Abstract
Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) nighttime light data has become a key tool of the environmental and social scientific fields, but suffers from several validity problems. We highlight one such problem—shifts in the digital number position in DMSP-OLS composites in the same satellite. We present techniques for identifying the problem, using moving window raster correlation and visual inspection, and for solving the problem, by assigning control points and manually shifting raster positions. To illustrate the importance of accounting for signal shift, we re-examine a recent analysis of the relationship between public goods provision and patterns of violence in the 2011 Syrian uprising and ensuing civil war. We find the statistical results change considerably when correcting for signal shift. We attribute this change to the systematic undercounting of light intensity in heavily populated areas. We close by identifying the types of research that would most benefit from our correction and suggest future refinements to our technique through automation.
Highlights
In the mid-1990s, the United States’ National Oceanic and Atmospheric Association’s (NOAA) Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) began publicly releasing annual composites of nighttime lights, quantifying the relative intensity of light emissions on a global scale
We identify a compatibility issue in some DMSP-OLS composites from the same satellite: the geoposition of digital numbers for the same physical location can shift from one year to another
In our re-analysis, we observe the possibility for false positives in statistical results utilizing shifted composites, which may be a result of systematic bias introduced by signal shift
Summary
In the mid-1990s, the United States’ National Oceanic and Atmospheric Association’s (NOAA) Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) began publicly releasing annual composites of nighttime lights, quantifying the relative intensity of light emissions on a global scale. DMSP-OLS nighttime lights data have several important limitations. They cannot capture variation in high-density urban areas due to pixel saturation [20] (though several corrections for saturation issues have been identified, including incorporating ground cover and census data [21]) and conflate electricity production with gas flares from oil production [22]. While saturation and signal decay are no longer issues with the release of monthly images from the Visible Infrared Imaging Radiometer Suite (VIIRS) satellite [24], DMSP-OLS data remain of interest to researchers using historical data (e.g., [25,26]). It is important to correct issues that may bias inferences for practitioners still using DMSP-OLS data
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