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author:

Wang, Lin (Wang, Lin.) [1] | Lei, Henggang (Lei, Henggang.) [2] | Xu, Hanqiu (Xu, Hanqiu.) [3]

Indexed by:

EI

Abstract:

The Russia-Ukraine conflict has persisted for over a year, posing challenges in assessing and verifying the extent of damage through on-site investigations. Nighttime light (NTL) remote sensing, an emerging approach for studying regional conflicts, can complement traditional methods. This article employs National Aeronautics and Space Administration's Black Marble products to reveal the response characteristics of NTL intensity at national and state scales during the first anniversary of the conflict (January 2022 to February 2023) in Ukraine. The article used the NTL ratio index to assess the relative intensity of NTL and month-on-month change rate, nighttime light change rate index (NLCRI), and the rate (R value) of linear regression analysis to depict spatiotemporal dynamics. In addition, Theil-Sen median trend analysis and Mann-Kendall tests were employed to analyze intensity trends, with a 'dual-threshold method' to reduce extensive noise interference. The results showed: At the national scale, the conflict resulted in an 84.0% decrease in NTL across Ukraine. At the state scale, the most severe NTL decline occurred near the southwestern border and eastern conflict zone under Ukrainian government control, witnessing over 80% decline rates. The correlation of decreases in NLCRI and R values with population displacement, infrastructure damage, or curfew measures demonstrated that the concentration of refugees and electricity facility restoration led to increased NLCRI and R values. Overall, NTL reflects critical moments at the national scale and provides insights into military intentions and humanitarian measures at the state scale. Therefore, NTL can effectively serve as a tool for observation and assessment in military conflicts. © 2008-2012 IEEE.

Keyword:

Marble NASA Noise abatement Population statistics Regression analysis Remote sensing

Community:

  • [ 1 ] [Wang, Lin]Fuzhou University, Fujian Provincial Key Laboratory of Remote Sensing of Soil Erosion and Disaster Prevention, Institute of Remote Sensing Information Engineering, College of Environmental and Safety Engineering, Fuzhou; 350108, China
  • [ 2 ] [Lei, Henggang]Fuzhou University, Academy of Digital China (Fujian), Fujian Provincial Key Laboratory of Remote Sensing of Soil Erosion and Disaster Prevention, Institute of Remote Sensing Information Engineering, Fuzhou; 350108, China
  • [ 3 ] [Xu, Hanqiu]Fuzhou University, Fujian Provincial Key Laboratory of Remote Sensing of Soil Erosion and Disaster Prevention, Institute of Remote Sensing Information Engineering, College of Environmental and Safety Engineering, Fuzhou; 350108, China

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Source :

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

ISSN: 1939-1404

Year: 2024

Volume: 17

Page: 4084-4099

4 . 7 0 0

JCR@2023

CAS Journal Grade:3

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ESI Highly Cited Papers on the List: 0 Unfold All

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30 Days PV: 0

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