Prolonged Dimming of Ukrainian Urban Illumination: A Measure of Conflict’s Impact
PAYNE INSTITUTE COMMENTARY SERIES: COMMENTARY
Prolonged Dimming of Ukrainian Urban Illumination:
A Measure of Conflict’s Impact
By M. Zhizhin
November 15, 2023
Variations in nighttime city illumination mirror regional socioeconomic trends. This correlation has been substantiated through multi-year and decadal analyses.
Natural disasters such as earthquakes, hurricanes or pandemics trigger a short-term dimming of city lights, followed by a rapid recovery . The war in Ukraine, however, has induced a novel pattern of city lights changes: an abrupt and sustained decline in illumination from the outset of the conflict, persisting for two years with partial recovery observed in some cities.
To investigate the alteration of nighttime illumination in Ukraine during the ongoing conflict, we compiled radiance profiles for the urban centers of 47 major Ukrainian cities with populations exceeding 100,000.
These radiance measurements, spanning from March 2012 to October 2023, were captured every night utilizing the Day-Night Band (DNB) sensor on the Suomi NPP satellite. The DNB data was collected within a 0.005-degree radius centered on city center coordinates provided by the Humanitarian OpenStreetMap Team .
The search radius employed aligns with the dimension of a single pixel in DNB imagery. To circumvent the influence of partially illuminated scenes during mid-latitude summers, we solely considered nighttime observations when the sun was more than 12 degrees below the horizon (nautical twilight).
Applying these constraints, we amassed a collection of approximately 60 nighttime radiance measurements per city center each month, totaling over 8,000 observations during the past eleven years. These observations underwent further filtering to retain only cloud-free satellite observations, as clouds can both obscure city lights (manifesting as lower radiance outliers) and reflect moonlight (manifesting as higher radiance outliers).
The resultant time series of cloud-free urban center radiance observations was segmented into stationary periods delineated by change points , where the average city lights radiance remained constant. Of particular interest were change points occurring after to the outbreak of the war on February 24, 2022. For these instances, the relative radiance alteration was quantified as a ratio, computed by dividing the pre-war mean radiance by the mean radiance within the subsequent stationary period following the change point.
A comprehensive analysis of nighttime radiance profiles for 47 major Ukrainian cities revealed a significant and prolonged dimming of city lights following the onset of the conflict. The ratio of pre-war and post-war radiance values exhibited substantial variation, ranging from 60-80% in Russian-controlled cities to a mere 3-5% of the pre-war level in the eastern cities of Kharkiv and Dnipro, both with populations exceeding one million (Figure 1).
The Ukrainian capital, Kyiv, experienced a 47% reduction in illumination following February 2022. This dimming intensified in October 2022 following Russian missile attacks on Ukrainian infrastructure, plummeting to 25% of pre-war levels. However, a partial recovery was observed in February 2023, with illumination reaching 48% of its pre-war state.
Most major cities in western Ukraine, with the exception of Ivano-Frankivsk, exhibited a less severe dimming effect and exhibited a gradual recovery towards pre-war illumination levels. Figure 1 illustrates this trend by depicting the radiance profile for the western Ukrainian city of Chernivtsi.
In contrast, Russian-controlled cities in Crimea and eastern Ukraine, including Sevastopol, Simferopol, Melitopol, Donetsk, and Luhansk, displayed minimal or no changes in city lights radiance. The observed changes in these cities were comparable to variations between pre-war change points.
Figure 2 visually depicts the spatial variation of city lights dimming in Ukraine following the outbreak of the conflict. The radius of the circle centered on each of the 47 major cities is inversely proportional to the ratio between the pre-war radiance and the mean radiance within the subsequent stationary period. Multiple circles with the same center illustrate the light ratios for the subsequent change points after the outbreak of the war. We have created a web page with interactive map combining the city lights change circles with the plots of the radiance profiles .
Figure 2. Map of nighttime city lights dimming in Ukraine following the onset of the conflict. The size of the circle corresponds to the magnitude of the radiance decrease. Larger circles indicate cities with a more significant drop in illumination.
The change points in nighttime radiance profiles also coincide with the onset of the first phase of the Ukraine-Russian conflict in February-March 2014, which culminated in the annexation of Crimea and parts of the Donetsk and Luhansk regions. The dimming ratios observed in Donetsk and Luhansk following the 2014 crisis align closely with the per capita Gross Regional Product (GRP) trends reported in [6, Section 4.2] (Table 1).
Given the ongoing correlation observed between dimming and GRP ratios before and after the outbreak of the Ukrainian war, we can extrapolate the anticipated declines in GRP for various major city regions, as outlined in Table 2.
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 Humanitarian OpenStreetMap Team https://www.hotosm.org/
 R. Killick, P. Fearnhead, and I. Eckley. Optimal detection of changepoints with a linear computational cost. Journal of the American Statistical Association, 107(500):1590–1598, 2012
 Interactive map of dimming lights in Ukraine https://eogdata.mines.edu/wwwdata/hidden/ukraine/Ukraine_city_lights_2012-2023.html
 . Julia Bluszcz, Marica Valente. The War in Europe: Economic Costs of the Ukrainian Conflict. URL: https://ideas.repec.org/p/diw/diwwpp/dp1804.html
ABOUT THE AUTHOR
Research Associate, Earth Observation Group
Zhizhin Mikhail Nikolaevich, M.Sci in mathematics from the Moscow State University in 1984, Ph.D. in computational seismology and pattern recognition from the Russian Acad. Sci. in 1992. Research positions from 1987 to 2012 in geophysics, space research and nuclear physics at Russian Acad. Sci., later at NOAA and CU Boulder. Currently he is a researcher at the Earth Observation Group at Colorado School of Mines. His applied research fields evolved from high performance computing in seismology, geodynamics, terrestrial and space weather to deep learning in remote sensing. He is developing new machine learning algorithms to better understand the Nature with Big Data.
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