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Author Lu, W.; Liu, Y.; Wang, J.; Xu, W.; Wu, W.; Liu, Y.; Zhao, B.; Li, H.; Li, P.
Title Global proliferation of offshore gas flaring areas Type Journal Article
Year (down) 2020 Publication Journal of Maps Abbreviated Journal Journal of Maps
Volume 16 Issue 2 Pages 396-404
Keywords Remote Sensing
Abstract The long-term venting and combustion of offshore associated gas have substantial adverse effects on the ecological environment, so characterizing the global proliferation of offshore gas flaring areas is very important for marine environmental protection and climate change research. However, the use of a single fire/light remote sensing product makes it difficult to conduct long-term observations. In this study, we detected global offshore gas flaring areas during the 27-year interval from 1992 to 2018, using temporal and spatial complementarity of six different remote sensing data products, which are as follows: DMSP-OLS Nighttime Lights; (A)ATSRs; MODIS and VIIRS activefire products; and VIIRS Night Fire and NighttimeLight. Our aim was to achieve more comprehensive extraction results and to analyze a longer time-interval than has been attempted previously. In addition, the resulting map ofthe global proliferation of offshore gas flaring areas enables their locational and temporal characteristics to be visualized.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1744-5647 ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number GFZ @ kyba @ Serial 2930
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Author Chen, M.; Zhang, S.
Title Measuring the regional non-observed economy in China with nighttime lights Type Journal Article
Year (down) 2020 Publication International Journal of Emerging Markets Abbreviated Journal Ijoem
Volume in press Issue Pages
Keywords Remote Sensing
Abstract Purpose

The non-observed economy (NOE) is a pervasive phenomenon worldwide, especially in developing countries, but the size of the NOE and its contributions to the overall economy are usually unknown. This paper presents an estimation of the average size of the NOE for the 31 provincial regions in China between 1992 and 2013.

Design/methodology/approach

This study uses the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) nighttime light data combined with 11 existing surveys on or measurements of NOE for 191 countries or regions throughout the world, to measure the size of the NOE.

Findings

The results show that the NOE share is unevenly distributed among China's provincial regions, with the smallest being 3.19% for Beijing and the largest being 69.71% for Ningxia. The national average is 43.11%, while the figures for the eastern region, middle region, northeastern region and western region are 39.3%, 47.6%, 44.7% and 43.6%, respectively. The NOE estimates are negatively correlated with the measured gross domestic product (GDP) and GDP per capita, which suggests that developed regions tend to have less NOE.

Originality/value

The nighttime lights are used to measure the NOE for China's provincial regions. Compared with traditional databases, one of the prominent features of nighttime lights is its objectivity, as there is little human interference; therefore, it can be used to achieve more accurate results.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1746-8809 ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number GFZ @ kyba @ Serial 2936
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Author Zangeneh, P.; Hamledari, H.; McCabe, B.
Title Quantifying Remoteness for Risk and Resilience Assessment Using Nighttime Satellite Imagery Type Journal Article
Year (down) 2020 Publication Journal of Computing in Civil Engineering Abbreviated Journal J. Comput. Civ. Eng.
Volume 34 Issue 5 Pages 04020026
Keywords Remote Sensing
Abstract Remoteness has a crucial role in risk assessments of megaprojects, resilience assessments of communities and infrastructure, and a wide range of public policymaking. The existing measures of remoteness require an extensive amount of population census and of road and infrastructure network data, and often are limited to narrow scopes. This paper presents a methodology to quantify remoteness using nighttime satellite imagery. The light clusters of nighttime satellite imagery are direct yet unintended consequences of human settled populations and urbanization; therefore, the absence of illuminated clusters is considered as evidence of remoteness. The proposed nighttime remoteness index (NIRI) conceptualizes the remoteness based on the distribution of nighttime lights within radii of up to 1,000 km. A predictive model was created using machine learning techniques such as multivariate adaptive regression splines and support vector machines regressions to establish a reliable and accurate link between nighttime lights and the Accessibility/Remoteness Index of Australia (ARIA). The model was used to establish NIRI for the United States and Canada, and in different years. The index was compared with the Canadian remoteness indexes published by Statistics Canada.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0887-3801 ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number GFZ @ kyba @ Serial 2937
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Author Wang, H.; Li, J.; Gao, M.; Chan, T.-C.; Gao, Z.; Zhang, M.; Li, Y.; Gu, Y.; Chen, A.; Yang, Y.; Ho, H.C.
Title Spatiotemporal variability in long-term population exposure to PM2.5 and lung cancer mortality attributable to PM2.5 across the Yangtze River Delta (YRD) region over 2010–2016: A multistage approach Type Journal Article
Year (down) 2020 Publication Chemosphere Abbreviated Journal Chemosphere
Volume in press Issue Pages 127153
Keywords Remote Sensing
Abstract The Yangtze River Delta region (YRD) is one of the most densely populated regions in the world, and is frequently influenced by fine particulate matter (PM2.5). Specifically, lung cancer mortality has been recognized as a major health burden associated with PM2.5. Therefore, this study developed a multistage approach 1) to first create dasymetric population data with moderate resolution (1 km) by using a random forest algorithm, brightness reflectance of nighttime light (NTL) images, a digital elevation model (DEM), and a MODIS-derived normalized difference vegetation index (NDVI), and 2) to apply the improved population dataset with a MODIS-derived PM2.5 dataset to estimate the association between spatiotemporal variability of long-term population exposure to PM2.5 and lung cancer mortality attributable to PM2.5 across YRD during 2010–2016 for microscale planning. The created dasymetric population data derived from a coarse census unit (administrative unit) were fairly matched with census data at a fine spatial scale (street block), with R2 and RMSE of 0.64 and 27,874.5 persons, respectively. Furthermore, a significant urban-rural difference of population exposure was found. Additionally, population exposure in Shanghai was 2.9–8 times higher than the other major cities (7-year average: 192,000 μg·people/m3·km2). More importantly, the relative risks of lung cancer mortality in high-risk areas were 28%–33% higher than in low-risk areas. There were 12,574–14,504 total lung cancer deaths attributable to PM2.5, and lung cancer deaths in each square kilometer of urban areas were 7–13 times higher than for rural areas. These results indicate that moderate-resolution information can help us understand the spatiotemporal variability of population exposure and related health risk in a high-density environment.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0045-6535 ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number GFZ @ kyba @ Serial 2938
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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 (down) 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
Corporate Author Thesis
Publisher Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2041-1723 ISBN Medium
Area Expedition Conference
Notes PMID:32444658 Approved no
Call Number GFZ @ kyba @ Serial 2939
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