Abstract
Urban and rural areas play an important role in the greenness change in China, despite exhibiting divergent landscape ecologies. Although recent studies have revealed an overall greening pattern in China, the relative contribution of urban and rural vegetation to nationwide greening trend and their driving mechanisms behind these changes remain poorly understood. Here, we first utilized a high-resolution land use/cover dataset (GlobeLand30) to establish a framework for distinguishing between urban and rural areas. We then assessed and compared the greenness changes in both urban and rural areas using multiple vegetation indices from 2000 to 2020. By employing Random Forest model and generalized linear model regression, we further investigated drivers behind the changes in urban and rural vegetation trends. Our results demonstrated a significant greening trend in China, and the greenness increased 13.71% from 2000 to 2020. Vegetation changes in both urban (+4.96%, 0.0011 yr−1) and rural areas (+14.25%, 0.0026 yr−1) have contributed positively to China’s greening trend, with their contribution being 11.3% and 88.7%, respectively. Urban core areas exhibited the largest trend magnitudes (0.0043 ± 0.0035 yr−1) among all the urban–rural subregions. Increased tree cover was identified as the primary driver of greening trends in both urban and rural areas, explaining 36% and 29% of the greening, respectively. However, the pathways of tree cover increase differed between urban and rural areas, with urban areas focusing on green space construction and rural areas implementing afforestation programs. In contrast, climate change and the CO2 fertilization effect had a greater contribution to the greening trend in rural areas than in urban areas. Our study demonstrates the positive role played by both urban and rural areas in China’s greening trends and elucidates the underlying mechanisms driving these changes, highlighting the need for differentiated strategies in urban and rural areas for future vegetation restoration.
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