UAS-SfM for Coastal Research: Geomorphic Feature Extraction and Land Cover Classification from High-Resolution Elevation and Optical Imagery

TitleUAS-SfM for Coastal Research: Geomorphic Feature Extraction and Land Cover Classification from High-Resolution Elevation and Optical Imagery
Publication TypeJournal Article
Year of Publication2017
AuthorsSturdivant, Emily, Lentz Erika, E. Thieler Robert, Farris Amy, Weber Kathryn, Remsen David, Miner Simon, and Henderson Rachel
JournalRemote Sensing
Volume9
Issue10
Pagination1020
Date PublishedJan-10-2017
Keywordscoastal change, drones, elevation model, geomorphic feature extraction, land cover classification, photogrammetry, structure-from-motion, unmanned aerial systems
Abstract

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.

URLhttp://www.mdpi.com/2072-4292/9/10/1020http://www.mdpi.com/2072-4292/9/10/1020/pdf
DOI10.3390/rs9101020
Short TitleRemote Sensing