The urban heat island (UHI) effect will persist for a long time and influence human health, energy consumption, and future urban planning. Understanding the cooling effect of water bodies and green spaces can help alleviate the frequency of extreme climate, especially during torridity seasons. In this study, correlation and regression analysis were used to measure the relationship between land surface temperature (LST) or cooling indicators and landscape factors. In addition, the cooling intensity, distance, and threshold value of efficiency (TVoE) of water bodies and green spaces were detected. The results confirmed that: first, the cooling effect of water bodies were stronger than that of vegetation in most cases and more water bodies’ layout in the region was advocated; second, increasing vegetation coverage within 27% of the region can effectively and economically alleviate the thermal environment; and third, the green samples with an area of 0.57 ha and a high vegetation index had a higher cost performance ratio. The results provided quantitative guidance for urban public service spatial planning of water bodies and green spaces to prevent the continuous increase of urban background temperature.

With the acceleration of global warming and urbanization, the problem of the thermal environment in urban areas has become increasingly prominent. In this paper, Fuzhou was selected to quantify the impact of land use cover change (LUCC) on land surface temperature (LST). The results showed that from 1993 to 2016, the land use/cover types of the study area changed greatly, especially the change of construction land, which led to an obvious change in the spatial pattern of LST. From 1993 to 2016, the spatial and temporal distribution of LST contributions in Fuzhou was uneven. The central urban area had a positive contribution to the rise of LST, while Minqing and Yongtai had a negative contribution. From the perspective of different land use/land cover types, forest or grass land, cultivated land, and water all made a negative contribution to the increase of surface temperature, while construction land made a positive contribution. Outcomes provided by the multi-distance spatial cluster analysis (Ripley’s K function) showed that there was a scale effect in the concentration and dispersion of LST; from 1993 to 2016, the concentration range of LST in the study area gradually expanded and the degree of concentration increased.