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Author Zheng, Q.; Weng, Q.; Wang, K.
Title Developing a new cross-sensor calibration model for DMSP-OLS and Suomi-NPP VIIRS night-light imageries Type Journal Article
Year 2019 Publication ISPRS Journal of Photogrammetry and Remote Sensing Abbreviated Journal ISPRS Journal of Photogrammetry and Remote Sensing
Volume 153 Issue Pages 36-47
Keywords Remote Sensing; Instrumentation
Abstract Night-time light (NTL) data provides a great opportunity to monitor human activities and settlements. Currently, global-scale NTL data are acquired by two satellite sensors, i.e., DMSP-OLS and VIIRS, but the data collected by the satellites are not compatible. To address this issue, we proposed a method for generating long-term and consistent NTL data. First, a logistic model was employed to estimate and smooth the missing DMSP-OLS data. Second, the Lomb-Scargle Periodogram technique was used to statistically examine the presence of seasonality of monthly VIIRS time series. The seasonal effect, noisy and unstable observations in VIIRS were eliminated by the BFAST time-series decomposition algorithm. Then, we proposed a residuals corrected geographically weighted regression model (GWRc) to generate DMSP-like VIIRS data. A consistent NTL time series from 1996 to 2017 was formed by combining the DMSP-OLS and synthetic DMSP-like VIIRS data. Our assessment shows that the proposed GWRc model outperformed existing methods (e.g., power function model), yielding a lower regression RMSE (6.36), a significantly improved pixel-level NTL intensity consistency (SNDI = 82.73, R2 = 0.986) and provided more coherent results when used for urban area extraction. The proposed method can be used to extend NTL time series, and in conjunction with the upcoming yearly VIIRS data and Black Marble daily VIIRS data, it is possible to support long-term NTL-based studies such as monitoring light pollution in ecosystems, and mapping human activities.
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Corporate Author Thesis
Publisher Place of Publication Editor
Language (up) Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0924-2716 ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number GFZ @ kyba @ Serial 2361
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Author Su, Z.; Zhong, X.; Zhang, G.; Li, Y.; He, X.; Wang, Q.; Wei, Z.; He, C.; Li, D.
Title High Sensitive Night-time Light Imaging Camera Design and In-orbit Test of Luojia1-01 Satellite Type Journal Article
Year 2019 Publication Sensors Abbreviated Journal Sensors
Volume 19 Issue 4 Pages 797
Keywords Remote Sensing; Instrumentation
Abstract Luojia1-01 satellite is the first scientific experimental satellite applied for night-time light remote sensing data acquisition, and the payload is an optical camera with high sensitivity, high radiation measurement accuracy and stable elements of interior orientation. At the same time, a special shaped hood is designed, which significantly improved the ability of the camera to suppress stray light. Camera electronics adopts the integrated design of focal plane and imaging processing, which greatly reduces the volume and weight of the system. In this paper, the design of the optical camera is summarized, and the results of in-orbit imaging performance tests are analyzed. The results show that the dynamic modulation transfer function (MTF) of the camera is better than 0.17, and the SNR is better than 35 dB under the condition of 10 lx illuminance and 0.3 reflectivity and all indicators meet the design requirements. The data obtained have been widely applied in many fields such as the process of urbanization, light pollution analysis, marine fisheries detection and military.
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Corporate Author Thesis
Publisher Place of Publication Editor
Language (up) Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1424-8220 ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number GFZ @ kyba @ Serial 2215
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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 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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Corporate Author Thesis
Publisher Place of Publication Editor
Language (up) 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 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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Corporate Author Thesis
Publisher Place of Publication Editor
Language (up) 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 Berman, S.
Title Opinion: Whither V(λ)? Type Journal Article
Year 2019 Publication Lighting Research & Technology Abbreviated Journal Lighting Research & Technology
Volume 51 Issue 1 Pages 4-4
Keywords Vision
Abstract
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language (up) Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1477-1535 ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number GFZ @ kyba @ Serial 2219
Permanent link to this record