This article attempts to detail time series characteristics of PM2.5 concentration in Guangzhou(China)from 1 June 2012 to 31 May 2013 based on wavelet analysis tools,and discuss its spatial distribution using geograph...This article attempts to detail time series characteristics of PM2.5 concentration in Guangzhou(China)from 1 June 2012 to 31 May 2013 based on wavelet analysis tools,and discuss its spatial distribution using geographic information system software and a modified land use regression model.In this modified model,an important variable(land use data)is substituted for impervious surface area,which can be obtained conveniently from remote sensing imagery through the linear spectral mixture analysis method.Impervious surface has higher precision than land use data because of its sub-pixel level.Seasonal concentration pattern and day-by-day change feature of PM2.5 in Guangzhou with a micro-perspective are discussed and understood.Results include:(1)the highest concentration of PM2.5 occurs in October and the lowest in July,respectively;(2)average concentration of PM2.5 in winter is higher than in other seasons;and(3)there are two high concentration zones in winter and one zone in spring.展开更多
Many efforts have been devoted to extracting impervious surfaces based on different methods from multiple spatial resolution images.Differences in extraction methods and spatial resolutions are significant and have le...Many efforts have been devoted to extracting impervious surfaces based on different methods from multiple spatial resolution images.Differences in extraction methods and spatial resolutions are significant and have led to discrepant performances in terms of the impervious surface extraction accuracy.However,which extraction method is more suitable for which kind of spatial resolution image in practice is poorly understood.This study systematically compared the performances of 12 methods of impervious surface extraction for four spatial resolution images(i.e.Landsat 8[30 m],Sentinel-2A[20 m],Sentinel-2A[10 m],and Gaofen-2[4 m])in three testing areas.The results indicated that for the mediumspatial resolutions of 30 and 20 m,the support vector machine(SVM)method was considered as the optimal classification method with the highest accuracy of impervious surface extraction.For the high-spatial resolutions of 10 and 4 m,the object based image analysis(OBIA)method obtained the highest accuracy of the impervious surface distribution.Furthermore,the perpendicular impervious surface index(PISI)outperformed the other indices in obtaining the impervious surface distribution,with the highest accuracy for four spatial resolution images.These comprehensive assessments can provide a valuable guidance for future impervious surface extraction from different spatial resolutions.展开更多
基金This work is supported by the National Nature Science Foundation of China[grant number:41201432],the National Science Foundation of Tibet[grant number:2016ZR-TU-05]the Foundation for Innovative Research for Young Teachers in Higher Educational Institutions of Tibet[grant number:QCZ2016-07].
文摘This article attempts to detail time series characteristics of PM2.5 concentration in Guangzhou(China)from 1 June 2012 to 31 May 2013 based on wavelet analysis tools,and discuss its spatial distribution using geographic information system software and a modified land use regression model.In this modified model,an important variable(land use data)is substituted for impervious surface area,which can be obtained conveniently from remote sensing imagery through the linear spectral mixture analysis method.Impervious surface has higher precision than land use data because of its sub-pixel level.Seasonal concentration pattern and day-by-day change feature of PM2.5 in Guangzhou with a micro-perspective are discussed and understood.Results include:(1)the highest concentration of PM2.5 occurs in October and the lowest in July,respectively;(2)average concentration of PM2.5 in winter is higher than in other seasons;and(3)there are two high concentration zones in winter and one zone in spring.
基金sponsored in part by the National Natural Science Foundation of China[grant numbers 41201432,41901347]Guangdong Basic and Applied Basic Research Foundation[grant number 2021A1515011411,2020A1515010562].
文摘Many efforts have been devoted to extracting impervious surfaces based on different methods from multiple spatial resolution images.Differences in extraction methods and spatial resolutions are significant and have led to discrepant performances in terms of the impervious surface extraction accuracy.However,which extraction method is more suitable for which kind of spatial resolution image in practice is poorly understood.This study systematically compared the performances of 12 methods of impervious surface extraction for four spatial resolution images(i.e.Landsat 8[30 m],Sentinel-2A[20 m],Sentinel-2A[10 m],and Gaofen-2[4 m])in three testing areas.The results indicated that for the mediumspatial resolutions of 30 and 20 m,the support vector machine(SVM)method was considered as the optimal classification method with the highest accuracy of impervious surface extraction.For the high-spatial resolutions of 10 and 4 m,the object based image analysis(OBIA)method obtained the highest accuracy of the impervious surface distribution.Furthermore,the perpendicular impervious surface index(PISI)outperformed the other indices in obtaining the impervious surface distribution,with the highest accuracy for four spatial resolution images.These comprehensive assessments can provide a valuable guidance for future impervious surface extraction from different spatial resolutions.
