Volume 14, Issue 51 (7-2025)
Haft Hesar J Environ Stud 2025, 14(51): 113-124 |
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Rezaei rad H. Spatial estimation of the intensity of the impact of environmental factors on thermal energy sustainability in the Tehran metropolitan area using the BCS algorithm. Haft Hesar J Environ Stud 2025; 14 (51) :113-124
URL: http://hafthesar.iauh.ac.ir/article-1-2224-en.html
URL: http://hafthesar.iauh.ac.ir/article-1-2224-en.html
Department of Urban Planning, Bu Ali Sina University, Hamadan
Abstract: (173 Views)
Introduction: Various factors disrupt the thermal energy balance and stability on urban surfaces, often due to unintended changes in climatic parameters and the systemic imbalance of energy systems in cities. These disruptions can lead to serious environmental consequences. Simultaneously, the rapid physical expansion of cities and their adverse environmental impacts have made urban morphological development a central concern. In many global metropolises, addressing the ecological implications of such development has become a key priority in efforts to achieve sustainable urban environmental development. The rising urban population, intensification of construction activities, and increased anthropogenic heat emissions have contributed to a noticeable increase in urban temperatures. This leads to the formation of a warm air canopy over city surfaces—commonly referred to as the Urban Heat Island (UHI) phenomenon. Consequently, the energy consumption for cooling and heating buildings rises significantly.
Methodology: The present study is applied in terms of its objective and descriptive-analytical in terms of methodology. In the theoretical framework, the influencing factors on the thermal energy balance of urban surfaces were identified through a library-based approach and a review of relevant literature.
Accordingly, to analyze the spatial estimation of the impact level of environmental factor indicators on the thermal energy balance of surfaces, and to assess the spatial correlation with this phenomenon resulting from urbanization and urban development changes, the metropolis of Tehran in the year 2021–2022 was selected as the case study.
A cloud-free and clear satellite image of Tehran was obtained by the Landsat 8 satellite using Envi software. The spatial variation patterns of surface thermal energy across the city were assessed using various remote sensing algorithms. Subsequently, the spatial correlation between environmental factor indicator layers and the surface thermal energy layer in the 22 municipal districts of Tehran was estimated using the Band Collection Statistical algorithm
Results: Among all the major environmental factor indicators, four indicators (number of urban parks, NDVI, NDMI, and NDWI) were selected based on their correlation coefficients exceeding 25%. The spatial estimation of environmental factor indicators in the thermal energy balance of Tehran metropolis shows that all four mentioned indicators have a direct association with the environmental dimension. In terms of absolute values, NDWI exhibited the highest correlation coefficient, whereas the number of parks and green spaces showed the lowest. These levels of correlation reflect the increasing role of anthropogenic activities and their impacts on environmental factor indicators. As a result, any change in these patterns will alter the surface heat levels, and consequently, the intensity of the heat island and ultimately the thermal energy balance on the surface.
Conclusion: Since urbanization and urban development are the main factors behind changes in the thermal energy distribution of Tehran's metropolitan surfaces, the implementation of targeted policies in these areas can lead to measurable shifts in environmental factor indicators. Ultimately, such shifts are likely to impact mobility, behavioral, and residential patterns, thereby affecting the degree of thermal energy sustainability across various districts of the Tehran metropolis. All the proposed strategies have been formulated to mitigate ambient and surface temperatures and cool the urban environment.
Methodology: The present study is applied in terms of its objective and descriptive-analytical in terms of methodology. In the theoretical framework, the influencing factors on the thermal energy balance of urban surfaces were identified through a library-based approach and a review of relevant literature.
Accordingly, to analyze the spatial estimation of the impact level of environmental factor indicators on the thermal energy balance of surfaces, and to assess the spatial correlation with this phenomenon resulting from urbanization and urban development changes, the metropolis of Tehran in the year 2021–2022 was selected as the case study.
A cloud-free and clear satellite image of Tehran was obtained by the Landsat 8 satellite using Envi software. The spatial variation patterns of surface thermal energy across the city were assessed using various remote sensing algorithms. Subsequently, the spatial correlation between environmental factor indicator layers and the surface thermal energy layer in the 22 municipal districts of Tehran was estimated using the Band Collection Statistical algorithm
Results: Among all the major environmental factor indicators, four indicators (number of urban parks, NDVI, NDMI, and NDWI) were selected based on their correlation coefficients exceeding 25%. The spatial estimation of environmental factor indicators in the thermal energy balance of Tehran metropolis shows that all four mentioned indicators have a direct association with the environmental dimension. In terms of absolute values, NDWI exhibited the highest correlation coefficient, whereas the number of parks and green spaces showed the lowest. These levels of correlation reflect the increasing role of anthropogenic activities and their impacts on environmental factor indicators. As a result, any change in these patterns will alter the surface heat levels, and consequently, the intensity of the heat island and ultimately the thermal energy balance on the surface.
Conclusion: Since urbanization and urban development are the main factors behind changes in the thermal energy distribution of Tehran's metropolitan surfaces, the implementation of targeted policies in these areas can lead to measurable shifts in environmental factor indicators. Ultimately, such shifts are likely to impact mobility, behavioral, and residential patterns, thereby affecting the degree of thermal energy sustainability across various districts of the Tehran metropolis. All the proposed strategies have been formulated to mitigate ambient and surface temperatures and cool the urban environment.
Keywords: Surface thermal energy sustainability, Environmental factor indices, BCS algorithm, Tehran metropolis.
Type of Study: Research |
Received: 2024/03/11 | Revised: 2025/07/1 | Accepted: 2024/09/20 | Published: 2025/07/1 | ePublished: 2025/07/1
Received: 2024/03/11 | Revised: 2025/07/1 | Accepted: 2024/09/20 | Published: 2025/07/1 | ePublished: 2025/07/1
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25. RezaeiRad, H., Rafieian, M., & Sozer, H. (2019). Evaluating the effects of increasing of building height on land surface temperature. International Journal Of Urban Management And Energy Sustainability, 1(1), 37-42.
26. Roy, D. P., Wulder, M. A., Loveland, T. R., Woodcock, C. E., Allen, R. G., Anderson, M. C., ... & Zhu, Z. (2014). Landsat-8: Science and product vision for terrestrial global change research. Remote sensing of Environment, 145, 154-172. [DOI:10.1016/j.rse.2014.02.001]
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28. Skelhorn, C. P. (2013). A fine scale assessment of urban greenspace impacts on microclimate and building energy in Manchester. The University of Manchester (United Kingdom).
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36. Weng, Q., Fu, P., & Gao, F. (2014). Generating daily land surface temperature at Landsat resolution by fusing Landsat and MODIS data. Remote sensing of environment, 145, 55-67. [DOI:10.1016/j.rse.2014.02.003]
37. Yang, X., Zhao, L., Bruse, M., & Meng, Q. (2013). Evaluation of a microclimate model for predicting the thermal behavior of different ground surfaces. Building and Environment, 60, 93-104. [DOI:10.1016/j.buildenv.2012.11.008]
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39. Zareie, S., Khosravi, H., & Nasiri, A. (2016). Derivation of land surface temperature from Landsat Thematic Mapper (TM) sensor data and analysing relation between land use changes and surface temperature. Solid Earth. Discuss, 1-15. [DOI:10.5194/se-2016-22] []
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42. رضاییراد، هادی. (1396). تحلیل اثرات برنامهریزی کالبدی بر تعادل انرژی در نواحی شهر تهران، رساله دکتری، دانشگاه تربیت مدرس، تهران.
43. رضاییراد، هادی، و رفیعیان، مجتبی (1396). روندیابی تغییرات حرارتی سطوح نواحی شهر تهران، نشریه علمی- پژوهشی پژوهشهای محیط زیست، 8 (16):176-167.
44. رفیعیان، مجتبی و رضاییراد، هادی (1396). سنجش اثرات سبزینگی گیاهی در تحولات فضایی شدت جزیره حرارتی سطح کلانشهر تهران با استفاده از تصاویر ماهوارهای LANDSAT8 و ASTER، نشریه علمی- پژوهشی تحلیل فضایی مخاطرات محیطی، 4 (3):1-16.
45. Bhang, K. J., & Park, S. S. (2009). Evaluation of the surface temperature variation with surface settings on the urban heat island in Seoul, Korea, using Landsat-7 ETM+ and SPOT. IEEE Geoscience and Remote Sensing Letters, 6(4), 708-712. [DOI:10.1109/LGRS.2009.2023825]
46. Bobrinskaya, M. (2012). Remote sensing for analysis of relationships between land cover and land surface temperature in ten megacities.
47. Collatz, G. J., Bounoua, L., Los, S. O., Randall, D. A., Fung, I. Y., & Sellers, P. J. (2000). A mechanism for the influence of vegetation on the response of the diurnal temperature range to changing climate. Geophysical Research Letters, 27(20), 3381-3384. [DOI:10.1029/1999GL010947]
48. Gartland, L. M. (2012). Heat islands: understanding and mitigating heat in urban areas. Routledge. [DOI:10.4324/9781849771559]
49. Guillevic, P. C., Privette, J. L., Coudert, B., Palecki, M. A., Demarty, J., Ottlé, C., & Augustine, J. A. (2012). Land Surface Temperature product validation using NOAA's surface climate observation networks-Scaling methodology for the Visible Infrared Imager Radiometer Suite (VIIRS). Remote Sensing of Environment, 124, 282-298. [DOI:10.1016/j.rse.2012.05.004]
50. Huang, C., Goward, S. N., Masek, J. G., Thomas, N., Zhu, Z., & Vogelmann, J. E. (2010). An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks. Remote Sensing of Environment, 114(1), 183-198. [DOI:10.1016/j.rse.2009.08.017]
51. Kerr, Y. H., Lagouarde, J. P., Nerry, F., & Ottlé, C. (2004). Land surface temperature retrieval techniques and applications: Case of the AVHRR. In Thermal remote sensing in land surface processing (pp. 33-109). CRC Press. [DOI:10.1201/9780203502174-c3]
52. Li, H. (2016). Pavement materials for heat island mitigation. Science Direct, 79-96. [DOI:10.1016/B978-0-12-803476-7.00005-2]
53. Markham, B. L., Storey, J. C., Williams, D. L., & Irons, J. R. (2004). Landsat sensor performance: history and current status. IEEE transactions on geoscience and remote sensing, 42(12), 2691-2694. [DOI:10.1109/TGRS.2004.840720]
54. Meng, Q. Y., Spector, D., Colome, S., & Turpin, B. (2009). Determinants of indoor and personal exposure to PM2. 5 of indoor and outdoor origin during the RIOPA study. Atmospheric Environment, 43(36), 5750-5758. [DOI:10.1016/j.atmosenv.2009.07.066] [PMID] []
55. Moran, M. S., Scott, R. L., Keefer, T. O., Emmerich, W. E., Hernandez, M., Nearing, G. S., ... & O'Neill, P. E. (2009). Partitioning evapotranspiration in semiarid grassland and shrubland ecosystems using time series of soil surface temperature. agricultural and forest meteorology, 149(1), 59-72. [DOI:10.1016/j.agrformet.2008.07.004]
56. Niu, C. Y., Musa, A., & Liu, Y. (2015). Analysis of soil moisture condition under different land uses in the arid region of Horqin sandy land, northern China. Solid Earth, 6(4), 1157-1167. [DOI:10.5194/se-6-1157-2015]
57. Oke, T. R. (1982). The energetic basis of the urban heat island. Quarterly journal of the royal meteorological society, 108(455), 1-24.
https://doi.org/10.1256/smsqj.45501 [DOI:10.1002/qj.49710845502]
58. Oke, T. R. (2002). Boundary layer climates. Routledge. [DOI:10.4324/9780203407219] []
59. Owen, T. W., Carlson, T. N., & Gillies, R. R. (1998). Remotely sensed surface parameters governing urban climate change. Int. J. Remote Sens, 19, 1663-1681. [DOI:10.1080/014311698215171]
60. Pitman, A. J., Avila, F. B., Abramowitz, G., Wang, Y. P., Phipps, S. J., & de Noblet-Ducoudré, N. (2011). Importance of background climate in determining impact of land-cover change on regional climate. Nature Climate Change, 1(9), 472-475. [DOI:10.1038/nclimate1294]
61. Rajeshwari, A., & Mani, N. D. (2014). Estimation of land surface temperature of Dindigul district using Landsat 8 data. International journal of research in engineering and technology, 3(5), 122-126. [DOI:10.15623/ijret.2014.0305025]
62. RezaeiRad, H., & Afzali, N. (2021). Measuring effects of building orientation and vegetation on thermal comfort by ENVI-Met (Case Study: Maslak Area, Istanbul).
63. RezaeiRad, H., & Afzali, N. (2024). The Role of City Information Modelling (CIM) in Evaluating the Spatial Correlation Between Vegetation Index Changes and Heat Island Severity in the Last Two Decades in Tehran Metropolis. In City Information Modelling (pp. 111-138). Singapore: Springer Nature Singapore. [DOI:10.1007/978-981-99-9014-6_7]
64. RezaeiRad, H., & Rafieiana, M. (2016). Evaluating The Effects of High rise building On Urban Heat Island by Sky View Factor: A case study of Narmak neighborhood, Tehran. Naqshejahan- Basic studies and New Technologies of Architecture and Planning, 5(4), 13-22.
65. RezaeiRad, H., & Rafieyan, M. (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), 47-60.
66. RezaeiRad, H., Rafieian, M., & Sozer, H. (2019). Evaluating the effects of increasing of building height on land surface temperature. International Journal Of Urban Management And Energy Sustainability, 1(1), 37-42.
67. Roy, D. P., Wulder, M. A., Loveland, T. R., Woodcock, C. E., Allen, R. G., Anderson, M. C., ... & Zhu, Z. (2014). Landsat-8: Science and product vision for terrestrial global change research. Remote sensing of Environment, 145, 154-172. [DOI:10.1016/j.rse.2014.02.001]
68. Shukla, J., & Mintz, Y. (1982). Influence of land-surface evapotranspiration on the earth's climate. Science, 215(4539), 1498-1501. [DOI:10.1126/science.215.4539.1498] [PMID]
69. Skelhorn, C. P. (2013). A fine scale assessment of urban greenspace impacts on microclimate and building energy in Manchester. The University of Manchester (United Kingdom).
70. Sobrino, J. A., Caselles, V., & Coll, C. (1993). Theoretical split-window algorithms for determining the actual surface temperature. Il Nuovo Cimento C, 16, 219-236. [DOI:10.1007/BF02524225]
71. Sun, J. (2011). Parameter estimation of coupled water and energy balance models based on stationarity constraints of soil moisture and temperature. Boston University. [DOI:10.1029/2010WR009293]
72. Svensson, M. K., & Eliasson, I. (2002). Diurnal air temperatures in built-up areas in relation to urban planning. Landscape and urban planning, 61(1), 37-54. [DOI:10.1016/S0169-2046(02)00076-2]
73. Tan, J., Zheng, Y., Tang, X., Guo, C., Li, L., Song, G., ... & Chen, H. (2010). The urban heat island and its impact on heat waves and human health in Shanghai. International journal of biometeorology, 54, 75-84. [DOI:10.1007/s00484-009-0256-x] [PMID]
74. Tran, N., Powell, B., Marks, H., West, R., & Kvasnak, A. (2009). Strategies for design and construction of high-reflectance asphalt pavements. Transportation Research Record, 2098(1), 124-130. [DOI:10.3141/2098-13]
75. Voogt, J. (2006). How researchers measure urban heat islands. In United States Environmental Protection Agency (EPA), state and local climate and energy program, heat island effect, urban heat island webcasts and conference calls.
76. Voogt, J. A., & Oke, T. R. (2003). Thermal remote sensing of urban climates. Remote sensing of environment, 86(3), 370-384. [DOI:10.1016/S0034-4257(03)00079-8]
77. Weng, Q., Fu, P., & Gao, F. (2014). Generating daily land surface temperature at Landsat resolution by fusing Landsat and MODIS data. Remote sensing of environment, 145, 55-67. [DOI:10.1016/j.rse.2014.02.003]
78. Yang, X., Zhao, L., Bruse, M., & Meng, Q. (2013). Evaluation of a microclimate model for predicting the thermal behavior of different ground surfaces. Building and Environment, 60, 93-104. [DOI:10.1016/j.buildenv.2012.11.008]
79. 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. [DOI:10.1016/j.rse.2006.09.003]
80. Zareie, S., Khosravi, H., & Nasiri, A. (2016). Derivation of land surface temperature from Landsat Thematic Mapper (TM) sensor data and analysing relation between land use changes and surface temperature. Solid Earth. Discuss, 1-15. [DOI:10.5194/se-2016-22] []
81. Zhou, Y., & Ren, G. (2011). Change in extreme temperature event frequency over mainland China, 1961− 2008. Climate Research, 50(2-3), 125-139. [DOI:10.3354/cr01053]
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