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The Use of Unmanned Aerial Systems to Map Intertidal Sediment

Iain Fairley, Anouska Mendzil, Michael Togneri Orcid Logo, Dominic Reeve Orcid Logo

Remote Sensing, Volume: 10, Issue: 12, Start page: 1918

Swansea University Authors: Iain Fairley, Michael Togneri Orcid Logo, Dominic Reeve Orcid Logo

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DOI (Published version): 10.3390/rs10121918

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 n...

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Published in: Remote Sensing
ISSN: 2072-4292 2072-4292
Published: MDPI 2018
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URI: https://cronfa.swan.ac.uk/Record/cronfa47910
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spelling 2019-01-08T12:31:29.9039132 v2 47910 2018-12-07 The Use of Unmanned Aerial Systems to Map Intertidal Sediment 568e6f260489dc8139afe77757553513 Iain Fairley Iain Fairley true false 7032d5a521c181cea18dbb759e1ffdeb 0000-0002-6820-1680 Michael Togneri Michael Togneri true false 3e76fcc2bb3cde4ddee2c8edfd2f0082 0000-0003-1293-4743 Dominic Reeve Dominic Reeve true false 2018-12-07 FGSEN 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. Journal Article Remote Sensing 10 12 1918 MDPI 2072-4292 2072-4292 Intertida, sediment, unmanned aerial systems, multispectral, artificial neural network, environmental impact assessment 30 11 2018 2018-11-30 10.3390/rs10121918 COLLEGE NANME Science and Engineering - Faculty COLLEGE CODE FGSEN Swansea University UKRI, NE/R014485/1 2019-01-08T12:31:29.9039132 2018-12-07T14:25:05.8149947 Faculty of Science and Engineering School of Engineering and Applied Sciences - Uncategorised Iain Fairley 1 Anouska Mendzil 2 Michael Togneri 0000-0002-6820-1680 3 Dominic Reeve 0000-0003-1293-4743 4 0047910-07122018142552.pdf APCE036.remotesensing-10-01918-1.pdf 2018-12-07T14:25:52.9400000 Output 8159312 application/pdf Version of Record true 2018-12-07T00:00:00.0000000 Distributed under the terms of a Creative Commons CC-BY 4.0 Licence true eng
title The Use of Unmanned Aerial Systems to Map Intertidal Sediment
spellingShingle The Use of Unmanned Aerial Systems to Map Intertidal Sediment
Iain Fairley
Michael Togneri
Dominic Reeve
title_short The Use of Unmanned Aerial Systems to Map Intertidal Sediment
title_full The Use of Unmanned Aerial Systems to Map Intertidal Sediment
title_fullStr The Use of Unmanned Aerial Systems to Map Intertidal Sediment
title_full_unstemmed The Use of Unmanned Aerial Systems to Map Intertidal Sediment
title_sort The Use of Unmanned Aerial Systems to Map Intertidal Sediment
author_id_str_mv 568e6f260489dc8139afe77757553513
7032d5a521c181cea18dbb759e1ffdeb
3e76fcc2bb3cde4ddee2c8edfd2f0082
author_id_fullname_str_mv 568e6f260489dc8139afe77757553513_***_Iain Fairley
7032d5a521c181cea18dbb759e1ffdeb_***_Michael Togneri
3e76fcc2bb3cde4ddee2c8edfd2f0082_***_Dominic Reeve
author Iain Fairley
Michael Togneri
Dominic Reeve
author2 Iain Fairley
Anouska Mendzil
Michael Togneri
Dominic Reeve
format Journal article
container_title Remote Sensing
container_volume 10
container_issue 12
container_start_page 1918
publishDate 2018
institution Swansea University
issn 2072-4292
2072-4292
doi_str_mv 10.3390/rs10121918
publisher MDPI
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Uncategorised{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Uncategorised
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description 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.
published_date 2018-11-30T03:58:07Z
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