UAS-SfM for coastal research : geomorphic feature extraction and land cover classification from high-resolution elevation and optical imagery Sturdivant, Emily Lentz, Erika E. Thieler, E. Robert Farris, Amy S. Weber, Kathryn M. Remsen, David P. Miner, Simon Henderson, Rachel E. 2017-11-13T20:29:06Z 2017-11-13T20:29:06Z 2017-10-03
dc.description © 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. en_US
dc.description.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. en_US
dc.description.sponsorship This project was funded by the U.S. Geological Survey (USGS) Coastal and Marine Geology Program and the Department of the Interior Northeast Climate Science Center. en_US
dc.identifier.citation Remote Sensing 9 (2017): 1020 en_US
dc.identifier.doi 10.3390/rs9101020
dc.language.iso en_US en_US
dc.publisher MDPI AG en_US
dc.rights Attribution 4.0 International *
dc.rights.uri *
dc.subject Coastal change en_US
dc.subject Drones en_US
dc.subject Elevation model en_US
dc.subject Geomorphic feature extraction en_US
dc.subject Land cover classification en_US
dc.subject Photogrammetry en_US
dc.subject Structure-from-motion en_US
dc.subject Unmanned aerial systems en_US
dc.title UAS-SfM for coastal research : geomorphic feature extraction and land cover classification from high-resolution elevation and optical imagery en_US
dc.type Article en_US
dspace.entity.type Publication
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