UAS-SfM for coastal research : geomorphic feature extraction and land cover classification from high-resolution elevation and optical imagery
Lentz, Erika E.
Thieler, E. Robert
Farris, Amy S.
Weber, Kathryn M.
Remsen, David P.
Henderson, Rachel E.
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KeywordCoastal 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.
© The Author(s), 2017. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Remote Sensing 9 (2017): 1020, doi:10.3390/rs9101020.
Suggested CitationArticle: Sturdivant, Emily, Lentz, Erika E., Thieler, E. Robert, Farris, Amy S., Weber, Kathryn M., Remsen, David P., Miner, Simon, Henderson, Rachel E., "UAS-SfM for coastal research : geomorphic feature extraction and land cover classification from high-resolution elevation and optical imagery", Remote Sensing 9 (2017): 1020, DOI:10.3390/rs9101020, https://hdl.handle.net/1912/9359
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