Konya'da kentsel ısı adası ve karbon monoksit değişiminin Google Earth Engine kullanılarak incelenmesi

Yıl 2023, Cilt: 12 Sayı: 4, 1185 - 1193, 15.10.2023

Duygu Arıkan , Ferruh Yıldız

https://doi.org/10.28948/ngumuh.1279129

Öz

Nüfusun artması kentlerde ve çevresinde değişiklikler meydana getirmektedir. Bu duruma bağlı olarak kentlerdeki ısı miktarı ve hava kirliliğinin kırsal alana göre daha fazla olduğu gözlemlenmektedir. Kentsel ısı adası insanın yaşam kalitesini etkileyen bir faktördür. Bu sebeple ısı değişikliklerinin izlenmesi, bölgesel olarak tedbir ve önlemlerin alınması gerekmektedir. Bu çalışmada, 2019 – 2021 yılları arasında Landsat 8 uydusu kullanılarak kentsel ısı adası ve Sentinel-5P uydusu kullanılarak karbonmonoksit (CO) durumu belirlenmiştir. Çalışma bölgesi olarak Konya’nın merkez ilçeleri seçilmiştir. Kentsel ısı adasını (KIA) belirlemek için her mevsime ait toplam 12 adet Landsat 8 uydu görüntüsünün termal bandı kullanılarak yer yüzey sıcaklığı (YYS) haritaları oluşturulmuştur. Ayrıca aynı bölge için Sentinel-5P uydusu kullanılarak on iki aya ait 36 adet CO haritası oluşturulmuştur. Oluşturulan haritalar değerlendirildiğinde sıcaklık ve CO arasında anlamlı bir sonuç olduğu belirlenmiştir. YYS sıcaklığının fazla olduğu kısımlarda karbonmonoksit miktarının fazla olduğu tespit edilmiştir.

Anahtar Kelimeler

Kentsel ısı adası, Yer yüzey sıcaklığı, Hava kalitesi, Karbon monoksit, Ulusal hava kalitesi izleme istasyonu

Investigation of urban heat island and carbon monoxide change using Google Earth engine in Konya

Öz

The increasing population has been causing changes in and around urban areas. As a result of this situation, it is observed that the amount of heat and air pollution in cities is higher than in rural areas. Urban heat islands (UHI) are a factor that affects people's quality of life. Therefore, monitoring temperature changes and taking regional measures is necessary. In this study, urban heat island (UHI) and carbon monoxide (CO) levels were determined using Landsat 8 satellite for the years 2019-2021 and Sentinel-5P satellite, respectively. The central districts of Konya were selected as the study area. To determine the urban heat island (UHI), surface temperature (ST) maps were created using the thermal band of a total of 12 Landsat 8 satellite images for each season. Additionally, 36 CO maps were generated using the Sentinel-5P satellite for the same region, covering twelve months. Upon evaluation of the generated maps, a significant correlation between temperatures and CO was observed. It was determined that areas with higher surface temperature also exhibited higher levels of carbon monoxide.

Anahtar Kelimeler

Urban heat islands, Land Surface Temperature, Air quality, Carbon monoxide, National air quality monitoring station

Kaynakça

• Ş. Durak, Geleneksel kırsal konutların ekolojik açıdan değerlendirilmesine yönelik bir model önerisi: Yalova örneği. 2021. Doktora Tezi, Kocaeli Üniversitesi Üniversitesi Fen Bilimleri Enstitüsü, Türkiye, 2021.
• Ü.D. Yüksel and O. Yılmaz, Ankara kentinde kentsel isi adasi etkisinin yaz aylarinda uzaktan algilama ve meteorolojik gözlemlere dayali olarak saptanmasi ve değerlendirilmesi. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 2008. 23(4). https://dergipark.org.tr/en/download/article-file/75772
• T.R. Oke, The energetic basis of the urban heat island. Quarterly Journal of the Royal Meteorological Society, 108(455), 1-24, 1982. https://www.patarnott.com/pdf/Oake1982_UHI.pdf
• Ö. Akyürek, Termal uzaktan algılama görüntüleri ile yüzey sıcaklıklarının belirlenmesi: Kocaeli örneği. Doğal Afetler ve Çevre Dergisi, 6(2), 377-390, 2020. https://doi.org/10.21324/dacd.667594
• B. Esin and N.S. Partigöç, İklim Değişikliğine Uyum Sürecinde Kent Planlamanın Rolü. Resilience, 6(1), 127-143, 2022. https://doi.org/10.32569/resilience.1026712
• R.C. Estoque and Y. Murayama, Monitoring surface urban heat island formation in a tropical mountain city using Landsat data (1987–2015). ISPRS Journal of Photogrammetry and Remote Sensing, 133, 18-29, 2017. https://doi.org/10.1016/j.isprsjprs.2017.09.008
• X. Zhang, R.C. Estoque and Y. Murayama, An urban heat island study in Nanchang City, China based on land surface temperature and social-ecological variables. Sustainable cities and society, 32, 557-568, 2017. https://doi.org/10.1016/j.scs.2017.05.005
• X. Zhang, R. C. Estoque, Y. Murayama and M. Ranagalage, Capturing urban heat island formation in a subtropical city of China based on Landsat images: implications for sustainable urban development. Environmental Monitoring and Assessment, 193(3), 1-13, 2021. https://doi.org/10.1007/s10661-021-08890-w
• Turkish Statistical Institute https://data.tuik.gov.tr/Bulten/Index?p=Dunya-Nufus-Gunu-2022-45552 , Accessed 07 December 2022.
• M. Poumadere, C. Mays, S. Le Mer and R. Blong, The 2003 heat wave in France: dangerous climate change here and now. Risk Analysis: an International Journal, 25(6), 1483-1494, 2005. https://doi.org/10.1111/j.1539-6924.2005.00694.x
• W. Zhou, G. Huang and M.L. Cadenasso, Does spatial configuration matter? Understanding the effects of land cover pattern on land surface temperature in urban landscapes. Landscape and urban planning, 102(1), 54-63, 2011. https://doi.org/10.1016/j.landurbplan.2011.03.009
• D. Duarte, Padrões de ocupação do solo e microclimas urbanos na região de clima tropical continental. Pós. Revista do Programa de Pós-Graduação em Arquiteturae Urbanismo da FAUUSP, 9, 88-107, 2001. https://doi.org/10.11606/issn.2317-2762.v0i9p88-107
• C.R.D. Almeida, L. Furst, A. Gonçalves and A. C Teodoro, Remote Sensing Image-Based Analysis of the Urban Heat Island Effect in Bragança, Portugal. Environments, 9(8), 98, 2022. https://doi.org/10.3390/environments9080098
• T. Mushore, J. Odindi and O. Mutanga, “Cool” Roofs as a Heat-Mitigation Measure in Urban Heat Islands: A Comparative Analysis Using Sentinel 2 and Landsat Data. Remote Sensing, 14(17), 4247, 2022. https://doi.org/10.3390/rs14174247
• Y.J. Choe and J.H. Yom, Improving accuracy of land surface temperature prediction model based on deep-learning. Spatial Information Research, 28(3), 377-382, 2020. https://doi.org/10.1007/s41324-019-00299-5
• C. Ketterer and A. Matzarakis, Comparison of different methods for the assessment of the urban heat island in Stuttgart, Germany. International journal of biometeorology, 59(9), 1299-1309, 2015. https://doi.org/10.1007/s00484-014-0940-3
• O. Adeyeri, A. Akinsanola and K. Ishola, Investigating surface urban heat island characteristics over Abuja, Nigeria: Relationship between land surface temperature and multiple vegetation indices. Remote Sensing Applications: Society and Environment, 7, 57-68, 2017. https://doi.org/10.1016/j.rsase.2017.06.005
• Ü. Güler and K. Kalkan, Sentinel-3 Verileri ile Aktif Yangın Tespiti ve Sentinel-2 Verileri ile Doğrulanması. Turkish Journal of Remote Sensing and GIS, 3(2), 86-97, 2022. https://doi.org/10.48123/rsgis.1095460
• Q. Zhao, L. Yu, X. Li, D. Peng, Y. Zhang and P. Gong, Progress and trends in the application of Google Earth and Google Earth Engine. Remote Sensing, 13(18), 3778, 2021. https://doi.org/10.3390/rs13183778
• A. Midekisa, F. Holl, D. J. Savory, R. Andrade-Pacheco, P. W. Gething, A. Bennett and H. J. Sturrock, Mapping land cover change over continental Africa using Landsat and Google Earth Engine cloud computing. PloS one, 12(9), e0184926, 2017. https://doi.org/10.1371/journal.pone.0184926
• B. DeVries, C. Huang, J. Armston, W. Huang, J. W. Jones and M. W. Lang, Rapid and robust monitoring of flood events using Sentinel-1 and Landsat data on the Google Earth Engine. Remote Sensing of Environment, 240, 111664, 2020. https://doi.org/10.1016/j.rse.2020.111664
• R. Goldblatt, W. You, G. Hanson and A.K. Khandelwal, Detecting the boundaries of urban areas in india: A dataset for pixel-based image classification in google earth engine. Remote Sensing, 8(8), 634, 2016. https://doi.org/10.3390/rs8080634
• J. Xiong, P. S. Thenkabail, J. C. Tilton, M. K. Gumma, P. Teluguntla, A. Oliphant and N. Gorelick, Nominal 30-m cropland extent map of continental Africa by integrating pixel-based and object-based algorithms using Sentinel-2 and Landsat-8 data on Google Earth Engine. Remote Sensing, 9(10), 1065, 2017. :https://doi.org/10.3390/rs9101065
• D. Arikan and F. Yildiz, Investigation of Antalya forest fire's impact on air quality by satellite images using Google earth engine. Remote Sensing Applications: Society and Environment, 100922, 2023. https://doi.org/10.1016/j.rsase.2023.100922
• J.A. Sobrino, R. Oltra-Carrió, G. Sòria, J. C. Jiménez-Muñoz, B. Franch, V. Hidalgo and M. Paganini, Evaluation of the surface urban heat island effect in the city of Madrid by thermal remote sensing. International journal of remote sensing, 34(9-10), 3177-3192, 2013. https://doi.org/10.1080/01431161.2012.716548
• S. Çobanyıldız, Konya'da şehirleşmenin sıcaklık ve yağış üzerine etkisi. Yüksek Lisans Tezi, Necmettin Erbakan Üniversitesi Fen Bilimleri Enstitüsü, Türkiye, 2016.
• Ş. Yaman and E.T. Görmüş, Orman Zararlılarının Verdiği Zararın Google Earth Engine Kullanılarak İzlenmesi. Turkish Journal of Remote Sensing and GIS, 3(2), 139-149, 2022. https://doi.org/10.48123/rsgis.1116907
• O.S. Yılmaz, M. S. Oruç, A. M. Ateş and F. Gülgen, Orman Yangın Şiddetinin Google Earth Engine ve Coğrafi Bilgi Sistemleri Kullanarak Analizi: Hatay-Belen Örneği. Journal of the Institute of Science and Technology, 11(2), 1519-1532, 2021. https://doi.org/10.21597/jist.817900
• N. You and J. Dong, Examining earliest identifiable timing of crops using all available Sentinel 1/2 imagery and Google Earth Engine. ISPRS Journal of Photogrammetry and Remote Sensing, 161, 109-123, 2020. https://doi.org/10.1016/j.isprsjprs.2020.01.001
• N. Aslan, Landsat uydu görüntülerinden kentsel ısı adalarının belirlenmesi: Batı Akdeniz Bölgesi örneği. 2016. Yüksek Lisans Tezi, Akdeniz Üniversitesi Fen Bilimleri Enstitüsü, Türkiye, 2016.
• Landsat Satellite 8 Information. https://www.usgs.gov/landsat-missions/landsat-8?qt-science_support_page_related_con=0#qt-science_support_page_related_con , Accessed 07 December 2022.
• R. Yunita, A. Wibowo and A. Rais. Urban Heat Island Mitigation Strategy based on Local Climate Zone Classification using Landsat 8 satellite imagery. in IOP Conference Series: Earth and Environmental Science. 2022. IOP Publishing. https://doi.org/10.1088/1755-1315/1039/1/012013
• S.S. Arifin, B. Hamzah, R. Mulyadi and A. R. Rasyid, Effects of Vegetation on Urban Heat Island Using Landsat 8 OLI/TIRS Imagery in Tropical Urban Climate, 10, 395-405, 2022. https://doi.org/10.13189/cea.2022.100134
• C. Xu, G. Chen, Q. Huang, M. Su, Q. Rong, W. Yue and D. Haase, Can improving the spatial equity of urban green space mitigate the effect of urban heat islands? An empirical study. Science of The Total Environment, 841, 156687, 2022. https://doi.org/10.1016/j.scitotenv.2022.156687
• X. Yu, X. Guo and Z. Wu, Land surface temperature retrieval from Landsat 8 TIRS—Comparison between radiative transfer equation-based method, split window algorithm and single channel method. Remote sensing, 6(10), 9829-9852, 2014. https://doi.org/10.1016/j.isprsjprs.2020.01.001
• M. Jin, J. Li, C. Wang and R. Shang, A practical split-window algorithm for retrieving land surface temperature from Landsat-8 data and a case study of an urban area in China. Remote sensing, 7(4), 4371-4390, 2015. https://doi.org/10.3390/rs70404371
• A. Sekertekin, S.H. Kutoglu and S. Kaya, Evaluation of spatio-temporal variability in Land Surface Temperature: A case study of Zonguldak, Turkey. Environmental monitoring and assessment, 188(1), 1-15, 2016. https://doi.org/10.1007/s10661-015-5032-2
• F. Wang, Z. Qin, C. Song, L. Tu, A. Karnieli and S. Zhao, An improved mono-window algorithm for land surface temperature retrieval from Landsat 8 thermal infrared sensor data. Remote sensing, 7(4), 4268-4289, 2015. https://doi.org/10.3390/rs70404268
• D. Arıkan and F. Yıldız, Türkiye’de COVID-19 döneminde NO2 emisyonunun analizi. Türkiye Ulusal Fotogrametri ve Uzaktan Algılama Birliği, sayfa 6-9, Mersin, Türkiye, 12-14 Mayıs 2022.
• N. Polat, Mardin ilinde uzun yıllar yer yüzey sıcaklığı değişiminin incelenmesi. Türkiye Uzaktan Algılama Dergisi, 2(1), 10-15, 2020. https://dergipark.org.tr/en/pub/tuzal/issue/52699/649526
• J. Zhang, Y. Wang and Y. Li, A C++ program for retrieving land surface temperature from the data of Landsat TM/ETM+ band6. Computers & geosciences, 32(10), 1796-1805, 2006. https://doi.org/10.1016/j.cageo.2006.05.001
• J.W. Rouse Jr, R. H. Haas, D. W. Deering, J. A. Schell and J. C. Harlan, Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation. 1974. https://ntrs.nasa.gov/api/citations/19750020419/downloads/19750020419.pdf
• N. Çağlayan, Seralar için led lambalı aydınlatma otomasyon sisteminin tasarlanmasına ve uygulanmasına yönelik bir çalışma. 2013.
• K.J. McCree, Test of current definitions of photosynthetically active radiation against leaf photosynthesis data. Agricultural meteorology, 10, 443-453, 1972. https://doi.org/10.1016/0002-1571(72)90045-3
• National Air Quality Monitoring Network. http://sim.csb.gov.tr/ , Accessed 09 December 2022.