Selch, Donna

Spectral signatures quickly aid the analysis of sand composition because specific wavelengths
correspond with distinct minerals. This provides objectivity to traditional microscopic methods, with the
option to create a custom spectral library for Hyperspectral Remote Sensing HRS applications.
Removal of salt as a precipitated solid from sea water is useful for clearer microscopic viewing of sand
because certain grains are less likely to be misidentified as crystalized salt. Though removal of salts
aids in qualitative visual identification, it is problematic for studies requiring spectral reflectance data to
match real-life conditions. Spectroradiometric techniques were used to assess the effects of salt in
spectral signatures of sand. Sand samples of mixed siliciclastic-carbonate composition were collected
from 15 locations across the southeastern Florida coast. Spectral plots were generated from laboratory
collected data with an ASD Spectroradiometer. Spectral data was collected before and after samples
were prepared for microscopic study. Laboratory-prepared samples show negative slope at
approximately 1500 nm and 2000 nm ranges on the generated plots. These wavelengths are indicative
of grains having either predominately carbonate or siliciclastic compositions, which agrees with the
microscopic analysis. Salts present in a sample affect the spectral signature, thus salt removal yields
spectral plots not necessarily concurrent with plots generated from raw, unprepared samples. For
studies utilizing airborne HRS data, the order of data collection and preparation is important. To ensure
a more precise match between the spectral library and the hyperspectral imagery, spectral data must
be collected before the sample is prepared for microscopic analysis.

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.

The canal system of South Florida has become a new distributive focus for the invasive Lionfish (Pterois volitans). Lionfish are considered a pest here, having up to 18 venomous spines that can inflict pain if stepped on or handled. These fish also eat a variety of juvenile species affecting the commercial and recreational fishing industry. The canal system in south Florida is also a center for recreational activities. Water land cover information will aid in species removal by offering species information to areas with a high percent of water land cover and who are more likely to come into contact with Lionfish. This research, comparing classification techniques to map water land cover, is the first step to mitigate the stronghold the lionfish have in South Florida. Once mapped, species information can then be distributed to residents that have close proximity to danger zones.

The evaporation of water in the ocean can lead to hyper salinity caused by the extra substances left behind during the process. The Florida bay is surrounded by the Florida loop current and the Florida Keys, its salinity reading has been recorded as high as 70 ppt, double the normal capacity of seawater. The bay salinity depends on the amount of fresh water released from the Everglades and the magnitude of water outputted to the Gulf of Mexico and the Atlantic Ocean, as well as the ratio between the amount of water evaporated and the amount of precipitates left in the remaining water pool. For this research nine saltwater treatments from 0 to 40 ppt in increments of 5 ppt were constructed to examine how evaporation rates are affected by varying salinities. During this study, data were collected from each of the nine treatments before and after evaporation. The data recorded included salinity readings, pH levels and volume of water evaporated. The analyses of this data will determine the relationship between salinity levels and evaporation rates.

Quantitative assessment of substrate classification for sand properties is needed for land management and conservation. Establishing a sand spectral library is the first step in this process. Hyperspectal analysis allows for rapid, nondestructive data acquisition. This process uses an ASD spectrometer in a laboratory setting with an artificial light source to collect the spectra. Sand collected worldwide was also analyzed for grain size and composition. Development of spectral libraries of sand is an essential factor to facilitate analytical techniques to monitor coastal problems including erosion and beach nourishment. This in turn can affect various flora and fauna which requires specific substrate to grow, nest, or live. Preliminary results show that each sand sample has a unique signature that can be identified using hyperspectral data.

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.