Use of remote sensing as an indicator of the urban heat island effect: the case of the municipality of Guelma (north-east of Algeria)

• Autorzy: Khallef Boubaker

Identyfikatory

DOI

10.15576/GLL/2023.3.61

• Języki publikacji: EN

Abstrakty

EN

The main objective of this study is to show which of the LST-NDVI and LST-NDBI relationships can determine the most accurate index that can be used as an indicator of the effects of urban heat islands in the municipality of Guelma, using Landsat data. 8 OLI/TIRS and the geographic information system. The application of the calculation formulas made it possible to extract the Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built up Index (NDBI) of the municipality of Guelma for the four seasons of 2019. This calculation led to the determination of the relationship between all three indicators. The results obtained show a strong correlation between the LST and the NDBI for the four seasons of the year. They suggest that the NDBI is an accurate indicator of the heat island effect in Guelma. This indicator can serve as a tool for future urban planning by those in charge of this department. However, there is currently and urgent need to strengthen strategies for reducing the effects of urban heat islands in order to preserve the quality of urban life of the inhabitants and by setting up emergency programs.

Słowa kluczowe

EN

Landsat 8; LST; land surface temperature; NDVI; normalized difference vegetation index; NDBI; Guelma; Algeria

PL

Landsat 8; LST; temperatura powierzchni ziemi; NDVI; znormalizowany różnicowy wskaźnik wegetacji; Guelma; Kalima; Algieria

• Wydawca: Wydawnictwo Uniwersytetu Rolniczego w Krakowie
• Czasopismo: Geomatics, Landmanagement and Landscape
• Rocznik: 2023
• Tom: no. 3
• Strony: 61--72
• Opis fizyczny: Bibliogr. 23 poz., rys., tab.

Twórcy

autor

Khallef Boubaker

• Department of Earth Sciences Institute of Architecture and Earth Sciences University Abbas Ferhat, Algeria

• https://orcid.org/0000-0001-5144-8567

Bibliografia

• 1. Bhatti S.S., Tripathi K.N. 2014. Built-up area extraction using Landsat 8 OLI imagery. GIScience & Remote Sensing, 51(4), 445–467. http://dx.doi.org/10.1080/15481603.2014.939539.
• 2. Carlson T.N., Ripley D.A. 1973. On the Relation between NDVI, Fractional Vegetation, Cover and Leaf Area Index. Remote Sensing of Environment, 62, 241–252, 1997. http://dx.doi.org/10.1016/S0034-4257 (97)00104-1.
• 3. Khalaf A. 2016. Utilization of thermal bands of Landsat 8 data and geographic information system for analysis of urban heat island in Baghdad governorate. MATEC Web of Conferences, 162, 03026 (2018), P 5. The 3rd International Conference on Buildings, Construction and Environmental Engineering. https://doi.org/10.1051/matecconf/201816203026
• 4. Khalid N.J. 2014. Urban Heat Island in Erbil City. Master degree thesis, 30 credits in Physical Geography Ecosystem Analysis. Department of Physical Geography and Ecosystems Science, Lund University. 57 P, 2014. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.830.1644&rep=pdf
• 5. Khallef B., Biskri Y., Mouchara N., Brahamia K. 2020. Analysis of Urban Heat Islands Using Landsat 8 OLI/ TIRS Data: Case of the City of Guelma (Algeria). Asian Journal of Environment & Ecology, 12(4), 42–51. Article AJEE.57316. https://doi.org/10.9734/AJEE/2020/v12i430167
• 6. Khallef B., Brahamia K., Oularbi A. 2020. Application of remote sensing indexes to the mapping of urban areas and bare soil: Case of the city of Guelma (North-East of Algeria). International Journal of Innovation and Applied Studies, 28, 2, 452–457. http://www.ijias.issrjournals.org/abstract.php?article=IJIAS-19-306-02
• 7. Kleerekoper L., Van Esch M., Salcedo T.B. 2011. How to make a city climate-proof, addressing the urban heat island effect. Resour. Conserv. Recycl., 64, 30–38, 2012. https://doi.org/10.1016/j.resconrec.2011.06.004.
• 8. Liu L., Zhang Y. 2011. Urban heat island analysis using the Landsat TM data and ASTER data: A case study in Hong Kong. Remote Sensing, 3, 1535–1552. https://doi.org/10.3390/rs3071535
• 9. Macarof P., Statescu F. 2017. Comparison of NDBI and NDVI as indicators of surface urban heat island effect in Landsat 8 imagery: a case study of Iasi. PESD, 11, 2. https://doi.org/10.1515/pesd-2017-0032.
• 10. Orhan O., Ekercin S., Dadaser-Celik F.2014. Use of Landsat Land Surface Temperature and Vegetation Indices for Monitoring Drought in the Salt Lake Basin Area. The Scientific World Journal. Article ID 142939. https://doi.org/10.1155/2014/142939
• 11. Peyrache-Gadeau V., Pecqueur B. 2011. Villes durables et changement climatique: quelques enjeux sur le renouvellement des „ressources urbaines”. Environnement Urbain / Urban Environment, 5. http://journals.openedition.org/eue/723.
• 12. Rouse J.W., Hass R.H.,. Schell J.A., Deering D.W. 1973 . Monitoring vegetation systems in the Great Plains with ERTS. Proceedings of the third ERTS symposium, Goddard Space Flight Center, December, NASA SP-351, 309−317. Washington, DC: NASA. https://ntrs.nasa.gov/citations/19740022614
• 13. Sun D., Kafatos M. 2007. Note on the NDVI‐LST relationship and the use of temperature‐related drought indices over North America. Geophysical Research Letters, 34, 24, December https://doi.org/10.1029/2007GL031485.
• 14. Tsou J., Zhuan G., Li Y., Zhang Y. 2017.Urban Heat Island Assessment Using the Landsat 8 Data: A Case Study in Shenzhen and Hong Kong. Urban Sci., 1, 10 https://doi.org/10.3390/urbansci1010010.
• 15. USGS. 2019. Using the USGS Landsat Level-1 Data Product. www.usgs.gov/land-resources/nli/landsat/using-usgs-landsat- level-1-data-product.
• 16. Valette E., Cordeau E. 2010 Les îlots de chaleur urbains. Répertoire de fiches connaissance. Département Environnement Urbain et Rural. IAU Île-de-France. https://www.iauidf.fr/fileadmin/NewEtudes/Etude_774/Les_ilots_de_chaleur_urbains_REPERTOIRE.pdf
• 17. Wang J., Yan Z., Quan X.W., Feng J. 2016. Urban warming in the 2013 summer heat wave in eastern China. Climate Dynamics, 48, 3015–3033. https://doi.org/10.1007/s00382-016-3248-7.
• 18. Weng Q., Lu D., Schubring J. 2003. Estimation of Land Surface Temperature Vegetation Abundance Relationship for Urban Heat Island Studies. Remote Sensing of Environment, 89, 467–483. https://doi.org/10.1016/j.rse.2003.11.005
• 19. Yuan F., Bauer M.E. 2007. Comparison of impervious surface area and normalized difference vegetation index as indicators of surface Urban Heat Island effects in Landsat Imagery. Remote Sensing of Environment, 06674. https://doi.org/10.1016/j.rse.2006.09.003
• 20. Yuan X., Li L., Chen X., Shi H. 2015. Effects of Precipitation Intensity and Temperature on NDVI-Based Grass Change over Northern China during the Period from 1982 to 2011 Remote Sens.,7(8), 10164–10183. https://doi.org/10.3390/rs70810164.
• 21. Yue W., Tan W., Xu L. 2007. The relationship between land surface temperature and NDVI with remote sensing: application to Shanghai Landsat 7 ETM+ data. International Journal of Remote Sensing, 28, 15. https://doi.org/10.1080/01431160500306906
• 22. Zha Y., Gao J., Ni S. 2013. Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing, 24(3), 583−594. https://doi.org/10.1080/01431160304987
• 23. Zoca C., Papin O. 2014. Ilot de chaleur urbain : conséquences sur les bâtiments. Bureau d’études ECIC Bordeaux. https://conseils.xpair.com/actualite_experts/ilot-chaleur-urbain-consequences- batiments.htm.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).