Accurate identification of forage species is essential for sustainable management of pastures. Monitoring changes in forage species within paddocks is necessary for estimating the forage productivity of each paddock. In this study, we utilized high-resolution satellite (SkySAT/Planetscope) and UAV imagery to detect changes in forage species resulting from a rotational stocking management strategy. The study was conducted on a rangeland located at the Texas A&M AgriLife Research Station–Beeville in Bee County (28°27'05.3'N 97°42'10.3'W). We examined the spatial and temporal change in forage species composition by generating classification maps using machine/deep learning models applied to time-series PlanetScope satellite imagery from 2020 to 2025. The change in species composition were validated using high-resolution SkySat/UAV derived species classification maps of 2024 and 2025. The results of this study demonstrate the potential use of integrating high-resolution satellite and UAV imagery to monitor the spatio-temporal forage species dynamics in mid and small-scale grassland.