| Since the "13th Five-Year Plan",Yunnan Province’s economy has developed rapidly.The economic growth of Yunnan Province is of great significance to Yunnan Province’s realization of the large-scale development of the western region and the balanced development of the whole country.Gross National Product(GDP)is one of the main indicators of a country’s or region’s economic level,however,the collection of traditional statistical GDP data and its use in assessing the level of economic development have many limitations.Night light data can effectively reflect the intensity of human nocturnal activity,before that,researchers at home and abroad generally use DMSP/OLS night light data to participate in GDP spatialization research,and the release of late NPP-VIIRS night light data is used by researchers as a new generation of night light data because of its higher resolution,but the main disadvantage is that its image is unprocessed transient light source and background noise,which affects the accuracy of its application.And due to the lack of annual NPP-VIIRS night lighting data,its application development is greatly limited.Traditional annual data synthesis is based directly on the annual mean image,but this allows errors to accumulate.In order to solve the above problems,this paper proposes to combine the threshold method and median method of construction land data to correct the NPP-VIIRS monthly image month by month,remove the background noise and extremely high value,and then synthesize the annual data,and use GDP and other data to evaluate its accuracy.The results show that the correlation between the annual night lighting data and GDP reaches 0.996,which greatly improves the reliability of the data,and also indicates that the synthesis method is more scientific.Then,using the corrected NPP-VIIRS annual night lighting data as the data source,the GDP of Yunnan Province was spatialized,the GDP distribution density map of Yunnan Province for 6years from 2015 to 2020 was obtained,and the quantitative and qualitative analysis of the spatialization results of Yunnan Province’s GDP based on NPP-VIIRS images was carried out,and on this basis,the development dynamic process and spatial distribution pattern of GDP growth in Yunnan Province in the past six years were analyzed.Finally,the results of spatialization of GDP in 2020 are used to evaluate and analyze the economic development level of prefectures and cities in Yunnan Province.The main conclusions of this study are as follows:(1)Combined with the construction land data,the NPP-VIIRS night lighting images in Yunnan Province for a total of 72 months from 2015 to 2020 were corrected and synthesized month by month by using the threshold method and the median method,and the annual NPP-VIIRS night lighting image data of Yunnan Province from 2015 to 2020 was obtained,and it was concluded through the test of the data that it was feasible to use the construction land data to correct the NPP-VIIRS night lighting images,and the correction effect was better than the traditional correction method.(2)The spatialization method was used to spatialize the GDP of Yunnan Province on the basis of the annual night lighting image,and the development dynamic process,spatial distribution pattern and dynamic development trend of Yunnan Province in recent years were analyzed.Based on the above analysis,relevant suggestions and discussions are put forward on the economic development of Yunnan Province.The results show that the research on the use of night lighting images to participate in economic development can visually analyze the spatial differences,development dynamic processes and change trends of GDP.(3)Taking the spatialization results of Yunnan Province’s GDP in 2020 as the initial data,the AND index was created as a new evaluation index to evaluate the economic development level of each state and city in Yunnan Province,and analyze the assessment results.The results show that NPP-VIIRS night light images are feasible and have more advantages for participating in economic development assessment. |
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