Research on the Spatio-Temporal Changes of Vegetation and Its Driving Forces in Shaanxi Province in the Past 20 Years

(1) Background: Vegetation is an important component of ecosystems. Investigating the spatio-temporal dynamic changes in vegetation in various Shaanxi Province regions is crucial for the preservation of the local ecological environment and sustainable development. (2) Methods: In this study, the KNDVI vegetation index over the 20-year period from 2003 to 2022 was calculated using MODIS satellite image data that was received from Google Earth Engine (GEE). Sen and MK trend analysis as well as partial correlation analysis were then utilized to examine the patterns in vegetation change in various Shaanxi Province regions. This paper selected meteorological factors, such as potential evapotranspiration (PET), precipitation (PRE), and temperature (TMP); human activity factors, such as land-use type and population density; and terrain factors, such as surface elevation, slope direction, and slope gradient, as the influencing factors for vegetation changes in the research area in order to analyze the driving forces of vegetation spatio-temporal changes. These factors were analyzed using a geo-detector. (3) Results: The vegetation in the research area presented a growth trend from 2003 to 2022, and the area of vegetation improvement was 189,756 km2, accounting for 92.15% of the total area. Among them, the area of significantly improved regions was 174,262 km2, accounting for 84.63% of the total area, and the area of slightly improved regions was 15,495 square kilometers, accounting for 7.52% of the total area. (4) Conclusions: The strengthening of bivariate factors and nonlinear enhancement were the main interaction types affecting vegetation changes. The combination of interaction factors affecting vegetation change in Shaanxi Province includes PRE boolean AND PET as well as TMP boolean AND PET. Therefore, climate conditions were the main driving force of KNDVI vegetation changes in Shaanxi Province. The data supported by this research are crucial for maintaining the region's natural ecosystem.