Fronts, which are sharp boundaries between distinct water masses, play a substantial role in managing biodiversity of marine species and preserving a resilient ecosystem. The overarching aim of this study is to compare different methodologies for detecting marine fronts. Many marine fronts are identifiable by their strong temperature gradient. For that reason, this study tests how two different edge detection methodologies (Laplacian and Canny) performs on detecting marine once applied on the Sea Surface Temperature (SST) product of the Sentinel-3 SLSTR instrument. In a few words, the results of this study showed that the Laplacian edge detection overestimates fronts, while the Canny Edge detection algorithm underestimates them. It worth highlighting though that the results are significantly improved using the appropriate filtering and/or image enhancements. The results of the Canny Edge detection algorithm were improved when a histogram equalisation image enhancement was applied before the Canny Edge and the results of the Laplacian detector were improved with median filtering.
SEO-DWARF (Semantic Earth Observation Data Web Alert and Retrieval Framework) is a project funded by the European Union Horizon 2020 research and innovation programme. The main objective of the project is to realize the content-based search of Earth Observation (EO) images on an application specific basis. The satellite images, which come from EO satellites such as Sentinels 1, 2 and 3, as well as ENVISAT, are distributed with few correlated meta-data which do not describe the phenomena and the objects included in the image. Innovative approaches to process remote sensing images can extract relevant information which semantically describes the land type, the region area border, objects and events such as oil spill. This information can be modeled as structured information through ontologies to be processed by algorithms to perform information retrieval and filtering. The proposed system is aware of the semantic elements which are relevant for final user and will be able to answer natural language queries such as “Show me the images of the Mediterranean Sea which include an algal bloom”. The possibility to retrieve a specific set of land images starting from a query expressed by a final user can quickly increase the interoperability and the diffusion of applications able to efficiently use EO data. In this work, we present a brief overview of the most successful application of this formalization strategy focusing on the tools and approaches for creating a robust and efficient domain geo-ontology. Furthermore, we describe the approach adopted to define the specific ontology used in the SEO-DWARF project, including the strategy adopted for implementing and populating it.
Main objectives of this paper are to evaluate SeaWiFS, MODIS, and AVHRR satellite imagery performances against in situ data around two Northeast Atlantic seamounts, Sedlo and Seine. The temporal and spatial variability of satellite-derived near-surface chlorophyll a (Chl a) and sea surface temperature (SST) is also analysed. SeaWiFS tends to show good accuracy with the in situ data for Sedlo seamount, while for Seine it tends to slightly overestimate the values. Oppositely, MODIS tends to underestimate Chl a for both seamounts. Match-up SST analyses show that MODIS underestimates the in situ values on Seine seamount. The best correlation was attained with AVHRR on Sedlo. Seasonal variations are clearly pronounced on Sedlo with typical spring and autumn Chl a blooms, while further to the south, on Seine, less intense blooms are registered, as expected. Higher/lower SST values are observed during summer/winter respectively, showing clear seasonal patterns. A time lag of about one month for the maximum SST heating/cooling from Sedlo to Seine is noted.
SST spatial variability in the Azores region is investigated using a method of least-square fit of a spatially averaged seasonal curve to monthly mean temperature variability curves at each pixel. The zero-order coefficient of the least-square fit is considered to represent a stationary anomaly field over the Azores region. The anomaly field reflects that the Mid-Atlantic Ridge (MAR) forms a barrier for heat exchange between the western and the eastern subtropical North Atlantic basins. Two SST frontal interfaces are identified to the east of MAR: the North Azores Flow (NAzF), which crosses MAR at 42-43oN, and the northern border of the Azores current. Several regions with significantly cooler surface waters are identified on stationary SST anomaly field. The temperature difference inside and outside of the cold-water pools sometimes exceeds 0.5oC. The most prominent pools are observed at the eastern flank of the MAR, between the Central and Eastern groups of the Azores islands and at a seamount chain south of the Azores. Those cooler water pools can be related to bottom-trapped advection, intensification of cyclones over bottom rises or enhanced tidal mixing near abrupt bottom topography.
Ocean Colour (OC) sensors have been primarily used in biological studies. More recently, OC information has been attracting the attention of oceanographers, as a potential method for revealing physical structures in the ocean. In this study, OC data obtained from SeaWiFS imagery is used, for the first time, to detect the weak Azores Current (AzC) and the associated Azores Front (AzF). Previous studies show that the frontal interface is well seen on SST imagery only during the cold season, while it is disguised during the warm season through the formation of a strong seasonal thermocline. With SeaWiFS imagery, the frontal interface is well identified around 34° N as an asymmetric zonally stretched band of higher near-surface chlorophyll a (CHL a) values north of the AzF, accompanied by a sharp decrease to the south. Quasi-stationary meanders, previously derived from SST fields for the same region, are also well observed in OC imagery. Monthly-averaged Chl a along a meridional cross-section shows that, from spring to autumn, the front is clearly visible. In winter, differences across the front are less pronounced, and the front is more easily identified on SST fields. OC gradients weaken to the east, corresponding to the general weakening of the AzC. In situ CTD data reveal a sharp and meandering thermohaline and dissolved oxygen front ocated at 33-34.5° N and 31° W. This study suggests that OC imagery, combined with other sensors, provide an important tool to investigate ocean dynamic variability, by helping to detect frontal zones with great precision.
Using 1.1 km resolution imagery from NOAA-12, -14, -16, and -17 recorded from April 2001 to May 2003 by "HAZO" HRTP mid-Atlantic satellite receiving station, 8-day average image are calculated to investigate AVHRR-derived SST distributions and associated dominant space and time scales around the Azores archipelago (34° to 42° N, 33° to 23° W). Eight-day average images together with zonal and meridional averages show a distinct seasonal cycle and typical gradi-ents, which emphasise the dual influence of the Gulf Stream and the Azores Current in this region. In late spring, iso-therms start moving to the north and retreat in early autumn. Low horizontal gradients are found during summertime, with warmer waters located to the south and west. Orientation of SST patterns changes with time from SW-NE (e.g. July 2001) to NNW-SSE (e.g. July 2002, August 2001 and 2002). The later orientation involves the sudden warming of the waters surrounding the northwestern group of islands of the Azores archipelago. This warming persists during 3 to 6 weeks with mean temperature differences of the order of 0.8° C. At a more local scale (2° x 2° in size) SST variability is also observed. In some cases, it is found that wind-driven coastal upwelling, a few km wide, occurs to the south of the islands during spring and summer months. Field data demonstrate that upwelling events increase local biomass. This result highlights the relevance of SST data to improve stock assessment and fishery management studies.
Since 4th April 2001, Sea Surface Temperature (SST) of Azorean waters is obtained from 1.1 km resolution NOAA- 12, -14, and -16 imagery recorded at the “HAZO” HRTP mid-Atlantic satellite receiving station. One year of data is processed to investigate AVHRR-derived SST distributions and associated dominant space and time scales. Daily
SST is calculated with the MultiChannel Sea Surface Temperature algorithm. Comparison with in situ historical data demonstrates that satellite SST ranges include low value pixels that may be attributed to undetected clouds. Images show that remnant clouds are associated to zones of strong cloud coverage. However, contamination of pixels in
contact with clouds is found to be limited. Therefore, to remove erroneous values, rather than systematically erode pixels around clouds, images are filtered inputting to each image threshold values equal to the mean more or less 4 times the standard deviation of 8-day SST histograms. Nighttime SST averages show a distinct seasonal cycle for the Azores region. Increased surface heating and decreased horizontal gradients are found during summertime. Eight-day zonal and meridional SST averages show typical latitudinal and longitudinal gradients, providing some insight into the physical mechanisms responsible for surface temperature variability in the region. These data are relevant to improve stock assessment and fishery management studies in the Azores.
The Arcachon tidal inlet (France) is a sandy place where tidal currents, swells and storms produce important sand movements. For example the emerged sand banks move several tens of meter a year. To study and understand these movements traditional methods are often inefficient. Remote sensing seems to be the best means to follow and quantify year after year sand movements. Two SPOT scenes have been processed to carry out bathymetric maps. From these maps we extrapolated both channels' and sand banks' movements and computed the sediment volumes between the water surface and a depth of 5 meters. Below this depth, the radiances keep a constant value whatever the depth change.
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