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The Use of Unmanned Aerial Systems to Map Intertidal Sediment / Iain Fairley; Anouska Mendzil; Michael Togneri; Dominic Reeve

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

Swansea University Author: Reeve, Dominic

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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
Published: 2018
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URI: https://cronfa.swan.ac.uk/Record/cronfa48087
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spelling 2019-02-25T16:02:54Z v2 48087 2019-01-08 The Use of Unmanned Aerial Systems to Map Intertidal Sediment Dominic Reeve Dominic Reeve true 0000-0003-1293-4743 false 3e76fcc2bb3cde4ddee2c8edfd2f0082 f6e6e27217b3dad5b1d74665cb301402 hlZUD5zXP9CNssJHdRBCXggr5y2nBRz3haj4DmVVDsQ= 2019-01-08 EEN 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 2072-4292 intertidal; sediment; unmanned aerial systems; multispectral; artificial neural network; environmental impact assessment 30 11 2018 2018-11-30 10.3390/rs10121918 College of Engineering Engineering CENG EEN Swansea University Coastal Hydrology None 2019-02-25T16:02:54Z 2019-01-08T12:40:23Z College of Engineering Engineering Iain Fairley 1 Anouska Mendzil 2 Michael Togneri 3 Dominic Reeve 4 0048087-08012019124147.pdf fairley2018v5.pdf 2019-01-08T12:41:47Z Output 8159312 application/pdf VoR true Updated Copyright 25/02/2019 2019-01-08T00:00:00 true eng
title The Use of Unmanned Aerial Systems to Map Intertidal Sediment
spellingShingle The Use of Unmanned Aerial Systems to Map Intertidal Sediment
Reeve, Dominic
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 3e76fcc2bb3cde4ddee2c8edfd2f0082
author_id_fullname_str_mv 3e76fcc2bb3cde4ddee2c8edfd2f0082_***_Reeve, Dominic
author Reeve, Dominic
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
doi_str_mv 10.3390/rs10121918
college_str College of Engineering
hierarchytype
hierarchy_top_id collegeofengineering
hierarchy_top_title College of Engineering
hierarchy_parent_id collegeofengineering
hierarchy_parent_title College of Engineering
department_str Engineering{{{_:::_}}}College of Engineering{{{_:::_}}}Engineering
document_store_str 1
active_str 1
researchgroup_str Coastal Hydrology
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 2018-11-30T13:05:46Z
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