Disentangling the roles of land-use-related drivers on vegetation greenness across China

Abstract

Abstract Quantifying the drivers of terrestrial vegetation dynamics is critical for monitoring ecosystem carbon sequestration and bioenergy production. Large scale vegetation dynamics can be observed using the leaf area index (LAI) derived from satellite data as a measure of ‘greenness’. Previous studies have quantified the effects of climate change and carbon dioxide (CO2) fertilization on vegetation greenness. In contrast, the specific roles of land-use-related drivers (LURDs) on vegetation greenness have not been characterized. Here, we combined the Interior-Point Method-optimized ecosystem model and the Bayesian model averaging statistical method to disentangle the roles of LURDs on vegetation greenness in China from 2000 to 2014. Results showed a significant increase in growing season LAI (greening) over 35% of the land area of China, whereas less than 6% of it exhibited a significantly decreasing trend (browning). The overall impact of LURDs on vegetation greenness over the whole country was comparatively low. However, the local effects of LURDs on the greenness trends of some specified areas were considerable due to afforestation and urbanization. Southern Coastal China had the greatest area fractions (35.82% of its corresponding area) of the LURDs effects on greening, following by Southwest China. It was because of these economic regions with great afforestation programs. Afforestation effects could explain 27% of the observed greening trends in the forest area. In contrast, the browning impact caused by urbanization was approximately three times of the greening effects of both climate change and CO2 fertilization on the urban area. And they made the urban area had a 50% decrease in LAI. The effects of residual LURDs only accounted for less than 8% of the corresponding observed greenness changes. Such divergent roles would be valuable for understanding changes in local ecosystem functions and services under global environmental changes.