Study On Macro Dynamic Monitoring And Simulation&Prediction Model Of The Urban Heat Island Effect In Xi’an City

In 2000, the State Council issued ―Notice on Printing and Distributing the Outline of the National Ecological Environmental Protection‖, it stated: "By 2030, the country should reach the standards of more than 30% of the urban eco-city and garden city. By 2050, the country should strive to improve the overall ecological environment". January 1, 2015, the latest revision of the implementation of the "The People’s Republic of China Environmental Protection Law" clearly required: "C hina shall strengthen the atmosphere, water and soil protection, establish and improve the relevant investigation, monitoring, evaluation and repair system. " Strengthening urban environmental management and creating a harmonious livable home, is one of the priorities of building smart city under the new normal. It is the way the city healthy and sustainable development and has far-reaching significance. Urban heat island effect as a branch of atmospheric pollution becomes an important indicator of environmental studies in urban environment, which will even affect the quality of the environment, production and life. With the accelerating process of urbanization, urban heat island effect is becoming increasingly prominent in Xi’an city. So, systematic understanding of the characteristics o f urban heat island effect has a very positive role of rational planning, improving living environment.For the Geographic Situation Monitor project of National Administration of Surveying, Mapping and Geoinformation of C hina as the background, taking Xi’an C ity as the research area, using 8 periods Landsat TM / ETM+ / O LI data from 1995-2013 as the main data sources, combined with Xi’an Statistical Yearbook data, based on RS and GIS technology, this research at first quantitatively inversed and verified the surface temperatures in Xi’an city; secondly, it determined the existence of the urban heat island effect in Xi’an city; the third, it first introduced Moving Split-Window Analysis and did quantitatively research of urban heat island effect edges, then analyzed the correlation between urban heat island effect and urbanization expansion; the forth, it autonomously put forward of urban heat island effect distribution index, which discussed the internal distribution law of urban heat island effect and coverage, created a comprehensive and quantitative evaluation index of urban heat island effect in Xi’an city; the fifth, it quantitatively studied the contribution of each factor to urban heat island effect, sequenced the influence in Xi’an city; the sixth, it built my own transverse GM relational model and improved longitudinal UHI-G-ES prediction model and predicted the ―urban heat island effect year‖. In summary, from the I to VI chapter,it is the macroscopically dynamic monitor of urban heat island effect in Xi’an city, from the Part VII to VIII chapter, it is the simulation and forecasting study of urban heat island effect in Xi’an city.Through research, the mainly drew the following conclusions:1. The land surface temperature inversed accurately. From 1995 to 2013, the overall strength trend was still decreasing from the city center to the periphery. The characteristic of distribution was from surface to the points. High and low heat island areas reduced, medium areas increased.2. The border based on Moving Split-Window Analysis was within a reasonable range. In 18 years, the range was expanding and the boundary was clearly changed. From 1995 to 2013, the area was increasing from 95.01 km2 to 415.48 km2. The greatest span change was about 17.4 km in the north-south direction; the minimum span was about 8.4 km in the southwest-northeast direction.3. The urban heat island effect and urban space expansion was positively correlated.In 18 years, the area of the urban heat island effect in Xi’an city had expanded by about 4.3 times, while the urban area of Xi’an city had expanded by about 4.3 times too. It was rapidly expanding from 1995 to 2006 and steadly growing from 2006 to 2013, coincided with the development in recent years.4. The heat island distribution index can quantitatively assess urban heat island effect, when P>1, that shows the urban heat island effect is obvious, and vice versa. Vegetation and water was advantage distribution in the 1&2&3-stage of urban heat island effect; it was disadvantage distribution in the 5&6-stage of urban heat island effect. Construction land and bare land was on the contrary. The 4-stage of urban heat island effect was the transition zone of different objects development. In 18 years, the intensity of the urban heat island effect reached the minimum in 2000, and the highest in 2010.5. The sorting of correlation factors of the urban heat island effect in Xi’an city was: rainfall> average wind speed> emissions> built area> Population> artificial gas energy consumption> Energy consumption LPG> traffic> green area > GDP> industrial GDP. Natural factors and human factors both influence the urban heat island effect in Xi’an city and the impact of human factors was increasingly.6. It autonomously built the lateral association model GM(0, N)of urban heat island effect in Xi’an city, which was proved that the average error was less than 1 percent, reaching one level precision. Improved the gray model, it autonomously built UHI-G-ES model,which was proved that the average error of annual average temperature simulation was less than 1 percent and the accuracy was from 2.42% to 0.259%, from level 2 to level 1. By prediction, from 2014 to 2025 years were all the urban heat island effect high years.This article has been innovative in the fo llowing aspects:1. Based on remote sensing method can quickly and quantitatively reveal the relationship between urban heat island and urban scale urbanization. 2. It introduced the Moving Split-Window Analysis in order to quantitatively determine the boundaries of urban heat island effect. The results show that this method is resonable and feasible. 3. I autonomously proposed heat island distribution index. The index can quantitatively obtain the advantages of different land cover distribution in different levels of the urban heat island effect area and can macroscopically obtain quantitative indicators of urban heat island effect. 4. Introducing gray correlation theory, the method realized the quantitative calculation and sequence of multi-factor’s influence level. 5. Introduced the GM(0, N) model usually for water conservancy, it proposed GM model used for horizontally simulating which reached one level precision; it proposed improved UHI-G-ES model used for vertically predicting, which improved the precision from level two to level one.