|Title||UAS-SfM for Coastal Research: Geomorphic Feature Extraction and Land Cover Classification from High-Resolution Elevation and Optical Imagery|
|Publication Type||Journal Article|
|Year of Publication||2017|
|Authors||Sturdivant, Emily, Lentz E., E. Thieler Robert, Farris Amy, Weber Kathryn, Remsen David, Miner Simon, and Henderson Rachel|
|Keywords||coastal change, drones, elevation model, geomorphic feature extraction, land cover classification, photogrammetry, structure-from-motion, unmanned aerial systems|
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.
|Short Title||Remote Sensing|