Modeling the effect of city structural parameters on city surface temperature based on segments obtained from object-oriented segmentation in Tehran city

Document Type : Original Research

Authors
Department of Remote Sensing and GIS, Humanities faculty, Tarbiat Modares University
Abstract
The warming of the urban environment is one of the consequences of unsustainable growth. This research aims to investigate the possibility of modeling the effect of the structural parameters on the city’s surface temperature in the summer season in Tehran. For this purpose, the Landsat-8 image taken in 2018 was used to calculate the surface temperature. In order to determine the study units in this research, the segmentation method was used on the Sentinel-2 image of 2018, and the ratio of the vegetation cover and the separation of built-up areas from non-built-up ones were extracted using this image. The multi-layer perceptron neural network and the convolutional neural network methods were used to model the effect of urban structural parameters on the surface temperature during the summer. The results obtained from random forest feature selection for the summer indicates that the presence of vegetation and urban uses that include residential and industrial areas, the presence of mixed residential/commercial/administrative areas, and the presence of vegetation affect changes in the urban surface temperature. Further, the information layers of road and population density in this season have an effect on the changing temperature of the earth's surface. Additionally, the results obtained through modeling and t-test of paired samples demonstrate the superiority of the convolutional neural network method, with a root mean square error of 0.61, determination coefficient of 0.62, and 17.75% estimation error, compared to the multi-layer perceptron model, which had 0.82 root mean square error, 0.26 determination coefficient, and 23.34% estimation error.

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_ حقیقت خرازی،ا.(1397).انرژی های حرارتی ناشی از حضور انسان ها در محیط های شهری مطالعه ی موردی شهر تهران. نشریه ی پژوهش های سیاستگذاری و برنامه ریزی شهری،4، 135-691 http://epprjournal.ir/article-1-169-fa.html
_ حسینی،س.،رفیعیان،م.،علوی،ع.(1398).تبین نظری فضاهای نوظهور شهری و بازتاب فضایی آن در شهر تهران.برنامه ریزی و آمایش فضا، 1، 45-67http://hsmsp.modares.ac.ir/article-21-43141-fa.html
_ حسین قلی زاده،ع.،ضیائیان فیروزآبادی،پ.،بیرانوند،پ،(1398).مقایسه ی الگوریتم های مختلف برآورد دما حاصل از گسیلمندی های مختلف با استفاده از تصاویر سنجش از دور.نشریه ی فضای جغرافیایی،72، 39-56http://geographical-space.iau-ahar.ac.ir/article-1-3433-fa.html
_ علیجانی،ب.،طولابی نژاد،م.،صیادی،ف.(1396).محاسبه ی شدت جزایر حرارتی براساس هندسه ی شهری مورد مطالعه:محله ی کوچه باغ تبریز.نشریه ی تحلیل فضایی مخاطرات محیطی،3، 99-112 SID. https://sid.ir/paper/264738/fa
_ عبدالهی،ع.،خبازی،م.،درانی زاده،ز.(1399).مدل سازی تغییرات کاربری اراضی با استفاده از شبکه عصبی پرسپترون چند لایه مطاله ی موردی شهر لاهیجان،نشریه ی برنامه ریزی و آمایش فضا، 1، 49-79 http://hsmsp.modares.ac.ir/article-21-35084-fa.html
_ هادی پور،م.،دارابی،ح.،داودی راد،ع.(1396).بررسی جزایر حرارتی شهری و ارتباط آن با شرایط آلودگی هوا و شاخص های NDVIو NDBI در شهر اراک. فصلنامه ی علمی پژوهشی اطلاعات جغرافیایی سپهر، 112، 249-264
https://doi.org/10.22131/sepehr.2020.38619
Alijani, B. , Toolabi, M. , & Sayadi, F. (2017) . The calculation of the heat islands intensity is based on the urban geometry of the study area : the neighborhood of the rue tabriz. reasech of environmental hazard analysis. No.3, 99-112.
. SID. https://sid.ir/paper/264738/fa ( In Persian).
Abdollahi,A., Khabbazi., M. , & Darani , Z. (2020) . Modeling of land use changes using artificial neural network ( mlp ) for the case study of lahijan city , the issue of planning and space preparation. No.1, 49-79.
http://hsmsp.modares.ac.ir/article-21-35084-fa.html( In Persian).
Akinyemi, F. O., Ikanyeng, M., & Muro, J. (2019). Land cover change effects on land surface temperature trends in an African urbanizing dryland region. City and environment interactions, 4, 100029. Doi:https://doi.org/10.1016/j.cacint.2020.100029
_ Baatz, M. (2000). Multiresolution segmentation: an optimization approach for high quality multi-scale image segmentation. Angewandte geographische informationsverarbeitung, 12-23. doi:https://doi.org/10.20659/jfp.13.1_85
_ Blaschke, T. (2010). Object based image analysis for remote sensing. ISPRS journal of photogrammetry and remote sensing, 65(1), 2-16. doi:https://doi.org/10.1016/j.isprsjprs.2009.06.004
Barnard, G. (1984). Comparing the means of two independent samples. Journal of the Royal Statistical Society Series C: Applied Statistics, 33(3), 266-271. doi:https://doi.org/10.2307/2347702
_ Chen, L., Wang, X., Cai, X., Yang, C., & Lu, X. (2021). Seasonal variations of daytime land surface temperature and their underlying drivers over Wuhan, China. Remote Sensing, 13(2), 323. doi:https://doi.org/10.3390/rs13020323
_ Drǎguţ, L., Tiede, D., & Levick, S. R. (2010). ESP: a tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data. International Journal of Geographical Information Science, 24(6), 859-871. doi:https://doi.org/10.1080/13658810903174803
_ Dey, V., Zhang, Y., & Zhong, M. (2010). A review on image segmentation techniques with remote sensing perspective (Vol. 38, pp. 31-42). Vienna, Austria: na.
_ Feng, Y., Du, S., Myint, S. W., & Shu, M. (2019). Do urban functional zones affect land surface temperature differently? A case study of Beijing, China. Remote Sensing, 11(15), 1802. doi:https://doi.org/10.3390/rs11151802
_ Hu, Y., Zhang, Q., Zhang, Y., & Yan, H. (2018). A deep convolution neural network method for land cover mapping: A case study of Qinhuangdao, China. Remote Sensing, 10(12), 2053. doi:https://doi.org/10.3390/rs10122053
Haghighat kharazi ,A. (2016) . Thermal energy induced by human presence in urban environments of tehran city . journal of policy research and urban planning , No.4 , 135 – 691.
http://epprjournal.ir/article-1-169-fa.html (In Persian).
Hussaini , S. , Rafiean,M., & Alavi , A. (2017) . The theoretical explanation of the emerging urban spaces and its spatial reflection in tehran . the issue of planning and space preparation,No.1, 45-67.
http://hsmsp.modares.ac.ir/article-21-43141-fa.html ( In Persian).
Hussain gholizadeh, A. ,Zeiaeian ferozabadi, P., & Beyranvand,P.(2018) Comparison of differents algorithms for estimating the temperature obtained from various LSE using remote sensing images . geographical space publication. No.72, 39-56.
http://geographical-space.iau-ahar.ac.ir/article-1-3433-fa.html( In Persian).
Hadipoor ,M., Darabi, H., & Davoodi rad,A. (2017) . Investigation of urban heat islands and its relation with air pollution conditions and NDVIand NDBI indices in arak city . journal of scientific information in the sphere. No.112, 246-264.
https://doi.org/10.22131/sepehr.2020.38619 ( In Persian)
Huang, G., Hu, C., & Wang, Z. Exploring the seasonal relationship between spatial and tem-poral features of land surface temperature and its potential drivers: the case of Chengdu metropolitan area, China. Frontiers in Earth Science, 11, 1226795.doi:https://doi.org/10.3389/feart.2023.1226795
Kim, J. H., Gu, D., Sohn, W., Kil, S. H., Kim, H., & Lee, D. K. (2016). Neighborhood landscape spatial patterns and land surface temperature: An empirical study on single-family residential areas in Austin, Texas. International journal of environmental research and public health, 13(9), 880.
doi: https://doi.org/10.3390/ijerph13090880
_ Koller, D., & Sahami, M. (1996). Toward optimal feature selection. Paper presented at the ICML.http://hdl.handle.net/1721.1/9658
Lin, L., Chen, J., & Cai, C. (2012). High rate of nitrogen fertilization increases the crop water stress index of corn under soil drought. Communications in soil science and plant analysis, 43(22), 2865-2877. doi:. https://doi.org/10.1080/00103624.2012.728265
_ Mallick, J., & Rahman, A. (2012). Impact of population density on the surface temperature and micro-climate of Delhi. Current Science, 1708-1713. doi:https://www.jstor.org/stable/24084829
Morabito, M., Crisci, A., Georgiadis, T., Orlandini, S., Munafò, M., Congedo, L., ... & Zazzi, M. (2017). Urban imperviousness effects on summer surface temperatures nearby residential buildings in different urban zones of Parma. Remote Sensing, 10(1), 26.
doi:https://doi.org/10.3390/rs10010026
Oke, T. R. (1982). The energetic basis of the urban heat island. Quarterly Journal of the Royal Meteorological Society, 108(455), 1-24.doi:https://doi.org/10.1002/qj.49710845502
_ Lin, L., Chen, J., & Cai, C. (2012). High rate of nitrogen fertilization increases the crop water stress index of corn under soil drought. Communications in soil science and plant analysis, 43(22), 2865-2877. doi:. https://doi.org/10.1080/00103624.2012.728265
Peng, S., Piao, S., Ciais, P., Friedlingstein, P., Ottle, C., Bréon, F. M., ... & Myneni, R. B. (2012). Surface urban heat island across 419 global big cities. Environmental science & technology, 46(2), 696-703.doi:https://doi.org/10.1021/es2030438
_ Sedgwick, P. (2010). Independent samples t test. Bmj, 340. doi:https://doi.org/10.1136/bmj.c2673
_ Sekertekin, A., & Bonafoni, S. (2020). Sensitivity analysis and validation of daytime and nighttime land surface temperature retrievals from Landsat 8 using different algorithms and emissivity models. Remote Sensing, 12(17), 2776. doi:https://doi.org/10.3390/rs12172776
Ullah, S., Ullah, R., Javed, M. F., Sajjad, R. U., Ullah, I., Mohamed, A., & Ullah, W. (2023). Land Use Land Cover (LULC) and Land Surface Temperature (LST) Changes and its Relationship with Human Modification in Islamabad Capital Territory, Pakistan.doi:https://doi.org/10.21203/rs.3.rs-2487695/v1
_ Wu, W., Li, L., & Li, C. (2021). Seasonal variation in the effects of urban environmental factors on land surface temperature in a winter city. Journal of Cleaner Production, 299, 126897. doi:https://doi.org/10.1016/j.jclepro.2021.126897
_ Wang, R., Hou, H., Murayama, Y., & Derdouri, A. (2020). Spatiotemporal analysis of land use/cover patterns and their relationship with land surface temperature in Nanjing, China. Remote Sensing, 12(3), 440. doi:https://doi.org/10.3390/rs12030440
_ Xiao, R., Weng, Q., Ouyang, Z., Li, W., Schienke, E. W., & Zhang, Z. (2008). Land surface temperature variation and major factors in Beijing, China. Photogrammetric Engineering & Remote Sensing, 74(4), 451-461. doi:https://doi.org/10.1016/j.proenv.2011.12.037
_ Xiaolu, S., & Bo, C. (2011). Change detection using change vector analysis from Landsat TM images in Wuhan. Procedia Environmental Sciences, 11, 238-244. doi:https://doi.org/10.14358/PERS.74.4.451
_ Zhao, Z. Q., He, B. J., Li, L. G., Wang, H. B., & Darko, A. (2017). Profile and concentric zonal analysis of relationships between land use/land cover and land surface temperature: Case study of Shenyang, China. Energy and Buildings, 155, 282-295.‌ doi:https://doi.org/10.1016/j.enbuild.2017.09.046