Abstract
Abstract. Due to urbanization and changes in the urban thermal environment and because the land surface temperature (LST) in urban areas are a few degrees higher than in surrounding non-urbanized areas, identifying spatial factors affecting on LST in urban areas is very important. In this regard, due to the unique properties of spatial data, in this study, a geographically weighted regression (GWR) was used to identify effective spatial factors. The GWR is a suitable method for spatial regression issues, because it is compatible with two unique properties of spatial data, i.e. the spatial autocorrelation and spatial non-stationarity. In this study, the Landsat 8 satellite data on 18 August 2014 and Tehran land use data in 2006 was used for determining the land surface temperature and its effective factors. As a result, R2 value of 0.765983 was obtained by taking the Gaussian kernel. The results showed that the industrial,military, transportation, and roads areas have the highest surface temperature.
Highlights
Rapid urban sprawl and population growth alter the physical properties of the urban land surface, resulting in significant variation in urban thermal environments (S. Li, Zhao, Miaomiao, & Wang, 2010)
In order to evaluate the effect of different land use on land surface temperature (LST), the geographically weighted regression (GWR) was used
Because the temperature of cities was higher than the countryside and this phenomena is due to an increase in land surface temperature and thereby creating urban heat islands are happened
Summary
Rapid urban sprawl and population growth alter the physical properties of the urban land surface, resulting in significant variation in urban thermal environments (S. Li, Zhao, Miaomiao, & Wang, 2010). Rapid urban sprawl and population growth alter the physical properties of the urban land surface, resulting in significant variation in urban thermal environments One of the most familiar local climatic effects is the urban heat island (UHI) phenomenon, in which land surface temperatures (LST) in urban areas are a few degrees higher than in surrounding non-urbanized areas (Xian & Crane, 2005). Recent significant advances in the data and technological integration between remote sensing and GIS suggest that the integration is a powerful and effective tool in urban studies. Remote sensing from airborne or satellite platforms cannot only provide thermal infrared data, and land use and land cover (LULC), building height, and other urban biophysical variables. Considerable research has been carried out using remote sensing and GIS to detect thermal characteristics of urban surfaces Considerable research has been carried out using remote sensing and GIS to detect thermal characteristics of urban surfaces (S. Li et al, 2010; Pu, Gong, Michishita, & Sasagawa, 2006; Streutker, 2003; Weng, 2001)
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