Identifying Anomalous Regions of Vegetation Change From 2000 to 2020, China: Driving Forces, Probability, and Colocation Patterns

Abstract

Differentiating between the effects of climate change and human activities on vegetation change is important in the context of vegetation restoration and management. In this study, we used the Theil–Sen slope and Mann–Kendall test to analyze the spatiotemporal changes in vegetation cover in China from 2000 to 2020, defining a series of anomalous regions. The probability of vegetation greening and browning under different climates and population pressures was evaluated using copula functions, and their spatial aggregation was studied through the colocation quotient. Geodetector was then used to analyze the influencing factors of vegetation change in these anomalous regions. Our findings have shown that the vegetation recovery rate in northern China has surpassed that of southern China. Precipitation and temperature across the entire region showed a positive feedback relationship with normalized difference vegetation index, indicating substantial spatial heterogeneity. Anomalous regions of vegetation change were predominantly concentrated in eastern China. The statistical probability of the copula function reflects that the synchronization probability of vegetation response to the external environment is higher. The sensitivity of temperature to vegetation is higher than that of precipitation and higher than that of population density. However, in the Tibetan Plateau and western arid zone, the feedback of population density has exceeded that of precipitation due to improvements in land management from population concentration. The findings have also shown that the vegetation dynamics are primarily influenced by soil water content, with the slope aspect having a minimal influence. Nonlinear interactions were observed among most of the influencing factors, with the interaction between soil water content and altitude being the strongest.

Keywords

• Anomalous region
• climate change
• colocation patterns
• human activity
• normalized difference vegetation index (NDVI)