| In the context of global warming and accelerated urbanization,urban temperatures are rising much faster than global temperatures.The urban heat island effect caused by population build-up and high-density buildings is one of the typical ecological effects of urbanization,which not only hurts people’s health,but also constrains urban development.The urban thermal environment is inextricably linked to land cover and other factors.A systematic study of the spatial and temporal evolution mechanisms of the urban thermal environment and its relationship with other related factors will help deepen the understanding of the thermal environment and provide scientific reference for the optimization of the urban thermal environment and rational urban planning in Nanjing.This paper uses Nanjing as the study area.Based on Landsat 8 imagery to obtain the surface temperature and land cover classification results of the study area,spatial analysis methods such as transfer area scale matrix,standard deviation ellipse,and global Moran’s I index were used to explore the spatial and temporal evolution mechanism of the urban thermal environment in Nanjing from 2013-2021.A quantitative study of the relationship between land cover and surface temperature using mathematical and statistical methods such as correlation and regression analysis.Finally,a geographical probe was used to identify the impact of different drivers and their interactions on the thermal environment of Nanjing and to forecast the thermal environment of Nanjing in 2025 with the help of the PLUS model.The main findings of the study are as follows:(1)The urban thermal environment in the study area shows a distribution pattern of "hot in the middle and cold around,sporadically distributed",with the high-temperature zone expanding to the southwest;the thermal environment classes in the study area are mainly medium-temperature zones,accounting for 43.03%,49.41% and 38.05% respectively in the three time periods,and the transformation of thermal environment classes mainly occurs between the sub-high-temperature zone,medium-temperature zone and sub-low-temperature zone,The transformation of thermal environment classes mainly occurred between the sub-high temperature zone,the medium temperature zone and the sub-low temperature zone;the sub-low temperature zone showed the greatest overall change during 2013-2021,and the transformation between thermal environment classes mainly occurred between adjacent classes;the centripetal force of the spatial distribution of the high temperature zone showed a slightly stronger trend,while the directionality of the distribution of all other thermal environment classes was weakened;the high temperature zone showed a trend of shifting to the southwest direction,while the directional distribution of other thermal environment classes showed less change.The distribution direction of the other thermal environment classes shows less variation,and the distribution direction always maintains a northwest-southeast distribution close to the north-south distribution;the thermal environment in the study area shows a high positive spatial correlation,with significant spatial clustering,and the spatial correlation gradually decreases with increasing spatial scale.(2)The average surface temperatures of the different land cover types from 2013-2021 are,from largest to smallest,bare land,building land,cropland,forest land,and water bodies.In the three periods,cropland is mainly distributed in the medium-temperature zone,but also in the subhigh-temperature zone and sub-low-temperature zone;woodland and water bodies are mainly distributed in the lower-temperature zone in the three periods,which can alleviate the urban heat island effect to a certain extent;bare land and construction land are mainly distributed in the higher-temperature zone in the three periods,which contribute significantly to the urban thermal environment.The correlation study shows that construction land is positively correlated with surface temperature,water bodies,and forest land are negatively correlated with surface temperature,and cultivated land is weakly correlated with surface temperature.The highest fitting coefficient of 0.642 was obtained by building a linear regression model of the share of different land cover types in the grid area and the average surface temperature of the grid,indicating that the multi-factor regression model fits best at the 600 m scale.(3)From the results of the factor detection of the drivers of the urban thermal environment effect,it can be concluded that the magnitude of the influence of each driver on the thermal environment is in the following order: 2013: NDWI>NDVI>NDBI>distance from water bodies>nighttime lights>elevation>distance from tertiary roads>distance from major roads>distance from secondary roads>distance from highways;2017: NDWI>NDVI>NDBI>distance to water bodies>nighttime lights >distance to major roads >distance to secondary roads >distance to tertiary roads >distance to highways >elevation;2021: NDBI>NDVI>NDWI>nighttime lights>distance to secondary roads>distance to tertiary roads>distance to major roads>distance to water bodies>elevation>distance to highways.The interaction detection showed that the influence of the driving factors on the spatial differentiation characteristics of the thermal environment in the study area had a synergistic enhancement,and the effects of all the driving factors were two-factor or nonlinear enhancement.(4)The PLUS(Patch-generating Land Use Simulation)model constructed in this study is highly reliable in predicting the future spatial distribution of the thermal environment in the simulated study area,with a kappa coefficient of 0.78 and an overall accuracy of 85%.Based on the data of Nanjing thermal environment grading results in 2021,and the prediction of the distribution of thermal environment in Nanjing in 2025 with the help of PLUS model,the results show that the urban high temperature area of Nanjing will continue to expand in the future,based on this result,suggestions are put forward to alleviate the urban heat island effect,mainly including:appropriately increasing the distribution of water bodies,giving priority to the use of building materials conducive to heat exchange,reducing dependence on traditional energy,and promotion of new air conditioning systems. |
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