The quantitative evaluation and prediction of ecosystem service value (ESV) in the Lhasa River Basin can provide a basis for ecological environment assessment and land-use optimization and adjustment in the future. Previous studies on the ESV in the Lhasa River Basin have focused mainly on static assessment and evolution analysis based on historical data, and have not considered future development trends. Moreover, most of the current driving factors selected in land use and ESV prediction studies are homogeneous, and do not reflect the geographical and cultural characteristics of the study area well. With the Lhasa River Basin as the research focus, 20 driving factors were selected according to the characteristics of the plateau alpine area, and the land-use changes under three developmental orientations, namely, natural evolution, ecological protection, and agricultural development, were predicted for the year 2030 with the FLUS-Markov model. Based on these predictions, the values of ecosystem services were calculated, and their spatiotemporal dynamic characteristics were analyzed. The results show that the model has high accuracy in simulating land-use change in the Lhasa River Basin, with a kappa coefficient of 0.989 and an overall accuracy of 99.33%, indicating a high applicability. The types of land use in the Lhasa River basin are dominated by the existence of grassland, unused land, and forest, with a combined proportion of 94.3%. The change trends of each land-use type in the basin under the three scenarios differ significantly, with grassland, cropland, and building land showing the most significant changes. The area of cropland increased only in the agricultural development scenario; the areas of forest and grassland increased only in the ecological protection scenario; and the expansion of building land was most effectively controlled in the ecological protection scenario. The ESV increased in all three scenarios, and the spatial distribution of the ESV per unit area in the middle and lower reaches was greater than that in the upper reaches. The ESV was the greatest in the ecological protection scenario, with grasslands, forests, and water bodies contributing more to the ESV of the basin. This study provides decision-making references for the effective utilization of land resources, ecological environmental protection planning, and urban construction in the Lhasa River Basin in the future.

Mountain cities with complex topographies have always been highly vulnerable areas to global environmental change, prone to geological hazards, climate change, and human activities. Exploring and analyzing the vulnerability of coupling systems in mountain cities is highly important for improving regional resilience and promoting sustainable regional development. Therefore, a comprehensive framework for assessing the spatial vulnerability of mountain cities is proposed. A vulnerability assessment index system is constructed using three functional systems, ecological protection, agricultural production, and urban construction. Subsequently, the BP neural network and the genetic algorithm (GA) are combined to establish a vulnerability assessment model, and geographically weighted regression (GWR) is introduced to analyze the spatial influence of one-dimensional systems on the coupling system. Linzhou, a typical mountain city at the boundary between China’s second- and third-step terrains, was selected as a case study to demonstrate the feasibility of the framework. The results showed that the vulnerability of the ecological protection system was highly aggregated in the east–central region, that of the agricultural production system was high in the west, and that of the urban construction system was low in the central region and high in the northwestern region. The coupling system vulnerability was characterized by multispatial distribution. The complex topography and geomorphology and the resulting natural hazards are the underlying causes of the vulnerability results. The impact of ecological and urban systems on the coupling system vulnerability is more prominent. The proposed framework can serve as a reference for vulnerability assessments of other similar mountain cities with stepped topographies to support the formulation of sustainable development strategies.

Based on the fundamental principles of spatial heterogeneity and landscape ecology, landscape mosaic (LM) offers a more effective method for capturing variations in landscape spatial components, patterns, and ecological functions compared to land use and land cover (LULC). This advantage is particularly pronounced when employing the InVEST model to evaluate habitat quality (HQ), as field surveys often yield highly variable results that challenge the accuracy and applicability of LULC-based assessments. This paper focuses on the Taihang Mountain area in Hebei Province as the study region, utilizing the Principal Component Analysis (PCA), Self-Organizing Map (SOM), and Euclidean Distance (ED) model to achieve LM classification of the area. Based on this, the InVEST-HQ assessment is conducted, employing both OLS and GWR models to analyze the correlation between HQ and LM landscape patterns. The results indicate that (1) seven major LULC types were reclassified into nine pillar LM types and eleven transitional LM types, with a significant number of ecotone types emerging between different LULC types, among which cultivated land plays the most prominent role; (2) from 2000 to 2020, the overall HQ in the study area exhibited a continuous deterioration trend, particularly marked by a notable increase in functional areas of HQ areas classified as Level I; (3) factors such as the complexity of patch edges, the continuity between patches, and the diversity of patch types all significantly impact HQ. This study introduces an innovative methodological framework for HQ assessment using LM classifications within InVEST model, offering a robust foundation for comprehensive biodiversity monitoring and informed ecological management in the study area.