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The Indonesian fisheries management system is now equipped with the state-of-the-art technologies to deter and combat Illegal, Unreported and Unregulated (IUU) fishing. Since October 2014, non-cooperative fishing vessels can be detected from spaceborne Vessel Detection System (VDS) based on high resolution radar imagery, which directly benefits to coordinated patrol vessels in operation context. This study attempts to monitor the amount of illegal fishing in the Arafura Sea based on this new source of information. It is analyzed together with Vessel Monitoring System (VMS) and satellite-based Automatic Identification System (Sat-AIS) data, taking into account their own particularities. From October 2014 to March 2015, i.e. just after the establishment of a new moratorium by the Indonesian authorities, the estimated share of fishing vessels not carrying VMS, thus being illegal, ranges from 42 to 47%. One year later in January 2016, this proportion decreases and ranges from 32 to 42%. [ABSTRACT FROM AUTHOR]

Numerous methods have been developed to geolocate fish from data storage tags. Whereas demersal species have been tracked using tide-driven geolocation models, pelagic species which undertake extensive migrations have been mainly tracked using light-based models. Here, we present a new HMM-based model that infers pelagic fish positions from the sole use of high-resolution temperature and depth histories. A key contribution of our framework lies in model parameter inference (diffusion coefficient and noise parameters with respect to the reference geophysical fields—satellite SST and temperatures derived from the MARS3D hydrodynamic model), which improves model robustness. As a case study, we consider long time series of data storage tags (DSTs) deployed on European sea bass for which individual migration tracks are reconstructed for the first time. We performed a sensitivity analysis on synthetic and real data in order to assess the robustness of the reconstructed tracks with respect to model parameters, chosen reference geophysical fields and the knowledge of fish recapture position. Model assumptions and future directions are discussed. Finally, our model opens new avenues for the reconstruction and analysis of migratory patterns of many other pelagic species in relatively contrasted geophysical environments. [ABSTRACT FROM AUTHOR]

From top-of-atmosphere (TOA) observations, atmospheric correction for ocean color inversion aims at distinguishing atmosphere and water contributions. From a methodological point of view, our approach relies on a Bayesian inference using Gaussian Mixture Model prior distributions on reference spectra of aerosol and water reflectance. A reference spectrum for the aerosol characterizes the specific signature of the aerosols on the observed aerosol reflectance. A reference spectrum for the water characterizes the specific signature of chlorophyll-a, suspended particulate matters and colored dissolved organic matters on the observed sea surface reflectance. In our Bayesian inversion scheme, prior distributions of the marine and aerosol variables are set conditionally to the observed values of covariates, typically acquisition geometry acquisition conditions and pre-estimates of the aerosol and water reflectance in the near-infrared part of the spectrum. The numerical inversion exploits a gradient-based optimization from quasi-randomized initializations. We evaluate our estimates of the sea surface reflectance from the MERIS TOA observations. Using the MERMAID radiometric in-situ dataset, we obtain significant improvements in the estimation of the sea surface reflectance, especially for the 412, 442, 490 and 510 nm bands, compared with the standard ESA MEGS algorithm and the a state-of-the-art neural network approach (C2R). The mean gain value on the relative error for the 13 bands between 412 and 885 nm is of 57% compared with MEGS algorithm and 10% compared with the C2R. The water leaving reflectances are used in Ocean Color for the estimation of the chl-a concentration, the colored dissolved organic matters absorption and the suspended particulate matters concentration underlying the potential of such approach to improve the standard level 2 products in coastal areas. We further discuss the potential of MEETC2 for the incoming OLCI/Sentinel 3 mission that will be launched in December 2015. [ABSTRACT FROM AUTHOR]

Abstract: In this paper is presented a novel image processing tool for the extraction of geometric information in otolith images. It relies on the reconstruction of individual otolith shape histories from otolith images. Based on the proposed non-parametric level-set representation of otolith shape history, applications to the extraction of growth axes and ring structures in otolith images are first considered. A second category of applications concern the analysis of 2D otolith growth. The potential of the proposed framework is illustrated on real otolith images for various species (e.g., cod, pollock) and discussed with a particular emphasis on the genericity of the approach and on applications such as otolith shape analysis, multi-proxy otolith analysis, otolith modeling. [Copyright &y& Elsevier]

Abstract: Computer-assisted tools need to be developed to help in the accurate and efficient acquisition of fish age and growth data for ecological and assessment issues. Stating fish age and growth analysis as pattern classification issues, the proposed approach relies on a statistical learning strategy. Given otolith images interpreted by an expert, probabilistic kernel-based methods (namely Kernel Logistic Regression) are used to infer interpretation rules. More precisely, two different probabilistic models are introduced: one to infer fish age from otolith images and a second one aiming at evaluating whether or not a given otolith growth pattern is realistic w.r.t. training examples. These probabilistic models provide us with the basis for coping with three different issues: the automated estimation of fish age from otolith images, the estimation of individual otolith growth patterns, and the definition of a confidence measure of otolith interpretations. These computer-assisted ageing tools are validated for a dataset of plaice otoliths. [Copyright &y& Elsevier]

Abstract: The acquisition of age and growth data is of key importance for fisheries research (assessment, marine ecology issues, etc.). Consequently, automating this task is of great interest. In this paper, we investigate the use of statistical learning techniques for fish age estimation. The core of this study lies in the definition of relevant image-related features. We rely on the computation of a 1D representation summing up the content of otolith images within a predefined area of interest. Features are then extracted from this non-stationary representation depicting the alternation of seasonal growth rings. Thus, fish age estimation can be viewed as a multi-class classification issue using statistical learning strategies. In particular, a procedure based on demodulation and remodulation of fish growth patterns is used to improve the generalization properties of the trained classifiers. The experimental evaluation is carried out over a dataset of 320 plaice otolith images from age groups 1–6. We analyze both, the performances of several statistical classifiers, namely SVMs (support vector machines) and neural networks, and the relevance of the proposed image-based feature sets. In addition, the combination of additional biological and shape features to the image-related ones is considered. We reach a rate of correct age estimation of 88% w.r.t. the expert ground truth. This demonstrates the relevance of the proposed approach for the automation of routine aging and for computer-assisted aging. [Copyright &y& Elsevier]

A quantitative understanding of the integrated ocean heat content depends on our ability to determine how heat is distributed in the ocean and identify the associated coherent patterns. This study demonstrates how this can be achieved using unsupervised classification of Argo temperature profiles. The classification method used is a Gaussian Mixture Model (GMM) that decomposes the Probability Density Function of a dataset into a weighted sum of Gaussian modes. It is determined that the North Atlantic Argo dataset of temperature profiles contains 8 groups of vertically coherent heat patterns, or classes. Each of the temperature profile classes reveals unique and physically coherent heat distributions along the vertical axis. A key result of this study is that, when mapped in space, each of the 8 classes is found to define an oceanic region, even if no spatial information was used in the model determination. The classification result is independent of the location and time of the ARGO profiles. Two classes show cold anomalies throughout the water column with amplitude decreasing with depth. They are found to be localized in the subpolar gyre and along the poleward flank of the Gulf Stream and North Atlantic Current (NAC). One class has nearly zero anomalies and a large spread throughout the water column. It is found mostly along the NAC. One class has warm anomalies near the surface (50 m) and cold ones below 200 m. It is found in the tropical/equatorial region. The remaining four classes have warm anomalies throughout the water column, one without depth dependance (in the southeastern part of the subtropical gyre), the other three with clear maximums at different depths (100 m, 400 m and 1000 m). These are found along the southern flank of the North Equatorial Current, the western part of the subtropical gyre and over the West European Basin. These results are robust to both the seasonal variability and to method parameters such as the size of the analyzed domain. [ABSTRACT FROM AUTHOR]

This paper presents a deep auto-encoder model and a phased framework approach to predict the next 12 h of vessel trajectories using 1 to 3 h of Automatic Identification System data as input. The strategy involves fusing spatiotemporal features from AIS messages with probabilistic features engineered from historical AIS data to reduce forecasting uncertainty. The probabilistic features have an F1-Score of approximately 85% and 75% for the vessel route and destination prediction, respectively. Under such circumstances, we achieved an R2 Score of over 98% with different layer structures and varying feature combinations; the high R2 Score is a natural outcome of the well-defined shipping lanes in the study region. However, our proposal stands out among competing approaches as it demonstrates the capability of complex decision-making during turnings and route selection. Furthermore, we have shown that our model achieves more accurate forecasting with average and median errors of 11km and 6km, respectively, a 25% improvement from the current state-of-the-art approaches. The resulting model from this proposal is deployed as part of a broader Decision Support System to safeguard whales by preventing the risk of vessel-whale collisions under the smartWhales initiative and acting on the Gulf of St. Lawrence in Atlantic Canada. [Display omitted] • Probabilistic feature augmentation for deriving trajectory route and destination. • Conditional probability model for spatial feature distillation from AIS data streams. • Feature fusion and augmentation for problem shifting into trajectory reconstruction. • AutoEncoder designed for faster trajectory reconstruction with fewer parameters. • Module of a Decision Support System that avoids vessel-whale collisions in Canada. [ABSTRACT FROM AUTHOR]

Abstract: The analysis of the shape of calcified structures is known to be particularly relevant to address species identification and stock discrimination. Previous work mainly relied on Fourier shape descriptors, which achieve a global characterization of shape. In contrast, we investigate the potential of shape geodesics which rely on local shape features for classification issues. Classification performances are reported for several real datasets of calcified structures. The proposed shape geodesics scheme is shown to significantly outperform the standard Fourier approaches. [Copyright &y& Elsevier]

The physical forcing of the ocean surface includes a variety of energetic processes, ranging from internal wave (IW) to submesoscale and mesoscale, associated with characteristic horizontal scales. While the description of mesoscale ocean dynamics has greatly benefited from the availability of satellite data, observations of finer scale patterns remain scarce. Recent studies showed that the vertical displacements of the oxycline depth, which separates the well-mixed oxygenated surface layer from the less oxygenated deeper ocean, estimated by acoustics, provide a robust proxy of isopycnal displacements over a wide range of horizontal scales. Using a high-resolution and wide-range acoustic data set in the Northern Humboldt Current System (NHCS) off Peru, the spatial and temporal patterns of fine-scale-to-mesoscale upper ocean dynamics are investigated. The spectral content of oxycline/pycnocline profiles presents patterns characteristic of turbulent flows, from the mesoscale to the fine scale, and an energization at the IW scale (2 km–200 m). On the basis of a typology performed on 35,000 structures we characterized six classes of physical structures according to their shape and scale range. The analysis reveals the existence of distinct features for the fine-scale range below ∼2–3 km, and clearly indicates the existence of intense IW and submesoscale activity over the entire NHCS region. Structures at scales smaller than ∼2 km were more numerous and energetic in spring than in summer. Their spatiotemporal variability supports the interpretation that these processes likely relate to IW generation by interactions between tidal flows, stratification and the continental slope. Given the impact of the physical forcing on the biogeochemical and ecological dynamics in EBUS, these processes should be further considered in future ecosystem studies based on observations and models. The intensification of upper ocean stratification resulting from climate change makes such high-resolution analyses even more critical. [ABSTRACT FROM AUTHOR]

Understanding the spatiotemporal behavior of fishermen at the fleet scale is key for defining effective strategies for fisheries management. Here we classify the spatial patterns exhibited by fishing trip trajectories in the world's largest monospecific fishery, the Peruvian anchovy fishery. Our goal is to identify spatial strategies and their possible changes over 2000–2009. The data comprise more than 350,000 fishing trips, recorded using a vessel monitoring system. On-board observers monitored a small fraction of those trips (>2000), providing data for inferring the type of activity (fishing, searching, and cruising) from the position records, for use in a state-space model. Each fishing trip was characterized by its duration, maximum distance to the coast, geographical extension, and time spent fishing, searching and cruising. Using clustering techniques, we identified four types of fishing trips, associated with differences in management among regions, fleet segments, and skippers’ behavior. The methodology could be used to investigate fishing spatial strategies using VMS trajectories in other fisheries. [ABSTRACT FROM AUTHOR]

A major goal in marine ecology is the understanding of the interactions between the dynamics of the different ecosystem components, from physics to top predators. While fishermen are among the main top predators at sea, almost none of the existing studies on ecology from physics to top predators contemplate fishermen as part of the system. The present work focuses on the coastal processes in the Northern Humboldt Current System, which encompasses both an intense climatic variability and the largest monospecific fishery of the world. From concomitant satellite, acoustic survey and Vessel Monitoring System data (∼90,000 fishing trips) for a ten-year period (2000–2009), we quantify the associations between the dynamics of the spatial behavior of fishermen, environmental conditions and anchovy ( Engraulis ringens ) biomass and spatial distribution. Using multivariate statistical analyses we show that environmental and anchovy conditions do significantly shape fishermen spatial behavior and present evidences that environmental fluctuations smoothed out along trophic levels. We propose a retrospective analysis of the study period in the light of the ecosystem scenarios evidenced and we finally discuss the potential use of fishermen spatial behavior as ecosystem indicator. [ABSTRACT FROM AUTHOR]

Abstract: The Advanced Very High Resolution Radiometer (AVHRR) instrument on-board the METOP satellite is designed to provide very accurate measurements of Sea Surface Temperature (SST). In this work, using one year of METOP-AVHRR data and a geostatistical approach, we characterize the spatial anisotropy and non-stationarity of the SST variability using oriented ellipsoids. The method is also able to separate the true SST variability from the artificial error introduced by the METOP-AVHRR sensor. These spatial parameters are then used for producing variability atlases (available on-line) over the whole ocean. [Copyright &y& Elsevier]

• A residual-based deep 3D CNN architecture for super-resolution. • Comprehensive performance analysis of key elements of neural networks. • Multi-scale training approach to handle arbitrary scale factors. • Multimodal CNN for super-resolution. The purpose of super-resolution approaches is to overcome the hardware limitations and the clinical requirements of imaging procedures by reconstructing high-resolution images from low-resolution acquisitions using post-processing methods. Super-resolution techniques could have strong impacts on structural magnetic resonance imaging when focusing on cortical surface or fine-scale structure analysis for instance. In this paper, we study deep three-dimensional convolutional neural networks for the super-resolution of brain magnetic resonance imaging data. First, our work delves into the relevance of several factors in the performance of the purely convolutional neural network-based techniques for the monomodal super-resolution: optimization methods, weight initialization, network depth, residual learning, filter size in convolution layers, number of the filters, training patch size and number of training subjects. Second, our study also highlights that one single network can efficiently handle multiple arbitrary scaling factors based on a multiscale training approach. Third, we further extend our super-resolution networks to the multimodal super-resolution using intermodality priors. Fourth, we investigate the impact of transfer learning skills onto super-resolution performance in terms of generalization among different datasets. Lastly, the learnt models are used to enhance real clinical low-resolution images. Results tend to demonstrate the potential of deep neural networks with respect to practical medical image applications. [ABSTRACT FROM AUTHOR]