Alan kullanım/arazi örtüsü ve bitki örtüsündeki değişimin arazi yüzey sıcaklığına etkisinin değerlendirilmesi: Aydın ili örneği

Abstract

Dünya genelinde nüfusun artması, kentsel alanlarda bina, yol vb. gibi geçirimsiz yüzeylerin artmasını tetikleyerek iklim değişikliğinin yanısıra ekosistemler, çevre ve insanlar üzerinde birçok olumsuz etkiyeye neden olmaktadır. Bu nedenle kentsel iklim araştırmaları önemli araştırma konularından biri olmaya başlamıştır. Buna bağlı olarak, farklı alan kullanımı / arazi örtüsü (AKAÖ) türlerinin ve bitki örtüsünün kentsel ısı adası (KIA) oluşumu üzerindeki etkilerinin nicel analizleri, kentsel planlama çalışmalarında büyük önem taşımaktadır. KIA oluşumu genel olarak arazi yüzey sıcaklığı (AYS) ile ölçülmektedir, bitki örtüsü ise havadan veya uydudan termal kızılötesi uzaktan algılama kullanılarak normalize edilmiş farksal bitki indeksi (NDVI) ile karakterize edilmektedir. Bu bağlamda, bu araştırma Aydın ilinde 1990-2017 yılları arasında AKAÖ, NDVI ve AYS'deki değişikliklerin etkilerini incelemeyi amaçlamaktadır. Bu makalede, AKAÖ ve NDVI'ın AYS üzerindeki etkilerini ölçmek için uzaktan algılama ve coğrafi bilgi sistemi tabanlı çeşitli nicel analiz yöntemleri kullanılmıştır. Bu bağlamda, Aydın ilinde yaz dönemi için 1990 (Landsat 5) ve 2017 (Landsat 8) yılına ait toplam on bulutsuz görüntü ile CORINE arazi örtüsü veri seti analizlerin ana materyallerini oluşturulmuştur. Araştırma sonucu, AKAÖ'deki bitki örtüsü yoğunluğunun/varlığının AYS üzerinde en güçlü etkiye sahip olduğunu göstermştir. Öte yandan, bitki örtüsü ve suyun varlığının (örneğin orman ve su ve sulak alanlar) hava sıcaklığının en yüksek olduğu durumda bile bu etkiyi azalttığı tespit edilmiştir.

Keywords

• Kentsel ısı adası
• Arazi yüzey sıcaklığı
• Landsat
• NDVI
• AKAÖ

Evaluation of the effect of land use / land cover and vegetation cover change on land surface temperature: The case of Aydın province

Abstract

Rising population in the world triggered the increase of artificial surfaces like buildings, roads, etc in urban areas and has many negative impacts on climate change, ecosystems, environment, and people. Accordingly urban climatical research has been a significant research topic. Therefore, quantitative analyses of the impact of different land use / land cover (LULC) types and vegetation cover on the urban heat island (UHI) is very essential for urban planning. The UHI formation is generally measured by land surface temperature (LST), vegetation cover is characterized by Normalized Different Vegetation Index (NDVI) through the use of airborne or satellite thermal infrared remote sensing. In this context, this research aims to analyse the effects of changes in LULC, NDVI and LST between the years of 1990 and 2017 in Aydın, Turkey. In this paper, various quantitative analysis methods based on remote sensing and geographic information system have been used to measure the effects of LULC and NDVI on AYS. In this respect, the main materials of the analyses are composed of ten cloud free Landsat 5 in 1990 and Landsat 8 images in 2017 for the summer period in Aydın as well as the CORINE land cover data set. The results showed that decreasing vegetation cover in LULC had the strongest influence on the LST. On the other hand, the presence of vegetation and water especially woody patches (e.g. forest and water and wetlands) reduced this effect even the air temperature was highest.

Keywords

• Urban heat island
• Land surface temperature
• Landsat
• NDVI
• LULC

References

1. Amiri, R., Weng, Q., Alimohammadi, A., Alavipanah, S.K., 2009. Spatial–temporal dynamics of land surface temperature in relation to fractional vegetation cover and land use/cover in the Tabriz urban area, Iran. Remote Sensing of Environment, 113(12): 2606-2617.
2. Alexandri, E., Jones, P., 2008. Temperature decrease in an urban canyon due to green walls and green roofs in di- verse climates. Building and Environment, 43: 480- 493.
3. Aygün, C., Sever, A.L., Kara, İ., Erdoğdu, İ., Atalay, A.K., 2016. Eskişehir meralarında otlatmanın planlamasında NDVI verilerinin kullanılması. Tarla Bitkileri Merkez Araştırma Enstitüsü Dergisi, 25(1): 66-77.
4. Barsi J.A., Schott J.R., Palluconi F.D., Hook S.J., 2005. Validation of a web-based atmospheric correction tool for single thermal band instruments. International Society for Optics and Photonics, 2005, San Diego, California, United States, pp. 58820.
5. Cao, X., Onishi, A., Chen, J., Imura, H., 2010. Quantifying the cool island intensity of urban parks using ASTER and IKONOS data. Landscape and Urban Planning, 96(4): 224-231.
6. Chander, G., Markham, B., 2003. Revised Landsat-5 TM radiometric calibration procedures and postcalibration dynamic ranges. IEEE Transactions on Geoscience and Remote Sensing, 41(11): 2674-2677.
7. Chen, A., Yao, X.A., Sun, R., Chen, L., 2014. Effect of urban green patterns on surface urban cool islands and its seasonal variations. Urban Forestry and Urban Greening, 13: 646–654.
8. Chen, X.L., Zhao, H.M., Li, P.X., Yin, Z.Y., 2006. Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes. Remote Sensing of Environment, 104(2): 133-146.

9. Chudnovsky, A., Ben-Dor, E., Saaroni, H., 2004. Diurnal thermal behavior of selected urban objects using remote sensing measurements. Energy and Buildings, 36(11): 1063-1074.
10. CLMS, 2020. Copernicus Land Monitoring Service (CLMS) Corine Land cover 2020. https://land.copernicus.eu/pan-european/corine-land-cover, Accessed: 15 June 2020.
11. Doğan, S., Tüzer, M., 2011. Küresel iklim değişikliği ve potansiyel etkileri. Cumhuriyet Üniversitesi İktisadi ve İdari Bilimler Dergisi, 12(1): 21-34.
12. Du, H., Wang, D., Wang, Y., Zhao, X., Qin, F., Jiang, H., Cai, Y., 2016. Influences of land cover types, meteorological conditions, anthropogenic heat and urban area on surface urban heat island in the Yangtze River Delta Urban Agglomeration. Science of the Total Environment, 571: 461-470.
13. Du, H., Cai, W., Xu, Y., Wang, Z., Wang, Y., Cai, Y., 2017. Quantifying the cool island effects of urban green spaces using remote sensing Data. Urban Forestry and Urban Greening, 27: 24-31.
14. EPA, 2012. Measuring Heat Islands, State and Local Climate and Energy Program, Heat Island Notes. Environmental Protection Agency. https://www.epa.gov/heatislands/measuring-heat-islands. Accessed: 22.06.2019.
15. Ersoy Tonyaloğlu, E., 2019. Kentleşmenin kentsel termal çevre üzerindeki etkisinin değerlendirilmesi, efeler ve İncirliova (Aydın) örneği. Türkiye Peyzaj Araştırmaları Dergisi, 2(1): 1-13.
16. Farina, A., 2012. Exploring the relationship between land surface temperature and vegetation abundance for urban heat island mitigation in Seville, Spain. Luma-Gis Thesis. Department of Physical Geography and Ecosystem Analysis Centre for Geographical Information Systems of Lund University, Lund, Sweden.
17. Fortuniak, K., 2009. Global environmental change and urban climate in central European cities. International Conference on Climate Change The environmental and socio-economic response in the southern Baltic region, 25 - 28 May 2009, University of Szczecin, Poland, pp 65-67.
18. Huang, L., Zhao, D., Wang, J., Zhu, J., Li, J., 2008. Scale impacts of land cover and vegetation corridors on urban thermal behavior in Nanjing, China. Theoretical and Applied Climatology, 94(3-4): 241-257.
19. Jennings, D.B., Jarnagin, S.T., Ebert, D.W., 2004. A modeling approach for estimating watershed impervious surface area from National Land Cover Data 92. Photogrammetric Engineering & Remote Sensing, 70(11): 1295-1307.
20. Julien, Y., Sobrino, J.A., Verhoef, W., 2006. Changes in land surface temperatures and NDVI values over Europe between 1982 and 1999. Remote Sensing of Environment, 103(1): 43-55.
21. Kaplan, G., Avdan, U., Avdan, Z.Y., 2018. Urban Heat Island Analysis Using the Landsat 8 Satellite Data: A Case Study in Skopje, Macedonia. In: Multidisciplinary Digital Publishing Institute, Proceedings, 2 (7): 358.
22. Karnieli, A., Agam, N., Pinker, R.T., Anderson, M., Imhoff, M.L., Gutman, G.G., Goldberg, A., 2010. Use of NDVI and land surface temperature for drought assessment: Merits and limitations. Journal of Climate, 23(3): 618-633.
23. Klein, P.M., Coffman, R., 2015. Establishment and performance of an experimental green roof under extreme climatic conditions. Science of the Total Environment, 512: 82-93.
24. Magee, N., Curtis, J., Wendler, G., 1999. The urban heat island effect at Fairbanks, Alaska. Theoretical and Applied Climatology, 64(1-2): 39-47.
25. MGM, 2020. Meteorolojı Genel Müdürlüğü 2020. https://www. mgm.gov.tr/veridegerlendirme/il-ve-ilceler-istatistik.aspx?m=AYDIN, Erişim: 10.08.2020.
26. Oguz, H., 2013. LST Calculator: a program retrieving land surface temperature from Landsat TM/ ETM+ Imagery. Environmental Engineering and Management Journal, 12(3): 549–555
27. Oke, T.R., 1973. City size and the urban heat island. Atmospheric Environment, 7(8): 769-779.
28. Önder, S., Akay, A., 2014. The roles of plants on mitigating the urban heat islands' negative effects. International Journal of Agriculture and Economic Development, 2(2): 18.
29. Pal, S., Ziaul, S.K., 2017. Detection of land use and land cover change and land surface temperature in English Bazar urban centre. The Egyptian Journal of Remote Sensing and Space Science, 20(1): 125-145.
30. Saaroni, H., Ziv, B., 2003. The impact of a small lake on heat stress in a Mediterranean urban park: The case of Tel Aviv, Israel. International journal of Biometeorology, 47(3): 156-165.
31. Sobrino, J.A., Jimenez-Munoz, J.C., Paolini, L., 2004. Land surface temperature retrieval from LANDSAT TM 5. Remote Sensing of Environment, 90(4): 434-440.
32. Stone, B., Norman, J.M., 2006. Land use planning and surface heat island formation: A parcel-based radiation flux approach. Atmospheric Environment, 40(19): 3561-3573.
33. Streutker, D.R., 2003. A study of the urban heat island of Houston, Texas. PhD dissertation, Rice University, Houston, Texas, USA.
34. TÜİK, 2019. Turkish Statistical Institute. http://www.tuik.gov.tr/ PreTablo.do?alt_id=1047, Accessed: 15 February 2019.
35. Türkeş, M., 2008. Küresel iklim değişikliği nedir? Temel kavramlar, nedenleri, gözlenen ve öngörülen değişiklikler. İklim Değişikliği ve Çevre, 1(1): 26-37.
36. Unger, J., Savić, S., Gál, T., 2011. Modelling of the annual mean urban heat island pattern for planning of representative urban climate station network. Advances in meteorology, 398613: 9.
37. USGS, 2018a. The United States Geological Survey. Revised Landsat-5 TM Radiometric Calibration Procedures and Postcalibration Dynamic Ranges. https://landsat.usgs.gov/ sites/default/files/documents/L5_TM_Cal_2003.pdf, Accessed: 7 July 2018.
38. USGS, 2018b. The United States Geological Survey. Landsat 8 Data Users Handbook - Section 5. https://landsat.usgs.gov/ landsat-8-l8-data-users-handbook-section-5, Accessed: 7 July 2018.
39. USGS, 2019. The United States Geological Survey. EarthExplorer – Home. https://earthexplorer.usgs.gov/, Accessed: 10 June 2019.
40. Voogt, J.A., Oke, T.R., 2003. Thermal remote sensing of urban climates. Remote sensing of environment, 86(3): 370-384.
41. Voogt, J.A., 2004. Urban Heat Island: Hotter Cities. America Institute of Biological Sciences. Action Bioscience, North Port, FL, USA.
42. Weng, Q., Lu, D., Schubring, J., 2004. Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies. Remote Sensing of Environment, 89(4): 467-483.
43. Xian, G., Crane, M., 2006. An analysis of urban thermal characteristics and associated land cover in Tampa Bay and Las Vegas using Landsat satellite data. Remote Sensing of Environment, 104(2): 147-156.
44. Xiao, H., Kopecká, M., Guo, S., Guan, Y., Cai, D., Zhang, C., Yao, W., 2018. Responses of urban land surface temperature on land cover: A comparative study of Vienna and Madrid. Sustainability, 10(2): 260. 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, 106(3): 375-386.
45. Yuan, X., Wang, W., Cui, J., Meng, F., Kurban, A., De Maeyer, P., 2017. Vegetation changes and land surface feedbacks drive shifts in local temperatures over Central Asia. Scientific Reports, 7(1): 1-8.
46. Yue, W., Xu, J., 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): 3205-3226.
47. Zhang, Y., Odeh, I.O., Han, C., 2009. Bi-temporal characterization of land surface temperature in relation to impervious surface area, NDVI and NDBI, using a sub-pixel image analysis. International Journal of Applied Earth Observation and Geoinformation, 11(4): 256-264.