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Elvidge, C. D., Ghosh, T., Hsu, F. - C., Zhizhin, M., & Bazilian, M. (2020). The Dimming of Lights in China during the COVID-19 Pandemic. Remote Sensing, 12(17), 2851.
Abstract: A satellite survey of the cumulative radiant emissions from electric lighting across China reveals a large radiance decline in lighting from December 2019 to February 2020—the peak of the lockdown established to suppress the spread of COVID-19 infections. To illustrate the changes, an analysis was also conducted on a reference set from a year prior to the pandemic. In the reference period, the majority (62%) of China’s population lived in administrative units that became brighter in March 2019 relative to December 2018. The situation reversed in February 2020, when 82% of the population lived in administrative units where lighting dimmed as a result of the pandemic. The dimming has also been demonstrated with difference images for the reference and pandemic image pairs, scattergrams, and a nightly temporal profile. The results indicate that it should be feasible to monitor declines and recovery in economic activity levels using nighttime lighting as a proxy.
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Elvidge, C. D., Keith, D. M., Tuttle, B. T., & Baugh, K. E. (2010). Spectral identification of lighting type and character. Sensors (Basel), 10(4), 3961–3988.
Abstract: We investigated the optimal spectral bands for the identification of lighting types and the estimation of four major indices used to measure the efficiency or character of lighting. To accomplish these objectives we collected high-resolution emission spectra (350 to 2,500 nm) for forty-three different lamps, encompassing nine of the major types of lamps used worldwide. The narrow band emission spectra were used to simulate radiances in eight spectral bands including the human eye photoreceptor bands (photopic, scotopic, and “meltopic”) plus five spectral bands in the visible and near-infrared modeled on bands flown on the Landsat Thematic Mapper (TM). The high-resolution continuous spectra are superior to the broad band combinations for the identification of lighting type and are the standard for calculation of Luminous Efficacy of Radiation (LER), Correlated Color Temperature (CCT) and Color Rendering Index (CRI). Given the high cost that would be associated with building and flying a hyperspectral sensor with detection limits low enough to observe nighttime lights we conclude that it would be more feasible to fly an instrument with a limited number of broad spectral bands in the visible to near infrared. The best set of broad spectral bands among those tested is blue, green, red and NIR bands modeled on the band set flown on the Landsat Thematic Mapper. This set provides low errors on the identification of lighting types and reasonable estimates of LER and CCT when compared to the other broad band set tested. None of the broad band sets tested could make reasonable estimates of Luminous Efficacy (LE) or CRI. The photopic band proved useful for the estimation of LER. However, the three photoreceptor bands performed poorly in the identification of lighting types when compared to the bands modeled on the Landsat Thematic Mapper. Our conclusion is that it is feasible to identify lighting type and make reasonable estimates of LER and CCT using four or more spectral bands with minimal spectral overlap spanning the 0.4 to 1.0 um region.
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Elvidge, C. D., Sutton, P. C., Ghosh, T., Tuttle, B. T., Baugh, K. E., Bhaduri, B., et al. (2009). A global poverty map derived from satellite data. Computers & Geosciences, 35(8), 1652–1660.
Abstract: A global poverty map has been produced at 30 arcsec resolution using a poverty index calculated by dividing population count (LandScan 2004) by the brightness of satellite observed lighting (DMSP nighttime lights). Inputs to the LandScan product include satellite-derived land cover and topography, plus human settlement outlines derived from high-resolution imagery. The poverty estimates have been calibrated using national level poverty data from the World Development Indicators (WDI) 2006 edition. The total estimate of the numbers of individuals living in poverty is 2.2 billion, slightly under the WDI estimate of 2.6 billion. We have demonstrated a new class of poverty map that should improve over time through the inclusion of new reference data for calibration of poverty estimates and as improvements are made in the satellite observation of human activities related to economic activity and technology access.
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Fan, J., He, H., Hu, T., Zhang, P., Yu, X., & Zhou, Y. (2019). Estimation of Landscape Pattern Changes in BRICS from 1992 to 2013 Using DMSP-OLS NTL Images. J Ind Soc Rem Sens, 47(5), 725–735.
Abstract: Nighttime light data from the Defense Meteorological Satellite Program’s Operational Linescan System are widely used for monitoring urbanization development. Brazil, Russia, India, China and South Africa (BRICS) countries have global economic and cultural influence in the new era. It was the first time for the researches about BRICS countries adopting nighttime light data to analyze the urbanization process. In this paper, we calibrated and extracted annual urbanized area patches from cities in BRICS based on a quadratic polynomial model. Nine landscape indexes were calculated to analyze urbanization process characteristics in BRICS. The results suggested that China and India both expanded more rapidly than other countries, with urban areas that increased by more than 100%. The expansion of large core cities was dominant in the urbanization of China, while emerging and expanding small urban patches were major forces in the urbanization of India. Since 1992, urbanization declined and urban areas shrunk in Russia, but core cities still maintained strength of urbanization. Due to economic recovery, urban areas near large cities in Russia began to expand. From 1992 to 2013, the urbanization process in South Africa developed slowly, as evidenced by time series fluctuations, but overall the development remained stable. The degree of urbanization in Brazil was greater than that in South Africa but less than that in Russia. Large-sized cities expanded slowly and small-sized cities clearly expanded in BRICS from 1992 to 2013.
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Gaughan, A. E., Oda, T., Sorichetta, A., Stevens, F. R., Bondarenko, M., Bun, R., Krauser, L., Yetman, G., & Nghiem, S. V. (2019). Evaluating nighttime lights and population distribution as proxies for mapping anthropogenic CO2 emission in Vietnam, Cambodia and Laos. Environmental Research Communications, 1(9), 091006.
Abstract: Tracking spatiotemporal changes in GHG emissions is key to successful implementation of the United Nations Framework Convention on Climate Change (UNFCCC). And while emission inventories often provide a robust tool to track emission trends at the country level, subnational emission estimates are often not reported or reports vary in robustness as the estimates are often dependent on the spatial modeling approach and ancillary data used to disaggregate the emission inventories. Assessing the errors and uncertainties of the subnational emission estimates is fundamentally challenging due to the lack of physical measurements at the subnational level. To begin addressing the current performance of modeled gridded CO2 emissions, this study compares two common proxies used to disaggregate CO2 emission estimates. We use a known gridded CO2 model based on satellite-observed nighttime light (NTL) data (Open Source Data Inventory for Anthropogenic CO2, ODIAC) and a gridded population dataset driven by a set of ancillary geospatial data. We examine the association at multiple spatial scales of these two datasets for three countries in Southeast Asia: Vietnam, Cambodia and Laos and characterize the spatiotemporal similarities and differences for 2000, 2005, and 2010. We specifically highlight areas of potential uncertainty in the ODIAC model, which relies on the single use of NTL data for disaggregation of the non-point emissions estimates. Results show, over time, how a NTL-based emissions disaggregation tends to concentrate CO2 estimates in different ways than population-based estimates at the subnational level. We discuss important considerations in the disconnect between the two modeled datasets and argue that the spatial differences between data products can be useful to identify areas affected by the errors and uncertainties associated with the NTL-based downscaling in a region with uneven urbanization rates.
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