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Water mass classification using band ratios
Descriptive
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Title
Water mass classification using band ratios
Name
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Ramadurai
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Ramya
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Ramya Ramadurai
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author
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Glenn
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Scott
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Advisory Committee
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Scott Glenn
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chair
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Schofield
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Oscar
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Advisory Committee
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Oscar Schofield
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co-chair
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Francis
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Jennifer
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Advisory Committee
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Jennifer Francis
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internal member
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Rutgers University
Role
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degree grantor
Name
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Graduate School - New Brunswick
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school
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Text
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theses
OriginInfo
DateCreated
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2008
DateOther
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2008-10
Language
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English
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electronic
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application/pdf
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text/xml
Extent
ix, 92 pages
Abstract
The Hudson River plume has been the topic of consideration and observation in order to try and understand the physical, chemical and biological behavior of the plume which is a key component of the oceanography of the Mid-Atlantic Bight (MAB) region off the east coast of the United States. One approach towards understanding the chlorophyll production for the plume would be to make use of satellite data to measure the optical ocean color properties of these waters. In this direction classifying the water masses of the Hudson River plume according to these optical properties would be an interesting method of analyzing the satellite data for the purpose of understanding and identifying the physical and biological changes and the correlation between them in this region.
The first step is to design and implement a water mass classification algorithm in the LaTTE (Lagrangian Transport and Transformation Experiment) region of the MAB. It takes about 1-2 weeks for the nutrients from the freshwater from the Hudson Estuary to be dissipated and mixed with the open ocean. This classification algorithm is developed using ocean color data from the Sea viewing WIde Field of view Sensor (SeaWiFS). The algorithm is validated by overlaying ship salinity tracks on the classified water masses to show that salinity values change at the boundaries of the classified regions, due to the mixing and export of freshwater across the shelf.
We analyze global Sea Surface Temperature (SST) data collected over the years 1995-2005 for summer and winter in order to find coastal estuarine ecosystems that may display similar behavior as the Hudson River Estuary. Looking at the seasonal variation in this data, we observe that the regions of MAB and the East Asian coast are found to have strikingly similar seasonal behavior.
This leads into the third and the last step of the process which involves applying the water mass classification algorithm to ocean color data from eastern coastal Asia. It is observed that the algorithm well in the seas of Okhotsk, Japan and East China where it is able to identify plume water and non river water.
The first step is to design and implement a water mass classification algorithm in the LaTTE (Lagrangian Transport and Transformation Experiment) region of the MAB. It takes about 1-2 weeks for the nutrients from the freshwater from the Hudson Estuary to be dissipated and mixed with the open ocean. This classification algorithm is developed using ocean color data from the Sea viewing WIde Field of view Sensor (SeaWiFS). The algorithm is validated by overlaying ship salinity tracks on the classified water masses to show that salinity values change at the boundaries of the classified regions, due to the mixing and export of freshwater across the shelf.
We analyze global Sea Surface Temperature (SST) data collected over the years 1995-2005 for summer and winter in order to find coastal estuarine ecosystems that may display similar behavior as the Hudson River Estuary. Looking at the seasonal variation in this data, we observe that the regions of MAB and the East Asian coast are found to have strikingly similar seasonal behavior.
This leads into the third and the last step of the process which involves applying the water mass classification algorithm to ocean color data from eastern coastal Asia. It is observed that the algorithm well in the seas of Okhotsk, Japan and East China where it is able to identify plume water and non river water.
Note
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M.S.
Note
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Includes bibliographical references (p. 86-92).
Subject
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Topic
Oceanography
Subject
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Topic
Water masses--Mathematical models
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Graduate School - New Brunswick Electronic Theses and Dissertations
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rucore19991600001
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http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.17549
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ETD_1098
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NjNbRU
Identifier
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doi:10.7282/T3RR1ZJN
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ETD graduate
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Rights
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The author owns the copyright to this work.
Copyright
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Copyright protected
Availability
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Open
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Name
Ramya Ramadurai
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Copyright holder
Affiliation
Rutgers University. Graduate School - New Brunswick
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Non-exclusive ETD license
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Author Agreement License
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I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
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