Semantically-Aware Retrieval of Oceanographic Phenomena Annotated on Satellite Images

Scientists in the marine domain process satellite images in order to extract information that can be used for monitoring, understanding, and forecasting of marine phenomena, such as turbidity, algal blooms and oil spills. The growing need for effective retrieval of related information has motivated the adoption of semantically aware strategies on satellite images with different spatiotemporal and spectral characteristics. A big issue of these approaches is the lack of coincidence between the information that can be extracted from the visual data and the interpretation that the same data have for a user in a given situation. In this work, we bridge this semantic gap by connecting the quantitative elements of the Earth Observation satellite images with the qualitative information, modelling this knowledge in a marine phenomena ontology and developing a question answering mechanism based on natural language that enables the retrieval of the most appropriate data for each user’s needs. The main objective of the presented methodology is to realize the content-based search of Earth Observation images related to the marine application domain on an application-specific basis that can answer queries such as “Find oil spills that occurred this year in the Adriatic Sea”

Can we actually monitor the spatial distribution of small pelagic fish based on Sentinel-3 data? An example from the North Aegean Sea (Eastern Mediterranean Sea)

Fish population spatial distribution data provide essential information for fleet monitoring and fishery spatial planning. Modern high resolution ocean color remote sensing sensors with daily temporal coverage can enable consistent monitoring of highly productive areas, giving insight in seasonal and yearly variations. Here is presented the methodology to monitor small pelagic fish spatial distribution by means of 500m resolution satellite data in a geographically and oceanographically complex area. Specifically, anchovy (Engraulis encrasicolus) and sardine (Sardina pilchardus) acoustic biomass data are modeled against environmental proxies obtained from the Sentinel-3 satellite mission. Three modeling techniques (Logistic Regression, Generalized Additive Models, Random Forest) were applied and validated against the in-situ measurements. The accuracy of anchovy presence detection peaked at 76% and for sardine at 78%. Additionally, the spatial distribution of the models’ output highlighted known fishing grounds. For anchovy, biomass modeling highlighted the importance of bathymetry, SST, and the distance from thermal fronts, whereas for sardine, bathymetry, CHL and chlorophyll fronts. The models are applied to a sample dataset to showcase a potential outcome of the proposed methodology and its spatial characteristics. Finally, the results are discussed and compared to other habitat studies and findings in the area

Marine Litter Windrows: A Strategic Target to Understand and Manage the Ocean Plastic Pollution

Windrow is a long-established term for the aggregations of seafoam, seaweeds, plankton and natural debris that appear on the ocean surface. Here, we define a "litter windrow" as any aggregation of floating litter at the submesoscale domain (<10 km horizontally), regardless of the force inducing the surface convergence, be it wind or other forces such as tides or density-driven currents. The marine litter windrows observed to date usually form stripes from tens up to thousands of meters long, with litter densities often exceeding 10 small items ( 2 cm) per m2 or 1 large item ( 2 cm) per 10 m2. Litter windrows are generally overlooked in research due to their dispersion, small size and ephemeral nature. However, applied research on windrows offers unique possibilities to advance on the knowledge and management of marine litter pollution. Litter windrows are hot spots of interaction with marine life. In addition, since the formation of dense litter windrows requires especially high loads of floating litter in the environment, their detection from space-borne sensors, aerial surveys or other platforms might be used to flag areas and periods of severe pollution. Monitoring and assessing of management plans, identification of pollution sources, or impact prevention are identified as some of the most promising fields of application for the marine litter windrows. In the present Perspective, we develop a conceptual framework and point out the main obstacles, opportunities and methodological approaches to address the study of litter windrows.This study is an outcome of the research project entitled "MappingWindrows as Proxy for Marine Litter Monitoring from Space" (WASP), funded by the European Space Agency (ESA) Contract No. 4000130627/20/NL/GLC, within the Discovery Campaign in Marine Litter. AC had additional support from MIDaS (CTM2016-77106-R, AEI/FEDER/UE), and SA from PRIN 2017-2017WERYZP-EMME project. AI was supported by the Environmental Research and Technology Development Fund (JPMEERF18S20201) of the Ministry of the Environment, Japan, and by SATREPS of Japan International Cooperation Agency and Japan Science and Technology Agency. OB and AR contribution was funded through the EU's LIFE Program (LIFE LEMA project, grant agreement no. LIFE15 ENV/ES/000252). This is contribution number 1016 of AZTI, Marine Research, Basque Research and Technology Alliance (BRTA)

Synergistic exploitation of geoinformation methods for post-earthquake 3D mapping of Vrisa traditional settlement, Lesvos Island, Greece

The aim of this paper is to present the methodology followed and the results obtained by the synergistic exploitation of geo-information methods towards 3D mapping of the impact of the catastrophic earthquake of June 12th 2017 on the traditional settlement of Vrisa on the island of Lesvos, Greece. A campaign took place for collecting: a) more than 150 ground control points using an RTK system, b) more than 20.000 high-resolution terrestrial and aerial images using cameras and Unmanned Aircraft Systems and c) 140 point clouds by a 3D Terrestrial Laser Scanner. The Structure from Motion method has been applied on the high-resolution terrestrial and aerial photographs, for producing accurate and very detailed 3D models of the damaged buildings of the Vrisa settlement. Additionally, two Orthophoto maps and Digital Surface Models have been created, with a spatial resolution of 5cm and 3cm, respectively. The first orthophoto map has been created just one day after the earthquake, while the second one, a month later. In parallel, 3D laser scanning data have been exploited in order to validate the accuracy of the 3D models and the RTK measurements used for the geo-registration of all the above-mentioned datasets. The significant advantages of the proposed methodology are: a) the coverage of large scale areas; b) the production of 3D models having very high spatial resolution and c) the support of post-earthquake management and reconstruction processes of the Vrisa village, since such 3D information can serve all stakeholders, be it national and/or local organizations

Remote Sensing of Sea Surface Artificial Floating Plastic Targets with Sentinel-2 and Unmanned Aerial Systems (Plastic Litter Project 2019)

Remote sensing is a promising tool for the detection of floating marine plastics offering extensive area coverage and frequent observations. While floating plastics are reported in high concentrations in many places around the globe, no referencing dataset exists either for understanding the spectral behavior of floating plastics in a real environment, or for calibrating remote sensing algorithms and validating their results. To tackle this problem, we initiated the Plastic Litter Projects (PLPs), where large artificial plastic targets were constructed and deployed on the sea surface. The first such experiment was realised in the summer of 2018 (PLP2018) with three large targets of 10 &times; 10 m. Hereafter, we present the second Plastic Litter Project (PLP2019), where smaller 5 &times; 5 m targets were constructed to better simulate near-real conditions and examine the limitations of the detection with Sentinel-2 images. The smaller targets and the multiple acquisition dates allowed for several observations, with the targets being connected in a modular way to create different configurations of various sizes, material composition and coverage. A spectral signature for the PET (polyethylene terephthalate) targets was produced through modifying the U.S. Geological Survey PET signature using an inverse spectral unmixing calculation, and the resulting signature was used to perform a matched filtering processing on the Sentinel-2 images. The results provide evidence that under suitable conditions, pixels with a PET abundance fraction of at least as low as 25% can be successfully detected, while pinpointing several factors that significantly impact the detection capabilities. To the best of our knowledge, the 2018 and 2019 Plastic Litter Projects are to date the only large-scale field experiments on the remote detection of floating marine litter in a near-real environment and can be used as a reference for more extensive validation/calibration campaigns

Contribution to the Research on the Capability of SAR Images in Recognizing and Detecting Oil Spills on Sea Surface

The scope of the dissertation is to investigate the capabilities of remote sensing techniques in recognition and detection of oil spills on the sea surface using SAR images. Two new methodologies were developed. The first one is based on object-oriented approach and on fuzzy logic. It is fully automated and is differentiated from the existing systems. The second one is based on the use of neural networks. Two neural networks are required to detect and classify oil slicks; one to detect the black formations on the SAR images and one to classify them as oil spills or look-alikes. Furthermore, the statistical characteristics which should be used to distinguish dark formations to oil spills and natural phenomena were investigated. Combinations of 25 characteristics were examined using neural networks and genetic algorithms. The result was the selection of a specific combination of ten characteristics to classify oil spills. This combination was observed in the majority of the examined solutions and it was proved that it can distinguish the oil slicks from the look-alikes in most of the examined cases.JRC.G.4-Maritime affair

Incidence angle Normalization of Wide Swath SAR Data for Oceanographic Applications

A backscattering trend in the range direction of the signal received by Synthetic Aperture Radar (SAR) in Wide Swath (WS) mode results in a progressive reduction of brightness over images from near to far range, which affects the detection and classification of sea surface features on wide swath SARimages. The aim of the present paper is to investigate methods for limiting the issue of Normalized Radar Cross-Section (NRCS or 0) variation due to the incidence angle. Two sensor independent functions are investigated: a theoretical backscattering shape function derived from a minimum wind speed and an empirical range fit of NRCS against incidence angle . The former method exploits only the modeled NRCS values while the latter only the image content. The results were compared with the squared cosine correction, the most widely applied method for normalization, using six newly developed comparison factors. The results showed that the cosine squared normalization has the lowest efficiency while the proposed methods have similar behaviors and comparable results. Nevertheless, after the log-transformation and summation of the comparison factors, it was clearly shown that theoretical normalization performance is superior to the empirical one since it has the highest accuracy and requires less computational time

Oil spill feature selection and classification using decision tree forest on SAR image data

A novel oil spill feature selection and classification technique is presented, based on a forest of decision trees. The parameters of the two-class classification problem of oil spills and “look-alikes” are explored. The contribution to the final classification of the 25 most commonly used features in the scientific community was examined. The work is sought in the framework of a multi-objective problem, i.e. the minimization of the used input features and, at the same time, the maximization of the three most important combinations contain 4, 6 and 9 features. The latter feature combination can be seen as the most appropriate solution of the decision forest study. Examination of the robustness of the above result showed that the proposed combination achieved higher classification accuracy than other well-known statistical separation indexes. Moreover, comparisons with previous findings converge on the classification accuracy (up to 84.5 %) and to the number of selected features, but diverge on the actual features. This observation leads to the conclusion that there is not a single optimum feature combination; several sets of combinations exist which contain at least some critical features.JRC.G.4-Maritime affair