NDVI-based vegetation dynamics and their responses to climate change and human activities from 1982 to 2020: A case study in the Mu Us Sandy Land, China

Abstract

Critical ecological restoration and reconstruction information can be presented by distinguishing the vegetation dynamics due to human activities and climate changes and by determining the main driving factors. In this study, the long-term (1982–2020) satellite-derived Normalized Difference Vegetation Index (NDVI) data sets and climatic factors were employed to analyze the spatiotemporal patterns of the vegetation dynamics in Mu Us Sandy Land (MUSL). In addition, this study investigated their links to variations of temperatures (T) and precipitations (P), and the contribution of human activities and climate change to the vegetation dynamics using the residual analysis were investigated. The results revealed a statistically significant overall greening trend in NDVI changes in 1982–2020 at the MUSL regional scale. To be more specific, this greening trend underwent two distinct periods separated by the breakpoint in 2006. The NDVI first slowly increased before 2006, and then the greening trend rose dramatically after 2006. At the pixel scale, pixels exhibiting greening tendencies exceeded browning trends observably. The correlation between the NDVI and P and T was largely positive, indicating that the P and the T could well explain the dynamic vegetation changes. However, the climatic driving factors for the vegetation growth in the MUSL varied at different time scales. On the monthly scale, T was the main climatic driving factor for the vegetation growth, while P dominated at the annual scale. In addition, the residual analysis indicated that human activities accounted for 56.44% of the NDVI change, while climate change accounted for 43.56%, demonstrating that human activities acted as the dominant drive for vegetation changes. As opposed to the above, degradation was attributed to human activities (52.23%) and climate change (47.77%), respectively. These results are expected to deepen the insights into the driving factors of vegetation change and provide theoretical guidance for ecological management and sustainable development in the MUSL.