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Open AccessArticle

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

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U.S. Geological Survey, Woods Hole, MA 02543, USA
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Marine Biological Laboratory, Woods Hole, MA 02543, USA
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Woods Hole Analytics, Woods Hole, MA 02543, USA
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U.S. Geological Survey, St. Petersburg, FL 33701, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2017, 9(10), 1020; https://doi.org/10.3390/rs9101020
Received: 25 July 2017 / Revised: 16 September 2017 / Accepted: 26 September 2017 / Published: 3 October 2017
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
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. View Full-Text
Keywords: coastal change; drones; elevation model; geomorphic feature extraction; land cover classification; photogrammetry; structure-from-motion; unmanned aerial systems coastal change; drones; elevation model; geomorphic feature extraction; land cover classification; photogrammetry; structure-from-motion; unmanned aerial systems
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MDPI and ACS Style

Sturdivant, E.J.; Lentz, E.E.; Thieler, E.R.; Farris, A.S.; Weber, K.M.; Remsen, D.P.; Miner, S.; Henderson, R.E. UAS-SfM for Coastal Research: Geomorphic Feature Extraction and Land Cover Classification from High-Resolution Elevation and Optical Imagery. Remote Sens. 2017, 9, 1020.

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