Development of coastal upwelling edge detection algorithms associated with harmful algal blooms off the Washington coast using sea surface temperature imagery

2005 ◽  
Author(s):  
Nathan R. Evans ◽  
Dana L. Woodruff ◽  
Vera L. Trainer
Keyword(s):  
Edge Detection ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Harmful Algal Blooms ◽  
Algal Blooms ◽  
Coastal Upwelling ◽  
Sea Surface ◽  
Detection Algorithms

scholarly journals The Southeast Alaska Tribal Ocean Research (SEATOR) Partnership: Addressing Data Gaps in Harmful Algal Bloom Monitoring and Shellfish Safety in Southeast Alaska

Toxins ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 407 ◽  
Author(s):  
John R. Harley ◽  
Kari Lanphier ◽  
Esther G. Kennedy ◽  
Tod A. Leighfield ◽  
Allison Bidlack ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Harmful Algal Blooms ◽  
Algal Blooms ◽  
Harmful Algal Bloom ◽  
Sea Surface ◽  
Southeast Alaska ◽  
Paralytic Shellfish Toxins ◽  
Tribal Governments ◽  
Salinity Data

Many communities in Southeast Alaska harvest shellfish such as mussels and clams as an important part of a subsistence or traditional diet. Harmful algal blooms (HABs) of phytoplankton such as Alexandrium spp. produce toxins that can accumulate in shellfish tissues to concentrations that can pose a hazard for human health. Since 2013, several tribal governments and communities have pooled resources to form the Southeast Alaska Tribal Ocean Research (SEATOR) network, with the goal of minimizing risks to seafood harvest and enhancing food security. SEATOR monitors toxin concentrations in shellfish and collects and consolidates data on environmental variables that may be important predictors of toxin levels such as sea surface temperature and salinity. Data from SEATOR are publicly available and are encouraged to be used for the development and testing of predictive algorithms that could improve seafood risk assessment in Southeast Alaska. To date, more than 1700 shellfish samples have been analyzed for paralytic shellfish toxins (PSTs) in more than 20 locations, with potentially lethal concentrations observed in blue mussels (Mytilus trossulus) and butter clams (Saxidomus gigantea). Concentrations of PSTs exhibit seasonality in some species, and observations of Alexandrium are correlated to sea surface temperature and salinity; however, concentrations above the threshold of concern have been found in all months, and substantial variation in concentrations of PSTs remain unexplained.

scholarly journals Local and remote forcing of sea surface temperature in the coastal upwelling system off Chile

2001 ◽  
Vol 106 (C8) ◽  
pp. 16657-16671 ◽  
Author(s):  
Samuel Hormazabal ◽  
Gary Shaffer ◽  
Jaime Letelier ◽  
Osvaldo Ulloa
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Coastal Upwelling ◽  
Sea Surface ◽  
Upwelling System ◽  
Remote Forcing

scholarly journals Modeling of Coastal Upwelling Using Spatiogram and Structuring Elements

2018 ◽  
Author(s):  
Ketut Wikantika
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Coastal Upwelling ◽  
Sea Surface ◽  
Ekman Transport ◽  
Contour Lines ◽  
High Concentrations ◽  
Color Pixel ◽  
Lower Temperature ◽  
Surrounding Areas

Upwelling is an oceanographic phenomenon where themass of water from the sea floor, cold temperature and nutrientrich, moving up to the sea surface. Upwelling could be happeneddue to the interaction between the surface water with the wind,ocean currents, Ekman transport, and the Coriolis force, so there isa special characteristic of the upwelling waters. Nevertheless, acommon indicator of upwelling is the sea surface temperature(SST) lower than the surrounding areas, high concentrations ofchlorophyll-a, as well as high salinity and density. In coastalupwelling, a lower temperature will be detected near the coast,while warmer temperatures towards offshore. This condition leadsto form unique patterns contour of sea surface temperature. Byusing the data of SST derived from satellites, SST can berepresented with the color and/or contour. Each color pixel valuerepresents a particular SST. The initial phase of this research is todevelop a model for the identification of upwelling based on thepixel and contour patterns. By using sea surface temperature data,the average SST (each month) were analyzed and generate contourlines. Contour lines are formed from the pixels that have the sametemperature value. By calculating the distance between pixels in thesame temperature class and the distance to the surrounding pixels,we have been developing a model for the detection of upwelling.

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