Next Article in Journal
Structure-from-Motion Using Historical Aerial Images to Analyse Changes in Glacier Surface Elevation
Previous Article in Journal
Application of Landsat Imagery to Investigate Lake Area Variations and Relict Gull Habitat in Hongjian Lake, Ordos Plateau, China
Article Menu
Issue 10 (October) cover image

Export Article

Open AccessArticle
Remote Sens. 2017, 9(10), 1020; https://doi.org/10.3390/rs9101020

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

1
U.S. Geological Survey, Woods Hole, MA 02543, USA
2
Marine Biological Laboratory, Woods Hole, MA 02543, USA
3
Woods Hole Analytics, Woods Hole, MA 02543, USA
4
U.S. Geological Survey, St. Petersburg, FL 33701, USA
*
Author to whom correspondence should be addressed.
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)
View Full-Text   |   Download PDF [5970 KB, uploaded 11 October 2017]   |  

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. 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
Figures

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top