Fractional vegetation coverage (FVC) is an important indicator to assess ecological environments and vegetation growing. It is also an important parameter in many land surface and ecological models. It is generally estimated by satellite-based remote sensing and ground investigation. However, it is challenging to precisely estimate the FVC of woody and herbaceous plants in elm sparse forest grasslands. Unmanned aerial systems (UAS) provide a solution for effectively bridging the gap between satellite-based remote sensing and field-based measurements. The purpose of this study was to propose an integrative tool for quickly, accurately, and automatically classifying the vegetation types and estimating FVC by coupling a UAS monitoring platform with decision tree algorithms. We applied this tool to observe the vegetation dynamics in an elm (Ulmus pumila) sparse forest grassland ecosystem (ESFOGE) plot during a growing season in 2017. The spatial resolution of the digital orthophoto map (DOM) derived from UAS was 2.67 cm/pixel with UAV flights at a height of 100 m. The woody and herbaceous plants of the ESFOGE plot were classified and their FVC were estimated as (19±2)% and (50±8)%, respectively, on the DOM by decision tree algorithms. The FVC variation of herbaceous plants was larger than that of woody plants during a growing season. The FVC of the ESFOGE plot was (69±9)%. The contribution of woody and herbaceous plants to vegetation coverage was 27% and 73%, respectively. The FVC of the ESFOGE-plot was more influenced by herbaceous plants. Overall, this research proved that a UAS monitoring platform is an effective tool for observing the vegetation status at a landscape scale. It automatically and quickly classified the vegetation type and estimated the FVC by coupling with decision tree algorithms. To our knowledge, it is the first time that the FVC dynamics of woody and herbaceous plants were derived from a UAS platform during a growing season in the ESFOGE. This UAS platform can be applied to monitor and evaluate the vegetation status in hard-to-reach areas in the future.