长春市城市形态及风环境对地表温度的影响
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摘要
随着城市化进程的加快,城市热岛效应越来越受到关注。然而,很少有研究分析城市形态和城市风环境对地表温度(LST)的影响。利用建筑和遥感等多源数据,基于GIS空间方法,结合迎风面指数(FAI)和地表温度,研究长春市城区迎风面指数时空差异,探索城市形态对城市地表温度影响。结果表明:①迎风面指数呈现从中心城区向外扩散的空间趋势,高密度以及高建筑对风的阻碍程度大。朝阳区北部迎风面指数最大,最大值达到15.1,各个区边缘区迎风面指数最小,最小值为0.01。②研究区昼夜地表温度温差大,范围分别是18.15℃～31.73℃、4.27℃～18.43℃。空间分布上与迎风面指数存在相同特征,城市中心城区温度高,以同心圆方式逐渐向外扩散。受城市建筑形态、人为热源等因素影响,与夜间相比,白天高温地区范围更大。③迎风面指数与地表温度在一定程度上相关,昼夜相关系数分别为0.371和0.355。建筑在垂直方向和水平方向上空间形态不同,对地表温度影响存在差异。结合城市风环境状况开展城市形态信息的定量研究,对于城市气候学家、城市规划师探寻城市潜在通风廊道、改善城市通风环境具有重要理论价值和现实意义。
The urban heat island effect has received an increasing attention recently with the acceleration of urbanization. However, so far few studies have focuses on the effects of urban morphology and wind conditions on land surface temperature(LST). This study utilizes a range of multi-source data including architecture and remote sensing and applies a GIS spatial method combined with urban building frontal area index(FAI) and LST. This research aims to evaluate spatiotemporal differences in the FAI of urban built-up areas as well as to explore the influence of urban form on surface temperature. Results initially reveal that building FAI conforms to a spatial trend comprising outward diffusion from the city center and shows that high density, higher elevation buildings hinder the wind strongly. Data show that FAI values for the north of Chaoyang District are the largest, reaching a maximum of 15.1, while those for edge areas for each district are the smallest, falling to a minimum of 0.01. Secondly, the results of this analysis reveal large differences in surface temperature between day and night within the study area, ranging between 18.15 ℃ and 31.73 ℃ and between 4.27 ℃ and 18.43 ℃,respectively. Spatial distribution values exhibit the same characteristics as those for the FAI; the urban central city is characterized by high temperature, which gradually spreads out in a concentric manner. The range of high temperature areas during the day is also larger than that at night as these values are influenced by other variables including urban architectural form and artificial heat sources. Thirdly, the data assembled here show that FAI is related to surface temperature to a certain extent; recorded correlations between day and night are 0.371 and0.355, respectively, both significant at the 0.01 level. It is also the case that building spatial shape is distinct in both vertical and horizontal directions and that the influence of surface temperature varies. Wind environmental data is an important component of quantitative research on building form and is necessary if urban climate scientists and planners are to explore and enhance potential ventilated corridors within cities.
The urban heat island effect has received an increasing attention recently with the acceleration of urbanization. However, so far few studies have focuses on the effects of urban morphology and wind conditions on land surface temperature(LST). This study utilizes a range of multi-source data including architecture and remote sensing and applies a GIS spatial method combined with urban building frontal area index(FAI) and LST. This research aims to evaluate spatiotemporal differences in the FAI of urban built-up areas as well as to explore the influence of urban form on surface temperature. Results initially reveal that building FAI conforms to a spatial trend comprising outward diffusion from the city center and shows that high density, higher elevation buildings hinder the wind strongly. Data show that FAI values for the north of Chaoyang District are the largest, reaching a maximum of 15.1, while those for edge areas for each district are the smallest, falling to a minimum of 0.01. Secondly, the results of this analysis reveal large differences in surface temperature between day and night within the study area, ranging between 18.15 ℃ and 31.73 ℃ and between 4.27 ℃ and 18.43 ℃,respectively. Spatial distribution values exhibit the same characteristics as those for the FAI; the urban central city is characterized by high temperature, which gradually spreads out in a concentric manner. The range of high temperature areas during the day is also larger than that at night as these values are influenced by other variables including urban architectural form and artificial heat sources. Thirdly, the data assembled here show that FAI is related to surface temperature to a certain extent; recorded correlations between day and night are 0.371 and0.355, respectively, both significant at the 0.01 level. It is also the case that building spatial shape is distinct in both vertical and horizontal directions and that the influence of surface temperature varies. Wind environmental data is an important component of quantitative research on building form and is necessary if urban climate scientists and planners are to explore and enhance potential ventilated corridors within cities.
引文
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[10] Chen Liding, Sun Ranhao, Liu Hailian. Eco-environmental effects of urban landscape pattern changes:Progresses,problems, and perspectives. Acta Ecologica Sinica, 2013, 33(4):1043-1050.[陈利顶,孙然好,刘海莲.城市景观格局演变的生态环境效应研究进展.生态学报, 2013, 33(4):1043-1050.]
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[12] Shi Y, Katzschner L, Ng E. Modelling the fine-scale spatiotemporal pattern of urban heat island effect using land use regression approach in a megacity. Science of the Total Environment, 2017, 618:891-904.
[13] Zhang Jiahua, Meng Qianwen, Li Xin, et al. Urban heat island variations in Beijing region in multi spatial and temporal scales. Scientia Geographica Sinica, 2011, 31(11):1349-1354.[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.地理科学, 2011, 31(11):1349-1354.]
[14] Hsieh C M, Huang H C. Mitigating urban heat islands:A method to identify potential wind corridor for cooling and ventilation. Computers, Environment and Urban Systems, 2016, 57:130-143.
[15] Yang J, Jin S, Xiao X, et al. Local climate zone ventilation and urban land surface temperatures:Towards a performancebased and wind-sensitive planning proposal in megacities. Sustainable Cities and Society, 2019, 47:101487. DOI:10.1016/j.scs.2019.101487.
[16] Ren Chao, Yuan Chao, He Zhengjun, et al. A study of air path and its application in urban planning. Urban Planning Forum, 2014(3):52-60.[任超,袁超,何正军,等.城市通风廊道研究及其规划应用.城市规划学刊, 2014(3):52-60.]
[17] Ng E, Yuan C, Chen L, et al. Improving the wind environment in high-density cities by understanding urban morphology and surface roughness:A study in Hong Kong. Landscape and Urban Planning, 2011, 101(1):59-74.
[18] Yang Junyan, Zhang Tao, Tan Ying. Research on urban wind environment:Technology evolution and integration of evaluation systems. South Architecture, 2014, 1(3):31-38.[杨俊宴,张涛,谭瑛.城市风环境研究的技术演进及其评价体系整合.南方建筑, 2014, 1(3):31-38.]
[19] Wang Tianxing, Chen Songlin, Yan Guangjian. Estimation of land surface parameters and spatio-temporal characteristics of urban heat island. Scientia Geographica Sinica, 2009, 29(5):697-702.[王天星,陈松林,阎广建.地表参数反演及城市热岛时空演变分析.地理科学, 2009, 29(5):697-702.]
[20] Weng Q. Thermal infrared remote sensing for urban climate and environmental studies:Methods, applications, and trends. Isprs Journal of Photogrammetry and Remote Sensing, 2009, 64(4):335-344.
[21] Katoa S, Kouyamaa T, Nakamuraa R, et al. Simultaneous retrieval of temperature and area according to sub-pixel hotspots from nighttime Landsat 8 OLI data. Remote Sensing of Environment, 2018, 204:276-286.
[22] Yao Yonghui, Zhang Baiping. MODIS-based estimation of air temperature and heating-up effect of the Tibetan Plateau.Acta Geographica Sinica, 2013, 68(1):95-107.[姚永慧,张百平.基于MODIS数据的青藏高原气温与增温效应估算.地理学报, 2013, 68(1):95-107.]
[23] Xu Weiyan, Sun Rui, Jin Zhifeng, et al. Estimation of near surface air temperature based on MODIS data.Meteorological and Environmental Sciences, 2015, 38(1):1-6.[徐伟燕,孙睿,金志凤,等.基于MODIS数据的近地表气温估算.气象与环境科学, 2015, 38(1):1-6.]
[24] Gulbe L, Caune V, Korats G. Urban area thermal monitoring:Liepaja case study using satellite and aerial thermal data.International Journal of Applied Earth Observation and Geoinformation, 2017, 63:45-54.
[25] Yuan Zhen, Wu Xiangli, Zang Shuying, et al. Cooling effect of green patche based on TM image in Harbin downtown city. Scientia Geographica Sinica, 2017, 37(10):1600-1608.[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.地理科学, 2017, 37(10):1600-1608.]
[26] Yang J, Wang Y, Xiao X, et al. Spatial differentiation of urban wind and thermal environment in different grid sizes.Urban Climate, 2019, 28:100458. DOI:10.1016/j.uclim.2019.100458.
[27] Yang J, Sun J, Ge Q S, et al. Assessing the impacts of urbanization-associated green space on urban land surface temperature:A case study of Dalian, China. Urban Forestry&Urban Greening, 2017, 22:1-10.
[28] Zhou D C, Zhao S Q, Liu S G, et al. Surface urban heat island in China's 32 major cities:Spatial patterns and drivers.Remote Sensing of Environment, 2014, 152:51-61.
[29] Tran H, Uchihama D, Ochi S, et al. Assessment with satellite data of the urban heat island effects in Asian mega cities.International Journal of Applied Earth Observation and Geoinformation, 2006, 8(1):34-48.
[30] Code for Design of Civil Buildings(GB50352-2005). Beijing:China Architecture and Building Press, 2005.[民用建筑设计通则(GB50352-2005).北京:中国建筑工业出版社, 2005.]
[31] Wan Z, Zhang Y, Zhang Q, et al. Quality assessment and validation of the MODIS global land surface temperature.International Journal of Remote Sensing, 2004, 25(1):261-274.
[32] Peng F, Wong M S, Nichol J E, et al. Historical GIS data and changes in urban morphological parameters for the analysis of urban heat islands in Hong Kong. ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016, XLI-B2:55-62.
[33] Macdonald R W, Griffiths R F, Hall D J. An improved method for the estimation of surface roughness of obstacle arrays. Atmospheric Environment, 1998, 32(11):1857-1864.
[34] Hsieh C M, Huang H C. Mitigating urban heat islands:A method to identify potential wind corridor for cooling and ventilation. Computers, Environment and Urban Systems, 2016, 57:130-143.
[35] Bottema M, Mestayer P G. Urban roughness mapping-validation techniques and some first results. Journal of Wind Engineering&Industrial Aerodynamics, 1998, 76(2):163-173.
[2] Qiao Zhi, Tian Guangjin. Dynamic monitoring of the footprint and capacity for urban heat island in Beijing between2001 and 2012 based on MODIS. Journal of Remote Sensing, 2015, 19(3):476-484.[乔治,田光进.基于MODIS的2001年-2012年北京热岛足迹及容量动态监测.遥感学报, 2015, 19(3):476-484.]
[3] Zhao Jie, Du Ziqiang, Wu Zhitao, et al. Seasonal variations of day-and nighttime warming and their effects on vegetation dynamics in China's temperate zone. Acta Geographica Sinica, 2018, 73(3):395-404.[赵杰,杜自强,武志涛,等.中国温带昼夜增温的季节性变化及其对植被动态的影响.地理学报, 2018, 73(3):395-404.]
[4] Zhao L, Lee X, Smith R B, et al. Strong contributions of local background climate to urban heat islands. Nature, 2014,511(7508):216-219.
[5] Bai X, Dawson R J,ürge-Vorsatz D, et al. Six research priorities for cities and climate change. Nature, 2018, 555(7694):23-25.
[6] Irina M, Daniel J S. Solomon Susan. Pace of shifts in climate regions increases with global temperature. Nature Climate Change, 2013, 3(8):739-743.
[7] Santamouris M. Heat island research in Europe:The state of the art. Advances in Building Energy Research, 2007, 1(1):123-150.
[8] Zhou B, Rybski D, Kropp Jürgen P. The role of city size and urban form in the surface urban heat island. Scientific Reports, 2017, 7(1):1-9.
[9] Yang Jun, Guo Andong, Xi Jianchao, et al. Spatial-temporal differentiation of three-dimensional urban landscape pattern:A case study of Zhongshan District in Dalian. Acta Geographica Sinica, 2017, 72(4):646-656.[杨俊,国安东,席建超,等.城市三维景观格局时空分异特征研究:以大连市中山区为例.地理学报, 2017, 72(4):646-656.]
[10] Chen Liding, Sun Ranhao, Liu Hailian. Eco-environmental effects of urban landscape pattern changes:Progresses,problems, and perspectives. Acta Ecologica Sinica, 2013, 33(4):1043-1050.[陈利顶,孙然好,刘海莲.城市景观格局演变的生态环境效应研究进展.生态学报, 2013, 33(4):1043-1050.]
[11] Wong M S, Nichol J E, To P H, et al. A simple method for designation of urban ventilation corridors and its application to urban heat island analysis. Building and Environment, 2010, 45(8):1880-1889.
[12] Shi Y, Katzschner L, Ng E. Modelling the fine-scale spatiotemporal pattern of urban heat island effect using land use regression approach in a megacity. Science of the Total Environment, 2017, 618:891-904.
[13] Zhang Jiahua, Meng Qianwen, Li Xin, et al. Urban heat island variations in Beijing region in multi spatial and temporal scales. Scientia Geographica Sinica, 2011, 31(11):1349-1354.[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.地理科学, 2011, 31(11):1349-1354.]
[14] Hsieh C M, Huang H C. Mitigating urban heat islands:A method to identify potential wind corridor for cooling and ventilation. Computers, Environment and Urban Systems, 2016, 57:130-143.
[15] Yang J, Jin S, Xiao X, et al. Local climate zone ventilation and urban land surface temperatures:Towards a performancebased and wind-sensitive planning proposal in megacities. Sustainable Cities and Society, 2019, 47:101487. DOI:10.1016/j.scs.2019.101487.
[16] Ren Chao, Yuan Chao, He Zhengjun, et al. A study of air path and its application in urban planning. Urban Planning Forum, 2014(3):52-60.[任超,袁超,何正军,等.城市通风廊道研究及其规划应用.城市规划学刊, 2014(3):52-60.]
[17] Ng E, Yuan C, Chen L, et al. Improving the wind environment in high-density cities by understanding urban morphology and surface roughness:A study in Hong Kong. Landscape and Urban Planning, 2011, 101(1):59-74.
[18] Yang Junyan, Zhang Tao, Tan Ying. Research on urban wind environment:Technology evolution and integration of evaluation systems. South Architecture, 2014, 1(3):31-38.[杨俊宴,张涛,谭瑛.城市风环境研究的技术演进及其评价体系整合.南方建筑, 2014, 1(3):31-38.]
[19] Wang Tianxing, Chen Songlin, Yan Guangjian. Estimation of land surface parameters and spatio-temporal characteristics of urban heat island. Scientia Geographica Sinica, 2009, 29(5):697-702.[王天星,陈松林,阎广建.地表参数反演及城市热岛时空演变分析.地理科学, 2009, 29(5):697-702.]
[20] Weng Q. Thermal infrared remote sensing for urban climate and environmental studies:Methods, applications, and trends. Isprs Journal of Photogrammetry and Remote Sensing, 2009, 64(4):335-344.
[21] Katoa S, Kouyamaa T, Nakamuraa R, et al. Simultaneous retrieval of temperature and area according to sub-pixel hotspots from nighttime Landsat 8 OLI data. Remote Sensing of Environment, 2018, 204:276-286.
[22] Yao Yonghui, Zhang Baiping. MODIS-based estimation of air temperature and heating-up effect of the Tibetan Plateau.Acta Geographica Sinica, 2013, 68(1):95-107.[姚永慧,张百平.基于MODIS数据的青藏高原气温与增温效应估算.地理学报, 2013, 68(1):95-107.]
[23] Xu Weiyan, Sun Rui, Jin Zhifeng, et al. Estimation of near surface air temperature based on MODIS data.Meteorological and Environmental Sciences, 2015, 38(1):1-6.[徐伟燕,孙睿,金志凤,等.基于MODIS数据的近地表气温估算.气象与环境科学, 2015, 38(1):1-6.]
[24] Gulbe L, Caune V, Korats G. Urban area thermal monitoring:Liepaja case study using satellite and aerial thermal data.International Journal of Applied Earth Observation and Geoinformation, 2017, 63:45-54.
[25] Yuan Zhen, Wu Xiangli, Zang Shuying, et al. Cooling effect of green patche based on TM image in Harbin downtown city. Scientia Geographica Sinica, 2017, 37(10):1600-1608.[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.地理科学, 2017, 37(10):1600-1608.]
[26] Yang J, Wang Y, Xiao X, et al. Spatial differentiation of urban wind and thermal environment in different grid sizes.Urban Climate, 2019, 28:100458. DOI:10.1016/j.uclim.2019.100458.
[27] Yang J, Sun J, Ge Q S, et al. Assessing the impacts of urbanization-associated green space on urban land surface temperature:A case study of Dalian, China. Urban Forestry&Urban Greening, 2017, 22:1-10.
[28] Zhou D C, Zhao S Q, Liu S G, et al. Surface urban heat island in China's 32 major cities:Spatial patterns and drivers.Remote Sensing of Environment, 2014, 152:51-61.
[29] Tran H, Uchihama D, Ochi S, et al. Assessment with satellite data of the urban heat island effects in Asian mega cities.International Journal of Applied Earth Observation and Geoinformation, 2006, 8(1):34-48.
[30] Code for Design of Civil Buildings(GB50352-2005). Beijing:China Architecture and Building Press, 2005.[民用建筑设计通则(GB50352-2005).北京:中国建筑工业出版社, 2005.]
[31] Wan Z, Zhang Y, Zhang Q, et al. Quality assessment and validation of the MODIS global land surface temperature.International Journal of Remote Sensing, 2004, 25(1):261-274.
[32] Peng F, Wong M S, Nichol J E, et al. Historical GIS data and changes in urban morphological parameters for the analysis of urban heat islands in Hong Kong. ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016, XLI-B2:55-62.
[33] Macdonald R W, Griffiths R F, Hall D J. An improved method for the estimation of surface roughness of obstacle arrays. Atmospheric Environment, 1998, 32(11):1857-1864.
[34] Hsieh C M, Huang H C. Mitigating urban heat islands:A method to identify potential wind corridor for cooling and ventilation. Computers, Environment and Urban Systems, 2016, 57:130-143.
[35] Bottema M, Mestayer P G. Urban roughness mapping-validation techniques and some first results. Journal of Wind Engineering&Industrial Aerodynamics, 1998, 76(2):163-173.