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Author You, X.; Monahan, K.M.
Title A thirst for development: mapping water stress using night-time stable lights as predictors of province-level water stress in China Type Journal Article
Year 2017 Publication (up) Area Abbreviated Journal Area
Volume 49 Issue 4 Pages 477-485
Keywords Remote Sensing
Abstract Given the rapid development within China, the inequality of available water resources has been increasingly of interest. Current methods for assessing water stress are inadequate for province‐scale rapid monitoring. A more responsive indicator at a finer scale is needed to understand the distribution of water stress in China. This paper selected Defense Meteorological Satellite Program Operational Line‐scan System night‐time stable lights as a proxy for water stress at the province level in China from 2004 to 2012, as night‐time lights are closely linked with population density, electricity consumption and other social, economic and environmental indicators associated with water stress. The linear regression results showed the intensity of night‐time lights can serve as a predictive tool to assess water stress across provinces with an R2 from 0.797 to 0.854. The model worked especially well in some regions, such as East China, North China and South West China. Nonetheless, confounding factors interfered with the predictive relationship, including population density, level of economic development, natural resource endowment and industrial structures, etc. The model was not greatly improved by building a multi‐variable linear regression including agricultural and industrial indicators. A straightforward predictor of water stress using remotely sensed data was developed.
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ISSN 0004-0894 ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number GFZ @ kyba @ Serial 2030
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Author Cardiel, N.; Gorgas, J.; Gallego J.; Serrano, A.; Zamorano, J.; Garcia-Vargas, M.-L.; Gomez-Cambronero, P.; Filgueira, J.M.
Title Proper handling of random errors and distortions in astronomical data analysis. Type Report
Year 2002 Publication (up) Astronomical Telescopes and Instrumentation Abbreviated Journal
Volume Issue Pages 297–304
Keywords Remote Sensing
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Notes Approved no
Call Number LoNNe @ kagoburian @ Serial 911
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Author de Miguel, A.S.; Castano, J.G.; Zamorano, J.; Pascual, S.; Angeles, M.; Cayuela, L.; Martinez, G.M.; Challupner, P.; Kyba, C.C.M.
Title Atlas of astronaut photos of Earth at night Type Journal Article
Year 2014 Publication (up) Astronomy & Geophysics Abbreviated Journal Astronomy & Geophysics
Volume 55 Issue 4 Pages 4.36-4.36
Keywords Remote Sensing
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ISSN 1366-8781 ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number LoNNe @ christopher.kyba @ Serial 482
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Author Li, R.; Liu, X.; Li, X.
Title Estimation of the PM2.5 Pollution Levels in Beijing Based on Nighttime Light Data from the Defense Meteorological Satellite Program-Operational Linescan System Type Journal Article
Year 2015 Publication (up) Atmosphere Abbreviated Journal Atmosphere
Volume 6 Issue 5 Pages 607-622
Keywords Remote Sensing
Abstract Nighttime light data record the artificial light on the Earth’s surface and can be used to estimate the degree of pollution associated with particulate matter with an aerodynamic diameter of less than 2.5 μm (PM2.5) in the ground-level atmosphere. This study proposes a simple method for monitoring PM2.5 concentrations at night by using nighttime light imagery from the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS). This research synthesizes remote sensing and geographic information system techniques and establishes a back propagation neural-network (BP network) model. The BP network model for nighttime light data performed well in estimating the PM2.5 pollution in Beijing. The correlation coefficient between the BP network model predictions and the corrected PM2.5 concentration was 0.975; the root mean square error was 26.26 μg/m3, with a corresponding average PM2.5 concentration of 155.07 μg/m3; and the average accuracy was 0.796. The accuracy of the results primarily depended on the method of selecting regions in the DMSP nighttime light data. This study provides an opportunity to measure the nighttime environment. Furthermore, these results can assist government agencies in determining particulate matter pollution control areas and developing and implementing environmental conservation planning.
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Series Editor Series Title Abbreviated Series Title
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ISSN 2073-4433 ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number LoNNe @ christopher.kyba @ Serial 1173
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Author Zhao, X.; Shi, H.; Yu, H.; Yang, P.
Title Inversion of Nighttime PM2.5 Mass Concentration in Beijing Based on the VIIRS Day-Night Band Type Journal Article
Year 2016 Publication (up) Atmosphere Abbreviated Journal Atmosphere
Volume 7 Issue 10 Pages 136
Keywords Remote Sensing
Abstract In order to monitor nighttime particular matter (PM) air quality in urban area, a back propagation neural network (BP neural network) inversion model is established, using low-light radiation data from the day/night band (DNB) of the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership (S-NPP) satellite. The study focuses on the moonless and cloudless nights in Beijing during March–May 2015. A test is carried out by selecting surface PM2.5 data from 12 PM2.5 automatic monitoring stations and the corresponding night city light intensity from DNB. As indicated by the results, the linear correlation coefficient (R) between the results and the corresponding measured surface PM2.5 concentration is 0.91, and the root-mean-square error (RMSE) is 14.02 μg/m3 with the average of 59.39 μg/m3. Furthermore, the BP neural network model shows better accuracy when air relative humility ranges from 40% to 80% and surface PM2.5 concentration exceeds 40 μg/m3. The study provides a superiority approach for monitoring PM2.5 air quality from space with visible light remote sensing data at night.
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Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2073-4433 ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number LoNNe @ kyba @ Serial 1546
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