强度的量化方案。研究选取统计学中X-11-ARIMA时间序列建模方法,分离并分析城市热岛强度时间序列的结构性成分。分析发现,以平均城乡温差为指标的北京城市热岛强度季节性特征明显,与城乡土地利用状况、季节性地表覆盖、地物热特性以及气候因子等联系密切。趋势—循环特征与城市扩张速度及入选城市区域面积相关。以已发生城市热岛区域城乡平均温差为指标的北京城市热岛强度趋势—循环特性在12年间表现平稳。时间序列建模分析提取出不规则变动成分,为定量研究偶然因素对城市热岛的影响提供了可能。

关键词: 地表温度遥感空间分辨率提升X-11-ARIMA    
Abstract:

Urban heat island (UHI) effect has been the focus on the research of global and regional climate change.In this study, the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature products and their corresponding datasets of Normalized Difference Vegetation Index (NDVI)and Reflectance products acquired from 2001 to 2012 in Beijing were selected as the data sources to support the method of constructing surface temperature time series. Validation with in situ meteorological datasets revealed that the method of constructing surface temperature time series was applicable and feasible with high accuracy, and eventually quantization scheme of the UHI intensity was given. Statistical model X-11-ARIMA was selected to decompose and analyze the UHI time series. Results indicate that when using the average temperature difference between urban and rural areas as an index, Beijing UHI intensity shows obvious seasonal characteristics, which are closely related to urban and rural land use status, seasonal surface coverage, thermal characteristics of ground objects, and climate factors. In the meantime, cycle trend features are associated with urban expansion. When using the average temperature difference between urban, where UHI has occurred, and rural areas as an index, cycle trend features have a stable performance. The irregular factors extracted by time series modeling analysis make the quantitative study on the accidental factors influence the urban heat island possible.

Key words: X-11-ARIMA    Spatial resolution upscaling    Remote sensing.    Land surface temperature
出版日期: 2014-06-10
:  P96  
基金资助:

国家自然科学基金项目“基于时间尺度模型耦合的逐日城市热岛模拟与演变特征分析”(编号:41101380); 国家重点基础研究发展计划项目“复杂地表遥感信息动态分析与建模”(编号:2013CB733406)资助

通讯作者: 通讯作者:周纪(1983-),男,四川南充人,副教授,主要从事定量遥感研究.      E-mail: jzhou233@uestc.edu.cn
作者简介: 作者简介:权凌(1988-),女,甘肃兰州人,硕士研究生,主要从事热红外遥感及其应用研究. E-mail:qualing1988@126.com
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引用本文:

权凌, 周纪, 李明松, 代冯楠, 李国全. 基于时间序列建模的城市热岛时间尺度成分分离方法与应用[J]. 地球科学进展, 2014, 29(6): 723-733.

Quan Ling, Zhou Ji, Li Mingsong, Dai Fengnan, Li Guoquan. A Method for Separating Temporal Components of the Urban Heat Island Based on Time Series Modeling and Its Application. Advances in Earth Science, 2014, 29(6): 723-733.

链接本文:

http://www.adearth.ac.cn/CN/10.11867/j.issn.1001-8166.2014.06.0723        http://www.adearth.ac.cn/CN/Y2014/V29/I6/723

[1] E, Cai M. Impact of urbanization and land-use change on climate[J]. Nature, 2003, 423(6 939): 528-531.
[2] R A. Land use and climate change[J]. Science, 2005, 310(5 754): 1 625-1 626.
[3] J A, Oke T R. Thermal remote sensing of urban climates[J]. Remote Sensing of Environment, 2003, 86(3): 370-384.
[4] F, Bauer M E. Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in landsat imagery[J]. Remote Sensing of Environment, 2007, 106(3): 375-386.
[5] J A.Urban Heat Island·Ian Douglas·Encyclopediaof Global Environmental Change[M]. Chichester: John Wiley & Sons, 2002: 660-666.
[6] T. Encyclpediaof Global Environmental Change[M]. Chichester: John Wiley & Sons,2002: 660-666.
[7] Yunhao, Li Jing, Li Xiaobing. The Simulation and Effect on the Pattern and Process of Urban Heat Environment by Remote Sensing[M]. Beijing: Science Press, 2004.[陈云浩, 李京, 李晓兵. 城市空间热环境遥感分析——格局, 过程, 模拟与影响[M]. 北京: 科学出版社, 2004.]
[8] J, Wang X, Wang X, et al. Remote sensing evaluation of urban heat island and its spatial pattern of the Shanghai metropolitan area, China[J]. Ecological Complexity, 2009, 6(4): 413-420.
[9] A, Wu J. Urban heat islands and landscape heterogeneity: Linking spatiotemporal variations in surface temperatures to land-cover and socioeconomic patterns[J]. Landscape Ecology, 2010, 25(1): 17-33.
[10] B, Gourmelon F, Laaidi K, et al. Satellite monitoring of summer heat waves in the Paris metropolitan area[J]. International Journal of Climatology, 2011, 31(2): 313-323.
[11] Wenfeng, Chen Yunhao, Zhou Ji, et al. Spatial simulation of urban heat island intensity based on the support vector machine technique: A case study in Beijing[J]. Acta Geodaeticaet Cartographica Sinica, 2011, 40(1):96-103.[占文凤, 陈云浩, 周纪,等. 基于支持向量机的北京城市热岛模拟—热岛强度空间格局曲面模拟及其应用[J]. 测绘学报, 2011, 40(1): 96-103.]
[12] R, Bartholy J, Dezso Z. Remotely sensed thermal information applied to urban climate analysis[J]. Advances in Space Research, 2006, 37(12): 2 191-2 196.
[13] K, Wang J, Wang P, et al. Influences of urbanization on surface characteristics as derived from the Moderate-Resolution Imaging Spectroradiometer: A case study for the Beijing metropolitan area[J]. Journal of Geophysical Research: Atmospheres (1984-2012), 2007, 112(D22): 1-12.
[14] S, Dumitrescu A. The July urban heat island of Bucharest as derived from modis images[J]. Theoretical and Applied Climatology, 2009, 96(1/2): 145-153.
[15] Ji, Chen Yunhao, Li Jing, et al. A volume modelfor urban heat island based on remote sensing imageryand its application: A case study in Beijing[J]. Journal of Remote Sensing, 2008, 12(5): 734-742.[周纪, 陈云浩, 李京, 等. 基于遥感影像的城市热岛容量模型及其应用: 以北京地区为例[J]. 遥感学报, 2008, 12(5): 734-742.]
[16] M L, Zhang P, Wolfe R E, et al. Remote sensing of the urban heat island effect across biomes in the continental USA[J]. Remote Sensing of Environment, 2010, 114(3): 504-513.
[17] L, Brunsell N A. The impact of temporal aggregation of land surface temperature data for Surface Urban Heat Island (SUHI) monitoring[J]. Remote Sensing of Environment, 2013, 134: 162-174.
[18] J, Chen Y, Zhang X, et al. Modelling the diurnal variations of urban heat islands with multi-source satellite data[J]. International Journal of Remote Sensing, 2013, 34(21): 7 568-7 588.
[19] J, Chen Y, Wang J, et al. Maximum nighttime Urban Heat Island (UHI) intensity simulation by integrating remotely sensed data and meteorological observations[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2011, 4(1): 138-146.
[20] R, William D S, Lily P, et al. Characterizing the urban heat island in current and future climates in New Jersey[J]. Environmental Hazards, 2005, 6: 51-62.
[21] Yang, Wei Zhigang. Comparison of the precipitation cycle and trend in different areas of Northern China in recent 50 years[J]. Advances in Earth Science, 2012, 27(3): 337-346.[刘扬,韦志刚. 近50年中国北方不同地区降水周期趋势的比较分析[J]. 地球科学进展, 2012, 27(3): 337-346.]
[22] S, Anderson A, Carbone R, et al. The Forecasting Accuracy of Major Time Series Methods[M]. New York: Wiley, 1984.
[23] E B, Huot G, Morry M, et al. Seasonal adjustment in the eighties: Some problems and solutions[J]. Canadian Journal of Statistics,1988, 16(Suppl.1): 109-126.
[24] Yunfeng,Xu Zhiying,Liu Yue,et al. A review of the scaling issues of geospatial data[J]. Advances in Earth Science, 2013, 28(3): 297-304.[胡云锋,徐芝英,刘越,等. 地理空间数据的尺度转换[J]. 地球科学进展, 2013, 28(3): 297-304. ]
[25] W P, Norman J M, Anderson M C, et al. Estimating subpixel surface temperatures and energy fluxes from the vegetation index-radiometric temperature relationship[J]. Remote Sensing of Environment, 2003, 85(4): 429-440.
[26] S. Narrowband to broadband conversions of land surface albedo I: Algorithms[J]. Remote Sensing of Environment, 2001, 76(2): 213-238.
[27] Liping. Some understanding of seasonally adjusted time series of the West[J].Statistical Research, 2001, 12: 60-61.[刘丽萍. 对西方国家时间序列季节调整的几点认识[J]. 统计研究, 2001, 12: 60-61.]
[28] Xiaoling, Xie Bangchang. Data Mining Methods and Applications[M]. Beijing: China Renmin University Press, 2008.[吕晓玲,谢邦昌. 数据挖掘方法与应用[M]. 北京: 中国人民大学出版社, 2008.]
[29] R. Note on graduation by adjusted average[J]. Transaction of the Actuarial Society of America, 1916, 17: 43-48.
[30] R. A new method of graduation[J]. Transaction of the Actuarial Society of America, 1924, 25: 29-40.
[31] Ji. Land Surface Temperature Retrieval and Temporal Variations of Urban Heat Island Modeling based on Remote Sensing[D]. Beijing: Beijing Normal University, 2010.[周纪. 地表温度反演与城市热岛时间尺度模型[D]. 北京:北京师范大学, 2010.]
[32] Xingrong, Hu Fei, Shu Wenjun. Characteristics of Beijing spring urban heat islands and influencing factors of a strong urban heat island event[J]. Journal of Nanjing Institute of Meteorology, 2008, 31(1): 129-134.[李兴荣, 胡非, 舒文军. 北京春季城市热岛特征及强度影响因子[J]. 南京气象学院学报, 2008, 31(1): 129-134.]
[33] C J G, Sinmonds I, Plummer N. Quantification of the influences of wind and cloud on the noctumal urban heat island of a large city[J]. Journal of Applied Meteorology, 2001, 40(2): 169-182.
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