Scientific ecosystem management requires the clarification of the synergic and trade-off relationship between ecosystem services, particularly in the environmentally delicate Loess Plateau region. Previous studies have indirectly deduced that ecological restoration projects affect ESRs by analyzing their impacts on ecosystem services, but there is no direct evidence from the existing research to show whether and to what extent different ecological restoration projects have an impact on trade-off synergies, which weakens the explanatory strength of ecological restoration projects as an important factor affecting ESRs. In this study, based on the spatial mapping of three pairs of relationships between three typical ESs in Fugu County, Shaanxi Province, and the relative contribution of each ecological restoration projects, as well as Ecosystem services and the relationship between them, were explored through the boosted regression tree modeling (BRT). This study proved that different ecological restoration projects have different impacts on ESRs. The results indicated that the three pairs of ESRs obtained among the three ecosystem services in Fugu County could be categorized into two types. The relationship between carbon storage and soil conservation and the relationship between carbon storage and water conservation CS–WC were spatially predominantly trade-offs, and their spatial distributions were highly similar. Various ecological restoration projects have varying effects on ESRs. The connection between ecological restoration projects and ESRs involves a nonlinear transformation, and the change varies from project to project. Based on the above findings, this study further explores the influence process of various types of ecological restoration projects on ESRs, and provides scientific support for optimizing ecosystem management and comprehensive management of the region.

Resource-based cities, constrained by resource dependence and economic restructuring, exhibit high sensitivity and vulnerability when responding to external shocks. Enhancing human settlement resilience can strengthen the capacity of urban systems to withstand external pressures, maintain stability, and promote the well-being of residents and sustainable urban development. However, the differentiation patterns and obstacle factor identification across urban types at different development stages still require further investigation. This study constructs an evaluation index system based on five subsystems: natural, human, living, social, and support systems. The evolution of the human settlement resilience of resource-based cities in Northern China from 2012 to 2022 was analyzed using the entropy method and the Analytic Hierarchy Process (AHP), kernel density estimation, and the obstacle degree model. We further compare resilience differentiation characteristics of four types of cities, growing, mature, declining, and regenerating, while conducting a scientific diagnosis of the factors that hinder resilience enhancement. The results show that: (1) Human settlement resilience in resource-based cities exhibits phased characteristics. Regenerating cities demonstrate the highest level of resilience, followed by growing, mature, and declining cities. All city types show an upward trend in human settlement resilience across different stages, with an overall increase of 25%, although their development trends showed noticeable differentiation. Regenerating cities experienced relatively rapid improvement, accompanied by a narrowing gap in inter-city disparities, while declining cities also demonstrated an overall upward development trend. Mature cities exhibited relatively gradual improvements in resilience, while growth-type cities exhibited differences in their rates of increase. (2) At the criterion level, social resilience is the core obstacle for growing, mature, and regenerating cities, while in declining cities, the core obstacle shifts from social to living resilience. The obstacle degree of social resilience was nearly above 30% for all four city types in both 2012 and 2017, and remained above 20% in 2022. At the indicator level, water resource abundance, urbanization level, and economic development level are the major barriers to resilience improvement, with dominant factors differing significantly among city types. The study results provide insights for the development of resource-based cities in China and offer references for similar cities in industrialized countries.

Open-pit coal mining exerts long-lasting and cumulative disturbances on terrestrial ecosystems during the post-mining stage, and the associated environmental impacts and recovery processes exhibit pronounced temporal persistence and spatial heterogeneity. Long-term remote sensing monitoring is therefore essential for understanding post-mining ecosystem dynamics and evaluating restoration outcomes. In this study, we constructed a Mining Landscape Disturbance Index (MLDI) and a Mining Landscape Recovery Index (MLRI) based on the Enhanced Vegetation Index (EVI) and Land Surface Temperature (LST). Combined with the LandTrendr algorithm, post-mining disturbance-recovery trajectories of 46 open-pit coal mines in northern China were systematically monitored and analyzed over the period 1984-2025. Using disturbance and recovery trajectory information extracted by LandTrendr, ecosystem resilience was comprehensively assessed from three dimensions: resistance, recovery capacity, and stability. Based on disturbance magnitude and recovery magnitude, the 46 mining areas were classified into four types: high disturbance-high recovery (HH), high disturbance-low recovery (HL), low disturbance-high recovery (LH), and low disturbance-low recovery (LL). The results indicate that the median MLRI values of all four types show an overall increasing trend at the interannual scale, suggesting a general post-mining recovery tendency of ecosystems during the study period, although significant differences exist in recovery levels and recovery rates among different types. Meanwhile, the median MLDI values also exhibit a continuous upward trend, reflecting persistent cumulative degradation pressure on mining ecosystems under long-term mining activities. Distinct multidimensional differentiation patterns were observed among the four disturbance-recovery types in terms of resistance, recovery capacity, and stability. This study provides an effective remote sensing-based framework for monitoring long-term post-mining ecological dynamics and offers scientific support for differentiated environmental management and ecological restoration strategies in post-mining areas.