The Role of Land Use Changes in Shaping Surface Temperature in Cities: A Case Study of Isfahan"

Document Type : Full length article

Authors

1 Department of Natural Geography, Faculty of Geography, University of Tehran, Tehran, Iran

2 Department of Natural Geography, Faculty of Geographical Sciences and Planning, Isfahan University, Isfahan, Iran

10.22059/jphgr.2023.361681.1007779

Abstract

ABSTRACT
The expansion and development of cities and the increase of urbanization are some of the current characteristics of human societies, especially in developing countries. Density and population growth in cities have led to the expansion and development of urban areas and changes in urban land use. In this research, a remote sensing technique was used to identify the patterns and examine the spatial changes in the surface temperature in different areas of Isfahan City. Using thermal equations and a similarity centre algorithm, the temperature of the earth's surface has been calculated for the periods of 1365 to 1401. The supervised classification of the most similar method was used to evaluate the changes in the land use of the studied area, which includes built uses, gardens, vegetation, and lands without vegetation. The results of the comparison of land use between 1365 and 1401 within the scope of studies show that during the mentioned period residential use increased by 175.94 square kilometers and garden use by 74.28 km shows a decreasing trend. Also, we see a decrease of 39.03 km in the use of vegetation and a decrease of 217.75 km in the use of areas without vegetation. The results of the earth surface temperature survey show the expansion of the earth surface temperature during the study period in Isfahan city. Based on this, in the studied period, the earth's surface temperature increased in the east and southeast regions. Examining the isothermal lines shows that the temperature of the earth's surface around the city increased significantly in terms of temperature and extent
Extended abstract
Introduction
Howard first coined the term thermal island about a century ago, in 1833. Subsequently, numerous studies were conducted in major cities and industries around the world to find that urbanization caused significant changes. Consideration has been given to meteorological parameters and ground surface features, and consequently, many changes have been made in the local climate. Studies show that the role of thermoelectric sensing is crucial for studying the effects of thermal islands. Thermal remote sensing data have provided the opportunity to dynamically monitor and evaluate urban heat islands. Thermal remote sensing data is a unique source for defining the surface thermal island associated with the urban canopy thermal island. Synoptic meteorological station data have high temporal resolution and long-term overlap. Thermal remote sensing can provide an overview of each city and is particularly important for accurate city-level climate monitoring. Isfahan is at 51 degrees 39 minutes 40 seconds east longitude and 32 degrees 38 minutes 30 seconds north latitude. It is situated in a sedimentary plain that extends to relatively wide plains. Isfahan city covers an area of 106,786 square kilometers. Isfahan Province is one of the central provinces of Iran, with Isfahan city as its capital. The province ranks sixth in terms of area and third in population among all provinces in Iran and holds the first position in terms of urbanization in the country.
 
Methodology
This study used 7 images of Landsat OLI and EMT sensors and Landsat TM multilingual image. These images cover the period 1986 to 2022 (36 years period); due to the intensity of heat in the summer months, these images are for the summer months and then to reduce and eliminate image errors, a series of preprocessing and geometric and radiometric corrections were applied to the images. The supervised method is then used to classify the information. In this method, training samples were used to classify the pixels. Similarity centers algorithm was used to classify the monitoring. This method analyzes the value of bivariate and each unknown pixel based on the variance and covariance of that spectral reaction class. It assumes that the data distribution for each class is based on the normal distribution around the mean pixel of that class. Finally, the kappa method was used for model validation and classification. Analysis of land use changes in Isfahan County from 1986 to 2022 revealed that in 1986, the majority of land use categories were areas without vegetation cover, covering an area of 338.53 square kilometers (61.51%). The least land use category included water bodies and vegetation cover, with areas of 0.72 square kilometers (0.13%) and 40.90 square kilometers (0.89%), respectively. The built-up land use category was scattered in the northern, eastern, and central parts, covering an area of 73.88 square kilometers (24.02%). Orchards accounted for 132.18 square kilometers, representing 24.02% of the total area. The areas without vegetation cover were concentrated in the southern and southwestern regions, specifically in zones 5, 6, 9, and 13, in the eastern part of zone 15, and in the northern part of zone 12.
 
Results and discussion
The study's findings on land use changes in the area from 1986 to 2022 indicate that the built-up area increased from 8.73 square kilometers to 82.249 square kilometers. The vegetation covers also expanded from 9.40 square kilometers to 20.80 square kilometers. However, the area of orchards decreased from 132.01 square kilometers to 90.57 square kilometers. The areas without vegetation cover also decreased from 338.05 square kilometers to 78.120 square kilometers. Finally, the water bodies decreased from 0.72 square kilometers to 0 square kilometers in terms of land use. The examination of the relationship between isotherms (temperature lines) and land use in 1986 revealed that the highest temperature lines were associated with barren lands. Due to a lack of moisture, these lands become extremely hot during the day, leading to temperatures reaching up to 45 degrees Celsius (common in the plains and deserts of this county). Additionally, vegetation cover in the northeastern and northwestern directions acts as a natural modifier and cooler for the city. In this land use category, temperatures reach a minimum of around 30 degrees Celsius. For residential areas, which have experienced the most expansion in urban areas, the average temperature reaches around 40 degrees Celsius. This land use category acts as an intermediate zone among other land uses.
 
Conclusion
Investigations show that most articles have focused on land use changes or thermal islands but have not examined the role of land use and its changes on thermal island formation over 36 years. This study used the most modern and scientific remote sensing algorithms, first using Landsat satellite imagery to investigate the relationship between land use changes and surface temperature. Land use and temperature relationships indicate that residential areas have been replaced in urban areas due to population growth, buildings, cement, and asphalt organizations. These surfaces will absorb the sun rather than reflect it, raising the temperature in urban areas and dominating a particular climate. The results also showed that the Zayandeh Rud River and vegetation in the river water area were moderated.
 
Funding
There is no funding support.
 
Authors’ Contribution
All of the authors approved the content of the manuscript and agreed on all aspects of the work.
 
Conflict of Interest
Authors declared no conflict of interest.
 
Acknowledgments
We are grateful to all the scientific consultants of this paper.

Keywords

Main Subjects


  1. Ahmadi, M., & Dadashi, A. (2017). The Identification of Urban Thermal Islands based on an Environmental Approach, Case Study: Isfahan Province. Geography and Environmental Planning28(3), 1-20. doi: 10.22108/gep.2017.98318.0 [In Persian].
  2. Ahmadi, A., akbari, E., jamalabadi, J., & alemohammad, M. (2022). Impact of land use and vegetation on the formation of thermal islands Case Study: Qainat City. Journal of Applied Researches in geographical Sciences; 22 (64), 79-93 DOI:10.52547/jgs.22.64.79 [In Persian].
  3. Abedin, M., Ghale, E., Aghazadeh, N., & mohamadzadeh sheshegaran, M. (2023). Monitoring the surface temperature and studying the land use relationship with surface temperature using OLI and TM image sensors (Case study: Meshginshahr city). Journal of Applied Researches in geographical Sciences, 22 (67), 375-393. doi: ‌ 10.52547/jgs.22.67.375 [In Persian].
  4. Adel Effat, H., & Abdel Kader, O. (2014). Change detection of urban heat islands and somere lated parameters using multi- temporal Landsat images; a case study for Cairo city. journal of Urban Climate, 10, 171-188. DOI:10.1016/j.uclim.2014.10.011
  5. Allison, E. W. (1989). Mmonitoring drought affected vegetation with AVHRR Digest-Internation Geoscience and Remote Sensing Symposium, 4,1965-1967.
  6. Chen, Q., Ren. J., Li,Z., & Ni, C. (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, Beijing, China, 11–13, 1-5. DOI:10.1109/ICBBE.2009.5163730
  7. Fan, Fenglei., Wang, Yunpeng., & Wang, Zhishi. (2008). Temporal and spatial change detecting (1998-2003) and predicting of land use and land cover in Core corridor of PearlRiver Delta (China) by using TM and ETM+images. Environmental Monitoring and Assessment, 137(1), 127-147.DOI: 10.1007/s10661-007-9734-y
  8. Feizizadeh, B. (2017). Modeling the Trends of the Land Use/Cover Change and Its Impacts on the Erosion System of the Allavian Dam Based on the Remote Sensing and GIS Techniques. Hydrogeomorphology4(11), 21-38. Dio:  20.1001.1.23833254.1396.4.11.2.3[In Persian].
  9. Guo, G., Wu, Z., Xiao, R., Chen, Y., Liu, X., & Zhang, X. (2015). Impacts of urban biophysical composition on land surface temperature in urban heat island clusters. Landscape and Urban Planning, 135 (3), 1–10. DOI:10.1016/j.landurbplan.2014.11.007
  10. MODIS Data. In Proceedings of 3rd International Conference on Bioinformatics and Biomedical Engineering, ICBBE 2009. Beijing, China, 11–13, 1-5.
  11. Kassa, A. (1990). Droughy risk monitoring for Sudan using NDVI, 1882-1993.A Dissertation submitted to the University College London.
  12. Kashki, A., Karami, M., Zandi, R., & Roki, Z. (2021). Evaluation of the effect of geographical parameters on the formation of the land surface temperature by applying OLS and GWR, A case study Shiraz City, Iran. journal Urban Climate, (37), 100832. DOI:10.1016/j.uclim.2021.100832
  13. Kogan, F.N. (1993). United States droughts of late 1980s as seen by NOAA polar orbiting. DOI: 10.1109/IGARSS.1993.322522
  14. Khedmatzadeh, A., mousavi, M., Mohamadi Torkamani, H., & Mohammadi, M. S. (2021). An Analysis of Land Use Changes and Thermal Island Formation in Urmia City exclusion Using Remote Sensing. Regional Planning11(41), 119-134. doi: 10.30495/jzpm.2021.3965 [In Persian].
  15. Liu, L., & 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. DOI:10.3390/rs3071535
  16. Manabe, B. S., Knutson T. R., Stouffer, R. J & Del worth T. L. ) 2001). exploring natural and anthropogenic variation of climate. Q. J. R. Meteor. Soc, 127, 1-24. DOI:10.1002/qj.49712757102
  17. Mallick, J., Kant, Y., & Bharath, B.D. (2008), Estimation of land surface temperature over Delhi using Landsat-7 ETM+. Joural of the Indian geograplay union,12 (3),131-140.
  18. Mazidi, A., & Hoseini, F. S. (2015). Effects of Changing Land Use and Land Cover on the Heat Island in Urban Area of Yazd Using Remote Sensing Data. Geography and Development13(38), 1-12. doi: 10.22111/gdij.2015.1926[In Persian].
  19. Mazidi, A., Omidvar, C., Mozafari, G. A., & Taghizadeh, Z. (2019). Revealing the Changes in Esfahan Heat Island Considering Urban Development. The Journal of Geographical Research on Desert Areas7(1), 21-39. [In Persian].
  20. Maleki, S., Shojaeean, A., & Farahmand, G. (2018). Assessment of temporal-spatial variability of Heat Islands in relation to urban uses-Case study: Urmia City. Scientific-Research Quarterly of Geographical Data (SEPEHR)27(105), 183-197.‌  https://doi.org/10.22131/sepehr.2018.31488 [In Persian].
  21. Nonomura, A., Kitahara, M., & Masuda T. (2009). Impact of land use and land cover changes on theambient temperature in a middle scale city, Takamatsu, in Southwest Japan. Journal of environmental management, 90(11), 3297-3304. DOI:10.1016/j.jenvman.2009.05.004
  22. Rozenstein, O., Qin, Z., Derimian, Y., & Karnieli, A. (2014). Derivation of land surface temperature for Landsat-8 TIRS using a split window algorithm. Sensors, 14(4), 5768-5780. https://doi.org/10.3390/s140405768
  23. Streutker, d. r. (2002). Satellite-measured growth of the urban heat island of Houston, TX. Remote sensing ofenvironment, 85: 282-289. DOI:10.1016/S0034-4257(03)00007-5
  24. Shi, Y., Katzschner, L., & Ng, E. (2017). Modelling the fine-scale spatiotemporal Pattern of Urban heat island effect using land use regression approach on a megacity Science of the Total Environmment 618(8). 1461-1486. DOI:10.1016/j.scitotenv.2017.08.252
  25. Sobrni, J. A., Jimenez-Munoz, J.C., & Paolini, L. (2004). Land surface temperatuee retrieval from LANDSAT TM 5.Remote Sensing of environment, 90(4), 434-440. https://doi.org/10.1016/j.rse.2004.02.003
  26. Soltanimoghadas, R. (2019). Spatial Consequences of Land Use Change in Rural Settlements (Case Study: Qarchak County, Tehran Province). Physical Social Planning6(2), 79-94. doi: 10.30473/psp.2019.6069 [In Persian].
  27. sobhani, B., & mansori, M. (2024). Analyzing the role of temperature changes in urban land uses using Landsat satellite images (Case study of Amol city). Journal of Environmental Science Studies8(4), 7437-7448. doi: 10.22034/jess.2023.392524.2000[In Persian].
  28. Srivastava, P.K., Majumdr, T.J., & Bhattacharya, A.K. (2009). Surfaacetemperature estimatiomn in Singhbhum Shear thermal infrared data. Advances in space research, 4, 1563-1574. https://doi.org/10.1016/j.asr.2009.01.023
  29. Serrano, A., Mateos, V.L., & Garcia, J. A. (1999). Trend Analysis of Monthly Precipitation over the Iberian Peninsula fir the Period 1921-1995. Phys. Chem. EARTH(B), 24, 1-2: 85-90. https://doi.org/10.1016/S1464-1909(98)00016-1
  30. Wang, Y. Ch., Hu, B., Myint, S.W., Feng, Ch., CHOW, Ch.W. T. L., & Passy, P. F. (2018). Patterns of land change and their potential impacts on land surface temperature change in Yangon, Myanmar. Scince of The Total Environment, 643, 738-750. https://doi.org/10.1016/j.scitotenv.2018.06.209
  31. Wang, R., Cai, M., Ren, Ch., Bechtel, B., XU, Y., & Ng, E. (2019). Detecting multi-temporal land cove change and land surface temperature in pearI River Delta by adopting IocaI climate zone. Urban Climate, 28, 1-16. https://doi.org/10.1016/j.uclim.2019.100455
  32. Xiao, R.B., Ouyang, Z.Y., Zheng, H., Li, W.F., Schienke, E.W., & Wang, X.K. (2007). Spatial Pattern of impervious surfaces and their impacts on land surface temperature in Beijing, China. Journal of National Library of Medicine, National Institute of Health, U.S, 19, 250- 256.https://doi.org/10.1016/S1001-0742(07)60041-2
  33. Xingping, Wen., Xiaofeng, Yang., & Guangdao, Hu. (2011). Relationship Between Land Cover Ratio and Urban Heat Island from Remote Sensing and Automatic Weather Stations Data. J Indian Soc Remote Sens, 39, 193–20. https://doi.org/10.1007/s12524-011-0076-4
  34. Yuan, J., & Bauer, M.E. (2011). Comparison of impervious surface area and normalized difference vegetatinon index as indicators urban heat island effects in normalized imagery. Remote sensing of Environment, 106(3), 375-386. https://doi.org/10.1016/j.rse.2006.09.003