The Investigation of the Relationship between Urban Morphology Changes and Land Surface Temperature for Urban Heat Island Management (A Case Study: Tehran)

Document Type : Research Paper

Authors

1 Department of Environmental Planning, Faculty of Environment, University of Tehran, Tehran, Iran

2 Department of Environmental Planning, Faculty of Environment, University of Tehran, Tehran, Iran.

Abstract

Alteration of urban forms and geometries due to rapid unplanned urban development can result in localized elevation of land surface temperature - a phenomenon known as urban heat island. This study examins the impact of land development patterns on land surface temperatures in a heterogeneous urban environment. As many as 18 Landsat satellite images for 1995, 2008 and 2021 (average cloudless images per year) were used in this sutdy. First, land development patterns were generated from the processing of Landsat satellite images in SAGA GIS using the method of Stewart and Oke. At the second stage, land surface temperatures for 1995, 2008, and 2021 were extracted using the single-channel algorithm. At the third stage, the relationship between mean value of land surface temperatures and the heterogeneous form of Tehran was analyzed. The results showed that among all LCZs, the highest mean value of land surface temperatures belonged to the Heavy Industry LCZ with the average temperatures of 5.32, 48.18, and 51.87°C for 1995, 2008, and 2021, followed by the Bare Soil/Sand LCZ with the average temperatures of 47.25, 49.25 and 52.36°C for the same years. The lowest mean value of land surface temperatures belonged to the Water LCZ with the average temperatures of 20.5, 22.63, and 23.15°C for 1995, 2008, and 2021, respectively. It was also found that the Compact Low-Rise LCZ had higher mean value of land surface temperatures than both Compact High-Rise and Compact Mid-Rise LCZs. The differences observed between the highest and lowest land surface temperatures in the study area are indicative of the significant impacts of urban form on land surface temperatures. The findings can contribute to our understanding of urban environments and help to devise better plans for minimizing the adverse effects of land use and development on these environments.
 
Extended Abstract
1-Introduction
Rapid unplanned urban development tends to result in reduced surface albedo, decreased vegetation, increased anthropogenic heat generation due to production and transportation, and altered urban geometry, which can all play a major role in the emergence of Urban Heat Islands (UHIs).
Over the years, a variety of models and metrics have been developed for the assessment of UHIs, but there is still much research to be done in this area. In the great majority of previous studies in this field, UHIs have been assessed on a macro scale using urban-rural, urban-agricultural, urban-suburban, urban-nonurban and urban-water temperature differences. However, since temperature distribution over an urban area depends on a wide range of factors, modeling outputs such as Land Surface Temperature (LST) estimates can become unrealistic if these factors are not taken into account. Thus, such assessments need to be made on a local scale with more attention to details. Among the variety of models proposed for UHI assessment, the one called Local Climate Zone (LCZ) has managed to address many issues of its predecessors. LCZ stands out among models of this kind for its excellent classification, capability, and precision, and provides a relatively new approach to the climatic classification of urban and non-urban areas.
Much of the research done on UHIs has been focused on thermal conditions rather than urban form. This is while urban form, open spaces, and building dimensions can all affect the rate of energy absorption/reflection, and therefore, fossil energy consumption and LST. However, so far, not much research has been conducted on the impact of urban morphology, building dimensions and other urban form factors on the intensity of UHI, especially in Iran. To fill this gap, this study investigated the effect of urban form on LST and UHI in order to gain more insight into how it impacts urban temperature.
 
2-Materials and Methods
In this study, first, the study area and the factors related to urban form and land cover were examined, and LCZ classification was performed in Saga GIS based on the model’s 17 standard classes. Then, LST was estimated using the single channel algorithm based on satellite spatio-temporal data and the level of LST and UHI in different LCZ classes was examined. Finally, the relationship between LCZ classes and spatio-temporal changes of UHI and NDVI in 1995, 2008 and 2021 was investigated. Given the impact of climatic and seasonal conditions on LST, only cloud-free images were used in LST modeling. In the end, the mean value of LST for each year was obtained by averaging all LSTs obtained from all cloud-free images taken over that year.
 
3- Results and Discussion
The LST estimates obtained for hot and cold seasons of 1995, 2008 and 2021 showed that the northern parts of the study area have a low LST, which can be attributed to their loosely packed urban structure as well as green spaces. Natural covers like dense orchards and water bodies can also have very low LSTs because of evapotranspiration. The central parts of the city were found to have high LSTs because of high building density and poor green cover. The results also showed a steady decline in the area of “cold” and “very cold” LST classes over the past 16 years. An inverse correlation was observed between NDVI and normalized LST, in the sense that LST was higher wherever vegetation was lower. The lowest LSTs belonged to areas with the highest levels of vegetation. Also, LST was found to be dropping with movement toward more vegetated areas.
The 1995, 2008 and 2021 mean value of LSTs obtained for LCZs showed a steady rise in the average temperature of all zones. Over these years, the average temperature in “heavy industry”, “large low-rise”, “sparsely built”, “open low-rise”, “compact low-rise”, “compact mid-rise” zones has risen from 30-45°C in 1995 to 31-47°C in 2008 and to 33-5°C in 2021, which has a notable ascending trend. Of all LCZs, “heavy industry” had the highest LST and “water” the lowest LST for all three years. Also, all LCZs with vegetation land use (green cover) have exhibited rising temperatures over the study period.
 
4- Conclusion
In this study, LCZ classification was performed using satellite imagery and telemetry data. A total of 17 classes, including 10 built classes and 7 natural cover classes were identified in the study area. LST in three periods from 1995 to 2021 was extracted from the Operational Land Imager (OLI) data using the single-channel algorithm. The results demonstrated a relationship between urban form and LST and UHI.
The highest overall accuracy and kappa coefficient for LCZ classification were for 1995 with Acc=93.58% and κ=0.91 and the lowest were for 2018 with Acc=89.56% and κ=0.87.

Keywords

References

Alavipanah, S. K., Hashemi Darrehbadami, S., & Kazemzadeh, A. (2015). Spatial-Temporal Analysis of Urban Heat-Island of Mashhad City due to Land Use/Cover Change and Expansion. Geographical Urban Planning Research (GUPR), 3 (1), 1-17 (In Persian).
Alexander, P., & Mills, G. (2014). Local climate classification and Dublin’s urban heat island. Atmosphere, 5 (4), 755-774.
Alijani, B., Toulabinjad, M., & Sayadi, F. (2017). Calculating Of Heat Island Intensity Based On Urban Geometry (Case Study: District Of Kucheh Bagh In Tabriz), Journal of spatial analysis environmental hazarts, 4 (3), 99-112
Alijani, B., Toulabinjad, M., Sayadi, F. (2017). Calculating Of Heat Island Intensity Based On Urban Geometry (Case Study: District Of Kucheh Bagh In Tabriz). Journal of Spatial Analysis Environmental hazards, 4 (95), 375-387 (In Persian).
Almusaed, A. (2011). The Urban Heat Island Phenomenon upon Urban Components. In Biophilic and Bioclimatic Architecture. London: Springer
Asadi, Y., Hamzeh, S., & Kiavarz, M. (2020). Investigate the effects of land Use and vegetation on urban heat islands using landscape measurements (Case Study: region 6 of Tehran). Human Geography Research, 52 (2), 759-773 (In Persian).
Badaro-Saliba, N., Adjizian-Gerard, J., Zaarour, R., & Najjar, G. (2021). LCZ scheme for assessing Urban Heat Island intensity in a complex urban area (Beirut, Lebanon). Urban Climate, 37, 100846.
Bechtel, B., See, L., Mills, G., & Foley, M. (2016). Classification of local climate zones using SAR and multispectral data in an arid environment. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9 (7), 3097-3105.
Benas, N., Chrysoulakis, N., & Cartalis, C. (2017). Trends of urban surface temperature and heat island characteristics in the Mediterranean. Theoretical and applied climatology, 130 (3-4), 807-816.
Cai, M., Ren, C., Xu, Y., Lau, K. K. L., & Wang, R. (2018). Investigating the relationship between local climate zone and land surface temperature using an improved WUDAPT methodology–A case study of Yangtze River Delta, China. Urban climate, 24, 485-502.
Camilloni, I., & Barros, V. (1997). On the urban heat island effect dependence on temperature trends. Climatic Change, 37 (4), 665-681.
Cao, C., Lee, X., Liu, S., Schultz, N., Xiao, W., Zhang, M., & Zhao, L. (2016). Urban heat islands in China enhanced by haze pollution. Nature Communications, 7, 12509
Caprotti, F. (2018). Future cities: moving from technical to human needs. Palgrave Communications, 4 (1), 1-4.
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.
Chen, X., Xu, Y., Yang, J., Wu, Z., & Zhu, H. (2020). Remote sensing of urban thermal environments within local climate zones: A case study of two high-density subtropical Chinese cities. Urban Climate, 31, 100568.
Chow, W. T., & Roth, M. (2006). Temporal dynamics of the urban heat island of Singapore. International Journal of Climatology: A Journal of the Royal Meteorological Society, 26 (15), 2243-2260.
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.
Cilek, M. U., & Cilek, A. (2021). Analyses of land surface temperature (LST) variability among local climate zones (LCZs) comparing Landsat-8 and ENVI-met model data. Sustainable Cities and Society, 69, 102877.
Cui, Y., Xu, X., Dong, J., & Qin, Y. (2016). Influence of urbanization factors on surface urban heat island intensity: A comparison of countries at different developmental phases. Sustainability, 8 (8), 706.
Dousset, B., & Gourmelon, F. (2003). Satellite multi-sensor data analysis of urban surface temperatures and landcover. ISPRS journal of photogrammetry and remote sensing, 58 (1), 43-54.
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.
Elmarakby, E., Khalifa, M., Elshater, A., & Afifi, S. (2020). Spatial Morphology and Urban Heat Island: Comparative Case Studies. In Architecture and Urbanism: A Smart Outlook. Cham. Springer.
Firozjaei, M. K., Kiavarz, M., Alavipanah, S. K., Lakes, T., & Qureshi, S. (2018). Monitoring and forecasting heat island intensity through multi-temporal image analysis and cellular automata-Markov chain modelling: A case of Babol city, Iran. Ecological indicators, 91, 155-170.
Firozjaei, M. K., Kiavarz, M., Alavipanah, S. K., Lakes, T., & Qureshi, S. (2018). Monitoring and forecasting heat island intensity through multi-temporal image analysis and cellular automata-Markov chain modelling: A case of Babol city, Iran. Ecological indicators, 91, 155-170 (In Persian).
Firozjaei, M. K., Kiavarz, M., Alavipanah, S. K., Lakes, T., & Qureshi, S. (2018). Monitoring and forecasting heat island intensity through multi-temporal image analysis and cellular automata-Markov chain modelling: A case of Babol city, Iran. Ecological indicators, 91, 155-170 (In Persian).
Firozjaei, M. K., Kiavarz, M., Nematollahi, O., Karimpour Reihan, M., & Alavipanah, S. K. (2019). An evaluation of energy balance parameters, and the relations between topographical and biophysical characteristics using the mountainous surface energy balance algorithm for land (SEBAL). International Journal of Remote Sensing, 40 (13), 5230-5260.
Gallo, K. P., McNab, A. L., Karl, T. R., Brown, J. F., Hood, J. J., & Tarpley, J. D. (1993). The use of NOAA AVHRR data for assessment of the urban heat island effect. Journal of Applied Meteorology, 32 (5), 899-908
Gholami, R. M., & Beck, C. (2019). Towards the determination of driving factors of varying LST-LCZ relationships: A case study over 25 cities. Geographica Pannonica, 23 (4), 289-307.
Guhathakurta, S. &Gober, P. (2007). The Impact of the Phoenix Urban Heat Island on Residential Water Use. Journal of the American Planning Association, 73, 317-329
Hasanlou, M., & Mostofi, N. (2015). Investigating urban heat island estimation and relation between various land cover indices in Tehran city using Landsat 8 imagery. In Proceedings of the 1st International Electronic Conference on Remote Sensing, Basel, Switzerland.
Huang, X., & Wang, Y. (2019). Investigating the effects of 3D urban morphology on the surface urban heat island effect in urban functional zones by using high-resolution remote sensing data: A case study of Wuhan, Central China. ISPRS Journal of Photogrammetry and Remote Sensing, 152, 119-131.
Jimenez-Munoz J. C., J. Cristobal, J. A. Sobrino, G. Soria, M. Ninyerola and X. Pons. (2009). Revision of the Single-Channel Algorithm for Land Surface Temperature Retrieval From Landsat Thermal-Infrared Data. IEEE Transactions on Geoscience and Remote Sensing, 47 (1) 339-349.
Jiménez-Muñoz Juan C., José A. Sobrino, Cristian Mattar, and Yadvinder Malhi,(2014) Spatial and temporal patterns of the recent warming of the Amazon forest, Journal of Geophysical Research: Atmospheres, 118, 5204-5215.
Jin, M., Dickinson, R. E., & Zhang, D. A. (2005). The footprint of urban areas on global climate as characterized by MODIS. Journal of Climate, 18 (10), 1551-1565.
Jusuf, S. K., Wong, N. H., Hagen, E., Anggoro, R., & Hong, Y. (2007). The influence of land use on the urban heat island in Singapore. Habitat international, 31 (2), 232-242.
Khoshnoodmotlagh, S., Daneshi, A., Gharari, S., Verrelst, J., Mirzaei, M., & Omrani, H. (2021). Urban morphology detection and it's linking with land surface temperature: A case study for Tehran Metropolis, Iran. Sustainable Cities and Society, 74, 103228.
Kong, F., Yin, H., James, P., Hutyra, L. R., & He, H. S. (2014). Effects of spatial pattern of greenspace on urban cooling in a large metropolitan area of eastern China. Landscape and Urban Planning, 128, 35-47.
Kotharkar, R., & Bagade, A. (2018). Evaluating urban heat island in the critical local climate zones of an Indian city. Landscape and Urban Planning, 169, 92-104.
Kottmeier, C., Biegert, C., & Corsmeier, U. (2007). Effects of urban land use on surface temperature in Berlin: Case study. Journal of urban planning and development, 133 (2), 128-137.
Lazzarini, M., Molini, A., Marpu, P. R., Ouarda, T. B., & Ghedira, H. (2015). Urban climate modifications in hot desert cities: The role of land cover, local climate, and seasonality. Geophysical Research Letters, 42 (22), 9980-9989.
Lazzarini, M., Molini, A., Marpu, P. R., Ouarda, T. B., & Ghedira, H. (2015). Urban climate modifications in hot desert cities: The role of land cover, local climate, and seasonality. Geophysical Research Letters, 42 (22), 9980-9989.
Leconte, F., Bouyer, J., Claverie, R., & Pétrissans, M. (2015). Estimation of spatial air temperature distribution at sub-mesoclimatic scale using the LCZ scheme and mobile measurements. In Proceedings of the 9th International Conference on Urban Climate (ICUC9).
Li, X., Zhou, Y., Asrar, G. R., Imhoff, M., & Li, X. (2017). The surface urban heat island response to urban expansion: A panel analysis for the conterminous United States. Science of the Total Environment, 605, 426-435.
Liao, W., Liu, X., Wang, D., & Sheng, Y. (2017). The impact of energy consumption on the surface urban heat island in China’s 32 major cities. Remote Sensing, 9 (3), 250.
Myint, S. W., Zheng, B., Talen, E., Fan, C., Kaplan, S., Middel, A., & Brazel, A. (2015). Does the spatial arrangement of urban landscape matter? Examples of urban warming and cooling in Phoenix and Las Vegas. Ecosystem Health and Sustainability, 1 (4), 1-15.
Nadizadeh Shorabeh, S., Hamzeh, S., Kiavarz, M., & Afsharipoor, S. K. (2018). Effects of Spatial and Temporal Land Use Changes and Urban Development on the Increase of Land Surface Temperature Using Landsat Multi-Temporal Images (Case study: Gorgan City). Geographical Urban Planning Research (GUPR), 6 (3), 545-568 (In Persian).
Nadizadeh Shorabeh, S., Hamzeh, S., Zanganeh Shahraki, S., Firozjaei, M. K., & Jokar Arsanjani, J. (2020). Modelling the intensity of surface urban heat island and predicting the emerging patterns: Landsat multi-temporal images and Tehran as case study. International Journal of Remote Sensing, 41 (19), 7400-7426.
Oke, T. R. (1973). City size and the urban heat island. Atmospheric Environment (1967), 7 (8), 769-779.
Oke, T. R. (1982). The energetic basis of the urban heat island. Quarterly Journal of the Royal Meteorological Society, 108 (455), 1-24.
Oke, T. R. (1982). The energetic basis of the urban heat island. Quarterly Journal of the Royal Meteorological Society, 108 (455), 1-24.
Oke, T. R. (1988). Street design and urban canopy layer climate. Energy and buildings, 11 (1-3), 103-113.
Quattrochi, D. A., & Luvall, J. C. (1999). Thermal infrared remote sensing for analysis of landscape ecological processes: methods and applications. Landscape ecology, 14 (6), 577-598.
Qureshi, S., Shorabeh, S. N., Samany, N. N., Minaei, F., Homaee, M., Nickravesh, F., ... & Arsanjani, J. J. (2021). A new integrated approach for municipal landfill siting based on urban physical growth prediction: a case study Mashhad metropolis in Iran. Remote Sensing, 13 (5), 949.
Rao, P. K. (1972). Remote sensing of urban" heat islands" from an environmental satellite. Bulletin of the American meteorological society, 53 (7), 647-648.
Rose, A., & Devadas, M. D. (2009). 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 climate.
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), 51-62.
Roshan, G., Rousta, I., & Ramesh, M. (2009). Studying the effects of urban sprawl of metropolis on tourism-climate index oscillation: A case study of Tehran city. Journal of Geography and Regional Planning, 2 (12), 310-321.
Roth, M., Oke, T. R., & Emery, W. J. (1989). Satellite-derived urban heat islands from three coastal cities and the utilization of such data in urban climatology. International Journal of Remote Sensing, 10 (11), 1699-1720.
Runnalls, K. E., & Oke, T. R. (2000). Dynamics and controls of the near-surface heat island of Vancouver, British Columbia. Physical Geography, 21 (4), 283-304.
Sadeghinia, A., Alijani, B., & Zeaieanfirouzabadi, P. (2013). Analysis of spatial-temporal structure of the urban heat island in Tehran through remote sensing and geographical information system. Journal of Geography and Environmental hazards, 1 (4), 1-17.
Sadeghinia, A., Alijani, B., & Zeaieanfirouzabadi, P. (2013). Analysis of spatial-temporal structure of the urban heat island in Tehran through remote sensing and geographical information system. Journal of Geography and Environmental hazards, 1 (4), 1-17 (In Persian).
Sanagar Darbani, E., Rafieian, M., Hanaee, T., & Monsefi Parapari, D. (2020). The Effects of Urban Heat Islands Mitigation on Human Health through Change in Urban form Hot and Arid Climate of Mashhad (Case Study: Graticular Texture of Shahed and Organic Texture of Pachenar Neighborhoods). Journal of Environmental Science and Technology, 22 (4), 375-387 (In Persian).
Shahmohamadi, P., Che-Ani, A. I., Maulud, K. N. A., Tawil, N. M., & Abdullah, N. A. G. (2011). The impact of anthropogenic heat on formation of urban heat island and energy consumption balance. Urban Studies Research, 2011.
Shirani-Bidabadi, N., Nasrabadi, T., Faryadi, S., Larijani, A., & Roodposhti, M. S. (2019). Evaluating the spatial distribution and the intensity of urban heat island using remote sensing, case study of Isfahan city in Iran. Sustainable cities and society, 45, 686-692.
Shorabeh, S. N., Kakroodi, A. A., Firozjaei, M. K., Minaei, F., & Homaee, M. (2022). Impact assessment modeling of climatic conditions on spatial-temporal changes in surface biophysical properties driven by urban physical expansion using satellite images. Sustainable Cities and Society, 80, 103757.
Shorabeh, S. N., Varnaseri, A., Firozjaei, M. K., Nickravesh, F., & Samany, N. N. (2020). Spatial modeling of areas suitable for public libraries construction by integration of GIS and multi-attribute decision making: Case study Tehran, Iran. Library & Information Science Research, 42 (2), 101017.
Simanjuntak, R. M., Kuffer, M., & Reckien, D. (2019). Object-based image analysis to map local climate zones: The case of Bandung, Indonesia. Applied geography, 106, 108-121.
Stewart, I. (2011). Redefining the urban heat island. Ph. D. thesis, The University of British Columbia, Vancouver
Stewart, I. D., & Oke, T. R. (2010, August). Thermal differentiation of local climate zones using temperature observations from urban and rural field sites. In Preprints, 9th Symposium, on Urban Environment, Keystone 2-6.
Stewart, I. D., & Oke, T. R. (2012). Local climate zones for urban temperature studies. Bulletin of the American Meteorological Society, 93 (12), 1879-1900.
Sun, L., Chen, J., Li, Q., & Huang, D. (2020). Dramatic uneven urbanization of large cities throughout the world in recent decades. Nature communications, 11 (1), 1-9.
Taheri Shahraiyni, H., Sodoudi, S., El-Zafarany, A., Abou El Seoud, T., Ashraf, H., & Krone, K. (2016). A comprehensive statistical study on daytime surface urban heat island during summer in urban areas, case study: Cairo and its new towns. Remote Sensing, 8 (8), 643.
Tayyebi, A., Shafizadeh-Moghadam, H., & Tayyebi, A. H. (2018). Analyzing long-term spatio-temporal patterns of land surface temperature in response to rapid urbanization in the mega-city of Tehran. Land Use Policy, 71, 459-469.
Tomlinson, C. J., Chapman, L., Thornes, J. E., & Baker, C. J. (2012). Derivation of Birmingham's summer surface urban heat island from MODIS satellite images. International Journal of Climatology, 32 (2), 214-224.
Unger, J. (2004). Intra-urban relationship between surface geometry and urban heat island: review and new approach. Climate research, 27 (3), 253-264.
United Nations, Department of Economic and Social Affairs, Population Division. (2018). World urbanization prospects: The 2018 revision, online edition.
Voogt, J. A., & Oke, T. R. (2003). Thermal remote sensing of urban climates. Remote sensing of environment, 86 (3), 370-384.
Wang, Y., Zhan, Q., & Ouyang, W. (2017). Impact of urban climate landscape patterns on land surface temperature in Wuhan, China. Sustainability, 9(10), 1700.
Weng, Q., Firozjaei, M. K., Sedighi, A., Kiavarz, M., & Alavipanah, S. K. (2019). Statistical analysis of surface urban heat island intensity variations: A case study of Babol city, Iran. GIScience & remote sensing, 56 (4), 576-604.
Weng, Q.; Lu, D.; Schubring, J. Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies. Remote Sensing of Environment. 2004, 89, 467–483.
Wicki, A., & Parlow, E. (2017). Attribution of local climate zones using a multitemporal land use/land cover classification scheme. Journal of Applied Remote Sensing, 11(2), 026001.
Zhang, P., Imhoff, M. L., Wolfe, R. E., & Bounoua, L. (2010). Characterizing urban heat islands of global settlements using MODIS and nighttime lights products. Canadian Journal of Remote Sensing, 36 (3), 185-196.
Zhang, Y., & Liang, S. (2018). Impacts of land cover transitions on surface temperature in China based on satellite observations. Environmental Research Letters, 13 (2), 024010.
Zhou, D., Zhang, L., Hao, L., Sun, G., Liu, Y., & Zhu, C. (2016). Spatiotemporal trends of urban heat island effect along the urban development intensity gradient in China. Science of the Total Environment, 544, 617-626.

Files

Share

How to cite

Statistics