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Spatio-temporal variations of night-time lights at early stages of the COVID-19 epidemic in the United States

  • role： First author第一作者
  • Affiliation：

    School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China

  • Email：xugang@whu.edu.cn
  • Introduction：许刚，研究方向为城市化与复杂城市系统分析。E-mail： xugang@whu.edu.cn
XU Gang1,  
  • Affiliation：

    School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China

WANG Chuanli2,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China

  • Email：mqx@whu.edu.cn
  • Introduction：孟庆祥，研究方向为地理信息建模和时空数据挖掘。E-mail： mqx@whu.edu.cn
MENG Qingxiang2*,  
  • Affiliation：

    State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

XIU Tianyu3,  
  • Affiliation：

    State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

LI Xi3

résumé

The COVID-19 epidemic swept the world and continues to spread. Without effective medical treatments and vaccine during the early stage of the pandemic, local governments in various countries had to lock down cities and adopt non-pharmaceutical interventions (NPIs), such as the stay-at-home order and social distancing. The NPIs against the COVID-19 epidemic have significantly changed the socio-economic activities in cities. However, the characteristics and patterns of urban socio-economic activities under this influence are still unclear. Given the development of earth observation technologies, large-scale changes in socioeconomic activities can be captured by satellites through remotely sensed Night-Time Lights (NTLs). In this study, we selected 20 major cities in the United States, including Atlanta, Chicago, Dallas, Denver, Detroit, Houston, Kansas City, Los Angeles, Miami, Minneapolis, New York, Orlando, Philadelphia, Phoenix, Pittsburgh, Salt Lake City, San Francisco, Seattle, Saint Louis, Washington D.C., to analyze the spatio-temporal variations of NTLs caused by the lockdown of cities. The first round of the COVID-19 epidemic occurred in the United States in mid-March 2020. Since March 2020, American cities have successively issued stay-at-home orders, but differences in the time and strictness of policy implementation were noted. Large cities have higher population density and intensity of social activities, so they are more susceptible to infectious diseases. The diversity of lockdown dates and the strictness of lockdowns in the cities in the United States are conducive to investigating the spatio-temporal variations of NTL. We acquired monthly averaged NTLS products of February, March, and April 2020, which were from Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) onboard the Suomi National Polar-orbiting Platform NPP. We further analyzed the spatial pattern, distance decay, and disparities in land use types of changes in the NTLs. Results show that the NTLs generally dimmed by 5%—8% in U.S. cities because of the lockdown of cities. In six cities, the luminous brightness dropped by more than 10%: Chicago, Dallas, Denver, Detroit, Minneapolis, and St. Louis. Among them, Minneapolis showed the largest decrease in luminous brightness, with a decrease of approximately 40% in March. The spatial change of the NTLs shows an obvious “core-periphery” pattern, indicating that the reduction of the NTLs declined with the distance from the city center, mainly because the central area of the city is a concentrated commercial area. After the closure of the city, commercial activities dropped significantly, resulting in an obvious reduction in NTLs around the city centers. The reduction of the NTLs varied among the diverse urban land use types. In New York, the NTL decreased the most on land for residence and aviation facilities by 12% and 11%, respectively. In Chicago, the NTL generally decreased by 20% in all types of urban land, and NTL recovered after one month of the lockdown of the cities in other urban lands, except the sports facilities land. This study only analyzed the spatio-temporal changes of NTLs. In the future, the results of this study can be combined with multi-source data to explain the driving force of NTL changes. Night-time light remote sensing effectively reflects the urban socio-economic dynamics with an important application in monitoring and assessing the socio-economic impacts of emergencies.

mots-clés

night-time light remote sensing;COVID-19;NPP-VIIRS;spatio-temporal variation;the United States

References

  1. 1.
    Anand A and Kim D H. 2021. Pandemic induced changes in economic activity around African protected areas captured through night-time light data. Remote Sensing, 13(2): 314
  2. 2.
    Bennett M M and Smith L C. 2017. Advances in using multitemporal night-time lights satellite imagery to detect, estimate, and monitor socioeconomic dynamics. Remote Sensing of Environment, 192: 176-197
  3. 3.
    Beyer R C M, Franco-Bedoya S and Galdo V. 2021. Examining the economic impact of COVID-19 in India through daily electricity consumption and nighttime light intensity. World Development, 140: 105287
  4. 4.
    Bustamante-Calabria M, Sánchez De Miguel A, Martín-Ruiz S, Ortiz J L, Vílchez J M, Pelegrina A, García A, Zamorano J, Bennie J and Gaston K J. 2021. Effects of the COVID-19 lockdown on urban light emissions: ground and satellite comparison. Remote Sensing, 13(2): 258
  5. 5.
    Chen Z Q. 2017. A Multiscale Analysis on Urban Area and Spatial Structure based on Nighttime Light Data. Shanghai: East China Normal University
  6. 6.
    Chen Z Q, Yu B L, Yang C S, Zhou Y Y, Yao S J, Qian X J, Wang C X, Wu B and Wu J P. 2021. An extended time series (2000—2018) of global NPP-VIIRS-like nighttime light data from a cross-sensor calibration. Earth System Science Data, 13(3): 889-906
  7. 7.
    Diffenbaugh N S, Field C B, Appel E A, Azevedo I L, Baldocchi D D, Burke M, Burney J A, Ciais P, Davis S J, Fiore A M, Fletcher S M, Hertel T W, Horton D E, Hsiang S M, Jackson R B, Jin X M, Levi M, Lobell D B, Mckinley G A, Moore F C, Montgomery A, Nadeau K C, Pataki D E, Randerson J T, Reichstein M, Schnell J L, Seneviratne S I, Singh D, Steiner A L and Wong-Parodi G. 2020. The COVID-19 lockdowns: a window into the Earth System. Nature Reviews Earth & Environment, 1(9): 470-481
  8. 8.
    Elvidge C D, Baugh K E, Kihn E A, Kroehl H W, Davis E R and Davis C W. 1997. Relation between satellite observed visible-near infrared emissions, population, economic activity and electric power consumption. International Journal of Remote Sensing, 18(6): 1373-1379
  9. 9.
    Elvidge C D, Ghosh T, Hsu F C, Zhizhin M and Bazilian M. 2020. The dimming of lights in China during the COVID-19 pandemic. Remote Sensing, 12(17): 2851
  10. 10.
    Ghosh T, Elvidge C D, Hsu F C, Zhizhin M and Bazilian M. 2020. The dimming of lights in India during the COVID-19 pandemic. Remote Sensing, 12(20): 3289
  11. 11.
    Jechow A and Hölker F. 2020. Evidence that reduced air and road traffic decreased artificial night-time skyglow during COVID-19 lockdown in Berlin, Germany. Remote Sensing, 12(20): 3412
  12. 12.
    Jia J S, Lu X, Yuan Y, Xu G, Jia J M and Christakis N A. 2020. Population flow drives spatio-temporal distribution of COVID-19 in China. Nature, 582(7812): 389-394
  13. 13.
    Jiang W, He G J, Peng Y, Wang G Z and Wang M M. 2017. Application potentiality and prospects of nighttime light remote sensing in “the Belt and Road” initiative. Journal of University of Chinese Academy of Sciences, 34(3): 296-303
  14. 14.
    Jiang Y Q, Huang X and Li Z L. 2021. Spatiotemporal patterns of human mobility and its association with land use types during COVID-19 in New York City. ISPRS International Journal of Geo-Information, 10(5): 344
  15. 15.
    Lan T, Shao G F, Tang L N, Xu Z B, Zhu W and Liu L Y. 2021. Quantifying spatiotemporal changes in human activities induced by COVID-19 pandemic using daily nighttime light data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14: 2740-2753
  16. 16.
    Levin N, Kyba C C M, Zhang Q L, De miguel A S, Román M O, Li X, Portnov B A, Molthan A L, Jechow A, Miller S D, Wang Z S, Shrestha R M and Elvidge C D. 2020. Remote sensing of night lights: a review and an outlook for the future. Remote Sensing of Environment, 237: 111443
  17. 17.
    Li D R and Li X. 2015. An overview on data mining of nighttime light remote sensing. Acta Geodaetica et Cartographica Sinica, 44(6): 591-601
  18. 18.
    Li X, Xu H N, Chen X L and Li C. 2013. Potential of NPP-VIIRS nighttime light imagery for modeling the regional economy of China. Remote Sensing, 5(6): 3057-3081
  19. 19.
    Li X Y, Li X, Fan Z Y, Mi L, Kandakji T, Song Z, Li D R and Song X P. 2022. Civil war hinders crop production and threatens food security in Syria. Nature Food, 3(1): 38-46
  20. 20.
    Liu B J, Chen Z Q, Yu B L, Yang C S, Qiu B W and Tu Y. 2021. Kinetic energy assessment and similarity analysis of urban development based on NPP-VIIRS nighttime light remote sensing. National Remote Sensing Bulletin, 25(5): 1187-1200
  21. 21.
    Meng Y D, Zhu V and Zhu Y. 2021. Co-distribution of light at night (LAN) and COVID-19 incidence in the United States. BMC Public Health, 21(1): 1509
  22. 22.
    Pei T, Wang X, Song C, Liu Y X, Huang Q, Shu H, Chen X, Guo S H and Zhou C H. 2021. Review on spatiotemporal analysis and modeling of COVID-19 pandemic. Journal of Geo-information Science, 23(2): 188-210
  23. 23.
    Rocha L E C, Thorson A E and Lambiotte R. 2015. The non-linear health consequences of living in larger cities. Journal of Urban Health, 92(5): 785-799
  24. 24.
    Rowe F, Robinson C and Patias N. 2022. Sensing global changes in the local patterns of energy consumption in cities during the early stages of the COVID-19 pandemic. Cities, 129: 103808
  25. 25.
    Shao Z F, Tang Y, Huang X and Li D R. 2021. Monitoring work resumption of Wuhan in the COVID-19 epidemic using daily nighttime light. Photogrammetric Engineering and Remote Sensing, 87(3): 197-206
  26. 26.
    Shi K F, Huang C, Yu B L, Yin B, Huang Y X and Wu J P. 2014. Evaluation of NPP-VIIRS night-time light composite data for extracting built-up urban areas. Remote Sensing Letters, 5(4): 358-366
  27. 27.
    Shu S, Yu B L, Wu J P and Liu H X. 2011. Methods for deriving urban built-up area using night-light data: assessment and application. Remote Sensing Technology and Application, 26(2): 169-176
  28. 28.
    Small C and Sousa D. 2021. Spatiotemporal evolution of COVID-19 infection and detection within night light networks: comparative analysis of USA and China. Applied Network Science, 6: 10
  29. 29.
    Straka W, Kondragunta S, Wei Z G, Zhang H, Miller S D and Watts A. 2020. Examining the economic and environmental impacts of COVID-19 using earth observation data. Remote Sensing, 13(1): 5
  30. 30.
    Tao J H, Fan M, Gu J B and Chen L F. 2020. Satellite observations of the return-to-work over China during the period of COVID-19. Journal of Remote Sensing, 24(7): 824-836
  31. 31.
    Tian H Y, Liu Y H, Li Y D, Wu C H, Chen B, Kraemer M U G, Li B Y, Cai J, Xu B, Yang Q Q, Wang B, Yang P, Cui Y J, Song Y M, Zheng P, Wang Q Y, Bjornstad O N, Yang R F, Grenfell B T, Pybus O G and Dye C. 2020. An investigation of transmission control measures during the first 50 days of the COVID-19 epidemic in China. Science, 368(6491): 638-642
  32. 32.
    Wang T Y, Zhang G, Li P R, Li F T and Guo X Y. 2018. Analysis on the driving factors of urban expansion policy based on DMSP/OLS remote sensing image. Acta Geodaetica et Cartographica Sinica, 47(11): 1466-1473
  33. 33.
    Xie Y H, Weng Q H and Fu P. 2019. Temporal variations of artificial nighttime lights and their implications for urbanization in the conterminous United States, 2013—2017. Remote Sensing of Environment, 225: 160-174
  34. 34.
    Xu G, Wang W W, Lu D D, Lu B B, Qin K and Jiao L M. 2022. Geographically varying relationships between population flows from Wuhan and COVID-19 cases in Chinese cities. Geo-Spatial Information Science, 25(2): 121-131.
  35. 35.
    Xu G, Xiu T Y, Li X, Liang X L and Jiao L M. 2021. Lockdown induced night-time light dynamics during the COVID-19 epidemic in global megacities. International Journal of Applied Earth Observation and Geoinformation, 102: 102421
  36. 36.
    Yin R Y, He G J, Jiang W, Peng Y, Zhang Z M, Li M X and Gong C J. 2021. Night-time light imagery reveals China's city activity during the COVID-19 pandemic period in early 2020. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14: 5111-5122
  37. 37.
    Yu B L, Wang C X, Gong W K, Chen Z Q, Shi K F, Wu B, Hong Y C, Li Q X and Wu J P. 2021. Nighttime light remote sensing and urban studies: data, methods, applications, and prospects. National Remote Sensing Bulletin, 25(1): 342-364
  38. 38.
    Zhao N Z, Cao G F, Zhang W, Samson E L and Chen Y. 2020. Remote sensing and social sensing for socioeconomic systems: a comparison study between nighttime lights and location-based social media at the 500 m spatial resolution. International Journal of Applied Earth Observation and Geoinformation, 87: 102058
  39. 39.
    Zheng S, Fu Y Y, Sun Y, Zhang C J, Wang Y S and Lichtfouse E. 2021. High resolution mapping of nighttime light and air pollutants during the COVID-19 lockdown in Wuhan. Environmental Chemistry Letters, 19(4): 3477-3485
  40. 40.
    Zhou C H, Pei T, Du Y Y, Chen J, Xu J, Wang J E, Zhang G Y, Su F Z, Song C, Yi J W, Ma T, Ge Y, Zhang A and Jiang L L. 2020. Big data analysis on COVID-19 epidemic and suggestions on regional prevention and control policy. Bulletin of the Chinese Academy of Sciences, 35(2): 200-203

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