Analysis of Spatial - Temporal Structure of the Urban Heat Island in Tehran through Remote Sensing and Geographical Information System

جغرافیا و مخاطرات محیطی

Article 2, Volume 1, Issue 4, January 2013, Pages 1-17   

Document Type: Research Article

DOI: 10.22067/geo.v1i4.16950

Authors

Alireza Sadeghinia^* ; Bohlul Alijani; Parviz Zeaieanfirouzabadi

kharazmi University

Abstract

Introduction
Urbanisation changes the land cover types in urban areas and results in different climatic conditions, which has been termed as “Urban climate”. Urban climates are different from those of less built-up areas in terms of air temperature, humidity, wind speed and direction, and amount of precipitation. The previous researches show that urban areas are warmer than surrounding rural areas creating the "urban heat island" (UHI) (Rose and Devadas; 2009, 2). The buildings, concrete, asphalt and industrial activity of urban areas causes UHI. Replacing natural land cover with pavements, buildings and other infrastructures takes away the natural cooling effects. Also, tall buildings and narrow streets can heat the air trapped between them and reduce airflow. In addition, heat from vehicles, factories and air conditioners adds heat to the surroundings, further exacerbating the heat island effect. UHI can impact local weather and climate, altering local wind patterns, spurring the development of clouds and fog, increasing the number of lightning events, and influencing the rates of precipitation. Furthermore, the poor air quality that results from the increased energy usage for cooling purposes in heat-island city can cause discomfort for humans and affect health, aggravating asthma and promoting other respiratory illnesses (Liu and Zhang, 2011: 158).
Thermal remote sensing data play an important role in the study of UHI. First, UHI is studied using NOAA AVHRR thermal data (Balling and Brazell, 1988: 1289-1293; Gallo et al., 1993: 899-908; Gallo and Owen, 1988: 159-172; Streutker, 2002: 282-289). Later, thermal infrared data TM, ETM+ and ASTER are used to study UHI more precisely (Weng, 2001: 1999-2014; Weng et al., 2006: 1275-1286; Amiri et al., 2009: 2606; Flahatlar et al., 2011). Meanwhile, in some studies, The spatial and temporal variations of UHI are detected by using multi- temporal thermal images of landasat TM and ETM (Mo et al., 2011: 4616-4622).
Several researchers (Akbari, 1379; jangi, 1383; shakiba et al., 1388: 56-69; namdari, 1388; malekpour and taleai, 1389: 89-102, and 1390: 29-42) have studied the UHI in Tehran. But none of them have presented a comprehensive picture of its spatial and temporal variations. Thus, the main objective of this study is to study the spatial and temporal variations of UHI in Tehran urban area.
Study Area
The city of Tehran as capital of Islamic republic of Iran is located between latitudes of 35°33’10“N to 35°50’12“N and longitudes of 51°05’17“E to 51°37’36“E with an average elevation of 1600 m, is surrounded by high mountains in the north and north east. Tehran experiences warm summers and cold winters. Tehran is a major center of culture, industry, commerce and transportation. The biggest city of the country, Tehran exhibits rapid population growth and urban expansion in the form of encroachment to the limited agricultural areas (malekpour et al., 2010). Due to its complicated expansion and urban structure and function the temperature variations are very important for its life and existence. Thus this study has tried to analyse the spatial and temporal variations of its temperature.
Material and Methods
In this paper, multi- temporal Landsat TM images (13 images) are used for the analysis of UHI in Tehran. LST is extracted using the mono-window algorithm presented in Qin et al (2001). Then, using appropriate statistical methods, spatial and temporal variations of LST were analysed. Based on thermal radiance transfer equation, the mono-window algorithm only requires three parameters of emissivity, transmittance and effective mean atmospheric temperature - to retrieve LST from Landsat TM 6. The spatial model to retrieve LST from Landsat TM using ERDAS spatial model tool is designed. In this model, only two measured parameters are required; near-surface air temperature and water vapour content of the atmosphere at the time of satellite pass, which can be easily obtained from synoptic weather stations of Tehran. These two parameters are then converted to air transmittance and effective mean atmospheric temperature. The third required parameter is the emissivity, which can be calculated from the normalised difference vegetation index (NDVI). The remaining useful data can all be obtained from the Meta file in the satellite image data.
It is impossible to compare thermal data from different years directly using absolute LST. Therefore, a normalisation technique (Xu et al., 2009) has been used to compare the computed LST of different years. The LST images were rescaled to the same level between 0 and 1 using the maximum and minimum values for those images. Then temporal variations were explored. The intensity of UHI from 1986 to 2010 was evaluated by the urban heat island ratio index (URI) (Mu et al., 2011). The URI was proposed to quantitatively compare the UHI differences in different years based on the ratio of UHI area to whole city area.
Results and Discussion
The temporal changes of LST during the study period showed that the distribution pattern of LST has changed. The area of moderate and very cool temperature of the city has been decreased but the area of very high temperatures (very hot areas) has been increased significantly. It is completely clear that spatial extent of UHI along with the spatial development of Tehran have been increased during this period of 25 years (1986 to 2010) and the extent of UHI has increased from 83 square kilometres (13 percent) to 99 square kilometres (16 percent). The temporal change of the intensity of UHI also showed that URI has increased. The URI has increased from 0.11 in 1986 to 0.15 in 2010. According to the spatial distribution of LST, UHI has maintained its principal center (Mehrabad airport) and The new hot clusters have emerged in the west (District 21 and west of district 22) and south- west (portion of District 18 and 19) of the Mehrabad airport. Our study showed that UHI of Tehran has extended toward the areas with an intensive decrease in vegetation and the extensive development of industries, working places and warehouses (the west and south-west of Tehran).
Conclusion
In this study, the mono-window algorithm is applied to retrieve LST in Tehran using the Landsat TM data. Then, using appropriate statistical methods, spatial and temporal variations of LST are analysed. Consideration of spatial-temporal variation of LST during the study period showed that the distribution of the UHI in Tehran has changed. Center of the UHI that had already been located on Mehrabad airport has extended toward the west and southwest of Tehran. The metropolitan of Tehran has witnessed a very fast urban sprawl during the study years from 1986 to 2010. The urban expansion was accompanied by increased UHI intensity of 0.11 in 1986 to 0.15 in 2010 over an almost 25-year period and resulted in the development of the severe UHI in the region and thus the degradation of the liveability of the city.

Keywords

Air Pollution; Urbanisation; heat Islands; Tehran Metropolis

References

Akbari H. (2000). Consideration of Temperature distribution pattern of Tehran using Landsat TM thermal data. A dissertation submitted to tarbiatmodarres University for degree of MA of Remote sensing and GIS, department of human science.(In Persian with English Abstract).

Amiri K., Q. Weng., A. alimohamadi., K. alavipanah. (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, Vol. 113, pp. 2606-2617.

Balling R. C., S. W. Brazell. (1988). High resolution surface temperature patterns in a complex urban terrain. Photogrammetric Engineering and remote sensing, Vol. 54, No. 9, pp. 1289-1293.

Chen Q., J. Ren., Z. Li., C. Ni. (2009). Urban Heat Island Effect Research in Chengdu City Based on MODIS Data. In Proceedings of 3rd International Conference on Bioinformatics and Biomedical Engineering, ICBBE 2009, Beijing, China, 11–13 June, 1-5.

Dai X., Z. Guo., L. Zhang., D. Li. (2010). Spatio-temporal exploratory analysis of urban surface temperature field in Shanghai, China.Stoch Environ Res Risk Assess, Vol. 24, pp. 247–257.

Falahatkar S., S. M. Hosseini., A. R. Soffianian. (2011). The relationship between land cover changes and spatial-temporal dynamics of land surface temperature. Indian Journal of Science and Technology, Vol. 4,No.2, pp. 76-81.

Gallo K.P., A. L. McNab., T. R. Karl., J. F. Brown., J. J. Hood., J. D. Tarpley. (1993). The use of NOAA AVHRR data for assessment of the urban heat island effect. Journal of Applied Meteorology, Vol. 32, No.5, pp. 899-908.

Gallo K.P., T. W. Owen. (1998). Assessment of urban heat island: A multi-sensor perspective for the Dallas-Ft. Worth, USA region. Geocarto International, Vol. 13, No. 4, pp. 35-41.

Jangi A. A. (2004). The role of location and morphology of city in the air quality of city by GIS and RS (the case study: Tehran). A dissertation submitted to tarbiatmoalem University for degree of MA of Urban geography, department of geographical sciences. (In Persian with English Abstract).

Li J. (2006). Estimating land surface temperature from Landsat-5 TM. Remote Sens. Technol. Appl, Vol. 21, pp. 322-326.

Liu L., Y. Zhang. (2011). Urban heat island analysis using the landsat TM data and ASTER data.Remote sens, Vol. 3, pp. 1535- 1552.

Malekpour P., M. Taleai., Y. Rezaei. (2010). Remote sensing-based spatial-temporal analysis of urban land surface temperature related to urban development: a case study of Tehran, MRSS2010, 6th International Remote Sensing & GIS Conference and Exhibition, April 28-29, 2010, Putra World Trade Centre, Kuala Lumpur, Malaysia.

Malekpour P., M. taleai. (2010). Consideration of land surface temperature and land use/ land cover changes by using landsat ETM (case study: Tehran). Remote sensing and GIS Iran(Tehran), Vol. 2, No. 3, pp. 89-102. (In Persian with English Abstract).

Malekpour P., M. taleai. (2011). Modeling relationship land use/land cover to land surface temperature by using ASTER data. Ecology (Tehran), Vol. 58, pp. 29-42. (In Persian with English Abstract).

Mo X., C. Cheng., F. Zhai., H. Li. (2011). Study on temporal and spatial variation of the urban heat island based on Landsat TM/ETM+ in central city and Binhai New Area of Tianjin, Multimedia Technology (ICMT), 2011 International Conference on. 26-28 July 2011, pp. 4616-4622.

Musavibaighi M., B. Ashraf., A. Farid Hoseini., A. Mianabadi. (2012). consideration Urban Heat Island of Mashhad by applying satellite image and Fractal method. Geography and invironmental hazard, Vol. 1, pp. 35-49. (In Persian with English Abstract).

Namdari S. (2009). Extraction of urban heat island in urban regions by satellite images (the case study: Tehran). A dissertation submitted to shahidbeheshti University for degree of MA of remote sensing and GIS, department of geology sciences. (In Persian with English Abstract).

Qin Z., A. Karnieli., P. Berliner. (2001). A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region. Int. J. Remote Sensing, Vol. 22, No. 18, pp. 3719–3746.

Rose A.L., M. D. Devadas. (2009). Analysis of land surface temperature and land use/land cover types using remote sensing imagary a case inchennal city, india. The seventh International Conference on Urban Climate, 29 June - 3 July 2009, Yokohama, Japan.

Shakiba A., P. zieaiyan., D. Ashourlu., S. Namdari. (2009). Analysis of relationship land use/land over to urban heat island of Tehran. Remote sensing and GIS Iran(Tehran), Vol. 1, pp. 39-56. (In Persian with English Abstract).

Sobrino J. A., J. C. Jimenez-Munoz., J. El-Kharraz., M. Gomez., M. Romaguera., G. Soria. (2004). Single-channel and two-channel methods for land surface temperature retrieval from DAIS data and its application to the Barrax site.Int. J. Remote Sensing, Vol. 25, No. 1, pp. 215–230.

Streutker D. R. (2003). Satellite-measured growth of the urban heat island of Houston, Texas. Remote Sensing of Environment, Vol. 85, pp. 282–289.

Sun Q., J. Tan., Y. Xu. (2010). An ERDAS image processing method for rettieving LST and describing urban heat evolution: A case study in the Pearl River Delta Region in South China. Environ Earth Sci, Vol. 59, pp. 1047-1055.

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, Vol. 22, No. 10, pp. 1999-2014.

Weng Q., D. Lu., B. Liang. (2006). Urban Surface Biophysical Descriptors and Land Surface Temperature Variations. Photogrammetric Engineering & Remote Sensing, Vol. 72, No. 11. pp. 1275–1286.

Xu H., B. Chen. (2004). Remote sensing of the urban heat island and its changes in Xiamen City of SE China. Journal of Environmental Sciences, Vol. 16, pp. 276-281.

Xu H., F. Ding., X. Wen. (2009). Urban expansion and Heat Island Dynamics in the Quanzhou Region. China.IEEE journal of selected topics in applied earth observations and remote sensing, Vol. 2, No. 2, pp. 74- 79.

Xu H., Y. Chen., S. Dan., W. Qiu. (2011). Spatial and temporal analysis of urban heat island effect in Chengdu city by remote sensing. Geoinformatics, 2011 19th international conference on, shanghai, 24-26 June 2011, pp. 1-5.

Zhang J., Y. Wang., Y. Li. (2006) . A C++ program for retrieving land surface temperature from the data of Landsat TM/ETM+ band6. Computers & Geosciences, Vol. 32, pp. 1796–1805.