Abstract

The net primary productivity (NPP) of the spatiotemporal variation in vegetation directly reflects ecosystem function, and landscape pattern evolution is a key spatial correlate of its differentiation. Clarifying this relationship is critical for regional ecological management. We focused on the upper and middle Yangtze River, using 2000–2020 land use and NPP data to analyze how overall landscape patterns are differentially associated with NPP across vegetation types and identify key landscape pattern characteristics associated with NPP. Our results show the following: (1) Based on 4102 grid cells at a 10 km × 10 km spatial grain and an 8:2 random train–test split validation procedure, XGBoost outperformed all other machine learning models, achieving a test set coefficient of determination (R2) of 0.78. (2) Over the study period, 89.58% of the region showed an increased Normalized Difference Vegetation Index (NDVI), with extensive grassland and shrubland conversion to forest. Regional average NPP increased by 51.57 g C m−2 yr−1. (3) Associations between landscape patterns and NPP differed across vegetation types. Number of patches (NP) was most strongly associated with forest and overall NPP, largest patch index (LPI) showed the closest linkage with shrubland NPP, and proportion of like adjacency (PLADJ) correlated most strongly with grassland NPP. Integrating vegetation evolution trends and vegetation-specific associations between landscape patterns and NPP, we propose that future ecological restoration and territorial spatial governance can adopt targeted landscape optimization strategies based on key correlated landscape indicators to support sustainable improvement of regional ecosystem productivity.

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