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Conference Paper: Analyzing Long-Term Artificial Light at Night Using VIIRS Monthly Product with Land Use Data: Preliminary Result of Hong Kong

TitleAnalyzing Long-Term Artificial Light at Night Using VIIRS Monthly Product with Land Use Data: Preliminary Result of Hong Kong
Authors
Issue Date2021
Citation
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS How to Cite?
AbstractLong-term monitoring of artificial light at night (ALAN) is essential for our understanding of the source of light pollution and developing mechanisms to control it. In this study, based on the VIIRS monthly product and land use data, we analyzed the long-term ALAN in Hong Kong between 2012 and 2019. We could not detect any long-term trend in the level of ALAN of Hong Kong from this dataset over the eight years of observations at the level of detection accuracy of the VIIRS monthly data. We performed a detailed analysis of the ALAN from Hong Kong and its relationship with land use classes. We found that in Hong Kong, the public residential areas are brighter than the private ones, likely the consequence of a combination of population density and lighting designs. Using the clustering method, we were able to identify some persistently bright (or dark) facilities, such as the Hong Kong-Zhuhai-Macau Bridge Port, airport and port facilities. Transient phenomena such as wildfires were identified as well. Finally, we observed a brighter background ALAN associated with an elevated humidity level (R=0.54) , which can possibly be attributed to the dispersing effect of water vapor on radiation. Since large public transportation facilities emitted the most ALAN in Hong Kong, we suggest adopting sustainable design in future transportation projects to reduce the emitted ALAN to the space, thereby reducing light pollution.
Persistent Identifierhttp://hdl.handle.net/10722/323387

 

DC FieldValueLanguage
dc.contributor.authorLiu, S-
dc.contributor.authorSo, CW-
dc.contributor.authorPun, JCS-
dc.date.accessioned2022-12-16T10:04:32Z-
dc.date.available2022-12-16T10:04:32Z-
dc.date.issued2021-
dc.identifier.citation 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS-
dc.identifier.urihttp://hdl.handle.net/10722/323387-
dc.description.abstractLong-term monitoring of artificial light at night (ALAN) is essential for our understanding of the source of light pollution and developing mechanisms to control it. In this study, based on the VIIRS monthly product and land use data, we analyzed the long-term ALAN in Hong Kong between 2012 and 2019. We could not detect any long-term trend in the level of ALAN of Hong Kong from this dataset over the eight years of observations at the level of detection accuracy of the VIIRS monthly data. We performed a detailed analysis of the ALAN from Hong Kong and its relationship with land use classes. We found that in Hong Kong, the public residential areas are brighter than the private ones, likely the consequence of a combination of population density and lighting designs. Using the clustering method, we were able to identify some persistently bright (or dark) facilities, such as the Hong Kong-Zhuhai-Macau Bridge Port, airport and port facilities. Transient phenomena such as wildfires were identified as well. Finally, we observed a brighter background ALAN associated with an elevated humidity level (R=0.54) , which can possibly be attributed to the dispersing effect of water vapor on radiation. Since large public transportation facilities emitted the most ALAN in Hong Kong, we suggest adopting sustainable design in future transportation projects to reduce the emitted ALAN to the space, thereby reducing light pollution.-
dc.languageeng-
dc.relation.ispartof 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS-
dc.titleAnalyzing Long-Term Artificial Light at Night Using VIIRS Monthly Product with Land Use Data: Preliminary Result of Hong Kong-
dc.typeConference_Paper-
dc.identifier.emailSo, CW: socw@connect.hku.hk-
dc.identifier.emailPun, JCS: jcspun@hku.hk-
dc.identifier.authorityPun, JCS=rp00772-
dc.identifier.doi10.1109/IGARSS47720.2021.9553915-
dc.identifier.hkuros343068-

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