Rapid urbanization has significantly increased the impervious surface of urban areas and affected the urban thermal cycle.At the same time,the population agglomeration,the growth of traffic volume,and prosperous commercial activities have thus caused heat accumulation in urban areas,which leads to the urban heat island effect(UHI).This phenomenon has significantly increased energy consumption and carbon dioxide emissions,increased the risk of high-temperature disasters,damaged the ecological balance of climate,hydrology,material circulation,and energy metabolism in urban areas,and also aggravated the accumulation of urban air pollutants,which adversely affects the life and health of residents.Therefore,the monitoring and management of urban internal thermal environment have become an important problem for modern city managers,and the methods and techniques for rapid and accurately calculating UHI indices and accurately expressing the UHI spatial distribution have become a hot research topic in the academic community.The indices such as UHI intensity,UHI footprint,UHI capacity,and heat island proportion index are used by researchers to describe the development level of the UHI,and the spatial distribution form is expressed by the UHI intensity surface.However,due to the complex spatial structure and frequent human activities inside the city,it is difficult to access and calculate the urban thermal environment information and indices in a large-scale and highfrequency way.Thermal infrared remote sensing can detect the thermal radiation energy of land surface targets,with the development of remote sensing technology in recent decades,remote sensing data with long time series,wide coverage,and high spatial resolution has been emerging,which could be used for inversing of the land surface UHI distribution,but most of the traditional UHI indices are obtained by fitting methods,which are complicated to calculate and hard to reflect the details inside the city.To address the problems of low efficiency of UHI indices calculation and imprecise in expressing complex spatial distribution of urban internal thermal environment which exist in the fitting methods,we proposed a model that integrates UHI information with the GeoSOT(Geographic Coordinate Subdividing Grid with One-Dimension Integer Coding on 2n Tree)grid and subsequently designed the calculation method of UHI indices and expression method of UHI spatial form.And on this basis,we calculated the UHI indices and impact factors,expressed the UHI spatial distribution,and made the correlation analysis between UHI indices and impact factors in the fifth ring of Beijing with the GeoSOT grid Framework by using the Landsat7\8 images data with no cloud cover from 2014 to 2019.the innovative achievements are as follows:(1)The UHI is essentially a three-dimensionally distributed and dynamically changing field,the complexity of which cannot be reflected by a simple functional model,while an overly complex function brings a series of computational and expression difficulties.To solve this problem,we propose a model that integrates UHI information with the GeoSOT grid,using a discrete field model to describe the UHI,and achieves the mapping of UHI information to the grid model.On this basis,we designed the rules for grid-level selection and the calculation method of grid center point value to ensure the accuracy of UHI indices calculation and UHI spatial distribution expression,meanwhile,built the data attribute tables to achieve efficient storage and management of UHI information.(2)On the basis of the model that integrates UHI information with the GeoSOT grid,we designed the calculation methods of UHI indices such as UHI footprint,UHI capacity,UHI centroid,which supported the rapid calculation of UHI indices in different scales.(3)In order to monitor and analyze the UHI impact factors,we proposed a GeoSOT gridbased association model of UHI information and impact factors,and designed the calculation methods and correlation analysis method of normalized difference vegetation index,normalized difference building index,population density,and other factors with UHI indices,which can support accurate UHI management.(4)To solve the imprecise in expressing complex spatial distribution of urban internal thermal environment,we designed a multi-scale expression method of UHI spatial distribution,breaking the constraints of complex surface fitting and achieving a detailed description of the urban thermal environment spatial form by using the discrete subdivision way.Compared with the Gaussian Surface fitting method,the detail description and efficiency are significantly improved,which addressed the low efficiency and imprecise in describing the spatial form details of the thermal environment inside the city.(5)The methods in this paper were used to explore the change of UHI in the area within the Fifth Ring Road of Beijing from 2014 to 2019.Compared with the Gaussian Surface fitting method,the calculation efficiency is improved by 26321 ms,and the details of UHI spatial distribution within the city can be clearly expressed.The experimental results show that the order of significant degree of UHI effect in the study area is: summer>autumn>spring>winter.The UHI effect gradually increased from 2014 to 2018,peaking in 2018,which was alleviated in 2019,the measures formulated by Beijing for ecological environment problems have achieved initial results.Meanwhile,the associative analysis results show that the normalized difference building index(NDBI)is positively correlated with LST,and the UHI effect becomes more significant as the NDBI value increases.In addition,population density is also one of the factors that accelerate the deterioration of the urban thermal environment.Conversely,the normalized difference vegetation index(NDVI)showed a negative correlation with land surface temperature(LST),indicating that vegetation can mitigate the UHI effect.The achievements of this paper support a large range and high-frequency calculation of rapid calculation of UHI indices and accurate expression of UHI spatial distribution,it is significant for accurate monitoring the changes of UHI,for analysis of connections between urban thermal environment and complicated spatial distribution of cities,further to support accurate governance of UHI. |