Human activities in the past century have caused a variety of environmental
problems in South Florida. In 2000, Congress authorized the Comprehensive Everglades
Restoration Plan (CERP), a $10.5-billion mission to restore the South Florida ecosystem.
Environmental projects in CERP require salinity monitoring in Florida Bay to provide
measures of the effects of restoration on the Everglades ecosystem. However current
salinity monitoring cannot cover large areas and is costly, time-consuming, and laborintensive.
The purpose of this dissertation is to model salinity, detect salinity changes, and
evaluate the impact of salinity in Florida Bay using remote sensing and geospatial
information sciences (GIS) techniques. The specific objectives are to: 1) examine the
capability of Landsat multispectral imagery for salinity modeling and monitoring; 2)
detect salinity changes by building a series of salinity maps using archived Landsat images; and 3) assess the capability of spectroscopy techniques in characterizing plant
stress / canopy water content (CWC) with varying salinity, sea level rise (SLR), and
nutrient levels.
Geographic weighted regression (GWR) models created using the first three
imagery components with atmospheric and sun glint corrections proved to be more
correlated (R^2 = 0.458) to salinity data versus ordinary least squares (OLS) regression
models (R^2 = 0.158) and therefore GWR was the ideal regression model for continued
Florida Bay salinity assessment. J. roemerianus was also examined to assess the coastal
Everglades where salinity modeling is important to the water-land interface. Multivariate
greenhouse studies determined the impact of nutrients to be inconsequential but increases
in salinity and sea level rise both negatively affected J. roemerianus. Field spectroscopic
data was then used to ascertain correlations between CWC and reflectance spectra using
spectral indices and derivative analysis. It was determined that established spectral
indices (max R^2 = 0.195) and continuum removal (max R^2= 0.331) were not significantly
correlated to CWC but derivative analysis showed a higher correlation (R^2 = 0.515 using
the first derivative at 948.5 nm). These models can be input into future imagery to
predict the salinity of the South Florida water ecosystem. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2016. / FAU Electronic Theses and Dissertations Collection
| Identifer | oai:union.ndltd.org:fau.edu/oai:fau.digital.flvc.org:fau_33714 |
| Contributors | Selch, Donna (author), Zhang, Caiyun (Thesis advisor), Florida Atlantic University (Degree grantor), Charles E. Schmidt College of Science, Department of Geosciences |
| Publisher | Florida Atlantic University |
| Source Sets | Florida Atlantic University |
| Language | English |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation, Text |
| Format | 140 p., application/pdf |
| Rights | Copyright © is held by the author, with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder., http://rightsstatements.org/vocab/InC/1.0/ |
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