Spatiotemporal variations and driving forces of vegetation coverage in Three Gorges reservoir area during 2013—2023———A case study at Badong County, Hubei Province
[Objective] The spatiotemporal variations of vegetation coverage and its driving mechanisms at Badong County, Hubei Province, Three Gorges reservoir area were analyzed, in order to provide scientific references for vegetation restoration, soil and water conservation, and ecological protection. [Methods] Based on MODIS NDVI data, the spatiotemporal dynamics of fractional vegetation coverage in Badong County from 2013 to 2023 was analyzed and the differentiation characteristics of vegetation coverage influenced by topography, geology, and climate factors was explored. Furthermore, the geodetector and random forest models were employed to identify dominant factors and their interaction effects. [Results] ① From 2013 to 2023, vegetation coverage at Badong County exhibited a fluctuating upward trend at a rate of 0.001 7/a, with overall high coverage levels. ② Vegetation coverage showed distinct spatial differentiation across influencing factors: it correlated positively with the spatial variation of elevation, slope, and annual precipitation, but negatively with mean annual temperature. It was also influenced by aspect, lithology, vegetation type, and soil type. ③ Geodetector analysis identified elevation and mean annual temperature as primary drivers (explanatory power >40%), while lithology, geomorphological type, and annual precipitation were secondary factors. Multi-factor interactions demonstrated synergistic enhancement, with elevation and lithology jointly explaining 55% of variation. Random forest model further validated the importance ranking of dominant predictors: elevation > mean annual temperature > lithology > annual precipitation. [Conclusion] The geodetector and random forest model jointly reveal elevation as the core driver of fractional vegetation coverage changes at Badong County in Three Gorges reservoir area during 2013—2023, followed by mean annual temperature, lithology, and precipitation.