Increased association between climate change and vegetation index variation promotes the coupling of dominant factors and vegetation growth

Sci Total Environ. 2021 May 1:767:144669. doi: 10.1016/j.scitotenv.2020.144669. Epub 2020 Dec 30.

Abstract

Vegetation productivity dynamics are closely related to climate change, and water availability determines vegetation growth in water-limited ecosystems. Nevertheless, how changes in the interactions between climatic factors and vegetation activity variation regulate the relationship between their trends remains unclear. The Normalized Difference Vegetation Index (NDVI) is an effective proxy of vegetation growth. First, we investigated the NDVI trends, and the results revealed a vegetation activity with weaker greening and greater spatial heterogeneity after an obvious land-cover breakpoint in 1999 compared with that before 1999 in northwest China. Notably, the Loess Plateau greatly led the greenness trends, but the Tibet Plateau showed mean browning after 1999, which implied that the coupling of climate change and vegetation trends varied with spatio-temporal changes. Subsequently, using the Geographical Detector Method (GDM), we quantified and compared the association between climate change and the interannual variability of NDVI in the two stages. Vegetation productivity variation is more closely related to changes in climatic factors after 1999 compared with that before 1999. Precipitation (PPT) and vapor pressure deficit (VPD) are the primary constraints to vegetation growth in both stages. Patterns in NDVI trend increases are consistent with those of increased PPT and decreased VPD and vice versa after 1999. However, the same patterns were not observed before 1999 because of the weak association between climate change and NDVI variation. This implicated a great significance of the association between climate change and changes in vegetation activity for the prediction of potential carbon sequestration due to the shift of dominant factors and their trends under future climate change.

Keywords: Dominant climatic factors; Explanatory power; Land-surface cover breakpoint; NDVI interannual variability; Northwest China; Quantified association.