With the acceleration of urbanization, unreasonable land use poses a serious threat to ecological security. However, there is still some space for improvement in the existing assessment of ecological risks (ERs) caused by land use/land cover change (LUCC). Therefore, this study takes the central Yunnan Province (CYP) as an example, and uses the Patch-Generating Land Use Simulation (PLUS) model and the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model to simulate the land use/land cover (LULC) in different scenarios in the future, calculate the ecosystem services (ESs) from 2000 to 2020 and the next 20 years, as well as ERs of various types of ESs caused by LUCC. In addition, the Pearson correlation coefficient and geographically weighted regression (GWR) are used to analyze the trade-off and synergy relationships between various ecological risks, and the geographic detector method (GDM) is used to analyze their driving factors. The research results indicate that: (1) there have been significant changes in LULC in the CYP in the past 20 years. (2) There was significant spatial heterogeneity among various types of ESs in the CYP. (3) In the past 20 years, the distribution range of ERs has been relatively large, with significant spatial differences. (4) The ERs caused by LUCC in the next 20 years generally show a decreasing trend, and the ecological high-risk areas are mainly concentrated on construction land. (5) The ERs associated with ecosystem service types are mainly synergistic. ER is influenced by a combination of natural factors, socio-economic factors, and distance factors, with some factors having strong interactions. In short, the future ERs of CYP have decreased, and the ERs corresponding to various ecosystem services (ESs) interact with each other, causing a ripple effect. Finally, this study proposes policy recommendations to alleviate ERs in the CYP.

The study of the forest coverage rate (FCR) is related to the ecological environment and sustainable development goals (SDGs) of a region. In light of the lack of an organic integration method of “spatiotemporal evolution, correlation analysis, and change prediction” and the lack of a methodology that integrates methods of “remote sensing (RS) and GIS, multi-phase LUCC, and construction of econometric models” in the research methods at present, this study focus on Yunnan, a typical border province located in China with a relatively fragile “innate” ecological environment, as the research area. Based on the interpretation of land use/land cover (LULC) data retrieved from seven periods RS images (1990, 1995, 2000, 2005, 2010, 2015, and 2020), the spatiotemporal evolution of FCR in 129 counties was analyzed. Complementary research methods, such as the spatial econometric model, geographically weighted regression (GWR), and the geographic detector (GD), are used to reveal the influencing factors of FCR. Finally, this study predicts the FCRs of 129 counties in Yunnan from 2025 to 2050. The FCR in Yunnan presents an increasing trend year by year, increasing from 28.96% in 1990 to 49.05% in 2020. In addition, it exhibits spatial agglomeration characteristics with fewer values in the east and more in the west. The analysis of influencing factors show that the increases in the per capita GDP, land utilization rate, and annual average temperature, and the implementation of the Conversion of Cultivated Land into Forest Project (CCFP) will significantly improve the FCR, while the increases in the population density land reclamation rate, the proportion of construction land area, and the proportion of soil erosion land area will significantly reduce the FCR. Furthermore, the FCR is influenced by multiple factors, and the relative factors observed not only show significant spatial differences, but also present complex and diverse patterns, with the additional characteristics of being interwoven and overlapping. This study contributes to expanding and improving the methods and pathways of exploring the spatiotemporal evolution characteristics of FCR in ecologically fragile areas using RS methods, providing a reference for increasing FCR and improving the ecological environment’s quality in Yunnan Province and other ecologically fragile areas.