Study On Dynamic Change Of Urban Thermal Landscape Based On Remote Sensing

Recently,the acceleration of urbanization and the expansion of urban scale were followedwithchanges of urban underlying surface and the thermal environment.This thesis,the main research area is the main city of Xi'an and Xianyang City,which contains 8 districts.Land surface temperature retrieving was finished with remote sensing data,and delineated six thermal landscapes.Analyze the temporal and spatial characteristics of various types of thermal landscape;and effect of impervious surface and park green space in urbanization process.The evolution pattern and scale effect of urban thermal landscape were quantitatively analyzed by using landscape pattern index.The UHI-CA-Markov model was introduced to simulate and predict the thermal landscape pattern in the future.The main conclusions of this thesis including:(1)In 1995,the proportion of various types of thermal landscape in the total area was from large to smallmedium temperature thermal landscape,sub-moderate temperature thermal landscape,sub-high temperature area,high temperature thermal landscape,low temperature area,special high temperature area.In 2005,the largest proportion of various types of thermal landscape in the total area was sub-moderate temperature thermal landscape.In 2016,Sub-high temperature area of the total area of the proportion increased.From 1995 to 2016,bare land was clearly transformed into vegetation thermal landscape in Baqiao District.In Weiyang District and Weicheng District,the heat level was significantly increased,the construction site of the thermal landscape area increased by 108.7 km.In Qindu District and Weicheng District,bare land was significantly transformed into construction land and vegetation,medium temperature area of thermal landscape area increased 107.7 km2.(2)The diversity of the thermal landscape was the strongest in 1995;the overall thermal landscape was aggregated to urban construction land in 2005 and 2016.Class area(CA),largest patch index(LPI),Percentage of Landscape(PLAND),and Aggregation Index(AI)had a strong dependence on the scale,the cell size of 60m and 150m more sensitive.The scale was more sensitive when the cell size was 60m and 180m.(3)There was a positive correlation between impervious surface coverage and surface temperature,the level of thermal landscape;the vegetation coverage,park area/perimeter and surface temperature?the level of thermal landscape were negatively correlated.(4)Based on the UHI-CA-Markov model to simulate the 2021 surface temperature.The change of the thermal landscape grade in 2021 will be more extreme than that in 2016,and the thermal landscape area of the low temperature and high temperature area will be increased.