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Vegetation growth enhancement in urban environments of the Conterminous United States.

Wenxiao JiaShuqing ZhaoShuguang Liu
Published in: Global change biology (2018)
Cities are natural laboratories for studying vegetation responses to global environmental changes because of their climate, atmospheric, and biogeochemical conditions. However, few holistic studies have been conducted on the impact of urbanization on vegetation growth. We decomposed the overall impacts of urbanization on vegetation growth into direct (replacement of original land surfaces by impervious built-up) and indirect (urban environments) components, using a conceptual framework and remotely sensed data for 377 metropolitan statistical areas (MSAs) in the conterminous United States (CONUS) in 2001, 2006, and 2011. Results showed that urban pixels are often greener than expected given the amount of paved surface they contain. The vegetation growth enhancement due to indirect effects occurred in 88.4%, 90.8%, and 92.9% of urban bins in 2001, 2006, and 2011, respectively. By defining offset value as the ratio of the absolute indirect and direct impact, we obtained that growth enhancement due to indirect effects compensated for about 29.2%, 29.5%, and 31.0% of the reduced productivity due to loss of vegetated surface area on average in 2001, 2006, and 2011, respectively. Vegetation growth responses to urbanization showed little temporal variation but large regional differences with higher offset value in the western CONUS than in the eastern CONUS. Our study highlights the prevalence of vegetation growth enhancement in urban environments and the necessity of differentiating various impacts of urbanization on vegetation growth, and calls for tailored field experiments to understand the relative contributions of various driving forces to vegetation growth and predict vegetation responses to future global change using cities as harbingers.
Keyphrases
  • climate change
  • magnetic resonance imaging
  • risk assessment
  • magnetic resonance
  • human health
  • escherichia coli
  • big data
  • particulate matter
  • electronic health record
  • smoking cessation
  • data analysis