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Author Zhao, F.; Ding, J.; Zhang, S.; Luan, G.; Song, L.; Peng, Z.; Du, Q.; Xie, Z. url  doi
openurl 
  Title Estimating Rural Electric Power Consumption Using NPP-VIIRS Night-Time Light, Toponym and POI Data in Ethnic Minority Areas of China Type Journal Article
  Year 2020 Publication Remote Sensing Abbreviated Journal Remote Sensing  
  Volume 12 Issue 17 Pages 2836  
  Keywords Remote Sensing  
  Abstract Aiming at the problem that the estimation of electric power consumption (EPC) by using night-time light (NTL) data is mostly concentrated in large areas, a method for estimating EPC in rural areas is proposed. Rural electric power consumption (REPC) is a key indicator of the national socio-economic development. Despite an improved quality of life in rural areas, there is still a big gap between electricity consumption between rural residents and urban residents in China. The experiment takes REPC as the research target, selects Dehong (DH) Dai Jingpo Autonomous Prefecture of Yunnan Province as an example, and uses the NTL data from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day–Night Band (DNB) carried by the Suomi National Polar-orbiting Partnership (NPP) Satellite from 2012 to 2017, toponym and points-of-interest (POI) data as the main data source. By performing kernel density estimation to extract the urban center and rural area boundaries in the prefecture, and combining the county-level boundary data and electric power data, a linear regression model of the total rural NTL intensity and REPC is estimated. Finally, according to the model, the EPC in ethnic minority rural areas is estimated at a 1-km spatial resolution. The results show that the NPP-REPC model can simulate REPC within a small average error (17.8%). Additionally, there are distinct spatial differences of REPC in ethnic minority areas.  
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  Corporate Author Thesis  
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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN (down) 2072-4292 ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number GFZ @ kyba @ Serial 3129  
Permanent link to this record
 

 
Author Elvidge, C.D.; Ghosh, T.; Hsu, F.-C.; Zhizhin, M.; Bazilian, M. url  doi
openurl 
  Title The Dimming of Lights in China during the COVID-19 Pandemic Type Journal Article
  Year 2020 Publication Remote Sensing Abbreviated Journal Remote Sensing  
  Volume 12 Issue 17 Pages 2851  
  Keywords Remote Sensing; VIIRS; Day-night band (DNB); Nighttime lights; COVID-19; Pandemic; VIIRS-DNB  
  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.  
  Address Earth Observation Group, Payne Institute for Public Policy, Colorado School of Mines, Golden, CO 80401, USA; celvidge(at)mines.edu  
  Corporate Author Thesis  
  Publisher MDPI Place of Publication Editor  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN (down) 2072-4292 ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number IDA @ john @ Serial 3134  
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Author Ivan, K.; Holobâcă, I.-H.; Benedek, J.; Török, I. url  doi
openurl 
  Title VIIRS Nighttime Light Data for Income Estimation at Local Level Type Journal Article
  Year 2020 Publication Remote Sensing Abbreviated Journal Remote Sensing  
  Volume 12 Issue 18 Pages 2950  
  Keywords Remote Sensing  
  Abstract The aim of the paper is to develop a model for the real-time estimation of local level income data by combining machine learning, Earth Observation, and Geographic Information System. More exactly, we estimated the income per capita by help of a machine learning model for 46 cities with more than 50,000 inhabitants, based on the National Polar-orbiting Partnership–Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nighttime satellite images from 2012–2018. For the automation of calculation, a new ModelBuilder type tool was developed within the ArcGIS software called EO-Incity (Earth Observation–Income city). The sum of light (SOL) data extracted by means of the EO-Incity tool and the observed income data were integrated in an algorithm within the MATLAB software in order to calculate a transfer equation and the average error. The results achieved were subsequently reintegrated in EO-Incity and used for the estimation of the income value at local level. The regression analyses highlighted a stable and strong relationship between SOL and income for the analyzed cities. The EO-Incity tool and the machine learning model proved to be efficient in the real-time estimation of the income at local level. When integrated in the information systems specific for smart cities, they can serve as a support for decision-making in order to fight poverty and reduce social inequalities.  
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  ISSN (down) 2072-4292 ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number GFZ @ kyba @ Serial 3138  
Permanent link to this record
 

 
Author Ernst, S.; Łabuz, M.; Środa, K.; Kotulski, L. url  doi
openurl 
  Title Graph-Based Spatial Data Processing and Analysis for More Efficient Road Lighting Design Type Journal Article
  Year 2018 Publication Sustainability Abbreviated Journal Sustainability  
  Volume 10 Issue 11 Pages 3850  
  Keywords Lighting  
  Abstract The efficiency and affordability of modern street lighting equipment are improving quickly, but systems used to manage and design lighting installations seem to lag behind. One of their problems is the lack of consistent methods to integrate all relevant data. Tools used to manage lighting infrastructure are not aware of the geographic characteristics of the lit areas, and photometric calculation software requires a lot of manual editing by the designer, who needs to assess the characteristics of roads, define the segments, and assign the lighting classes according to standards. In this paper, we propose a graph-based method to integrate geospatial data from various sources to support the process of data preparation for photometric calculations. The method uses graph transformations to define segments and assign lighting classes. A prototype system was developed to conduct experiments using real-world data. The proposed approach is compared to results obtained by professional designers in a case study; the method was also applied to several European cities to assess its efficiency. The obtained results are much more fine-grained than those yielded by the traditional approach; as a result, the lighting is more adequate, especially when used in conjunction with automated optimisation tools.  
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  Corporate Author Thesis  
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  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN (down) 2071-1050 ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number GFZ @ kyba @ Serial 2051  
Permanent link to this record
 

 
Author Jechow, A. url  doi
openurl 
  Title Observing the Impact of WWF Earth Hour on Urban Light Pollution: A Case Study in Berlin 2018 Using Differential Photometry Type Journal Article
  Year 2019 Publication Sustainability Abbreviated Journal Sustainability  
  Volume 11 Issue 3 Pages 750  
  Keywords Skyglow; Remote Sensing  
  Abstract Earth Hour is one of the most successful coordinated mass efforts worldwide to raise awareness of environmental issues, with excessive energy consumption being one driver of climate change. The campaign, first organized by the World Wildlife Fund in Australia in 2007, has grown across borders and cultures and was celebrated in 188 countries in 2018. It calls for voluntarily reduction of electricity consumption for a single hour of one day each year. Switching off non-essential electric lights is a central theme and resulted in 17,900 landmarks going dark in 2018. This switch-off of lights during Earth Hour also leads to reduction of light pollution for this specific period. In principle, Earth Hour allows the study of light pollution and the linkage to electricity consumption of lighting. However, quantitative analysis of the impact of Earth Hour on light pollution (and electricity consumption) are sparse, with only a few studies published showing no clear impact or the reverse, suffering from residual twilight and unstable weather conditions. In this work, light pollution measurements during Earth Hour 2018 in an urban park (Tiergarten) in Berlin, Germany, are reported. A novel light measurement method using differential photometry with calibrated digital cameras enables tracking of the switching off and switching back on of the lights of Berlin’s iconic Brandenburg Gate and the buildings of Potsdamer Platz adjacent to the park. Light pollution reduction during the event was measurable, despite the presence of moonlight. Strategies for future work on light pollution using such events are discussed.  
  Address  
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  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN (down) 2071-1050 ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number GFZ @ kyba @ Serial 2198  
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