武汉市夏季城市热岛与不透水面增温强度时空分布

doi:10.12082/dqxxkx.2018.180495

地球信息科学学报 ›› 2019, Vol. 21 ›› Issue (2): 226-235.doi: 10.12082/dqxxkx.2018.180495

• 地理空间分析综合应用 • 上一篇下一篇

武汉市夏季城市热岛与不透水面增温强度时空分布

樊智宇^1,^2,³(), 詹庆明^1,^2,^3,^4,^*(), 刘慧民^1,^2,³, 杨晨^1,^2,³, 夏宇⁴

1. 武汉大学城市设计学院,武汉 430072
2. 武汉大学数字城市研究中心,武汉 430072
3. 地球空间信息技术协同创新中心, 武汉 430079
4. 武汉大学测绘遥感信息工程国家重点实验室,武汉 430079

收稿日期:2018-09-29 修回日期:2018-11-23 出版日期:2019-02-20 发布日期:2019-01-30
通讯作者: 詹庆明 E-mail:zhiyufan@whu.edu.cn;qmzhan@whu.edu.cn
作者简介:
作者简介：樊智宇（1996-）,男,江苏抚州人,硕士生,主要从事城市遥感与城市大数据分析的研究。E-mail: zhiyufan@whu.edu.cn
基金资助:
国家自然科学基金项目（51878515、51378399、41331175）

Spatial-temporal Distribution of Urban Heat Island and the Heating Effect of Impervious Surface in Summer in Wuhan

Zhiyu FAN^1,^2,³(), Qingming ZHAN^1,^2,^3,^4,^*(), Huimin LIU^1,^2,³, Chen YANG^1,^2,³, Yu XIA⁴

1. School of Urban Design, Wuhan University, Wuhan 430072, China
2. Digital City Research Center, Wuhan University, Wuhan 430072, China
3. Collaborative Innovation Center of Geospatial Technology, Wuhan 430079, China
4.The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

Received:2018-09-29 Revised:2018-11-23 Online:2019-02-20 Published:2019-01-30
Contact: Qingming ZHAN E-mail:zhiyufan@whu.edu.cn;qmzhan@whu.edu.cn
Supported by:
National Natural Science Foundation of China, No.51878515, 51378399, 41331175

摘要/Abstract

摘要：

城市化的不断发展使自然地表不断被不透水面所取代,城市地表温度高于乡村,形成了显著的热岛效应。城市热岛给城市生态发展与人类健康带来了严重的负面影响,对其空间模式与背后形成机制的研究意义重大。本文以武汉市为例,基于2001、2007和2016年夏季Landsat系列影像使用辐射传导方程法反演了城市地表温度,并采用MOD11A1数据进行了验证;同时,计算了不同时期的城市温度等级和热岛比例指数,分析了城市热岛的时空变化。此外,为了探究热岛效应形成的主要原因,即不透水面与热环境的关系,全局角度使用多元线性回归分析对比了其增温效果与植被水体降温效果的强弱,空间局部角度采用地理加权回归结合地形数据得到了其增温强度的时空变化。结果表明：① 辐射传导方程法适用于实验中研究区的反演;武汉市城市热岛比例指数先增后减,但温度等级高的地区仍在不断扩张;② 多元线性回归可以准确地反映不同地表覆盖对地表温度的影响, $R 2$ 值为0.910,总体上武汉市不透水面的增温效果强于植被的降温效果,并弱于水体的降温效果; ③ 2001-2016年不透水面增温强度较高区域的分布呈现“单中心”到“多中心”的变化趋势,由单一集中于中心城区变为了分散集中于三环线附近的汉阳沌口工业区、青山工业区、阳逻开发区和东西湖区等地区。综上所述,武汉市夏季热环境问题仍然较为严重,城市外部地区的不透水面增温强度正在逐渐增大,规划治理应当给予这些地区更多的关注。

关键词: 武汉市, 热岛效应, 热红外反演, 不透水面, 多元线性回归, 地理加权回归

Abstract:

With the advancement of urbanization, natural land cover has been continuously replaced by impervious surface which resulted in the phenomenon of Urban Heat Island (UHI). UHI can lead to serious negative effects on urban ecology and residents' health, so is of great significance to study the corresponding spatial pattern and dynamic change. Based on 3 summer Landsat images acquired in 2001, 2007 and 2016 of Wuhan, this paper retrieved Land Surface Temperature (LST) using Radiative Transfer Function (RTF) method and verified the results by MOD11A1 which is the daily LST product of MODIS. Furthermore, LST grade and UHI ratio index (URI) were calculated to analyze the corresponding spatial-temporal variation. We also explored the relationship between LST and impervious surface. Globally, the multiple linear regression method was applied to compare the heating effect of impervious surface with the cooling effect of vegetation and water. Locally, we used Geographically Weighted Regression (GWR) to analyze the spatial-temporal variation of the heating effect of impervious surface combined with topographic data. The results indicated that: ① RTF method is suitable for retrieving LST in the study area. URI of Wuhan ascended from 0.42 in 2001 to 0.54 in 2007, and then descended to 0.51 in 2016. However, the areas with high temperature are still expanding; ② The multiple linear regression achieved a desirable fitting accuracy with R^2 being 0.910 because it covered the impact of 3 land cover types on LST simultaneously. Overall, the heating effect caused by impervious surface in Wuhan is stronger than the cooling effect caused by vegetation, but weaker than the cooling effect caused by water; ③ From 2001 to 2016, the distribution of areas with high heating effect of impervious surface showed a trend from "single center" to "multi-center". The original single center which is located in the center of city expanded to multiple center areas covering the districts near the Third Ring Road such as Hanyang Zhuankou Industrial Area, Qingshan Industrial Area, Yangluo Open Economic Zone and Dongxihu District. Therefore, the UHI phenomenon in Wuhan is still serious in summer. The heating effect of impervious surface is intensifying in suburb areas. So urban planners should pay more attention to these areas to mitigate the heat stress.

Key words: Wuhan, heat island effect, thermal retrieve, impervious surface, multiple linear regression, Geographically Weighted Regression (GWR)

樊智宇, 詹庆明, 刘慧民, 杨晨, 夏宇. 武汉市夏季城市热岛与不透水面增温强度时空分布[J]. 地球信息科学学报, 2019, 21(2): 226-235.DOI:10.12082/dqxxkx.2018.180495

Zhiyu FAN, Qingming ZHAN, Huimin LIU, Chen YANG, Yu XIA. Spatial-temporal Distribution of Urban Heat Island and the Heating Effect of Impervious Surface in Summer in Wuhan[J]. Journal of Geo-information Science, 2019, 21(2): 226-235.DOI:10.12082/dqxxkx.2018.180495

图/表 9

表1

表2

辐射传导方程法相关参数"

卫星	$K 1$ /(W/(m²?sr?μm))	$K 2$ /K
Ladnsat5	607.76	1260.56
Ladnsat7	666.09	1282.71
Ladnsat8	774.89	1321.08

表2

表3

图1

图2

表4

回归模型建立结果"

模型名称	回归公式	类型	拟合系数 $R 2$
不透水面-地表温度	$Y$ =32.09+11.90 $X 1$ *	一元回归	0.812
水体-地表温度	$Y$ =38.48-9.81 $X 2$ *	一元回归	0.607
植被-地表温度	$Y$ =39.82-6.09 $X 3$ *	一元回归	0.166
不透水面、水体、植被-地表温度	$Y$ =38.64+5.05 $X 1$ -9.89 $X 2$ -4.70 $X 3$ *	多元回归	0.910

表4

表5

GWR全局拟合系数结果"

拟合系数	2001-07-22	2007-07-31	2016-07-23
$R 2$	0.918	0.915	0.929
$R 2 Adjusted$	0.917	0.914	0.928

表5

图3

图4

参考文献 36

[1]	潘涛,张弛,杜国明,等.城乡不透水面增长格局及地表温度的响应特征研究[J].地球信息科学学报,2016,18(1):134-142. doi: 10.3724/SP.J.1047.2017.00134
	[ Pan T, Zhang C, Du G M, et al.Pattern of impervious surface change and its effect on land surface temperature in urban and rural areas[J]. Journal of Geo-information Science, 2016,18(1):134-142. ] doi: 10.3724/SP.J.1047.2017.00134
[2]	唐菲,徐涵秋.城市不透水面与地表温度定量关系的遥感分析[J].吉林大学学报(地球科学版),2013,43(6):1987-1996.
	[ Tang F, Xu H Q.Quantitative relationship between impervious surface and land surface temperature based on remote sensing technology[J]. Journal of Jilin University (Earth Science Edition), 2013,43(6):1987-1996. ]
[3]	Weng Q.A remote sensing-GIS evaluation of urban expansion and its impact on surface temperature in the Zhujiang Delta, China[J]. International Journal of Remote Sensing, 2001,22(10):1999-2014. doi: 10.1080/713860788
[4]	侯浩然,丁凤,黎勤生.近20年来福州城市热环境变化遥感分析[J].地球信息科学学报,2018,20(3):385-395.
	[ Hou H R, Ding F, Li Q S.Remote sensing analysis of changes of urban thermal environment of Fuzhou city in China in the past 20 years[J]. Journal of Geo-information Science, 2018,20(3):385-395. ]
[5]	肖荣波,欧阳志云,李伟峰,等.城市热岛的生态环境效应[J].生态学报,2005,25(8):2055-2060. doi: 10.3321/j.issn:1000-0933.2005.08.032
	[ Xiao R B, Ouyang Z Y, Li W F, et al.A review of the eco-environmental consequences of urban heat islands[J]. Acta Ecologica Sinica, 2005,25(8): 2055-2060. ] doi: 10.3321/j.issn:1000-0933.2005.08.032
[6]	Peng J, Xie P, Liu Y, et al.Urban thermal environment dynamics and associated landscape pattern factors: A case study in the Beijing metropolitan region[J]. Remote Sensing of Environment, 2016,173:145-155. doi: 10.1016/j.rse.2015.11.027
[7]	Kolokotroni M, Zhang Y, Watkins R.The London Heat Island and building cooling design[J]. Solar Energy, 2007,81(1):102-110. doi: 10.1016/j.solener.2006.06.005
[8]	Liu H, Zhan Q, Yang C, et al.The multi-timescale temporal patterns and dynamics of land surface temperature using Ensemble Empirical Mode Decomposition[J]. The Science of the total environment, 2018,652:243-255.
[9]	Weng Q.Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends[J]. Remote Sensing of Environment, 2012,117(2):34-49. doi: 10.1016/j.rse.2011.02.030
[10]	董磊磊,潘竟虎,王卫国,等.基于遥感和GWR的兰州中心城区夏季热场格局及与土地覆盖的关系[J].土壤,2018,50(2):404-413.
	[ Dong L L, Pan J H, Wang W G, et al.Spatiotemporal pattern of summer thermal field and its relationship with land cover in Lanzhou based on RS and GWR[J]. Soils, 2018,50(2):404-413. ]
[11]	王佳,钱雨果,韩立建,等.基于GWR模型的土地覆盖与地表温度的关系——以京津唐城市群为例[J].应用生态学报,2016,27(7):2128-2136. doi: 10.13287/j.1001-9332.201607.008
	[ Wang J, Han Y G, Han L J, et al.Relationship between land surface temperature and land cover types based on GWR model: A case of Beijing-Tianjin-Tangshan urban agglomeration, China[J]. Chinese Journal of Applied Ecology, 2016,27(7):2128-2136. ] doi: 10.13287/j.1001-9332.201607.008
[12]	毛克彪,唐华俊,周清波,等.用辐射传输方程从MODIS数据中反演地表温度的方法[J].兰州大学学报(自然科学版),2007(4):12-17. doi: 10.3321/j.issn:0455-2059.2007.04.003
	[ Mao K B, Tang H J, Zhou Q B, et al.Retrieving land surface temperature from MODIS data by using radiance transfer equation[J]. Journal of Lanzhou University(Natural Sciences),2007(4): 12-17. ] doi: 10.3321/j.issn:0455-2059.2007.04.003
[13]	覃志豪,Zhang M H, Karnieli A,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. doi: 10.1142/S0252959901000401
	[ Qin Z H, Zhang M H, 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. ] doi: 10.1142/S0252959901000401
[14]	Sobrino J A, Jiménez-Muñoz J C, Paolini L. Land surface temperature retrieval from Landsat TM5[J]. Remote Sensing of Environment, 2004,90(4):434-440. doi: 10.1016/j.rse.2004.02.003
[15]	Jiménez-Muñoz J C, Sobrino J A. A generalized single-channel method for retrieving land surface temperature from remote sensing data[J]. Journal of Geophysical Research Atmospheres, 2003,108(D22):1-9. doi: 10.1029/2003JD003480
[16]	Yu X, Guo X, Wu Z.Land Surface Temperature retrieval from Landsat 8 TIRS-comparison between radiative transfer equation-based method, split window algorithm and single channel method[J]. Remote Sensing, 2014,6(10):9829-9852. doi: 10.3390/rs6109829
[17]	Su Y F, Foody G M, Cheng K S.Spatial non-stationarity in the relationships between land cover and surface temperature in an urban heat island and its impacts on thermally sensitive populations[J]. Landscape & Urban Planning, 2012,107(2):172-180. doi: 10.1016/j.landurbplan.2012.05.016
[18]	Li S, Zhao Z, Miaomiao X, et al.Investigating spatial non-stationary and scale-dependent relationships between urban surface temperature and environmental factors using geographically weighted regression[J]. Environmental Modelling & Software, 2010,25(12):1789-1800. doi: 10.1016/j.envsoft.2010.06.011
[19]	Estoque R C, Murayama Y, Myint S W.Effects of landscape composition and pattern on land surface temperature: An urban heat island study in the megacities of Southeast Asia[J]. Science of the Total Environment, 2017,577:349. doi: 10.1016/j.scitotenv.2016.10.195 pmid: 27832866
[20]	徐涵秋. 城市不透水面与相关城市生态要素关系的定量分析[J].生态学报,2009,29(5):2456-2462. doi: 10.3321/j.issn:1000-0933.2009.05.032
	[ Xu H Q.Quantitative analysis on the relationship of urban impervious surface with other components of the urban ecosystem[J]. Acta Ecologica Sinica 2009,29(5):2456-2462. ] doi: 10.3321/j.issn:1000-0933.2009.05.032
[21]	Xian G, Crane M.An analysis of urban thermal characteristics and associated land cover in Tampa Bay and Las Vegas using Landsat satellite data[J]. Remote Sensing of Environment, 2006,104(2):147-156. doi: 10.1016/j.rse.2005.09.023
[22]	赵梓淇,李丽光,王宏博,等.沈阳市区土地利用类型与地表温度关系研究[J].气象与环境学报,2016,32(6):102-108. doi: 10.3969/j.issn.1673-503X.2016.06.013
	[ Zhao Z Q, Li L G, Wang H B, et al.Study on the relationships between land use and land surface temperature in Shenyang urban districts[J]. Journal of Meteorology and Environment, 2016,32(6):102-108. ] doi: 10.3969/j.issn.1673-503X.2016.06.013
[23]	Kustas 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. doi: 10.1016/S0034-4257(03)00036-1
[24]	Florinsky I V, Kulagina T B, Meshalkina J L.Influence of topography on landscape radiation temperature distribution[J]. International Journal of Remote Sensing, 1994,15(16):3147-3153. doi: 10.1080/01431169408954317
[25]	覃志豪,李文娟,徐斌,等.陆地卫星TM6波段范围内地表比辐射率的估计[J].国土资源遥感,2004,16(3):28-32. doi: 10.3969/j.issn.1001-070X.2004.03.007
	[ Qin Z H, Li W J, Xu B, et al.The estimation of land surface emissivity for Landsat TM6[J]. Remote Sensing for Land & Resources, 2004,16(3):28-32. ] doi: 10.3969/j.issn.1001-070X.2004.03.007
[26]	Li J, Song C, Cao L, et al.Impacts of landscape structure on surface urban heat islands: A case study of Shanghai, China[J]. Remote Sensing of Environment, 2011,115(12):3249-3263. doi: 10.1016/j.rse.2011.07.008
[27]	Li X, Zhou W, Ouyang Z.Relationship between land surface temperature and spatial pattern of greenspace: What are the effects of spatial resolution[J]. Landscape & Urban Planning, 2013,114(8):1-8. doi: 10.1016/j.landurbplan.2013.02.005
[28]	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. doi: 10.1016/j.jag.2015.11.006
[29]	Fotheringham A S, Charlton M E, Brunsdon C.Geographically weighted regression: A natural evolution of the expansion method for spatial data analysis[J]. Environment & Planning A, 1998,30(11):1905-1927.
[30]	Brunsdon C, Fotheringham S, Charlton M.Geographically weighted regression-modelling spatial non-stationarity[J]. Journal of the Royal Statistical Society, 1998,47(3):431-443. doi: 10.1111/1467-9884.00145
[31]	覃文忠. 地理加权回归基本理论与应用研究[D].上海:同济大学,2007.
	[ Qin W Z.The Basic theoretics and application research on geographically weighted regression[D]. Shanghai: Tongji University, 2007. ]
[32]	Miliaresis G C.An unstandardized selective variance reduction script for elevation, latitude and longitude decorrelation stretch of multi-temporal LST imagery[J]. Modeling Earth Systems & Environment. 2016,2(1):41. doi: 10.1007/s40808-016-0103-0
[33]	王宏博,李丽光,赵梓淇,等.基于TM/ETM+数据的沈阳市各区城市热岛特征[J].生态学杂志,2015,34(1):219-226.
	[ Wang H B, Li L G, Zhao Z Q, et al.Urban heat island variation of each district in Shenyang based on TM /ETM+ data[J]. Chinese Journal of Ecology, 2015,34(1):219-226. ]
[34]	Xu H Q, Chen B Q.Remote sensing of the urban heat island and its changes in Xiamen City of SE China[J]. Journal of Environmental Sciences, 2004,16(2):276-281. doi: 10.3321/j.issn:1001-0742.2004.02.022 pmid: 15137654
[35]	张好,徐涵秋,李乐,等.成都市热岛效应与城市空间发展关系分析[J].地球信息科学学报,2014,16(1):70-78. doi: 10.3724/SP.J.1047.2014.00070
	[ Zhang H, Xu H Q, Li L, et al.Analysis of the Relationship between urban heat island effect and urban expansion in Chengdu, China[J]. Journal of Geo-information Science, 2014,16(1):70-78. ] doi: 10.3724/SP.J.1047.2014.00070
[36]	Liu X, Hu G, Chen Y, et al.High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth Engine Platform[J]. Remote Sensing of Environment, 2018,209:227-239. doi: 10.1016/j.rse.2018.02.055

卫星	影像日期	成像时间	气象温度/℃	相对湿度/%	气压/kpa	风向	风速/ mph
Landsat7	2001-07-22	10:56AM	34	63	100.2	SW	13
Landsat5	2007-07-31	10:49AM	35	67	100.2	SW	9
Landsat8	2016-07-23	10:45AM	35	59	100.2	SSW	13

数据	2001年7月22日			2007年7月31日			2016年7月23日
数据	最小温度	最大温度	平均温度	最小温度	最大温度	平均温度	最小温度	最大温度	平均温度
MOD11A1	28.39	48.57	37.54	25.55	44.45	34.77	25.29	46.10	36.22
Landsat影像反演	28.84	54.29	39.06	25.74	47.87	35.20	25.92	52.96	37.26

武汉市夏季城市热岛与不透水面增温强度时空分布

Spatial-temporal Distribution of Urban Heat Island and the Heating Effect of Impervious Surface in Summer in Wuhan

RichHTML

PDF (PC)

赞

可视化

被引次数

摘要/Abstract

引用本文

使用本文

图/表 9

参考文献 36

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	帅艳民, 马现伟, 曲歌, 邵聪颖, 刘涛, 刘守民, 黄华兵, 谷玲霄, 拉提帕·吐尔汗江, 梁继, 李玲. 协同多时相波谱特征的不透水面信息级联提取[J]. 地球信息科学学报, 2021, 23(1): 171-186.
[2]	刘菁菁, 刘雨思, 伊迪升, 杨静, 张晶. 北京市四环内街区尺度下的主题混合模式挖掘[J]. 地球信息科学学报, 2020, 22(6): 1370-1382.
[3]	柯新利, 肖邦勇, 郑伟伟, 马艳春, 李红艳. 城镇-农业-生态空间划定的多情景模拟[J]. 地球信息科学学报, 2020, 22(3): 580-591.
[4]	李培林, 刘小平, 黄应淮, 张鸿辉. 基于GEE平台的广州市主城区不透水面时间序列提取[J]. 地球信息科学学报, 2020, 22(3): 638-648.
[5]	刘稳, 詹庆明, 刘权毅, 司瑶, 黄启雷, 樊智宇. 地理国情监测成果与规划用地数据的关联转换方法[J]. 地球信息科学学报, 2020, 22(2): 161-174.
[6]	刘艳霞, 冯莉, 田慧慧, 阳少奇. 中国气候舒适度时空分布特征分析[J]. 地球信息科学学报, 2020, 22(12): 2338-2347.
[7]	汪小英, 李小漫, 沈镭, 王宜龙. 长江经济带城乡一体化对能源效率的空间效应分析[J]. 地球信息科学学报, 2020, 22(11): 2188-2198.
[8]	周佳, 赵亚鹏, 岳天祥, 卢涛. 结合HASM和GWR方法的省级尺度近地表气温估算[J]. 地球信息科学学报, 2020, 22(10): 2098-2107.
[9]	杜震洪, 吴森森, 王中一, 汪愿愿, 张丰, 刘仁义. 基于地理神经网络加权回归的中国PM_2.5浓度空间分布估算方法[J]. 地球信息科学学报, 2020, 22(1): 122-135.
[10]	蔡博文,王树根,王磊,邵振峰. 基于深度学习模型的城市高分辨率遥感影像不透水面提取[J]. 地球信息科学学报, 2019, 21(9): 1420-1429.
[11]	陈冰倩, 张友水, 程璟媛, 赵雪. 福州市地表温度热点及时空变化分析[J]. 地球信息科学学报, 2019, 21(5): 710-719.
[12]	邬群勇, 张良盼, 吴祖飞. 顾及空间异质性的出租载客与公交客流回归分析[J]. 地球信息科学学报, 2019, 21(3): 337-345.
[13]	董小刚,乔庆华,翟亮,孙立,甄云鹏. 基于改进引力模型的广场公园可达性研究[J]. 地球信息科学学报, 2019, 21(10): 1518-1526.
[14]	冯珊珊,樊风雷. Landsat/OLI与夜间灯光数据在提取城市不透水面中的精度差异分析[J]. 地球信息科学学报, 2019, 21(10): 1608-1618.
[15]	王菲菲, 赵小锋, 刘秀广, 刘嘉慧, 林剑艺. 城市地表净辐射通量的季相变化及与地表覆盖格局的关系研究[J]. 地球信息科学学报, 2018, 20(8): 1160-1168.