多空间尺度下城市热环境与不透水面聚集密度响应关系

孟庆岩; 王子安; 张琳琳; 孙震辉; 胡蝶; 杨天梁

doi:10.11834/jrs.20210263

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多空间尺度下城市热环境与不透水面聚集密度响应关系
Response relationship between urban thermal environment and impervious surface distribution density at multi-spatial scale： A case study of Beijing
2022年26卷第9期页码：1789-1801
DOI： 10.11834/jrs.20210263

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孟庆岩, 王子安, 张琳琳, 等. 多空间尺度下城市热环境与不透水面聚集密度响应关系[J]. 遥感学报, 2022,26(9):1789-1801.

MENG Qingyan, WANG Zi’an, ZHANG Linlin, et al. Response relationship between urban thermal environment and impervious surface distribution density at multi-spatial scale： A case study of Beijing[J]. National Remote Sensing Bulletin, 2022,26(9):1789-1801.
孟庆岩, 王子安, 张琳琳, 等. 多空间尺度下城市热环境与不透水面聚集密度响应关系[J]. 遥感学报, 2022,26(9):1789-1801. DOI： 10.11834/jrs.20210263.

MENG Qingyan, WANG Zi’an, ZHANG Linlin, et al. Response relationship between urban thermal environment and impervious surface distribution density at multi-spatial scale： A case study of Beijing[J]. National Remote Sensing Bulletin, 2022,26(9):1789-1801. DOI： 10.11834/jrs.20210263.

摘要

针对以往城市热环境与不透水面聚集密度响应规律研究不足的问题，本文以北京市为例，基于MODIS 数据计算城建区与边缘区平均温度差值为热岛强度，结合热岛强度等级特征，选取热岛强度等级期望值较高的6个典型区域（Ⅰ西二旗、西三旗、回龙观，Ⅱ东四、地安门、新街口，Ⅲ丰台区中部，Ⅳ四惠、定福庄、东坝，Ⅴ石景山区，Ⅵ北四环四季青桥附近），研究热岛强度等级与不透水面聚集密度的响应关系；进而利用Landsat数据反演地表温度，研究城建区与精细尺度下地表温度对不透水面聚集密度响应临界点与最优尺度。结果表明：（1）不同热岛强度等级的出现频率、其在白天与夜间的变化程度与不透水面聚集密度有一定相关性，且相近的不透水面聚集密度下所表现出热岛强度的变化特征相似。（2）主要城建区内地表平均温度与不透水面聚集密度的分布特征存在较显著的一致性，随着尺度半径,r,的增大其响应越明显。且地表温度对不透水面聚集密度的响应存在一个临界点，随着尺度半径,r,减小，响应临界点逐渐增大：尺度半径,r,=1000 m时，不透水面聚集密度达到60%后其影响趋于减弱；,r,=500 m、,r,=300 m时临界点分别为69%与83%。（3）不同典型区域的最优尺度存在差异，西二旗、西三旗、回龙观最优尺度为150 m，东四、地安门、新街口与四环四季青桥附近为60 m，而季节变化对最优尺度的影响较小。该最优尺度可在一定程度上衡量不同区域不透水面分布的破碎程度及地表类型的单一程度。本研究可为城市规划与管理、城市热岛治理提供参考依据。

Abstract

In view of the insufficient research on the response law of urban thermal environment and the Impervious Surface Distribution Density (ISDD), this paper selected Beijing as a case study. On the basis of the MODIS data, the average temperature difference between the urban construction and marginal areas was calculated as the UHI intensity (UHII). In combination with the characteristics of the UHII level, six typical regions (ⅠXierqi, Xisanqi, Huilongguan, ⅡDongsi, Xi’anmen, Xinjiekou, Ⅲthe middle of Fengtai District, ⅣSihui, Dingfuzhuang, Dongba, ⅤShijingshan District, Ⅵnear the Sijiqing bridge on the North Fourth Ring Road) with a high expected UHII level were selected to study the response relationship between the UHII level and the ISDD. Finally, Landsat data was used to invert the surface temperature to study the critical point and optimal scale of surface temperature response to the ISDD in urban construction areas and typical regions. Results show that the following. (1) The frequency of occurrence of UHII levels, their variation degree during the day and at night have a certain correlation with the ISDD, and the variation characteristics of the UHII under similar ISDDs are the same. (2) The distribution characteristics of the surface mean temperature in urban construction areas and the ISDD are obviously consistent. As the scale radius ,r, increases, the response is more obvious. Besides, there are critical points for the response of the surface temperature to ISDD. The response of critical points gradually increases with the decrease of scale radius ,r,. When scale radius ,r,=1000 m, the ISDD reaches 60%, and its influence tends to weaken; the critical points of ,r,=500 m and ,r,=300 m are 69% and 83%, respectively. (3) The optimal scales of different typical regions have differences. The optimal scale of Xierqi, Xisanqi, and Huilongguan is 150 m, and that of Dongsi, Di’anmen, Xinjiekou, and Qingqiao near the fourth ring is 60 m. However, seasonal changes have little impact on the optimal scale. The optimal scale can measure the degree of fragmentation of the impervious surface distribution in different regions and the single degree of surface type to some extent. This study can provide a reference for urban planning and UHI governance.

关键词

热岛效应不透水面聚集密度城建区临界点响应规律

Keywords

urban heat island effectimpervious surface distribution densityurban construction areacritical pointresponse law

references

Amiri R, Weng Q H, Alimohammadi A and Alavipanah S K. 2009. Spatial–temporal dynamics of land surface temperature in relation to fractional vegetation cover and land use/cover in the Tabriz urban area, Iran. Remote Sensing of Environment, 113(12): 2606-2617 [DOI: 10.1016/j.rse.2009.07.021http://dx.doi.org/10.1016/j.rse.2009.07.021]

Bauer M E, Heinert N J, Doyle J K and Yuan F. 2004. Impervious surface mapping and change monitoring using Landsat remote sensing, ASPRS Annual Conference Proceedings, May 23—28, Denver, Colorado. Bethesda, MD: American Society for Photogrammetry and Remote Sensing.

Beck B R. 1990. Measurement of the Magnetic and Temperature Dependence of the Electron-electron Anisotropic Temperature Relaxation Rate. San Diego: University of California.

Carlson T N and Arthur S T. 2000. The impact of land use — land cover changes due to urbanization on surface microclimate and hydrology: a satellite perspective. Global and Planetary Change, 25(1/2): 49-65 [DOI: 10.1016/S0921-8181(00)00021-7http://dx.doi.org/10.1016/S0921-8181(00)00021-7]

Deng C B and Wu C S. 2012. BCI: a biophysical composition index for remote sensing of urban environments. Remote Sensing of Environment, 127: 247-259 [DOI: 10.1016/j.rse.2012.09.009http://dx.doi.org/10.1016/j.rse.2012.09.009]

Ding H Y and Shi W Z. 2013. Land-use/land-cover change and its influence on surface temperature: a case study in Beijing city. International Journal of Remote Sensing, 34(15): 5503-5517 [DOI: 10.1080/01431161.2013.792966http://dx.doi.org/10.1080/01431161.2013.792966]

Estoque R C, Murayama Y and Myint S W. 2017. Effects of landscape composition and pattern on land surface temperature: an urban heat island study in the megacities of Southeast Asia. Science of the Total Environment, 577: 349-359 [DOI: 10.1016/j.scitotenv.2016.10.195http://dx.doi.org/10.1016/j.scitotenv.2016.10.195]

Gaffin S R, Rosenzweig C, Khanbilvardi R, Parshall L, Mahani S, Glickman H, Goldberg R, Blake R, Slosberg R B and Hillel D. 2008. Variations in New York city’s urban heat island strength over time and space. Theoretical and Applied Climatology, 94(1/2): 1-11 [DOI: 10.1007/s00704-007-0368-3http://dx.doi.org/10.1007/s00704-007-0368-3]

Imhoff M L, Zhang P, Wolfe R E and Bounoua L. 2010. Remote sensing of the urban heat island effect across biomes in the continental USA. Remote Sensing of Environment, 114(3): 504-513 [DOI: 10.1016/j.rse.2009.10.008http://dx.doi.org/10.1016/j.rse.2009.10.008]

Ji C P, Liu W D and Xuan C Y. 2006. Impact of urban growth on the heat island in Beijing. Chinese Journal of Geophysics, 49(1): 69-77

季崇萍, 刘伟东, 轩春怡. 2006. 北京城市化进程对城市热岛的影响研究. 地球物理学报, 49(1): 69-77 [DOI: 10.3321/j.issn:0001-5733.2006.01.010http://dx.doi.org/10.3321/j.issn:0001-5733.2006.01.010]

Li W F, Ouyang Z Y, Chen Q W and Mao J Q. 2008. Study on the spatial pattern of impervious surface using remote sensing data within the urban area of Beijing. Journal of Remote Sensing, 12(4): 603-612

李伟峰, 欧阳志云, 陈求稳, 毛劲乔. 2008. 基于遥感信息的北京硬化地表格局特征研究. 遥感学报, 12(4): 603-612 [DOI: 10.11834/jrs.20080479http://dx.doi.org/10.11834/jrs.20080479]

Lu D S and Weng Q H. 2006. Spectral mixture analysis of ASTER images for examining the relationship between urban thermal features and biophysical descriptors in Indianapolis, Indiana, USA. Remote Sensing of Environment, 104(2): 157-167 [DOI: 10.1016/j.rse.2005.11.015http://dx.doi.org/10.1016/j.rse.2005.11.015]

Mathew A, Khandelwal S and Kaul N. 2016. Spatial and temporal variations of urban heat island effect and the effect of percentage impervious surface area and elevation on land surface temperature: study of Chandigarh city, India. Sustainable Cities and Society, 26: 264-277 [DOI: 10.1016/j.scs.2016.06.018http://dx.doi.org/10.1016/j.scs.2016.06.018]

Qiao Z and Tian G J. 2015. Dynamic monitoring of the footprint and capacity for urban heat island in Beijing between 2001 and 2012 based on MODIS. Journal of Remote Sensing, 19(3): 476-484

乔治, 田光进. 2015. 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, 19(3): 476-484 [DOI: 10.11834/jrs.20154165http://dx.doi.org/10.11834/jrs.20154165]

Qin Z H, Li W J, Xu B and Zhang W C. 2003. Estimation method of land surface emissivity for retrieving land surface temperature from Landsat TM6 data//The 14th National Remote Sensing Technology Academic Exchange Meeting. Qingdao, China: Chinese Society for Oceanography

覃志豪, 李文娟, 徐斌, 张万昌. 2003. 利用Landsat TM6反演地表温度所需地表辐射率参数的估计方法. 第十四届全国遥感技术学术交流会. 青岛: 中国海洋学会

Quattrochi D, Luvall J C, Rickman D L, Estes Jr M G, Laymon C A and Howell B F. 2000. A decision support information system for urban landscape management using thermal infrared data. Photogrammetric Engineering and Remote Sensing, 66(10): 1195-1207 [DOI: 10.1080/01431160050121384http://dx.doi.org/10.1080/01431160050121384]

Ridd M K. 1995. Exploring a V-I-S (vegetation-impervious surface-soil) model for urban ecosystem analysis through remote sensing: comparative anatomy for cities. International Journal of Remote Sensing, 16(12): 2165-2185 [DOI: 10.1080/01431169508954549http://dx.doi.org/10.1080/01431169508954549]

Sobrino J A, Jiménez-Muñoz J C, Sòria G, Romaguera M, Guanter L, Moreno J, Plaza A and Martinez P. 2008. Land surface emissivity retrieval from different VNIR and TIR sensors. IEEE Transactions on Geoscience and Remote Sensing, 46(2): 316-327 [DOI: 10.1109/TGRS.2007.904834http://dx.doi.org/10.1109/TGRS.2007.904834]

Tang F and Xu H Q. 2013. Quantitative relationship between impervious surface and land surface temperature based on remote sensing technology. Journal of Jilin University (Earth Science Edition), 43(6): 1987-1996, 2017

唐菲, 徐涵秋. 2013. 城市不透水面与地表温度定量关系的遥感分析. 吉林大学学报(地球科学版), 43(6): 1987-1996, 2017 [DOI: 10.13278/j.cnki.jjuese.2013.06.030http://dx.doi.org/10.13278/j.cnki.jjuese.2013.06.030]

Tran H, Uchihama D, Ochi S and Yasuoka Y. 2006. Assessment with satellite data of the urban heat island effects in Asian mega cities. International Journal of Applied Earth Observation and Geoinformation, 8(1): 34-48 [DOI: 10.1016/j.jag.2005.05.003http://dx.doi.org/10.1016/j.jag.2005.05.003]

Unger J, Gál T, Rakonczai J, Mucsi L, Szatmári J, Tobak Z, Van Leeuwen B and Fiala K. 2009. Air temperature versus surface temperature in urban environment//The seventh International Conference on Urban Climate. Yokohama, Japan

Wang L, Meng Q Y, Wu J, Zhang J H and Zhang L L. 2015. Monitoring and analyzing spatio-temporal changes of heat island intensity in Beijing main urban construction area from 2005 to 2014. Journal of Geo-Information Science, 17(9): 1047-1054

王靓, 孟庆岩, 吴俊, 张佳晖, 张琳琳. 2015. 2005—2014年北京市主要城建区热岛强度时空格局分析. 地球信息科学学报, 17(9): 1047-1054 [DOI: 10.3724/SP.J.1047.2015.01047http://dx.doi.org/10.3724/SP.J.1047.2015.01047]

Wang X K, Meng Q Y, Li J., Hu X., Zhang L., 2021. Spatiotemporal evolution and thermal environmental effects of impervious surfaces in the main urban area of Beijing. Ecological Science, 40(1): 169–181

王宪凯, 孟庆岩, 李娟, 胡新礼, 张琳琳. 北京市主城区不透水面时空演变及其热环境效应研究. 生态科学, 2021, 40(1): 169-181 [DOI: 10.14108/j.cnki.1008-8873.2021.01.022http://dx.doi.org/10.14108/j.cnki.1008-8873.2021.01.022]

Xian G. 2008. Satellite remotely-sensed land surface parameters and their climatic effects for three metropolitan regions. Advances in Space Research, 41(11): 1861-1869 [DOI: 10.1016/j.asr.2007.11.004http://dx.doi.org/10.1016/j.asr.2007.11.004]

Xian G and Crane M. 2006. An analysis of urban thermal characteristics and associated land cover in Tampa Bay and Las Vegas using Landsat satellite data. Remote Sensing of Environment, 104(2): 147-156 [DOI: 10.1016/j.rse.2005.09.023http://dx.doi.org/10.1016/j.rse.2005.09.023]

Xiao R B, Ouyang Z Y, Zheng H, Li W F, Schienke E W and Wang X K. 2007. Spatial pattern of impervious surfaces and their impacts on land surface temperature in Beijing, China. Journal of Environmental Sciences, 19(2): 250-256 [DOI: 10.1016/S1001-0742(07)60041-2http://dx.doi.org/10.1016/S1001-0742(07)60041-2]

Xu H Q. 2009. Quantitative analysis on the relationship of urban impervious surface with other components of the urban ecosystem. Acta Ecologica Sinica, 29(5): 2456-2462

徐涵秋. 2009. 城市不透水面与相关城市生态要素关系的定量分析. 生态学报, 29(5): 2456-2462 [DOI: 10.3321/j.issn:1000-0933.2009.05.032http://dx.doi.org/10.3321/j.issn:1000-0933.2009.05.032]

Yang S B, Zhao X Y, Shen S H, Hai Y L and Fang Y X. 2010. Characteristics of urban heat island seasonal pattern in Beijing based on Landsat TM/ETM+ imagery. Transactions of Atmospheric Sciences, 33(4): 427-435

杨沈斌, 赵小艳, 申双和, 海玉龙, 方永侠. 2010. 基于Landsat TM/ETM+数据的北京城市热岛季节特征研究. 大气科学学报, 33(4): 427-435 [DOI: 10.3969/j.issn.1674-7097.2010.04.006http://dx.doi.org/10.3969/j.issn.1674-7097.2010.04.006]

Yang Y B and Pan P. 2011. Research on the impact of impervious surface area on urban heat island in Jiangsu Province//Proceedings of the SPIE 8286 International Symposium on Lidar and Radar Mapping. Nanjing: SPIE, 82861P [DOI: 10.1117/12.912517http://dx.doi.org/10.1117/12.912517]

Ye Z W. 2009. Relation between neighborhood with dense high-rises and thermal landscape using remote sensing. Beijing: Capital Normal University

叶智威. 2009. 基于遥感的高层密集街区与热景观关系研究. 首都师范大学

Yuan F and Bauer M E. 2007. Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery. Remote Sensing of Environment, 106(3): 375-386 [DOI: 10.1016/j.rse.2006.09.003http://dx.doi.org/10.1016/j.rse.2006.09.003]

Zhang J H, Hou Y Y, Li G C, Yan H and Yang L M. 2005. The diurnal and seasonal characteristics of urban heat island variation in Beijing city and surrounding areas and impact factors based on remote sensing satellite data. Science in China (Series D: Earth Sciences), 48(S2): 220-229

张佳华, 侯英雨, 李贵才, 延昊, 杨立民. 2005. 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. 中国科学(D辑: 地球科学), 35(S1): 187-194

Zhang X L, Zhang S W, Li Y, Xie Y F and Kuang W H. 2008. Correlation between urban thermal environment effect and land use pattern in Changchun. Resources Science, 30(10): 1564-1570

张新乐, 张树文, 李颖, 谢云峰, 匡文慧. 2008. 城市热环境与土地利用类型格局的相关性分析——以长春市为例. 资源科学, 30(10): 1564-1570 [DOI: 10.3321/j.issn:1007-7588.2008.10.018http://dx.doi.org/10.3321/j.issn:1007-7588.2008.10.018]

Zhang Y, Bao W J, Yu Q and Ma W C. 2012. Study on seasonal variations of the urban heat island and its interannual changes in a typical Chinese megacity. Chinese Journal of Geophysics, 55(4): 1121-1128

张艳, 鲍文杰, 余琦, 马蔚纯. 2012. 超大城市热岛效应的季节变化特征及其年际差异. 地球物理学报, 55(4): 1121-1128 [DOI: 10.6038/j.issn.0001-5733.2012.04.007http://dx.doi.org/10.6038/j.issn.0001-5733.2012.04.007]

Zhou J, Chen Y H, Li J, Weng Q H and Yi W B. 2008. A volume model for urban heat island based on remote sensing imagery and its application: a case study in Beijing. Journal of Remote Sensing, 12(5): 734-742

周纪, 陈云浩, 李京, 翁齐浩, 易文斌. 2008. 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. 遥感学报, 12(5): 734-742 [DOI: 10.11834/jrs.20080595http://dx.doi.org/10.11834/jrs.20080595]

Zhou W Q, Huang G L and Cadenasso M L. 2011. Does spatial configuration matter? Understanding the effects of land cover pattern on land surface temperature in urban landscapes. Landscape and Urban Planning, 102(1): 54-63 [DOI: 10.1016/j.landurbplan.2011.03.009http://dx.doi.org/10.1016/j.landurbplan.2011.03.009]

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