Weber Kathryn M.

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Weber
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Kathryn M.
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Subject: Drones × Start date: 2017 × End date: 2017 ×

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  • Article
    UAS-SfM for coastal research : geomorphic feature extraction and land cover classification from high-resolution elevation and optical imagery
    (MDPI AG, 2017-10-03) Sturdivant, Emily ; Lentz, Erika E. ; Thieler, E. Robert ; Farris, Amy S. ; Weber, Kathryn M. ; Remsen, David P. ; Miner, Simon ; Henderson, Rachel E.
    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.