Analyzing land use changes (LUC) in both past and future scenarios is critical to optimize local ecology and formulate policies for sustainable development. We analyzed LUC characteristics in Huaibei City, China from 1985 to 2020, and used the CLUE-S and PLUS models to simulate LU in 2020. Then, we compared the accuracy of the simulation phase and chose the PLUS model to project LU under four scenarios in 2025. The results showed the following: (1) Farmland and grassland areas decreased from 1985 to 2020, while forestland, water, and construction land increased. (2) The LU types in the region are explained well by the driving factors, with all receiver operation characteristic (ROC) values greater than 0.8. (3) The kappa indices for CLUE-S and PLUS analog modeling were 0.727 and 0.759, respectively, with figure of merit (FOM) values of 0.109 and 0.201. (4) Under the farmland and ecological protection scenario (FEP), farmland and forestland areas are protected, increasing by 1727.91 hm² and 86.22 hm², respectively, while construction land decreases by 2001.96 hm². These results confirm that PLUS is significantly better than the CLUE-S model in modeling forestland and water, and slightly better than the CLUE-S model in modeling the rest of the LU type. Urban sustainability is strong in the scenario FEP.

Vegetation is a crucial component of terrestrial ecology and plays a significant role in carbon sequestration. Monitoring changes in vegetation ecological quality has important guidance value for sustainable development. In this study, we investigated the spatial and temporal variation characteristics of Ecological Quality Index of Terrestrial Vegetation (EQI) in Anhui Province during the growing season from 2000 to 2020 using trend analysis, partial correlation analysis and bivariate spatial autocorrelation analysis. Based on the Multiscale Geographically Weighted Regression (MGWR), the spatial heterogeneity of the effects of average temperature, precipitation, elevation, slope, and human activity factors on EQI was explored. Our results showed an increasing trend in EQI during the growing season in Anhui Province from 2000 to 2020. The significantly increasing areas accounted for 43.49%, while the significantly decreasing areas accounted for 3.60%. EQI had a mostly positive correlation with precipitation and a negative correlation with average temperature (p < 0.1), showing a higher sensitivity to precipitation than to temperature. Additionally, EQI tended to increase initially and then decrease with increasing elevation and slope. Furthermore, our analysis revealed a significant negative spatial correlation between human activity intensity and EQI (p < 0.01). The bivariate global autocorrelation Moran index between EQI and human activity in 2000, 2005, 2010, 2015, and 2018 were −0.418, −0.427, −0.414, −0.487, and −0.470, respectively. We also found that the influencing factors explain 63–83% of the spatial variation of EQI, and the order of influence of factors on EQI is elevation > human activity > slope > average temperature > precipitation. MGWR results indicated that human activities and topographic factors had a stronger impact on EQI at the local scale, while climate factors tended to influence EQI at the global scale.