Spatial patterns and driving forces of urban vegetation greenness in China: A case study comprising 289 cities

Abstract

Urban vegetation in China has changed substantially in recent decades due to rapid urbanization and dramatic climate change. Nevertheless, the spatial differentiation of greenness among major cities of China and its evolution process and drivers are still poorly understood. This study examined the spatial patterns of vegetation greenness across 289 cities in China in 2000, 2005, 2010, 2015, and 2018 by using spatial autocorrelation analysis on the Normalized Difference Vegetation Index (NDVI); then, the influencing factors were analyzed by using the optimal parameters-based geographical detector (OPGD) model and 18 natural and anthropogenic indicators. The findings demonstrated a noticeable rise in the overall greenness of the selected cities during 2000–2018. The cities in northwest China and east China exhibited the rapidest and slowest greening, respectively, among the six sub-regions. A significant positive spatial correlation was detected between the greenness of the 289 cities in different periods, but the correlation strength weakened over time. The hot and very hot spots in southern and eastern China gradually shifted to the southwest. While the spatial pattern of urban greenness in China is primarily influenced by wind speed (WS) and precipitation (PRE), the interaction between PRE and gross domestic product (GDP) has the highest explanatory power. The explanatory power of most natural factors decreased and, conversely, the influence of anthropogenic factors generally increased. These findings emphasize the variations in the influence strength of multiple factors on urban greenness pattern, which should be taken into account to understand and adapt to the changing urban ecosystem.

Keywords

Vegetation greenness / Spatial heterogeneity / Influencing factors / Geographical detector / Urban areas

Declaration of competing interests

The author declares that there are no known competing financial interests or personal relationships that influenced the work reported in this paper.

Acknowledgements

This work was supported by the Foundation of High-level Talents of Qingdao Agricultural University (Grant No. 665/1120041), the Open Research Fund of the State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau (Grant No. A314021402-202221), the Natural Science Foundation of Shandong Province (Grants No. ZR2020QD114 and ZR2021ME167), and the Postgraduate Innovation Program of Qingdao Agricultural University (Grant No. QNYCX22031).

Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.geosus.2024.03.001.

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