A Robust Method to Generate a Consistent Time Series From DMSP/OLS Nighttime Light Data
The long time series of nighttime light (NTL) data collected by DMSP/OLS sensors provides a unique and valuable resource to study changes in human activities. However, its full time series potential has not been fully explored, mainly due to inconsistencies in the temporal signal. Previous studies h...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zhang, Qingling [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: IEEE transactions on geoscience and remote sensing - New York, NY : IEEE, 1964, 54(2016), 10, Seite 5821-5831 |
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| Übergeordnetes Werk: |
volume:54 ; year:2016 ; number:10 ; pages:5821-5831 |
| Links: |
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| DOI / URN: |
10.1109/TGRS.2016.2572724 |
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| Katalog-ID: |
OLC1981766731 |
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| 520 | |a The long time series of nighttime light (NTL) data collected by DMSP/OLS sensors provides a unique and valuable resource to study changes in human activities. However, its full time series potential has not been fully explored, mainly due to inconsistencies in the temporal signal. Previous studies have tried to resolve this issue in order to generate a consistent NTL time series. However, due to geographic limitations with the algorithms, these approaches cannot generate a coherent NTL time series globally. The purpose of this study is to develop a methodology to create a consistent NTL time series that can be applied globally. Our method is based on a novel sampling strategy to identify pseudoinvariant features. We select data points along a ridgeline-the densest part of a density plot generated between the reference image and the target image-and then use those data points to derive calibration models to minimize inconsistencies in the NTL time series. Results show that the algorithm successfully calibrates DMSP/OLS annual composites and generates a consistent NTL time series. Evaluation of the results shows that the calibrated NTL time series significantly reduces the differences between two images within the same year and increases the correlations between the NTL time series and gross domestic product as well as with energy consumption, and outperforms the Elvidge et al. (2014) method. The methodology is simple, robust, and easy to implement. The quality-enhanced NTL time series can be used in a myriad of applications that require a consistent data set over time. | ||
| 650 | 4 | |a Satellite broadcasting | |
| 650 | 4 | |a Earth | |
| 650 | 4 | |a Sensors | |
| 650 | 4 | |a ridgeline sampling regression (RSR) | |
| 650 | 4 | |a Calibration | |
| 650 | 4 | |a Time series analysis | |
| 650 | 4 | |a Systematics | |
| 650 | 4 | |a Satellites | |
| 650 | 4 | |a Calibration sites | |
| 650 | 4 | |a satellite image normalization | |
| 650 | 4 | |a ridge sampling and regression | |
| 650 | 4 | |a intersensor calibration | |
| 650 | 4 | |a ridge regression | |
| 700 | 1 | |a Pandey, Bhartendu |4 oth | |
| 700 | 1 | |a Seto, Karen C |4 oth | |
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10.1109/TGRS.2016.2572724 doi PQ20161012 (DE-627)OLC1981766731 (DE-599)GBVOLC1981766731 (PRQ)c1246-950aa6bdad2e5048c05a260fb2de4f1c4e853d24f036884b387424f1403115e20 (KEY)0048677920160000054001005821robustmethodtogenerateaconsistenttimeseriesfromdms DE-627 ger DE-627 rakwb eng 620 550 DNB Zhang, Qingling verfasserin aut A Robust Method to Generate a Consistent Time Series From DMSP/OLS Nighttime Light Data 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The long time series of nighttime light (NTL) data collected by DMSP/OLS sensors provides a unique and valuable resource to study changes in human activities. However, its full time series potential has not been fully explored, mainly due to inconsistencies in the temporal signal. Previous studies have tried to resolve this issue in order to generate a consistent NTL time series. However, due to geographic limitations with the algorithms, these approaches cannot generate a coherent NTL time series globally. The purpose of this study is to develop a methodology to create a consistent NTL time series that can be applied globally. Our method is based on a novel sampling strategy to identify pseudoinvariant features. We select data points along a ridgeline-the densest part of a density plot generated between the reference image and the target image-and then use those data points to derive calibration models to minimize inconsistencies in the NTL time series. Results show that the algorithm successfully calibrates DMSP/OLS annual composites and generates a consistent NTL time series. Evaluation of the results shows that the calibrated NTL time series significantly reduces the differences between two images within the same year and increases the correlations between the NTL time series and gross domestic product as well as with energy consumption, and outperforms the Elvidge et al. (2014) method. The methodology is simple, robust, and easy to implement. The quality-enhanced NTL time series can be used in a myriad of applications that require a consistent data set over time. Satellite broadcasting Earth Sensors ridgeline sampling regression (RSR) Calibration Time series analysis Systematics Satellites Calibration sites satellite image normalization ridge sampling and regression intersensor calibration ridge regression Pandey, Bhartendu oth Seto, Karen C oth Enthalten in IEEE transactions on geoscience and remote sensing New York, NY : IEEE, 1964 54(2016), 10, Seite 5821-5831 (DE-627)129601667 (DE-600)241439-9 (DE-576)015095282 0196-2892 nnns volume:54 year:2016 number:10 pages:5821-5831 http://dx.doi.org/10.1109/TGRS.2016.2572724 Volltext http://ieeexplore.ieee.org/document/7490418 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-GEO SSG-OLC-FOR SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_70 AR 54 2016 10 5821-5831 |
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10.1109/TGRS.2016.2572724 doi PQ20161012 (DE-627)OLC1981766731 (DE-599)GBVOLC1981766731 (PRQ)c1246-950aa6bdad2e5048c05a260fb2de4f1c4e853d24f036884b387424f1403115e20 (KEY)0048677920160000054001005821robustmethodtogenerateaconsistenttimeseriesfromdms DE-627 ger DE-627 rakwb eng 620 550 DNB Zhang, Qingling verfasserin aut A Robust Method to Generate a Consistent Time Series From DMSP/OLS Nighttime Light Data 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The long time series of nighttime light (NTL) data collected by DMSP/OLS sensors provides a unique and valuable resource to study changes in human activities. However, its full time series potential has not been fully explored, mainly due to inconsistencies in the temporal signal. Previous studies have tried to resolve this issue in order to generate a consistent NTL time series. However, due to geographic limitations with the algorithms, these approaches cannot generate a coherent NTL time series globally. The purpose of this study is to develop a methodology to create a consistent NTL time series that can be applied globally. Our method is based on a novel sampling strategy to identify pseudoinvariant features. We select data points along a ridgeline-the densest part of a density plot generated between the reference image and the target image-and then use those data points to derive calibration models to minimize inconsistencies in the NTL time series. Results show that the algorithm successfully calibrates DMSP/OLS annual composites and generates a consistent NTL time series. Evaluation of the results shows that the calibrated NTL time series significantly reduces the differences between two images within the same year and increases the correlations between the NTL time series and gross domestic product as well as with energy consumption, and outperforms the Elvidge et al. (2014) method. The methodology is simple, robust, and easy to implement. The quality-enhanced NTL time series can be used in a myriad of applications that require a consistent data set over time. Satellite broadcasting Earth Sensors ridgeline sampling regression (RSR) Calibration Time series analysis Systematics Satellites Calibration sites satellite image normalization ridge sampling and regression intersensor calibration ridge regression Pandey, Bhartendu oth Seto, Karen C oth Enthalten in IEEE transactions on geoscience and remote sensing New York, NY : IEEE, 1964 54(2016), 10, Seite 5821-5831 (DE-627)129601667 (DE-600)241439-9 (DE-576)015095282 0196-2892 nnns volume:54 year:2016 number:10 pages:5821-5831 http://dx.doi.org/10.1109/TGRS.2016.2572724 Volltext http://ieeexplore.ieee.org/document/7490418 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-GEO SSG-OLC-FOR SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_70 AR 54 2016 10 5821-5831 |
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10.1109/TGRS.2016.2572724 doi PQ20161012 (DE-627)OLC1981766731 (DE-599)GBVOLC1981766731 (PRQ)c1246-950aa6bdad2e5048c05a260fb2de4f1c4e853d24f036884b387424f1403115e20 (KEY)0048677920160000054001005821robustmethodtogenerateaconsistenttimeseriesfromdms DE-627 ger DE-627 rakwb eng 620 550 DNB Zhang, Qingling verfasserin aut A Robust Method to Generate a Consistent Time Series From DMSP/OLS Nighttime Light Data 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The long time series of nighttime light (NTL) data collected by DMSP/OLS sensors provides a unique and valuable resource to study changes in human activities. However, its full time series potential has not been fully explored, mainly due to inconsistencies in the temporal signal. Previous studies have tried to resolve this issue in order to generate a consistent NTL time series. However, due to geographic limitations with the algorithms, these approaches cannot generate a coherent NTL time series globally. The purpose of this study is to develop a methodology to create a consistent NTL time series that can be applied globally. Our method is based on a novel sampling strategy to identify pseudoinvariant features. We select data points along a ridgeline-the densest part of a density plot generated between the reference image and the target image-and then use those data points to derive calibration models to minimize inconsistencies in the NTL time series. Results show that the algorithm successfully calibrates DMSP/OLS annual composites and generates a consistent NTL time series. Evaluation of the results shows that the calibrated NTL time series significantly reduces the differences between two images within the same year and increases the correlations between the NTL time series and gross domestic product as well as with energy consumption, and outperforms the Elvidge et al. (2014) method. The methodology is simple, robust, and easy to implement. The quality-enhanced NTL time series can be used in a myriad of applications that require a consistent data set over time. Satellite broadcasting Earth Sensors ridgeline sampling regression (RSR) Calibration Time series analysis Systematics Satellites Calibration sites satellite image normalization ridge sampling and regression intersensor calibration ridge regression Pandey, Bhartendu oth Seto, Karen C oth Enthalten in IEEE transactions on geoscience and remote sensing New York, NY : IEEE, 1964 54(2016), 10, Seite 5821-5831 (DE-627)129601667 (DE-600)241439-9 (DE-576)015095282 0196-2892 nnns volume:54 year:2016 number:10 pages:5821-5831 http://dx.doi.org/10.1109/TGRS.2016.2572724 Volltext http://ieeexplore.ieee.org/document/7490418 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-GEO SSG-OLC-FOR SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_70 AR 54 2016 10 5821-5831 |
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10.1109/TGRS.2016.2572724 doi PQ20161012 (DE-627)OLC1981766731 (DE-599)GBVOLC1981766731 (PRQ)c1246-950aa6bdad2e5048c05a260fb2de4f1c4e853d24f036884b387424f1403115e20 (KEY)0048677920160000054001005821robustmethodtogenerateaconsistenttimeseriesfromdms DE-627 ger DE-627 rakwb eng 620 550 DNB Zhang, Qingling verfasserin aut A Robust Method to Generate a Consistent Time Series From DMSP/OLS Nighttime Light Data 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The long time series of nighttime light (NTL) data collected by DMSP/OLS sensors provides a unique and valuable resource to study changes in human activities. However, its full time series potential has not been fully explored, mainly due to inconsistencies in the temporal signal. Previous studies have tried to resolve this issue in order to generate a consistent NTL time series. However, due to geographic limitations with the algorithms, these approaches cannot generate a coherent NTL time series globally. The purpose of this study is to develop a methodology to create a consistent NTL time series that can be applied globally. Our method is based on a novel sampling strategy to identify pseudoinvariant features. We select data points along a ridgeline-the densest part of a density plot generated between the reference image and the target image-and then use those data points to derive calibration models to minimize inconsistencies in the NTL time series. Results show that the algorithm successfully calibrates DMSP/OLS annual composites and generates a consistent NTL time series. Evaluation of the results shows that the calibrated NTL time series significantly reduces the differences between two images within the same year and increases the correlations between the NTL time series and gross domestic product as well as with energy consumption, and outperforms the Elvidge et al. (2014) method. The methodology is simple, robust, and easy to implement. The quality-enhanced NTL time series can be used in a myriad of applications that require a consistent data set over time. Satellite broadcasting Earth Sensors ridgeline sampling regression (RSR) Calibration Time series analysis Systematics Satellites Calibration sites satellite image normalization ridge sampling and regression intersensor calibration ridge regression Pandey, Bhartendu oth Seto, Karen C oth Enthalten in IEEE transactions on geoscience and remote sensing New York, NY : IEEE, 1964 54(2016), 10, Seite 5821-5831 (DE-627)129601667 (DE-600)241439-9 (DE-576)015095282 0196-2892 nnns volume:54 year:2016 number:10 pages:5821-5831 http://dx.doi.org/10.1109/TGRS.2016.2572724 Volltext http://ieeexplore.ieee.org/document/7490418 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-GEO SSG-OLC-FOR SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_70 AR 54 2016 10 5821-5831 |
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10.1109/TGRS.2016.2572724 doi PQ20161012 (DE-627)OLC1981766731 (DE-599)GBVOLC1981766731 (PRQ)c1246-950aa6bdad2e5048c05a260fb2de4f1c4e853d24f036884b387424f1403115e20 (KEY)0048677920160000054001005821robustmethodtogenerateaconsistenttimeseriesfromdms DE-627 ger DE-627 rakwb eng 620 550 DNB Zhang, Qingling verfasserin aut A Robust Method to Generate a Consistent Time Series From DMSP/OLS Nighttime Light Data 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The long time series of nighttime light (NTL) data collected by DMSP/OLS sensors provides a unique and valuable resource to study changes in human activities. However, its full time series potential has not been fully explored, mainly due to inconsistencies in the temporal signal. Previous studies have tried to resolve this issue in order to generate a consistent NTL time series. However, due to geographic limitations with the algorithms, these approaches cannot generate a coherent NTL time series globally. The purpose of this study is to develop a methodology to create a consistent NTL time series that can be applied globally. Our method is based on a novel sampling strategy to identify pseudoinvariant features. We select data points along a ridgeline-the densest part of a density plot generated between the reference image and the target image-and then use those data points to derive calibration models to minimize inconsistencies in the NTL time series. Results show that the algorithm successfully calibrates DMSP/OLS annual composites and generates a consistent NTL time series. Evaluation of the results shows that the calibrated NTL time series significantly reduces the differences between two images within the same year and increases the correlations between the NTL time series and gross domestic product as well as with energy consumption, and outperforms the Elvidge et al. (2014) method. The methodology is simple, robust, and easy to implement. The quality-enhanced NTL time series can be used in a myriad of applications that require a consistent data set over time. Satellite broadcasting Earth Sensors ridgeline sampling regression (RSR) Calibration Time series analysis Systematics Satellites Calibration sites satellite image normalization ridge sampling and regression intersensor calibration ridge regression Pandey, Bhartendu oth Seto, Karen C oth Enthalten in IEEE transactions on geoscience and remote sensing New York, NY : IEEE, 1964 54(2016), 10, Seite 5821-5831 (DE-627)129601667 (DE-600)241439-9 (DE-576)015095282 0196-2892 nnns volume:54 year:2016 number:10 pages:5821-5831 http://dx.doi.org/10.1109/TGRS.2016.2572724 Volltext http://ieeexplore.ieee.org/document/7490418 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-GEO SSG-OLC-FOR SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_70 AR 54 2016 10 5821-5831 |
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However, its full time series potential has not been fully explored, mainly due to inconsistencies in the temporal signal. Previous studies have tried to resolve this issue in order to generate a consistent NTL time series. However, due to geographic limitations with the algorithms, these approaches cannot generate a coherent NTL time series globally. The purpose of this study is to develop a methodology to create a consistent NTL time series that can be applied globally. Our method is based on a novel sampling strategy to identify pseudoinvariant features. We select data points along a ridgeline-the densest part of a density plot generated between the reference image and the target image-and then use those data points to derive calibration models to minimize inconsistencies in the NTL time series. Results show that the algorithm successfully calibrates DMSP/OLS annual composites and generates a consistent NTL time series. Evaluation of the results shows that the calibrated NTL time series significantly reduces the differences between two images within the same year and increases the correlations between the NTL time series and gross domestic product as well as with energy consumption, and outperforms the Elvidge et al. (2014) method. The methodology is simple, robust, and easy to implement. 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A Robust Method to Generate a Consistent Time Series From DMSP/OLS Nighttime Light Data |
| abstract |
The long time series of nighttime light (NTL) data collected by DMSP/OLS sensors provides a unique and valuable resource to study changes in human activities. However, its full time series potential has not been fully explored, mainly due to inconsistencies in the temporal signal. Previous studies have tried to resolve this issue in order to generate a consistent NTL time series. However, due to geographic limitations with the algorithms, these approaches cannot generate a coherent NTL time series globally. The purpose of this study is to develop a methodology to create a consistent NTL time series that can be applied globally. Our method is based on a novel sampling strategy to identify pseudoinvariant features. We select data points along a ridgeline-the densest part of a density plot generated between the reference image and the target image-and then use those data points to derive calibration models to minimize inconsistencies in the NTL time series. Results show that the algorithm successfully calibrates DMSP/OLS annual composites and generates a consistent NTL time series. Evaluation of the results shows that the calibrated NTL time series significantly reduces the differences between two images within the same year and increases the correlations between the NTL time series and gross domestic product as well as with energy consumption, and outperforms the Elvidge et al. (2014) method. The methodology is simple, robust, and easy to implement. The quality-enhanced NTL time series can be used in a myriad of applications that require a consistent data set over time. |
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The long time series of nighttime light (NTL) data collected by DMSP/OLS sensors provides a unique and valuable resource to study changes in human activities. However, its full time series potential has not been fully explored, mainly due to inconsistencies in the temporal signal. Previous studies have tried to resolve this issue in order to generate a consistent NTL time series. However, due to geographic limitations with the algorithms, these approaches cannot generate a coherent NTL time series globally. The purpose of this study is to develop a methodology to create a consistent NTL time series that can be applied globally. Our method is based on a novel sampling strategy to identify pseudoinvariant features. We select data points along a ridgeline-the densest part of a density plot generated between the reference image and the target image-and then use those data points to derive calibration models to minimize inconsistencies in the NTL time series. Results show that the algorithm successfully calibrates DMSP/OLS annual composites and generates a consistent NTL time series. Evaluation of the results shows that the calibrated NTL time series significantly reduces the differences between two images within the same year and increases the correlations between the NTL time series and gross domestic product as well as with energy consumption, and outperforms the Elvidge et al. (2014) method. The methodology is simple, robust, and easy to implement. The quality-enhanced NTL time series can be used in a myriad of applications that require a consistent data set over time. |
| abstract_unstemmed |
The long time series of nighttime light (NTL) data collected by DMSP/OLS sensors provides a unique and valuable resource to study changes in human activities. However, its full time series potential has not been fully explored, mainly due to inconsistencies in the temporal signal. Previous studies have tried to resolve this issue in order to generate a consistent NTL time series. However, due to geographic limitations with the algorithms, these approaches cannot generate a coherent NTL time series globally. The purpose of this study is to develop a methodology to create a consistent NTL time series that can be applied globally. Our method is based on a novel sampling strategy to identify pseudoinvariant features. We select data points along a ridgeline-the densest part of a density plot generated between the reference image and the target image-and then use those data points to derive calibration models to minimize inconsistencies in the NTL time series. Results show that the algorithm successfully calibrates DMSP/OLS annual composites and generates a consistent NTL time series. Evaluation of the results shows that the calibrated NTL time series significantly reduces the differences between two images within the same year and increases the correlations between the NTL time series and gross domestic product as well as with energy consumption, and outperforms the Elvidge et al. (2014) method. The methodology is simple, robust, and easy to implement. The quality-enhanced NTL time series can be used in a myriad of applications that require a consistent data set over time. |
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