Net primary productivity (NPP) is a key metric for evaluating ecosystem carbon sink capacity and defining vegetation. Despite extensive research on vegetation NPP, much relies on coarse spatial resolution data, which often overlooks regional spatial heterogeneity, causing inaccuracies in NPP estimates. Therefore, this study employed the improved CASA model, based on GF-SG and kNDVI methods, to estimate vegetation NPP at a 30 m spatial resolution on the Zoigê Plateau from 2001 to 2020. The effects of anthropogenic and climatic factors on NPP were quantified through residual and partial correlation analyses. These results indicated the following: (1) NDVI derived from the GF-SG fusion method aligns closely with Landsat NDVI (R² ≈ 0.9). When contrasted with using NDVI alone, incorporating kNDVI into the CASA model enhances NPP assessment accuracy. (2) Vegetation NPP on the Zoigê Plateau has fluctuated upward by 2.09 gC·m⁻²·a⁻¹ over the last two decades, with higher values centrally and lower at the edges. (3) Monthly partial correlation analysis indicates almost no temporal effects in NPP response to temperature (97.42%) but significant cumulative effects in response to precipitation (80.3%), with longer accumulation periods in the south. Annual analysis reveals that NPP correlates more strongly with temperature than precipitation. (4) NPP changes are jointly influenced by climate change (48.46%) and human activities (51.54%), with the latter being the dominant factor. This study deepens the understanding of NPP dynamics in the Zoigê Plateau and offers insights for estimating NPP at high spatial-temporal resolutions.

The Wenchuan earthquake, an unexpected magnitude 8.0 mega-earthquake that struck on 12 May 2008, significantly changed land cover (LC), particularly affecting vegetation and rock cover. However, the long-term effects of LC changes on ecosystem services (ESs) remain unclear in earthquake-affected regions, especially across different spatial scales. This study, focusing on Wenchuan County, employs a multi-model framework that integrates fractional vegetation coverage (FVC), rock exposure rate (FR), and ecosystem services (ESs), combining correlation analysis, geographically weighted regression (GWR), Self-organizing map (SOM) clustering, and XGBoost-SHAP model, to analyze the spatiotemporal dynamics, interrelationships, and driving mechanisms of land cover (LC) and ESs before and after the earthquake. Results show that: (1) From 2000 to 2020, FVC and FR fluctuated markedly under earthquake influence, with slight declines in habitat quality (HQ) and carbon storage (CS) and notable improvements in soil conservation (SC) and water yield (WY). (2) With increasing elevation, the FVC–CS–SC group exhibited a downward trend and synergy, while the FR–HQ–WY group increased and also showed synergy; trade-offs and synergies became more pronounced at larger scales, displaying strong spatiotemporal heterogeneity. (3) Elevation (explaining 10–60% of variance) was the main driver for LC and ESs, with land use, slope, human activities, climate, and geological conditions significantly impacting individual indicators. At the same time, the existing geological hazard points are mainly concentrated along both sides of the river valleys, which may be associated with intensified human–land conflicts. These findings offer valuable insights into ecological restoration and sustainable development in earthquake-affected regions.

Constructing coastal ecological security barriers is beneficial for preventing environmental degradation and enhancing resilience to natural disasters. This study examines the functionality of these barriers from an ecosystem service perspective, developing an Ecosystem Security Barrier Function (ESBF) index to analyze its spatiotemporal variations. From 2000 to 2020, habitat quality in the study area experienced a slight decline, while water supply capacity initially increased and then decreased. Water purification capacity hit its lowest point in 2015 before improving. The ESBF generally ranged from moderate to high levels, with higher values in the northwest and lower values in the southeast, showing strong spatial autocorrelations. Despite mild degradation in some areas, overall stability was maintained with frequent transitions between ESBF levels. Utilizing the Multiscale Geographically Weighted Regression (MGWR) model, we conducted a grid-scale analysis of the driving mechanisms behind ESBF. We found that precipitation, elevation, and the Normalized Difference Vegetation Index (NDVI) positively correlated with ESBF, whereas population density, land use, and nighttime lights negatively correlated. The relationship between temperature and ESBF showed a “north-positive, south-negative” pattern. The study recommends enhancing coastal wetland restoration, strengthening protective forest construction, and effectively controlling pollutant sources entering the sea to safeguard and improve the ecological security barrier function.