Time series analysis of radiant heat using 75 hours VIIRS satellite day and night band nightfire data
The nightfires illuminated on the earth surface are caught by the satellite. These are emitted by various sources such as gas flares, biomass burning, volcanoes, and industrial sites such as steel mills. Amount of nightfires in an area is a proxy indicator of fuel consumption and CO2 emission. In th...
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EJAAM
2020-12-01
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| Series: | E-Journal of Analysis and Applied Mathematics |
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| Online Access: | https://ejaam.org/articles/2020/10.2478-ejaam-2020-0008.pdf |
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| author | Jyoti U. Devkota |
| author_facet | Jyoti U. Devkota |
| author_sort | Jyoti U. Devkota |
| collection | DOAJ |
| description | The nightfires illuminated on the earth surface are caught by the satellite. These are emitted by various sources such as gas flares, biomass burning, volcanoes, and industrial sites such as steel mills. Amount of nightfires in an area is a proxy indicator of fuel consumption and CO2 emission. In this paper the behavior of radiant heat (RH) data produced by nightfire is minutely analyzed over a period of 75 hour; the geographical coordinates of energy sources generating these values are not considered. Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS DNB) satellite earth observation nightfire data were used. These 75 hours and 28252 observations time series RH (unit W) data is from 2 September 2018 to 6 September 2018. The dynamics of change in the overall behavior these data and with respect to time and irrespective of its geographical occurrence is studied and presented here. Different statistical methodologies are also used to identify hidden groups and patterns which are not obvious by remote sensing. Underlying groups and clusters are formed using Cluster Analysis and Discriminant Analysis. The behavior of RH for three consecutive days is studied with the technique Analysis of Variance. Cubic Spline Interpolation and merging has been done to create a time series data occurring at equal minute time interval. The time series data is decomposed to study the effect of various components. The behavior of this data is also analyzed in frequency domain by study of period, amplitude and the spectrum. |
| format | Article |
| id | doaj-art-5dd03c32db404c199934f3f30d4e7e95 |
| institution | Kabale University |
| issn | 2544-9990 |
| language | English |
| publishDate | 2020-12-01 |
| publisher | EJAAM |
| record_format | Article |
| series | E-Journal of Analysis and Applied Mathematics |
| spelling | doaj-art-5dd03c32db404c199934f3f30d4e7e952025-02-08T18:35:22ZengEJAAME-Journal of Analysis and Applied Mathematics2544-99902020-12-01202010.2478/ejaam-2020-0008Time series analysis of radiant heat using 75 hours VIIRS satellite day and night band nightfire dataJyoti U. Devkota0Department of Mathematics, Kathmandu University, NepalThe nightfires illuminated on the earth surface are caught by the satellite. These are emitted by various sources such as gas flares, biomass burning, volcanoes, and industrial sites such as steel mills. Amount of nightfires in an area is a proxy indicator of fuel consumption and CO2 emission. In this paper the behavior of radiant heat (RH) data produced by nightfire is minutely analyzed over a period of 75 hour; the geographical coordinates of energy sources generating these values are not considered. Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS DNB) satellite earth observation nightfire data were used. These 75 hours and 28252 observations time series RH (unit W) data is from 2 September 2018 to 6 September 2018. The dynamics of change in the overall behavior these data and with respect to time and irrespective of its geographical occurrence is studied and presented here. Different statistical methodologies are also used to identify hidden groups and patterns which are not obvious by remote sensing. Underlying groups and clusters are formed using Cluster Analysis and Discriminant Analysis. The behavior of RH for three consecutive days is studied with the technique Analysis of Variance. Cubic Spline Interpolation and merging has been done to create a time series data occurring at equal minute time interval. The time series data is decomposed to study the effect of various components. The behavior of this data is also analyzed in frequency domain by study of period, amplitude and the spectrum.https://ejaam.org/articles/2020/10.2478-ejaam-2020-0008.pdfcluster analysisdiscriminant analysisprinciple components analysisspectrumviirs satellite dnb data |
| spellingShingle | Jyoti U. Devkota Time series analysis of radiant heat using 75 hours VIIRS satellite day and night band nightfire data E-Journal of Analysis and Applied Mathematics cluster analysis discriminant analysis principle components analysis spectrum viirs satellite dnb data |
| title | Time series analysis of radiant heat using 75 hours VIIRS satellite day and night band nightfire data |
| title_full | Time series analysis of radiant heat using 75 hours VIIRS satellite day and night band nightfire data |
| title_fullStr | Time series analysis of radiant heat using 75 hours VIIRS satellite day and night band nightfire data |
| title_full_unstemmed | Time series analysis of radiant heat using 75 hours VIIRS satellite day and night band nightfire data |
| title_short | Time series analysis of radiant heat using 75 hours VIIRS satellite day and night band nightfire data |
| title_sort | time series analysis of radiant heat using 75 hours viirs satellite day and night band nightfire data |
| topic | cluster analysis discriminant analysis principle components analysis spectrum viirs satellite dnb data |
| url | https://ejaam.org/articles/2020/10.2478-ejaam-2020-0008.pdf |
| work_keys_str_mv | AT jyotiudevkota timeseriesanalysisofradiantheatusing75hoursviirssatellitedayandnightbandnightfiredata |