Remote Sensing Mapping Of Urban Impermeable Surfaces And Its Impact On Surface Thermal Environment

The impervious surface area(ISA)is not only one of the main characteristics of urban land use,but also its spatial distribution is an important factor affecting urban surface temperature(LST).ISA has been widely recognized as an important indicator for assessing the level of urbanization and urban environmental quality.With the development of remote sensing technology,a variety of land use/land cover and ISA datasets have been produced one after another,however,due to the different data sources and classification methods adopted by different dataset products,there are inevitable differences in accuracy and consistency among the dataset products.Therefore,it is particularly important to evaluate and analyze the existing dataset products.So far,researchers have proposed a variety of feature classification and ISA extraction methods,but due to the complexity of urban features,the existing spatial heterogeneity problem and mixed image element problem still pose challenges to accurate feature classification and mapping,and urban feature classification and ISA mapping still have room for improvement and research significance.The rapid expansion of ISA has led to a series of urban thermal environmental problems,and vegetation has a significant role in reducing urban LST.Therefore,it is of practical significance to analyze the spatial pattern of UHI effect and the effect of different land cover types on urban LST,and to formulate a reasonable spatial combination of ISA and vegetation to alleviate the urban thermal environment problems.This paper first evaluates the accuracy of the existing ISA dataset products,and in view of the fact that the accuracy of the existing dataset can hardly meet the needs of urban heat island effect research,this paper produces a set of ISA dataset for the study area,elucidates the spatial pattern of ISA and UHI,and analyses the optimal combination of ISA and vegetation to mitigate the urban heat island effect.The main findings of this paper are as follows:(1)Elucidated the ISA precision and consistency differences of six 30 m spatial resolution dataset products(GAUD,CLCD,GAIA,Globe Land30,AGLC,GISA)in China,and analyzed the driving factors of ISA consistency differences among various products,and finally selected the optimal ISA products.In terms of precision performance,the GISA product has the best overall precision,user precision,producer precision,kappa coefficient,and Mathews correlation coefficient;the ISA user precision of the CLCD product is high,and the ISA user precision of the GAUD product is low;in terms of spatial consistency,the ISA of the six dataset products has roughly the same area distribution in the latitude and longitude directions,and the ISA of the Beijing-Tianjin-Hebei,Yangtze River Delta,and The highest consistency of dataset products in Beijing-Tianjin-Hebei,Yangtze River Delta and Pearl River Delta regions,and lower consistency in southwest and northwest regions;in terms of the correlation of ISA area share between products,GAUD has the highest correlation with GISA(R2=0.94);in terms of provincial scale consistency,the provinces with high consistency levels are Beijing(66.40%),Shanghai(57.39%),Zhejiang(51.75%)and Guangdong(51.43%),and provinces with low consistency grades were Tibet(4.81%),Xinjiang(8.28%),Inner Mongolia(10.34%),Qinghai(14.51%),Gansu(15.55%),and Ningxia(15.58%);topography(including elevation and slope)was an important factor affecting product consistency.(2)A random forest classifier combining three types of features was constructed based on screening multiple effective remote sensing indices as spectral features,using the minimum noise classification transformation to remove data redundancy of remote sensing images to obtain auxiliary features,and using a grayscale co-generation matrix to generate texture features to produce various types of features in the main urban area of Zhengzhou City in 2006,2010,2015 and 2018.The accuracy evaluation results showed that the overall accuracy of the 2010 ISA extracted by this classification method was 0.92,the kappa coefficient was 0.83,the user accuracy was 0.87,the producer accuracy was 0.92,and the Mathews correlation coefficient was 0.83.By comparing the accuracy analysis of the ISA extraction results obtained based on this method with the ISA feature classes of the GISA dataset,it was demonstrated that the The validity and usability of the classification method for feature classification and ISA extraction in urban areas were demonstrated by comparing the accuracy analysis of the ISA extraction results obtained based on this method with those of the GISA data set.(3)Based on the ISA produced in this article for the main urban area of Zhengzhou City in 2006,2010,2015,and 2018,the impervious surface density fraction(ISF)was obtained using Zonal Statistics algorithm;the LST was inverted by the radiative transfer equation;the vegetation cover(FVC)was calculated using the image dichotomous model,and the mean FVC was obtained using the circular focal density algorithm.the mean standard deviation method was used to plot the The spatial distribution of the UHI effect was plotted using the mean standard deviation method,and the contribution of different land cover types to the LST was evaluated.The slopes of linear fits of ISF and mean FVC with LST for multiple years and their trends were calculated by density interval partitioning,and the correlation models of ISF,mean FVC and LST in 2010 were constructed to analyze the correlation relationships among the three.Finally,the surface warming efficiency and surface cooling efficiency were used to quantify the warming effect of ISA and the cooling effect of vegetation,respectively,and the optimal combination of ISF and average FVC in 2010 was proposed by comparative analysis between subzones,which provided scientific basis for alleviating the urban thermal environment problems in the main city of Zhengzhou.