MAPPING BEACH MORPHOLOGY AND GRAIN SIZES OF SOUTHERN NEW ENGLAND BEACHES USING "3D BEACH PROFILES"

Beach profiles are a simple and affordable approach to monitoring changes in beach morphology. Advancements in technologies, specifically structure-from-motion and machine learning, can be integrated into conventional beach profiling techniques to better capture spatial and temporal changes in slope and sediment grain-sizes. The association between slope and grain-size on mixed sediment beaches, particularly those composed of gravel, remains significantly understudied relative to sandy beaches. This gap in research arises in part from the difficulties associated with quantifying grain-size distributions in these settings. Here we present a new technique for quantifying grain-sizes and capturing beach slopes referred to as ‘3D Beach Profiles’. The technique utilizes structure-from-motion technology to construct a three-dimensional digital surface model (DSM) and high-resolution orthomosaic of the surveyed beach surface. These digital surfaces enable the extraction of beach slopes with orders of magnitude more data, while machine-learning grain-size detection algorithms are applied to imagery to segment grains and estimate grain-size distributions. A fifty-five-meter beach profile extracted from one DSM contained 63,027 data points. In a two-by-two-meter square orthoimage raster, the grain-size detection algorithm, segmenteverygrain, was able to segment 524 individual grains and provide a shapefile of grain outlines. Baseline surveys have been conducted at four mixed sediment beaches located in southern New England, and monitoring will continue seasonally. The DSMs and orthomosaics produced from these surveys range in resolution from 0.83 to 1.36 mm. These data products can be used to identify trends at multiple temporal and spatial scales with regard to grain-size and beach morphology. Further, we will discuss observed seasonal changes between surveys and how we aim for the technique to be implemented by municipalities to collect data on their respective beaches that will ultimately contribute to better informed and effective beach management practices.