بررسی جزایر حرارتی شهری کلان‌شهر تبریز با استفاده از داده‌های چند زمانه ماهواره LANDSAT8 مبتنی بر روش تحلیل لکه های داغ

محورهای موضوعی : فصلنامه علمی برنامه ریزی منطقه ای

محمد آزادی مبارکی ¹ , محمود احمدی ²

1 - دانشجوی دکتری آب و هواشناسی شهری، دانشگاه شهید بهشتی، دانشکده علوم زمین، تهران، ایران
2 - دانشیار آب‌وهوا شناسی دانشگاه شهید بهشتی، دانشکده علوم زمین، تهران، ایران

تاریخ دریافت : 1398/10/15 تاریخ پذیرش : 1399/01/23 تاریخ انتشار : 1400/09/01

کلید واژه: تبریز, جزیره حرارتی شهری, شاخص LULC, شاخص*Getis-ord Gi, سنجنده TIRS,

چکیده مقاله :

شهرنشینی سریع در تبریز تأثیر قابل‌توجهی در محیط حرارتی شهری داشته و این تغییرات بر آب‌وهوا، محیط و کیفیت زندگی ساکنان تأثیر گذاشته است. پژوهش حاضر باهدف ارزیابی جزایر حرارتی شهری (UHI) تبریز با استفاده از روش­های خودهمبستگی فضایی و ارتباط آن با پارامترهای فیزیکی سطح انجام شد. برای محاسبه شاخص پوشش گیاهی و دمای سطح زمین از روش­های NDVI و الگوریتم پنجره مجزا (Split Window) بر اساس تصاویر سنجنده­های TIRS و OLI ماهواره Landsat 8 استفاده شد؛ سپس رابطه بین تغییرات LULC، NDVI و دمای سطح زمین (LST) موردبررسی قرار گرفت؛ برای شناسایی UHI از روش­های خودهمبستگی فضایی Moran’s I و Hot Spot استفاده شد. نتایج نشان داد در کلان‌شهر تبریز ارتباط معکوس معنی­دار در سطح 05/0 بین LST و NDVI وجود دارد. کمینه LST محاسباتی 99/11 و بیشینه آن 49/58 درجه سلسیوس به ترتیب در مناطق مرکزی و شمال غربی شهر به‌دست‌آمده است. همچنین ارزیابی دمای سطح زمین با LULC نیز نشان داده است سطوح نفوذناپذیر به همراه بافت فرسوده شهری مهم­ترین دلایل تشدید UHI تبریز هستند. روش خودهمبستگی فضایی Moran’s I نشان داد LST شهر تبریز دارای ساختار فضایی بوده یا به عبارتی دارای الگوی خوشه­ای است و مقدار آن بین 92/0 تا 95/0 متغیر است. UHI تبریز از نوع پیرامونی و مثلثی شکل است که از کانون به جهات بر شدت و وسعت جزایر حرارتی شهری افزوده می­شود. بزرگ‌ترین UHI شناسایی‌شده در منطقه 6 شهری به دلیل استقرار فرودگاه تبریز است، همچنین وجود زمین­های بایر و بافت فرسوده بیشینه LST را دارا می­باشند.

چکیده انگلیسی:

Abstract
Rapid urbanization in Tabriz has a significant impact on the urban thermal environment and these changes have affected the climate, environment and quality of life of residents. The aim of this study was to evaluate the urban heat islands (UHI) of Tabriz using spatial autocorrelation methods and its relationship with physical parameters of the surface. NDVI and Split Window algorithms based on TIRS and OLI Sensor of Landsat 8 satellites were used to calculate the vegetation index and land surface temperature; then the relationship between LULC, NDVI and Land Surface Temperature (LST) changes evaluated. Spatial autocorrelation methods Moran's I and Hot Spot were used to identify UHI. The results showed that in the metropolis of Tabriz, there is a significant inverse relationship between α = 0.05 between LST and NDVI. LST minimum and maximum 11.99 to 58.49 Celsius respectively in central and north-west of the city is obtained. Also, the assessment of land surface temperature with LULC has shown that the impervious surface along with the worn-out urban textures are the most important reasons for the intensification of Tabriz urban heat islands. The Moran's I space spatial autocorrelation method showed that the LST of Tabriz has a spatial structure or in other words has a cluster pattern and its value varies between 0.92 and 0.95. The urban heatislands of Tabriz are of the peripheral and triangular type, which increase the intensity and extent of urban heatislands from the center. The largest identified heatisland is located in District 6 of the city. Due to the existence of Tabriz Airport, as well as the existence of barren lands and worn-out urban textures, this area has the maximum land surface temperature (LST).
 
Keywords: urban heat island, LULC Index Index, Getis-ord Gi* Index, TIRS sensors, Tabriz.

 

ExpandedAbstract
 
Introduction:
Rapid urbanization in Tabriz has a significant impact on the urban thermal environment and these changes have affected the climate, environment and quality of life of residents. The aim of this study was to evaluate the urban heat islands (UHI) of Tabriz using spatial autocorrelation methods and its relationship with physical parameters of the surface. LULC is the interface between human activities and the environment, and it plays an important role in the urban climate because LULC changes are often required to support and implement urban planning policies and promote urban comfort. The increased temperatures in urban areas have created significant economic and health-related issues that affect more than half of the global population. Remote sensing is a powerful tool for environmental monitoring that can be used to help understand LULC and rapid urbanization and to estimate UHI properties at the Earth’s surface because the UHI phenomenon affects many millions of people worldwide. Urban areas cover only 2 to 3 percent of the total land area, however, due to the rapid growth of urbanization around the world, have attracted much attention. The city's thermal city (UHI), which in some way can be called the identity of a metropolitan area in modern times, is a phenomenon that has a higher temperature than surrounding areas. The thermal island phenomenon changes the level of surface energy by changing the natural cover of the earth with pavements, buildings, concrete, asphalt and other buildings. City-level frolics (SUHI) exist at any time of the day, and are more intense in the summer and mid-day. The present study was conducted with the aim of evaluating the thermal heat islands of Tabriz and its correlation with physical parameters of the surface using OLI and TIRS sensor data of LANDSAT8 satellite. The results of this research can be effective for urban management models, sustainable development and urban viability in the future.
Methodology:
NDVI and Split Window algorithms based on TIRS and OLI Sensor of Landsat 8 satellites were used to calculate the vegetation index and land surface temperature; then the relationship between LULC, NDVI and Land Surface Temperature (LST) changes evaluated. Spatial autocorrelation methods Moran's I and Hot Spot were used to identify UHI. The digital values of thermal images converted to spectral radiance to calculate the surface temperature. The NDVI index was used to assess the coverage of the earth's surface and its relationship with the surface temperature of the earth. Finally, the Hot Spot Analysis Index, Getis-ord Gi^* statistic was used to calculate the urban thermal islands.
Results and discussion:
Land surface temperature (LST) and normalized vegetation index (NDVI) in Tabriz, based on LANDSAT8 satellite data, OLI and TIRS sensors were. Accordingly, the average surface temperature In Tabriz during the study period in summer, it fluctuates between 32.12 and 46.41 ºC, respectively, for September and August 2015, the average amount of earth surface temperature was not expected to be unpredictable in winter. The average surface temperature in Tabriz is 49/37 ° C. Maximum deviation rate occurred in August 2015. The relationship between the two parameters studied, Land Temperature (LST) and (NDVI) showed a negative significant correlation at 0.05% level. The maximum correlation with Earth surface temperature in Tabriz is September 2015. The correlation between September and August in the three months of the summer months is stronger than in August. This may be due to rising rainfall in September, which has increased vegetation. There is a great difference in temperature between the northern and northwestern regions and the central regions of Tabriz (regions 9, 21, 22 and 19). Impermeable surfaces such as concrete and asphalt in urban space trap heat at the surface.
Conclusion:
The results showed that LST is sensitive to vegetation. Therefore, it can be concluded that LST can be used to detect LULC changes over time. The population of the metropolis of Tabriz has grown dramatically over the past two decades; the coincidence of this increase in population has certainly led to changes in the LULC, which has led to an increase in impermeable urban areas and the trapping of the temperature and the rise of urban hot springs. Increasing population numbers, especially in developing countries such as Iran, will increase the pressure on natural resources, which will result in widespread change in urban and urban conditions. One of the most important changes in pressure on natural ecosystems the city will change the city's bio-availability. The largest metropolitan thermal island is located in the 6th district. . The correlation coefficient of Pearson and the coefficient of determination (R2) calculated by the linear regression method showed that the relationship between LST and NDVI vegetation index is a significant inverse relationship at the level of α = 0.05. However, it should be noted that this relationship is necessarily is not linear. LST minimum and maximum 11.99 to 58.49 Celsius respectively in central and north-west of the city is obtained. Also, the assessment of land surface temperature with LULC has shown that the impervious surface along with the worn-out urban textures are the most important reasons for the intensification of Tabriz urban heat islands. The Moran's I space spatial autocorrelation method showed that the LST of Tabriz has a spatial structure or in other words has a cluster pattern and its value varies between 0.92 and 0.95. The urban heat islands of Tabriz are of the peripheral and triangular type, which increase the intensity and extent of urban heat islands from the center. The largest identified heat island is located in District 6 of the city. Due to the existence of Tabriz Airport, as well as the existence of barren lands and worn-out urban textures, this area has the maximum land surface temperature (LST).

منابع و مأخذ:

1. Agarwal, M., & Tandon, A (2010). Modeling of the urban heat island in the form of mesoscale wind and of its effect on air pollution dispersal. Applied Mathematical Modelling, 34(9), pp: 2520-2530.


2. Ahmadi, Mahmoud. And Dadashi-Roudbari, Abbasali (2016). “The biophysical effect of compound the formation of urban heat islands (Case Study: Mashhad)”, iranian remote sensing & GIS. 8(3), 39-58.


3. Ahmadi, Mahmoud. And Dadashi-Roudbari, Abbasali (2017). “The Identification of Urban Thermal Islands based on an Environmental Approach, Case Study: Isfahan Province”, Geography and Environmental Planning. 28(3), 1-20.


4. Ahmadi, Mahmoud. And Dadashi-Roudbari, Abbasali, and Esfandiari, Neda (2019). “Monitoring the urban heat islands with a Fractal Net Evolution (FNEA) Approach (Case Study: Tehran Metropolis)”, iranian remote sensing & GIS. 11(1), 95-11.


5. Akbari, H., and Kolokotsa, D (2016). Three decades of urban heat islands and mitigation technologies research. Energy and Buildings, 133, pp: 834-842.


6. Alibakhshi, Z., Ahmadi, M., and Asl, M. F (2020). Modeling Biophysical Variables and Land Surface Temperature Using the GWR Model: Case Study—Tehran and Its Satellite Cities. Journal of the Indian Society of Remote Sensing, 48(1), pp: 59-70.


7. Almusaed, A (2011): The urban heat island phenomenon upon urban components. In Biophilic and Bioclimatic Architecture (pp. 139-150). Springer, London.


8. Buyantuyev, A., & Wu, J (2010). Urban heat islands and landscape heterogeneity: linking spatiotemporal variations in surface temperatures to land-cover and socioeconomic patterns. Landscape ecology, 25(1), pp: 17-33.


9. Dadashi-roudbari, Abbasali. And aliabadi, Kazem (2017). “The Role of Geographic Components on the Temperature Dispersion at Urban Area Using Remote Sensing Techniques Case Study of Mashhad City”, Geographical Planning of Space, 7(24), pp: 131-142.


10. Fallah Ghalhari, G. F., Dadashi-Roudbari, A. D., And Asadi, M (2016). Identifying the spatial and temporal distribution characteristics of precipitation in Iran. Arabian Journal of Geosciences, 9(12), pp: 595.


11. Feng, X., And Myint, S. W (2016). Exploring the effect of neighboring land cover pattern on land surface temperature of central building objects. Building and Environment, 95, pp: 346-354.


12. Feng, Y., GAO, C., Tong, X., Chen, S., Lei, Z., and Wang, J (2019). Spatial Patterns of Land Surface Temperature and Their Influencing Factors: A Case Study in Suzhou, China. Remote Sensing, 11(2), pp: 182.


13. Fischer, M. M., and Getis, a (2009). Handbook of applied spatial analysis: software tools, methods and applications. Springer Science & Business Media.


14. Guhathakurta, S., and Gober, P (2007). The impact of the Phoenix urban heat island on residential water use. Journal of the American Planning Association, 73(3), pp: 317-329.


15. Guo, A., Yang, J., Xiao, X., Xia, J., Jin, C., and Li, X (2020). Influences of urban spatial form on urban heat island effects at the community level in China. Sustainable Cities and Society, 53, 101972.


16. Huang, Q., and Lu, Y (2015). The effect of urban heat island on climate warming in the Yangtze River Delta urban agglomeration in China. International journal of environmental research and public health, 12(8), pp: 8773-8789.


17. Kayet, N., Pathak, K., Chakrabarty, A., and Sahoo, S (2016). Spatial impact of land use/land cover change on surface temperature distribution in Saranda Forest, Jharkhand. Modeling Earth Systems and Environment, 2(3), pp: 127.


18. Lilly Rose, A., & Devadas, M. D (2009, June). ANALYSIS OF LAND SURFACE TEMPERATURE AND LAND USE/LAND COVER TYPES USING REMOTE SENSING IMAGERY-A CASE IN CHENNAI CITY, INDIA. In The seventh International Conference on Urban Clim held on (Vol. 29).


19. Liu, H., Zhan, Q., Chen, Y., and Wang, J (2018). Characterizing the Spatio-Temporal Pattern of Land Surface Temperature through Time Series Clustering: Based on the Latent Pattern and Morphology. Remote Sensing, 10(4), pp: 654.


20. Liu, L., and Zhang, Y (2011). Urban heat island analysis using the Landsat TM data and ASTER data: A case study in Hong Kong. Remote Sensing, 3(7), pp: 1535-1552.


21. Mallick, J., Kant, Y., and Bharath, B. D (2008). Estimation of land surface temperature over Delhi using Landsat-7 ETM+. J. Ind. Geophys. Union, 12(3), pp: 131-140.


22. Metz, M., Andreo, V., And Neteler, M. (2017). A New Fully Gap-Free Time Series of Land Surface Temperature from MODIS LST Data. Remote sensing, 9(12), 1333.


23. mousavi, Mirnajaf., Mohamadi Torkamani, Hojjat., Khedmatzadeh, Ali., Mohammadi, and Mir sadegh. (2020). “An Analysis of Land Use Changes and Thermal Island Formation in Urmia City exclusion Using Remote Sensing”, Regional Planning, Articles in Press.


24. Oke, T. R (1982). The energetic basis of the urban heat island. Quarterly Journal of the Royal Meteorological Society, 108(455), pp: 1-24.


25. Pauleit, S., Ennos, R., and Golding, Y (2005). Modeling the environmental impacts of urban land use and land cover change—a study in Merseyside, UK. Landscape and urban planning, 71(2-4), pp: 295-310.


26. Priyankara, P., Ranagalage, M., Dissanayake, D. M. S. L. B., Morimoto, T., and Murayama, Y (2019). Spatial Process of Surface Urban Heat Island in Rapidly Growing Seoul Metropolitan Area for Sustainable Urban Planning Using Landsat Data (1996–2017). Climate, 7(9), pp: 110.


27. Rezaeei Rad, Hadi., and Rafieyan, Mojtaba (2017). “Estimating the spatial-temporal Changes in intensity of the heat island in Tehran Metropolitan by Using ASTER and Landsat8 Satellite Images”, Regional Planning, 7(27), pp: 47-60.


28. Rinner, C., & Hussain, M (2011). Toronto’s urban heat island-Exploring the relationship between land use and surface temperature. Remote Sensing, 3(6), pp: 1251-1265.


29. Rosenfeld, A. H., Akbari, H., Romm, J. J., & Pomerantz, M (1998). Cool communities: strategies for heat island mitigation and smog reduction. Energy and Buildings, 28(1), pp: 51-62.


30. Sadeghinia, Ali reza., Alijani, Bohlol., and Zeaieanfirouzabadi, Parviz (2013). “Analysis of Spatial - Temporal Structure of the Urban Heat Island in Tehran through Remote Sensing and Geographical Information System”, GEOGRAPHY AND ENVIRONMENTAL HAZARDS, 1(4), pp: 1-17 (In Persian).


31. Shabani, Morteza., Darvishan, shadman., and Solaimani, Karim (2019). “Investigating the Effects of Land Use Change on Spatiotemporal Patterns of Land Surface Temperature and Thermal Islands (Case Study: Saqqez County)”, Geography and Environmental Planning, 30(1), pp: 37-54 (In Persian).


32. Sismanidis, P., Keramitsoglou, I., abd Kiranoudis, C. T (2015). A satellite-based system for continuous monitoring of surface urban heat islands. Urban Climate, 14, pp: 141-153.


33. Sultana, S., and Satyanarayana, A. N. V (2020). Assessment of urbanization and urban heat island intensities using landsat imageries during 2000–2018 over a sub-tropical Indian City. Sustainable Cities and Society, 52, pp: 101846.


34. Sun, S., Xu, X., Lao, Z., Liu, W., Li, Z., García, E. H., and Zhu, J (2017). Evaluating the impact of urban green space and landscape design parameters on thermal comfort in hot summer by numerical simulation. Building and Environment, 123, pp: 277-288.


35. Tayebi, safeh (2018). “Analysis and spatial modeling of the effects of urban expansion on the thermal islands of Tehran”, PhD thesis, Faculty Geography, University of Tehran.


36. Weng, Q., & Yang, S (2004). Managing the adverse thermal effects of urban development in a densely populated Chinese city. Journal of Environmental Management, 70(2), pp: 145-156.


37. Zhang, H., Qi, Z. F., Ye, X. Y., Cai, Y. B., Ma, W. C., and Chen, M. N (2013). Analysis of land use/land cover change, population shift, and their effects on spatiotemporal patterns of urban heat islands in metropolitan Shanghai, China. Applied Geography, 44, pp. pp: 121-133.


38. Zhang, Y., Chen, L., Wang, Y., Chen, L., Yao, F., Wu, P., and Zhang, T (2015). Research on the contribution of urban land surface moisture to the alleviation effect of urban land surface heat based on Landsat 8 data. Remote Sensing, 7(8), pp: 10737-10762.


39. Zhao, C., Jensen, J., & Zhan, B (2017). A comparison of urban growth and their influencing factors of two border cities: Laredo in the US and Nuevo Laredo in Mexico. Applied geography, 79, pp: 223-234.