基于遥感数据挖掘定量反演城市化区域地表温度研究
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摘要
城市化的快速发展最为直观的表现就是土地覆盖景观的转变。土地利用/土地覆盖的变化不仅会改变地球表面物理特征,而且又能影响到地表与大气之间的能量和水分的交换过程、改变地表生物地球化学的循环过程,对区域甚至全球生态系统的结构和功能等产生极其深刻的影响。尤其对于我国重要的经济中心城市—上海,在社会经济的高速发展中,城市景观布局和土地利用方式变化对城市生态环境演变产生了深远影响。在各种城市化的生态环境效应中,城市热岛效应的产生及演变与城市地表覆被变化、人类社会经济活动密切相关,是城市生态环境状况的综合概括与体现。
本文以上海市中心城区为研究区域,通过综合应用定量遥感方法、地理信息系统空间分析技术与空间数据挖掘技术,开展城市化过程中的土地利用时空演变格局及城市热岛效应形成机制研究。通过Landsat TM/ETM+遥感影像混合像元分解和亚像元空间定位,获得了较高精度的地表覆盖分类结果,揭示城市化过程中上海中心城区土地利用时空演变格局及城市用地空间扩展模式。在此基础上,运用改进后的Landsat TM/ETM+热波段单窗反演算法,对地表温度和地表发射率进行定量反演,并应用决策树方法和探索性空间数据分析技术来揭示上海中心城区地表温度场的时空演变特征,挖掘城市热岛效应的形成机制。研究成果不仅对于提高遥感影像解译及定量化反演精度、深入城市生态环境系统的研究具有重要的理论意义,而且对于制定合理的城市用地布局与规划以及治理和改善城市生态环境具有较高的实践价值。论文共分为五个章节。
第一章首先论述了研究背景与立题意义,其次对遥感影像数据挖掘、遥感影像混合像元分类、亚像元信息的空间定位、地表温度的遥感反演这四个相关领域的国内外研究进展进行概述。在此基础上,提出了论文的研究内容、研究方法、技术路线及创新之处。
第二章基于可能性理论和中心点聚类方法的基本原理,建立了可能性C中心点(PCRMDD)方法。根据减法聚类法所提供的初始聚类中心,运用该算法对研究区域的Landsat TM/ETM+遥感影像进行混合像元分解,并自动获取各类地物端元盖度分布图和影像端元光谱,解混精度的检验结果表明该方法能在噪声环境下获得精度较高的分类结果和端元光谱信息。根据获得的不同时期研究区域的地表覆盖分类结果,应用GIS空间分析功能,进一步探讨在城市化过程中上海中心城区土地利用时空演变格局,揭示城市用地空间扩展模式。
第三章利用小波变换的时频局域化特性和多尺度分析能力及神经网络的自学习、预测功能,通过小波分析与神经网络的松散型结合方式,建立小波分析和径向基函数神经网络(Wavelet-RBFNN)预测模型。根据混合像元分解获得的像元组分比率信息,基于小波系数的近邻依赖性假设,通过亚像元组分比率值的预测和硬分类两个步骤,实现了遥感影像亚像元的空间定位。对复杂程度不同的人工图像、QuickBird影像和Landsat TM/ETM+遥感影像的实验结果表明,本文提出的亚像元定位方法能成功实现影像空间分辨率的增强,并且与三次样条插值法、克立格插值法相比,具有更好的视觉效果和更高的预测精度。采用该模型对研究区Landsat TM/ETM+遥感影像在较高空间分辨率水平上的亚像元定位结果证实,在高分辨遥感影像不易获得或成本过高时,运用本文提出的Wavelet-RBFNN模型能有效地模拟较高空间分辨率影像,实现高分辨率上地表覆盖类型的自动识别与定位。
第四章在详细介绍大气校正法、普适性单通道算法和单窗算法这三种基于Landsat TM/ETM+热波段数据反演地温方法的基础上,对单窗算法中地表发射率的计算方法进行了改进。运用改进后的单窗算法对上海中心城区1989、1997、2000和2002年四个特征年份的地表温度和地表发射率进行定量反演,并运用RBF神经网络建立多时相遥感影像的相对辐射校正模型,对不同时相影像进行标准化处理。在此基础上,采用决策树方法来构造城市热环境系统的分类和预测模型,建立中心城区地表温度场空间分布及其驱动因素之间的定量关系,挖掘城市热岛效应的形成机制;并采用热环境成因分类图的形式对分类规则进行可视化表示,以显示多种影响因素综合作用下上海中心城区的热环境空间格局差异。进而,利用探索性空间数据分析(ESDA)技术,通过全局和局部空间自相关分析,采用Global Moran's I、Local Moran'I,和G~*统计量等空间统计指标及半变异函数来定量描述不同尺度和时期上海市中心城区热力景观的空间变异和时间演变特征。
第五章对论文的研究成果进行了概括和总结,并提出未来需进一步开展的工作和研究重点。
Land use / land cover transformations due to the accelerated development of urbanization not only result in a change of the Earth surface physical properties,but also influence the exchange processes of energe and water between land surface and atmosphere,and biological and geochemical circulation of the Earth,and generally have profound effect on structure and function of regional and even global ecosystem. Especially in Shanghai,the important economic central city in China,the change of urban landscape and land use pattern with high-speed social and economic development has brought far-reaching influence to the evolvement of urban ecological environment.Among varoius urbanized ecological environment effect,the formation and evolution of urban heat island(UHI)effect is closely related with urban land cover change and human social and economic activity,and can be used to generalize and embody the condition of urban ecological environment.
The research presented in this paper focuses on the study of spatio-temporal evolvement pattern of land use in course of urbanization and mechanism of UHI effect in the selected study area,Shanghai central city,by the intergration of quantitative remote sensing(RS)method,Geographic Information System(GIS) spatial analytical technique,and spatial data mining technique.Land cover classification with high accuracy,by means of mixed-pixel classification and sub-pixel mapping for Landsat TM/ETM+ images,is used to represent the pattern of land use spatio-temporal evolvement and urban land spatial spraw with urbanization in Shanghai central city.Based on these,land surface temperature(LST)and land surface emissivity(LSE)are retrieved by means of modified mono-window algorithm for Landsat TM6 or ETM+6 data.Furthermore,Decision Tree method and Exploratory Spatial Data Analysis(ESDA)are applied to reveal the spatio-temporal evolution characteristics of LST field in Shanghai central city and mine the mechanism of UHI effect.The results are of important theoretical value in improving the accuracy of remote sensing imagery interpretation and quantitative retrieval and deep study of urban ecological environmental system,and are helpful to establish reasonable urban land use arrangement and planning,and manage and improve urban ecological environment in practice.Five chapters are included in this paper.
Chapter one firstly discusses study background and significance,then summarizes recent study results of related field including remote sensing image data mining,mixed-pixel classification and sub-pixel mapping for remote sensing imagery, and remote sensing retrieval of LST.Based on these,the research content, methodology,technical route and innovation features of the dissertation are put forward.
Chapter two sets up a Possibilistic C Repulsive Medoids(PCRMDD)clustering algorithm,based on possibility theory and basic principle of c-medoids clustering method.By utilizing initial cluster centers obtained through Subtractive Clustering, mixed-pixel classification is implemented on Landsat TM/ETM+ images of the study area by means of the algorithm,and class proportions of each endmember and spectral reflectance of endmember on images are automatically acquired.Accuracy analysis demonstrates that PCRMDD represents a robust and efficient tool to obtain reliable soft classification results and endmember spectral information in noisy environment. Furthermore,according to the obtained multi-temporal land cover classification of the study area,the pattern of spatio-temporal land use evolvement and urban land spatial sprawl with urbanization in Shanghai central city are explored with the application of spatial analytical function of GIS.
By making up of time-frequency local property and multi-scale analytical capability of wavelet transformation and self-learning and prediction function of artificial neural network,chapter three develops a prediction model loose combining wavelet analysis and Radial Basis Functions(RBF)neural network,abbreviated as Wavelet-RBFNN.According to the proportion of each land cover class within each pixel from mixed-pixel classification,based on the assumption of neighbourhood dependence of wavelet coefficients,sub-pixel mapping on remote sensing image is accomplished through two steps,i.e.,prediction of proportion of each land cover class within sub-pixel and soft classification hardening.The experimental results obtained on artificial images,QuickBird image,and Landsat TM/ETM+ images indicate that the sub-pixel mapping method proposed in this paper,can successfully achieve remote sensing image super-resolution enhancement,outperforming cubic spline and Kriging interpolation method in visual effect and prediction accuracy.The sub-pixel mapping results of Wavelet-RBFNN model applied to Landsat TM/ETM+ image of study area at higher spatial resolution demonstrate that the model can be used to simulate higher spatial resolution imagery,and automatically identify and map land cover targets at the subpixel scale,when the cost and availability of fine spatial resolution imagery prohibit its use in many areas of work.
Three methods to retrieve the land surface temperature(LST)from the Landsat thermal channel,including Radiative Transfer Equation(RTE),a generalized single-channel method developed by Jimenez-Munoz and Sobrino,and Qin et al.'s mono-window algorithm,are presented in chapter four.In this paper,the method to estimate land surface emissivity(LSE)is modified when the mono-window algorithm is applied to retrieve LST and LSE from Landsat TM6 and ETM+6 data of the study area in 1989,1997,2000 and 2002.Besides,the resultant multi-temporal LST images are normalized radiometrically through relative radiometric correction based on RBF neural network.Based on these,the quantitative relationship between the spatial distribution of LST field and its driving factors in Shanghai central city is set up and the mechanism of UHI effect is mined,by applying decision tree to developing a classification and prediction model of urban thermal environment system.The obtained classification rules are visually represented in the form of classification image of causes of thermal environment formation to reveal the spatial pattern difference of the thermal environment in Shanghai central city under compositive effect of various influencing factors.Furthermore,by utilizing Exploratory Spatial Data Analysis technique and global and local spatial autocorrelation analysis,several spatial statistical indices,such as Global Moran's I,Local Moran's I and Getis-Ord local G,and semivariogram are adopted to qualitatively describe the characteristics of spatial heterogeneity and temporal evolution of thermal landscape at different scales and periods in Shanghai central city.
In chapter five,the research results are concluded.Furthermore,future research keys are discussed,too.
本文以上海市中心城区为研究区域,通过综合应用定量遥感方法、地理信息系统空间分析技术与空间数据挖掘技术,开展城市化过程中的土地利用时空演变格局及城市热岛效应形成机制研究。通过Landsat TM/ETM+遥感影像混合像元分解和亚像元空间定位,获得了较高精度的地表覆盖分类结果,揭示城市化过程中上海中心城区土地利用时空演变格局及城市用地空间扩展模式。在此基础上,运用改进后的Landsat TM/ETM+热波段单窗反演算法,对地表温度和地表发射率进行定量反演,并应用决策树方法和探索性空间数据分析技术来揭示上海中心城区地表温度场的时空演变特征,挖掘城市热岛效应的形成机制。研究成果不仅对于提高遥感影像解译及定量化反演精度、深入城市生态环境系统的研究具有重要的理论意义,而且对于制定合理的城市用地布局与规划以及治理和改善城市生态环境具有较高的实践价值。论文共分为五个章节。
第一章首先论述了研究背景与立题意义,其次对遥感影像数据挖掘、遥感影像混合像元分类、亚像元信息的空间定位、地表温度的遥感反演这四个相关领域的国内外研究进展进行概述。在此基础上,提出了论文的研究内容、研究方法、技术路线及创新之处。
第二章基于可能性理论和中心点聚类方法的基本原理,建立了可能性C中心点(PCRMDD)方法。根据减法聚类法所提供的初始聚类中心,运用该算法对研究区域的Landsat TM/ETM+遥感影像进行混合像元分解,并自动获取各类地物端元盖度分布图和影像端元光谱,解混精度的检验结果表明该方法能在噪声环境下获得精度较高的分类结果和端元光谱信息。根据获得的不同时期研究区域的地表覆盖分类结果,应用GIS空间分析功能,进一步探讨在城市化过程中上海中心城区土地利用时空演变格局,揭示城市用地空间扩展模式。
第三章利用小波变换的时频局域化特性和多尺度分析能力及神经网络的自学习、预测功能,通过小波分析与神经网络的松散型结合方式,建立小波分析和径向基函数神经网络(Wavelet-RBFNN)预测模型。根据混合像元分解获得的像元组分比率信息,基于小波系数的近邻依赖性假设,通过亚像元组分比率值的预测和硬分类两个步骤,实现了遥感影像亚像元的空间定位。对复杂程度不同的人工图像、QuickBird影像和Landsat TM/ETM+遥感影像的实验结果表明,本文提出的亚像元定位方法能成功实现影像空间分辨率的增强,并且与三次样条插值法、克立格插值法相比,具有更好的视觉效果和更高的预测精度。采用该模型对研究区Landsat TM/ETM+遥感影像在较高空间分辨率水平上的亚像元定位结果证实,在高分辨遥感影像不易获得或成本过高时,运用本文提出的Wavelet-RBFNN模型能有效地模拟较高空间分辨率影像,实现高分辨率上地表覆盖类型的自动识别与定位。
第四章在详细介绍大气校正法、普适性单通道算法和单窗算法这三种基于Landsat TM/ETM+热波段数据反演地温方法的基础上,对单窗算法中地表发射率的计算方法进行了改进。运用改进后的单窗算法对上海中心城区1989、1997、2000和2002年四个特征年份的地表温度和地表发射率进行定量反演,并运用RBF神经网络建立多时相遥感影像的相对辐射校正模型,对不同时相影像进行标准化处理。在此基础上,采用决策树方法来构造城市热环境系统的分类和预测模型,建立中心城区地表温度场空间分布及其驱动因素之间的定量关系,挖掘城市热岛效应的形成机制;并采用热环境成因分类图的形式对分类规则进行可视化表示,以显示多种影响因素综合作用下上海中心城区的热环境空间格局差异。进而,利用探索性空间数据分析(ESDA)技术,通过全局和局部空间自相关分析,采用Global Moran's I、Local Moran'I,和G~*统计量等空间统计指标及半变异函数来定量描述不同尺度和时期上海市中心城区热力景观的空间变异和时间演变特征。
第五章对论文的研究成果进行了概括和总结,并提出未来需进一步开展的工作和研究重点。
Land use / land cover transformations due to the accelerated development of urbanization not only result in a change of the Earth surface physical properties,but also influence the exchange processes of energe and water between land surface and atmosphere,and biological and geochemical circulation of the Earth,and generally have profound effect on structure and function of regional and even global ecosystem. Especially in Shanghai,the important economic central city in China,the change of urban landscape and land use pattern with high-speed social and economic development has brought far-reaching influence to the evolvement of urban ecological environment.Among varoius urbanized ecological environment effect,the formation and evolution of urban heat island(UHI)effect is closely related with urban land cover change and human social and economic activity,and can be used to generalize and embody the condition of urban ecological environment.
The research presented in this paper focuses on the study of spatio-temporal evolvement pattern of land use in course of urbanization and mechanism of UHI effect in the selected study area,Shanghai central city,by the intergration of quantitative remote sensing(RS)method,Geographic Information System(GIS) spatial analytical technique,and spatial data mining technique.Land cover classification with high accuracy,by means of mixed-pixel classification and sub-pixel mapping for Landsat TM/ETM+ images,is used to represent the pattern of land use spatio-temporal evolvement and urban land spatial spraw with urbanization in Shanghai central city.Based on these,land surface temperature(LST)and land surface emissivity(LSE)are retrieved by means of modified mono-window algorithm for Landsat TM6 or ETM+6 data.Furthermore,Decision Tree method and Exploratory Spatial Data Analysis(ESDA)are applied to reveal the spatio-temporal evolution characteristics of LST field in Shanghai central city and mine the mechanism of UHI effect.The results are of important theoretical value in improving the accuracy of remote sensing imagery interpretation and quantitative retrieval and deep study of urban ecological environmental system,and are helpful to establish reasonable urban land use arrangement and planning,and manage and improve urban ecological environment in practice.Five chapters are included in this paper.
Chapter one firstly discusses study background and significance,then summarizes recent study results of related field including remote sensing image data mining,mixed-pixel classification and sub-pixel mapping for remote sensing imagery, and remote sensing retrieval of LST.Based on these,the research content, methodology,technical route and innovation features of the dissertation are put forward.
Chapter two sets up a Possibilistic C Repulsive Medoids(PCRMDD)clustering algorithm,based on possibility theory and basic principle of c-medoids clustering method.By utilizing initial cluster centers obtained through Subtractive Clustering, mixed-pixel classification is implemented on Landsat TM/ETM+ images of the study area by means of the algorithm,and class proportions of each endmember and spectral reflectance of endmember on images are automatically acquired.Accuracy analysis demonstrates that PCRMDD represents a robust and efficient tool to obtain reliable soft classification results and endmember spectral information in noisy environment. Furthermore,according to the obtained multi-temporal land cover classification of the study area,the pattern of spatio-temporal land use evolvement and urban land spatial sprawl with urbanization in Shanghai central city are explored with the application of spatial analytical function of GIS.
By making up of time-frequency local property and multi-scale analytical capability of wavelet transformation and self-learning and prediction function of artificial neural network,chapter three develops a prediction model loose combining wavelet analysis and Radial Basis Functions(RBF)neural network,abbreviated as Wavelet-RBFNN.According to the proportion of each land cover class within each pixel from mixed-pixel classification,based on the assumption of neighbourhood dependence of wavelet coefficients,sub-pixel mapping on remote sensing image is accomplished through two steps,i.e.,prediction of proportion of each land cover class within sub-pixel and soft classification hardening.The experimental results obtained on artificial images,QuickBird image,and Landsat TM/ETM+ images indicate that the sub-pixel mapping method proposed in this paper,can successfully achieve remote sensing image super-resolution enhancement,outperforming cubic spline and Kriging interpolation method in visual effect and prediction accuracy.The sub-pixel mapping results of Wavelet-RBFNN model applied to Landsat TM/ETM+ image of study area at higher spatial resolution demonstrate that the model can be used to simulate higher spatial resolution imagery,and automatically identify and map land cover targets at the subpixel scale,when the cost and availability of fine spatial resolution imagery prohibit its use in many areas of work.
Three methods to retrieve the land surface temperature(LST)from the Landsat thermal channel,including Radiative Transfer Equation(RTE),a generalized single-channel method developed by Jimenez-Munoz and Sobrino,and Qin et al.'s mono-window algorithm,are presented in chapter four.In this paper,the method to estimate land surface emissivity(LSE)is modified when the mono-window algorithm is applied to retrieve LST and LSE from Landsat TM6 and ETM+6 data of the study area in 1989,1997,2000 and 2002.Besides,the resultant multi-temporal LST images are normalized radiometrically through relative radiometric correction based on RBF neural network.Based on these,the quantitative relationship between the spatial distribution of LST field and its driving factors in Shanghai central city is set up and the mechanism of UHI effect is mined,by applying decision tree to developing a classification and prediction model of urban thermal environment system.The obtained classification rules are visually represented in the form of classification image of causes of thermal environment formation to reveal the spatial pattern difference of the thermal environment in Shanghai central city under compositive effect of various influencing factors.Furthermore,by utilizing Exploratory Spatial Data Analysis technique and global and local spatial autocorrelation analysis,several spatial statistical indices,such as Global Moran's I,Local Moran's I and Getis-Ord local G,and semivariogram are adopted to qualitatively describe the characteristics of spatial heterogeneity and temporal evolution of thermal landscape at different scales and periods in Shanghai central city.
In chapter five,the research results are concluded.Furthermore,future research keys are discussed,too.
引文
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