Geomatics and Environmental Engineering, vol. 15, no. 2

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Vol. 15 No. 2 (2021): Geomatics and Environmental Engineering

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Submitted
Feb 1, 2021
Accepted
Feb 23, 2021
Published
Mar 24, 2021
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This work is licensed under a Creative Commons Attribution 4.0 International License.

Comparison of Land Surface Temperature Before, During and After the Covid‑19 Lockdown Using Landsat Imagery: A Case Study of Casablanca City, Morocco

https://doi.org/10.7494/geom.2021.15.2.105
Meryem Taoufik
Laboratory of Applied Geology, Geomatics and Environment, Faculty of Sciences Ben M’Sik, Hassan II University of Casablanca, Casablanca, Morocco
https://orcid.org/0000-0001-8572-3459
Meriem Laghlimi
Laboratory of Applied Geology, Geomatics and Environment, Faculty of Sciences Ben M’Sik, Hassan II University of Casablanca, Casablanca, Morocco
https://orcid.org/0000-0003-0192-075X
Ahmed Fekri
Laboratory of Applied Geology, Geomatics and Environment, Faculty of Sciences Ben M’Sik, Hassan II University of Casablanca, Casablanca, Morocco

Corresponding Author(s) : Meryem Taoufik

meryem.taoufik@gmail.com

Geomatics and Environmental Engineering, Vol. 15 No. 2 (2021): Geomatics and Environmental Engineering

Abstract

Land Surface Temperature (LST) is an important variable within global climate change. With the appearance of remote sensing techniques and advanced  GIS software, it is now possible to estimate LST. In this study, the effect of lockdown during COVID-19 on the LST was assessed using Landsat 8 Imagery.  LST dynamic was investigated for three different periods: Before, during  and after the COVID-19 lockdown. The study was conducted in Casablanca  City. The results showed that during the emergence of COVID-19 with lockdown policy applied, the LST decreases remarkably compared to the previous 4-years´ average LST. After the easing of restrictions, the LST increased to exceed the previous 4-year mean LST. Furthermore, throughout all studied periods, the LST recorded its higher values in industrial zones and areas with high urban density and urban transportation, which indicates the conspicuous impact of anthropogenic activities on the LST variation. These findings indicate an ability to assess the feasibility of planned lockdowns intended as a potential preventive mechanism to reduce LST peaks and the loss of air quality in metropolitan environments in the future.

Keywords

Land Surface Temperature Landsat 8 COVID-19 spatiotemporal analysis Remote Sensing Morocco
Taoufik, M., Laghlimi, M., & Fekri, A. (2021). Comparison of Land Surface Temperature Before, During and After the Covid‑19 Lockdown Using Landsat Imagery: A Case Study of Casablanca City, Morocco. Geomatics and Environmental Engineering, 15(2), 105–120. https://doi.org/10.7494/geom.2021.15.2.105
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References
  1. Carlos W.G., Dela Cruz C.S., Cao B., Pasnick S., Jamil S.: Novel Wuhan (2019-nCoV) Coronavirus. American Journal of Respiratory and Critical Care Medicine, vol. 201, 2020, pp. 7–8.
  2. Wuhan Municipal Health Commission: Report on current pneumonia epidemic situation in the city. 2019. http://wjw.wuhan.gov.cn/front/web/showDetail/ 2019123108989 [access: 8.10.2020].
  3. Huang C., Wang Y., Li X., Ren L., Zhao J., Hu Y., Zhang L., Fan G., Xu J., Gu X., Cheng Z., Yu T., Xia J., Wei Y., Wu W., Xie X., Yin W., Li H., Liu M., Xiao Y., Gao H., Guo L., Xie J., Wang G., Jiang R., Gao Z., Jin Q., Wang J., Cao B.: Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The Lancet, vol. 395, 2020, pp. 497–506. https://doi.org/10.1016/S0140-6736(20)30183-5.
  4. Cascella M., Rajnik M., Cuomo A., Dulebohn S.C., Di Napoli R.: Features, Evaluation and Treatment Coronavirus (COVID-19). StatPearls, 2020. https://www.ncbi.nlm.nih.gov/books/NBK554776/ [access: 1.02.2021].
  5. Shereen M.A., Khan S., Kazmi A., Bashir N., Siddique R.: COVID-19 infection: Origin, transmission, and characteristics of human coronaviruses. Journal of Advanced Research, vol. 24, 2020, pp. 91–98. https://doi.org/10.1016/ j.jare.2020.03.005.
  6. World Health Organization: Coronavirus disease 2019 (COVID-19) Situation Report – 67. WHO, 2020. https://apps.who.int/iris/bitstream/handle/10665/331613/nCoVsitrep27Mar2020-eng.pdf?sequence=1&isAllowed=y [ac‑ cess: 27.03.2020].
  7. World Health Organization: Coronavirus disease 2019 (COVID-19) situation report – 50. WHO, 2020. https://apps.who.int/iris/bitstream/handle/10665/331450/nCoVsitrep10Mar2020-eng.pdf?sequence=1&isAllowed=y [ac‑ cess: 10.03.2020].
  8. World Health Organization: Weekly Operational Update on COVID-19: 30 November 2020. Interim Guidance, 2020. https://www.who.int/publications/m/ item/weekly-operational-update---30-november-2020 [access: 27.03.2020].
  9. Firano Z., Filali Adib F.: The COVID-19: macroeconomics scenarii and role of containment in Morocco. One Health, vol. 10, 2020, art. no. 100152. https://doi. org/10.1016/j.onehlt.2020.100152.
  10. Otmani A., Benchrif A., Tahri M., Bounakhla M., Chakir E.M., El Bouch M., Krombid M.H.: Impact of Covid‑19 lockdown on PM10, SO2 and NO2 concentrations in Salé City (Morocco). Science of the Total Environment, vol. 735, 2020, art. no. 139541. https://doi.org/10.1016/j.scitotenv.2020.139541.
  11. Wang C., Li Y., Myint S.W., Zhao Q., Wentz E.A.: Impacts of spatial clustering of urban land cover on land surface temperature across Köppen climate zones in the contiguous United States. Landscape and Urban Planning, vol. 192, 2019, art. no. 103668. https://doi.org/10.1016/j.landurbplan.2019.103668.
  12. Hu T., Renzullo L.J., Dijk A.I.J.M.v., He J., Tian S., Xu Z., Zhou J., Liu T., Liu Q.: Monitoring agricultural drought in Australia using MTSAT-2 land surface temperature retrievals. Remote Sensing of Environment, vol. 236, 2020, art. no. 111419. https://doi.org/10.1016/j.rse.2019.111419.
  13. Shah H.L., Zhou T., Huang M., Mishra V.: Strong Influence of Irrigation on Water Budget and Land Surface Temperature in Indian Subcontinental River Basins. IGR Atmospheres, vol. 124, issue 3, 2019, pp. 1449–1462. https://doi.org/doi.org/10.1029/2018JD029132.
  14. Weng Q., Firozjaei M.K., Kiavarz M., Alavipanah S.K., Hamzeh S.: Normalizing land surface temperature for environmental parameters in mountainous and ur‑ ban areas of a cold semi‑arid climate. Science of The Total Environment, vol. 650, 2019, pp. 515–529. https://doi.org/10.1016/j.scitotenv.2018.09.027.
  15. Zhana Q., Meng F., Xiao Y.: Exploring the relationships of between land surface temperature, ground coverage ratio and building volume density in an urbanized environment. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XL-7/W3, 2015, pp. 255–260. https://doi.org/10.5194/isprsarchives-XL-7-W3-255-2015.
  16. Amir Siddique M., Dongyun L., Li P., Rasool U., Ullah Khan T., Javaid Aini Farooqi T., Wang L., Fan B., Rasool M.: Assessment and simulation of land use and land cover change impacts on the land surface temperature of Chaoyang District in Beijing, China. PeerJ, vol. 8, 2020. https://doi.org/10.7717/peerj.9115.
  17. Buyantuyev A., Wu J.: Urban heat islands and landscape heterogeneity: linking spatiotemporal variations in surface temperatures to land‑cover and socioeconomic patterns. Landscape Ecology, vol. 25, 2010, pp. 17–33. https://doi.org/10.1007/s10980-009-9402-4.
  18. Li X., Li W., Middel A., Harlan S.L., Brazel A.J., Turner II B.L.: Remote sensing of the surface urban heat island and land architecture in Phoenix, Arizona: Combined effects of land composition and configuration and cadastral‑demographic‑economic factors. Remote Sensing of Environment, vol. 174, issue 1, 2016, pp. 233–243.
  19. Anderson M.C., Norman J.M., Kustas W.P., Houborg R., Starks P.J., Agam N.: A thermal‑based remote sensing technique for routine mapping of land‑surface carbon, water and energy fluxes from field to regional scales. Remote Sensing of Environment, vol. 112, 2008, pp. 4227–4241. https://doi.org/10.1016/j.rse.2008.07.009.
  20. Brunsell N.A., Gillies R.R.: Length scale analysis of surface energy fluxes derived from remote sensing. Journal of Hydrometeorology, vol. 4, 2002, pp. 1212–1219.
  21. Smith T.M., Reynolds R.W., Peterson T.C., Lawrimore J.: Improvements to NOAA’s Historical Merged Land–Ocean Surface Temperature Analysis (1880–2006). Journal of Climate, vol. 21, issue 10, 2008, pp. 2283–2296. https://doi.org/10.1175/2007JCLI2100.1.
  22. Maithani S., Nautiyal G., Sharma A.: Investigating the Effect of Lockdown During COVID-19 on Land Surface Temperature: Study of Dehradun City, India. Journal of the Indian Society of Remote Sensing, vol. 48, 2020, pp. 1297–1311. https://doi.org/10.1007/s12524-020-01157-w.
  23. Hadibasyir H.Z., Rijal S.S., Sari D.R.: Comparison of Land Surface Temperature During and Before the Emergence of Covid‑19 using Modis Imagery in Wuhan City, China. Forum Geografi, vol. 34, issue 1, 2020. https://doi.org/10.23917/forgeo.v34i1.10862.
  24. González‑Márquez L.C., Torres‑Bejarano F.M., Torregroza‑Espinosa A.C., Hansen‑Rodríguez I.R., Rodríguez‑Gallegos H.B.: Use of LANDSAT 8 images for depth and water quality assessment of El Guájaro reservoir, Colombia. Jour‑ nal of South American Earth Sciences, vol. 82, 2018, pp. 231–238. https://doi. org/10.1016/j.jsames.2018.01.004.
  25. USGS: Using the USGS Landsat Level‑1 Data Product. https://www.usgs.gov/core-science-systems/nli/landsat/using-usgs-landsat-level-1-data-product [access: 1.02.2020].
  26. Latif M.S.: Land Surface temperature retrival of Landsat‑8 data using split window algorithm – a case study of Ranchi district. International Journal of Engineering Development and Research, vol. 2, issue 4, 2014, pp. 3840–3849.
  27. X H.-Q.,Chen B.-Q.: Remote sensing of the urban heat island and its changes in Xiamen City of SE China. Journal of Environmental Sciences, vol. 16, issue 2, 2004, pp. 276–281.
  28. Jimenez‑Munoz J.C., Sobrino J.A., Gillespie A., Sabol D., Gustafson W.T.: Improved land surface emissivities over agricultural areas using ASTER NDVI. Remote Sensing of Environment, vol. 103, issue 4, 2006, pp. 474–487.
  29. Carlson T., Ripley D.: On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sensing of Environment, vol. 62, issue 3, 1997, pp. 241–252. https://doi.org/10.1016/S0034-4257(97)00104-1.
  30. Stathopoulou M., Cartalis C.: Daytime urban heat islands from Landsat ETM+ and Corine land cover data: an application to major cities in Greece. Solar Energy, vol. 81, issue 3, 2007, pp. 358–368.
  31. Bouhrara I.: Etat d’urgence sanitaire 3: Casablanca, unereprise en douce. 2020. https://www.ecoactu.ma/etat-durgence-sanitaire-3-casablanca-reprise/ [ac‑ cess: 5.12.2020].
  32. Karimi A., Pahlavani P., Bigdeli B.: Land use analysis on Land Surface Temperature in urban areas using a geographically weighted regression and Landsat 8 imagery, a case study: Tehran, Iran. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XLII-4/W4, 2017, pp. 117–122. https://doi.org/10.5194/isprs-archives-XLII-4-W4-117-2017.
  33. Bahi H., Rhinane H., Bensalmia A., Fehrenbach U., Scherer D.: Effects of Urbanization and Seasonal Cycle on the Surface Urban Heat Island Patterns in the CoastalGrowing Cities: A Case Study of Casablanca, Morocco. Remote Sensing, vol. 8, issue 829, 2016, art. no. 829. https://doi.org/10.3390/rs8100829.
  34. Li L., Tan Y., Ying S., Yu Z., Li Z., Lan H.: Impact of land cover and population density on land surface temperature: case study in Wuhan, China. Journal of Applied Remote Sensing, vol. 8, issue 1, 2014, art. no. 084993. https://doi.org/10.1117/1.JRS.8.084993.
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