A compilation of digitized satellite imagery of the Gulf Stream (1982, 1983, and 1985)

Ninety plots of digitized temperature boundaries from infared satellite images of the Gulf Stream along with corresponding image snapshots were compiled to determine stream width propagation speed. The satellite images are from the years 1982, 1983, and 1985 and are often of consecutive days. In this report, these images and digitized plots are presented.Funding was provided by the Office of Naval Research through contract Number N00014-87-K-0007, and by the National Science Foundation under grant Numbers OCE 87-00601 and OCE 85-10828

Cloud Detection from a Sequence of SST Images

A cloud detection algorithm was designed as an adjunct to a companion edge-detection algorithm. The cloud detection integrates two distinct algorithms: one based on multiimage processing, the other on single-image analysis. The multiimage portion of the cloud detection algorithm operates on a time sequence of sea surface temperature (SST) images. It is designed to detect clouds associated with regions of apparently lower temperatures than the underlying SST field. A pixel in the current image is initially considered to be corrupted by clouds if it is significantly cooler than the corresponding pixel in a neighbor image. To refine the initial classification, the algorithm checks the current image and the neighbor image for the presence of water masses, which through displacement could explain the change in temperature. The single-image cloud detection algorithm is designed to detect clouds associated with regions of the SST image where gradient vectors have a large magnitude. These regions are flagged in the map of potential clouds. multiimage processing is integrated with the single-image algorithm by adding pixels classified as cloudy at the multiimage level to the map of potential clouds. Further analysis of the gradient vector field and of the shapes of potentially cloudy areas allows one to determine whether these regions correspond to clouds or SST fronts. A previous study has shown that the clouds identified by the single-image algorithm were in close agreement with those detected by a human expert. To validate the additional multiimage processing, the effect of the integrated cloud detection on the performance of a companion edge detection algorithm is examined. These results and a direct comparison with the cloud masks produced by a human expert indicate that, compared to the single-image algorithm, the multiimage algorithm successfully identify additional cloud-corrupted regions while keeping a low rate for the detection of false clouds

Edge Detection Algorithm for SST Images

An algorithm to detect fronts in satellite-derived sea surface temperature fields is presented. Although edge detection is the main focus, the problem of cloud detection is also addressed since unidentified clouds can lead to erroneous edge detection. The algorithm relies on a combination of methods and it operates at the picture, the window, and the local level. The resulting edge detection is not based on the absolute strength of the front, but on the relative strength depending on the context thus, making the edge detection temperature-scale invariant. The performance of this algorithm is shown to be superior to that of simpler algorithms commonly used to locate edges in satellite-derived SST images. This evaluation was performed through a careful comparison between the location of the fronts obtained by applying the various methods to the SST images and the in situ measures of the Gulf Stream position

Edge Detection Applies to SST Fields

The wide availability of workstations has made the creation of sophisticated image processing algorithms economically possible. Here the latest version of an algorithm designed to detect fronts automatically in satellite-derived Sea Surface Temperature (SST) fields, is presented. The Algorithm operates at three levels: picture level, window level, and local/pixel level, much as humans seem to. Following input of the data, the most obvious clouds (based on temperature and shape) are identified and tagged so that data which do not represent sea surface temperature are not used in the subsequent modules. These steps operate at the picture and then at the window level. The procedure continues at the window level with the formal portion of the edge detection. Using techniques for unsupervised learning, the temperature distribution (histogram) in each window is analyzed to determine the statistical relevance of each possible front. To remedy the weakness related to the fact that clouds and water masses do not always form compact populations, the algorithm also includes a study of the spatial properties instead of relying entirely on temperatures. In this way, temperature fronts are unequivocally defined. Finally, local operators are introduced to complete the contours found by the region based algorithm. The resulting edge detection is not based on the absolute strength of the front, but on the relative strength depending on the context, thus making the edge detection temperature-scale invariant. The performance of this algorithm is shown to be superior to that of other algorithms commonly used to locate edges in satellite-derived SST images

Comparative Study of Two Recent Edge-Detection Algorithms Designed to Process Sea-Surface Temperature Fields

Two algorithms used for the detection of fronts in satellite-derived sea-surface temperature fields are compared. The two algorithms produced surprisingly comparable results considering the substantial differences in the two approaches: multilevel (Algorithm 1) versus locally based (Algorithm 2). Algorithm 1 offers the advantage of shorter run times. Algorithm 2 can be made faster if one is willing to accept less reliable edge detection. Algorithm 1 also offers the advantage of being adaptive and therefore automatic in its application to different data sets. However, when direct control with regard to detection of the edges is demanded, Algorithm 2 contains two tunable parameters to select the smoothness and the strength of edges, while Algorithm 1 as presently coded does not

On the front line: integrated habitat mapping for olive ridley sea turtles in the southeast Atlantic

notes:types: JOURThis study demonstrates that it is imperative that marine conservation policy recognizes the spatial extent of highly migratory species with expansive ranges. It also highlights that deficiencies exist in current knowledge of bycatch, both in gear specificity and in catch per unit effort. With integration of vessel monitoring system (VMS) data and those on fisheries catch, knowledge and understanding of bycatch may be improved, and this will ultimately facilitate development of appropriate management strategies and long-term sustainability of fisheries and their supporting ecosystems

Seasonal Spatial Segregation in Blue Sharks (Prionace glauca) by Sex and Size Class in the Northeast Pacific Ocean

Aim: Animal tracking can provide unique insights into the ecology and conservation of marine species, such as the partitioning of habitat, including differences between life history stages or sexes, and can inform fisheries stock assessments, bycatch reduction and spatial management such as dynamic management. Location: Northeast Pacific Ocean. Methods: We used satellite tracking data from 47 blue sharks (Prionace glauca) from the Northeast Pacific to determine movements and home range along the west coast of North America, and sex–size class (immature females, mature males) specific habitat preferences using boosted regression trees. Using a suite of static and dynamic environmental variables, we determined distribution and habitat preferences across summer and fall for each sex–size class. Results: We found that there was spatial segregation between sex–size classes particularly in the summer months with immature females found largely north of 33°N, and males south of 35°N. In fall, females travelled south, resulting in an overlap in distributions south of 37°N. Sea surface temperature (SST), latitude and longitude were top predictors. However, immature females and adult males demonstrated unique habitat preferences including SST, with immature females preferring cooler temperatures (SST \u3c 15°C) than adult males in summer, and a broader band of SST than adult males in fall. All models performed well, explaining 50%–67% of deviance, and 23%–41% of deviance when predictions were cross‐validated. Main conclusions: We provide first insights into coastal movements and habitat preferences of blue sharks in the Northeast Pacific. We found that immature females undergo a seasonal southward migration in this more coastal habitat, similar to patterns observed in the North Atlantic. We also found some overlap between adult males and immature females in fall months, suggesting the importance of more coastal habitat in managing this species, particularly in determining population structure for blue shark stock assessments, and reducing blue shark bycatch

Integrated monitoring of mola mola behaviour in space and time

Over the last decade, ocean sunfish movements have been monitored worldwide using various satellite tracking methods. This study reports the near-real time monitoring of finescale (< 10 m) behaviour of sunfish. The study was conducted in southern Portugal in May 2014 and involved satellite tags and underwater and surface robotic vehicles to measure both the movements and the contextual environment of the fish. A total of four individuals were tracked using custom-made GPS satellite tags providing geolocation estimates of fine-scale resolution. These accurate positions further informed sunfish areas of restricted search (ARS), which were directly correlated to steep thermal frontal zones. Simultaneously, and for two different occasions, an Autonomous Underwater Vehicle (AUV) videorecorded the path of the tracked fish and detected buoyant particles in the water column. Importantly, the densities of these particles were also directly correlated to steep thermal gradients. Thus, both sunfish foraging behaviour (ARS) and possibly prey densities, were found to be influenced by analogous environmental conditions. In addition, the dynamic structure of the water transited by the tracked individuals was described by a Lagrangian modelling approach. The model informed the distribution of zooplankton in the region, both horizontally and in the water column, and the resultant simulated densities positively correlated with sunfish ARS behaviour estimator (r(s) = 0.184, p < 0.001). The model also revealed that tracked fish opportunistically displace with respect to subsurface current flow. Thus, we show how physical forcing and current structure provide a rationale for a predator's finescale behaviour observed over a two weeks in May 2014

Scales and structure of frontal adjustment and freshwater export in a region of freshwater influence

Sea surface temperature satellite imagery and a regional hydrodynamic model are used to investigate the variability and structure of the Liverpool Bay thermohaline front. A statistically based water mass classification technique is used to locate the front in both data sets. The front moves between 5 and 35 km in response to spring-neap changes in tidal mixing, an adjustment that is much greater than at other shelf-sea fronts. Superimposed on top of this fortnightly cycle are semi-diurnal movements of 5-10 km driven by flood and ebb tidal currents. Seasonal variability in the freshwater discharge and the density difference between buoyant inflow and more saline Irish Sea water give rise to two different dynamical regimes. During winter, when cold inflow reduces the buoyancy of the plume, a bottom-advected front develops. Over the summer, when warm river water provides additional buoyancy, a surface-advected plume detaches from the bottom and propagates much larger distances across the bay. Decoupled from near-bed processes, the position of the surface front is more variable. Fortnightly stratification and re-mixing over large areas of Liverpool Bay is a potentially important mechanism by which freshwater, and its nutrient and pollutant loads, are exported from the coastal plume system. Based on length scales estimated from model and satellite data, the erosion of post-neap stratification is estimated to be responsible for exporting approximately 19% of the fresh estuarine discharge annually entering the system. Although the baroclinic residual circulation makes a more significant contribution to freshwater fluxes, the episodic nature of the spring-neap cycle may have important implications for biogeochemical cycles within the ba