In this work, multibeam echosounder (MBES) and dual frequency sidescan sonar (SSS) data are combined to map the shallow (5–100 m) benthic habitats of the National Marine Park of Zakynthos (NMPZ), Greece, a Marine Protected Area (MPA). NMPZ hosts extensive prairies of the protected Mediterranean phanerogams Posidonia oceanica and Cymodocea nodosa, as well as reefs and sandbanks. Seafloor characterization is achieved using the multi-frequency acoustic backscatter of: (a) the two simultaneous frequencies of the SSS (100 and 400 kHz) and (b) the MBES (180 kHz), as well as the MBES bathymetry. Overall, these high-resolution datasets cover an area of 84 km² with ground coverage varying from 50% to 100%. Image texture, terrain and backscatter angular response analyses are applied to the above, to extract a range of statistical features. Those have different spatial densities and so they are combined through an object-based approach based on the full-coverage 100-kHz SSS mosaic. Supervised classification is applied to data models composed of operationally meaningful combinations between the above features, reflecting single-sonar or multi-sonar mapping scenarios. Classification results are validated against a detailed expert interpretation habitat map making use of extensive ground-truth data. The relative gain of one system or one feature extraction method or another are thoroughly examined. The frequency-dependent separation of benthic habitats showcases the potentials of multi-frequency backscatter and bathymetry from different sonars, improving evidence-based interpretations of shallow benthic habitats.

Artificial Neural Networks (ANNs) have wide applications in aquatic ecology and specifically in modelling water quality and biotic responses to environmental predictors. However, data scarcity is a common problem that raises the need to optimize modelling approaches to overcome data limitations. With this paper, we investigate the optimal k-fold cross validation in building an ANN using a small water-quality data set. The ANN was created to model the chlorophyll-a levels of a shallow eutrophic lake (Mikri Prespa) located in N. Greece. The typical water quality parameters serving as the ANN’s inputs are pH, dissolved oxygen, water temperature, phosphorus, nitrogen, electric conductivity, and Secchi disk depth. The available data set was small, containing only 89 data samples. For that reason, k-fold cross validation was used for training the ANN. To find the optimal k value for the k-fold cross validation, several values of k were tested (ranging from 3 to 30). Additionally, the leave-one-out (LOO) cross validation, which is an extreme case of the k-fold cross validation, was also applied. The ANN’s performance indices showed a clear trend to be improved as the k number was increased, while the best results were calculated for the LOO cross validation as expected. The computational times were calculated for each k value, where it was found the computational time is relatively low when applying the more expensive LOO cross validation; therefore, the LOO is recommended. Finally, a sensitivity analysis was examined using the ANN to investigate the interactions of the input parameters with the Chlorophyll-a, and hence examining the potential use of the ANN as a water management tool for nutrient control.

In the present paper, a methodological scheme, bringing together common Acoustic Seabed Classification (ASC) systems and a powerful data decomposition approach, called Independent Component Analysis (ICA), is demonstrated regarding its suitability for detecting small targets in Side Scan Sonar imagery. Traditional ASC systems extract numerous texture descriptors, leading to a large feature vector, the dimensionality of which is reduced by means of data decomposition techniques, usually Principal Component Analysis (PCA), prior to classification. However, in the target detection issue, data decomposition should point towards finding components that represent sub-ordinary image information (i.e., small targets) rather than a dominant one. ICA has long been proved to be suitable for separating targets from a background, and this study represents a novel exhibition of its applicability to Side Scan Sonar (SSS) images. The present study attempts to build a fully automated target detection approach that combines image based feature extraction, ICA, and unsupervised classification. The suitability of the proposed approach has been demonstrated using an SSS data-set containing more than 70 manmade targets, most of them metallic, validated through a marine magnetic survey or ground truthing inspection. The method exhibited very good performance as it was able to detect more than 77% of the targets and it produced less than seven false alarms per km². Moreover, it was compared to cases where, in the exact same methodological scheme, no decomposition technique is used, or PCA is employed instead of ICA, achieving the highest detection rate, but, more importantly, producing more than six times less false alarms, thus proving that ICA successfully manages to maximize target to background separation.

A visual census of marine litter on the seafloor of the Saronikos Gulf (Greece) is presented in the current work. The abundance and qualitative composition of benthic marine litter were investigated in two selected locations of the Saronikos Gulf with a Remote Operated Vehicle, where other sampling strategies couldn’t be implemented. The assessment of marine litter was combined with environmental education within the PERSEUS (FP7) Research Project, in a novel 2-day research cruise where schoolchildren actively participated. Two transects of total length 2.36 km were carried out. A relevant marine litter protocol was developed where marine litter was categorized into six different categories according to their material type and where possible, the source of the items was identified. Plastics (55 %) and metals (36 %) had the biggest share among the recorded marine litter items. Marine litter proved to be an ideal theme in order to enhance the environmental awareness of schoolchildren.

Up until the last ten years, remote sensing data and especially high-resolution satellite data and airphotos were mainly used in shallow water mapping. The evolution and low cost of unmanned aerial vehicles (UAVs) provides a new tool for coastal area monitoring. This paper presents the synergistic use of a small commercial UAV and an unmanned surface vehicle (USV) for beachrock mapping in Syros Island, Greece. RGB images collected with a quadcopter were processed using Structure from Motion (SFM) photogrammetry in order to create digital surface models (DSMs) and orthophotos of the coastline. A beachrock lying in shallow waters was detected and mapped using the UAV derived products. At the same time, a USV equipped with a compact side scan sonar (SSS) and bathymetric sonar system, provided the shape of the beachrock by mosaicking the backscatter strength of the SSS. In order to evaluate the results of the UAV and USV data derivatives, the beachrock perimeter and its depth were also mapped using a differential global navigation satellite system (GNSS) receiver. During the fieldwork, samples from the beachrock were collected and analyzed in the laboratory. The mineralogical composition of the bulk samples was determined by powder X-ray diffraction (XRD). Further petrographic study was also performed by petrographic polarizing microscope, macroscope, and scanning electron microscopy (SEM). Beachrock samples are classified as fine to medium grain sandstones and conglomerates. The mineral compositions of their grains and lithoclasts reflect the bedrocks of Syros Island (mainly metamorphic rocks) while a micritic high-Mg calcite constitutes the cement of these rocks.