Shoreline habitats and infrastructure already are impacted by sea level rise (SLR) and the increasing intensity of extreme events. As global temperatures continue to rise, these impacts will become more severe. The ability of and ways in which governments and individuals adapt to SLR could have profound consequences for adjacent ecosystems, transportation systems, and urban settings. The costs associated with implementing adaptations will also increase over time. Natural systems often attenuate impacts of SLR and storms, providing a free, often unrecognized and under-appreciated, protective service for shoreline infrastructures. In San Francisco Bay, CA, SLR has exceeded 20 cm over the past 100 years. In US coastal areas, it is likely to rise by at least another 30-45 cm by mid-century. Accurate and timely information about the actual extent of flooding and erosional impacts to shorelines from SLR will be critical during infrastructure adaptation. To provide this critically needed information, we describe three interacting methods that we are applying in California and Georgia to monitor shoreline and infrastructure changes in response to SLR across a wide range of time and space scales: 1) Time lapse cameras for two years along coastal areas in two US states to track low and high tide inundation. We have found that the method was sensitive to vertical changes in sea level of <1 cm, roughly equivalent to 1-2 years of SLR under the current global trajectory. The method described is feasible for near-term (1 to 10 years) to long-term application and can be used for measuring fine-resolution shoreline changes in response to SLR and storm events. 2) RTK-GPS (real-time kinetic geo-positioning systems) was used in transects across tidal marshes adjacent to and independent of infrastructure to measure inter-annual changes in elevation. This method has very high vertical resolution (1 cm), but because of effort requirements is typically only carried out on an annual timeframe. In 2008 and 2018, we measured ground and vegetation elevation/composition across ~7,000 ac of tidal marshes abutting a state highway. We found no estimated loss of marsh surface sediment over this decade, but there was a substantial reorganization of the sediment. 3) Airborne (drone and satellite) imagery to provide historical shoreline data and automatically and manually identify areas and degree of shoreline change. This method has low spatial resolution in comparison to other methods, but is freely available (satellite) and can be carried out over very large areas. Using satellite imagery, we found retreat of the shoreline edge in certain parts of the San Francisco Bay shoreline and increase in complexity of the shoreline edge, with meter-scale areas of loss and other areas without loss. These approaches can be used to test predictive model and expectations and provide a critical missing piece in the assessment and decision-making process of SLR adaptation in shoreline areas.