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

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 Trees 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¿ have been reduced, selecting the most important features while increasing the true recognition rate. A series of tests have been conducted and results showed increased performance in oil spill recognition compared with previous studies. It is shown that from the initial 25 parameters used in these tests, only 6 are sufficient for robust recognition, which is also enhances the speed of processing.JRC.G.3-Maritime affair