关键词: 蓝绿空间, 热环境, 地表温度, 景观格局

Abstract: Urbanization in China is characteristically rapid, large-scale and involves significant resource use. Thus, it can exacerbate current ecological problems. Problems associated with the thermal environment are particularly prominent, and which is mainly manifested by heat island effect is the most intuitive reflection. Therefore, optimizing the urban layout to develop the cooling effect associated with blue and green space is of great importance toward improving urban thermal environment-compared with other methods, this is believed to be the most feasible way of addressing the problem. However, most existing studies on urban thermal environment have focused on identifying the driving effects of different land-use patterns, and thus, lack an in-depth discussion on blue and green space. Tianjin is located in the northeast region of the North China Plain and typically has a warm, temperate and sub-humid monsoon climate. In the current study, six districts of Tianjin City were chosen to form the research area, and these cover the Heping, Nankai, Hongqiao, Hexi, and Hedong districts. Furthermore, the research area covered a total area of approximately 181.18km2. Information on the typical climate and urbanization characteristics of Tianjin can provide reference for cities with similar characteristics. First, the surface temperature was calculated using the mono-window algorithm, with small errors based on the Landsat satellite remote sensing images from the years 2004, 2011 and 2017. The results were divided into seven temperature zones using the standard-deviation classification method. Meanwhile, the land-use classification of the data source was performed using the maximum likelihood classifier. The land-use patterns were then divided into blue space, green space and non-blue-green space. Next, the correlation between the landscape pattern index of the blue-green space and thermal environment was analyzed using the landscape-pattern analytical method. Furthermore, the connection characteristics under different space scales were compared using the moving window method. The results demonstrated that from 2004 to 2017, the space in high-temperature scopeareas continually expanded via river crossings in the direction of urbanization, while the space in low-temperature scopeareas was concentrated in blue and green space. Blue and green space can be developed to improve the thermal environment in urban areas. At the landscape-pattern level, surface temperature was found to be significantly negatively correlated with the plaque perimeter index of the blue and green space, but it was significantly positively correlated with perimeter/area ratio. At the spatial-pattern level, surface temperature showed the highest correlation with the proportion of blue-space plaque in landscape areas in the samples within the scale of 1500m×1500m. Furthermore, it showed the highest correlation with green spaces within 300m×300m. Moreover, the improvement efficiency of the thermal environment in blue space was found to be higher. Finally, the study proposes some suggestions to optimize urban blue and green space through its orientation to the thermal environment. To effectively address thermal environmental problems and improve the ecology and habitability of cities, it is suggested that blue and green space landscape patterns be optimized, that the blue space in the high temperature areas in the north be increased, that rivers be widened and reconstructed, and that riverside greenways be constructed. It is expected the current study will provide new ideas and reference points for the construction of ecological and habitable cities against a background of rapid urbanization. The current study nevertheless still has some limitations with respect to the spatial scale due to the data source and calculation methods used. Future studies in this area should examine the landscape laws underpinning thermal environments as well as the blue and green space at the refined scale.

Key words: blue and green space, urban thermal environment, land surface temperature, landscape pattern