| Urban agglomeration is an important carrier of the shift of the world economic center of gravity,as the strategic core area of China's economic development and the main area of the new urbanization.As a new data source for the study of urban agglomeration,noctilucent remote sensing data has a strong application and analysis ability.Night lights brightness reflects a area of human activity at night,a relatively developed city night lighting levels significantly higher than the small city,so the night light data can be in a certain extent,reflects the development of the social economic,and for the development of the city,the development of social economy is one of the important indicators to measure urban development situation.In this paper,the Beijing-Tianjin-Hebei urban agglomeration was taken as the study area,and DMSP-OLS noctilucent remote sensing data and NPP-VIIRS noctilucent remote sensing data were used to synthesize the long time series noctilucent remote sensing data set from 1993 to 2018.In this way,the urban areas with weak human activities in the statistical data can be separated from the active and rapidly developing urban areas,and the extraction perspective of urban built-up areas with noctilucent remote sensing data as the core can be innovated.Then,the change of economic center of gravity of Beijing-Tianjin-Hebei urban agglomeration is analyzed by combining the night-light remote sensing data and GDP data.Finally,based on the spatiotemporal simulation of social and economic parameters,the paper comprehensively analyzes the urban development process in Beijing-Tianjin-Hebei region,and understands the characteristics of Beijing-Tianjin-Hebei urban agglomeration development from a new perspective,which provides reference for the formulation of regional development policies.The main conclusions of the study are as follows:(1)The data integration process based on DMSP-OLS night-light remote sensing data and NPP-VIIRS night-light remote sensing data is realized by establishing a function model in the year when the two kinds of data overlap.After the simulation operation,the correlation between DMSP-OLS nocturnal remote sensing data simulated by NPP-VIIRS and original DMSP-OLS nocturnal remote sensing data is significantly higher than that between the original NPP-VIIRS nocturnal remote sensing data and original DMSP-OLS nocturnal remote sensing data.The standard error is significantly decreased.Overlapping in 2012 and 2013,Pearson correlation coefficients are 0.85 and 0.81 respectively,the standard errors also fell from 20.5 and19.2 to 6.1 and 7.2.The overall deviation of urban built-up area extracted from the integrated night-light remote sensing data is only 7.2% in one year,which indicates that the integration method is feasible and the integrated data can be used in the study of urban spatial change.(2)The spatial comparison method of statistical data is used to determine the optimal threshold extracted from urban built-up areas based on night light.The extracted results are close to the actual statistical data to the greatest extent,and have a high consistency with the urban built-up areas.Although the results of urban built-up areas extracted from night light data can reflect the urban development situation in the Beijing-Tianjin-Hebei region on the spatial scale,there are still some areas that have not been counted by the statistical data.In this study,noctilucent remote sensing data with different time spans and attributes are integrated,which extends the time series span of applying noctilucent remote sensing data to the analysis of geographical phenomena,and is a great significance for the study of urban spatiotemporal changes.(3)Combined with noctilucent remote sensing data,GDP data and extracted data of built-up areas,the change of economic gravity center of each prefecture-level city in Beijing-Tianjin-Hebei region was analyzed respectively,which includes the direction of the offset of the center of gravity,offset distance.The results show that from 1993 to 2018,Beijing,Xingtai,Shijiazhuang and Handan mainly developed eastward;Zhangjiakou,Cangzhou mainly developed to the northeast;Chengde developed to the northwest;Qinhuangdao developed to the west;Tangshan's economic center of gravity shifted to the south;Tianjin's economic center of gravity shifted northward;Langfang,Baoding and Hengshui's economic center of gravity shift is not much,the change is not significant.(4)The total amount of nighttime light TNL was calculated by using noctilucent remote sensing data,and the regression equations of total amount of nighttime light TNL,GDP and population data were constructed respectively by combining with the statistical data of GDP and population data of Beijing-Tianjin-Hebei region.The fitting analysis results showed that both of them had a good fitting effect with total amount of nighttime light.(5)Utilizing the fitting results of GDP,population and total nighttime light,the spatial simulation of GDP and population in the Beijing-Tianjin-Hebei region was realized.The results of spatialization of GDP directly reflect the spatial differences within the economy of Beijing-Tianjin-Hebei region.The overall economic development of the Beijing-Tianjin-Hebei region was rapid from 1993 to 2018.However,there were differences among different regions and the development speed was also different.Beijing,Tianjin,Shijiazhuang,Tangshan and other cities developed more rapidly than other cities,while Hengshui and Chengde developed more slowly than other cities,and they were relatively weak regions.At the same time,the economic growth rate in the Beijing-Tianjin-Hebei region was relatively flat from1993 to 2002,and gradually accelerated after 2002.The results of spatialized population reflect the spatial differences of population distribution in Beijing-Tianjin-Hebei region.The population of the Beijing-Tianjin-Hebei region also grew rapidly from 1993 to 2018,with cities such as Beijing,Tianjin and Shijiazhuang having large populations and fast growth rates.In Baoding,Hengshui,Chengde,Xingtai and Cangzhou,the growth rate was relatively fast at the beginning and then gradually leveled off. |
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