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Remote Sensing of Open Water Fraction and Melt Ponds in the Beaufort Sea Using Machine Learning Algorithms.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Remote Sensing of Open Water Fraction and Melt Ponds in the Beaufort Sea Using Machine Learning Algorithms./
Author:	Ortiz, Macarena D.
Description:	1 online resource (191 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 79-02(E), Section: B.
Subject:	Climate change. -
Online resource:	click for full text (PQDT)
ISBN:	9780355242126

Remote Sensing of Open Water Fraction and Melt Ponds in the Beaufort Sea Using Machine Learning Algorithms.
Ortiz, Macarena D.

Remote Sensing of Open Water Fraction and Melt Ponds in the Beaufort Sea Using Machine Learning Algorithms. - 1 online resource (191 pages)

Source: Dissertation Abstracts International, Volume: 79-02(E), Section: B.

Thesis (Ph.D.)--University of Miami, 2017.

Includes bibliographical references

Classification of open water fraction (OWF) from synthetic aperture radar (SAR) images in the marginal ice zone can be significantly difficult during the summer months, where melt-onset can alter the backscatter and melt ponds contaminate OWF estimates. In this dissertation, we explore five different machine learning algorithms including Neural Networks, Linear Support Vector Machines, Naive Bayes, K-Nearest Neighbor and Discriminant Analysis to quantify OWF using TerraSAR-X Stripmap images during the boreal summer of 2014. To validate our methods, we use nearly-coincident high resolution panchromatic optical images. We find that overall, the classification algorithms attained comparable accuracies, however the Naive Bayes achieved the fastest computation time. Faster computation can be very practical for users on vessels wishing to have accurate "on-the-fly" methods to calculate ice/water from SAR for navigational purposes and for modelers working with near real-time ice forecasting. We also present a prototype algorithm using linear support vector machines designed to quantify the evolution of melt pond fraction from the optical dataset in an area where several in-situ instruments were deployed by the British Antarctic Survey and the Marginal Ice Zone Program, during April-September 2014. We explore both the temporal evolution of melt ponds and spatial statistics such as pond fraction, pond area, and number pond density, to name a few. We also introduce a linear regression model that can potentially be used to estimate average pond area by ingesting several melt pond statistics and shape parameters.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355242126Subjects--Topical Terms:

1009004
Climate change.
Index Terms--Genre/Form:

554714
Electronic books.

Remote Sensing of Open Water Fraction and Melt Ponds in the Beaufort Sea Using Machine Learning Algorithms.
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500 $a Source: Dissertation Abstracts International, Volume: 79-02(E), Section: B.
500 $a Adviser: Hans C. Graber.
502 $a Thesis (Ph.D.)--University of Miami, 2017.
504 $a Includes bibliographical references
520 $a Classification of open water fraction (OWF) from synthetic aperture radar (SAR) images in the marginal ice zone can be significantly difficult during the summer months, where melt-onset can alter the backscatter and melt ponds contaminate OWF estimates. In this dissertation, we explore five different machine learning algorithms including Neural Networks, Linear Support Vector Machines, Naive Bayes, K-Nearest Neighbor and Discriminant Analysis to quantify OWF using TerraSAR-X Stripmap images during the boreal summer of 2014. To validate our methods, we use nearly-coincident high resolution panchromatic optical images. We find that overall, the classification algorithms attained comparable accuracies, however the Naive Bayes achieved the fastest computation time. Faster computation can be very practical for users on vessels wishing to have accurate "on-the-fly" methods to calculate ice/water from SAR for navigational purposes and for modelers working with near real-time ice forecasting. We also present a prototype algorithm using linear support vector machines designed to quantify the evolution of melt pond fraction from the optical dataset in an area where several in-situ instruments were deployed by the British Antarctic Survey and the Marginal Ice Zone Program, during April-September 2014. We explore both the temporal evolution of melt ponds and spatial statistics such as pond fraction, pond area, and number pond density, to name a few. We also introduce a linear regression model that can potentially be used to estimate average pond area by ingesting several melt pond statistics and shape parameters.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
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650 4 $a Physical oceanography. $3 1178843
650 4 $a Artificial intelligence. $3 559380
655 7 $a Electronic books. $2 local $3 554714
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710 2 $a University of Miami. $b Applied Marine Physics. $3 1186001
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