Urban agglomeration ecosystems are impacted by human activities and natural disasters, so analyzing the spatial and temporal evolution of landscape ecological resilience from the perspective of adaptive cycling is crucial. Using the Changsha–Zhuzhou–Xiangtan urban agglomeration in China as a case study, this research constructs a “Risk-Potential-Connectivity” framework to evaluate ecological resilience. This framework applies exploratory spatial data analysis methods to examine the spatiotemporal evolution and associated patterns of resilience and the Geodetector model to measure the driving factors of spatial variation. This study constructs an adaptive cycle model based on ecological resilience analysis, integrating potential and connectivity indices to classify the development stages of urban agglomeration regions dynamically. The results showed that the overall spatial distribution pattern of ecological risk decreased from the center outward, whereas ecological potential and connectivity increased. The average resilience index from 2000 to 2020 was 0.31, with a declining trend and shifting center of gravity from northwest to southeast. The spatial and temporal distribution of toughness exhibited high and low aggregation, with an overall Moran index greater than 0.75. Land-use intensity had the strongest explanatory power (q = 0.3662) for the spatial differentiation of landscape ecological resilience drivers and the joint effects of factor interaction had a higher explanatory power than single factors. Adaptive cycle analysis revealed that Furong District is in the protection stage, Xiangtan County in the development stage, and Liling City in the reorganization stage, with no region yet in the release stage. The findings offer a better understanding of the interactive adaptation characteristics and evolutionary patterns of social-ecological systems over extended periods, providing scientific support for the formulation of protection strategies to respond to dynamic changes in urban agglomeration ecosystems.

The study of the spatial and temporal evolution of annual mean PM2.5 concentration and population exposure risk in Hunan Province can further analyze the influence of the landscape pattern index on the changes in annual mean PM2.5 concentration and population exposure risk. The spatial and temporal evolution of the coupling relationship between people, air, and land at a fine scale and its spatial heterogeneity were investigated by the population exposure risk model, the moving window method, the spatial auto correlation model, and correlation analysis. The results show that: (1) from 2000 to 2020, the annual average PM2.5 concentration in Hunan Province showed a slow increase and then a significant decrease, and its spatial distribution was high in the northeast and low in the southwest. (2) From 2000 to 2016, the average proportion of the population exposed to high annual average PM2.5 concentrations (>45 μg/m3) in Hunan Province reached 74.24%, and from 2016 to 2020, the proportion of the population exposed to low annual average PM2.5 concentrations (<35 μg/m3) increased year by year. In 2020, there were regions with annual average concentrations of 15-25 μg/m3, and the regional concentrations were all less than 45 μg/m3. The spatial distribution is dominated by low-risk areas, with an overall scattered distribution, and high-risk areas are concentrated in the Chang - Zhu - Tan urban agglomeration. (3) Cultivated land and forested land, as the main land types, have relatively obvious characteristics of changes in dynamics and attitudes; water bodies and impervious surfaces are second; cultivated land has a high degree of patch aggregation, high edge density and low fragmentation, which increases the risk of annual average PM2.5 concentration and population exposure; forested land has a high degree of patch aggregation, low edge density and low fragmentation, which is conducive to reducing the risk of annual average PM2.5 concentration and population exposure; and water bodies and impervious surfaces have a high degree of aggregation, low edge density, and low fragmentation, which is conducive to reducing the risk of annual average PM2.5 concentration and population exposure. The higher the degree of aggregation, the higher the edge density, the more complex the degree of fragmentation of water bodies and impervious surfaces, and the higher the risk of annual average PM2.5 concentration and population exposure. The results of this study can provide a theoretical basis for mitigating air pollution, improving the human environment, optimizing the landscape pattern, and promoting ecologically sustainable development.

Urban agglomerations are complex adaptive social–ecological systems, and understanding the spatiotemporal evolution of landscape ecological resilience is important for their sustainable management. This study extended the previously developed Risk–Potential–Connectivity (RPC) framework for the Changsha–Zhuzhou–Xiangtan Urban Agglomeration (CZXUA) by identifying adaptive cycle categories at the grid scale using Local Moran’s I and explicit classification rules. Previously generated SD–PLUS land use projections were used to evaluate future changes in the RPC dimensions, integrated resilience, and adaptive cycle composition. (1) From 2000 to 2020, mean landscape ecological resilience decreased from 0.3642 to 0.3345, representing a net decline of 8.15%. Global Moran’s I increased from 0.746 to 0.787, indicating stronger spatial clustering. The spatial pattern remained relatively stable, with higher resilience in peripheral ecological areas, lower resilience in urban cores, and a persistent northwest–southeast orientation. (2) Adaptive cycle composition changed only moderately. The conservation (K) category accounted for more than 45% of the study area, while the release (Ω) category accounted for approximately 19% and was concentrated mainly in urban cores. The exploitation (r) category declined after 2010, whereas the smaller reorganization (α) and transitional (t) categories were more sensitive to classification settings. (3) Using previously generated land use projections as scenario inputs, future RPC responses differed clearly among scenarios. SSP126 maintained more intact ecological patches, relatively stable connectivity, and the smallest resilience change, with increases in r and K. SSP245 showed the greatest connectivity decline, expansion of areas with declining resilience, a reduction in r, and increases in Ω and α. SSP585 produced the highest landscape ecological risk, the largest declines in ecological potential and integrated resilience, reductions in r and K, and increases in α and t. These conditional results indicate that limiting construction land expansion, protecting ecological land, and maintaining ecological connectivity may reduce resilience losses under future urban development. The RPC adaptive cycle framework can support the identification of priorities for ecological protection, restoration, connectivity enhancement, and risk reduction.