The integration of large-scale Low Earth Orbit satellite constellations (hereinafter referred to as "LEO constellations") and artificial intelligence (AI) technology presents a historic opportunity for a paradigm shift in geography research, heralding a new era for geography to evolve from qualitative geography, quantitative geography, and digital geography into the "LEO constellation-AI-driven Geography". Under this framework, future geographic research can rely on the high spatio-temporal resolution monitoring data provided by LEO constellations to accurately capture the high-frequency dynamic changes of geographic elements at multiple scales, particularly at the global scale. By coupling physical models with AI, it becomes feasible to conduct simulation experiments on the complex interactions between natural and human elements, system states, and interface changes. This will facilitate a deeper understanding of core geographic issues such as variable coupling, multi-process cascading effects, and teleconnection mechanisms. To propel "LEO constellation-AI-driven Geography", there is an urgent need to establish a new-generation data acquisition and sharing platform relying on LEO constellation, seamlessly creating a "dynamic map" of global geographic resources and elements. Additionally, a geographic process simulator that couples physical models and AI needs to be developed to intelligently simulate and predict changes and impacts of geographic elements and landscapes.

Vegetation on the Qinghai-Xizang Plateau exhibits high-altitude and vertical zonation distribution characteristics, which pose significant challenges for fine-scale vegetation classification based on remote sensing. A major issue is the limited separability of remote sensing features among certain vegetation types, necessitating the effective integration of additional non-remote sensing features to improve separability. To address this problem, the present study developed a novel method for fine-scale vegetation remote sensing classification by progressively incorporating coarse spatial resolution vegetation and environmental features. This approach aims to improve both the accuracy and precision of classification. The new method comprises three primary components. First, vegetation and environmental features that substantially enhance vegetation classification and exhibit distinct feature differences are selected. These features are then used to calculate the prior probabilities for each class through a generalized additive model. Concurrently, machine learning classification with remote sensing features is employed to obtain the posterior probabilities for each class. Finally, by applying the Bayesian algorithm, the prior probabilities derived from coarse spatial resolution data are employed to adjust the posterior probabilities obtained from high spatial resolution data, resulting in refined classification outcomes. The method was rigorously tested and applied to the Qilian Mountains, Yellow River Source Area, and Hengduan Mountains on the Qinghai-Xizang Plateau. Sentinel-2 remote sensing data with a spatial resolution of 10 m, vegetation and environmental data with spatial resolutions ranging from 90 m to 10000 m, and ground survey data were utilized. The fine-scale vegetation classification results with a spatial resolution of 10 m were achieved. Compared to using only remote sensing features, the new method improved classification accuracy by 8% to 24%. This new classification method provides effective technical support for improving the accuracy and precision of vegetation classification and offers significant reference value for fine-scale vegetation classification on the Qinghai-Xizang Plateau and similar regions.