Quantifying influences of natural and anthropogenic factors on vegetation changes using structural equation modeling: A case study in Jiangsu Province, China

Abstract

Vegetation coverage in highly developed areas has been significantly altered in response to multiple disturbances over recent decades. However, the major driving factor of vegetation coverage change in these areas remains unclear, with climate change and anthropogenic factors playing interactive roles under different soil and terrain conditions. Comprehensively understanding the underlying drivers of vegetation change can provide references for regulating environmental management and prevention of vegetation degradation. In this paper, a structural equation modeling (SEM) method was employed to quantify the effects of fundamental natural environment (i.e. the relative stable variables including soil and topography), climate change and human activity change on vegetation coverage change in Jiangsu province, China from 2000 to 2015. Four variables including land use, population density, road impact and night lights were used to indicate human activities. The results showed that the increase of NDVI smaller than 0.10 covered 39.13% of the study area while the decrease of NDVI larger than 0.10 accounted for 20.23%. Areas with NDVI increase mainly distributed in croplands in northern Jiangsu. This could be explained by the increase of crop yield due to the development of modern agriculture. The decrease of NDVI was mainly observed in southern Jiangsu with higher urbanization level and city centers in northern Jiangsu, indicating the effect of rapid urbanization on vegetation degradation. The constructed SEM model suggested that the total effects (influential coefficients) of fundamental natural environment, climate change, and human activity change on NDVI change in Jiangsu were −0.24, 0.17, and −0.74, respectively. Although the fundamental natural environment didn’t have a direct effect on NDVI change, but it had an indirect effect through interactions with human activities. We also constructed SEM models for northern and southern Jiangsu separately, due to their different natural environment and changing patterns of climate change. The results indicated the different driving mechanisms of NDVI change in northern and southern Jiangsu. Furthermore, the results suggested night light as the best indicator of human activity change, followed by the road impact index. We concluded that our study offered a framework to better understand and explain the complex interrelationships behind the spatial temporal change of NDVI.