| Records |
| Author |
Li, Q.F.; Yang, G.X.; Yu, L.H.; Zhang, H. |
| Title |
A survey of the luminance distribution in the nocturnal environment in Shanghai urban areas and the control of luminance of floodlit buildings |
Type |
Journal Article |
| Year |
2006 |
Publication |
Lighting Research & Technology |
Abbreviated Journal |
Lighting Research & Technology |
| Volume |
38 |
Issue |
3 |
Pages |
185-189 |
| Keywords |
Lighting |
Abstract  |
A survey of the luminance distribution of the nocturnal environment in Shanghai urban areas, which included 11 locations and 16 buildings, was made. The 11 locations could be categorized as commercial, administration, leisure or residential. The average environmental luminance of these was recorded. The authors identified the effects of excessive exterior lighting. The luminance was measured and subjective appraisals made of 16 buildings. The writers have developed an empirical formula for arriving at the brightness level rating for floodlit buildings and recommended corresponding working ranges of luminance. |
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| ISSN |
1477-1535 |
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| Call Number |
GFZ @ kyba @ |
Serial |
2715 |
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| Author |
L.Imhoff, M.; Lawrence, W.T.; Stutzer, D.C.; Elvidge, C.D. |
| Title |
A technique for using composite DMSP/OLS “City Lights” satellite data to map urban area |
Type |
Journal Article |
| Year |
1997 |
Publication |
Remote Sensing of Environment |
Abbreviated Journal |
Remote Sensing of Environment |
| Volume |
61 |
Issue |
3 |
Pages |
361-370 |
| Keywords |
Remote Sensing |
| Abstract |
A Tresholding technique was used to convert a prototype “city lights” data set from the National Oceanic and Atmospheric Administration's National Geophysical Data Center (NOAAINGDC) into a map of “urban areas” for the continental United States. Thresholding was required to adapt the Defense Meteorological Satellite Program's Operational Linescan System (DMSPIOLS)-based NGDC data set into an urban map because the values reported in the prototype represent a cumulative percentage lighted for each pixel extracted from hundreds of nighttime cloud screened orbits, rather than any suitable land-cover classification. The cumulative percentage lighted data could not be used alone because the very high gain of the OLS nighttime photomultiplier configuration can. lead to a pixel (2.7X2.7 km) appearing “lighted” even with very low intensity, nonurban light sources. We found that a threshold of %89% yielded the best results, removing ephemeral light sources and “blooming” of light onto water when adjacent to cities while still leaving the dense urban core intact. This approach gave very good results when compared with the urban areas as defined by the 1990 U. S. Census; the “urban” area from our analysis being only 5% less than that of the Census. The Census was also used to derive population.- and housing-density statistics for the continent-wide “city lights” analysis; these averaged 1033 persons/km2 and 426 housing units/ king, respectively. The use of a nighttime sensor to determine the location and estimate the density of population based on light sources has proved feasible in this exploratory effort. However, issues concerning the use of census data as a benchmark for evaluating the accuracy of remotely sensed imagery are discussed, and potential improvements in the sensor regarding spatial resolution, instrument gain, and pointing accuracy are addressed. |
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0034-4257 |
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no |
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GFZ @ kyba @ |
Serial |
2220 |
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| Author |
Davies, T.W.; McKee, D.; Fishwick, J.; Tidau, S.; Smyth, T. |
| Title |
Biologically important artificial light at night on the seafloor |
Type |
Journal Article |
| Year |
2020 |
Publication |
Scientific Reports |
Abbreviated Journal |
Sci Rep |
| Volume |
10 |
Issue |
1 |
Pages |
12545 |
| Keywords |
Ecology; Skyglow; Remote Sensing |
| Abstract |
Accelerating coastal development is increasing the exposure of marine ecosystems to nighttime light pollution, but is anthropogenic light reaching the seafloor in sufficient quantities to have ecological impacts? Using a combination of mapping, and radiative transfer modelling utilising in situ measurements of optical seawater properties, we quantified artificial light exposure at the sea surface, beneath the sea surface, and at the sea floor of an urbanised temperate estuary bordered by an LED lit city. Up to 76% of the three-dimensional seafloor area was exposed to biologically important light pollution. Exposure to green wavelengths was highest, while exposure to red wavelengths was nominal. We conclude that light pollution from coastal cities is likely having deleterious impacts on seafloor ecosystems which provide vital ecosystem services. A comprehensive understanding of these impacts is urgently needed. |
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Plymouth Marine Laboratory, Prospect Place, Devon, Plymouth, PL1 3DH, UK |
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English |
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2045-2322 |
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PMID:32719492; PMCID:PMC7385152 |
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no |
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GFZ @ kyba @ |
Serial |
3071 |
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| Author |
Li, S.; Cheng, L.; Liu, X.; Mao, J.; Wu, J.; Li, M. |
| Title |
City type-oriented modeling electric power consumption in China using NPP-VIIRS nighttime stable light data |
Type |
Journal Article |
| Year |
2019 |
Publication |
Energy |
Abbreviated Journal |
Energy |
| Volume |
189 |
Issue |
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Pages |
116040 |
| Keywords |
Energy; Remote Sensing; China; electric power consumption; Night lights; Nighttime light; VIIRS-DNB |
| Abstract |
Accelerating urbanization has created tremendous pressure on the global environment and energy supply, making accurate estimates of energy use of great importance. Most current models for estimating electric power consumption (EPC) from nighttime light (NTL) imagery are oversimplified, ignoring influential social and economic factors. Here we propose first classifying cities by economic focus and then separately estimating each category’s EPC using NTL data. We tested this approach using statistical employment data for 198 Chinese cities, 2015 NTL data from the Visible Infrared Imaging Radiometer Suite (VIIRS), and annual electricity consumption statistics. We used cluster analysis of employment by sector to divide the cities into three types (industrial, service, and technology and education), then established a linear regression model for each city's NTL and EPC. Compared with the estimation results before city classification (R2: 0.785), the R2 of the separately modeled service cities and technology and education cities increased to 0.866 and 0.830, respectively. However, the results for industrial cities were less consistent due to their more complex energy consumption structure. In general, using classification before modeling helps reflect factors affecting the relationship between EPC and NTL, making the estimation process more reasonable and improving the accuracy of the results. |
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School of Geography and Ocean Science, Nanjing University, Nanjing, 210023, China |
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0360-5442 |
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no |
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GFZ @ kyba @ |
Serial |
2672 |
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| Author |
Yeh, C.; Perez, A.; Driscoll, A.; Azzari, G.; Tang, Z.; Lobell, D.; Ermon, S.; Burke, M. |
| Title |
Using publicly available satellite imagery and deep learning to understand economic well-being in Africa |
Type |
Journal Article |
| Year |
2020 |
Publication |
Nature Communications |
Abbreviated Journal |
Nat Commun |
| Volume |
11 |
Issue |
1 |
Pages |
2583 |
| Keywords |
Remote Sensing |
| Abstract |
Accurate and comprehensive measurements of economic well-being are fundamental inputs into both research and policy, but such measures are unavailable at a local level in many parts of the world. Here we train deep learning models to predict survey-based estimates of asset wealth across ~ 20,000 African villages from publicly-available multispectral satellite imagery. Models can explain 70% of the variation in ground-measured village wealth in countries where the model was not trained, outperforming previous benchmarks from high-resolution imagery, and comparison with independent wealth measurements from censuses suggests that errors in satellite estimates are comparable to errors in existing ground data. Satellite-based estimates can also explain up to 50% of the variation in district-aggregated changes in wealth over time, with daytime imagery particularly useful in this task. We demonstrate the utility of satellite-based estimates for research and policy, and demonstrate their scalability by creating a wealth map for Africa's most populous country. |
| Address |
National Bureau of Economic Research, 1050 Massachusetts Avenue, Cambridge, MA, 02138-5398, USA. mburke@stanford.edu |
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English |
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Edition |
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2041-1723 |
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PMID:32444658 |
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no |
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GFZ @ kyba @ |
Serial |
2939 |
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