Schwab, W.C.; Baldwin, W.E.; Hapke, C.J.; Lentz, E.E.; Gayes, P.T.; Denny, J.F.; List, J.H., and Warner, J.C., 2013. Geologic evidence for onshore sediment transport from the inner continental shelf: Fire Island, New York. Sediment budget analyses along the south shore of Fire Island, New York, have been conducted and debated in the scientific and coastal engineering literature for decades. It is well documented that a primary component of sediment transport in this system is directed alongshore from E to W, but discrepancies in volumetric sediment budget calculations remain. An additional quantity of sand, averaging about 200,000 m3/y is required to explain the growth of the western segment of the barrier island, a prograding spit. Littoral sediment derived from updrift erosion of the coast, addition of beach nourishment fill, and onshore transport of inner continental shelf, shoreface sediments, or both have all been proposed as potential sources of the additional sediment needed to balance the...

The vulnerability of coastal systems to hazards such as storms and sea-level rise is typically characterized using a combination of ground and manned airborne systems that have limited spatial or temporal scales. Structure-from-motion (SfM) photogrammetry applied to imagery acquired by unmanned aerial systems (UAS) offers a rapid and inexpensive means to produce high-resolution topographic and visual reflectance datasets that rival existing lidar and imagery standards. Here, we use SfM to produce an elevation point cloud, an orthomosaic, and a digital elevation model (DEM) from data collected by UAS at a beach and wetland site in Massachusetts, USA. We apply existing methods to (a) determine the position of shorelines and foredunes using a feature extraction routine developed for lidar point clouds and (b) map land cover from the rasterized surfaces using a supervised classification routine. In both analyses, we experimentally vary the input datasets to understand the benefits and limitations of UAS-SfM for coastal vulnerability assessment. We find that (a) geomorphic features are extracted from the SfM point cloud with near-continuous coverage and sub-meter precision, better than was possible from a recent lidar dataset covering the same area; and (b) land cover classification is greatly improved by including topographic data with visual reflectance, but changes to resolution (when <50 cm) have little influence on the classification accuracy.