Livestock can have negative impacts on freshwater and riparian ecosystems, including streambank degradation. The effect of livestock abundance on a landscape can vary, with some streams more susceptible than others based on landscape features alone. Indeed, there has been much research on the effects of landscape variables, such the slope of the riparian area and distance to alternative water sources, on the likelihood of cattle frequenting a stream. However, to date, this research has not been combined and summarized in a way that is easily accessible to land managers. Here, I will present a preliminary model for predicting the susceptibility of streams to livestock degradation using Google Earth Engine. The model uses remotely sensed data (e.g., DEM’s, NDVI) and landscape-scale models (e.g., Wetland Evaluation Tool, Rangeland Analysis Platform) to generate an index of stream susceptibility for a given stream reach. I present a pilot study applying the model to streams managed by the Bureau of Land Management (BLM) in Idaho, and my efforts to validate and optimize the model using BLM Lotic Assessment, Inventory, and Monitoring (AIM) and PacFish/InFish Biological Opinion (PIBO) data. Ultimately, this work will generate a decision support tool in Google Earth Engine, linking the modeled stream susceptibility to grazing impacts with local knowledge of grazer abundance and management actions to create a holistic predicted grazing impact score. Such a tool could be used by local land managers to make reach-specific management decisions, and when linked with monitoring at the state or regional scale, could determine the impact of livestock on aquatic ecosystems more broadly.