The majority of streams in the Colorado River Watershed are highly degraded systems with low floodplain connectivity and simplified, planar riverbeds. The consequences of stream deterioration have been accentuated by recent drought and are evident in Utah's failing hydroelectric dams and the Great Salt Lake's receding shoreline. Many organizations are working to restore these streams in order to support native fish populations, increase stream flow length and volume, and prevent or reverse desertification in watersheds; however, these projects require frequent and robust monitoring over large areas which is time intensive and expensive. Limited budgets and unwieldy, census-like monitoring methods are significant hurdles to watershed recovery. Fluvial geomorphologists have identified several riverscape entities (such as rapids, pools, woody debris, and floodplains), but the correlative relationships between these features are relatively unexplored. A geomorphologist can tell you that fallen logs are good for slowing down stream flow, allowing water to seep into the surrounding water table and increase base flows that persist through drought, but it's difficult to say exactly how many trees need to fall into the channel before there is a measurable positive effect. I aim to bridge the gap between data-collection and geomorphic understanding by statistically analyzing the relationships between geomorphic characteristics, data-collection methods, and how much a stream's flow is interacting with the land and vegetation that surrounds it (i.e., floodplain connectivity). Correlative statistical tests will highlight what riverscape units are the best indicators of stream health (and, therefore, most important to monitor/restore) and whether they are most efficiently measured in the field or remotely via satellite and/or drone imagery. This information will allow for field work protocol streamlining, budget flexibility, and expanded restoration progress in western watersheds.