Open-pit coal mining plays an important role in supporting national economic development; however, it has caused ecological problems and even seriously impacted regional ecological stability. Given the importance of maintaining ecological stability in semi-arid coal mining areas, this study used a coupling coordination degree approach based on the structural and functional state transition model (SFSTM) to evaluate the spatio–temporal variation of ecological stability from 2002 to 2017 by using MODIS and Landsat datasets in the semi-arid open-pit coal mining area. Besides, random points were created for different ecological stability levels (containing natural areas, coal mining areas, and reclamation areas) and segment linear regression was conducted to determine the structural change threshold for negative state transitions based on mining and positive state transitions based on reclamation. Furthermore, the impact factors of ecological stability were analyzed. Results showed that ecological stability fluctuated significantly over 16 years, showing a trend of first increasing and then decreasing. It was found that precipitation and temperature were the key natural factors affecting ecological stability, and mining activities constituted the dominant factor. The average perturbation distances to ecological stability from mining activities in the west, southwest, and east mining groups were 7500, 5500, and 8000 m, respectively. SFSTM is appliable to the coal mining ecosystem. Quantitative models of ecological stability response can help resolve ambiguity about management efficacy and the ecological stability results facilitate iterative updating of knowledge by using monitoring data from coal mining areas. Moreover, the proposed ecological structural threshold provides a useful early warning tool, which can aid in the reduction of ecosystem uncertainty and avoid reverse transformations of the positive state in the coal mining areas.

Understanding farmers’ participation is crucial for achieving an effective impact on rural living environmental governance and promoting sustainable development. Taking Sandu Town in eastern China as a case study, in-depth semi-structured interviews with farmers, village cadres, and town managers were conducted in this study. Then, a conceptual framework incorporating comprehensive factors is presented to analyze the driving factors and mechanisms of farmer participation in rural domestic waste management. The results show that farmers’ participation in pro-environmental actions is a response to an integrated network of both internal and external factors. Life inertia, loss of personal interests, and objective conditions are the barriers to farmers deciding to participate. Meanwhile, environmental awareness can increase farmers’ internal motivations, and factors such as household benefits, social-cultural influences, and appraisal systems, including household possession protection, very low economic costs, better life experiences, demonstrations from society, “following the crowd”, peer pressure, and reward and criticism measures, are the external forces that mobilize farmers to participate in rural environmental governance. Policy recommendations are proposed based on the findings.

Knowledge about the spatial-temporal pattern of cropland abandonment is the premise for the management of abandoned croplands. Traditional mapping approaches of abandoned croplands usually utilize a multi-date classification-based land cover change trajectory. It requires quality training samples for land cover classification at each epoch, which is challenging in regions of smallholder agriculture in the absence of high-resolution imagery. Facing these challenges, a theoretical model is proposed to recognize abandoned croplands based on post-abandonment secondary succession. It applies the continuous change detection and classification (CCDC) temporal segmentation algorithm to Landsat time series (1986~2021) to obtain disjoint segments, representing croplands’ status. The post-abandonment secondary succession showing a greening trend is recognized using NDVI-based harmonic analysis, so as to capture its preceding abandonment. This algorithm is applied to a mountainous area in southwest China, where cropland abandonments are widespread. Validation based on stratified random samples referenced by a vegetation index time series and satellite images shows that the detected abandoned croplands have user accuracy, producer accuracy and an F1 score ranging from 43% to 71%, with variation among abandonment year. The study area has a potential cropland extent of 22,294 km2, within which 9252 km2 of the cropland was abandoned. The three peak years of abandonment were 1994, 2000, and 2011. The algorithm is suitable to be applied to large-scale mapping due to its automatic manner.