城市高度异质下垫面监测及热环境分析

发布时间：2018-04-23 12:20

  本文选题：土地覆盖/土地利用 + 上海　； 参考：《华东师范大学》2016年博士论文

【摘要】：在对地观测卫星技术迅猛发展、遥感应用研究成果丰硕的今天,如何准确区分城市中具有复杂混合光谱的地表覆盖仍然是困扰国内外遥感研究的一个核心问题。这个问题在历史悠久的中国城市中尤显突出,其城区形成过程经历过千百年的朝代更迭和文化兴衰,无论是空间结构布局还是建筑材质用料,复杂程度远远超出西方发达国家的同等规模城市。这些历史文化和空间结构的特殊性导致了高度复杂的地物光谱特征,给中国城市遥感研究带来了严重挑战。而过去三十年的改革开放,大规模的城市化进一步强化了城市地表的光谱异质性。从乡村农田到城市土地利用类型的快速转型,自然地表环境开发为以不透水面为主体的居住、工业或商业用地,中心城区历史建筑的遗存及土地利用的集约型高强度重组,以及对城市水体的改造利用乃至污染,形成了高度破碎化、异质化的城市景观,使城市下垫面各要素的光谱特征更加错综复杂。面对上述严峻的技术和应用挑战,本文以上海为例,针对中国城市景观破碎度大、地表覆盖空间变异强度高、光谱同质性低的特点,利用多时相、多源遥感数据,研发和测试一系列适合高度异质城市下垫面地表覆盖特征量化的方法,研明城市发展模式与轨迹,并进一步分析其对城市热环境的影响。研究旨在准确、高效和及时评估和监测高度异质的城市地表覆盖范围、分布结构、地物成分以及时空变化,以利于从宏观和中观层面上了解城市化进程,为合理利用与规划土地覆盖,缓解城市化所带来的一系列问题提供科学依据。本研究主要工作包括以下五个方面：(1)在城市水体的提取研究中,设计了一种由粗到细(coarse-to-fine, CTF)的城市水体提取策略,方法结合不同时相的热红外和光学影像数据来应对城市水体的高度光谱复杂性和季节性变化。传统的提取方法多基于像元水平上的水体探测,无法准确地识别城市内部细小的水体,且水体的识别易受低照不透水面和阴影的干扰。而本研究中的CTF方法可结合线性光谱混合分解模型和多时相变化检测技术来准确识别亚像元水平上的水体特征,方法尤其适用于河网水系复杂的城市地区。CTF方法通过对试验区(上海)不同时相的Landsat ETM+影像的分析结果表明：方法可以有效地在高度异质的城市环境内识别永久性和季节性水体,并达到满意的精度。(2)在城市不透水面的提取研究中,利用四面体模型(植被-高照不透水面-低照不透水面-土壤,V-H-L-S)解决了传统的植被-不透水面-土壤(V-I-S)理论模型与实际影像所提取端元不匹配的问题。方法首先通过四面体模型来描述最小噪声分离(minimum noise fraction, MNF)变换后的像元在三维空间的分布特征,然后通过多目标优化的遗传算法来确定四面体的顶点,即城市地表的四个端元分别位于各个顶点的小四面体内,进而确定三维空间内端元位置,计算出各地物覆盖类型的影像端元光谱。上海主城区实例数据的验证充分表明基于四面体的V-H-L-S模型能较好地解释城市地表分解为四组分的情况,相比传统的端元获取方法(像元纯净度指数法和二维散点图法)自动化程度更高,且可以获得更为理想的不透水面定量结果。(3)在城市植被的估测研究中,将增强型时空自适应反射率融合模型(enhanced temporal adaptive reflectance fusion model, ESTARFM)用于融合Landsat和MODIS数据以生成高空间分辨率和高时相分辨率的NDVI时间序列,进而利用时空混合分析技术(temporal mixture analysis, TMA)对NDVI时相曲线进行分解,以估测农田、常绿、落叶植被覆盖情况。研究发现利用TMA方法分解NDVI时间序列生成的植被覆盖度与地面参考数据的一致性较好(R2大于0.79,RMSE小于0.11,MAE小于0.84)。基于时空融合模型获取高空间和高时相分辨率的遥感数据对于复杂城市环境的植被监测是十分关键的,所提取的时相端元相比传统的光谱端元,可以更准确地区分出更多的植被类型。(4)在城市土地覆盖的变化检测研究中,采用了基于地表物理组分的长时间序列变化检测技术。方法首先利用不同年份(1990,1995,2000,2003,2007,2013)30m空间分辨率NDVI时序数据,基于时空混合分析模型获取各个年份的城市不透水面信息,进而采用Z-score分析对亚像元尺度的历年不透水面覆盖度进行变化监测。方法相比传统的基于像元尺度的变化检测技术,可以获取土地覆盖更为精细和稳定的变化信息,包括二值变化信息、变化强度和方向信息。航拍影像的精度验证表明方法可以提高不透水面估测精度,且能精细地检测出复杂的城市和郊区内土地覆盖变化特征。(5)一种新型的非线性处理方法——极端学习机(extreme learning machine, ELM)用于探索城市地表温度(land surface temperature, LST)和不透水面组分之间的非线性关系。通过不同年份上海夏季城市热岛数据的测试比较发现ELM方法所建立的模型在不同年份LST的预测上都表现出比线性模型更高的精度,且算法的效率远高于传统的非线性方法。同时考虑邻近像元的不透水面信息可以进一步提高模型的精度。研究结果充分说明ELM模型可以准确地处理LST模拟过程中的非线性,并且这种非线性关系可能是周边像元的地表环境共同作用的结果。
[Abstract]:With the rapid development of earth observation satellite technology and the fruitful research achievements of remote sensing applications, it is still a core problem that how to accurately distinguish the surface coverage of complex mixed spectra in cities. This problem is particularly prominent in Chinese cities with a long history, and the process of urban formation has experienced hundreds of thousands of years. The changes in the dynasties and the rise and fall of the culture in the years, whether the spatial structure or the material of the building material, are far more complex than the same scale cities in the western developed countries. The particularity of these historical and cultural and spatial structures has led to the highly complex spectral characteristics of the ground objects, which have brought serious challenges to the research of urban remote sensing in China. In the past three Ten years of reform and opening up, large-scale urbanization further strengthened the spectral heterogeneity of the urban surface. From rural farmland to urban land use type, the natural surface environment is developed into inhabitation, industrial or commercial land, the remains of historical buildings in the central city and intensive high-strength of land use. Degree reorganization, as well as the transformation and utilization of urban water and even pollution, form a highly fragmented and heterogeneous urban landscape, and make the spectral characteristics of the urban underlying elements more complex. In the face of the severe technical and application challenges mentioned above, this paper takes Shanghai as an example, and has a large fragmentation of the Chinese urban landscape and the strong surface coverage of the space variation. With the characteristics of high degree and low spectral homogeneity, using multi time phase and multi source remote sensing data, we developed and tested a series of methods suitable for the quantification of surface cover characteristics of the undercover of highly heterogeneous cities, studied the urban development model and trajectory, and further analyzed its impact on the urban thermal environment. The research aims to accurately, efficiently and timely assess and monitor the height. Heterogeneous urban surface coverage, distribution structure, composition of ground objects and temporal and spatial changes in order to understand the process of urbanization from the macroscopic and meso level, and provide scientific basis for the rational utilization and planning of land cover and alleviating a series of problems brought by urbanization. The main work of this study includes the following five aspects: (1) in urban water In the study of body extraction, a city water extraction strategy from coarse-to-fine (CTF) is designed. The method is combined with the thermal infrared and optical image data of different phases to cope with the high spectral complexity and seasonal variation of urban water. The traditional extraction method is based on the water body detection on the pixel level and can not be accurately identified. In this study, the CTF method can be used to identify the water characteristics at the sub pixel level accurately, which is especially suitable for the.CTF square in the complex urban area of the river network. The method of analysis of Landsat ETM+ images of different phase in the test area (Shanghai) shows that the method can effectively identify permanent and seasonal water bodies in highly heterogeneous urban environment and achieve satisfactory accuracy. (2) in the study of the extraction of urban impermeable water, the tetrahedral model (vegetation - high exposure to water and low illumination) Water surface soil, V-H-L-S) solves the problem that the Traditional Vegetation - impermeable surface - soil (V-I-S) theory model does not match the end element of the actual image. Method first, a tetrahedral model is used to describe the distribution characteristics of the image element in the three-dimensional space after the minimum noise separation (minimum noise fraction, MNF) transformation, and then through multi-objective optimization. The genetic algorithm is used to determine the vertex of tetrahedron, that is, the four endpoints of the urban surface are located in the small tetrahedron of each vertex, and then the end element position in the three-dimensional space is determined and the image end spectrum of each cover type is calculated. The verification of the example data of the Shanghai main city area fully shows that the V-H-L-S model based on the tetrahedron can be better. To explain the situation of urban surface decomposition to four components, it is more automated than the traditional method of endpoint acquisition (pixel pure index method and two-dimensional scatter plot method), and can obtain more ideal quantitative results of impermeable surface. (3) an enhanced spatio-temporal adaptive reflectivity fusion model (enhance) is used in the study of urban vegetation. D temporal adaptive reflectance fusion model, ESTARFM) is used to fuse Landsat and MODIS data to generate high spatial resolution and high phase resolution of NDVI time series, and then decomposes the phase curve with a spatio-temporal hybrid analysis technique (temporal mixture) to estimate farmland, evergreen, and deciduous vegetation. The study found that the vegetation coverage generated by the TMA method to decompose the NDVI time series is in good agreement with the ground reference data (R2 is greater than 0.79, RMSE is less than 0.11, MAE is less than 0.84). The remote sensing data of high space and high phase resolution based on the spatio-temporal fusion model is critical to the vegetation monitoring in complex urban environment. The time phase end element can be more accurately divided into more vegetation types compared with the traditional spectral endpoints. (4) in the study of the change detection of urban land cover, the long time series change detection technology based on the surface physical components is adopted. First, the spatial resolution NDVI of different years (199019952000200320072013) 30m is used. The time series data, based on the spatio-temporal hybrid analysis model, is used to obtain the urban water surface information of each year, and then the Z-score analysis is used to monitor the variation of the surface coverage of the sub pixel scales. The method can obtain more precise and stable change letters of land coverage compared with the traditional change detection technology based on pixel scale. Interest, including two value change information, change intensity and direction information. The accuracy verification of aerial image shows that the method can improve the accuracy of impermeable surface estimation, and can accurately detect the complex characteristics of land cover change in the complex city and suburb. (5) a new nonlinear processing square method (extreme learning machine, ELM) It is used to explore the nonlinear relationship between the urban surface temperature (land surface temperature, LST) and the impermeable water components. Through the comparison of the summer urban heat island data in different years in Shanghai, it is found that the model established by the ELM method shows a higher accuracy than the linear model in the prediction of the LST in different years, and the efficiency of the algorithm is far away. It is higher than the traditional nonlinear method. Considering the impermeable surface information of adjacent pixels can further improve the accuracy of the model. The results of the study fully demonstrate that the ELM model can accurately deal with the nonlinearity in the LST simulation process, and this nonlinear relationship may be the result of the joint action of the surrounding surface environment.

【学位授予单位】：华东师范大学
【学位级别】：博士
【学位授予年份】：2016
【分类号】：P237;X16;X87
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本文编号：1791957