Name
Abstract | ||
|---|---|---|
Historically, satellite-based nighttime lights datasets have been limited by their coarse spatial and radiometric resolution, as well as their lack of calibration across time. As a result, most studies have used nighttime lights to understand processes at the aggregate urban scale-e.g. differentiating between urban and non-urban land, or downscaling national energy, social, and economic variables. In contrast, the new Black Marble Product Suite, a science-quality product derived from the Suomi-NPP VIIRS Day Night Band (DNB), opens up novel opportunities to look inside cities-understanding internal patterns and processes of urban areas, as well as how human settlements change throughout time.In this article, we provide a brief overview of the innovations of the Black Marble Product Suite [1] that are specifically important for urban applications. We also discuss three case studies that have demonstrated the capability of nighttime lights to track changing conditions in human settlements-e.g. urban energy access, migration, and disaster impacts and recovery. As the time series expands, Black Marble data has the potential to increase our understanding of changes in urban infrastructure and human factors that influence a myriad of urban sustainability and resilience outcomes. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/JURSE.2019.8809074 | 2019 Joint Urban Remote Sensing Event (JURSE) |
Keywords | Field | DocType |
urbanization,energy infrastructure,nighttime lights,satellite,urban land use,human settlement,Suomi-NPP,NOAA-20,NASA Black Marble,VIIRS-DNB,Night Lights,NTL | Psychological resilience,Downscaling,Urbanization,Satellite,Environmental resource management,Suite,Environmental science,Human settlement,Urban infrastructure,Sustainability | Conference |
ISSN | ISBN | Citations |
2334-0932 | 978-1-7281-0010-4 | 0 |
PageRank | References | Authors |
0.34 | 4 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eleanor C. Stokes | 1 | 0 | 0.34 |
| Miguel O. Roman | 2 | 26 | 4.19 |
| Zhuosen Wang | 3 | 42 | 6.78 |
| Ranjay M. Shrethsa | 4 | 0 | 0.34 |
| Tian Yao | 5 | 0 | 0.68 |
| Ginny Kalb | 6 | 0 | 0.34 |