Next Article in Journal
Fog and Low Cloud Frequency and Properties from Active-Sensor Satellite Data
Next Article in Special Issue
Building Detection from VHR Remote Sensing Imagery Based on the Morphological Building Index
Previous Article in Journal
An Enhanced Single-Pair Learning-Based Reflectance Fusion Algorithm with Spatiotemporally Extended Training Samples
Previous Article in Special Issue
Applying High-Resolution Imagery to Evaluate Restoration-Induced Changes in Stream Condition, Missouri River Headwaters Basin, Montana
Article Menu
Issue 8 (August) cover image

Export Article

Open AccessArticle
Remote Sens. 2018, 10(8), 1208; https://doi.org/10.3390/rs10081208

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.
Received: 18 June 2018 / Revised: 27 July 2018 / Accepted: 28 July 2018 / Published: 2 August 2018
Full-Text   |   PDF [12078 KB, uploaded 2 August 2018]   |  

Abstract

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
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top