Scale-dependent and driving relationships between spatial features and carbon storage and sequestration in an urban park of Zhengzhou, China

Research indicates that urban ecosystems can store large amounts of carbon. However, few studies have examined how the spatial features of park greenspace affect its carbon-carrying capacity, and how those effects vary with the spatial scale. Lidar point clouds and remote sensing images were extracted for the 196 ha green space in the China Green Expo to study carbon storage and sequestration in parks. Full subset regression, stepwise regression, HP analysis, and structural equation modeling were used to examine the scale dependency and the driving relationship between carbon storage and carbon sequestration in parks. The results show that the optimal statistical sample diameters for carbon density and carbon sequestration density in parks are 100 m. Under the influence of impermeable surfaces and water bodies, the statistical values of carbon density were minimized when the sample plot diameter was 700 m. Biodiversity and forest structure are the main drivers of carbon density, with the influence of water bodies being more prominent on a larger scale. Texture characteristics explain more carbon density than the vegetation index, and RVI could better explain the variation of carbon sequestration than NDVI. This study explores scaled changes in carbon density, carbon sequestration density in parks, and their driving relationships, which can aid in developing carbon sequestration strategies based on parks.