Seabed Mapping in Coastal Shallow Waters Using High Resolution Multispectral and Hyperspectral Imagery

Coastal ecosystems experience multiple anthropogenic and climate change pressures. To monitor the variability of the benthic habitats in shallow waters, the implementation of effective strategies is required to support coastal planning. In this context, high-resolution remote sensing data can be of...

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Main Authors: Javier Marcello, Francisco Eugenio, Javier Martín, Ferran Marqués
Format: Article
Language:English
Published: MDPI AG 2018-08-01
Series:Remote Sensing
Subjects:
Online Access:http://www.mdpi.com/2072-4292/10/8/1208
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spelling doaj-368c16babb5341f8bcf03adfca079dc02020-11-25T00:44:41ZengMDPI AGRemote Sensing2072-42922018-08-01108120810.3390/rs10081208rs10081208Seabed Mapping in Coastal Shallow Waters Using High Resolution Multispectral and Hyperspectral ImageryJavier Marcello0Francisco Eugenio1Javier Martín2Ferran Marqués3Instituto de Oceanografía y Cambio Global, IOCAG, Universidad de las Palmas de Gran Canaria, ULPGC, Parque Científico Tecnológico Marino de Taliarte, s/n, Telde, 35214 Las Palmas, SpainInstituto de Oceanografía y Cambio Global, IOCAG, Universidad de las Palmas de Gran Canaria, ULPGC, Parque Científico Tecnológico Marino de Taliarte, s/n, Telde, 35214 Las Palmas, SpainDepartamento de Física, Universidad de las Palmas de Gran Canaria, ULPGC, 35017 Las Palmas, SpainSignal Theory and Communications Department, Universitat Politecnica de Catalunya BarcelonaTECH, 08034 Barcelona, SpainCoastal ecosystems experience multiple anthropogenic and climate change pressures. To monitor the variability of the benthic habitats in shallow waters, the implementation of effective strategies is required to support coastal planning. In this context, high-resolution remote sensing data can be of fundamental importance to generate precise seabed maps in coastal shallow water areas. In this work, satellite and airborne multispectral and hyperspectral imagery were used to map benthic habitats in a complex ecosystem. In it, submerged green aquatic vegetation meadows have low density, are located at depths up to 20 m, and the sea surface is regularly affected by persistent local winds. A robust mapping methodology has been identified after a comprehensive analysis of different corrections, feature extraction, and classification approaches. In particular, atmospheric, sunglint, and water column corrections were tested. In addition, to increase the mapping accuracy, we assessed the use of derived information from rotation transforms, texture parameters, and abundance maps produced by linear unmixing algorithms. Finally, maximum likelihood (ML), spectral angle mapper (SAM), and support vector machine (SVM) classification algorithms were considered at the pixel and object levels. In summary, a complete processing methodology was implemented, and results demonstrate the better performance of SVM but the higher robustness of ML to the nature of information and the number of bands considered. Hyperspectral data increases the overall accuracy with respect to the multispectral bands (4.7% for ML and 9.5% for SVM) but the inclusion of additional features, in general, did not significantly improve the seabed map quality.http://www.mdpi.com/2072-4292/10/8/1208benthic mappingseagrassairborne hypespectral imageryWorldview-2atmospheric correctionsunglint correctionwater column correctiondimensionality reduction techniquesSVM classificationlinear unmixing
collection DOAJ
language English
format Article
sources DOAJ
author Javier Marcello
Francisco Eugenio
Javier Martín
Ferran Marqués
spellingShingle Javier Marcello
Francisco Eugenio
Javier Martín
Ferran Marqués
Seabed Mapping in Coastal Shallow Waters Using High Resolution Multispectral and Hyperspectral Imagery
Remote Sensing
benthic mapping
seagrass
airborne hypespectral imagery
Worldview-2
atmospheric correction
sunglint correction
water column correction
dimensionality reduction techniques
SVM classification
linear unmixing
author_facet Javier Marcello
Francisco Eugenio
Javier Martín
Ferran Marqués
author_sort Javier Marcello
title Seabed Mapping in Coastal Shallow Waters Using High Resolution Multispectral and Hyperspectral Imagery
title_short Seabed Mapping in Coastal Shallow Waters Using High Resolution Multispectral and Hyperspectral Imagery
title_full Seabed Mapping in Coastal Shallow Waters Using High Resolution Multispectral and Hyperspectral Imagery
title_fullStr Seabed Mapping in Coastal Shallow Waters Using High Resolution Multispectral and Hyperspectral Imagery
title_full_unstemmed Seabed Mapping in Coastal Shallow Waters Using High Resolution Multispectral and Hyperspectral Imagery
title_sort seabed mapping in coastal shallow waters using high resolution multispectral and hyperspectral imagery
publisher MDPI AG
series Remote Sensing
issn 2072-4292
publishDate 2018-08-01
description Coastal ecosystems experience multiple anthropogenic and climate change pressures. To monitor the variability of the benthic habitats in shallow waters, the implementation of effective strategies is required to support coastal planning. In this context, high-resolution remote sensing data can be of fundamental importance to generate precise seabed maps in coastal shallow water areas. In this work, satellite and airborne multispectral and hyperspectral imagery were used to map benthic habitats in a complex ecosystem. In it, submerged green aquatic vegetation meadows have low density, are located at depths up to 20 m, and the sea surface is regularly affected by persistent local winds. A robust mapping methodology has been identified after a comprehensive analysis of different corrections, feature extraction, and classification approaches. In particular, atmospheric, sunglint, and water column corrections were tested. In addition, to increase the mapping accuracy, we assessed the use of derived information from rotation transforms, texture parameters, and abundance maps produced by linear unmixing algorithms. Finally, maximum likelihood (ML), spectral angle mapper (SAM), and support vector machine (SVM) classification algorithms were considered at the pixel and object levels. In summary, a complete processing methodology was implemented, and results demonstrate the better performance of SVM but the higher robustness of ML to the nature of information and the number of bands considered. Hyperspectral data increases the overall accuracy with respect to the multispectral bands (4.7% for ML and 9.5% for SVM) but the inclusion of additional features, in general, did not significantly improve the seabed map quality.
topic benthic mapping
seagrass
airborne hypespectral imagery
Worldview-2
atmospheric correction
sunglint correction
water column correction
dimensionality reduction techniques
SVM classification
linear unmixing
url http://www.mdpi.com/2072-4292/10/8/1208
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