基于半变异函数的重庆市地表温度空间异质性建模及多尺度特征分析
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摘要
城市地表温度空间异质性的研究对理解城市地表温度空间结构有重要意义。本文利用大气校正法反演地表温度,基于半变异函数构建城市地表温度空间异质性模型,并进一步分析不同空间尺度下地表温度空间异质性结构参数的变化规律。以2013年6月16日的Landsat 8为数据源,以重庆为研究区开展实验,研究结果表明:①不同空间尺度下重庆地表温度空间异质性均呈现指数模型分布特征;②在30 m空间尺度下,地表温度空间异质性主要是由空间结构引起,但随机因素引起的空间变异占比为0.45,呈现出明显的块金效应,表明该尺度下随机因素引起的空间变异不可忽略;③从空间尺度(30～1500 m)整体变化上看,地表温度空间异质性主要由空间结构引起,同时表现出明显的尺度效应;随着空间尺度增大,块金值(C_0)、偏基台值(C)、基台值(C_0+C)以及块基比(C_0/(C_0+C))逐渐减小,表明地表温度空间异质性逐渐减弱但空间自相关性逐渐增强。变程(A)逐渐增大,表示空间自相关性范围逐渐扩大;④随机因素引起的空间变异占比为0.23～0.46,呈现出波动变化,这是因为地表温度在像元内部也存在空间异质性。空间结构引起的空间变异相对平缓,这是因为空间尺度的变化不会改变地形结构;⑤从尺度域来看,基台值与块金值在尺度域(690 m,1500 m)内呈现出较大幅度波动变化状态,且变化趋势相似,表明地表温度空间异质性的变化与随机因素有较大关联。综上所述,分析地表温度空间结构需要选取合适的空间尺度,尺度较小时,容易受到随机因素干扰,从而影响地表温度在空间结构上的空间变异性;尺度较大时,地表温度空间异质性较弱且变化不稳定。
Analyzing the spatial heterogeneity of land surface temperature(LST) is important for understanding the spatial structure of LST. This study retrieved LST by the atmospheric correction method, and constructed a spatial heterogeneity model of LST by using the semi-variogram function. It then took a multi-scale perspective to discuss LST's spatial heterogeneity in the study area of Chongqing. A Landsat 8 OLI imagery in June 16,2013 was the primary data source. Results show that: ① The LST's spatial heterogeneity was exponentially distributed at different spatial scales. ②At the 30 m spatial scale, the spatial heterogeneity was mainly caused by spatial structure, though the proportion of spatial variation caused by random factors accounted for 0.45,showing obvious nugget effect; thus, random factors cannot be ignored at this scale. ③ On the whole spatial scale(30～1500 m), the spatial heterogeneity was mainly caused by spatial structure, and showed obvious spatial scale effect. As the spatial scale increases, the nugget(C_0), the partial sill(C), the sill(C_0+C), and the nugget-sill ratios(C_0/(C_0 + C)) gradually decreased, indicating that the spatial heterogeneity declined and the spatial autocorrelation gradually increased. Meanwhile, the range(A) gradually increased, indicating that spatially autocorrelated regions gradually enlarged. ④ On one hand, the proportion of spatial variation caused by random factors ranged from 0.23 to 0.46, showing obvious volatility, because the LST also had spatial heterogeneity within each pixel. On the other hand, the spatial variability caused by spatial structure was relatively flat,because the change of spatial scale did not affect the topographic structure. ⑤ From the scale effect perspective,both sill and nugget showed large fluctuations, and the trend was similar from 690 m to 1500 m, indicating that the change of the LST's spatial heterogeneity was related to random factors. In summary, choosing the appropriate spatial scale is very important for analyzing the spatial structure of LST. When the scale is small, the spatial distribution of LST is easily disturbed by random factors, which affects the variability in spatial structure.When the scale is large, the spatial heterogeneity of LST is weak and unstable.
Analyzing the spatial heterogeneity of land surface temperature(LST) is important for understanding the spatial structure of LST. This study retrieved LST by the atmospheric correction method, and constructed a spatial heterogeneity model of LST by using the semi-variogram function. It then took a multi-scale perspective to discuss LST's spatial heterogeneity in the study area of Chongqing. A Landsat 8 OLI imagery in June 16,2013 was the primary data source. Results show that: ① The LST's spatial heterogeneity was exponentially distributed at different spatial scales. ②At the 30 m spatial scale, the spatial heterogeneity was mainly caused by spatial structure, though the proportion of spatial variation caused by random factors accounted for 0.45,showing obvious nugget effect; thus, random factors cannot be ignored at this scale. ③ On the whole spatial scale(30～1500 m), the spatial heterogeneity was mainly caused by spatial structure, and showed obvious spatial scale effect. As the spatial scale increases, the nugget(C_0), the partial sill(C), the sill(C_0+C), and the nugget-sill ratios(C_0/(C_0 + C)) gradually decreased, indicating that the spatial heterogeneity declined and the spatial autocorrelation gradually increased. Meanwhile, the range(A) gradually increased, indicating that spatially autocorrelated regions gradually enlarged. ④ On one hand, the proportion of spatial variation caused by random factors ranged from 0.23 to 0.46, showing obvious volatility, because the LST also had spatial heterogeneity within each pixel. On the other hand, the spatial variability caused by spatial structure was relatively flat,because the change of spatial scale did not affect the topographic structure. ⑤ From the scale effect perspective,both sill and nugget showed large fluctuations, and the trend was similar from 690 m to 1500 m, indicating that the change of the LST's spatial heterogeneity was related to random factors. In summary, choosing the appropriate spatial scale is very important for analyzing the spatial structure of LST. When the scale is small, the spatial distribution of LST is easily disturbed by random factors, which affects the variability in spatial structure.When the scale is large, the spatial heterogeneity of LST is weak and unstable.
引文
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[19]丁凤,徐涵秋.TM热波段图像的地表温度反演算法与实验分析[J].地球信息科学学报,2006,8(3):125-130,135.[Ding F,Xu H Q.Comparison of two new algorithms for retrieving land surface temperature from Landsat TMthermal band[J].Journal of Geo-information Science,2006,8(3):125-130,135.]
[20]Weng Q H,Lu D S,Schubring J.Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies[J].Remote sensing of Environment,2004,89(4):467-483.
[21]Li J X,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.
[22]Qin Z H,Li W J,Xu B,et al.The estimation of land surface emissivity for Landsat TM6[J].Remote Sens.Land Resour,2004,3:28-32.
[23]Luo X B,Li W S.Scale effect analysis of the relationships between urban heat island and impact factors:case study in Chongqing[J].Journal of Applied Remote Sensing,2014,8(6):284-292.
[24]Curran P J,Atkinson P M.Geostatistics and remote sensing[J].Progress in Physical Geography,1998,22(1):61-78.
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[2]Rao,Krishna P.Remote sensing of urban heat islands from an environmental satellite[J].Bulletin of the American Meteorological Society,1972,53(7):647-648.
[3]Owen T W,Carlson T N,Gillies R R.An assessment of satellite remotely-sensed land cover parameters in quantitatively describing the climatic effect of urbanization[J].International Journal of Remote Sensing,1998,19(9):1663-1681.
[4]Carlson T N,Arthur S T.The impact of land use-Land cover changes due to urbanization on surface microclimate and hydrology:A satellite perspective[J].Global and Planetary Change,2000,25(1):49-65.
[5]周淑贞,吴林.上海下垫面温度与城市热岛--气象卫星在城市气候研究中的应用之一[J].环境科学学报,1987,7(3):261-268.[Zhou S Z,Wu L.Surface temperature and urban heat island of Shanghai[J].Acta Scientiae Circumstantiae,1987,7(3):261-268.]
[6]Streutker,David R.A remote sensing study of the urban heat island of Houston,Texas[J].International Journal of Remote Sensing,2002,23(13):2595-2608.
[7]Kato S,Yamaguchi Y.Analysis of urban heat-island effect using ASTER and ETM+Data:Separation of anthropogenic heat discharge and natural heat radiation from sensible heat flux[J].Remote Sensing of Environment,2005,99(1-2):44-54.
[8]Zhang Y S,Balzter H,Zou C C,et al.Characterizing bitemporal patterns of land surface temperature using landscape metrics based on sub-pixel classifications from Landsat TM/ETM+[J].International Journal of Applied Earth Observation&Geoinformation,2015,42:87-96.
[9]Bai Y,Meng H,Su J H,et al.Spatial and temporal changes of urban thermal landscape pattern in Shanghai[J].Environmental Science&Technology,2013,36(3):196-201.
[10]于琛,胡德勇,曹诗颂,等.2005-2016年北京中心城区热岛时空格局及影响因子多元建模[J].地球信息科学学报,2017,19(11):1485-1494.[Yu C,Hu D Y,Cao S S,et al.Spatio-temporal pattern of heat island and multivariate modeling of impact factors of Beijing downtown from 2005 to 2016[J].Journal of Geo-information Science,2017,19(11):1485-1494.]
[11]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.]
[12]周洋,祝善友,华俊玮,等.南京市高温热浪时空分布研究[J].地球信息科学学报,2018,20(11):1613-1621.[Zhou Y,Zhu S Y,Hua J W,et al.Spatio-temporal distribution of high temperature heat wave in Nanjing[J].Journal of Geo-information Science,2018,20(11):1613-1621.]
[13]陈公德,徐建华,戴晓燕,等.运用遥感数据挖掘解析城市地表温度的空间变异规律[J].遥感技术与应用,2008,23(4):405-409.[Chen G D,Xu J H,Dai X Y,et al.Applying Geo-data mining to analysis spatial variance characters of urban land surface temperature[J].Remote Sensing Technology and Application,2008,23(4):405-409.]
[14]Tian H,Liu Q H,Du Y M,et al.Analysis of land surface temperature spatial heterogeneity using variogram model[C]//Geoscience&Remote Sensing Symposium.2015.
[15]Duan J P,Li L,Fang Y J.Seasonal spatial heterogeneity of warming rates on the Tibetan Plateau over the past 30years[J].Scientific Reports,2015,5:11725.
[16]陶于祥,许凯丰,易宗旺,等.基于半变异函数的城市热岛空间异质性分析[J].西南大学学报(自然科学版),2018,40(10):151-158.[Tao Y X,Xu K F,Yi Z W,et al.A semivariogram-based analysis of spatial heterogeneity of urban heat islands[J].Journal of Southwest University(Natural Science Edition),2018,40(10):151-158.]
[17]Garrigues S,Allard D,Baret F,et al.Quantifying spatial heterogeneity at the landscape scale using variogram models[J].Remote Sensing of Environment,2006,103(1):81-96.
[18]Garrigues S,Allard D,Baret F,et al.Multivariate quantification of landscape spatial heterogeneity using variogram models[J].Remote Sensing of Environment,2008,112(1):216-230.
[19]丁凤,徐涵秋.TM热波段图像的地表温度反演算法与实验分析[J].地球信息科学学报,2006,8(3):125-130,135.[Ding F,Xu H Q.Comparison of two new algorithms for retrieving land surface temperature from Landsat TMthermal band[J].Journal of Geo-information Science,2006,8(3):125-130,135.]
[20]Weng Q H,Lu D S,Schubring J.Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies[J].Remote sensing of Environment,2004,89(4):467-483.
[21]Li J X,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.
[22]Qin Z H,Li W J,Xu B,et al.The estimation of land surface emissivity for Landsat TM6[J].Remote Sens.Land Resour,2004,3:28-32.
[23]Luo X B,Li W S.Scale effect analysis of the relationships between urban heat island and impact factors:case study in Chongqing[J].Journal of Applied Remote Sensing,2014,8(6):284-292.
[24]Curran P J,Atkinson P M.Geostatistics and remote sensing[J].Progress in Physical Geography,1998,22(1):61-78.
[25]Johnston K,Hoef J V,Krivoruchko K et al.Using arcgis geostatistical Analyst[M].ESRI Press,2001.
[26]张仁铎.空间变异理论及应用[M].北京:科学出版社,2005.[Zhang R D.Spatial variability theory and application[M].Beijing:Science Press,2005.]
[27]王政权.地统计学及其在生态学中的应用[M].北京:科学出版社,1999.[Wang Z Q.Application of geostatistics in ecology[M].Beijing:Science Press,1999.]
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