Trends in Urban Vegetation Growth in China from 2000 to 2022

Abstract

Over the past two decades, urbanization in China has been advancing rapidly. The intricate effects of urbanization on vegetation growth in the urban core have been studied and reported. However, the percentage of impervious surfaces in the urban core, as defined in previous studies, was relatively low, and included some pixels containing farmland and water bodies. Consequently, their results may be affected by urbanization processes, such as the transformation of land types. Hence, this paper extracted 100% impervious surfaces from 2000 to 2022 as urban core areas in China using a 30 m resolution China land cover dataset (CLCD), which completely excluded the effect of urbanization itself on the experimental results, obtaining the trend of vegetation change in the real urban core area. Employing the remote sensing imagery of the Enhanced Vegetation Index (EVI) from 2000 to 2022, we analyzed the growth of vegetation in 1559 urban cores and the surrounding rural areas in China. The study’s findings revealed that the majority of the core areas (85.3%) studied in this paper exhibited a significant (p < 0.05) increase in vegetation, indicating that the various urban greening policies in China have been effective. However, only about 23.7% (369) of the urban core areas showed a faster increase in vegetation than the rural areas. This suggests that for most urban cores (1190), vegetation increase is not as pronounced as it is in surrounding rural areas. Additionally, the EVI rate of change in the urban cores obtained using CLCD versus MODIS land cover data significantly differed. The latter obtained a less pronounced trend of vegetation growth compared to the former, attributable to the disparity in their spatial resolution and the methodology used to define urban areas. The study underscores the importance of vegetation growth and its distribution in various urban core areas to comprehend the dynamics of urban cores’ vegetation growth and to offer insights for the subsequent formulation of greening policies. Moreover, data with different resolutions will significantly impact the results, thus highlighting the necessity of employing high spatial resolution data for more comprehensive research.