Global nighttime light dataset from 1992 to 2022 with focus on low-light areas
Abstract Traditional nighttime light (NTL) research has largely focused on urban areas, neglecting approximately 80% of Earth’s low-light or dark sky regions. This oversight may result in a significant underestimation of light pollution, especially in global protected areas that are often biodiversi...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-06-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-05246-8 |
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| author | Hui Tang Yongde Zhong Jinyang Deng Hongling Xia Juan Wei |
| author_facet | Hui Tang Yongde Zhong Jinyang Deng Hongling Xia Juan Wei |
| author_sort | Hui Tang |
| collection | DOAJ |
| description | Abstract Traditional nighttime light (NTL) research has largely focused on urban areas, neglecting approximately 80% of Earth’s low-light or dark sky regions. This oversight may result in a significant underestimation of light pollution, especially in global protected areas that are often biodiversity hotspots. Our study employs a novel approach combining a residual neural network with a raster function model to tackle key challenges, including NTL restoration in high-latitude regions, long-term data continuity, and gap alignment across different sensor types. For the first time, we enable continuous calibration and temporal extension of global DVNL and DMSP/OLS data. Our dataset outperforms similar products by offering greater explanatory power for economic activities, enhanced temporal stability, and improved spatial distribution accuracy. Furthermore, it exhibits heightened sensitivity to subtle changes in low-light areas across global, national, urban, and protected scales, making it especially valuable for monitoring human activities and assessing environmental impacts in critical regions like World Heritage Sites, Dark Sky Preserves, and national parks. |
| format | Article |
| id | doaj-art-2ce9a287ff0349a8a88b9b218276df7f |
| institution | OA Journals |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-2ce9a287ff0349a8a88b9b218276df7f2025-08-20T02:06:23ZengNature PortfolioScientific Data2052-44632025-06-0112112210.1038/s41597-025-05246-8Global nighttime light dataset from 1992 to 2022 with focus on low-light areasHui Tang0Yongde Zhong1Jinyang Deng2Hongling Xia3Juan Wei4College of National Parks and Tourism, Central South University of Forestry & TechnologyCollege of National Parks and Tourism, Central South University of Forestry & TechnologyDepartment of Hospitality, Hotel Management and Tourism, Texas A & M University, College StationDepartment of Architecture, Hunan Urban Construction CollegeCollege of Forestry, Central South University of Forestry & TechnologyAbstract Traditional nighttime light (NTL) research has largely focused on urban areas, neglecting approximately 80% of Earth’s low-light or dark sky regions. This oversight may result in a significant underestimation of light pollution, especially in global protected areas that are often biodiversity hotspots. Our study employs a novel approach combining a residual neural network with a raster function model to tackle key challenges, including NTL restoration in high-latitude regions, long-term data continuity, and gap alignment across different sensor types. For the first time, we enable continuous calibration and temporal extension of global DVNL and DMSP/OLS data. Our dataset outperforms similar products by offering greater explanatory power for economic activities, enhanced temporal stability, and improved spatial distribution accuracy. Furthermore, it exhibits heightened sensitivity to subtle changes in low-light areas across global, national, urban, and protected scales, making it especially valuable for monitoring human activities and assessing environmental impacts in critical regions like World Heritage Sites, Dark Sky Preserves, and national parks.https://doi.org/10.1038/s41597-025-05246-8 |
| spellingShingle | Hui Tang Yongde Zhong Jinyang Deng Hongling Xia Juan Wei Global nighttime light dataset from 1992 to 2022 with focus on low-light areas Scientific Data |
| title | Global nighttime light dataset from 1992 to 2022 with focus on low-light areas |
| title_full | Global nighttime light dataset from 1992 to 2022 with focus on low-light areas |
| title_fullStr | Global nighttime light dataset from 1992 to 2022 with focus on low-light areas |
| title_full_unstemmed | Global nighttime light dataset from 1992 to 2022 with focus on low-light areas |
| title_short | Global nighttime light dataset from 1992 to 2022 with focus on low-light areas |
| title_sort | global nighttime light dataset from 1992 to 2022 with focus on low light areas |
| url | https://doi.org/10.1038/s41597-025-05246-8 |
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