Vegetation Coverage Evolution Mechanism and Driving Factors in Dongting Lake Basin (China), 2000 to 2020

The Dongting Lake Basin (DLB), a region of key importance in the national project of the Yangtze River Protection and Economic Belt Construction, experienced dramatic land use changes caused by anthropogenic disturbances and climate change. Understanding vegetation dynamics is crucial for improving ecosystem structure and function and environmental sustainability. Here, a long-term (2000–2020) Normalized Difference Vegetation Index (NDVI) dataset, integrated with multiple statistical methods, was applied to investigate the spatiotemporal characteristics of vegetation coverage in the DLB. The Geodetector model and partial correlation analysis were then applied to determine the main factors affecting spatial and temporal vegetation coverage change. The results showed the following: (1) The DLB showed an overall increasing NDVI at a rate of 0.37% per year from 2000 to 2020; NDVI dynamics shifted in 2010, changing from a slow to a significant increase. The seasonal average NDVI increased differently among the four seasons, in the following descending order: winter (0.56%) > spring (0.22%) > summer (0.17%) > autumn (0.05%). (2) The area with an upward NDVI trend was primarily distributed in the forest zones in the eastern and western parts, accounting for 87.55% of the total area, whereas the area with a decreasing trend was mainly clustered in the northern plains of the DLB, accounting for 6.27% of the total area. (3) The annual variation rate of the NDVI during 2010–2020 was faster than that from 2000 to 2010; the gains and losses of the transmission area were varied among different vegetation levels. (4) The DEM and slope comprised a stronger influence on the NDVI spatial variation, while the annual average temperature was the controlling climate factor, with a q-value of 26.09%. The interaction of each independent factor showed a strengthening effect for explaining the spatial variability of the NDVI. (5) Climatic factors exerted a positive correlation with the NDVI, and the temperature had a stronger influence on vegetation coverage change than that of precipitation. These results can guide the development of ecosystem models to enhance their predictive accuracy, which can provide a scientific basis for the sustainable management of vegetation resources.