No abstract

On October 10 2011 an underwater eruption gave rise to a novel shallow submarine volcano south of the island of El Hierro, Canary Islands, Spain. During the eruption large quantities of mantle-derived gases, solutes and heat were released into the surrounding waters. In order to monitor the impact of the eruption on the marine ecosystem, periodic multidisciplinary cruises were carried out. Here, we present an initial report of the extreme physical-chemical perturbations caused by this event, comprising thermal changes, water acidification, deoxygenation and metal-enrichment, which resulted in significant alterations to the activity and composition of local plankton communities. Our findings highlight the potential role of this eruptive process as a natural ecosystem-scale experiment for the study of extreme effects of global change stressors on marine environments.

Remote sensing of coastal areas requires multispectral satellite images with a high spatial resolution. In this sense, WorldView-2 is a very high resolution satellite, which provides an advanced multispectral sensor with eight narrow bands, allowing the proliferation of new environmental monitoring and mapping applications in shallow coastal ecosystems. These challenges need the accurate determination of the water radiance, which is not often valued compared to other sources such as atmosphere and specular water reflection (sun glint). In this context, the atmospheric correction and the glinting removal have demonstrated to be critical steps in the preprocessing chain of high resolution images. In this work, the Second Simulation of a Satellite Signal in the Solar Spectrum (6S) is used to compensate the atmospheric effects and to compute part of the deglinting algorithm using the modeled direct normalized irradiance. This paper describes a novel automatic deglinting procedure, integrated in the Radiative Transfer Modeling (RTM) inversion of the shallow water environments, which allows computing the water Inherent Optical Properties (IOPs), bathymetry and seafloor albedo contributions. The proposed methodology has demonstrated a proper performance for environmental monitoring in shallow water areas.

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.