城市地表端元丰度与地表亮温关系
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摘要
由于城市人口急剧增长,城市化进程成为全球变暖的主要贡献来源:城市裸露、半裸露土地和不透水面温度较高。针对城市地表物质丰度与温度的关系,以武汉市2013年夏季Landsat-8遥感数据为数据源,使用多端元光谱混合分析法动态提取每个像元中的植被-不透水面-土壤(V-I-S)端元组分,并采用大气顶层辐射值反演地表亮温,通过聚类分析典型地表物质类型的地表亮温区位表现,结合三角图分析每个像元内的端元丰度与相应地表亮温之间的关系,拟合多元回归方程探究端元丰度对地表温度的影响能力,以及地表亮温对端元丰度数值变化的表现。实验结果表明,V-I-S端元丰度情况可解释夏季地表亮温98.563%的变化,其中不透水面丰度对夏季亮温的影响最大,而植被丰度的大小与植被温度调节能力成正比。
The relationship between fraction of urban land surface material and temperature has always been a hot issue in the research of urban environment.In this paper,a multiple endmember spectral mixture analysis method was applied to extract vegetation-impervious surface-soil fraction in every pixel,and surface brightness temperature was derived by using the radiation on the upper atmosphere based on Landsat-8image.Then a clustering analysis and a ternary triangular Chart,as well as a multivariate statistical analysis,were applied to ascertain the relationship between the fraction in each pixel and the surficial brightness temperature.The results show that,the endmember fraction of V-I-S can explain 98.563% of the urban surface temperature changes,and the fraction of impervious surface has a positive impact on land surface temperature while the fraction of vegetation has a negative impact on it.
The relationship between fraction of urban land surface material and temperature has always been a hot issue in the research of urban environment.In this paper,a multiple endmember spectral mixture analysis method was applied to extract vegetation-impervious surface-soil fraction in every pixel,and surface brightness temperature was derived by using the radiation on the upper atmosphere based on Landsat-8image.Then a clustering analysis and a ternary triangular Chart,as well as a multivariate statistical analysis,were applied to ascertain the relationship between the fraction in each pixel and the surficial brightness temperature.The results show that,the endmember fraction of V-I-S can explain 98.563% of the urban surface temperature changes,and the fraction of impervious surface has a positive impact on land surface temperature while the fraction of vegetation has a negative impact on it.
引文
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[28]向东进,李宏伟,刘小雅.实用多元统计分析[M].武汉:中国地质大学出版社,2005.
[2]夏俊士,杜培军,张海荣,等.基于遥感数据的城市地表温度与土地覆盖定量研究[J].遥感技术与应用,2010,25(1):15-23.
[3]田平,田光明,王飞儿,等.基于TM影像的城市热岛效应和植被覆盖指数关系研究[J].科技通报,2006,22(5):708-713.
[4]周红妹,周成虎,葛伟强,等.基于遥感和GIS的城市热场分布规律研究[J].地理学报,2001,56(2):189-197.
[5]陈云浩,王洁,李晓兵.夏季城市热场的卫星遥感分析[J].国土资源遥感,2002,14(4):55-59.
[6]MICHISHITA R,JIANG Z B,XU B.Monitoring two decades of urbanization in the Poyang Lake area,China through spectral unmixing[J].Remote Sensing of Environment,2012,117(1):3-18.
[7]刘正春.多端元光谱混分分析方法研究[D].长沙:中南大学,2012.
[8]LU D S,MORAN E,BATISTELIA M.Linear mixture model applied to amazonian vegetation classification[J].Remote Sensing of Environment,2003,87(4):456-469.
[9]PHINN S,STANFORD M,SCARTH P.Monitoring the composition of urban environments based on the vegetationimpervious surface-soil(VIS)model by subpixel analysis techniques[J].Remote Sensing of Environment,2002,23(20):4131-4153.
[10]ROBERTS D A,GARDNER M,CHURCH R,et al.Mapping chaparral in the santa monica mountains using multiple endmember spectral mixture models[J].Remote Sensing of Environment,1998,65(3):267-279.
[11]DENNISON P E,HALLIGAN K Q,ROBERTS D A.A comparison of error metrics and constraints for multiple endmember spectral mixture analysis and spectral angle mapper[J].Remote Sensing of Environment,2004,93(3):359-367.
[12]DENNISON P E,ROBERTS D A.Endmember selection for multiple endmember spectral mixture analysis using endmember average RMSE[J].Remote Sensing of Environment,2003,87(3):123-135.
[13]张萌,黄思静,冯文新,等.巧解砂岩分类三角图[J].成都理工大学学报(自然科学版),2005,32(4):421-429.
[14]叶虎平.黄东海海洋光谱分类方法研究[D].天津:国家海洋技术中心,2009.
[15]ROBERT A.Remotesensing of inherent optical properties:fundamentals,tests of algorithms,and applications[J].IOCCG Report Number 5,2006,5:95-100.
[16]辛鑫,张平宇.基于三角图法的矿业城市人地系统脆弱性分类[J].煤炭学报,2009,34(2):284-288.
[17]徐福留,赵珊珊,张颖,等.经济发展可持续性状态与趋势定量评价方法研究[J].环境科学学报,2005,25(6):711-720.
[18]李博,韩增林.基于三角图法的沿海城市系统脆弱性分类方法研究[J].海洋开发与管理,2011,28(11):100-102.
[19]曹丽琴.城市热环境及其影像因子的定量遥感研究[D].武汉:武汉大学,2009.
[20]张叙葭.单/双端元线性模型分解混合像元的应用效果对比[J].闽江学院学报,2015(2):129-140.
[21]DASH P,GTTSCHE FM,OLESEN F S,et al.Land surface temperature and emissivity estimation from passive sensor data:theory and practice-current trends[J].International Journal of Remote Sensing,2002,23(13):2563-2594.
[22]RIDD M K.Exploring a VIS(vegetation-impervious surface-soil)model for urban ecosystem analysis through remote sensing:comparative anatomy for cities[J].International Journal of Remote Sensing,1995,16(12):2165-2185.
[23]潘本锋,汪巍,李亮,等.我国大中型城市秋冬季节雾霾天气污染特征与成因分析[J].环境与持续发展,2013(1):33-36.
[24]王宏,冯宏芳,隋平,等.福州市灰霾气象要素场特征分析[J].气象科技,2009,37(6):670-675.
[25]赵桂香,杜丽,卫丽萍,等.一次持续性区域雾霾天气的综合分析[J].干旱区研究,2011,28(5):871-878.
[26]叶光营,吴毅伟,刘必桔.福州区域雾霾天气时空分布特征分析[J].环境科学与技术,2010,33(10):114-119.
[27]周涛,汝小龙.北京市雾霾天气成因及治理措施研究[J].华北电力大学学报(社会科学版),2012(2):12-16.
[28]向东进,李宏伟,刘小雅.实用多元统计分析[M].武汉:中国地质大学出版社,2005.