With the development of urbanization, urban heat island effect issue is becoming more and more severe. What's more, the vegetation eco-environmental quality (VEEQ) is severely damaged, resulting in the decline of urban ecosystem function. Therefore, it is of great significance to use remote sensing technique to analyze the response of urban heat island to VEEQ quantitatively. As is known to all, vegetation is the main body in the vegetation ecological environment system. Water and heat conditions are the important driving forces for its formation and evolution. Good soil condition is the basis for vegetation survival. Besides, the terrain is conducive to the judgment of the vegetation distribution. Accordingly, several indexes involving vegetation index, heat index, soil moisture index, soil brightness index, elevation factor and slope factor were selected and extracted from Landsat8 OLI images to establish the evaluation index system of VEEQ. Based on Landsat8 TIRS images, this paper applied the radiative transfer equation method to retrieve land surface temperature (LST) and the urban island grade was divided based on the mean and standard deviation values of LST. The principal component analysis method was utilized to determine the weigh value of each index and then a comprehensive evaluation model of VEEQ was established. Furthermore, the quantitative relationship between LST and VEEQ was analyzed. The results showed that, there existed obvious heat island effects in Haidian District of Beijing city and its surrounding areas. The poor quality areas and the high quality areas of vegetation ecological environment had strengthening and weakening thermal environment effects respectively. There was a strong negative relationship between LST and VEEQ.
Saiping Xu, Qianjun Zhao, Kai Yin, Bei Cui, and Xiupeng Zhang, "Study on urban heat island effect and its response to the vegetation eco-environmental quality," Proc. SPIE 10008, Remote Sensing Technologies and Applications in Urban Environments, 100080D (Presented at SPIE Remote Sensing: September 26, 2016; Published: 26 October 2016); https://doi.org/10.1117/12.2240913.
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