Spatiotemporal Evolution and Driving Mechanisms of kNDVI in Different Sections of the Yangtze River Basin Using Multiple Statistical Methods and the PLSPM Model

Spatiotemporal vegetation changes serve as a key indicator of regional ecological environmental quality and provide crucial guidance for developing strategies for regional ecological protection and sustainable development. Currently, vegetation change studies in the Yangtze River Basin primarily rely on the Normalized Difference Vegetation Index (NDVI). However, the NDVI is susceptible to atmospheric and soil conditions and exhibits saturation phenomena in areas with high vegetation coverage. In contrast, the kernel NDVI (kNDVI) demonstrates significant advantages in suppressing background noise and improving saturation thresholds through nonlinear kernel transformation, thereby enhancing sensitivity to vegetation changes. To elucidate the spatiotemporal characteristics and driving mechanisms of vegetation changes in the Yangtze River Basin, this study constructed a temporal kNDVI using MOD09GA data from 2000 to 2022. Considering sectional heterogeneity, rather than analyzing the entire region as a whole as in previous studies, this research examined spatiotemporal evolution characteristics by sections using four statistical metrics. Subsequently, Partial Least Squares Path Modeling (PLSPM) was innovatively introduced to quantitatively analyze the influence mechanisms of topographic, climatic, pedological, and socioeconomic factors. Compared to traditional correlation analysis and the geographical detector method, PLSPM, as a theoretically driven statistical method, can simultaneously process path relationships among multiple latent variables, effectively revealing the intensity and pathways of driving factors’ influences, while providing more credible and interpretable explanations for kNDVI variation mechanisms. Results indicate that the overall kNDVI in the Yangtze River Basin exhibited an upward trend, with the midstream demonstrating the most significant improvement with minimal interannual fluctuations, the upstream displaying an east-increasing and west-stable spatial pattern, and the downstream demonstrating coexisting improvement and degradation characteristics, with these trends expected to persist. Driving mechanism analysis reveals that the upstream was predominantly influenced by the climatic factor, the midstream was dominated by terrain, and the downstream displayed terrain–soil coupling effects. Based on these findings, it is recommended that the upstream focus on enhancing vegetation adaptation management to climate change, the midstream need to coordinate the relationship between topography and human activities, and the downstream should concentrate on controlling the negative impacts of urban expansion on vegetation.