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Author Su, Y.; Yue, J.; Liu, X.; Miller, S.D.; Ш, W.C.S.; Smith, S.M.; Guo, D.; Guo, S.
Title Mesospheric Bore Observations Using Suomi-NPP VIIRS DNB during 2013–2017 Type Journal Article
Year 2018 Publication Remote Sensing Abbreviated Journal (down) Remote Sensing
Volume 10 Issue 12 Pages 1935
Keywords Airglow; Remote Sensing
Abstract This paper reports mesospheric bore events observed by Day/Night Band (DNB) of the Visible/Infrared Imaging Radiometer Suite (VIIRS) on the National Oceanic and Atmospheric Administration/National Aeronautics and Space Administration (NOAA/NASA) Suomi National Polar-orbiting Partnership (NPP) environmental satellite over five years (2013–2017). Two types of special mesospheric bore events were observed, enabled by the wide field of view of VIIRS: extremely wide bores (>2000 km extension perpendicular to the bore propagation direction), and those exhibiting more than 15 trailing crests and troughs. A mesospheric bore event observed simultaneously from space and ground was investigated in detail. DNB enables the preliminary global observation of mesospheric bores for the first time. DNB mesospheric bores occurred more frequently in March, April and May. Their typical lengths are between 300 km and 1200 km. The occurrence rate of bores at low latitudes is higher than that at middle latitudes. Among the 61 bore events, 39 events occurred in the tropical region (20°S–20°N). The high occurrence rate of mesospheric bores during the spring months in the tropical region coincides with the reported seasonal and latitudinal variations of mesospheric inversion layers.
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ISSN 2072-4292 ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number GFZ @ kyba @ Serial 2128
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Author Coesfeld, J.; Anderson, S.; Baugh, K.; Elvidge, C.; Schernthanner, H.; Kyba, C.
Title Variation of Individual Location Radiance in VIIRS DNB Monthly Composite Images Type Journal Article
Year 2018 Publication Remote Sensing Abbreviated Journal (down) Remote Sensing
Volume 10 Issue 12 Pages 1964
Keywords Remote Sensing; Instrumentation
Abstract With the growing size and use of night light time series from the Visible Infrared Imaging Radiometer Suite Day/Night Band (DNB), it is important to understand the stability of the dataset. All satellites observe differences in pixel values during repeat observations. In the case of night light data, these changes can be due to both environmental effects and changes in light emission. Here we examine the stability of individual locations of particular large scale light sources (e.g., airports and prisons) in the monthly composites of DNB data from April 2012 to September 2017. The radiances for individual pixels of most large light emitters are approximately normally distributed, with a standard deviation of typically 15–20% of the mean. Greenhouses and flares, however, are not stable sources. We observe geospatial autocorrelation in the monthly variations for nearby sites, while the correlation for sites separated by large distances is small. This suggests that local factors contribute most to the variation in the pixel radiances and furthermore that averaging radiances over large areas will reduce the total variation. A better understanding of the causes of temporal variation would improve the sensitivity of DNB to lighting changes.
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Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2072-4292 ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number GFZ @ kyba @ Serial 2129
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Author Zhao, X.; Yu, B.; Liu, Y.; Chen, Z.; Li, Q.; Wang, C.; Wu, J.
Title Estimation of Poverty Using Random Forest Regression with Multi-Source Data: A Case Study in Bangladesh Type Journal Article
Year 2019 Publication Remote Sensing Abbreviated Journal (down) Remote Sensing
Volume 11 Issue 4 Pages 375
Keywords Remote Sensing
Abstract Spatially explicit and reliable data on poverty is critical for both policy makers and researchers. However, such data remain scarce particularly in developing countries. Current research is limited in using environmental data from different sources in isolation to estimate poverty despite the fact that poverty is a complex phenomenon which cannot be quantified either theoretically or practically by one single data type. This study proposes a random forest regression (RFR) model to estimate poverty at 10 km × 10 km spatial resolution by combining features extracted from multiple data sources, including the National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) Day/Night Band (DNB) nighttime light (NTL) data, Google satellite imagery, land cover map, road map and division headquarter location data. The household wealth index (WI) drawn from the Demographic and Health Surveys (DHS) program was used to reflect poverty level. We trained the RFR model using data in Bangladesh and applied the model to both Bangladesh and Nepal to evaluate the model’s accuracy. The results show that the R2 between the actual and estimated WI in Bangladesh is 0.70, indicating a good predictive power of our model in WI estimation. The R2 between actual and estimated WI of 0.61 in Nepal also indicates a good generalization ability of the model. Furthermore, a negative correlation is observed between the district average WI and the poverty head count ratio (HCR) in Bangladesh with the Pearson Correlation Coefficient of -0.6. Using Gini importance, we identify that proximity to urban areas is the most important variable to explain poverty which contribute to 37.9% of the explanatory power. Compared to the study that used NTL and Google satellite imagery in isolation to estimate poverty, our method increases the accuracy of estimation. Given that the data we use are globally and publicly available, the methodology reported in this study would also be applicable in other countries or regions to estimate the extent of poverty.
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Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2072-4292 ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number GFZ @ kyba @ Serial 2217
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Author Elvidge, C.; Zhizhin, M.; Baugh, K.; Hsu, F.; Ghosh, T.
Title Extending Nighttime Combustion Source Detection Limits with Short Wavelength VIIRS Data Type Journal Article
Year 2019 Publication Remote Sensing Abbreviated Journal (down) Remote Sensing
Volume 11 Issue 4 Pages 395
Keywords Remote Sensing
Abstract The Visible Infrared Imaging Radiometer Suite (VIIRS) collects low light imaging data at night in five spectral bands. The best known of these is the day/night band (DNB) which uses light intensification for imaging of moonlit clouds in the visible and near-infrared (VNIR). The other four low light imaging bands are in the NIR and short-wave infrared (SWIR), designed for daytime imaging, which continue to collect data at night. VIIRS nightfire (VNF) tests each nighttime pixel for the presence of sub-pixel IR emitters across six spectral bands with two bands each in three spectral ranges: NIR, SWIR, and MWIR. In pixels with detection in two or more bands, Planck curve fitting leads to the calculation of temperature, source area, and radiant heat using physical laws. An analysis of January 2018 global VNF found that inclusion of the NIR and SWIR channels results in a doubling of the VNF pixels with temperature fits over the detection numbers involving the MWIR. The addition of the short wavelength channels extends detection limits to smaller source areas across a broad range of temperatures. The VIIRS DNB has even lower detection limits for combustion sources, reaching 0.001 m2 at 1800 K, a typical temperature for a natural gas flare. Comparison of VNF tallies and DNB fire detections in a 2015 study area in India found the DNB had 15 times more detections than VNF. The primary VNF error sources are false detections from high energy particle detections (HEPD) in space and radiance saturation on some of the most intense events. The HEPD false detections are largely eliminated in the VNF output by requiring multiband detections for the calculation of temperature and source size. Radiance saturation occurs in about 1% of the VNF detections and occurs primarily in the M12 spectral band. Inclusion of the radiances affected by saturation results in temperature and source area calculation errors. Saturation is addressed by identifying the presence of saturation and excluding those radiances from the Planck curve fitting. The extremely low detection limits for the DNB indicates that a DNB fire detection algorithm could reveal vast numbers of combustion sources that are undetectable in longer wavelength VIIRS data. The caveats with the DNB combustion source detection capability is that it should be restricted to pixels that are outside the zone of known VIIRS detected electric lighting.
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Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2072-4292 ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number GFZ @ kyba @ Serial 2218
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Author Liu, A.; Wei, Y.; Yu, B.; Song, W.
Title Estimation of Cargo Handling Capacity of Coastal Ports in China Based on Panel Model and DMSP-OLS Nighttime Light Data Type Journal Article
Year 2019 Publication Remote Sensing Abbreviated Journal (down) Remote Sensing
Volume 11 Issue 5 Pages 582
Keywords Remote Sensing
Abstract The cargo handling capacity of a port is the most basic and important indicator of port size. Based on the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) nighttime light data and panel model, this study attempts to estimate the cargo handling capacity of 28 coastal ports in China using satellite remote sensing. The study confirmed that there is a very close correlation between DMSP-OLS nighttime light data and the cargo handling capacity of the ports. Based on this correlation, the panel data model was established for remote sensing-based estimation of cargo handling capacity at the port and port group scales. The test results confirm that the nighttime light data can be used to accurately estimate the cargo handling capacity of Chinese ports, especially for the Yangtze River Delta Port Group, Pearl River Delta Port Group, Southeast Coastal Port Group, and Southwest Coastal Port Group that possess huge cargo handling capacities. The high accuracy of the model reveals that the remote sensing analysis method can make up for the lack of statistical data to a certain extent, which helps to scientifically analyze the spatiotemporal dynamic changes of coastal ports, provides a strong basis for decision-making regarding port development, and more importantly provides a convenient estimation method for areas that have long lacked statistical data on cargo handling capacity.
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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 2072-4292 ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number GFZ @ kyba @ Serial 2337
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