Document Type: ORIGINAL RESEARCH PAPER

Authors

1 Department of Environmental Sciences, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran

2 Department of Environmental Sciences, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran

3 Renewable Energy and Energy Efficiency Group, Department of Infrastructure Engineering, Melbourne School of Engineering, The University of Melbourne, VIC 3010, Australia

Abstract

Cities are experiencing rapid population growth and consequently extensive urbanization. Land-use/land-cover change is one of the important elements worldwide, which significantly affect the environment. This study aims to describe the emergence of urban heat and cool islands as a result of changes in land-use/land-cover. Land surface temperature over a 32-year period in Isfahan city, Iran was retrieved. The results confirmed the effect of land-use/land-cover change on Landsat land surface temperature. The average land surface temperature changed from 37.5°C in 1985 to 42.7°C in 2017 during August. The highest land surface temperature in the study area for both years occurred on bare soils (40.66°C in 1985 and 45.88°C in 2017). The second highest Landsat land surface temperature was recorded in central parts of the city with dense built-up covers (36.93°C in 1985 vs 42.45°C in 2017). The central parts of the city were found to have a lower Landsat land surface temperature compared to bare soils, which contributes to the formation of urban cool islands. As expected, water bodies and vegetation had a lower Landsat land surface temperature compared to other land covers. The results also showed changes in land use types during 1985 and 2017, with an increase in water bodies (148.82%) and built-up areas (39.67%) and a decrease in vegetation (20.08%) and bare soil (12.42%). The areas converted from vegetation to built-up experienced an increase in Landsat land surface temperature, which confirmed the effect of land-use/land-cover on microclimate.

Graphical Abstract

Highlights

  • LULC and consequently LST had experienced extensive alterations in a 32-year period in Isfahan, Iran;
  • This study confirmed the decreasing effect of vegetation and the increasing effect of built-up areas on LST variations, which are also emphasized in other studies;
  • Increase of  Zayandehrud water content resulted in cool temperature in some parts of the city in 2017 compared to 1985;
  • Urban green areas significantly reduced the urban temperature through evaporative cooling. Therefore, urban centers experienced a lower temperature compared to the surrounding bare lands.

Keywords

Main Subjects

Ahmad, F., (2012). Detection of change in vegetation cover using multi-spectral and multi-temporal information for District Sargodha, Pakistan. Soc. Nat., 24(3): 557-571 (15 pages).

Ahmad, A.; Quegan, S., (2012). Analysis of maximum likelihood classification on multispectral data. Appl. Math. Sci., 6(129): 6425-6436 (12 pages).  

Al-Ali, A.; Mubarak, H., (2015). The effect of land cover on the air and surface urban heat island of a desert Oasis. Ph.D., Durham University.

Alagu Raja, R.A.; Anand, V.; Senthil Kumar, A.; Sandeep Maithani, V.; Kumar, A., (2013). Wavelet-based post-classification change detection technique for urban growth monitoring. J. Indian Soc. Remote Sens., 41: 35-43 (9 pages).

Alavipanah, S.; Wegmann, M.; Qureshi, S.; Weng, Q.; Koellner, T., (2015). The role of vegetation in mitigating urban land surface temperatures: A case study of Munich, Germany during the warm season. Sustainability,7(4): 4689-4706 (18 pages).

Amanollahi, J.; Tzanis, C.; Ramli, M. F.; Abdullah, A. M., (2016). Urban heat evolution in a tropical area utilizing Landsat imagery. Atmos. Res., 167: 175-182 (8 pages).

Bai, L.; Woodward, A.; Liu, Q., (2016). County-level heat vulnerability of urban and rural residents in Tibet, China. J. Environ. Health., 15(1): 3-13 (11 pages).

Barsi, J. A.; Schott, J. R.; Hook, S. J.; Raqueno, N. G.; Markham, B. L.; Radocinski, R. G., (2014). Landsat-8 thermal infrared sensor (TIRS) vicarious radiometric calibration. Remote Sens., 6(11): 11607-11626 (20 pages).

Bogoliubova, A.; Tymków, P., (2014). Accuracy assessment of automatic image processing for land cover classification of ST. Petersburg protected area. Geodesia et Descriptio Terrarum, 13(1-2): 5-22 (18 pages).

Briottet, X.; Chehata, N.; Oltra-Carrio, R.; Le Bris, A.; Weber, C., (2017). Optical Remote Sensing in Urban Environments, in Baghdadi, N., Zribi, M. (Eds.), Land Surf. Remote Sens. Urban Coast. Areas. Elsevier. 1- 62 (62 pages).

Butt, A.; Shabbir, R.; Ahmad, S. S.; Aziz, N., (2015). Land use change mapping and analysis using remote sensing and GIS: A case study of Simly watershed, Islamabad, Pakistan. Egypt. J. Remote Sens. Space Sci., 18(2): 251-259 (9 pages).

Carlson, T.; Augustine, J.; Boland, F., (1977). Potential application of satellite temperature measurements in the analysis of land use over urban areas. Bull. Am. Meteorol. Soc., 1301-1303 (3 pages).

Charabi, Y.; Bakhit, A., (2011). Assessment of the canopy urban heat island of a coastal arid tropical city: The case of Muscat, Oman. Atmos. Res., 101(1-2): 215-227 (13 pages).

Chen, F.; Yang, X.; Zhu, W., (2014). WRF simulations of urban heat island under hot-weather synoptic conditions: The case study of Hangzhou City, China. Atmos. Res., 138: 364-377 (14 pages).

Clinton, N.; Gong, P., (2013). MODIS detected surface urban heat islands and sinks: Global locations and controls. Remote Sens. Environ., 134: 294-304 (11 pages).

Cui, Y. Y.; De Foy, B., (2012). Seasonal variations of the urban heat island at the surface and the near-surface and reductions due to urban vegetation in Mexico City. J. Appl. Meteorol. Climatol., 51(5): 855-868 (14 pages).

Deng, Y.; Wang, S.; Bai, X.; Tian, Y.; Wu, L.; Xiao, J.; Chen, F.; Qian, Q., (2018). Relationship among land surface temperature and LUCC, NDVI in typical karst area. Sci. Rep., 8(1): 641-653 (13 pages).

Dong, F.; Chen, J.; Yang, F., (2018). A Study of Land Surface Temperature Retrieval and Thermal Environment Distribution Based on Landsat-8 in Jinan City. Conf. Ser.: Earth Environ. Sci., 118 (11 pages).

El-Hattab, M. M., (2016). Applying post-classification change detection technique to monitor an Egyptian coastal zone (Abu Qir Bay). Egypt. J. Remote Sens. Space Sci., 19(1): 23-36 (14 pages).

Gebeyehu Admasu, T., (2017). Monitoring trends of greenness and LULC (land use/land cover) change in Addis Ababa and its surrounding using MODIS time-series and LANDSAT Data. Master of Science, Lund University. Sweden.

Georgescu, M.; Moustaoui, M.; Mahalov, A.; Dudhia, J., (2011). An alternative explanation of the semiarid urban area “Oasis Effect”. J. Geophys. Res. D: Atmos., 116(D24). (13 pages).

Giannini, M.; Belfiore, O.; Parente, C.; Santamaria, R., (2015). Land Surface Temperature from Landsat 5 TM images: comparison of different methods using airborne thermal data. J. Eng. Sci. Technol. Rev., 8(3): 83-90 (8 pages).

Howard, L., (1818). The climate of London: Deduced from Meteorological Observations. Cambridge.

Islam, M. S.; Islam, K. S., (2013). Application of thermal infrared remote sensing to explore the relationship between land use-land cover changes and urban heat Island effect: a case study of Khulna City. Journal of BIP, 6: 49-60 (12 pages).

Jeevalakshmi, D.; Reddy, S.; Manikiam, B., (2017). Land Surface Temperature Retrieval from LANDSAT data using Emissivity Estimation. Int. J. Appl. Eng. Res, 12(20): 9679-9687 (9 pages).

Joshi, J. P.; Bhatt, B., (2012). Estimating temporal land surface temperature using remote sensing: A study of Vadodara urban area, Gujarat. JGEE, 2(1): 123-130 (8 pages).

Kayet, N.; Pathak, K.; Chakrabarty, A.; Sahoo, S., (2016). Urban heat island explored by co-relationship between land surface temperature vs multiple vegetation indices. Spat. Inf. Res., 24(5): 515-529 (15 pages).

Keramitsoglou, I.; Kiranoudis, C. T.; Ceriola, G.; Weng, Q.; Rajasekar, U., (2011). Identification and analysis of urban surface temperature patterns in Greater Athens, Greece, using MODIS imagery. Remote Sens. Environ., 115(12): 3080-3090 (11 pages).

Kim, S.; Ryu, Y., (2015). Describing the spatial patterns of heat vulnerability from urban design perspectives. Int J Sust Dev World., 22(3): 189-200 (12 pages).

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. Geophys. Res. Lett., 42(22): 9980-9989 (10 pages).

Li, J.; Wang, X.; Wang, X.; Ma, W.; Zhang, H., (2009). Remote sensing evaluation of urban heat island and its spatial pattern of the Shanghai metropolitan area, China. Ecol. Complexity, 6(4): 413-420 (8 pages).

Liu, H.; Weng, Q., (2012). Enhancing temporal resolution of satellite imagery for public health studies: A case study of West Nile Virus outbreak in Los Angeles in 2007. Remote Sens. Environ., 117: 57-71 (15 pages).

Mutiibwa, D.; Strachan, S.; Albright, T., (2015). Land surface temperature and surface air temperature in complex terrain. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 8(10): 4762-4774 (13 pages).

NASA., (2010). Landsat 7 Science Data User Handbook: National Aeronautics and Space Administration.

Njoku, E.G., (2014). Encyclopedia of Remote Sensing., Berlin. Germany. Ed. Springer.

Omran, E., (2012). Detection of land-use and surface temperature change at different resolutions. J. Geogr. Inf. Syst., 4(03): 189-203 (15 pages).

Orhan, O.; Yakar, M., (2016). Investigating Land Surface Temperature Changes Using Landsat Data in Konya, Turkey. ISPRS Archives, 41 (B8): 12-19 (8 pages).

Pal, S.; Ziaul, S., (2017). Detection of land use and land cover change and land surface temperature in English Bazar urban center. Egypt. J. Remote Sens. Space Sci., 20(1): 125-145 (21 pages).

Rajeshwari, A.; Mani, N., (2014). Estimation of land surface temperature of Dindigul district using Landsat 8 data. IJRET, 3(5): 122-126 (5 pages).

Rasul, A.; Balzter, H.; Smith, C., (2016). Diurnal and seasonal variation of surface urban cool and heat islands in the semi-arid city of Erbil, Iraq. Climate, 4(3): 42-58 (17 pages).

Rasul, A.; Balzter, H.; Smith, C.; Remedios, J.; Adamu, B.; Sobrino, J. A.; Srivanit, M.; Weng, Q., (2017). A review on remote sensing of urban heat and cool islands. Land, 6(2): 38.

Seif, A.; Mokarram, M., (2012). Change detection of Gil Playa in the Northeast of Fars Province. Iran Am. J. Sci. Res, 86: 122-130 (9 pages).

Singh, P.; Kikon, N.; Verma, P., (2017). Impact of land use change and urbanization on urban heat island in Lucknow city, Central India. A remote sensing based estimate. Sustain Cities Soc., 32: 100-114 (15 pages).

Sisodia, P. S.; Tiwari, V.; Kumar, A., (2014). Analysis of supervised maximum likelihood classification for remote sensing image. In Recent Advances and Innovations in Engineering (ICRAIE). Jaipur, India.

Sun, Q.; Tan, J.; Xu, Y., (2010). An ERDAS image processing method for retrieving LST and describing urban heat evolution: a case study in the Pearl River Delta Region in South China. Environ. Earth Sci., 59(5): 1047-1055 (9 pages).

Tomlinson, C. J.; Chapman, L.; Thornes, J. E.; Baker, C., (2011). Remote sensing land surface temperature for meteorology and climatology: A review. Meteorol. Appl., 18(3): 296-306 (11 pages).

Varamesh, S.; Hosseini, S.M.; Rahimzadegan, M., (2017). Comparison of conventional and advanced classification approaches by Landsat-8 imagery. App.l Ecol. Environ. Res., 15(3): 1407-1416 (10 pages).

Vlassova, L.; Perez-Cabello, F.; Nieto, H.; Martín, P.; Riaño, D.; 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 Sens., 6(5): 4345-4368 (24 pages).

Xiao, H.; Kopecká, M.; Guo, S.; Guan, Y.; Cai, D.; Zhang, C.; Zhang, X.; Yao, W., (2018). Responses of Urban Land Surface Temperature on Land Cover: A Comparative Study of Vienna and Madrid. Sustainability, 10(2): 1-19 (19 pages).

Yıldırım, Ü.; Erdoğan, S.; Uysal, M., (2011). Changes in the coastline and water level of the Akşehir and Eber Lakes between 1975 and 2009. Water Resour. Manage., 25(3): 941-962 (22 pages).

Youneszadeh, S.; Amiri, N.; Pilesjo, P., (2015). The effect of land use change on land surface temperature in the Netherlands. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W5: 745-748 (4 pages).

Yousefi, S.; Mirzaee, S.; Tazeh, M; Pourghasemi, H.; Karimi, H., (2015). Comparison of different algorithms for land use mapping in dry climate using satellite images: a case study of the Central regions of Iran. Desert., 20 (1): 1-10 (10 pages).

Zhang, Y.; Balzter, H.; Zou, C.; Xu, H.; Tang, F., (2015). Characterizing bi-temporal patterns of land surface temperature using landscape metrics based on sub-pixel classifications from Landsat TM/ETM+. Int. J. Appl. Earth Obs. Geoinf., 42: 87-96 (10 pages).

Zhou, W.; Huang, G.; Cadenasso, M. L., (2011). Does spatial configuration matter? Understanding the effects of land cover pattern on land surface temperature in urban landscapes. Landscape Urban Plann., 102(1): 54-63 (10 pages).

Zhou, W.; Qian, Y.; Li, X.; Li, W.; Han, L., (2014). Relationships between land cover and the surface urban heat island: seasonal variability and effects of spatial and thematic resolution of land cover data on predicting land surface temperatures. Landscape Ecol., 29(1): 153-167 (15 pages).

Zhou, X.; Wang, Y., (2011). Dynamics of Land Surface Temperature in Response to Land‐Use/Cover Change. Geographical Research, 49(1): 23-36 (14 pages). 


Letters to Editor


GJESM Journal welcomes letters to the editor for the post-publication discussions and corrections which allows debate post publication on its site, through the Letters to Editor. Letters pertaining to manuscript published in GJESM should be sent to the editorial office of GJESM within three months of either online publication or before printed publication, except for critiques of original research. Following points are to be considering before sending the letters (comments) to the editor.

[1] Letters that include statements of statistics, facts, research, or theories should include appropriate references, although more than three are discouraged.
[2] Letters that are personal attacks on an author rather than thoughtful criticism of the author’s ideas will not be considered for publication.
[3] Letters can be no more than 300 words in length.
[4] Letter writers should include a statement at the beginning of the letter stating that it is being submitted either for publication or not.
[5] Anonymous letters will not be considered.
[6] Letter writers must include their city and state of residence or work.
[7] Letters will be edited for clarity and length.

CAPTCHA Image