Investigating the Effects of Environmental and Demographic Parameters on the Spatial Distribution of Surface Temperature of Tehran by Combining Statistical and Mono-Window Models

Document Type : Full length article

Authors

1 PhD Candidate in Remote Sensing and GIS, Faculty Geography, University of Tehran, Tehran, Iran

2 Associate Professor of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran, Iran

Abstract

Introduction
One of the emerging environmental hazards arising from the expansion of urbanization is the phenomenon of urban heat island. Urban heat island is a phenomenon in which urban areas experience warmer temperatures than the surrounding countryside. This phenomenon has been studied and recorded in the world over 150 years ago and generally, along with natural vegetation changes; It usually can appear in impenetrable surfaces such as pavement streets, cement, asphalt, concrete, etc. The effects of urban heat island on human life include increased energy consumption due to increased demand for building cooling during the warm seasons, increased heat stress and reduced staffing efficiency, increased water consumption and increased urban air pollution. Also, UHI has caused a change in the urban and global climate. Given the increase in population, the importance of energy and the issue of global warming will be increased over the coming decades.
The Tehran city is the capital of Iran and center of economic activity in the country. Each year, a large number of different provinces migrate to the province to work, which will destroy the green space and increase population in the city. This causes a lot of problems including increasing the surface temperature. Therefore, the aim of this paper is to investigate the effect of demographic and environmental parameters on the spatial distribution of Tehran's metropolis surface temperature.
Materials and methods
In this study, the Landsat 5 satellite image of TM sensor has been used for the studied area. In order to complete input parameters for mapping surface temperature using satellite imagery of meteorological data, and for providing field samples, we used Google Earth images and topographic maps of Iran Cartographic Center to provide surface weather maps.
At first, the preprocessing steps were applied to prepare images including atmospheric correction. Then, the images were classified using a Maximum Likelihood and were classified into four landuses, built up, fallow, water and green space. After classification, each of the images was categorized using precision classification controls. In the next step, using the Mono Window algorithm, surface temperature was obtained for each image.
Results and discussion
Given the existing landuses, the area was classified by 4 types of built-up, green space, bare land, and water using the supervised classification method. The area built-up, green space, bare land and water are 37061.46, 9512.91, 11470.05 and 44.91 ha, respectively. The most of the landuse is built environment. The surface temperature values here are ranged from 294 to 328 degrees Kelvin. The lowest average temperature is for water use and natural areas such as green spaces, forest and urban parks, while the maximum temperatures are in shallow land and impenetrable lands such as asphalt, street paving and other Man-made coatings, as well as industrial and commercial lands, and the surfaces of residential and transportation facilities.
Based on the total population of Tehran in 2011, the districts of 21, 22, 12 and 9 are considered as the main core of the urban heat island among the low population areas of Tehran. Due to various landuses (industrial, commercial, transportation, etc.), low population density in these areas appears natural. While the urban heat island in these areas is mainly resulted from industrial activities, airports, transportation, commercial land use, and bare land degradation. Map of surface temperature and population distribution in different regions of Tehran show that the regions with high temperatures in districts of 4, 5, 15, 2 and 14, have a high population density. Although these areas are not among the main heat islands in Tehran; however, due to high population density, high traffic volume, and air pollution from these areas is endangered by the emergence and expansion of urban heat islands. 
Conclusion
The purpose of this research is to investigate the effect of demographic and environmental parameters on the spatial distribution of Tehran's surface temperature. The results of the study indicate that heterogeneous spatial distribution factors of surface temperature in Tehran are different. These factors are deliberately due to different land use and vegetation in the region. In the northern regions of Tehran, an uncompromising urban structure along with green space has caused these areas to have a low surface temperature, while it is high in the major proportion of the central part of the city with high building density and poor green space. Finally, the results of the relationship between surface temperature and population distribution in different regions of Tehran show that the regions with high temperatures in districts of 4, 5, 15, 2 and 14 have a high population density.

Keywords

Main Subjects

References

صادقی‏نیا، ع.؛ علیجانی، ب. و ضیائیان، پ. (1391). تحلیل فضایی- زمانی جزیرة حرارتی کلان‏شهر تهران با استفاده از سنجش از دور و سیستم اطلاعات جغرافیایی، جغرافیا و مخاطرات محیطی، 4: ۱-۱۷.
علوی‏پناه، س.ک.؛ هاشمی دره بادامی، س. و کاظم‏زاده، ع. (1394). تحلیل زمانی‏- مکانی جزیرۀ حرارتی شهر مشهد با توجه به گسترش شهر و تغییرات کاربری‏- پوشش زمین، پژوهش‏های جغرافیایی برنامه‏ریزی شهری، 3(1): 17-1.
کریمی فیروزجایی، م. و کیاورز مقدم، م. (1395). بررسی ارتباط بین دما، شار تابش خالص با خصوصیات بیوفیزیکی، و کاربری اراضی با استفاده از تصاویر ماهواره‏ای لندست 8، سنجش از دور و سامانه اطلاعات جغرافیایی در منابع طبیعی، 7(4): 96-79.
نادی‏زاده شورابه، س.؛ حمزه، س. و افشاری، س.ک. (1396). پایش مکانی- زمانی تغییرات جزیرة حرارتی شهری و ارتباط آن با تغییرات کاربری اراضی و پوشش با ادغام داده‏های اپتیک و حرارتی سنجش از دور، همایش ملی ژئوماتیک.
هاشمی دره بادامی، س.؛ نورایی‏صفت، ا.؛ کریمی، س. و نظری، س. (1394). تحلیل روند توسعة جزیرة حرارتی شهری در رابطه با تغییر کاربری اراضی/ پوشش با استفاده از سری زمانی تصاویر لندست، سنجش از دور و سامانه اطلاعات جغرافیایی در منابع طبیعی، 6(3): 28-15.
Akbari, H. (2005). Energy Saving Potentials and Air Quality Benefits of Urban Heat Island Mitigation, Lawrence Berkeley National Laboratory, pp. 1-19.
Alavipanah, S.; Hashemi Darrehbadami, S. and 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.
Almusaed, A. (2011). The urban heat island phenomenon upon urban components, In Biophilic and Bioclimatic Architecture, Springer London, pp. 139-150.
Balling, R. and Brazel, S.W. (1988). High-resolution surface temperature patterns in a complex urban terrain, Photogrammetric engineering and remote sensing, 54(9): 1289-1293.
Behbahani, S.M.R.; Rahimikhoob, A. and Nazarifar, M.H. (2009). Comparison of Some Split-window Algorithms to Estimate Land Surface Temperature from AVHRR Data in Southeastern Tehran, Desert, 14(2): 157-161.
Bokaie, M.; Zarkesh, M.K.; Arasteh, P.D. and Hosseini, A. (2016). Assessment of Urban Heat Island based on the relationship between land surface temperature and land use/land cover in Tehran, Sustainable Cities and Society, 23: 94-104.
Chander, G. and 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.
Chander, G.; Markham, B.L. and Helder, D.L. (2009). Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors, Remote sensing of environment, 113(5): 893-903.
Chen, X.L.; Zhao, H.M.; Li, P.X. and 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.
Chi, M.; Feng, R. and Bruzzone, L. (2008). Classification of hyperspectral remote-sensing data with primal SVM for small-sized training dataset problem, Advances in space research, 41(11): 1793-1799.
Deng, C. and Wu, C. (2013). Examining the impacts of urban biophysical compositions on surface urban heat island: A spectral unmixing and thermal mixing approach, Remote Sensing of Environment, 131: 262-274.
Ding, H. and Shi, W. (2013). Land-use/land-cover change and its influence on surface temperature: a case study in Beijing City, International Journal of Remote Sensing, 34(15): 5503-5517.
Dontree, S. (2010). Relation of land surface temperature (LST) and land use/land cover (LULC) from remotely sensed data in Chiang Mai—Lamphun basin, In SEAGA conference.
Effat, H.A. and Hassan, O.A.K. (2014). Change detection of urban heat islands and some related parameters using multi-temporal Landsat images; a case study for Cairo city, Egypt, Urban Climate, 10: 171-188.
Firozjaei, M.K.; Kiavarz, M.; Alavipanah, S.K.; Lakes, T. and 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.
Founda, D. (2011). Evolution of the air temperature in Athens and evidence of climatic change: A review, Advances in Building Energy Research, 5(1): 7-41.
Gallo, K.P. and Owen, T.W. (1999). Satellite-based adjustments for the urban heat island temperature bias, Journal of Applied Meteorology, 38(6): 806-813.
Gallo, K.P.; Mc Nab, A.L.; Karl, T.R.; Brown, J.F.; Hood, J.J. and 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.
Gao, B.C. (1996). NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space, Remote sensing of environment, 58(3): 257-266.
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): 317-329.
Guo, G.; Wu, Z.; Xiao, R.; Chen, Y.; Liu, X. and Zhang, X. (2015). Impacts of urban biophysical composition on land surface temperature in urban heat island clusters, Landscape and Urban Planning, 135: 1-10.
Haashemi, S.; Weng, Q.; Darvishi, A. and Alavipanah, S.K. (2016). Seasonal variations of the surface urban heat island in a semi-arid city, Remote Sensing, 8(4): 352.
Habibi, S.M. and Hourcade, B. (2005). Atlas of Tehran metropolis, Tehran: Urban Processing and Planning Co, 1.
Heinl, M.; Hammerle, A.; Tappeiner, U. and Leitinger, G. (2015). Determinants of urban–rural land surface temperature differences–A landscape scale perspective, Landscape and Urban Planning, 134: 33-42.
Hu, Y. and Jia, G. (2010). Influence of land use change on urban heat island derived from multi‐sensor data, International Journal of Climatology, 30(9): 1382-1395.
Jensen, J.R. (1986). Introductory digital image processing: a remote sensing perspective, Univ. of South Carolina, Columbus.
Jiang, J. and Tian, G. (2010). Analysis of the impact of land use/land cover change on land surface temperature with remote sensing, Procedia environmental sciences, 2: 571-575.
Jimenez-Munoz, J.C. and Sobrino, J.A. (2008). Split-window coefficients for land surface temperature retrieval from low-resolution thermal infrared sensors, IEEE geoscience and remote sensing letters, 5(4): 806-809.
Kachar, H.; Vafsian, A.R.; Modiri, M.; Enayati, H. and Nezhad, A.S. (2015). Evaluation of Spatial and Temporal Distribution Changes of LST Using Landsat Images (Case Study: Tehran). The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(1): 351-356.
Karimi Firozjaei, M. and Kiavrz Mogadam, M. (2017). Investigating the relationship between temperature, net radiation flux by biophysical properties and lanuse using LandSat 8 satellite imagery. Journal of RS and GIS for Natural Resources, 7(4), 79-96.
Lilly Rose, A. and 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 climate, Yokohama, Japan (Vol. 29).
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): 1535-1552.
Liu, Y.; Wu, C.; Peng, D.; Xu, S.; Gonsamo, A.; Jassal, R.S.; ... and Chen, J.M. (2016). Improved modeling of land surface phenology using MODIS land surface reflectance and temperature at evergreen needleleaf forests of central North America, Remote Sensing of Environment, 176: 152-162.
Li, X.; Li, W.; Middel, A.; Harlan, S.L.; Brazel, A.J. and Turner, B.L. (2016). Remote sensing of the surface urban heat island and land architecture in Phoenix, Arizona: Combined effects of land composition and configuration and cadastral–demographic–economic factors, Remote Sensing of Environment, 174: 233-243.
Li, X.; Zhou, W.; Ouyang, Z.; Xu, W. and Zheng, H. (2012). Spatial pattern of greenspace affects land surface temperature: evidence from the heavily urbanized Beijing metropolitan area, China, Landscape ecology, 27(6): 887-898.
Lo, C.P.; Quattrochi, D.A. and Luvall, J.C. (1997). Application of high-resolution thermal infrared remote sensing and GIS to assess the urban heat island effect, International Journal of Remote Sensing, 18(2): 287-304.
Karimi Firozjaei, M., Kiavrz Mogadam, M. (2017). Investigating the relationship between temperature, net radiation flux by biophysical properties and lanuse using LandSat 8 satellite imagery. Journal of RS and GIS for Natural Resources, 7(4), 79-96.
Mather, A.S. (1986). Land use, London; New York: Longman, p. 286.
Nadizadeh Shorabeh, S.; Hamzeh, Saeid. and Afshary poor, K. (2017). Spatial-temporal monitoring of urban heat island variations and their relationship to land use and cover changes by integrating remote sensing optical and thermal data, National Geomatics Conference.
Oommen, T.; Misra, D.; Twarakavi, N.K.; Prakash, A.; Sahoo, B. and Bandopadhyay, S. (2008). An objective analysis of support vector machine based classification for remote sensing, Mathematical geosciences, 40(4): 409-424.
Otukei, J.R. and Blaschke, T. (2010). Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms, International Journal of Applied Earth Observation and Geoinformation, 12, S27-S31.
Qin, Z.; Karnieli, A. and Berliner, P. (2001). A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region, International Journal of Remote Sensing, 22(18): 3719-3746.
Rajasekar, U. and Weng, Q. (2009). Urban heat island monitoring and analysis using a non-parametric model: A case study of Indianapolis, ISPRS Journal of Photogrammetry and Remote Sensing, 64(1): 86-96.
Sadeghinia, A.; Alijani, B. and Zeaiean, P. (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), 1-17.
Streutker, D.R. (2002). A remote sensing study of the urban heat island of Houston, Texas, International Journal of Remote Sensing, 23(13): 2595-2608.
Streutker, D.R. (2003). Satellite-measured growth of the urban heat island of Houston, Texas, Remote Sensing of Environment, 85(3): 282-289.
Tayyebi, A. and Jenerette, G.D. (2016). Increases in the climate change adaption effectiveness and availability of vegetation across a coastal to desert climate gradient in metropolitan Los Angeles, CA, USA, Science of the Total Environment, 548: 60-71.
Tayyebi, A.; Shafizadeh-Moghadam, H. and 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.
Vlassova, L.; Perez-Cabello, F.; Nieto, H.; Martín, P.; Riaño, D. and De la Riva, J. (2014). Assessment of methods for land surface temperature retrieval from Landsat-5 TM images applicable to multiscale tree-grass ecosystem modeling, Remote Sensing, 6(5): 4345-4368.
Wang, F.; Qin, Z.; Song, C.; Tu, L.; Karnieli, A. and  Zhao, S. (2015). An improved mono-window algorithm for land surface temperature retrieval from Landsat 8 thermal infrared sensor data, Remote Sensing, 7(4): 4268-4289..
Weng, Q. (2001). A remote sensing? GIS evaluation of urban expansion and its impact on surface temperature in the Zhujiang Delta, China, International journal of remote sensing, 22(10): 1999-2014.
Weng, Q.; Liu, H. and Lu, D. (2007). Assessing the effects of land use and land cover patterns on thermal conditions using landscape metrics in city of Indianapolis, United States, Urban ecosystems, 10(2): 203-219.
Xian, G. and 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.
Yuan, F. and 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.
Zareie, S.; Khosravi, H.; Nasiri, A. and Dastorani, M. (2016). Using Landsat Thematic Mapper (TM) sensor to detect change in land surface temperature in relation to land use change in Yazd, Iran, Solid Earth, 7(6): 1551-1564.
Zha, Y.; Gao, J. and Ni, S. (2003). Use of normalized difference built-up index in automatically mapping urban areas from TM imagery, International journal of remote sensing, 24(3): 583-594.
Zhang, Z.; Tang, B.H. and Li, Z.L. (2018). Retrieval of leaf water content from remotely sensed data using a vegetation index model constructed with shortwave infrared reflectances, International Journal of Remote Sensing, 1-11.
Zhou, D.; Zhang, L.; Hao, L.; Sun, G.; Liu, Y. and 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.
Zhou, W.; Huang, G. and Cadenasso, M.L. (2011). 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.
 
Physical Geography Research
Volume 51, Issue 2
July 2019
Pages 263-282

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History

  • Receive Date: 30 October 2018
  • Revise Date: 29 January 2019
  • Accept Date: 29 January 2019

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