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Salinity Assessment, Change, and Impact on Plant Stress / Canopy Water Content (CWC) in Florida Bay using Remote Sensing and GIS

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Date Issued:
2016
Summary:
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.
Title: Salinity Assessment, Change, and Impact on Plant Stress / Canopy Water Content (CWC) in Florida Bay using Remote Sensing and GIS.
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Name(s): Selch, Donna, author
Zhang, Caiyun, Thesis advisor
Florida Atlantic University, Degree grantor
Charles E. Schmidt College of Science
Department of Geosciences
Type of Resource: text
Genre: Electronic Thesis Or Dissertation
Date Created: 2016
Date Issued: 2016
Publisher: Florida Atlantic University
Place of Publication: Boca Raton, Fla.
Physical Form: application/pdf
Extent: 140 p.
Language(s): English
Summary: 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.
Identifier: FA00004686 (IID)
Degree granted: Dissertation (Ph.D.)--Florida Atlantic University, 2016.
Collection: FAU Electronic Theses and Dissertations Collection
Note(s): Includes bibliography.
Subject(s): Environmental management
Florida Bay (Fla.)
Geographic information systems
Geospatial data
Marine ecology
Plant water relationships
Remote sensing
Salinity -- Florida -- Florida Bay -- Measurement
Held by: Florida Atlantic University Libraries
Sublocation: Digital Library
Links: http://purl.flvc.org/fau/fd/FA00004686
Persistent Link to This Record: http://purl.flvc.org/fau/fd/FA00004686
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Host Institution: FAU
Is Part of Series: Florida Atlantic University Digital Library Collections.