Understanding the spatial distribution of urban forest ecosystem services is essential for urban planners and managers to effectively manage cities and is an essential part of sustainable urban development. Mapping the spatial distribution of urban forest ecosystem services and improving the accuracy of its assessment scale will undoubtedly provide a more accurate reference basis for later management. In this study, we used the i-Tree Eco model and kriging interpolation to quantify and map urban forest ecosystem services and their spatial distribution in Zhengzhou, a city along the lower reaches of the Yellow River in China; analyzed the mapping errors and applicable conditions; and further explored the spatial differences using geographic probes. The i-Tree Eco model estimation results showed that the total carbon storage in the urban forest of Zhengzhou city was 75.7 tons, the annual carbon sequestration was 14.66 tons, the trees and shrubs in the urban area of Zhengzhou city could effectively avoid a total of 307.86 m3 of surface runoff per year, and trees and shrubs removed 411.8 kg/year of air pollution (O3, CO, NO2, PM2.5, PM10, and SO2). The spatial distribution of all urban forest ecosystem services showed significant heterogeneity, but the spatial evaluation precision of different factors varied. GDP and population data showed a negative correlation with ecosystem services, and ecosystem services were abundant in watershed and woodland areas. This study differs from traditional assessments based on regional data due to its improved spatial evaluation accuracy, and the results, discussion, and analysis not only help Zhengzhou's own urban development, but also provide a basis for the future construction and management of other cities, the Central Plains urban agglomeration, and the surrounding larger regions. This will contribute to the enhancement of ecosystem services and thus improve the ecological conditions of the region. This will also have a positive effect on the health of urban residents.
Copyright: © 2023 Yang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.