AG  >> Vol. 7 No. 5 (October 2017)

    夏季城市地表高温区划的遥感监测研究
    Satellite-Based Surface High Temperature Regionalization Index in Summer City

  • 全文下载: PDF(4402KB) HTML   XML   PP.695-707   DOI: 10.12677/AG.2017.75070  
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作者:  

石涛:安徽省气象灾害防御技术中心,安徽 合肥;芜湖市气象局,安徽 芜湖;安徽省大气科学与卫星遥感重点实验室,安徽 合肥;
程向阳:安徽省气象灾害防御技术中心,安徽 合肥;安徽省大气科学与卫星遥感重点实验室,安徽 合肥;
张安伟,马菊:芜湖市气象局,安徽 芜湖;
杨元建:安徽省大气科学与卫星遥感重点实验室,安徽 合肥

关键词:
高温区划城市热环境地表温度人口加权谐波分析Harmonic Analysis of Time Series High Temperature Regionalization Population Weighted Urban Thermal Environment Land Surface Temperature

摘要:

本文基于MODIS地表温度产品和NPP-VIIRS夜间灯光遥感影像,应用时间序列谐波分析和相关性分析进行处理,以安徽省代表城市为例,研究了考虑空间化人口权重的夏季城市地表高温区划的卫星遥感监测指标。结果表明:时间序列谐波分析可以去除遥感影像中的云层遮挡现象,而且能较好地保留原始数据的重要特征信息,重新构成平滑的时间序列遥感影像。与以往的夜间灯光数据相比,NPP-VIIRS空间分辨率和辐射分辨率更高,由此得到的人口格网模型也更加接近实际人口分布。人口加权的城市地表高温区划指标相对于传统的单一地表高温区划指标,是一个形态分布相对稳定、可操作性较强的应用指标,利用该指标体系能够有效进行城市地表高温灾害的区划评估,继而进行城市规划管理或者推广节能减排技术,发展绿色生态环保技术缓解城市地表热环境格局不均衡造成和加剧的城市热岛效应和夏季高温热浪。

In this paper, we processed surface temperature product of MODIS and night light remote sensing image of NPP-VIIRS covered Anhui province by the method of harmonic analysis of time series (HANTS) and correlation analysis. At the same time, we constructed the index of population weighted Urban Surface High Temperature Regionalization (IPWUSHTR), and we analyzed and studied the spatial distribution characteristics of urban thermal environment in summer. Results show: HANTS could remove the clouds from remote sensing images and well reserved the important characteristic information of the original data, to reconstruct the smooth time series of remote sensing images. Compared with the previous night light data, NPP-VIIRS had higher spatial resolution and radiometric resolution, and the population grid model thus obtained was closer to the actual population distribution. Relative to the traditional MODIS-based thermal environment index, IPWUSHTR was an indicator of stable distribution and convenient operation. In addition, we could use IPWUSHTR to carry out urban planning and management or promote energy-saving emission reduction technology, ultimately to ease the urban heat island effect and summer heat wave caused by imbalance of urban thermal environment pattern.

文章引用:
石涛, 程向阳, 张安伟, 马菊, 杨元建. 夏季城市地表高温区划的遥感监测研究[J]. 地球科学前沿, 2017, 7(5): 695-707. https://doi.org/10.12677/AG.2017.75070

参考文献

[1] 叶彩华, 刘勇洪, 刘伟东, 等. 城市地表热环境遥感监测指标研究及应用[J]. 气象科技, 2011, 39(1): 95-101.
[2] 陈云浩, 李晓兵, 史培军, 等. 上海城市热环境的空间格局分析[J]. 地理科学, 2004, 22(3): 317-323.
[3] 张佳华, 侯英雨, 李贵才, 等. 北京城市及周边热岛日变化及季节特征的卫星遥感研究与影响因子分析[J]. 中国科学, 2005, 35(A01): 187-194.
[4] Mirzaei, P.A. (2015) Recent Challenges in Modeling of Urban Heat Island. Sustainable Cities and Society, 19, 200- 206.
https://doi.org/10.1016/j.scs.2015.04.001
[5] Grimmond. (2007) Urbanization and Global Environmental Change: Local Effects of Urban Warming. Geographical Journal, 173, 83-88.
https://doi.org/10.1111/j.1475-4959.2007.232_3.x
[6] 刘建军, 郑有飞, 吴荣军. 热浪灾害对人体健康的影响及其方法研究[J]. 自然灾害学报, 2008, 17(1): 151-156.
[7] Wong, M.S. and Nichol, J.E. (2013) Spatial Variability of Frontal Area Index and Its Relationship with Urban Heat Island Intensity. International Journal of Remote Sensing, 34, 885-896.
https://doi.org/10.1080/01431161.2012.714509
[8] Ren, G.Y. and Zhou, Y.Q. (2014) Urbanization Effect on Trends of Extreme Temperature Indices of National Stations over Mainland China. Journal of Climate, 27, 2340-2360.
https://doi.org/10.1175/JCLI-D-13-00393.1
[9] Cao, C., Xu, H.L., Liu, S., et al. (2016) Urban Heat Islands in China Enhanced by Haze Pollution. Nature Communications, 7, 12509.
https://doi.org/10.1038/ncomms12509
[10] UN. (2014) Revision of the World Urbanization Prospects. United Nations, New York.
[11] Wu, Q., Li, H.Q., Wang, R.S., et al. (2006) Monitoring and Predicting Land Use Change in Beijing Using Remote Sensing and GIS. Landscape Urban Planning, 78, 322-333.
https://doi.org/10.1016/j.landurbplan.2005.10.002
[12] Minh, D., Van, T.L. and Toan, T. (2015) Mapping Ground Subsidence Phenomena in Ho Chi Minh City through the Radar Interferometry Technique Using ALOS PALSAR Data. Remote Sensing, 7, 8543-8562.
https://doi.org/10.3390/rs70708543
[13] 谢志清, 杜银, 曾燕, 等. 上海城市集群化发展显著增强局地高温热浪事件[J]. 气象学报, 2015, 73(6): 1104-1113.
[14] 程兴宏, 徐祥德, 张胜军, 等. 北京地区热岛非均匀分布特征的卫星遥感–地面观测[J]. 气候与环境研究, 2015, 12(5): 683-692.
[15] Gallo, K.P., Easterling, D.R. and Peterson, T.C. (1996) The Influence of Land Use/Land Cover on Climatological Values of the Diurnal Temperature Range. Journal of Climate, 9, 2941-2944.
https://doi.org/10.1175/1520-0442(1996)009<2941:TIOLUC>2.0.CO;2
[16] Gallo, K.P. and Tarpley, J.D. (1996) The Comparison of Vegetation Index and Surface Temperature Composites for Urban Heat Island Analysis. Interna-tional Journal of Remote Sensing, 17, 3071-3076.
https://doi.org/10.1080/01431169608949128
[17] Gallo, K.P., Owen, T.W., Easterling, D.R., et al. (1999) Tem-perature Trends of the US Historical Climatology Network Based on Satellite-Designated Land Use/Land Cover. Journal of Climate, 12, 1344-1348.
https://doi.org/10.1175/1520-0442(1999)012<1344:TTOTUS>2.0.CO;2
[18] 但尚铭, 安海锋, 但玻, 等. 基于AVHRR和DEM的重庆城市热岛效应分析[J]. 长江流域资源与环境, 2009, 18(7): 680-685.
[19] 闫峰, 覃志豪, 李茂松, 等. 基于MODIS 数据的上海市热岛效应研究[J]. 武汉大学学报(信息科学版), 2007, 32(7): 576-580.
[20] 王文杰, 申文明, 刘晓曼, 等. 基于遥感的北京市城市化发展与城市热岛效应变化关系研究[J]. 环境科学研究, 2006, 19(2): 44-48.
[21] 王艳姣, 闫峰, 张培群, 等. 基于植被指数和地表反照率影响的北京城市热岛变化[J]. 环境科学研究, 2009, 22(2): 215-220.
[22] 张宏群, 杨元建, 荀尚培, 等. 安徽省植被和地表温度季节变化及空间分布特征[J]. 应用气象学报, 2011, 22(2): 232-240.
[23] 石涛, 杨元建, 马菊, 等. 基于MODIS的安徽省代表城市热岛效应时空特征[J]. 应用气象学报, 2013, 24(4): 484-494.
[24] 易予晴, 龙腾飞, 焦伟利, 等. 武汉城市群夏季热岛特征及演变[J]. 长江流域资源与环境, 2015, 24(8): 1279-1285.
[25] Weng, Q. (2001) A Remote Sens-ing-GIS Evaluation of Urban Expansion and Its Impact on Surface Temperature in the Zhujiang Delta, China. Interna-tional Journal of Remote Sensing, 22, 1999-2014.
[26] 李福建, 马安青, 丁原东, 等. 基于Landsat数据的城市热岛效应研究[J]. 遥感技术与应用, 2009, 24(4): 553-558.
[27] 石涛, 杨元建, 张爱民, 等. 基于TM和GIS的合肥市热环境研究[J]. 遥感技术与应用, 2011, 26(2): 156-162.
[28] Shi, T., Yong, H., Hong, W., et al. (2015) Influence of Urbanization on the Thermal Environment of Meteorological Stations: Satellite-Observational Evidence. Advances in Climate Change Research, 6, 7-15.
https://doi.org/10.1016/j.accre.2015.07.001
[29] Zhao, M., Cai, H., Qiao, Z., et al. (2016) Influence of Urban Expansion on the Urban Heat Island Effect in Shanghai. International Journal of Geographical Information Science, 30, 1-21.
https://doi.org/10.1080/13658816.2016.1178389
[30] Oke, T.R. (1983) The Energetic Basis of the Urban Heat Island. Quarterly Journal of the Royal Meteorological Society, 108, 1-24.
[31] 东高红, 尉英华, 解以扬, 等. 天津地区城市热岛环流与海风环流相互作用的研究[J]. 气象, 2015, 41(12): 1447-1455.
[32] 刘树华, 等. 植被覆盖度对大气边界层热力影响的数值模拟[J]. 气象学报, 1996, 54(3): 303-311.
[33] 刘树华, 李洁, 文平辉. 城市及乡村大气边界层结构的数值模拟[J]. 北京大学学报(自然科学版), 2002, 38(1): 91-97.
[34] 刘树华, 周彬. 应用–改进的模式对北京夏季风、温和湿度场的模拟[J]. 北京大学学报(自然科学版), 2007, 43(1): 42-47.
[35] Zhang, N., Wang, X., Chen, Y., et al. (2016) Numerical Simulations on Influence of Urban Land Cover Expansion and Anthropogenic Heat Release on Urban Meteorological Environment in Pearl River Delta. Theoretical and Applied Climatology, 126, 469-479.
https://doi.org/10.1007/s00704-015-1601-0
[36] 李新宇, 刘扬阳, 蒋雪娜, 等. 基于高维空间几何信息学的遥感图像去薄云算法[J]. 电子学报, 2011, 39(5): 1002-1006.
[37] 姜澒月, 周坚华. 遥感图像薄云雾的梯度改正[J]. 遥感技术与应用, 2013, 28(4): 640-646.
[38] 张立杰, 李磊, 江崟, 等. 基于自动站观测资料的深圳城市热岛研究[J]. 气候与环境研究, 2011, 16(4): 479-486.
[39] 高义, 王辉, 王培涛, 等. 基于人口普查与多源夜间灯光数据的海岸带人口空间化分析[J]. 资源科学, 2013, 35(12): 2517-2523.
[40] 周玉洁, 王卷乐, 郭海会. 基于谐波分析和线性光谱模型的耕地信息提取[J]. 遥感技术与应用, 2015, 30(4): 706-713.