Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/82141
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dc.contributorDepartment of Land Surveying and Geo-Informatics-
dc.creatorLin, J-
dc.creatorShi, W-
dc.date.accessioned2020-05-05T05:58:50Z-
dc.date.available2020-05-05T05:58:50Z-
dc.identifier.urihttp://hdl.handle.net/10397/82141-
dc.language.isoenen_US
dc.publisherMolecular Diversity Preservation International (MDPI)en_US
dc.rights© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).en_US
dc.rightsThe following publication Lin J, Shi W. Statistical Correlation between Monthly Electric Power Consumption and VIIRS Nighttime Light. ISPRS International Journal of Geo-Information. 2020; 9(1):32, is available at https://doi.org/10.3390/ijgi9010032en_US
dc.subjectElectric power consumptionen_US
dc.subjectMonthlyen_US
dc.subjectNighttime lighten_US
dc.subjectRemote sensingen_US
dc.subjectVIIRS DNBen_US
dc.titleStatistical correlation between monthly electric power consumption and VIIRS nighttime lighten_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume9-
dc.identifier.issue1-
dc.identifier.doi10.3390/ijgi9010032-
dcterms.abstractThe 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.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationISPRS international journal of geo-information, 2020, v. 9, no. 1, 32-
dcterms.isPartOfISPRS international journal of geo-information-
dcterms.issued2020-
dc.identifier.isiWOS:000514631100004-
dc.identifier.scopus2-s2.0-85078024041-
dc.identifier.eissn2220-9964-
dc.identifier.artn32-
dc.description.validate202006 bcma-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Scopus/WOSen_US
dc.description.pubStatusPublisheden_US
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