Mapping the refined land-cover classification mapping (RLCM) is a primary and essential strategy for evaluating the ecological change and understanding the ecosystem services. A common problem during the generation of RLCM is a scale mismatch between remote sensing (RS) data and field quadrat, which leads to inaccuracy of the classification result. A multi-scale transformation method was developed via integrating RS, unmanned aerial vehicle (UAV), and field surveys in Sanjiangyuan National Park (SNP). With the help of UAV, a large number of virtual biomass quadrats were resampled and interpolated, and the quantitative thresholds of different vegetation coverage in alpine meadow and steppe were determined to improve land-cover classification accuracy. Based on the spatial-temporal analysis of RLCM from 1990 to 2017, the whole ecological coverage was becoming better, and its driving factor was attributed to government policy and climate change. This study can provide a practical suggestion for the management and sustainable development in SNP.