Study Of Urban Thermal Environment Based On Remote Sensing And Urban Spatial Morphology

With the rapid development of economy, urbanization is inevitable. Building density augment changes the geometry shape and surface properties in the large area of ctiy, which masks urban thermal environment more complex and urban heat island effect aggravate at local areas. It can’t be ignored and increasingly significant that the pattern and height of buildings in city has an effect on urban heat island and air temperature variances. Therefore, it is of great significance to further study the influence of the spatial morphology of buildings to urban thermal environment change.In the research, many urban morphological parameters including sky view factor (SVF) and frontal area index (FAI) were estimated by using 3D building data with a high spatial resolution (1m) for Australia Adelaide central city. On the basis, this study calculated solar irradiation absorbed by street surface combined with remote sensing technology and albedo data, and analyzed the change of urban heat island intensity using the measured temperature data of 19 sites from October 2010 to October 2011. Then, the paper discussed systematically the effect of urban space morphological parameters, land surface absorbed solar irradiation on urban heat island distribution and air temperature in different time of various seasons.The main conclusions of the research include:(1) UHII in Adelaide city has a similar pattern at four seasons under sunny weather conditions that UHII become strong at nighttime and weak at daytime. At night, the largest UHII occurs at Spring and Autumn, followed by Winter, and Summer has the smallest UHII. UHII during the daytime at all seasons was not significant. The maximum daytime UHII is at Summer,and the minimum occurs at Spring, even "cold island" phenomenon appears at Spring.(2) SVFs estimated by using 3D building data with a high spatial resolution is very consistent with SVFfish.eye, calculated from fish-eye photos. The correlation coefficient between SVFS and SVFfish-eye is 0.97, which shows that SVFS has a higher precision. A variety of urban morphological parameters, such as aspect ratio, the density of buildings, the mean buildings height, the average distance between buildings, were estimated by using a high resolution 3D building data. The results show the correlation between SVF and aspect ratio is the greatest among the above parameters, while there is no obvious correlation between SVF and the average distance between buildings.(3) There exists a high negative linear relationship between SVFS and UHII at night time for all seasons. The correlations are significant positive in the noon of the Spring, Autumn and Winter, while it is not obvious in Summer. Under clear, cloudy and rainy weather conditions, there are little difference between SVF and UHII at night time. But, due to cloud and precipitation, the positive correlation under clear sky is greater than that under cloudy and rainy weather conditions in daytime.(4) There exist a logarithmic relationship between FAI and UHII at night and morning time, while a linear relationship exists at daytime for all seasons. And in the afternoon of spring and winter, a significant positive linear relationship between FAI and UHII. At the spatial scales of 200m, the correlation of FAI-UHII is greater than that of SVF-UHII at night time of Spring and Winter, while it is greater at daytime of Autumn. For the other moments, there are little difference between FAI-UHII and SVF-UHII correlations. In addition, the universality of the correlation between SVF and UHII at different search radius is higher than that of FAI.(5) By useing 3D building data to determine whether one site is in the shadow at different times, this study then calculated the direct solar irradiation of the surface, the diffuse irradiation and the reflected irradiation from adjacent building. Based on the measured surface albedo data, this paper calculated the amount of absorption of solar radiation incident at each site in different times, then analyzed the correlation between the total absorbed solar irradiation (TASIT) and air temperature. The results show thatthere is a significant linear correlation between TASIT and air temperature during daytime; The relationship between TASIT and air temperature change with time, because the net short wave radiation has different contribution to the net radiation of the surface at different moments. There exist a significant negative linear relationship between them at sunrise. From 9:00 to 16:30, there exist a linear positive correlation, and the correlation coefficient is larger than 0.4. And it shows a significant linear correlation between 13:00 and 15:30. From 16:30 to sunset, the correlations become poor.