عنوان مقاله [English]
The expansion of cities and urbanization and the gradual increase in the number of major cities in the world, especially in developing countries on the one hand, and the growth of cities, concentration and population accumulation on the other, cause problems such as the emergence of heat islands. Urban heat islands are the obvious negative impacts of urbanization that largely depends on land use and land cover type, most aspects of urbanization become apparent when humans interfere with the natural structure of the earth and alter the natural landscape of the earth, causing many environmental problems such as urban heat islands in general, urban heat island is the result of the complex impacts of urban processes on its climate. These processes cause cities to be surrounded by a hot air mass that is about 120 meters in height during the day and more than twice as much at night. In this phenomenon, the city center has the highest temperature in comparison to the surrounding rural areas, and with the departure from the city center this temperature and height of the hot air mass decreases. This is called the urban heat island. In most studies, it is concluded that urban vegetation reduces surface temperature, as opposed to the inconsistency between land surface temperature and land use in land use. The objectives of this study are to determine the distribution of land surface temperature in land use types, as well as to investigate the difference between land surface temperature with land use types and vegetation composition, and finally to analyze the impact of human factors on land surface temperature. This study is expected to provide a better understanding of urban heat islands by analyzing land use types and land cover and social and economic interactions. the results of this study can guide managers in planning to regulate urban socio-economic activities to reduce urban heat islands.
District 6 of Tehran is one of the relatively old districts of Tehran which is located in the central area of Tehran. District 6, as one of the busiest areas in Tehran, has a residential density of 75 percent, with 30 percent allocated to transportation networks.This study uses the Landsat 8 satellite image on August 7, 2016. The images are available free of charge at the US Geological Survey. In order to complete the input parameters for mapping the surface temperature using satellite images, the Modis water vapor product with a spatial resolution of 5000 m was used. For visual interpretation, 1: 10000 maps were used, and the type of files of the target area extracted from 1: 2000 maps for 2015 extracted from the Tehran Information Technology Organization, In this study we consider only the vegetation case with respect to available data. There are 5 types of land uses in this area and the user map has been prepared using Arc GIS software.
1- Old residential area (over 25 years old)
2- New residential area (between 5 and 15 years)
3. Wasteland (landfills vacant)
4- Industrial areas (including various industrial activities such as factory and warehouse space)
5. Organizational Areas (Infrastructure Related to Schools, Colleges, Universities and Research Institutions)
2. Results and discussion
Approximately 49% of the total area under study was considered for all types of land uses, including 6.4% new residential, 21% old residential, 2.5% waste, 0.6% industrial and 18.3% Percentage of organizational usage. Data provided by the municipality's ICT and satellite imagery used to match the time of preparation. In this study, based on the findings, the highest surface temperature is related to industrial and organizational use, and the surface temperature of the wasteland is ranked third after organizational use, The average value of land surface temperature varies with different types of land use, indicating that the factors affecting the land surface temperature vary by land use. Linear correlation analysis showed that the mean land surface temperature significantly depended on both land cover composition and land use. The results show that there is a slight correlation between vegetation indices in each land use type. The land cover composition has a direct relationship with the land cover metrics, but the correlation coefficient of the vegetation cover composition and measures varies among different land use types. The findings show that the composition of the green space in small plots is more sensitive to the surface temperature of the earth. The results also suggest that there is a complex mechanism in urban heat islands, which may be caused not only by the biophysical process but also by human resources. The variations in land surface temperature between different types of land use indicate that energy consumption and human heat emission have important effects on the land surface temperature. In human sources of heat dissipation, such as human metabolism, the presence of buildings and traffic significantly contribute to the increase of urban heat and the creation of urban islands. The intensity of heat varies according to the type of climate, population density and intensity of industrial and commercial activities.
The findings of this study show that land use has the potential to explain the effects of complex human activities in urban areas compared to land cover. these findings highlight the contradictory effects of land use composition and land cover on urban heat islands, which not only help to better understand the mechanism of urban heat islands but also provide practical solutions for urban planning and management. Surface temperatures can be reduced by optimizing vegetation patterns (based on the relationship between land surface temperature and land cover) for any human-assisted use. to reduce the effect of urban heat islands, increasing vegetation density and scattering, it is also advisable to create green roofs or roof gardens and maintain plants in buildings.
Keywords: "Surface temperature", "Landsat 8", "Landscape measurements", "Urban heat islands".
10. Han, M. Y.; Shao, L.; Li, J. S.; Guo, S.; Meng, J.; Ahmad, B.; ... and Chen, G. Q., 2014, Emergy-based hybrid evaluation for commercial construction engineering: a case study in BDA, Ecological indicators, Vol. 47, PP. 179-188.
11. Hubacek, K. and Sun, L., 2001, A scenario analysis of China's land use and land cover change: incorporating biophysical information into input–output modeling, Structural change and economic dynamics, Vol. 12, No. 4, PP. 367-397.
12. Hu, Y. and Jia, G., 2010, Influence of land use change on urban heat island derived from multi‐sensor data, International Journal of Climatology, Vol. 30, No. 9, PP.1382-1395.
13. Jiménez-Muñoz, J. C.; Sobrino, J. A.; Skoković, D.; Mattar, C. and Cristóbal, J., 2014, Land surface temperature retrieval methods from Landsat-8 thermal infrared sensor data, IEEE Geoscience and remote sensing letters, Vol. 11, No. 10, PP. 1840-1843.
14. Jahanbakhsh, S., 1993, Investigating the effective microclimate factors in city temperature,Journal of Geographical Research, No. 26, PP.107-120
15. Khaledi, SH., 1996, Practical meteorologist. Tehran, Qom. PP. 28-30
16. Li, H. B.; Yu, S.; Li, G. L. and Deng, H., 2012, Lead contamination and source in Shanghai in the past century using dated sediment cores from urban park lakes, Chemosphere, Vol. 88, No. 10, PP. 1161-1169.
17. Li, J.; Song, C.; Cao, L.; Zhu, F.; Meng, X. and Wu, J., 2011, Impacts of landscape structure on surface urban heat islands: A case study of Shanghai, China, Remote Sensing of Environment, Vol. 115, No. 12, PP. 3249-3263.
18. Li, X.; Zhou, W. and Ouyang, Z., 2013, Relationship between land surface temperature and spatial pattern of greenspace: What are the effects of spatial resolution?, Landscape and Urban Planning, No. 114, PP. 1-8.
19. 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, Vol. 3, No. 7, PP. 1535-1552.
20. Pal, S. and Ziaul, S. K., 2017, Detection of land use and land cover change and land surface temperature in English Bazar urban centre, The Egyptian Journal of Remote Sensing and Space Science, Vol. 20, No. 1, PP. 125-145.
21. Mcgarigal, K. and Marks, B. J., 1995, Spatial pattern analysis program for quantifying landscape structure. Gen. Tech. Rep. PNW-GTR-351, US Department of Agriculture, Forest Service, Pacific Northwest Research Station, PP. 1-122.
23. Sailor, D. J., 2011, A review of methods for estimating anthropogenic heat and moisture emissions in the urban environment, International journal of climatology, Vol. 31, No. 2, PP. 189-199.
24. Sobrino, J. A. and Raissouni, N., 2000, Toward remote sensing methods for land cover dynamic monitoring: Application to Morocco, International journal of remote sensing, Vol. 21, No. 2, PP. 353-366.
25. Shahgedanova, M. and Burt, T., 1998, Urban heat islands, Geography Review, No. 11, PP. 36-41.
26. Valor, E. and Caselles, V., 1996, Mapping land surface emissivity from NDVI: Application to European, African, and South American areas, Remote sensing of Environment, Vol. 57, No. 3, PP. 167-184.
27. 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, Vol. 6, No. 5, PP. 4345-4368.
28. 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, Vol. 10, No. 2, PP. 203-219.
29. Weifeng, L.; Yang, B.; Qiuwen, CH.; Kate, H.; Xiaohua, J. and Chunmeng, H., 2014, Discrepant impacts of land use and land cover on urban heat islands: A case study of Shanghai, China, Ecological Indicators, No. 47, PP. 171-178.
30. Wiedmann, T.; Schandl, H.; Lenzen, M.; Moran, D.; Suh, S.; West, J. and Kanemoto, K., 2013, The material footprint of nation, PNAS.Vol.112, No. 20, PP. 6271-6276
31. Yu, X.; Guo, X. and Wu, Z., 2014, Land surface temperature retrieval from Landsat 8 TIRS—Comparison between radiative transfer equation-based method, split window algorithm and single channel method, Remote Sensing, Vol. 6, No. 10, PP. 9829-9852.
32. 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, Vol. 7, No. 6, PP. 1551-1564.
33. Zhang, X.; Zhong, T.; Feng, X. and Wang, K., 2009, Estimation of the relationship between vegetation patches and urban land surface temperature with remote sensing, International Journal of Remote Sensing, Vol. 30, No. 8, PP. 2105-2118.
34. Zhou, Y.; Weng, Q.; Gurney, K. R.; Shuai, Y. and Hu, X., 2012, Estimation of the relationship between remotely sensed anthropogenic heat discharge and building energy use, ISPRS Journal of Photogrammetry and Remote Sensing, No. 67, PP. 65-72.
35. Zhou, X.; Su, Z.; Anishkin, A.; Haynes, W. J.; Friske, E. M.; Loukin, S. H.; ... and Saimi, Y., 2007, Yeast screens show aromatic residues at the end of the sixth helix anchor transient receptor potential channel gate, Proceedings of the National Academy of Sciences, Vol. 104, No. 39, PP. 15555-15559.
36. 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, Vol. 102, No. 1, PP. 54-63.