Accurate regional carbon sequestration estimates are essential for China’s emission reduction and carbon sink enhancement efforts to address climate change. Enhancing the spatial precision of vegetation carbon sink estimates is crucial for a deeper understanding of the underlying response mechanisms, yet this remains a significant challenge. In this study, the Beijing–Tianjin–Hebei (BTH) region was selected as the study area. We employed the GF-SG (Gap filling and Savitzky–Golay filtering) model to fuse Landsat and MODIS data, generating high-resolution imagery to enhance the accuracy of NPP (Net Primary Productivity) and NEP (Net Ecosystem Productivity) estimates for this region. Subsequently, the Sen+MK model was used to analyze the spatiotemporal variations in carbon sinks. Finally, the land use intensity index, which reflects human activity disturbances, was applied, and the bivariate Moran’s spatial autocorrelation method was used to analyze the response mechanisms of carbon sinks. The results indicate that the fused GF-SG NDVI (Normalized Difference Vegetation Index) data provided highly accurate 30 m resolution imagery for estimating NPP and NEP. The spatial distribution of carbon sinks in the study area showed higher values in the northeastern forest regions, relatively high values in the southeastern plains, and lower values in the northwestern plateau and central urban areas. Additionally, 58.71% of the area exhibited an increasing trend, with 11.73% showing significant or strongly significant growth. A generally negative spatial correlation was observed between land use intensity and carbon sinks, with the impact of land use intensity on carbon sinks exceeding 0.3 in 2010. This study provides methodological insights for obtaining vegetation monitoring data and estimating carbon sinks in large urban agglomerations and offers scientific support for developing ecological and carbon reduction strategies in the BTH region.

This study aims to investigate the spatial distribution and structural characteristics of urban greening in Beijing, focusing on three typologies: Single Tree (S-T), Tree–ush (T-B), and Tree–Bush–Grass (T-B-G). The analysis examines how socio-economic factors and the COVID-19 pandemic have influenced these structures across three time periods: pre-pandemic, during the pandemic, and post-pandemic recovery. To achieve this, a deep learning-based approach utilizing the DeepLabV3+ neural network was applied to analyze the features extracted from Baidu Street View (BSV) images. This method enabled the precise quantification of the structural characteristics of urban greening. The findings indicate that greening structures are significantly influenced by commercial activity, population mobility, and economic conditions. During the pandemic, simpler forms like S-T proved more resilient due to their lower maintenance requirements, while complex systems such as T-B-G experienced reduced support. These results underscore the vulnerability of green infrastructure during economic strain and highlight the need for urban greening strategies that incorporate flexibility and resilience to adapt to changing socio-economic contexts while maintaining ecological and social benefits.

Historic districts face increasing challenges due to the coexistence of intensive tourism, residents' everyday life, and sensitive ecological environments within constrained urban spaces. To address this issue, using the Shichahai historic district in Beijing as a case study, this study develops a Physarum-inspired multi-agent simulation framework to examine activity-pressure patterns under different temporal and behavioral scenarios. Multi-source spatial data were translated into GAMA model inputs representing movement constraints, agent origins, temporal attraction fields, and redistribution targets. Resident, tourist, mixed-activity, and spatial attraction redistribution scenarios were constructed and repeatedly simulated to evaluate spatial coverage, path concentration, mean visit intensity, resident-tourist overlap, activity-pressure coupling, and model stability. The results show that the tourist scenario produced a more concentrated path, with active units ranging from 9,056 to 9,267 across the four observation dates, compared with approximately 10,639 -10,660 in the resident scenario and 10,721 -10,766 in the mixed scenario. The mixed scenario showed the highest overall mean visit intensity, with a mean-of-means visit value of 228.80, followed by the tourist scenario at 217.09 and the resident scenario at 212.82. Resident-tourist overlap remained low at the district scale, with a mean overlap index of approximately 0.023, while localized maximum values reached 0.958 -0.997, indicating that the co-concentration of simulated resident- and tourist-related movement was limited to specific corridors and nodes. Under the spatial attraction redistribution scenario, path concentration decreased, with HHI declining from approximately 5.45 -5.56 × 10 ^-4 to 4.19 × 10 ^-4 and the high-density visit share decreasing from 2.45% -2.76% to 0.95%. These findings indicate that adjusting high-density attraction clusters can reshape simulated path concentration and support a balanced distribution of activity pressure without changing the physical structure of the historic district. The proposed framework provides a dynamic modeling approach for identifying localized pressure concentration and supporting low-intervention environmental adaptive regulation in high-density historic districts.