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Remote Sensing for Land & Resources    2020, Vol. 32 Issue (1) : 247-254     DOI: 10.6046/gtzyyg.2020.01.33
Study on the relationship between impervious surface coverage and artificial heat in new urban districts: A case study of Xixian New District, Shaanxi Province
Ru WANG1,2, Yanfang ZHANG1,2(), Hongmin ZHANG1,2, Yun LI1,2
1. College of Geography and Tourism, Shaanxi Normal University, Xi’an 710119, China
2. National Experimental Teaching Demonstration Center of Geography (Shaanxi Normal University), Xi’an 710119, China
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Based on Landsat data, the authors extracted the impervious surface coverage of the two sceneries in Xixian New District in 2007 and 2016 by the linear spectral mixture model decomposition method, and extracted the artificial thermal information by the surface energy balance method in the same period, and investigated the relationship between them. The results are as follows: ① From 2007 to 2016, the impervious surface expanded from 294.93 km 2 to 362.62 km 2, and gradually changed from natural surface and low coverage to medium and high coverage. ② In 2016, the regional differences of anthropogenic heat in the study area were significant. The high-value areas were concentrated in the north-central part of Fengdong New Town and around Xianyang International Airport of Airport New Town, and were scattered in the central part of Qinhan New Town, northern part of Fengxi New Town and part of Jinghe New Town. ③ The mean values of impervious coverage and anthropogenic thermal mean values of land use showed the tendency of construction land>cultivated land>woodland>water body. ④ There was a positive correlation between impervious coverage and artificial heat, with a correlation coefficient of 0.97. The rate of increase of artificial heat values with impervious coverage had the tendency of Airport New Town>Fengdong New Town>Jinghe New Town>Qinhan New Town>Fengxi New Town.

Keywords Xixian New District      impervious surface coverage      anthropogenic heat      linear spectral mixing model      surface energy balance method     
:  TP79  
Corresponding Authors: Yanfang ZHANG     E-mail:
Issue Date: 14 March 2020
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Yanfang ZHANG
Hongmin ZHANG
Yun LI
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Ru WANG,Yanfang ZHANG,Hongmin ZHANG, et al. Study on the relationship between impervious surface coverage and artificial heat in new urban districts: A case study of Xixian New District, Shaanxi Province[J]. Remote Sensing for Land & Resources, 2020, 32(1): 247-254.
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Fig.1  Extent of study area
参数 建设用地 水体 耕地 林地
Zom/m 0.33 0.000 03 0.1 0.3
Zoh/m 0.003 3 0.000 088 0.001 0.000 3
d/m 1.66 0.05 0.1 1.5
Tab.1  Zom, Zoh and d for different surface coverage types
Fig.2  Spatial distribution of ISC
覆盖度类型 2016年
自然地表 低覆盖度 中覆盖度 高覆盖度 极高覆盖度 合计
2007年 自然地表 61.01 77.05 61.76 31.89 17.89 249.60
低覆盖度 113.75 90.39 92.37 57.29 27.02 380.82
中覆盖度 34.87 32.65 44.79 31.93 9.67 153.91
高覆盖度 8.97 10.99 23.57 18.17 4.79 66.49
极高覆盖度 3.82 6.25 16.11 11.08 4.25 41.51
合计 222.42 217.33 238.60 150.36 63.62 892.33
Tab.2  ISC transfer matrix from 2007 to 2016(km2)
Fig.3  Distribution of proportion of impervious surfaces in each region from 2007 to 2016
Fig.4  Anthropogenic heat spatial distribution
Fig.5  Anthropogenic heat profile analysis
Fig.6  Comparison of ISC and anthropogenic heat in different regions
Fig.7  Land use in the study area in 2016
地表类型 ISC均值 人为热均值/(W·m-2)
水体 0.02 -70.76
林地 0.32 1.79
耕地 0.37 46.06
建筑用地 0.59 140.02
Tab.3  Statistical results of ISC and anthropogenic heat mean values of land use
Fig.8  Scattered plots of the relationship between ISC and anthropogenic heat for each land use
Fig.9  Relationship between ISC and anthropogenic heat
地区 拟合方程 R2
泾河新城 y=93.25x+11.76 0.94
空港新城 y=113.49x+11.52 0.97
秦汉新城 y=77.17x+29.18 0.95
沣东新城 y=105.22x+38.04 0.94
沣西新城 y=68.55x+20.28 0.94
全区 y=83.19x+23.56 0.97
Tab.4  Fitting equation of ISC and anthropogenic heat in the study area in 2016
[1] Weng Q . Remote sensing of impervious surfaces in the urban areas:Requirements,methods,and trends[J]. Remote Sensing of Environment, 2012,117:34-49.
[2] 袁超 . 基于光谱混合分解模型的城市不透水面遥感估算方法研究[D]. 长沙:中南大学, 2008.
[2] Yuan C . Estimation Method of Urban Impervious Surfaces Based on Spectral Mixed Decomposition Model[D]. Changsha:Central South University, 2008.
[3] Torrance K E, Shun J S W . Time-varying energy consumption as a factor in urban climate[J]. Atmospheric Environment, 1976,10(4):329-337.
[4] 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.
[4] Xiao R B, Ouyang Z Y, Li W F , et al. Eco-environmental effects of urban heat island[J]. Journal of Ecology, 2005,25(8):2055-2060.
[5] 王业宁, 孙然好, 陈利顶 . 北京市区车辆热排放及其对小气候的影响[J]. 生态学报, 2017,37(3):953-959.
[5] Wang Y N, Sun R H, Chen L D . Study on the impact of vehicle emissions on microclimate in Beijing metropolis[J]. Acta Ecologica Sinica, 2017,37(3):953-959.
[6] 伶华, 刘辉志, 桑建国 , 等. 城市人为热对北京热环境的影响[J]. 气候与环境研究, 2004. 9(3):409-421.
[6] Ling H, Liu H Z, Sang J G , et al. Effects of urban anthropogenic heat on Beijing’s thermal environment[J]. Climate and Environment Studies, 2004,9(3):409-421.
[7] 姬翠翠, 贾永红, 李晓松 , 等. 线性/非线性光谱混合模型估算白刺灌丛植被覆盖度[J]. 遥感学报, 2016,20(6):1402-1412.
[7] Ji C C, Jia Y H, Li X S , et al. Estimation of vegetation coverage of Nitraria spinosa shrubs by linear/non-linear spectral hybrid model[J]. Journal of Remote Sensing, 2016,20(6):1402-1412.
[8] 王浩, 吴炳方, 李晓松 , 等. 流域尺度的不透水面遥感提取[J]. 遥感学报, 2011,15(2):388-400.
[8] Wang H, Wu B F, Li X S , et al. Remote sensing extraction of impervious surface at watershed scale[J]. Journal of Remote Sensing, 2011,15(2):388-400.
[9] Wu C, Murray A T . Estimating impervious surface distribution by spectral mixture analysis[J]. Remote Sensing of Environment, 2003,84(4):493-505.
[10] 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: 10.6046/gtzyyg.2004.03.07.
[10] Qin Z H, Li W J, Xu B , et al. Estimation of surface emissivity within TM6 band of Landsat[J]. Land and Resources Remote Sensing, 2004,16(3):28-32.doi: 10.6046/gtzyyg.2004.03.07.
[11] 宋挺, 段峥, 刘军志 , 等. Landsat 8数据地表温度反演算法对比[J]. 遥感学报, 2015,19(3):451-464.
[11] Song T, Duan Z, Liu J Z , et al. Comparison of land surface temperature inversion algorithms for Landsat8 data[J]. Journal of Remote Sensing, 2015,19(3):451-464.
[12] 徐涵秋 . 新型Landsat8卫星影像的反射率和地表温度反演[J]. 地球物理学报, 2015,58(3):741-747.
[12] Xu H Q . New Landsat 8 satellite image reflectivity and surface temperature inversion[J]. Journal of Geophysics, 2015,58(3):741-747.
[13] 赵英时 . 遥感应用分析原理与方法[M]. 北京: 科学出版社, 2003: 235.
[13] Zhao Y S. Principles and Methods of Remote Sensing Application Analysis[M]. Beijing: Science Press, 2003: 235.
[14] 王煜东, 赵小艳, 徐向华 , 等. 南京地区地表热通量的遥感反演分析[J]. 生态环境学报, 2016,25(4):636-646.
[14] Wang Y D, Zhao X Y, Xu X H , et al. Remote sensing inversion analysis of surface heat flux in Nanjing area[J]. Journal of Ecology and Environment, 2016,25(4):636-646.
[15] Bastiaanssen W G M . SEBAL-based sensible and latent heat fluxes in the irrigated Gediz Basin,Turkey[J]. Journal of hydrology, 2000,229(1):87-100.
[16] 李宝富, 陈亚宁, 李卫红 , 等. 基于遥感和SEBAL模型的塔里木河干流区蒸散发估算[J]. 地理学报, 2011,66(9):1230-1238.
[16] Li B F, Chen Y N, Li W H , et al. Estimation of evapotranspiration in the main stream of Tarim River based on remote sensing and SEBAL model[J]. Journal of Geography, 2011,66(9):1230-1238.
[17] Zhou Y, Weng Q, Gurney K R , et al. Estimation of the relationship between remotely sensed anthropogenic heat discharge and building energy use[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2012,67(none):65-72.
[18] Kato S, Yamaguchi Y . A nalysis of urban heat-island effect using ASTER and ETM+ data:Separation of anthropo-genic heat discharge and natural heat radiation from sensible heat flux[J]. Remote Sensing of Environment, 2005,99(1):44-54.
[19] Nishida K, Nemani R R, Running S W , et al. An operational remote sensing algorithm of land surface evaporation[J]. Journal of Geophysical Research:Atmospheres, 2003,108(D9):1-14.
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