The interpretation of landform responses to environmental forcing and change is important for improving understanding natural hazards and climate change impacts, yet is dependent on the accuracy of methods and data used for observing and detecting geomorphic processes and geomorphic responses. Despite methodological advances in detection of geospatially statistically significant change, new methods and lack of consistent reporting by authors continue to confound how various sources and levels of uncertainty are handled when identifying and calculating what is real change (i.e., erosion and deposition across space and time in a landscape). This presents challenges for identifying key geomorphic responses, both quantitative (e.g., sediment budget responses) and qualitative (e.g., patterns and locations of change), critical for morphodynamic research, ecosystem restoration, and natural hazards assessments that use sequential digital elevation models (DEMs). This study tests the performance of various popular change detection methods using repeat, high resolution, UAS-derived datasets from 2019-2023 at a dynamic beach-dune setting at the Oceano Dunes, California. This work identifies the ability of existing change detection methods to detect real change or noise in coastal dune landscapes, although the methods and findings can be applied to other environments. We examine application of the raster-based Geomorphic Change Detection (GCD) tool (Wheaton et al., 2010), a GCD Fuzzy Inference System variation, CloudCompare M3C2 (Lague et al., 2013), and a probability map (Wernette et al., 2020) to the same 8 datasets to identify variations amongst multiple methods of change detection. The results identify the importance of method selection and how geomorphic processes and responses are represented by each method. The representation of landscape scale sediment movement is important to aid management decisions when site specific goals are highly dependent on the geomorphic responses observed, such as in resilience assessments or restoration effectiveness. This work addresses the continuing need to detect real change with the transparent reporting of data collection, processing methods, and uncertainty assessments within geomorphic change tools by comparing and replicating existing methods.