| As an urban disaster, urban heat island effect (UHI) has been highlighted as a symbol of urban thermal environment deterioration, the adverse impacts of UHI on urban atmospheric environment, socio-economic development, people's physical and mental health cannot be underestimated. It also consumes considerable amount of energy. Therefore, it is helpful to improve the thermal environment, reduce energy consumption, and make urban human settlements development sustainable by analyzing UHI qualitatively and quantitatively. In this thesis, we employ remote sensing, GIS and numerical simulation technology to analyze the generation and impact mechanism of UHI on an urban scale in Hangzhou, and explore the UHI mitigation technology and system development. Therefore, researching UHI from the perspective of urban planning and combining the city planning and urban environmental issues can provide basic ideas for coordinating the relationship between human and land and minimizing impacts from human activities on the urban thermal environment, and thus has important significance both in theory and practice.The first chapter mainly elaborates on the background and significance of the research, and reviews related domestic and foreign literature on urban heat island. We "find that previous studies of urban heat island mitigation lacks systematic and integrated analyses, it does not distinguish the influencing factors of UHI as well as mitigation strategies on different spatial scales The majority of the studies are based on a specific aspect and ignores the comprehensive characteristic of UHI research. Finally, we establish a research framework, aiming to provide theoretical basis for following research.The second chapter focuses on analyzing the characteristics of multi-source remote sensing data such as Landsat TM/ETM+, EOS/MODIS, ASTER, NOAA/AVHRR, categorizing the spectral characteristics and resolution of different band for each remote sensing images, and presenting their advantages and disadvantages. Then, according to different characteristics of remote sensing data, the calculation methods for surface temperature, vegetation index, land cover classification and surface albedo are established respectively,, which provides the basis and model libraries for factor extraction module of UHI mitigation simulation system.Chapter 3, based on RS and GIS technologies, the reveals the spatial and temporal variation of UHI in Hangzhou as well as the differences of spatial distribution. The research results show:①The average annual temperature of urban and suburban areas and UHI intensity have been increased significantly since 1991.②The expansion rate of built-up land is positively correlated with the increase rate of high temperature area.③The center of UHI is mainly distributed in the industrial land, commercial land and residential land-intensive areas. Based on the previous quantitative study, we discussed the mechanism of generation and impact on urban scale. Several conclusions have been drawn as follows:①The source of UHI generation is the rapid conversion of suburban land to urban land.②Anthropogenic heat emissions and air pollution are an important factors for the UHI.③The changes of urban spatial pattern and the destruction of ecological environment aggravate the UHI.④The differences of urban land also have effects on the UHI. Corresponding to these investigated UHI generation mechanisms on urban scale, many mitigation strategis from urban planning point,such as controlling the expansion scale and speed of urban land, constructing urban ecological open space, optimizing land use and transportation system, and appropriately reducing building density of urban are put forward.In chapter 4, the paper mainly analyzes mesoscale numerical model MM5 and the urban canopy model, and simulates the variations of temperature by changing the characteristics of urban land surface parameters in Hangzhou city using numerical model simulation technology. The results show:①The average temperature of the whole simulation region increases by 1.75℃when water is replaced with urban land.②The average temperature of the whole simulation region increases by 4.26℃when vegetation is replaced with urban land.③The average temperature of the whole simulation region increases by 6.32℃when the water and vegetation are all replaced with urban land.④The average temperature of the whole simulation region falls 2.36℃when part of the urban land is replaced by vegetation.In chapter 5, section I analyzes the relationship between surface temperature and land surface albedo, NDVI, building density, floor area ratio and average height of the different planning plots. The results show:①The surface temperature is negatively correlated with NDVI, surface albedo, and the average height, while positively correlated with building density and plot ratio.②Surface albedo, NDVI and the building density have more effects on land surface temperature than plot ratio and average height.③Surface temperature is most closely related with NDVI and surface albedo. The correlation coefficients are 0.843 and 0.829, followed by building density, whose correlation coefficient is 0.662. The last are the plot ratio and average height, whose correlation coefficients are 0.429 and 0.416 respectively. In section II, the paper establishes a fine interpolation of the temperature using mobile observation data, which generates accurate grid temperature data using mobile observe temperature data by the methods of triangulation linear interpolation, thin section sample interpolation and minimum curvature interpolation. This method provides the most valuable temperature data for the study of the distribution of UHI status. And it has more practical significance to improve the spacial accuracy of the temperature.Based on the theories and case studies of the previous five chapters, chapter 6 developes the "Urban Heat Island Mitigation Analysis and Optimization System" by employing IDL language and secondary development of library development platform of ENVI. The system has four main functional modules:data management module, remote sensing factor extraction module, data statistics query module, and analysis of simulation and optimization module. Its features include:①Supporting multiple data file formats.②overlaying raster and vector data.③Extracting information such as surface temperature, NDVI, and surface albedo from remote sensing data:NOAA,MODIS,Landsat TM/ETM+,ASTER, and so on.④Allowing inquiries into surface temperatures of different types of construction land, including demarcating the extreme on plan land, calculating the average temperature, producing the average land temperature curve at different times, producing different temperature profiles curve on different lands.⑤Allowing the single factor analysis and multiple linear regression analysis. Users can use the same block in the same location to collect point data randomly, and use curve fitting method to obtain surface temperature and the impact factor relational database.⑥Fine interpolation function using temperature observation data.
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