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Author Elvidge, C.; Zhizhin, M.; Hsu, F.-C.; Baugh, K.
Title VIIRS Nightfire: Satellite Pyrometry at Night Type Journal Article
Year 2013 Publication Remote Sensing Abbreviated Journal Remote Sensing
Volume 5 Issue 9 Pages 4423-4449
Keywords SNPP; VIIRS; fire detection; gas flaring; biomass burning; fossil fuel carbon emissions
Abstract The Nightfire algorithm detects and characterizes sub-pixel hot sources using multispectral data collected globally, each night, by the Suomi National Polar Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS). The spectral bands utilized span visible, near-infrared (NIR), short-wave infrared (SWIR), and mid-wave infrared (MWIR). The primary detection band is in the SWIR, centered at 1.6 μm. Without solar input, the SWIR spectral band records sensor noise, punctuated by high radiant emissions associated with gas flares, biomass burning, volcanoes, and industrial sites such as steel mills. Planck curve fitting of the hot source radiances yields temperature (K) and emission scaling factor (ESF). Additional calculations are done to estimate source size (m2), radiant heat intensity (W/m2), and radiant heat (MW). Use of the sensor noise limited M7, M8, and M10 spectral bands at night reduce scene background effects, which are widely reported for fire algorithms based on MWIR and long-wave infrared. High atmospheric transmissivity in the M10 spectral band reduces atmospheric effects on temperature and radiant heat retrievals. Nightfire retrieved temperature estimates for sub-pixel hot sources ranging from 600 to 6,000 K. An intercomparison study of biomass burning in Sumatra from June 2013 found Nightfire radiant heat (MW) to be highly correlated to Moderate Resolution Imaging Spectrometer (MODIS) Fire Radiative Power (MW).
Address Earth Observation Group, NOAA National Geophysical Data Center, Boulder, CO 80305, USA
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ISSN (down) 2072-4292 ISBN Medium
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Call Number IDA @ john @ Serial 199
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Author Li, X.; Xu, H.; Chen, X.; Li, C.
Title Potential of NPP-VIIRS Nighttime Light Imagery for Modeling the Regional Economy of China Type Journal Article
Year 2013 Publication Remote Sensing Abbreviated Journal Remote Sensing
Volume 5 Issue 6 Pages 3057-3081
Keywords nighttime light; gross regional product; Visible Infrared Imaging Radiometer Suite; linear regression
Abstract Historically, the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) was the unique satellite sensor used to collect the nighttime light, which is an efficient means to map the global economic activities. Since it was launched in October 2011, the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor on the Suomi National Polar-orbiting Partnership (NPP) Satellite has become a new satellite used to monitor nighttime light. This study performed the first evaluation on the NPP-VIIRS nighttime light imagery in modeling economy, analyzing 31 provincial regions and 393 county regions in China. For each region, the total nighttime light (TNL) and gross regional product (GRP) around the year of 2010 were derived, and a linear regression model was applied on the data. Through the regression, the TNL from NPP-VIIRS were found to exhibit R2 values of 0.8699 and 0.8544 with the provincial GRP and county GRP, respectively, which are significantly stronger than the relationship between the TNL from DMSP-OLS (F16 and F18 satellites) and GRP. Using the regression models, the GRP was predicted from the TNL for each region, and we found that the NPP-VIIRS data is more predictable for the GRP than those of the DMSP-OLS data. This study demonstrates that the recently released NPP-VIIRS nighttime light imagery has a stronger capacity in modeling regional economy than those of the DMSP-OLS data. These findings provide a foundation to model the global and regional economy with the recently availability of the NPP-VIIRS data, especially in the regions where economic census data is difficult to access.
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Call Number IDA @ john @ Serial 201
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Author Li, X.; Ge, L.; Chen, X.
Title Detecting Zimbabwe's Decadal Economic Decline Using Nighttime Light Imagery Type Journal Article
Year 2013 Publication Remote Sensing Abbreviated Journal Remote Sensing
Volume 5 Issue 9 Pages 4551-4570
Keywords Zimbabwe; economic decline; nighttime light; DMSP-OLS; remote sensing; light at night
Abstract Zimbabwe’s economy declined between 2000 and 2009. This study detects the economic decline in different regions of Zimbabwe using nighttime light imagery from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS). We found a good correlation (coefficient = 0.7361) between Zimbabwe’s total nighttime light (TNL) and Gross Domestic Product (GDP) for the period 1992 to 2009. Therefore, TNL was used as an indicator of regional economic conditions in Zimbabwe. Nighttime light imagery from 2000 and 2008 was compared at both national and regional scales for four types of regions. At the national scale, we found that nighttime light in more than half of the lit area decreased between 2000 and 2008. Moreover, within the four region types (inland mining towns, inland agricultural towns, border towns and cities) we determined that the mining and agricultural sectors experienced the most severe economic decline. Some of these findings were validated by economic survey data, proving that the nighttime light data is a potential data source for detecting the economic decline in Zimbabwe.
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Notes Approved no
Call Number IDA @ john @ Serial 212
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Author Miller, S.; Straka, W.; Mills, S.; Elvidge, C.; Lee, T.; Solbrig, J.; Walther, A.; Heidinger, A.; Weiss, S.
Title Illuminating the Capabilities of the Suomi National Polar-Orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band Type Journal Article
Year 2013 Publication Remote Sensing Abbreviated Journal Remote Sensing
Volume 5 Issue 12 Pages 6717-6766
Keywords Instrumentation; satellite imagery; nighttime visible/near-infrared; moonlight
Abstract Daytime measurements of reflected sunlight in the visible spectrum have been a staple of Earth-viewing radiometers since the advent of the environmental satellite platform. At night, these same optical-spectrum sensors have traditionally been limited to thermal infrared emission, which contains relatively poor information content for many important weather and climate parameters. These deficiencies have limited our ability to characterize the full diurnal behavior and processes of parameters relevant to improved monitoring, understanding and modeling of weather and climate processes. Visible-spectrum light information does exist during the nighttime hours, originating from a wide variety of sources, but its detection requires specialized technology. Such measurements have existed, in a limited way, on USA Department of Defense satellites, but the Suomi National Polar-orbiting Partnership (NPP) satellite, which carries a new Day/Night Band (DNB) radiometer, offers the first quantitative measurements of nocturnal visible and near-infrared light. Here, we demonstrate the expanded potential for nocturnal low-light visible applications enabled by the DNB. Via a combination of terrestrial and extraterrestrial light sources, such observations are always available—expanding many current existing applications while enabling entirely new capabilities. These novel low-light measurements open doors to a wealth of new interdisciplinary research topics while lighting a pathway toward the optimized design of follow-on satellite based low light visible sensors.
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Call Number LoNNe @ christopher.kyba @ Serial 468
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Author Mann, M.; Melaas, E.; Malik, A.
Title Using VIIRS Day/Night Band to Measure Electricity Supply Reliability: Preliminary Results from Maharashtra, India Type Journal Article
Year 2016 Publication Remote Sensing Abbreviated Journal Remote Sensing
Volume 8 Issue 9 Pages 711
Keywords Remote Sensing; NPP-VIIRS; VIIRS-DNB; India; South Asia
Abstract Unreliable electricity supplies are common in developing countries and impose large socio-economic costs, yet precise information on electricity reliability is typically unavailable. This paper presents preliminary results from a machine-learning approach for using satellite imagery of nighttime lights to develop estimates of electricity reliability for western India at a finer spatial scale. We use data from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar Partnership (SNPP) satellite together with newly-available data from networked household voltage meters. Our results point to the possibilities of this approach as well as areas for refinement. With currently available training data, we find a limited ability to detect individual outages identified by household-level measurements of electricity voltage. This is likely due to the relatively small number of individual outages observed in our preliminary data. However, we find that the approach can estimate electricity reliability rates for individual locations fairly well, with the predicted versus actual regression yielding an R2 > 0.5. We also find that, despite the after midnight overpass time of the SNPP satellite, the reliability estimates derived are representative of daytime reliability.
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Notes Approved no
Call Number LoNNe @ kyba @ Serial 1515
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