Detection of Critical Transitions and Heterogeneity Analysis of Vegetation Resilience in Northeast China

Terrestrial ecosystems are facing increasingly severe threats driven by the dual pressures of climate change and anthropogenic activities. However, current remote sensing-based ecological research still exhibits notable deficiencies in the integration of multi-source data. This study develops a Critical Transition Index (CTI) for Northeast China. The CTI integrates four remotely sensed vegetation variables (LAI, NDVI, SIF, and VOD) with time series decomposition (STL), multiple early-warning signals (ar1, variance, skewness, and kurtosis), consistency scoring, and Mahalanobis distance. The framework systematically assesses vegetation resilience and its spatiotemporal responses to climatic stressors. Results reveal pronounced differences among variables: the structural indicator LAI identified the highest proportion of high-risk areas (60.8%, CTI ≥ 0.8), whereas the functional indicator SIF showed relatively high stability, with a mean CTI of 0.619 and a high-risk proportion of only 16.0%. High-risk areas are primarily concentrated in cropland–grassland mosaics, while forested regions maintain lower risk. Temporal analysis of land cover composition within high-risk areas shows a clear “structural diffusion” trend: the proportion of deciduous broadleaf forests in the high-risk category increased from being negligible in early periods (2003–2007) to approximately 20% in later periods (2013–2017) for both SIF and VOD indicators. This study underscores the necessity of multi-indicator frameworks for detecting critical transitions and provides quantitative, spatially explicit scientific insights for ecosystem early-warning and regional management strategies.