The interaction between rural household livelihood strategies and land use behavior constitutes the core of the human–land relationship in a region, especially in protected area villages with high ecological sensitivity that require the integration of social–economic and ecological benefits. This study, grounded in the concept of sustainable livelihoods, utilizes survey data from 399 households in the Chang-Zhu-Tan Green Heart Ecological Protection Zone. By employing the entropy method to calculate indicator weights, a coupling coordination degree model is constructed to analyze the coupling degree, coordination degree, and spatial differentiation patterns between household livelihood strategies and land use behavior. The findings reveal that (1) there is a coupling relationship between household livelihood strategies and land use behavior in the protected area, characterized by moderate coupling coordination. (2) Agricultural sideline households exhibit the highest degree of coupling coordination. The sideline activities of these households not only enhance income but also exert positive impacts on the ecological environment while they are engaged in agricultural production. (3) There is a significant spatial differentiation in the coupling coordination degree of different types of households in the protected area: it increases with the distance from the urban center, with higher values in the central areas than in the peripheral regions. The results of this study provide a basis for understanding micro-level land use changes and guiding household land use behavior, as well as offering theoretical and practical references for improving sustainable livelihoods of households in the study area, promoting rural revitalization, and implementing ecological protection policies.

In the strategic orientation of promoting high-quality development of metropolitan areas, ecological space is not only the core carrier for maintaining regional ecological balance and harmony but also a key element in shaping the scientific layout of metropolitan areas and promoting harmonious coexistence between cities and nature. This paper takes the Chang-Zhu-Tan metropolitan area as the research object and identifies and classifies ecological space based on the “Production-Life-Ecology” concept, extracts core ecological source areas through the minimum resistance model, and generates ecological resistance surfaces. Then, three types of ecological space corridors are constructed by using the MCR gravity model. This study finds that the ecological space in the Chang-Zhu-Tan metropolitan area is seriously fragmented, the number of corridors is insufficient, and the spatial configuration of the “Production-Life-Ecology” is imbalanced in the region and proposes optimization models and strategies in combination with the spatial network approach to identify and classify ecological space patterns for the metropolitan area. Accordingly, the study proposes optimization models and strategies based on the spatial network approach to provide theoretical support and practical guidance for the ecological spatial pattern and sustainable development of the metropolitan area.

Ecological changes in mining areas are characterized by long-term dynamics, temporal stages, and spatial heterogeneity. Existing monitoring methods often face challenges in balancing multidimensional ecological condition characterization with spatially consistent analysis and have limited capability to identify complex disturbance–recovery trajectories. To address these issues, an Object-based Ecological Index-driven Framework (OEI-GTCD) is proposed to monitor ecological disturbance and recovery trajectories in mining areas. The framework uses the Mining-area Ecological Index (MEI) to characterize multidimensional ecological conditions and conducts analysis based on spatially consistent object units. On this basis, a Globally Optimized Time-series Change Detection method (GTCD) is proposed to improve the identification of complex ecological disturbance–recovery trajectories by integrating trend-change detection, abrupt-change detection, and segmentation optimization. Landsat imagery from 2000 to 2024 was used to characterize the spatiotemporal patterns of ecological disturbance and recovery across the Daye Mining Area, China. The findings suggest that MEI captures multiple dimensions of ecological conditions, while object-scale analysis characterizes ecological changes through overall object-level trajectories, supporting their spatial interpretation. OEI-GTCD achieved disturbance and recovery detection accuracies of 88.4% and 84.8%, respectively, representing improvements of 6.0 and 5.2 percentage points over OEI-LandTrendr. Ablation analyses further highlighted the contributions of abrupt-change detection and the improved segmentation criterion to event identification, as well as the role of local breakpoint refinement in temporal localization. From 2000 to 2024, stable and continuously improving ecological states together accounted for 82.07% of the area represented by the non-water objects used in the ecological analysis, while disturbance and recovery processes exhibited pronounced spatiotemporal heterogeneity. Overall, OEI-GTCD provides an integrated approach for monitoring ecological disturbance and recovery and characterizing their spatiotemporal patterns in mining areas.

Long-term mining activities can cause complex ecological and environmental problems, including mineral surface exposure, dust-related disturbance, and vegetation degradation, while conventional ecological indices may not fully characterize the distinctive ecological conditions of mining areas. This study developed a mining-area Modified Remote Sensing Ecological Index (MRSEI) by incorporating the Lithological Mineral Index (LMI), Normalized Dust Difference Index (NDDI), and Vegetation Health Index (VHI) into the Remote Sensing Ecological Index (RSEI) framework. Object-based analytical units were constructed using Landsat time-series data from 2000 to 2024 and G-means clustering, followed by the development of an Object-Based XGBoost-SHAP (OB-XGBoost-SHAP) framework to quantify the relative model contributions and nonlinear associations of explanatory factors. The results showed that, in the Daye mining area (DMA), MRSEI provided clearer differentiation of ecological quality among land-use types than RSEI and Mining-Specific Eco-Environment Index (MSEEI), with an overall gradient of forest land > cultivated land > construction land > mining land. Annual Principal Component Analysis (Annual-PCA) and Global Principal Component Analysis (Global-PCA) results showed high consistency (Pearson r = 0.985). Across the seven evaluation years, the mean MRSEI of the DMA increased from 0.5493 in 2000 to 0.6035 in 2024, representing an overall change of 9.87%. The object-based model consistently outperformed the regular-grid model under random splitting, while model performance decreased under spatially separated validation but retained moderate predictive capability. Particulate Matter (PM10), Digital Elevation Model (DEM), Temperature (TEM), and Land Use Intensity (LU) showed relatively high model contributions, with their relative importance varying across different periods. This study integrates mining-specific ecological characterization, object-based spatial analysis, and explanatory-factor contribution analysis, providing a methodological reference for ecological quality assessment and long-term change monitoring in mining areas.