NDUI+: A fused DMSP-VIIRS based global normalized difference urban index dataset
Fuente:
arXiv
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| Autori principali: | , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866929597236379648 |
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| author | Singh, Manmeet Ghosh, Subhasis Kamath, Harsh Saxena, Shivam SB, Vaisakh Mitra, Chandana Sudharsan, Naveen Rao, Suryachandra Dashtian, Hassan Magruder, Lori Shepherd, Marshall Niyogi, Dev |
| author_facet | Singh, Manmeet Ghosh, Subhasis Kamath, Harsh Saxena, Shivam SB, Vaisakh Mitra, Chandana Sudharsan, Naveen Rao, Suryachandra Dashtian, Hassan Magruder, Lori Shepherd, Marshall Niyogi, Dev |
| contents | Urbanization is advancing rapidly, covering less than 2% of Earth's surface yet profoundly influencing global environments and experiencing disproportionate impacts from extreme weather events. Effective urban management and planning require high-resolution, temporally consistent datasets that capture the complexity of urban growth and dynamics. This study presents NDUI+, a novel global urban dataset addressing critical gaps in urban data continuity and quality. NDUI+ integrates data from the Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS), VIIRS Nighttime Light, and Landsat 7 NDVI using advanced remote sensing and deep learning techniques. The dataset resolves sensor discontinuity challenges, offering a seamless 30-meter spatial and annual temporal resolution time series from 1999 to the present. NDUI+ demonstrates high precision and granularity, aligning closely with high-resolution satellite data and capturing urban dynamics effectively. The dataset provides valuable insights for urban climate studies, IPCC assessments, and urbanization research, complementing resources like UT-GLOBUS for urban modeling. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_02794 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | NDUI+: A fused DMSP-VIIRS based global normalized difference urban index dataset Singh, Manmeet Ghosh, Subhasis Kamath, Harsh Saxena, Shivam SB, Vaisakh Mitra, Chandana Sudharsan, Naveen Rao, Suryachandra Dashtian, Hassan Magruder, Lori Shepherd, Marshall Niyogi, Dev Physics and Society Atmospheric and Oceanic Physics Urbanization is advancing rapidly, covering less than 2% of Earth's surface yet profoundly influencing global environments and experiencing disproportionate impacts from extreme weather events. Effective urban management and planning require high-resolution, temporally consistent datasets that capture the complexity of urban growth and dynamics. This study presents NDUI+, a novel global urban dataset addressing critical gaps in urban data continuity and quality. NDUI+ integrates data from the Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS), VIIRS Nighttime Light, and Landsat 7 NDVI using advanced remote sensing and deep learning techniques. The dataset resolves sensor discontinuity challenges, offering a seamless 30-meter spatial and annual temporal resolution time series from 1999 to the present. NDUI+ demonstrates high precision and granularity, aligning closely with high-resolution satellite data and capturing urban dynamics effectively. The dataset provides valuable insights for urban climate studies, IPCC assessments, and urbanization research, complementing resources like UT-GLOBUS for urban modeling. |
| title | NDUI+: A fused DMSP-VIIRS based global normalized difference urban index dataset |
| topic | Physics and Society Atmospheric and Oceanic Physics |
| url | https://arxiv.org/abs/2306.02794 |