Volume 27, Issue 3 (2023)
MJSP 2023, 27(3): 132-158 |
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Shamsoddini A, Arianezhad S. Modeling the effect of city structural parameters on city surface temperature based on segments obtained from object-oriented segmentation in Tehran city. MJSP 2023; 27 (3) :132-158
URL: http://hsmsp.modares.ac.ir/article-21-72815-en.html
URL: http://hsmsp.modares.ac.ir/article-21-72815-en.html
1- Department of Remote Sensing and GIS, Humanities faculty, Tarbiat Modares University , ali.shamsoddini@modares.ac.ir
2- Department of Remote Sensing and GIS, Humanities faculty, Tarbiat Modares University
2- Department of Remote Sensing and GIS, Humanities faculty, Tarbiat Modares University
Abstract: (643 Views)
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.
Keywords: Land surface temperature, object-oriented segmentation, urban structural parameters, Deep Learning, Machine Learning
Article Type: Original Research |
Subject:
techniques if spatial / locational data processing in environmental planning
Received: 2023/12/7 | Accepted: 2024/03/18 | Published: 2023/10/2
Received: 2023/12/7 | Accepted: 2024/03/18 | Published: 2023/10/2
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