Date of Award
2012
Document Type
Thesis
Publication Status
Version of Record
Submission Date
October 2026
Abstract
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.
Extent
viii, 56 p. : ill. (some col.)
Recommended Citation
Selch, Donna, "Comparing salinity models in Whitewater Bay using remote sensing" (2012). Electronic Theses and Dissertations 2010 - 2019. 476.
https://digitalcommons.fau.edu/etd_general_6/476
Identifier
FADT3356015
Comments
by Donna Selch.|Thesis (M.A.)--Florida Atlantic University, 2012.|Includes bibliography.|Mode of access: World Wide Web.|System requirements: Adobe Reader.|by Donna Selch.