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Open AccessArticle

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

1
Instituto 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, Spain
2
Departamento de Física, Universidad de las Palmas de Gran Canaria, ULPGC, 35017 Las Palmas, Spain
3
Signal Theory and Communications Department, Universitat Politecnica de Catalunya BarcelonaTECH, 08034 Barcelona, Spain
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(8), 1208; https://doi.org/10.3390/rs10081208
Received: 18 June 2018 / Revised: 27 July 2018 / Accepted: 28 July 2018 / Published: 2 August 2018
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. View Full-Text
Keywords: benthic mapping; seagrass; airborne hypespectral imagery; Worldview-2; atmospheric correction; sunglint correction; water column correction; dimensionality reduction techniques; SVM classification; linear unmixing benthic mapping; seagrass; airborne hypespectral imagery; Worldview-2; atmospheric correction; sunglint correction; water column correction; dimensionality reduction techniques; SVM classification; linear unmixing
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MDPI and ACS Style

Marcello, J.; Eugenio, F.; Martín, J.; Marqués, F. Seabed Mapping in Coastal Shallow Waters Using High Resolution Multispectral and Hyperspectral Imagery. Remote Sens. 2018, 10, 1208.

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