Next Article in Journal
Inversion of Nearshore X-Band Radar Images to Sea Surface Elevation Maps
Previous Article in Journal
Residuals of Tropospheric Delays from GNSS Data and Ray-Tracing as a Potential Indicator of Rain and Clouds
Article Menu
Issue 12 (December) cover image

Export Article

Open AccessArticle
Remote Sens. 2018, 10(12), 1918; https://doi.org/10.3390/rs10121918

The Use of Unmanned Aerial Systems to Map Intertidal Sediment

Zienkiewicz Centre for Computational Engineering, College of Engineering, Swansea University, Swansea SA1 8EN, UK
*
Author to whom correspondence should be addressed.
Received: 28 September 2018 / Revised: 16 November 2018 / Accepted: 26 November 2018 / Published: 30 November 2018
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
Full-Text   |   PDF [10943 KB, uploaded 30 November 2018]   |  

Abstract

This paper describes a new methodology to map intertidal sediment using a commercially available unmanned aerial system (UAS). A fixed-wing UAS was flown with both thermal and multispectral cameras over three study sites comprising of sandy and muddy areas. Thermal signatures of sediment type were not observable in the recorded data and therefore only the multispectral results were used in the sediment classification. The multispectral camera consisted of a Red–Green–Blue (RGB) camera and four multispectral sensors covering the green, red, red edge and near-infrared bands. Statistically significant correlations (>99%) were noted between the multispectral reflectance and both moisture content and median grain size. The best correlation against median grain size was found with the near-infrared band. Three classification methodologies were tested to split the intertidal area into sand and mud: k-means clustering, artificial neural networks, and the random forest approach. Classification methodologies were tested with nine input subsets of the available data channels, including transforming the RGB colorspace to the Hue–Saturation–Value (HSV) colorspace. The classification approach that gave the best performance, based on the j-index, was when an artificial neural network was utilized with near-infrared reflectance and HSV color as input data. Classification performance ranged from good to excellent, with values of Youden’s j-index ranging from 0.6 to 0.97 depending on flight date and site. View Full-Text
Keywords: intertidal; sediment; unmanned aerial systems; multispectral; artificial neural network; environmental impact assessment intertidal; sediment; unmanned aerial systems; multispectral; artificial neural network; environmental impact assessment
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

Fairley, I.; Mendzil, A.; Togneri, M.; Reeve, D.E. The Use of Unmanned Aerial Systems to Map Intertidal Sediment. Remote Sens. 2018, 10, 1918.

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