Long-Term Variability of Vegetation Cover and Its Driving Factors and Effects over the Zuli River Basin in Northwest China

Vegetation information is a critical factor in regional environment management under climate change. In this study, a typical arid and semi-arid watershed on the Loess Plateau, the Zu Li River Basin (ZRB), was selected to study the long-term changes in vegetation cover and its drivers and impacts. Unlike existing normalized vegetation index (NDVI) products, which have coarse spatial resolution and short time horizons, this study used the 30 m Landsat dataset analyzed in the Google Earth Engine (GEE) to generate high-resolution and long-term NDVI data, which are the most ideal for monitoring vegetation dynamics using long-time-series data products. The results showed that the annual mean maximum NDVI (normalized vegetation index) in the ZRB increased during 1987-2021, with a significant (p < 0.05) increasing trend in most areas. Upstream vegetation cover increased more than midstream and downstream, but the increase was smaller. Precipitation in the ZRB area was significantly (p < 0.05) correlated with the NDVI series, except for the upstream pass area, where human activities played an important role. NDVI was significantly (p < 0.05) negatively correlated with runoff coefficient and sand content, indicating that vegetation cover was an important reason for the decrease in runoff coefficient and sand content.

Automated summary

Vegetation information is crucial for managing the regional environment in the face of climate change. This study focused on the Zu Li River Basin (ZRB), a typical arid and semi-arid watershed on the Loess Plateau, to investigate the long-term changes in vegetation cover and their drivers and impacts. By utilizing high-resolution and long-term NDVI data generated from the 30 m Landsat dataset analyzed in the Google Earth Engine (GEE), this study provided valuable insights into vegetation dynamics using long-time-series data products. The findings revealed an increasing trend in the annual mean maximum NDVI in the ZRB from 1987 to 2021, with a significant difference among different areas.