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Urban green spaces play a crucial role in maintaining urban ecosystem sustainability by providing numerous ecosystem services. How to quantify and evaluate the ecological benefits and services of urban green spaces remains a hot topic currently, while the evaluation is barely applied or implemented in urban design and planning. In this study, super-high-resolution aerial images were used to acquire the spatial distribution of urban green spaces, a modified pre-stratified random sampling method was applied to obtain the vegetation information of the four types of urban green spaces in Luohe, a common plain city in China, and i-Tree Eco model was further used to assess the vegetation structure and various ecosystem services including air quality improvement, rainfall interception, carbon storage, and sequestration provided by four types of urban green spaces. The modeling results reveal that there were about 1,006,251 trees in this area. In 2013, all the trees in these green spaces could store about 54,329 t of carbon, sequester about 4973 t of gross carbon, remove 92 t of air pollutants, and avoid 122,637 m&sup3, of runoff. The study illustrates an innovative method to reveal different types of urban green spaces with distinct ecosystem service productivity capacity to better understand their various roles in regulating the urban environment. The results could be used to assist urban planners and policymakers to optimize urban green space structure and composition to maximize ecosystem services provision.

While green roofs have been deemed promising in mitigating environmental issues caused by rapid urban development, city-scale green roof studies have faced various obstacles, especially difficulties in obtaining accurate data for analysis. This study developed a new, cost-effective approach to assessing green roof development potential by using ultra-high-resolution (UHR) (0.09 m) Unmanned Aerial Vehicle (UAV) imagery in a case study site (Central Luohe with an area of 158 km2) in China. Specifically, the data was processed, interpreted, and classified to create highly accurate land-use and building roof spatial resources databases. A decision-making flowchart was developed for preliminary determination of a building stock’s suitability for green roof implementation and the preferred type based on the five influencing factors and building roof classification. Subsequently, a two-stage strategy for large-scale green roof development was proposed. The approach demonstrated in this research greatly improves the accuracy of city-scale studies on roof spatial resources and enables better planning and development of urban green spaces at the local level.

Rapidly growing cities often struggle with insufficient green space, although information on when and where more green space is needed can be difficult to collect. Big data on the density of individuals in cities collected from mobile phones can estimate the usage intensity of urban green space. Taking Zhengzhou&rsquo, s central city as an example, we combine the real-time human movement data provided by the Baidu Heat Map, which indicates the density of mobile phones, with vector overlays of different kinds of green space. We used the geographically weighted regression (GWR) method to estimate differentials in green space usage between weekdays and weekends, utilizing the location and the density of the aggregation of people with powered-up mobile phones. Compared with weekends, the aggregation of people in urban green spaces on workdays tends to vary more in time and be more concentrated in space, while the highest usage is more stable on weekends. More importantly, the percentage of weekday green space utilization is higher in small parks and green strips in the city, with the density increasing in those small areas, while the green space at a greater distance to the city center is underutilized. This study validates the potential of applying Baidu Heat Map data to provide a dynamic perspective of green space use, and highlights the need for more green space in city centers.