Long-term satellite observations show continuous increase of vegetation growth enhancement in urban environment.

Urbanization shows continuous expansion and development, ushering in the co-evolution of urban environments and vegetation over time. Recent remote sensing-based studies have discovered prevalent vegetation growth enhancement in urban environments. However, whether there is a temporal evolution of the growth enhancement remains unknown and unexplored. Here we expanded the existing framework for assessing the long-term impact of urbanization on vegetation greenness (enhanced vegetation index, EVI) using long time series of remote sensing images and applied it in Changsha, the capital city of Hunan province in China. Results showed that vegetation growth experienced widespread enhancement from 2000 to 2017, and increased 1.8 times from 2000 to 2017, suggesting strong continuous adaptive capability of vegetation to urban conditions. Although the overall impact of urbanization was negative due to the replacement of vegetated surfaces, the growth enhancement nevertheless offset or compensated the direct loss of vegetated cover during urbanization in the magnitude of 28 % in 2000 to 44 % in 2017. Our study also revealed large spatial heterogeneity in vegetation growth response among various districts at different urbanization levels and found an emergent trend under the observed spatial heterogeneity toward an asymptotic maximum with urbanization, showing EVI converges to 0.22 in highly urbanized areas. We further found that the positive effect of urbanization on vegetation growth is a function of urbanization intensity and time, which implies that the effect of the urban environment on vegetation can be simulated and predicted, and can be verified in more cities in the future. Our study is the first to successfully quantify long-term spatial patterns on the co-evolution of urbanization and vegetation, providing a new understanding of the continuous adaptive responses of vegetation growth to urbanization and shedding light on predicting biological responses to future environmental change.