The study of vegetation net primary productivity (NPP) is essential in the Yanshan–Taihang Mountain Ecological Conservation Zone (YTECZ). Serving as an ecological security barrier for the Beijing–Tianjin–Hebei region, understanding the spatiotemporal dynamics and drivers of NPP in the YTECZ is fundamental for supporting effective sustainable development policies. Utilizing MODIS NPP, climatic data (temperature and precipitation), and the Human Footprint Index (HFP, a comprehensive metric of anthropogenic pressure), this study employed univariate linear regression, ArcGIS spatial analysis, and the Geographical Detector to investigate the spatiotemporal patterns and drivers of vegetation NPP in the YTECZ from 2004 to 2023 and to project its future trends through time series analysis. Our findings reveal a significant fluctuating upward trend in vegetation NPP over the 21-year period (mean annual increase: 4.58 g C·m⁻²), displaying a distinct spatial gradient characterized by higher values in western and northern sectors relative to eastern and southern areas. The interannual variability of vegetation NPP was primarily dominated by precipitation fluctuation, while its spatial heterogeneity was jointly driven by vapor pressure deficit (VPD) and temperature. Notably, human activities exhibited significant explanatory power on NPP’s spatial pattern, and their interaction with climatic factors (e.g., VPD) resulted in non-linear enhancements. Future projections suggest that the current increasing trend is unlikely to be sustained in the long term, indicating substantial uncertainty in vegetation carbon sequestration patterns. This study provides critical insights into vegetation response mechanisms to global change drivers, offering a scientific foundation for ecological management strategies toward sustainable development in the YTECZ.

Time-series InSAR is an important means for early identification and monitoring of landslides. However, in complex mountainous areas, it still faces challenges such as significant geometric distortions and complicated deformation mechanisms. To address these issues, this paper proposes a landslide identification and prediction framework that integrates ascending and descending orbits InSAR observations with physics-guided deep learning. Taking Yangbi County, Yunnan Province, as a case study, we combined ascending and descending Sentinel-1A data and employed the SBAS-InSAR method to identify potential landslides, detecting a total of 41 hazardous sites. The cumulative displacement time series of typical landslides were further extracted along the slope aspect to more realistically reflect landslide movement characteristics. On this basis, wavelet decomposition was introduced to separate the displacement series into trend and periodic components. Gray relational analysis was then used to select influencing factors such as precipitation and temperature, and a stepwise prediction model based on LSTM (WT-LSTM) was constructed. The results indicate that the model achieves significantly higher prediction accuracy at characteristic points of the representative landslide (RMSE = 1.16–2.19 mm) compared to standalone LSTM and SVR models. These findings demonstrate its effectiveness and potential applicability in landslide deformation monitoring and prediction in complex mountainous areas, while also providing a useful reference for landslide risk early warning.

Soil microbial communities face the combined pressures of climate change and biodiversity loss, yet how these stressors interact to shape ecosystem function remains a critical uncertainty. To investigate this, we established a constructed grassland plant community and conducted a fully factorial experiment manipulating plant diversity (1, 3, and 6 species), temperature (ambient, +2 °C), and precipitation (ambient, +50%). High-throughput 16S rRNA gene sequencing revealed that plant diversity exerted a stronger influence on soil bacterial community structure than did warming or precipitation changes. Beta diversity analysis revealed a distinct clustering of bacterial communities corresponding to the plant diversity gradient. This shift was characterized by a consistent enrichment of the metabolically versatile genus Sphingomonas in medium-diversity plots that experienced elevated precipitation, suggesting a predicted potential for enhanced organic matter decomposition. Despite overall stability in alpha diversity, the interaction between plant diversity and warming significantly modulated bacterial diversity and dominance patterns. Our findings highlight that plant diversity plays a key role in mediating soil bacterial responses to simulated climate factors in the short term. Incorporating these plant–soil feedback mechanisms into ecological models appears crucial for advancing predictions of ecosystem dynamics under future climate conditions.

Sustainable land governance requires timely and accurate monitoring of land-use change to balance ecological, agricultural, and urban demands. Yet policymakers rarely receive actionable insights fast enough, because large-scale geospatial computation and rapid delivery remain disconnected. To close this gap, we introduce a Computational-Visualization Co-design (CVC) framework that welds a distributed high-performance computing engine to a real-time, preprocessing-free visualization system. Our approach represents a system-level innovation. It co-designs computational shards as visualization units, eliminating intermediate data reorganization. This co-design paradigm makes analytical results immediately visible. CVC processes a 20 TB imagery dataset and overlays millions of parcels 5–9 times faster than conventional engines. Map service publishing plummets from 168 h to just 7—a 24-fold speed-up—while client-side performance stays robust. The framework directly supports sustainable land management. It enables proactive monitoring, rapid impact assessment, and evidence-based policy formulation. Our work thus contributes to key Sustainable Development Goals related to land and cities. Validated with national survey data from China, the system merges analysis with instantaneous visual feedback, offering a practical route to sustainable land governance.

Understanding spatiotemporal changes in landscape ecological risk is essential for regional ecological security and sustainable land management, particularly in environmentally sensitive areas. Using land-use data from five time points (2000, 2005, 2010, 2015, and 2020) together with topographic, climatic, vegetation, and socioeconomic data, we assessed landscape ecological risk dynamics in Xinzhou City, a typical ecologically sensitive region in the middle Yellow River Basin. We integrated a landscape ecological risk assessment model, spatial autocorrelation analysis, and an optimal-parameter geographical detector (OPGD) to identify the spatiotemporal patterns and key drivers of ecological risk. Built-up land expanded by 131.47% over the study period, while cropland and grassland both contracted. Land-use change was dominated by bidirectional conversion between cropland and grassland, alongside a net shift of both to built-up land. Overall, ecological risk declined: high- and relatively high-risk areas decreased by 1354.15 km². Spatially, the pattern remained stable, with higher risk in basins and lower risk in mountainous regions, although the trend varied across phases. Risk exhibited significant spatial clustering: low–low clusters remained stable in mountainous ecological source areas, whereas high–high clusters persisted in basin agricultural areas. Elevation was the dominant factor in spatial risk differentiation. Combined natural and anthropogenic factors shaped the overall risk pattern. Despite the regional decline, urbanization-induced disturbance remains the principal driver of local risk increases. Our findings demonstrate the value of coupling long-term land-use data with OPGD to evaluate the relative and interactive contributions of terrain constraints and anthropogenic drivers in complex mountain–basin systems, with implications for spatially targeted ecological restoration and sustainable land management in ecologically sensitive transition zones.

Urban expansion exerts substantial pressure on regional ecosystems; however, how these effects propagate through direct and indirect pathways at the urban agglomeration scale remains insufficiently understood. Using nighttime light (NTL) and statistical yearbook data, we extracted the built-up areas across the Beijing–Tianjin–Hebei (BTH) region using a threshold-based approach to characterize expansion dynamics from 1992 to 2022. Furthermore, we applied the PKU GIMMS NDVI dataset within a conceptual framework to quantify the direct and indirect effects of built-up area expansion on vegetation. Expansion patterns varied markedly among cities: Beijing demonstrated concentric outward growth, Tianjin exhibited dual-core development, and cities in eastern and southern Hebei followed a leapfrog, dispersed pattern. Expansion rates and intensities also diverged considerably: Beijing and Tianjin sustained prolonged, high-intensity growth, whereas cities in northern and western Hebei expanded more gradually. The centroid of built-up areas remained relatively stable in Beijing and Tianjin but shifted in other cities, where expansion concentrated along one or two primary axes. Direct negative effects, driven by impervious surface expansion, intensified with increasing urbanization pressure, reaching approximately −54% in rapidly expanding urban agglomerations by 2007 and peaking at −67.43% at the city scale in Beijing by 2022. In contrast, indirect effects exhibited pronounced spatiotemporal heterogeneity: they fluctuated in rapidly expanding urban agglomerations but increased markedly in other areas after 2012. The strongest indirect positive effect peaked at the city level (64.85% in Handan in 2022), suggesting gradual vegetation recovery toward the end of the study period. These findings suggest that moderate-growth cities with remaining green space may exhibit relatively stronger ecological compensation potential, but this pattern requires further validation across broader urban agglomerations. This underscores the need for spatially differentiated planning to balance urban growth and ecological sustainability.