大连市中山区城市建筑垂直特征对城市热环境效应的影响
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摘要
以大连市中山区的遥感和建筑数据为基础,利用单窗算法和相关分析方法,研究2007年、2017年研究区的地表温度空间分异特征,以及地表温度与建筑高度的相关性。研究结果表明:2007年、2017年研究区最低温度由22.497℃增长到29.015℃,最高温度由36.091℃增长到43.213℃。研究区北部、解放路和中南路沿线、青泥洼-天津街商贸集聚区附近温度明显增高。2007年、2017年,研究区区域内建筑都有所增高,中山区北部和青泥洼-天津街商贸集聚区附近建筑明显增高,研究区建筑有向南蔓延的趋势。2007年、2017年,地表温度与建筑高度呈现低相关性,相关系数分别为0.346、0.331。
Land surface temperature(LST) is one of the most important indicators of the urban thermal environment. The aggravation of urban thermal environment effect is closely related to the substitution of urban underlying surface by impervious layer and urban development. The urban space grows rapidly and the building height increases. The building height is an important index to measure the spatial distribution of buildings in a region. The vertical characteristics of urban buildings are remarkable. This article is based on the Zhongshan District of Dalian city building and remote sensing data, by using the single window algorithm and correlation analysis method, variation of the surface temperature of 2007 and 2017 space in the study area. The correlation between land surface temperature and the height of the building. The results show that: 1) The minimum temperature in the 2007 and 2017 study area increases from 22.497℃ to 29.015℃, and the maximum temperature increases from 36.091℃ to 43.213℃. Owing to the sea reclamation and the establishment of the Donggang Sub-district, the temperature in the northeastern part of the study area increased significantly. The study area in the northern Jiefang Road, and along the south, Qingniwa-Tianjin Street business gathering area near the temperature increases significantly. 2) In 2007 and 2017, the buildings are mostly distributed in the north and central parts of the study area. The buildings in the north of the study area are relatively high, while those in the middle of the study area(along Jiefang Road and Zhongnan Road) are relatively low. The regional construction has increased the north of Zhongshan District and Qingniwa-Tianjin Street business gathering area near the building of district construction has increased significantly, the trend spread south. 3) The data of land surface temperature and building height are divided grid(30 m×30 m) and the correlation analysis of ground surface temperature and building height is carried out by bivariate correlation analysis method. In 2007 and 2017, land surface temperature and building height show low correlation, the correlation coefficients are 0.346 and 0.331,respectively. To a certain degree, the correlation between land surface temperature and building height is discussed.
Land surface temperature(LST) is one of the most important indicators of the urban thermal environment. The aggravation of urban thermal environment effect is closely related to the substitution of urban underlying surface by impervious layer and urban development. The urban space grows rapidly and the building height increases. The building height is an important index to measure the spatial distribution of buildings in a region. The vertical characteristics of urban buildings are remarkable. This article is based on the Zhongshan District of Dalian city building and remote sensing data, by using the single window algorithm and correlation analysis method, variation of the surface temperature of 2007 and 2017 space in the study area. The correlation between land surface temperature and the height of the building. The results show that: 1) The minimum temperature in the 2007 and 2017 study area increases from 22.497℃ to 29.015℃, and the maximum temperature increases from 36.091℃ to 43.213℃. Owing to the sea reclamation and the establishment of the Donggang Sub-district, the temperature in the northeastern part of the study area increased significantly. The study area in the northern Jiefang Road, and along the south, Qingniwa-Tianjin Street business gathering area near the temperature increases significantly. 2) In 2007 and 2017, the buildings are mostly distributed in the north and central parts of the study area. The buildings in the north of the study area are relatively high, while those in the middle of the study area(along Jiefang Road and Zhongnan Road) are relatively low. The regional construction has increased the north of Zhongshan District and Qingniwa-Tianjin Street business gathering area near the building of district construction has increased significantly, the trend spread south. 3) The data of land surface temperature and building height are divided grid(30 m×30 m) and the correlation analysis of ground surface temperature and building height is carried out by bivariate correlation analysis method. In 2007 and 2017, land surface temperature and building height show low correlation, the correlation coefficients are 0.346 and 0.331,respectively. To a certain degree, the correlation between land surface temperature and building height is discussed.
引文
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[15]张佳华,孟倩文,李欣,等.北京城区城市热岛的多时空尺度变化[J].地理科学, 2011, 31(11):1349-1354.[Zhang Jiahua,Meng Qianwen,Li Xin et al. Urban heat island variations in beijing region in multi spatial temporal scales. Scientia Geographica Sinica, 2011, 31(11):1349-1354.]
[16] Zhao G, Dong J, Liu J et al. Different patterns in daytime and nighttime thermal effects of urbanization in Beijing-Tianjin-Hebei Urban Agglomeration[J]. Remote Sensing, 2017,9(2):121.
[17] Chun B, Guldmann J M. Spatial statistical analysis and simulation of the urban heat island in high-density central cities[J].Landscape&Urban Planning, 2014, 125(3):76-88.
[18] Chun B, Guhathakurta S. The impacts of three-dimensional surface characteristics on urban heat islands over the Diurnal Cycle[J]. The Professional Geographer, 2017, 69(2):191-202.
[19]王美雅,徐涵秋,付伟,等.城市地表水体时空演变及其对热环境的影响[J].地理科学,2016, 36(7):1099-1105.[Wang Meiya,Xu Hanqiu,Fu Wei et al. Spatiotemporal variation of urban surface water and its influence on urban thermal environment. Scientia Geographica Sinica, 2016, 36(7):1099-1105.]
[20] Wu H, Ye L P, Shi W Z et al. Assessing the effects of land use spatial structure on urban heat islands using HJ-1B remote sensing imagery in Wuhan, China[J]. International Journal of Applied Earth Observation&Geoinformation, 2014, 32(1):67-78.
[21]袁振,吴相利,臧淑英,等.基于TM影像的哈尔滨市主城区绿地降温作用研究[J].地理科学, 2017, 37(10):1600-1608.[Yuan Zhen, Wu Xiangli, Zang Shuying et al. Cooling effect of green patches based on TM Image in Harbin Downtown City.Scientia Geographica Sinica, 2017, 37(10):1600-1608.]
[22] Wang J, Zhan Q, Guo H et al. Characterizing the spatial dynamics of land surface temperature——Impervious surface fraction relationship[J]. International Journal of Applied Earth Observation&Geoinformation, 2016, 45:55-65.
[23] Qiao Z, Xu X, Wu F et al. Urban ventilation network model:a case study of the core zone of capital function in Beijing metropolitan area[J]. Journal of Cleaner Production, 2017, 168:526-525.
[24] Zhi Q, Ming Z, Karnieli A et al. Mono-window algorithm for retrieving land surface temperature from Landsat TM6 data[J].Acta Geographica Sinica, 2001, 56(4):456-466.
[25] Wang F, Qin Z, Song C et al. An improved mono-window algorithm for land surface temperature retrieval from Landsat 8Thermal Infrared Sensor Data[J]. Remote Sensing, 2015, 7(4):4268-4289.
[26]杨俊,孙静,葛全胜.大连市城区绿地时空特征的热环境效应研究[J].地球信息科学学报, 2016,18(8):1087-1093.[Yang Jun, Sun Jing, Ge Quansheng. Study on thermal environmental effect of spatial-temporal characteristics of urban green space in the city of Dalian. Geo-Information Science, 2016,18(8):1087-1093.]
[27]中华人民共和国建设部. GB 50352-2005民用建筑设计通则[S].北京:中国建筑出版社, 2005.[The Ministry of Construction of the People’s Republic of China. Code for design of civil buildings in GB 50352-2005. Beijing:China Architure Building Press, 2005.]
[28]张文彤,邝春伟. SPSS统计分析基础教程[M].北京:高等教育出版社, 2011.[Zhang Wentong,Kuang Chunwei. Basic course of spss statistical analysis. Beijing:Higher Education Press,2011.]
[2] Howard L. The climate of London[M]. London:Harvey and Dorton, 1833.
[3] Manley G. On the frequency of snowfall in metropolitan England[J]. Quarterly Journal of the Royal Meteorological Society,1958, 84(359):70-72.
[4] Rao P K. Remote sensing of urban heat islands from an environmental satellite[J]. Bulletin of the American Meteorological society, 1972,53:647-648.
[5]覃志豪,Minghua Zhang,Karnieli Arnon,等.用陆地卫星TM6数据演算地表温度的单窗算法[J].地理学报, 2001, 56(4):456-466.[Qin Zhihao, Minghua Zhang, Karnieli Arnon et al.Mono-window algorithm for retrieving land surface temperature from Landsat TM6 data. Acta Geographica Sinica, 2001, 56(4):456-466.]
[6] Qin Z, Karnieli A, Berliner P. A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region[J]. International Journal of Remote Sensing, 2001, 22(18):3719-3746.
[7]刘帅,李琦,朱亚杰.基于HJ-1B的城市热岛季节变化研究——以北京市为例[J].地理科学, 2014, 34(1):84-88.[Liu Shuai,Li Qi,Zhu Yajie. Study on seasonal variation of urban heat island with HJ-1B satellite imagery:A case study of Beijing.Scientia Geographica Sinica, 2014, 34(1):84-88.]
[8]张建明,王鹏龙,马宁,等.河谷地形下兰州市城市热岛效应的时空演变研究[J].地理科学, 2012, 32(12):1530-1537.[Zhang Jianming,Wang Penglong,Ma Ning et al. Spatial-temporal evolution of urban heat island effect in basin valley——A case study of Lanzhou City. Scientia Geographica Sinica, 2012, 32(12):1530-1537.]
[9] Ye X, Ren H, Liu R et al. Land surface temperature estimate from Chinese Gaofen-5 satellite data using split-window algorithm[J]. IEEE Transactions on Geoscience&Remote Sensing,2017, PP(99):1-12.
[10]覃志豪,Karnieli Arnon.用NOAA-AVHRR热通道数据演算地表温度的劈窗算法[J].国土资源遥感,2001(2):33-42.[Qin Zhihao, Karnieli Arnon. Split window algorithms for retrieving land surface temperature from NOAA-AVHRR data. Remote Sensing for Land&Resources,2001(2):33-42.]
[11]杨何群,周红妹,尹球,等. FY-3A/MERSI数据在典型大城市热环境监测预报中的应用——以上海市为例[J].测绘通报.2013(11):25-28.[Yang Hequn, Zhou Hongmei, Yin Qiu et al.Application of FY-3A/MERSI satellite data for thermal environment monitoring and forecast of typical cities——A case study of Shanghai. Bulletin of Surveying and Mapping, 2013(11):25-28.]
[12]祝善友,张桂欣,刘莹.苏州下垫面格局演变及其局地热环境效应[J].地理科学, 2012, 32(7):859-865.[Zhu Shanyou,Zhang Guixin, Liu Ying. Change of underlying surface patten and its local thermal environment effect in Suzhou City. Scientia Geographica Sinica, 2012, 32(7):859-865.]
[13]邹春城,张友水,黄欢欢.福州市城市不透水面景观指数与城市热环境关系分析[J].地球信息科学学报, 2014, 16(3):490-498.[Zou Chuncheng, Zhang Youshui, Huang Huanhuan. Impacts of impervious surface area and landscape metrics on urban heat environment in Fuzhou City, China. Geo-Information Science, 2014, 16(3):490-498.]
[14]唐菲,徐涵秋.旧城改造与城市热岛效应关系的遥感研究——以福州市苍霞片区为例[J].地理科学, 2011, 31(10):1228-1234.[Tang Fei,Xu Hanqiu. The relationship between urban reconstruction and urban heat island effect based on remote sensing technology:Cangxia District of Fuzhou City, China. Scientia Geographica Sinica, 2011, 31(10):1228-1234.]
[15]张佳华,孟倩文,李欣,等.北京城区城市热岛的多时空尺度变化[J].地理科学, 2011, 31(11):1349-1354.[Zhang Jiahua,Meng Qianwen,Li Xin et al. Urban heat island variations in beijing region in multi spatial temporal scales. Scientia Geographica Sinica, 2011, 31(11):1349-1354.]
[16] Zhao G, Dong J, Liu J et al. Different patterns in daytime and nighttime thermal effects of urbanization in Beijing-Tianjin-Hebei Urban Agglomeration[J]. Remote Sensing, 2017,9(2):121.
[17] Chun B, Guldmann J M. Spatial statistical analysis and simulation of the urban heat island in high-density central cities[J].Landscape&Urban Planning, 2014, 125(3):76-88.
[18] Chun B, Guhathakurta S. The impacts of three-dimensional surface characteristics on urban heat islands over the Diurnal Cycle[J]. The Professional Geographer, 2017, 69(2):191-202.
[19]王美雅,徐涵秋,付伟,等.城市地表水体时空演变及其对热环境的影响[J].地理科学,2016, 36(7):1099-1105.[Wang Meiya,Xu Hanqiu,Fu Wei et al. Spatiotemporal variation of urban surface water and its influence on urban thermal environment. Scientia Geographica Sinica, 2016, 36(7):1099-1105.]
[20] Wu H, Ye L P, Shi W Z et al. Assessing the effects of land use spatial structure on urban heat islands using HJ-1B remote sensing imagery in Wuhan, China[J]. International Journal of Applied Earth Observation&Geoinformation, 2014, 32(1):67-78.
[21]袁振,吴相利,臧淑英,等.基于TM影像的哈尔滨市主城区绿地降温作用研究[J].地理科学, 2017, 37(10):1600-1608.[Yuan Zhen, Wu Xiangli, Zang Shuying et al. Cooling effect of green patches based on TM Image in Harbin Downtown City.Scientia Geographica Sinica, 2017, 37(10):1600-1608.]
[22] Wang J, Zhan Q, Guo H et al. Characterizing the spatial dynamics of land surface temperature——Impervious surface fraction relationship[J]. International Journal of Applied Earth Observation&Geoinformation, 2016, 45:55-65.
[23] Qiao Z, Xu X, Wu F et al. Urban ventilation network model:a case study of the core zone of capital function in Beijing metropolitan area[J]. Journal of Cleaner Production, 2017, 168:526-525.
[24] Zhi Q, Ming Z, Karnieli A et al. Mono-window algorithm for retrieving land surface temperature from Landsat TM6 data[J].Acta Geographica Sinica, 2001, 56(4):456-466.
[25] Wang F, Qin Z, Song C et al. An improved mono-window algorithm for land surface temperature retrieval from Landsat 8Thermal Infrared Sensor Data[J]. Remote Sensing, 2015, 7(4):4268-4289.
[26]杨俊,孙静,葛全胜.大连市城区绿地时空特征的热环境效应研究[J].地球信息科学学报, 2016,18(8):1087-1093.[Yang Jun, Sun Jing, Ge Quansheng. Study on thermal environmental effect of spatial-temporal characteristics of urban green space in the city of Dalian. Geo-Information Science, 2016,18(8):1087-1093.]
[27]中华人民共和国建设部. GB 50352-2005民用建筑设计通则[S].北京:中国建筑出版社, 2005.[The Ministry of Construction of the People’s Republic of China. Code for design of civil buildings in GB 50352-2005. Beijing:China Architure Building Press, 2005.]
[28]张文彤,邝春伟. SPSS统计分析基础教程[M].北京:高等教育出版社, 2011.[Zhang Wentong,Kuang Chunwei. Basic course of spss statistical analysis. Beijing:Higher Education Press,2011.]