The Tibet Autonomous Region (TAR) is located in the mid-latitude and high-cold regions, and the ecological environment in most areas is fragile. Studying its surface vegetation coverage can identify the ecosystem’s development trends and provide a specific contribution to global environmental change. The normalized difference vegetation index (NDVI) can better reflect the coverage of surface vegetation. Therefore, based on remote sensing data with a resolution of 1 km², air temperature, precipitation, and other data in the same period in the study area from 1998 to 2019, this paper uses trend analysis, F-significance tests, the Hurst index, and the Geodetector model to obtain the spatial distribution, change characteristics, and evolution trends of the NDVI in the TAR in the past 22 years. At the same time, the quantitative relationship between natural and human factors and NDVI changes is also obtained. The study results show that the NDVI in the southern and southeastern parts of the TAR is higher, with mean values greater than 0.5 showing that vegetation cover is better. The NDVI in the western and northwestern parts of the TAR is lower, with mean values less than 0.3, indicating vegetation cover is worse. NDVI in the TAR showed an overall increasing trend from 1998 to 2019 but a decreasing trend in ridgelines, snow cover, and glacier-covered areas. The areas where NDVI values show a trend of increasing and then decreasing in the future account for 53.69% of the total area of the TAR. The most crucial factor affecting NDVI changes in the TAR is soil type, followed by influencing factors such as vegetation cover type, average annual air temperature, and average annual precipitation. The influence of natural elements is generally more significant than anthropogenic factors. The influencing factors have synergistic effects, and combining anthropogenic factors and other factors will show mutual enhancement and non-linear enhancement relationships. This study provides a theoretical basis for natural resource conservation, ecosystem restoration, and sustainable human development strategies in the TAR.

Investigating spatio-temporal differentiation patterns of land-use conflicts in mountainous and flatland regions provides critical insights for optimizing spatial regulation strategies and advancing sustainable regional development. Using the Urban Agglomeration in Central Yunnan (UACY) as a case study, the production–living–ecological space (PLES) was classified through land-use functional dominance analysis based on 2010–2020 geospatial datasets. Spatio-temporal evolution patterns and mountain–dam differentiation were analyzed using spatial superposition, dynamic degree analysis, transfer matrices, and geospatial TuPu methods. A multi-scale conflict index incorporating landscape metrics was developed to assess PLES conflict intensities across spatial scales, with contribution indices identifying key conflict-prone spatial types. Analysis revealed distinct regional differentiation in PLES distribution and evolutionary trajectories during 2010–2020. Forest Ecological Space (FES) and Agricultural Production Space (APS) dominated both the entire study area and mountainous zones, with APS exhibiting particular dominance in dam regions. Grassland Ecological Space (GES) and Other Ecological Space (OES) experienced rapid conversion rates, contrasting with stable or gradual expansion trends in other space types. Change intensity was significantly greater in mountainous zones compared to flatland area (FA). PLES conflict exhibited marked spatial heterogeneity. FA demonstrated substantially higher conflict levels than mountainous zones, with evident scale-dependent variations. Maximum conflict intensity occurred at the 4000 m scale, with all spatial scales demonstrating consistent escalation trends during the study period. ULS, FES, and WES predominantly occurred in low-conflict zones characterized by stability, whereas APS, Industrial and Mining Production Space (IMPS), RLS, GES, and OES were primarily associated with high-conflict areas, constituting principal conflict sources.

Rapid climate change has exacerbated global ecosystem degradation, leading to habitat fragmentation and landscape connectivity loss. Constructing ecological networks (EN) with resilient conduction functions and conservation priorities is crucial for maintaining regional ecological security and promoting sustainable development. However, the spatiotemporal modeling and dynamic resilience assessment of EN under the combined impacts of future climate and land use/land cover (LULC) changes remain underexplored. This study focuses on the Central Yunnan Urban Agglomeration (CYUA), China, and integrates landscape ecology with complex network theory to develop a dynamic resilience assessment framework that incorporates multi-scenario LULC projections, multi-temporal EN construction, and node-link disturbance simulations. Under the Shared Socioeconomic Pathways and Representative Concentration Pathways (SSP-RCP) scenarios, we quantified spatiotemporal variations in EN resilience and identified resilience-based conservation priority areas. The results show that: (1) Future EN patterns exhibit a westward clustering trend, with expanding habitat areas and enhanced connectivity. (2) From 2000 to 2040, EN resilience remains generally stable, but diverges significantly across scenarios—showing steady increases under SSP1-2.6 and SSP5-8.5, while slightly declining under SSP2-4.5. (3) Approximately 20% of nodes and 40% of links are identified as critical components for maintaining structural-functional resilience, and are projected to form conservation priority patterns characterized by larger habitat areas and more compact connectivity under future scenarios. The multi-scenario analysis provides differentiated strategies for EN planning and ecological conservation. This framework offers adaptive and resilient solutions for regional ecosystem management under climate change.

Revealing the trade-offs, synergies, and driving mechanisms among land use functions is essential for mitigating conflicts between functions, optimizing territorial spatial patterns, and providing policy support for regional sustainable development. Taking the Central Yunnan Urban Agglomeration as a case study, this study adopts a grid-based evaluation unit and employs a multi-model fusion approach to systematically analyze the interaction mechanisms among land use functions. By integrating the Pearson correlation method and root mean square deviation (RMSD) model, the trade-off and synergy relationships and their spatiotemporal evolution were quantitatively assessed. The XGBoost–SHAP model and optimized parameter-based geographical detector (OPGD) were introduced to identify the nonlinear characteristics and interaction effects of influencing factors on land use function trade-offs and synergies. In addition, a geographically weighted regression (GWR) model was used to explore spatial heterogeneity in these effects. The results indicate that (1) from 2010 to 2020, the overall synergy between production and ecological functions (PF&EF) in the urban agglomeration was enhanced, while trade-offs between production and living functions (PF&LF) intensified, and the trade-off intensity between living and ecological functions (LF&EF) decreased. Significant spatial heterogeneity exists among land use function interactions: PF&EF and PF&LF trade-offs are concentrated in the central and eastern parts of the urban agglomeration, while LF&EF trade-offs are more scattered, mainly occurring in highly urbanized and ecologically sensitive areas; (2) the dominant factors influencing land use function trade-offs and synergies include precipitation, slope, land use intensity, elevation, NDVI, Shannon diversity index (SHDI), distance to county centers, and distance to expressways; (3) these dominant factors exhibit strong nonlinear effects and significant threshold responses in shaping trade-offs and synergies among land use functions; and that (4) compared with the OLS model, the GWR model demonstrated higher fitting accuracy. This reveals that the impacts of natural, socio-economic, and landscape pattern factors on land use function interactions are characterized by pronounced spatial heterogeneity.