内モンゴル大興安嶺におけるベイジアンネットワークに基づく植生純生産力の進化と空間パターン最適化

This study aims to explore the spatiotemporal heterogeneity patterns and driving factors of the net primary productivity （NPP） of vegetation in the Greater Xing'an Mountains of Inner Mongolia from 2000 to 2022, which is of great significance for optimizing ecological protection and restoring the ecosystem in the ecological hotspot area. Based on the annual average NPP data of MOD17A3 and topographic, meteorological, and human activity data, this study used trend analysis, the Hurst index, and geographic detector to construct a Bayesian network； identified the interaction of driving variables； analyzed the spatiotemporal change patterns of the NPP of different vegetation types and the key variable influence； and depicted the ecological restoration optimization area in spatial pattern. The results show that： ① From 2000 to 2022, the NPP of vegetation in the Greater Xing'an Mountains of Inner Mongolia increased annually, with an average value of 341.14 g·（m² a）^-1. The overall growth rate from 2000 to 2008 was higher than that from 2009 to 2022. Spatially, it presented a north-south gradient with 60.89% and 12.02% of vegetation showing improvement and degradation trends, respectively. ② The future change characteristics of NPP were marked by a clear countertrend, with the explanatory power of the dominant factors in descending order being： normalized difference vegetation index （NDVI）, annual precipitation （PRE）, evapotranspiration （ET）, plant available water content （PAWC）, rainfall erosion force （RE）, and annual mean temperature （TEM）. The dominant interactive factors were： TEM and ET, TEM and PAWC, and PRE and RE. ③ The sensitivity analysis showed that the annual NPP was greatly influenced by the key subsets of PRE, RE, and PAWC, and different states of conditions could divide the ecological pattern optimization space. The spatiotemporal heterogeneity patterns of NPP of vegetation in the Greater Xing'an Mountains of Inner Mongolia were obvious.