Spatial modelling of thermal islands according to the effect of environmental factors (Tehran city)

Subject Areas : Journal of Radar and Optical Remote Sensing and GIS

Ebad Noorifar ¹ , Azita Rajabi ² , Masoumeh Hadavand ³

1 - Department of Geography and Urban Planning, Central Tehran Branch, Islamic Azad University, Tehran, Iran
2 - Associate professor in Department of Geography and Urban planning, central Tehran Branch, Islamic Azad University , Tehran ,Iran
3 - Department of Geography, University of Tehran, Tehran, Iran

Received: 2022-11-28 Accepted : 2023-02-18 Published : 2023-03-06

Keywords: GIS, Tehran, remote sensing, urban thermal island, spatial statistics,

Abstract :

In recent decades, the city of Tehran has experienced vast changes in its various levels with the increase in population and various activities and infrastructures related to it, and these human activities and changes in land use on an urban scale lead to an increase in temperature and the formation of urban heat islands. has been This issue has become one of the serious challenges and has occupied the minds of the scientific community. Considering the importance of the topic, the purpose of this research was to model thermal islands according to the environmental factors of Tehran city. The research conducted is based on a quantitative, descriptive-analytical method. First, Landsat 5 and 8 satellite images were obtained to calculate the surface temperature of the earth using TM, OLI, TIRS sensors from the United States Earth Science Base, related to the summer season of 2010 and 2021, identification of high-risk and low-risk urban heat island clusters from the global Moran test (spatial Moran autocorrelation) and local Moran (hot spot and cold spot analysis) were performed. After ensuring that the spatial distribution pattern of urban heat island is clustered, high-risk and low-risk clusters of urban heat island were identified. According to the spatial distribution pattern of high-risk and low-risk clusters, the possible influencing factors such as height, slope, direction, solar radiation, traffic density, etc., were investigated and analyzed. In the ordinary least squares (OLS) model, the three independent variables of height, vegetation and construction density had the greatest influence in the model