We developed a new approach using a cloud-based remote sensing and geospatial analysis platform, Google Earth Engine, to quantify temporal changes in river channel location and adjacent riparian vegetation extent and fraction. Our new method uses publicly available 1 m aerial images and eliminates manual processing need by incorporating an automatic image classification algorithm. Classification of riparian vegetation is enhanced by increasing the temporal resolution to monthly and by mapping vegetation at high spatial resolution. We illustrate the application of our method by characterizing temporal and spatial trends in riparian vegetation and river channel position for the mainstem of the Genesee River, New York, from 2006 to 2015. Annual change in riparian vegetation extent along the Genesee River ranged from a loss of 17% to a gain of 13%, and 64 km (28%) of the river demonstrated channel migration across consecutive aerial images. Our method also successfully distinguished differences between sections of the river above and below a dam. The enhanced capacity to map vegetation monthly allowed us to identify that seasonal active vegetation fractions along the Genesee River peaked at 75% in the summer and remained lower than 25% in winter. Our method allows stakeholders and managers to process remotely sensed imagery and investigate trends in river channel and riparian vegetation dynamics over time, while reducing the costs of data processing and storage.