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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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 |
work_keys_str_mv |
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