Analysis of the Spatial and Temporal Scale Effects of Landscape Pattern Indexes on Carbon Sinks in Greenspaces in Taiyuan, China

Calculating the carbon sinks in greenspaces is crucial for carbon neutrality and sustainable urban development. In this study, we chose Taiyuan, China, and calculated the indexes of landscape patterns and carbon sinks to understand the relationship between urban carbon sinks and landscape patterns. Based on the Google Earth Engine and hyperspectral remote-sensing satellites (Sentinel-2A), we estimated the greenspace carbon sinks in Taiyuan in 2021 at a fine scale by using the Carnegie–Ames–Stanford Approach model and soil microbial respiration model (Rh) to calculate net ecosystem productivity (NEP). Besides, we calculated the landscape pattern index in Taiyuan and analyzed the relationship and the spatial dissimilarity between the index and carbon sink using the Geodetector and the Pearson correlation analysis to find the key indexes for assessing the greenspace NEP. The results showed the following: (1) The carbon sequestration of various land types in each county and district was either limited or zero, and the order of yearly mean values of NEP of diverse greenspaces in Taiyuan in 2021 was forestland > grassland > cropland. (2) The index of dispersion and juxtaposition (IJI) had the most significant influence on cropland and grassland NEP. By contrast, the largest patch index was the predominant determinant for forestland NEP at the type level. (3) At the landscape level, the main factors affecting the NEP of greenspace in Taiyuan were patch density (PD) and number of patches (NP), and each landscape index showed significant interaction effects on the NEP of greenspace in Taiyuan, with the strongest interaction between PD/NP and IJI. These findings have implications for carbon management and urban landscape pattern development and provide a theoretical basis for improving urban carbon sink capacity.