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Comparing salinity models in Whitewater Bay using remote sensing

This study compared models that used remote sensing to assess salinity in Whitewater Bay. The quantitative techniques in this research allow for a less costly and quicker assessment of salinity values. Field observations and Landsat 5 TM imagery from 2003-2006 were separated into wet and dry seasons and temporally matched. Interpolation models of Inverse Distance Weighting and Kriging were compared to empirical regression models (Ordinary Least Squares and Geographically Weighted Regression - GWR) via their Root Mean Square Error. The results showed that salinity analysis is more accurate in the dry season compared with the wet season. Univariate and multivariate analysis of the Landsat bands revealed the best band combination for salinity analysis in this local area. GWR is the most conducive model for estimating salinity because field observations are not required for future predictions once the local formula is established with available satellite imagery. / by Donna Selch. / Thesis (M.A.)--Florida Atlantic University, 2012. / Includes bibliography. / Mode of access: World Wide Web. / System requirements: Adobe Reader.

Identiferoai:union.ndltd.org:fau.edu/oai:fau.digital.flvc.org:fau_3974
ContributorsSelch, Donna, Charles E. Schmidt College of Science, Department of Geosciences
PublisherFlorida Atlantic University
Source SetsFlorida Atlantic University
LanguageEnglish
Detected LanguageEnglish
TypeText, Electronic Thesis or Dissertation
Formatviii, 56 p. : ill. (some col.), electronic
CoverageFlorida, Whitewater Bay, Florida, Whitewater Bay, Florida, Whitewater Bay, Whitewater Bay (Fla.)
Rightshttp://rightsstatements.org/vocab/InC/1.0/

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