Abstract
The nighttime light (NTL) imagery acquired from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) enables feasibility of investigating socioeconomic activities at monthly scale, compared with annual study using nighttime light data acquired from the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS). This paper is the first attempt to discuss the quantitative correlation between monthly composite VIIRS DNB NTL data and monthly statistical data of electric power consumption (EPC), using 14 provinces of southern China as study area. Two types of regressions (linear regression and polynomial regression) and nine kinds of NTL with different treatments are employed and compared in experiments. The study demonstrates that: (1) polynomial regressions acquire higher reliability, whose average R square is 0.8816, compared with linear regressions, whose average R square is 0.8727; (2) regressions between denoised NTL with threshold of 0.3 nW/(cm2·sr) and EPC steadily exhibit the strongest reliability among the nine kinds of processed NTL data. In addition, the polynomial regressions for 12 months between denoised NTL with threshold of 0.3 nW/(cm2·sr) and EPC are constructed, whose average values of R square and mean absolute relative error are 0.8906 and 16.02%, respectively. These established optimal regression equations can be used to accurately estimate monthly EPC of each province, produce thematic maps of EPC, and analyze their spatial distribution characteristics.
Highlights
In recent years, the technology and application of remote sensing of nighttime light have attracted increasingly extensive attention [1,2,3,4,5,6,7,8,9,10,11,12,13,14]
R square, mean absolute relative error (MARE), maximum relative error (MRE), and root mean squared error (RMSE) were employed to describe the quality of each regression equation
This paper investigated the relationship between Electric power consumption (EPC) and Nighttime light (NTL) data on a monthly scale, using monthly Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) NTL composite data from January 2013 to December 2018 and the corresponding monthly statistical data of EPC of 14 provinces in southern China
Summary
The technology and application of remote sensing of nighttime light have attracted increasingly extensive attention [1,2,3,4,5,6,7,8,9,10,11,12,13,14]. Nighttime light (NTL) imagery acquired by remote sensing technology intuitively exhibit the distributions of artificial nocturnal radiances, which is an increasingly useful indicator in investigating socioeconomic activities of human being [4,5,6,7,8,9,10,11,12,13,14]. Electric power consumption (EPC) is a basic index in measuring regional energy consumption, which can objectively reflect economic performance situation, and exhibit industrial structure change and energy consumption level. Two kinds of remotely sensed NTL data, the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) and the Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS DNB) onboard the Suomi National Polar Partnership (SNPP) satellite, were often used for remote sensing-based estimation of EPC [3,5,6]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
