Shoreline Feature Mapping Using UAS-SfM Terrain Models

In this study, the structure from motion (SfM) processing methods in Drone2Map and Agisoft software were investigated for use in shoreline feature extraction from unmanned aircraft system (UAS) images. LIDAR and satellite imagery can be expensive and unsuitable for high resolution projects dependent on temporal variation; therefore, SfM may be used to document features with greater complexity in their shape and positioning. Photogrammetric survey performed by an UAS captures images tagged with geographic information for positioning. The referenced photos are stitched together in the SfM processing software, and a three dimensional point cloud of the surface is created. The stitched imagery and point cloud can be used to create a variety of products such as orthomosaics and digital elevation models (DEM). The SfM exports are used to identify and extract shoreline features for input into NOAA’s Shoreline Attribution Machine (SHAM) software for additional attribution. The assigned attributes detail the feature’s depth, cover, and various other characteristics useful for symbolization on a nautical chart. The UAS-SfM post-processing workflow proposed and documented in this study will streamline the acquisition and post-processing of mapped shoreline data for standardized field use and aid in the long-term efforts of ensuring the preservation of resilient coastal communities.