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Letu, H.; Hara, M.; Tana, G.; Nishio, F. |
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Title |
A Saturated Light Correction Method for DMSP/OLS Nighttime Satellite Imagery |
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2012 |
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IEEE Transactions on Geoscience and Remote Sensing |
Abbreviated Journal |
IEEE Trans. Geosci. Remote Sensing |
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50 |
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2 |
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389-396 |
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DMSP-OLS; remote sensing; light at night; radiometry; calibration |
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Several studies have clarified that electric power consumption can be estimated from the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) stable light imagery. As digital numbers (DNs) of stable light images are often saturated in the center of city areas, we developed a saturated light correction method for the DMSP/OLS stable light image using the nighttime radiance calibration image of the DMSP/OLS. The comparison between the nonsaturated part of the stable light image for 1999 and the radiance calibration image for 1996-1997 in major areas of Japan showed a strong linear correlation (R2 = 92.73) between the DNs of both images. Saturated DNs of the stable light image could therefore be corrected based on the correlation equation between the two images. To evaluate the new saturated light correction method, a regression analysis is performed between statistic data of electric power consumption from lighting and the cumulative DNs of the stable light image before and after correcting for the saturation effects by the new method, in comparison to the conventional method, which is, the cubic regression equation method. The results show a stronger improvement in the determination coefficient with the new saturated light correction method (R2 = 0.91, P = 1.7 ·10-6 <; 0.05) than with the conventional method (R2 = 0.81, P = 2.6 ·10-6 <; 0.05) from the initial correlation with the uncorrected data (R2 = 0.70, P = 4.5 · 10-6 <; 0.05). The new method proves therefore to be very efficient for saturated light correction. |
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0196-2892 |
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IDA @ john @ |
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204 |
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Schwarting, T., McIntire, J., Oudrari, H., & Xiong, X |
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Title |
JPSS-1/NOAA-20 VIIRS Day-Night Band Prelaunch Radiometric Calibration and Performance |
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2019 |
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IEEE Transactions on Geoscience and Remote Sensing |
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1-13 |
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Instrumentation |
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The Visible Infrared Imaging Radiometer Suite (VIIRS) on board the first Joint Polar-Orbiting Satellite System series 1 (JPSS-1) has a panchromatic, three gain stage, day-night band (DNB) capable of imaging the Earth under illumination conditions ranging from reflected moonlight to daytime scenes. The DNB has four charged-coupled devices (CCDs) with 32 different modes of time-delay integration and subpixel aggregation to achieve high SNR in low light conditions while maintaining roughly constant spatial resolution across scan. During the prelaunch testing phase, these 32 different aggregation modes are separately calibrated over a large dynamic range (covering seven orders of magnitude) through a series of radiometric tests designed to generate initial calibration coefficients for the sensor data record (SDR) operational algorithm, assess radiometric performance, and determine compliance with the sensor design requirements. Early in the environmental testing at the Raytheon El Segundo facility, nonlinear behavior was discovered in some DNB edge of scan aggregation modes at low signal levels. In response to this nonlinearity, the test program was altered to characterize the radiometric performance both in the baseline configuration and with a modified aggregation scheme that eliminates the modes used at the end of scan, replacing them with an unaffected adjacent mode and trading off spatial resolution for improved linearity. Presented in this paper is the radiometric performance under both sensor configurations including dynamic range, sensitivity, radiometric uncertainty, and nonlinearity along with a discussion of the potential impact to DNB on-orbit calibration and SDR performance. |
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2541 |
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Polivka, T.N.; Wang, J.; Ellison, L.T.; Hyer, E.J.; Ichoku, C.M. |
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Improving Nocturnal Fire Detection With the VIIRS Day-Night Band |
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2016 |
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IEEE Transactions on Geoscience and Remote Sensing |
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IEEE Trans. Geosci. Remote Sensing |
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54 |
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9 |
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5503-5519 |
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Remote Sensing |
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Building on existing techniques for satellite remote sensing of fires, this paper takes advantage of the day-night band (DNB) aboard the Visible Infrared Imaging Radiometer Suite (VIIRS) to develop the Firelight Detection Algorithm (FILDA), which characterizes fire pixels based on both visible-light and infrared (IR) signatures at night. By adjusting fire pixel selection criteria to include visible-light signatures, FILDA allows for significantly improved detection of pixels with smaller and/or cooler subpixel hotspots than the operational Interface Data Processing System (IDPS) algorithm. VIIRS scenes with near-coincident Advanced Spaceborne Thermal Emission and Reflection (ASTER) overpasses are examined after applying the operational VIIRS fire product algorithm and including a modified “candidate fire pixel selection†approach from FILDA that lowers the 4-μm brightness temperature (BT) threshold but includes a minimum DNB radiance. FILDA is shown to be effective in detecting gas flares and characterizing fire lines during large forest fires (such as the Rim Fire in California and High Park fire in Colorado). Compared with the operational VIIRS fire algorithm for the study period, FILDA shows a large increase (up to 90%) in the number of detected fire pixels that can be verified with the finer resolution ASTER data (90 m). Part (30%) of this increase is likely due to a combined use of DNB and lower 4-μm BT thresholds for fire detection in FILDA. Although further studies are needed, quantitative use of the DNB to improve fire detection could lead to reduced response times to wildfires and better estimate of fire characteristics (smoldering and flaming) at night. |
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0196-2892 |
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LoNNe @ kyba @ |
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1781 |
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Zhao, N., Zhang, W., Liu, Y., Samson, E. L., Chen, Y., & Cao, G. |
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Improving Nighttime Light Imagery With Location-Based Social Media Data |
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2018 |
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IEEE Transactions on Geoscience and Remote Sensing |
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57 |
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4 |
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Remote Sensing |
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Location-based social media have been extensively utilized in the concept of “social sensing” to exploit dynamic information about human activities, yet joint uses of social sensing and remote sensing images are underdeveloped at present. In this paper, the close relationship between the number of Twitter users and brightness of nighttime lights (NTL) over the contiguous United States is calculated and geotagged tweets are then used to upsample a stable light image for 2013. An associated outcome of the upsampling process is the solution of two major problems existing in the NTL image, pixel saturation, and blooming effects. Compared with the original stable light image, digital number (DN) values of the upsampled stable light image have larger correlation coefficients with gridded population (0.47 versus 0.09) and DN values of the new generation NTL image product (0.56 versus 0.52), i.e., the Visible Infrared Imaging Radiometer Suite day/night band image composite. In addition, total personal incomes of states are disaggregated to each pixel in proportion to the DN value of the pixel in the NTL images and then aggregate by counties. Personal incomes distributed by the upsampled NTL image are closer to the official demographic data than those distributed by the original stable light image. All of these results explore the potential of geotagged tweets to improve the quality of NTL images for more accurately estimating or mapping socioeconomic factors. |
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IDA @ intern @ |
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2353 |
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Zhao, M., Zhou, Y., Li, X., Zhou, C., Cheng, W., Li, M., & Huang, K. |
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Title |
Building a Series of Consistent Night-Time Light Data (1992–2018) in Southeast Asia by Integrating DMSP-OLS and NPP-VIIRS |
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2020 |
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IEEE Transactions on Geoscience and Remote Sensing |
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58 |
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3 |
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1843-1856 |
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Remote Sensing |
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Satellite-derived nighttime light (NTL) data from the Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) and the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) have been extensively used for monitoring human activities and urbanization processes. Differences of these two datasets in their spatial and radiometric properties make it difficult for a temporally consistent analysis using these two datasets together. In this article, we developed a new approach to integrate these two datasets and generated a temporally consistent NTL dataset from 1992 to 2018. First, we performed the pixel-level spatial resampling of VIIRS data using a kernel density method after preprocessing the raw VIIRS data. Second, we conducted a logarithmic transformation of the aggregated VIIRS data. Third, we proposed a sigmoid function between DMSP and processed VIIRS data to characterize their relationship. Using the proposed method, we generated a series of consistent DMSP NTL data in Southeast Asia from 1992 to 2018 and analyzed the dynamic of resulted NTL at different scales. The evaluations based on profile curves, spatial patterns, scatter correlations, and histograms, of NTLs, indicate that our approach can achieve a good agreement between DMSP and simulated DMSP data in the same year. Our approach offers the potential for generating a time series of global DMSP NTL data from 1992 to present, which can contribute a more continuous and consistent monitoring of human activities and a better understanding of the urbanization process. |
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IDA @ intern @ |
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2962 |
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