Comparing Salinity Models in Whitewater Bay Using Remote Sensing

COMPARING SALINITY MODELS IN WHITEWATER BAY

USING REMOTE SENSING

Donna Selch

A Thesis Submitted to the Faculty of

The Charles E. Schmidt College of Science

in Partial Fulfillment of the Requirements for the Degree of

Master of Arts

Florida Atlantic University

Boca Raton, Florida

August 2012

ACKNOWLEDGEMENTS

I wish to express my gratitude to Dr. Caiyun Zhang for her support, encouragement, and guidance throughout my graduate work at Florida Atlantic

University. I am grateful for the time and interest she has invested in me in the midst of all her activities, teaching, and research projects. I am also thankful for having the opportunity to work with the other members of my advisory committee: Dr. Charles

Roberts and Dr. Zhixiao Xie.

In the development and implementation of this thesis, I received the support of many faculty members and graduate student colleagues in the Geoscience department who stayed up late with me, answered my questions, and helped make this thesis possible. Thank you all.

Finally, I owe a deep debt of gratitude to my family: my mother, Katharine, has been supportive of my desire to continue with my education; my sister, Julia, for reminding me to relax and have a little fun; and my fellow Banditos, without whom my life would have taken a different direction. Thank you Callie and Megan for helping me always see the bigger picture.

ABSTRACT

Author: Donna Selch

Title: Comparing Salinity Models in Whitewater Bay using Remote Sensing

Institution: Florida Atlantic University

Thesis Advisor: Dr. Caiyun Zhang

Degree: Master of Arts

Year: 2012

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.

COMPARING SALINITY MODELS IN WHITEWATER BAY

USING REMOTE SENSING

LIST OF TABLES ...... vii LIST OF FIGURES ...... viii INTRODUCTION ...... 1 LITERATURE REVIEW ...... 9 Directly Assessing Salinity via Remote Sensing ...... 10 Indirectly Assessing Salinity via Remote Sensing ...... 11 METHODOLOGY ...... 15 Study Site ...... 15 Salinity Data ...... 18 Landsat 5 TM Imagery ...... 18 Preprocessing ...... 20 Models ...... 23 RESULTS AND DISCUSSION ...... 30 CONCLUSIONS...... 43 REFERENCES ...... 46

TABLES

Table 1 Water Years of Salinity Data and Corresponding Landsat TM Imagery used in this study ...... 19 Table 2 Data Correlation Matrix for the dry season ...... 35 Table 3 Data Correlation Matrix for the wet season ...... 35 Table 4 Univariate and Multivariate Regression Analysis (R square values of Landsat TM bands) ...... 36 Table 5 Multivariate Regression Analysis Collinearity Statistics (Wet Season) ...... 37 Table 6 Multivariate Regression Analysis Collinearity Statistics (Dry Season) ...... 37 Table 7 OLS R2 values and formulas for the wet and dry season where Y is the

dependent variable (salinity); B0 is the intercept coefficient; Bn are the coefficients (by bands); Xn are the explanatory variables (Landsat band extracted numbers) ...... 40 Table 8 GWR R2 values for the wet and dry season ...... 41 Table 9 RMSE values ...... 42

vii

FIGURES

Figure 1 Map of South Florida and Florida Bay. Inset image is of the study site, Whitewater Bay, FL...... 17 Figure 2 Salinity Field Collected Data in the dry season ...... 25 Figure 3 Salinity Field Collected Data in the wet season ...... 26 Figure 4 Scatterplots of dry season (Nov 2005) by individual bands ...... 31 Figure 5 Scatterplots of wet season (June 2006) by individual bands ...... 32 Figure 6 Scatterplots of dry season (Nov 2005) by band combinations ...... 33 Figure 7 Scatterplots of wet season (June 2006) by band combinations ...... 34 Figure 8 Comparison of Interpolation Models for salinity analysis in Whitewater Bay. . 39 Figure 9 Comparison of Regression Models for salinity analysis in Whitewater Bay ..... 42

viii

INTRODUCTION

The Kissimmee – Okeechobee – Everglades (KOE) ecosystem extends from the

Kissimmee River Basin south past Lake Okeechobee extending until Florida Bay. The

KOE watershed has been categorized into different environments based on its physiography. The true Everglades is a “river of grass” that slightly slopes south from

Lake Okeechobee releasing its freshwater into Florida Bay and the Gulf of Mexico. This sawgrass marsh is lower in altitude than surrounding environments with water draining slowly through it. The slope of the land gradually decreases from approximately 14 feet above sea level at Lake Okeechobee to nearly sea level when the Everglades meets

Whitewater Bay (Rand and Bachman, 2008; McPherson et al., 1996).

The Everglades is a vast freshwater wetland that historically encompassed more than 10 000 km2. It is primarily fed by the Kissimmee River Basin, located south of

Orlando, Lake Okeechobee, the second largest freshwater lake in the United States, and the precipitation that falls on the region (Rand and Gardinali, 2004). The Everglades is a heterogeneous mosaic of freshwater lakes, tree islands, shrubs, sawgrass, sloughs, and estuaries (Harwell, 1998). Its slight changes in depth result in long incubation periods for the water in the Everglades (Sklar et al., 2001); water may take up to a year to complete the journey. The interannual variability in precipitation was historically an important

1 driver in the long term dynamics and sustainability of the biodiversity of the region

(Harwell, 1998).

Historically, the Everglades “river of grass” was 120 miles long, 50 miles wide, and less than a foot deep (Alles, 2007). Agriculture and urban development has reduced the Everglades by 50% (Redfield, 2000; Chimney et al., 2001; Sklar et al., 2001). After

World War II, two events drastically changed Florida. First was the rapid increase in population. Second was an increased amount of rainfall which caused flooding in many urban and agricultural lands (Kiker et al., 2001). The hurricanes of the late 1940s that left most of South Florida underwater for months had contrasted with the drought of the

1930s (Harwell, 1998).

The variability in precipitation including droughts and floods, was vital to the historical Everglades, but not desired by the citizens of urbanized South Florida. In conjunction with the state of Florida, the US Army Corps of Engineers implemented the

Central and Southern Florida Project for Flood Control (C & SF Project). The primary objective of this plan was flood protection and providing water supply to the urban areas of South Florida (Harwell, 1998; Redfield, 2000; Skylar et al., 2001). From 1950 until into the 1970s the project redirected, controlled, and regulated the massive water system resulting in “720 miles of levees, 1000 miles of canals, 200 gates and water control structures and 16 massive pumping stations” (Kiker et al., 2001, p 405). In actuality the policy succeeded in altering the entire water flow from Kissimmee (south of Orlando) hundreds of miles south to Florida Bay. The Kissimmee River, once a meandering shallow river 166 km (~100 miles) long, was canalized into a 90 km (56 miles) long channel (Koebel, 1995). The natural overflow of water from Lake Okeechobee to the

2 south during the wet season was permanently altered with the Herbert Hoover Dike

(Clarke and Dalrymple, 2003). The natural ecosystem, habitat for hundreds of species, and the historic water quality and filtration system for Florida Bay was not taken into consideration. As a result of the C & SF Project vegetation was lost, invertebrates and fish numbers decreased, and migrating birds lost their habitats. Even worse, the water quality was severely decreased with the increase of agriculture and farming waste and the loss of natural vegetation to clean the water (Rand and Gardinali, 2004; Rand and

Bachman, 2008).

Restoration efforts began in 1976 with the Kissimmee River Restoration Act

(Koebel, 1995). The Everglades Forever Act of 1994 mandated an increase in freshwater flow through the Everglades (Harwell, 1998; Sklar et al., 2001). These plans were further expanded into parts of the current Comprehensive Everglades Restoration Plan

(CERP) plan to include the entire KOE watershed. CERP is a $7.8 billion, 30 year federal and state restoration project compromising the water demands and flood control of the urban population with the needed restoration for a healthy Florida Everglades

(Clarke and Dalrymple, 2003; Kranzer, 2003). The CERP is the framework to restore, protect, and preserve the ecosystem of central and South Florida (Perry, 2004). The plan is centered on the water resources from the Kissimmee – Okeechobee – Everglades

(KOE) watershed. Restoration efforts are ongoing and continuous. Parts of the

Kissimmee River are being dechannelized; levees and other water control structures are slowly being removed. CERP is attempting to restore the historic water flow of the KOE watershed (Kranzer, 2003).

CERP consists of 68 critical environmental projects designed to restore the

Florida Everglades while still maintaining the urban population of South Florida.

Generally, CERP intends to improve the health of the 2.4+ million acres of the South

Florida ecosystem and Lake Okeechobee, virtually eliminate damaging freshwater releases to the estuaries east and west of Lake Okeechobee, improve the water quality and quantity to Florida (including Whitewater) and Biscayne Bays, and enhance water supply and maintain flood protection for South Florida (Clarke and Dalrymple, 2003).

In Whitewater Bay, salinity historically fluctuated with the inflow of freshwater from the mainland of Florida and the tidal salinity fluctuations from the Gulf of Mexico.

This freshwater discharge has become regulated and managed in the past half century by a complex canal system. Water management agencies use this system for flood control.

They can rapidly release large quantities of freshwater during the land’s wet season and refrain from water discharge during the dry season (Satula et al., 2001). In addition, massive releases of freshwater from Lake Okeechobee and the canal system is routed in large amounts (on average – 1.7 billion gallons per day) in anticipation of storm events to minimize flooding (Clarke and Dalrymple, 2003; Kranzer, 2003). This also means that during the dry season, when salinity naturally increases in Florida Bay, a sudden decrease in salinity can be experienced with a release of freshwater from the canal system

(Montague and Leyi, 1993). More frequent tides in mangrove wetlands (like Whitewater

Bay) can occur from August through October as a result of seasonally higher sea levels in the wet season of south Florida (Chen and Twilley, 1999).

The rapid changes of salinity can cause reductions in the spread and total numbers of plant and animal species in the water and on land. Most species can only survive

4 within a specific window of salinity. The Florida Everglades ecosystem maintains a fragile balance between fresh to salt water. Anthropological influences upset and disrupt this balance (Clarke and Dalrymple, 2003). When the natural flow of water is altered too greatly, a cascade of reactions will cause the ecosystem to simplify over time. This will result in a degraded wetland habitat and will disturb the distribution and abundance of the species in the bay ecosystem (Copeland, 1966; Parsons et al., 1999; Richter et al., 2003;

Restrepo et al., 2006).

Whitewater Bay is an important estuary that is the home for a rich biodiversity of wildlife and vegetation. It is also an important recreational and tourist site for the South

Florida economy. Salinity is considered the most important hydrobiological parameter to maintain an estuary. Increased salinity, due to upstream changes, can result in higher salinity levels to the detriment of the biological communities of the estuary (Copeland,

1966) and the economic value of the ecosystem. Precipitation in Whitewater Bay can also affect salinity. Increased rainfall (freshwater) decreases salinity levels. Annual rainfall has varied from this region from 867 mm to 2200 mm with an average approximate rainfall of 1400 mm. However up to 75% of the precipitation falls during the wet season, approximately June through September (Chen and Twilley, 1999;

Childers et al., 2006; Rand and Bachman, 2008). This makes South Florida prone to flooding but also adds to the variability of salinity in the entire region. During times of less rainfall, water management offices are less eager to allow the flow of freshwater to balance the Everglades ecosystem. They would prefer to retain the freshwater for human and agricultural requirements. During period of floods, additional freshwater is released to accommodate the increased rainfall. Both droughts and floods are natural variances in

5 the Everglades ecosystem (Fourqurean and Robblee, 1999). However, the unnatural releasing or holding of freshwater only further degrades and complicates a fragile ecosystem.

Historically, water would flow from Lake Okeechobee through the Everglades and Shark River Slough before empting in estuaries such as Whitewater Bay. A majority of the water in the Everglades system formally travelled through an area called Northeast

Shark Slough. This area is located east of where current water is anthropologically directed through the Everglades National Park (ENP). Water flows into Northeast Shark

Slough have been virtually eliminated. By default, Western Shark River Slough receives most of the water flow. Water levels here are unnaturally too high for too long while other parts of the ENP including Northeast Shark Slough are too low and short in duration (Clarke and Dalrymple, 2003). This water mismatch results in not enough water in the right place, time, quantity, or quality to satisfy the needs of the natural Everglades and the urbanized areas of South Florida (Kranzer, 2003). Restoration of historic water flow into Northeast Shark River Slough is a major component of CERP (Gunderson,

2001; Clarke and Dalrymple, 2003).

Whitewater Bay can be affected by many natural and unnatural events that alter biodiversity and water quality of the ecosystem. Changes in freshwater flow from the

Everglades and altered entry points have decreased the volume of freshwater input into the system, increasing salinity values. While the Everglades is a relatively low nutrient environment, anthropologic influences have significantly increased nutrient concentrations from agriculture and urbanization (Gunderson, 2001). Seagrass, the natural dominating vegetation of the Everglades, thrives in low nutrient areas. Increased

6 nutrients encourage the growth of certain types of algae. Hurricanes act as a natural flushing agent for bays destroying seagrass beds and moving sediments. Seagrass vegetation is also kept in check by grazing herbaceous keystone species such as manatees and sea turtles. Increased salinity can also change the structure and abundance of seagrass and other submerged aquatic vegetation (Gunderson, 2001; Fourqurean et al.,

2003). Construction of transportation activities such as roads and railroads has filled in passageways through which water would normally flow. Sea level has also risen drastically this century. Changes in water depth inevitably alter an ecosystem

(Gunderson, 2001). These factors directly and indirectly affect the distribution, flow, and concentration of salinity in Whitewater Bay.

There are many properties of water that are regularly tested. These properties include temperature, dissolved oxygen, nutrients, turbidity (light), sediments, and salinity. Salinity is the measure of how much salt is in a substance. All organisms need a specific range of salinity to survive. However, salinity outside of the species range can result in a hypertonic death. Hypersalinty has been suggested to be a major limiting factor in seagrass and mangrove forest structure and growth (Chen and Twilley, 1999;

Fourqurean et al., 2003).

Much research has investigated the present and historical water quality and salinity levels in Florida Bay. Scientists are examining the effects of anthropogenic influences such as the construction of canals on the water quality of Florida Bay.

Speculation suggests that increased salinity is a leading factor in the decrease of water quality and biodiversity of its living resources (Swart et al., 1996). Consequently,

7 assessing salinity is a top priority for projects such as CERP, which are attempting to restore the natural flow of water in the KOE watershed.

Water salinity has historically been collected by field research. However, this can be costly and time consuming. Using remote sensing to assess salinity levels could be an answer to many problems. Due its density, deep water levels of salinity are difficult to ascertain at this time. Shallower water found in estuaries, lagoons, and bays are potential areas for further investigations. Using remote sensing to conserve and manage shallow wetlands resources is extremely advantageous especially in large geographic areas.

Remote sensing allows for seasonal and annual assessment (Ozesmi and Bauer, 2002).

Remote sensing is an option that uses data assessed either by airplane or satellite.

Landsat TM imagery is free and easily available to the public. It has also been shown to be a good indicator of environmental quality over large geographic areas (Bauer, et al.,

2004). Determining salinity levels by Landsat TM remote sensing will allow more money to go into the cost of restoring the water flow rather than evaluating the results of restoration efforts. This research seeks to determine salinity levels in Whitewater Bay,

Florida using Landsat 5 TM. In this research, the following questions seek to be answered:

1. Is using remote sensing effective to assess salinity?

2. What bands are most effective in assessing salinity?

3. What differences in salinity can be observed in the wet (summer, fall) and dry

(winter, spring) seasons?

LITERATURE REVIEW

Traditional methods of collecting water quality data consist of devices used in the field. These instruments either record data stationally at fixed positions in the study area or are used on a boat. Only a portion of these areas have been ground based mapped because of time, human resources, and financial constraints (Sawaya et al., 2003; Bauer et al., 2004). Scientists have been investigating the use of remote sensing to quantify water quality. This data is often available at a reduced cost and includes sites that are inaccessible in the field.

Gathering field data in inaccessible or large areas is difficult, cost-prohibitive, and unnecessary with the accessibility of remotely sensed data. Aerial and satellite images are two alternatives to conventional terrestrial surveys. Both aerial and satellite images have been used in wetland and water quality monitoring (Clarke et al., 1991; Kasischke et al., 1997; Ozesmi et al., 2002). Kasischke and Bourgeau-Chavez (1997) evaluated the use of both remote sensors in South Florida wetlands. High resolution images are needed to accurately define the ecological and hydrological characteristics of a small spatial scale.

Directly Assessing Salinity via Remote Sensing

Previous research described in the literature elucidates that remote sensing has the efficacy to assess water salinity. Scientific research has been conducted since the 1950s using radiometric measurements to passively assess salinity. Matter emits electromagnetic radiation as a result of the temperature activated random motion of atoms. From a complex formula calculating the radiation emitted, a relationship can be established between salinity, temperature, and the brightness temperature of seawater

(Lagerloef et al., 1995; Topliss et al., 2002; Klemas, 2011). Radiometric measurements can be calculated either from airborne measurements or from space-based satellites. The lower microwave frequency (L-band 1.4 GHz) is the optimal spectral channel to directly sense salinity and changes in salinity. The L band is highly sensitive to emissivity which contains a term proportional to the conductivity. Conductivity is directed by temperature and salinity. With known temperature, salinity can thus be directly measured

(Droppleman et al., 1970; Lagerloef et al., 1995; Zhang et al., 2012).

Scientific studies have used NOAA’s Airborne Scanning Low-Frequency

Microwave Radiometers (SLFMRs) to map the salinities of bays, estuaries, rivers, and coastal waters for decades (Droppleman et al., 1970; Blume et al., 1978; Le Vine et al.,

1990; D’Sa et al., 2000; Burrage et al., 2002; Klemas, 2011). The SLFMR is an imaging sensor that can provide regional coverage of sea surface salinity (SSS). Its range for SSS can be determined by doubling the airplane’s altitude (Miller, 1995). SLFMR has mapped the coastal plumes from Chesapeake and Mobile Bays allowing salinity maps to be generated quickly over waters that range from oligohaline to hypersaline (Burrage et al., 2002). Salinity patterns in Florida Bay were estimated using the SLFMR in a pilot

10 attempt to measure the ecologically sensitive ecosystem (D’Sa et al., 2000). However, due to its high cost in data collection, the use of SLFMR for long-term salinity monitoring purposes is unpractical (Zhang et al., 2012).

SSS is an oceanic variable that can also be assessed from satellite based radiometers. In the 1970’s the earth-orbiting satellite, Skylab, used radiometric measurements to quantitatively measure sea surface conditions. Data collected intermittently from a 1.4 GHz microwave radiometer was compared to standard SSS charts where a correlation was detected between the sensor data and SSS (Lerner and

Hollinger, 1977; Lagerloef et al., 1995). Advancements in technology satellite sensing salinity have been continuously made since Skylab leading to the launching of a new satellite mission to measure SSS, Aquarius, on June 10, 2011. Aquarius is a NASA radiometer designed to provide global salinity maps on a monthly basis. However, with a spatial resolution between 76 and 156 km, Aquarius is designed to monitor SSS in the open ocean. Its coarse spatial resolution makes its application in the Florida Bay coastal region (Klemas, 2011; Zhang et al., 2012).

Indirectly Assessing Salinity via Remote Sensing

The use of microwave radiometers for salinity monitoring purposes in support of

CERP has many concerns. There is a high cost associated with the frequent measurements needed for continuous evaluations with the changes in the South Florida ecosystem. The new satellite, Aquarius, launched in 2011 has a spatial resolution too coarse to use in local coastal communities. Therefore scientists have explored the use of multispectral sensors of satellite imagery to assess salinity. However they cannot assess

11 salinity directly. Instead, scientists have developed a method of indirectly measuring salinity using CDOM.

Colored dissolved organic matter (CDOM) is an optically measurable and important component of salt and fresh water. It is produced naturally in water by the decay of organic material. However, anthropologic processes such as agriculture, wetland drainage, and effluent discharge can affect CDOM levels. In general, CDOM levels are larger in fresh water and estuaries than the open ocean (Maie et al., 2006).

Estuaries occur at the extreme end of freshwater sources, like the Shark River Slough.

Thus the concentration of CDOM at the terminal end of an estuary can be much greater than at the mouth. A gradient of CDOM can be measured and observed (Bowers and

Brett, 2008). A variation of CDOM can also be observed in the wet and dry seasons of

Florida (Chen, 2006). CDOM and salinity vary in proportion to the input of freshwater into the estuary.

Scientific research most commonly uses Sea-viewing Wide Field-of-view Sensor

(SeaWiFS), Moderate Resolution Imaging Spectrometer (MODIS), and Landsat imagery to indirectly assess salinity via satellite multispectral sensors. CDOM, which is actually measured, serves as an intermediary function between the remote sensing reflectance bands and sea surface salinity. The inverse relationship of salinity and CDOM has been exploited by many scientists to assess salinity in coastal regions and estuaries (Bowers and Brett, 2008).

Satellite imagery is often restricted by their spectral, spatial, and temporal resolutions (Ritchie et al., 2003). SeaWiFS has a temporal resolution of 1 day; MODIS every 1 – 2 days; Landsat 5 TM every 16 days. SeaWiFS carried eight bands ranging in

12 bandwidth from 402 nm – 885 nm which provided data from a variety of oceanographic qualities such as ocean primary production, ocean influences on climate processes, and many biogeochemical cycles (Jensen, 2007). With an 1130 m resolution, SeaWiFS data is not applicable in shallow water local areas (Liu et al., 2003). For 13 years, SeaWiFS transmitted data to Earth until December 2010 when the satellite it was on stopped communicating with ground stations. Two months later it was declared unrecoverable.

Both MODIS and Landsat TM imagery can be downloaded for free from the

USGS. MODIS Bands 8 – 16 are most optimal in open ocean conditions to globally estimate ocean color, phytoplankton, and biochemistry. However, while MODIS has a spatial resolution range of 250-1000 m depending on the band, Landsat has a spatial resolution of 30 m (for bands 1 – 5 and 7) and 120 m for band 6 (Sawaya et al., 2003;

Jensen, 2007). The more detail required of the study site, the higher spatial resolution is needed (Stumpf et al., 2003). The objective of the study will determine the choice of satellite imagery. MODIS has been used to assess salinity using many models in

Chesapeake Bay (Urquhart et al., 2012). However, due to its affordability, mission continuity, and absolute calibration many scientists prefer to use Landsat to monitor salinity (Zhang et al., 2012).

Landsat satellites were first launched in 1972 with Landsat 1. For 40 years, the

Landsat satellites have provided scientists with continuous data that has contributed to human knowledge of the water cycle, climate, ecosystems, and the changing Earth and more. Landsat 5 TM contains seven bands. Bands 1 – 3 contain the visible spectrum; band 4 – 5 the near infrared; band 6 the thermal band; band 7 a mid-infrared (Jensen,

2007).

Landsat calibrates data on its own with on-board radiometric calibration devices

(Zhang et al., 2012). Its spatial, spectral, and temporal resolution makes it appealing for salinity analysis. Landsat imagery has been used to assess salinities of bays, estuaries, rivers, and coastal waters for decades. In 1982, Landsat MSS bands 1 – 4 satellite imagery was used with photographs to map SSS in the San Francisco Bay Delta to confirm correlations between Landsat color bands and SSS in an estuary (Khorram,

1982). Empirical models to estimate salinity have been calculated from Landsat data

(Lavery et al., 1993; Vuille and Baumgartner, 1993, Baban, 1997; Dewidar and Khedr,

2001). The basis for this study was a project using Landsat TM data to assess salinity in

Northeastern Florida Bay. The potential of using Landsat imagery in small areas was examined and established with algorithms for quantitative and qualitative analysis (Zhang et al., 2012).

METHODOLOGY

Study Site

The climate of the South Florida region, including the Everglades, is distinctly separated into a wet and dry season. The wet season is loosely defined as the time period of June through September; the dry season is roughly October through May. The wet versus dry designation is based on the amount of precipitation. Rain that falls at the beginning of the KOE watershed in central Florida can take an extended period of time to reach Florida Bay at the end of the Everglades. As a result, water levels are the highest in

October, at the end of the wet season and lowest in May at the end of the dry season

(Beckage et al., 2003).

Florida Bay is a broad, shallow, estuarine lagoon located off the southern tip of the Florida peninsula. It is bounded by the Everglades to the North, the Florida Keys to the east and south, and opens to the Gulf of Mexico to the west. Florida Bay is part of the larger recreational and commercial fishery of the Gulf of Mexico with parts adjacent to the protected Florida Keys National Marine Sanctuary (Nuttle et al., 2000) and is fed freshwater by a system of rivers, streams, canals, and the grassy wetlands. Florida Bay is often likened to a lagoonal structure because of its relatively shallow depth and varying salinity of its seawater due to ranges of freshwater input (Burd and Jackson, 2002). It is these ranges that can cause ecological changes inside of Florida Bay, visually observed

15 by algal blooms and the death of Thallassia testudium (turtle grass), the normal dominant covering of the muddy benthic environment (Boyer et al., 1997; Fourqurean and

Robblee, 1999; Rudnick et al., 1999).

Whitewater Bay is located on the extreme southern tip of the Florida Peninsula

(Figure 1). Whitewater Bay, approximately 13 miles by 6 miles, is open to the Gulf of

Mexico via several narrow, shallow passes (Scholl, 2004). Florida Bay and Whitewater

Bay are separated by fragmented land pieces that seasonally connect to the southern tip of

Florida. Freshwater empties into Whitewater Bay from rivers originating in the Shark

River Slough in the Florida Everglades (Boyer, 2006) while red, black, and white mangroves line these rivers and streams of the slough (Boyer et al., 1997). Separate slough systems flow into Whitewater and Florida Bay, yet similarities of the bays exist.

Their climatic conditions are nearly identical since they are located so near each other and their tidal exchange with their respective sloughs and saltwater are alike (Boyer et al., 1997). Since it is directly affected by the canal system and the megalopolis of

Miami-Dade, a majority of the research has been done on water quality in Florida Bay over Whitewater Bay. Yet Shark River Slough has also been affected by impacts of humans on the KOE watershed. By establishing remote sensing techniques in

Whitewater Bay, a more complete analysis on the changes of salinity can be constructed.

Figure 1 Map of South Florida and Florida Bay. Inset image is of the study site, Whitewater Bay, FL.

Salinity Data

Salinity surveys were completed from 2004-2006 in Whitewater Bay by the

United States Geological Survey (USGS). Four boats were mobilized over a one day span to collect water variable data including salinity and temperature every five seconds via a boat mounted flow-through cell to a continuous water quality meter. Geographic coordinates (latitude and longitude) were accrued with a Garmon 76C handheld unit.

Data files available included date of collection, temperature in degrees Celsius, specific conductance in microsiemens per centimeter, salinity in parts per thousand, and latitude and longitude in decimal degrees. Salinity and temperature meters were checked in known conductivity standards before and after data collection surveys (Zhang et al.,

2012). This data is available for free from the USGS SOFIA’s data exchange database.

Salinity data for the area has been divided into three “water years.” Water year 2004 contains salinity data from expeditions in December 2003 and June 2004; water year

2005 covers October 2004 and April 2006; water year 2006 encompasses November

2005 and June 2006 (Table 1). The data collected in these surveys were used in this study to develop salinity assessment models in Whitewater Bay, Florida.

Landsat 5 TM Imagery

The raw salinity data was cross referenced to existing Landsat 5 TM images. The date of Landsat imagery and ground truthed data do not need to match exactly because salinity does not fluctuate as rapidly as temperature. Data that corresponds with contaminated or cloud covered images were removed from analysis. Landsat 5 TM data, collecting images over Whitewater Bay every 16 days, is available for free from the

USGS’s Earth Explorer database (http://earthexplorer.usgs.gov/). Images of my study area are categorized under WRS-1 Path 15 Row 43. Table 1 matches the salinity data used in this study to the closest Landsat 5 TM images.

Water Date of Salinity Landsat 5 TM Date Year Acquisition

December 11, 2003++ December 6, 2003++ 2004 June 2, 2004* May 30, 2004*

October 14, 2004++ October 21, 2004++ 2005 April 6, 2006* April 2, 2006*

November 10, 2005++ November 9, 2005++ 2006 June 28, 2006* June 21, 2006*

Table 1 Water Years of Salinity Data and Corresponding Landsat TM Imagery used in this study

* Represents Wet Season Data; ++ Represents Dry Season

In Arc Map, the salinity data was open and displayed as XY data. Landsat 5 TM data images were defined as UTM Zone 17N (Bauer et al, 2004). The salinity data projections were also defined in the same projection. The salinity data was then exported into a shapefile which projects over the corresponding Landsat 5 TM image with matching XY Coordinates. By clipping the study site from other data and images collected, a polygon shapefile of Whitewater Bay was created. Outside salinity data was then excluded from further analysis. The surviving data is the data of the study site which will be the only data used in future computations. 19

Preprocessing

Atmospheric correction of satellite images is required to remove or greatly reduce the influence of the atmosphere (Wang and DeLiberty, 2005). There are variations in atmospheric conditions due to differences in dates of Landsat TM acquisition. As a result surface reflectance values cannot be accurately compared without first correcting for those differences (Goslee, 2011). There are two basic methods of atmospherically correcting images. Relative normalization methods match one image to another based on the spectral characteristics of the reference image. This results in the images appearing like they have been taken using the same sensor and the same atmospheric conditions

(Goslee, 2011). Relative correction techniques were developed to be used when absolute atmospheric corrections could not. Most often this occurs with historical or land-cover change detection satellites whose atmospheric properties required for absolute atmospheric correction is not available (Jensen, 2005).

Absolute atmospheric correction methods rely on adjusting each image individually based on each image’s mechanical and technical characteristics (Goslee,

2011). The complicated algorithms required for this correction can be completed by modern computer software if specific atmospheric condition information is available.

This information may either be provided by the user or contained within the dataset of certain atmospheric absorption bands. Most often the following information is required: latitude and longitude of the remotely sensed image scene, date and exact time of acquisition, image collection altitude and mean elevation of the scene (above sea level), an atmospheric model (for Whitewater Bay this is tropical), radiometrically calibrated image radiance data, the mean and full-width at half-maximum information about each 20 specific band, and the local atmospheric visibility at the time of image collection. This information is put into the atmospheric model selected (for Whitewater Bay, this is the tropical model) where the absorption and scattering characteristics of the atmosphere are evaluated (Jensen, 2005).

Atmospheric and satellite data are available for the Landsat TM imagery used in this study. Therefore, absolute atmospheric correction was performed on each image.

There are many atmospheric correction algorithms available. Atmospheric Correction using ENVI’s Quick Atmospheric Correction (QUAC) was applied to each training and validation Landsat TM image. QUAC is a visible near infrared through short wave infrared correction method for multispectral (including Landsat) imagery. It defines atmospheric correction parameters from information gathered in the pixel spectra in the scene. QUAC requires a minimum of three bands and valid wavelengths. It is based on the empirical finding of the average reflectance of at least ten diverse materials in the scene. With those materials and sufficient dark material to allow a baseline spectrum,

QUAC results in fast and accurate atmospheric corrections (Bernstein et al., 2005;

FLAASH Module User’s Guide, 2009).

After data was collected via Landsat and ground truthing, they were divided into wet (summer and fall) and dry seasons (winter and spring) for analysis. Since these two seasons vary in water amount, separation into categories is paramount for meaningful analysis. The digital numbers for each sample location on the Landsat TM images were extracted and geographically matched to the surveyed data to develop relationships with measured salinity (Sawaya et al., 2003). It is from this information that further research can be gathered to identify the best model to generate an accurate salinity map of

Whitewater Bay. Field gathered salinity point locations positioned on pixels of clouds on their corresponding Landsat image for both the training and validation data must be deleted. The threshold of 50 was determined in the extracted Band 4 data. The matched datasets were then resampled into a resolution of 100 meters by using a moving window

(3 x 3) which outputs the average reflection of the window into the central kernel of the window. This is done to remove potential noise and reduce errors (Mayo et al., 1995;

Zhang et al., 2012).

The Landsat 5 TM sensor has seven spectral bands. Bands 1-3 are known collectively as the Visible Spectrum. Band 1 (blue: 450 – 520 nm) is especially important in water quality analysis because of its increased penetration of water bodies

(FGDC, 1992; Ozesmi and Bauer, 2002; Jensen, 2007). Band 2 (green: 520 – 600 nm) reacts to the green reflectance of healthy vegetation. Band 3 (red: 630 – 690 nm) is the absorption band of healthy green vegetation and is useful in boundary delineations. Band

4 (near infrared: 760 – 900 nm) emphasizes land water contrasts. Band 5 (mid infrared:

1550 – 1750 nm) is used to examine water amounts in plants and used to differentiate among clouds, snow, and ice. Band 6 (thermal infrared: 10400 – 12500 nm) measures infrared radiant energy (temperature) emitted from surfaces. Thermal band 6 also has a higher spatial resolution (120 m) than bands 1 – 5 and 7 (30 m). Band 7 (mid-infrared:

2080 – 2350 nm) is useful in rock zonation identification and soil moisture conditions

(Jensen, 2007). Different combinations of bands are often used to provide information about different ecosystems. Bands 1 – 4 provide useful information about water quality because light passes through the water in this spectral range (Song et al., 2011). Bands 5 and 7 are useful in wetland areas because of their sensitivity to soil and plant moisture

22 contents (Jensen, 2007). However, band combination patterns are site-specific and vary from individual region to region (Zhang, et al., 2012).

Models

Empirical models are based on observed data rather than theoretical (semi- empirical). Since ground truthed data were available and used in this research, empirical analysis was employed. Correlation matrixes of the data (using SPSS analysis) show which bands correlate most with salinity. Regression analysis on the bands also revealed the optimum bands for the empirical models. The empirical models created in this research resulted in algorithms that generate estimated salinity maps of Whitewater Bay.

Comparisons of these maps to contour salinity maps from the field data show the accuracy of remotely sensed predictions. Regression modeling techniques are widely used for predicting a variable’s spatial distribution (Palialexis et al., 2011). Four models were run and analyzed to compare predicted salinity data to actual field data.

The interpolation methods of Inverse Distance Weighting (IDW) and Kriging were performed first. These methods do not need the Landsat TM imagery and corresponding digital numbers extracted to predict values. Interpolation estimates a predicted variable from ground observations (Lark, 2000). The final two models used the

Landsat TM imagery and extracted digital numbers to envision salinity: Ordinary Least

Squares (OLS) and Geographically Weighted Regression (GWR).

Interpolation predicts values from sample points. IDW is an interpolation technique that uses the known measured values to predict values in unknown locations.

It is based on the premise that the further away the unknown data location is from the

23 known data location, the less alike the results will be and vice versa. IDW is directly based on the surrounding measured values to determine the resulting prediction

(Palialexis et al., 2011). IDW was performed twice in this study on the wet and dry season data individually. With a large dataset, the number of neighbors to include in the weighting included at least 20 and up to 100.

Kriging is very similar to IDW but requires an exploration of the training data before interpolation. In this example, a first order trend was observed and removed to provide a more accurate assessment of interpolation. Kriging, unlike IDW, uses the statistical relationship between the measured points (Secor et al., 2006; Nas and Berktay,

2010). Distance and direction between sample points reflect a spatial correlation that can be used to explain variation in the surface (Palialexis et al., 2011). Neither interpolation method (IDW and Kriging) can predict salinity estimates for the future or in the past without ground observations. In this study, Kriging was executed on the wet and dry seasons individually. The parameters used in IDW were also used in Kriging in terms of neighbor influence and datasets. However, the results of data exploration influenced variogram and model type.

The Interpolation models of Inverse Distance Weighting (IDW) and Kriging

(Krig) do not allow for the prediction of salinity values. Instead, they interpolate data from field collected data. Validation sample points were randomly selected using a random point generator. A majority of the total samples were used for training purposes; approximately 30% of the total points were selected for each season individually for validation purposes. Of the 1127 total resampled points in the dry season, 400 were designated as validation or “check-in” points. In the wet season, 200 of the 549 total

24 points were set aside. These points were used to calculate RMSE values to compare the two interpolation models. Figures 2 and 3 show the locations of the training and validation points for the interpolation models.

Figure 2 Salinity Field Collected Data in the dry season

Figure 3 Salinity Field Collected Data in the wet season

OLS is the basic linear regression and the beginning point for regression modeling. It is often used as an exploratory analysis because it assumes that the errors from fitted models are statistically independent (Lark, 2000). In this study, the dependent

26 variable was salinity and the explanatory variables were the bands chosen in statistical analysis. The salinity variance in the diagnostic output table shows a very low R2 values for both the wet and dry seasons. This shows the non-linearity of sea surface salinity in the study site.

Both OLS and GWR use the imagery data (digital numbers) to extract prediction models. However, many relationships between geostastical data are non-linear. Thus, linear regression models such as OLS have their limitations (Xie et al, 2012). Due to the complex relationship between land and water in terms of sea surface salinity levels, GWR was run after the explanatory features of OLS were explored and established. GWR allows for a multi-value regression model to be run and analyzed.

GWR has more appeal than other regression models. Once a formula has been established with known data and satellite imagery, it can be applied to other imagery.

This makes it possible to examine salinity historically with the availability of the satellite imagery. Thus it is also possible to predict salinity values without the cost and time of field observations (Xie et al., 2012).

For this study, the GWR model can be described as the following:

Yi = B0 + B1iX1 + B2iX2 + B3iX3 + B4iX4 + B5iX5 + B7iX7 where Y is the surface salinity at location i; B0 is the intercept coefficient; Bn are the band coefficients that vary in terms of i (location); and Xn are the spectral information extracted from the imagery bands.

Water years 2004 and 2005 were used as training data for the empirical regression models (OLS and GWR). An average of the formulas (from each year) that established the relationship between the salinity and Landsat TM bands was used to estimate salinity

27 values in water year 2006. The predicted salinity values were compared to field collected data for model analysis using Root Mean Square Error (RMSE).

Band determination was dictated by correlation and collinearity analysis via SPSS analysis. Once the training data was implemented to determine the GWR formula (for the wet and dry seasons individually), the resulting formula was applied to the validation data. To keep parameters consistent, the same bands were used in the analysis of all models.

A mask was constructed for the wet and dry season to reduce clutter and improve accuracy in the final results of analysis. The mask was created using the near infrared band (band 4) of the validation Landsat image using unsupervised classification via Iso

Clustering. This was done to remove cloud and land features stated in preprocessing. The near infrared band (Band 4) has a strong absorption of water in this spectral region

(Zhang et al., 2012). The image was verified by visually determining the image with the least amount of cloud and cloud clutter. The mask allows only the water features to be assigned a value; the land and cloud features are given a “no data” classification. This is done to ensure the area associated with the masked pixels would be ignored in analysis as well as aesthetic purposes. The raster mask was applied to each of the models before final analysis and salinity estimation calculations.

Root Mean Square Error (RMSE) values are often used to assess accuracy and performance of models (Melesse et al., 2008) and were easily calculated in each of the models run in this study. The RMSE is ideally as small as possible (Nas and Berktay,

2010). RMSE was then calculated to compare the results of the four models. This was accomplished by first establishing the actual salinity and estimated (predicted) salinity in

28 each of the models. The error (or residual) was generated using the formula: Error =

Observed – Predicted. When calculating the error in the GWR model, in the wet and dry season individually, “blunder points” were observed. Spatial analysis observed that these points were located off the grid of the GWR model, hence producing error values of -

9999. These points were removed from analysis in all four models even though the blunder was only observed in the GWR analysis. This was done for consistency purposes. The error squared was calculated in the following formula: Error Squared =

[Error]^2. The square root of the mean of the error square was then evaluated. It was this RMSE value that was compared among the models.

RESULTS AND DISCUSSION

Using IBM SPSS Version 19 software, field gathered (training) salinity data and

Landsat 5 TM bands (1, 2, 3, 4, 5, and 7) were explored for statistical significance.

Scatterplots visualized the relationship between salinity and each of the bands individually in Figures 4 (dry season – November 2005) and 5 (wet season – June 2006).

The plots show a linear relationship between salinity and each band. Scatterplots were also developed between salinity and different band combinations (Figures 6 and 7).

Although not as linear as the relationship between salinity and individual bands, the band combination plots are also generally linear. The scatterplots do not signal any major problems with normality. Scatterplots are a preliminary tool of inspection and therefore further analysis of the relationship of salinity and bands is required.

Figure 4 Scatterplots of dry season (Nov 2005) by individual bands

Figure 5 Scatterplots of wet season (June 2006) by individual bands

Figure 6 Scatterplots of dry season (Nov 2005) by band combinations

Figure 7 Scatterplots of wet season (June 2006) by band combinations

Correlation matrices of the wet and dry seasons identify the bands as not being highly correlated. The larger the number in the correlation matrix, the stronger the correlation. In the dry season salinity is most correlated to bands 4 and 5 (Table 2); in the wet season salinity is most correlated to band 4 (Table 3). The correlation matrix of the dry season also reveals some correlation between bands 1 and 2; bands 1 and 5; and bands 1 and 7. The wet season does not show the same strength of relationships but the highest correlation amongst the bands is between bands 1 and 2 and bands 1 and 5.

Dry Season Salinity Band 1 Band 2 Band 3 Band 4 Band 5 Salinity Band 1 0.038 Band 2 -0.121 0.517 Band 3 -0.138 0.294 0.291 Band 4 0.149 0.439 0.124 0.309 Band 5 0.275 0.632 0.222 0.104 0.465 Band 7 0.084 0.673 0.462 0.234 0.229 0.458

Table 2 Data Correlation Matrix for the dry season

Wet Season Salinity Band 1 Band 2 Band 3 Band 4 Band 5 Salinity Band 1 -0.087 Band 2 -0.400 0.270 Band 3 -0.175 0.168 0.141 Band 4 0.103 0.153 0.111 0.302 Band 5 -0.043 0.296 0.231 0.192 0.244 Band 7 -0.548 0.014 -0.165 0.045 0.168 0.038

Table 3 Data Correlation Matrix for the wet season

Univariate and multivariate regression analysis was performed to determine optimum bands for the empirical models (Table 4). In both tests, salinity was the dependent variable while the Landsat TM band or combinations of bands were the independent variables. Univariate analysis shows that all bands are not strong statistical significance predictors of salinity, yet bands 2 and 7 for the wet season and bands 4 and 5 for the dry season explain the highest variance among the individual bands. However, the highest any band can explain is 30% of the variation in salinity. Therefore, multiple bands are necessary for salinity estimation. Using all six bands can explain the highest variance in salinity; removing bands decreases the R2 values and hence the variance.

Wet Dry

Band 1 0.008 0.001

Band 2 0.160 0.015

Band 3 0.031 0.019

Band 4 0.011 0.022

Band 5 0.002 0.076

Band 7 0.300 0.007

Bands 1, 2, 3, 4, 5, 7 0.434 0.147

Bands 1, 2, 3, 4, 7 0.433 0.088

Bands 1, 2, 3, 4 0.211 0.071

Bands 1, 3, 4 0.063 0.059

Table 4 Univariate and Multivariate Regression Analysis (R square values of Landsat TM bands)

Further multivariate analyses were examined to determine Landsat TM band selection for the most optimum empirical model. Variance Inflation Factor (VIF) is an indicator of multicollinearity. VIF delivers a quantitative measure of how much variance

(for a given regression coefficient) is increased compared to if all predictors (in this study, Landsat TM bands) were uncorrelated (Tables 5 and 6). A VIF value of 1 represents the ideal situation of no correlation with other predictors. The VIF scores in the tables below represent almost such an ideal situation. High VIF values are indicators for multicollinearity problems and can also lead to the removal of specific bands in analysis because of collinearity. However, the VIF scores are all very low. The tolerance is the reciprocal of VIF. Therefore, while low VIF scores are ideal, high tolerance values are desired (closest to 1).

Bands 1, 2, 3, 4, 5, 7 Bands 1, 2, 3, 4, 7 Bands 1, 2, 3, 4 Bands 1, 3, 4 Wet Collinearity Statistics Collinearity Statistics Collinearity Statistics Collinearity Statistics Season Tolerance VIF Tolerance VIF Tolerance VIF Tolerance VIF Band 1 0.857 1.166 0.901 1.110 0.902 1.109 0.960 1.041 Band 2 0.863 1.159 0.882 1.134 0.915 1.092 - - Band 3 0.881 1.135 0.887 1.127 0.887 1.127 0.894 1.119 Band 4 0.846 1.182 0.869 1.068 0.896 1.116 0.898 1.113 Band 5 0.845 1.184 ------Band 7 0.935 1.069 0.936 1.068 - - - -

Table 5 Multivariate Regression Analysis Collinearity Statistics (Wet Season)

Bands 1, 2, 3, 4, 5, 7 Bands 1, 2, 3, 4, 7 Bands 1, 2, 3, 4 Bands 1, 3, 4 Dry Collinearity Statistics Collinearity Statistics Collinearity Statistics Collinearity Statistics Season Tolerance VIF Tolerance VIF Tolerance VIF Tolerance VIF Band 1 0.338 2.961 0.411 2.435 0.588 1.699 0.780 1.283 Band 2 0.664 1.506 0.673 1.487 0.690 1.450 - - Band 3 0.814 1.229 0.835 1.197 0.837 1.195 0.873 1.145 Band 4 0.679 1.473 0.743 1.347 0.748 1.337 0.772 1.296 Band 5 0.528 1.895 ------Band 7 0.518 1.930 0.526 1.902 - - - -

Table 6 Multivariate Regression Analysis Collinearity Statistics (Dry Season) 37

Figure 8 shows the comparison of the interpolation models based on the field collected data. For comparison, observed (field collected) salinity values are shown. The raster has been masked to estimate only salinity levels in the water. Both IDW and

Kriging show a general trend of lower salinity values in the eastern portion of Whitewater

Bay with increasing salinity values as one travels west. This concurs with the freshwater release of Shark River Slough from the Florida mainland and the saltwater influence from the Gulf of Mexico and outer Florida Bay.

Figure 8 Comparison of Interpolation Models for salinity analysis in Whitewater Bay.

The Ordinary Least Squares method is a basic linear regression model which builds a formula to establish salinity patterns. Data from Landsat images can then be placed into this formula (depending on season) to estimate salinity (Table 7). However, in this study, the R2 values are low. An explained variance of 7.8 – 43.8 % for the dry season and 12.4 – 19.6 % for the wet season was obtained. This indicates a low probability of accurately assessing salinity using this regression model.

OLS OLS Formulas R2

Base Y = B0 + B1X1 + B2X2 + B3X3 + B4X4 + B5X5 + B7X7 Dec-2003 Y = 0.771 + 2.896X + 4.926X + -3.994X + -0.234X + -0.240X + -0.105X 0.438 Dry 1 2 3 4 5 7 Oct-2004 Y = 14.994 + 0.0124X1 + -0.095X2 + -0.123X3 + -0.040X4 + 0.133X5 + 0.014X7 0.078 Jun-2004 Y = 27.145 + 0.006X + -0.031X + -0.363X + 0.132X + 0.179X + 0.047X 0.196 Wet 1 2 3 4 5 7 Apr-2006 Y = 21.900 + 0.041X1 + -0.037X2 + 0.049X3 + -0.036X4 + 0.276X5 + -0.010X7 0.124

2 Table 7 OLS R values and formulas for the wet and dry season where Y is the dependent variable (salinity); B0 is the intercept coefficient; Bn are the coefficients (by bands); Xn are the explanatory variables (Landsat band extracted numbers)

The R2 values have excluded OLS models as a desired salinity estimating model.

However, the R2 values for the Geographically Weighted Regression (GWR) models show that they are a preferred model for Whitewater Bay in terms of estimating salinity

(Table 8). An explained variance of 85.3 – 99.6 % for the dry season and 98.6 – 99.4% for the wet season was obtained. GWR uses a formula which Bn (the coefficients by bands) takes into consideration the geographical location of the point. Therefore the formulas used in GWR regression have Bn values which can be different at every location.

GWR R2 Values Dec-2003 0.853 Dry Oct-2004 0.996 Jun-2004 0.986 Wet Apr-2006 0.994

Table 8 GWR R2 values for the wet and dry season

The derived empirical models were validated using the field collected data in

Water Year 2006 (November 2005 for the dry season and June 2006 for the wet season).

The root mean square error (RMSE) was used to evaluate the accuracy of estimations.

Salinity data from sample locations in December 2003 and October 2004 was used to build an average formula for the dry season. Data was extracted from Landsat TM imagery (collected 9 November 2005) to estimate salinity values and then compared with field surveyed data collected on 10 November 2005. The wet season used salinity data taken in June 2004 and April 2004 to build an average GWR formula. Data collected from Landsat TM imagery on 21 June 2006 was compared to field salinity measurements on 28 June 2012. The RMSE values shown in Table 9 indicate that the interpolation methods are more precise at estimating salinity. However, the interpolation methods used field data collected on the validation dates to estimate salinity. The GWR model used a formula derived from previous years’ salinity relationships and Landsat TM imagery. Figure 9 shows the salinity maps of the empirical regression (GWR) models.

For comparison, observed (field collected) salinity values are shown. This map depicts a salinity gradient of freshwater influence from Shark River Slough (lower salinity in blue) and saltwater influx (higher salinity in red).

RMSE (ppt) IDW Krig GWR Dry Season Nov 2005 0.458 0.385 12.336 Wet Season June 2006 0.407 0.348 12.550

Table 9 RMSE values

Figure 9 Comparison of Regression Models for salinity analysis in Whitewater Bay

CONCLUSIONS

This study estimated salinity using four different models. IDW and Kriging are both excellent at interpolating salinity. However, neither model can predict salinity without field data. For this reason, these models are not ideal for continuous salinity estimation in Whitewater Bay.

Empirical regression models allow for prediction of salinity once a formula has been established. Using OLS to predict salinity is not optimum due to its low percentage of explained variance (7.8 – 43.8 % for the dry season and 12.4 – 19.6 % for the wet season). However, GWR has a high percentage of explained variance (85.3 – 99.6 % for the dry season and 98.6 – 99.4% for the wet season). Therefore, GWR models are the optimal choice for salinity estimation in Whitewater Bay based on Landsat TM imagery.

The GWR salinity maps are not flawless. Previous research in the literature indicated

Landsat TM band selection would be essential for building the models. Highly correlated bands would be removed and using only the most effective variables (bands) would be required for salinity prediction. This was not the case in this study. Statistical analysis and data exploration revealed low band correlation. Hence all bands were used in this analysis. However, using all six Landsat TM bands (Bands 1, 2, 3, 4, 5, and 7) is potentially the explanation for the imperfections in the salinity map. The model is still a great model as evident from the R2 analysis and up to 99.6% of explained variance.

Current restoration work through CERP assesses salinity levels as a monitoring tool as historic freshwater flow is reinstated throughout South Florida and into

Whitewater and Florida Bays. This study explored the use of remote sensing to assess salinity in Whitewater Bay. The quantitative techniques modeled in this research allow for a less costly and quick assessment of salinity values and hence freshwater input into the ecosystem. The effectiveness of restoration projects can thus be evaluated.

Using one or two years of training salinity data to estimate and predict a different year has a range of issues. Weather including atmospheric conditions varies from day to day. Atmospheric corrections using QUAC were performed on the Landsat TM images, but still could not perfectly balance the images. Cloud cover was another issue wrestled in this study. The cloud cover in the shorter wet season occurs more often and more persistently than in the longer dry season. Estimated salinity possibly differs from observed salinity due to influence of the cloud cover and thus the differences of digital number. Although visible clouds were accounted for and corresponding points removed from training data, their presence in the validation Landsat image skewed the results.

This is especially evident in the wet season.

Increased rainfall in the wet season also increases the amount of clouds that impact the salinity estimation analysis. During the summer (the wet season) there is also an enlargement of evapotranspiration (ET). One might theorize that during periods of profuse precipitation, runoff levels of freshwater would increase; thus decreasing the levels of salinity. However, Mulholland et al, (1997) suggests that evapotranspiration might be greater than the rainfall during the wet season resulting in conditions where freshwater levels would decrease. Precipitation varies from year to year in south Florida

44 especially because hurricane season coincides directly with the wet season in Florida.

Large amounts of rainfall can be brought to a region by a large thunderstorm or hurricane snatching rain from another region.

The dry season in Florida seems to have lower salinity values overall than the wet season (examining the salinity maps). In addition, the dry season of Florida, characterized by less rainfall, proved to have a more accurate estimation of predicted salinity levels than the wet season (examining GWR RMSE values). This increases the need for further analysis of salinity assessment and values in Whitewater Bay.

Understanding the variability of salinity in Whitewater Bay is important not only to the restoration efforts but to the various ecosystems that are affected as a result.

Further studies would benefit from the exploration of evapotranspiration and precipitation levels for the days and months prior to the dates data was acquired. Water traveling through the KOE watershed may take up to a year to traverse the entire system. Heavy rainfall or extreme drought months prior to data procurement can affect salinity levels when released into the ocean. Investigating the salinity patterns throughout the entire south Florida region, including Whitewater Bay, will allow scientists a better understanding of a changing environment.

REFERENCES

Alles, David L. Western Washington University. “Biodiversity Hot Spots: The Florida

Everglades.” 3/7/07. Accessed at

http://fire.biol.wwu.edu/trent/alles/Everglades.pdf. June 26, 2012.

Baban, Serwan MJ. (1997) “Environmental Monitoring of Estuaries: Estimating and

Mapping Various Environmental Indicators in Breydon Water Estuary, UK, using

Landsat TM Imagery.” Estuarine, Coastal and Shelf Science, 44(5), 589-598.

Bauer, Marvin E., Nathan J. Heinert, Jean K. Doyle, and Fei Yuan. (2004) “Impervious

Surface Mapping and Change Monitoring Using Landsat Remote Sensing.”

ASPRS Annual Conference Proceedings. Denver.

Beckage, Brian, William J. Platt, Matthew G. Slocum, and Bob Panko. (2003)

“Influence of the El Niño Southern Oscillation on Fire Regimes in the Florida

Everglades.” Ecology, 84(12), 3124–3130.

Bernstein, L. S., S. M. Adler-Golden, R. L. Sundberg, R. Y. Levine, T. C. Perkins, A.

Berk, A. J. Ratkowski, G. Felde, and M. L. Hoke. (2005). “A New Method for

Atmospheric Correction and Aerosol Optical Property Retrieval for VIS-SWIR

Multi- and Hyperspectral Imaging Sensors: QUAC (QUick Atmospheric

Correction).” Proceedings of the International Geoscience and Remote Sensing

Symposium, IGARSS’05, 2005 IEEE International, 5, 3549-3552.

Blume, Hans-Juergen C., Bruce M. Kendall, and John C. Fedors. (1978) “Measurement

of Ocean Temperature and Salinity via Microwave Radiometry.” Boundary-

Layer Meteorology, 13, 295-308.

Bowers, D. and H.L. Brett. (2008) “The Relationship between CDOM and Salinity in

Estuaries: An Analytical and Graphical Solution.” Journal of Marine Systems,

73, 1–7.

Boyer, Joseph N. (2006) “Shifting N and P limitation along a north-south gradient of

mangrove estuaries in South Florida.” Hydrobiologia, 569, 167–177.

Boyer, Joseph N., James W. Fourqurean, and Ronald D. Jones. (1997) “Spatial

Characterization of Water Quality in Florida Bay and Whitewater Bay by

Multivariate Analyses: Zones of Similar Influence.” Estuaries, 20 (4), 743-758.

Burd, Adrian B. and George A. Jackson. (2002) “An Analysis of Water Column

Distributions in Florida Bay.” Estuaries, 25 (4), 570-585.

Burrage, Derek, Jerry Miller, Don Johnson, Joel Wesson, and Jeff Johnson. (2002)

“Observing Sea Surface Salinity in Coastal Domains Using an Airborne Surface

Salinity Mapper.” In: Proceedings IEEE/MTS Marine Frontiers Oceans 2002,

Biloxi, 29–31 October 2002 (Proceedings CDROM).

Chen, Ronghua and Robert R. Twilley. (1999) “Patterns of Mangrove Forest Structure

and Soil Nutrient Dynamics along Shark River Estuary, Florida.” Estuaries, 22

(4), 955-970.

Chen, Zhiqiang. (2006) "Monitoring Water Quality in Tampa Bay: Coupling in situ and

Remote Sensing." University of South Florida Graduate School Theses and

Dissertations. Paper 2479. http://scholarcommons.usf.edu/etd/2479

Childers, Daniel L., Joseph N. Boyer, Stephen E. Davis, Christopher J. Madden, David T.

Rudnick, and Fred K. Sklar. (2006) “Relating Precipitation and Water

Management to Nutrient Concentrations in the Oligotrophic ‘Upside-Down’

Estuaries of the Florida Everglades.” Limnology and Oceanography, 51 (1), 602-

616.

Chimney, M. J. & G. Goforth. (2001) “Environmental Impacts to the Everglades

Ecosystem: A Historical Perspective and Restoration Strategies.” Water Science

and Technology, 44 (11-12), 93-100.

Clarke, Alice L. and George H. Dalrymple. (2003) “$7.8 Billion for Everglades

Restoration: Why Do Environmentalists Look so Worried?” Population and

Environment, 24 (6), 541-569.

Clarke, Sharon E., Denis White, and Andrew L. Schaedel. (1991) “Oregon, USA,

Ecological Regions and Subregions for Water Quality Management.”

Environmental Management, 15 (6), 847-856.

Copeland, B. J. (1966) “Effects of Decreased River Flow on Estuarine Ecology.”

Journal (Water Pollution Control Federation), 38 (11), 1831-1839.

D’Sa, E. J., J. B. Zaitzeff, and R. G. Steward. (2000) “Monitoring Water Quality in

Florida Bay with Remotely Sensed Salinity and in situ Bio-Optical Observations.”

International Journal of Remote Sensing, 21 (4), 811-816. 48

Dewidar, Kh. and A. Khedr. (2001) “Water Quality Assessment with Simultaneous

Landsat-5 TM at Manzala Lagoon, Egypt.” Hydrobiologia, 457, 49–58.

Droppleman, J. D., R. A. Mennella, and D. E. Evans. (1970) “An Airborne Measurement

of the Salinity Variations of the Mississippi River Outflow.” Journal of

Geophysical Research, 75(30), 5909–5913.

FLAASH Module User’s Guide. (2009) “Atmospheric Correction Module: QUAC and

FLAASH User’s Guide, Version 4.7.” ITT Visual Information Solutions, Inc.

Fourqurean, James W., Joseph N. Boyer, Michael J. Durako, Lee N. Hefty, Bradley J.

Peterson. (2003) “Forecasting Responses of Seagrass Distributions to Changing

Water Quality Using Monitoring Data.” Ecological Applications, 13 (2), 474-

489.

Fourqurean, James W. and Michael B. Robblee. (1999) “Florida Bay: A History of

Recent Ecological Changes.” Estuaries, 22 (2), 345-357.

Goslee, Sarah C. (2011) “Analyzing Remote Sensing Data in R: The Landsat package.”

Journal of Statistical Software, 43 (4), 1-25.

Gunderson, Lance H. (2001) “Managing Surprising Ecosystems in Southern Florida.”

Ecological Economics, 37, 371–378.

Harwell, Mark A. (1998) “Science and Environmental Decision Making in South

Florida.” Ecological Applications, 8 (3), 580-590.

Jensen, John R. (2005) Introductory Digital Image Processing: A Remote Sensing

Perspective, 3rd edition. Upper Saddle River, NJ: Prentice Hall.

Jensen, John R. (2007) Remote Sensing of the Environment: An Earth Resource

Perspective, 2nd edition. Upper Saddle River, NJ: Prentice Hall.

Kasischke, Eric S. and Laura L. Bourgeau-Chavez. (1997) “Monitoring South Florida

Wetlands Using ERS-1 SAR Imagery.” Photogrammetric Engineering & Remote

Sensing, 63 (3), 281-291.

Khorram, Siamak. (1982) “Remote Sensing of Salinity in the San Francisco Bay Delta.”

Remote Sensing of Environment, 12 (1), 15-22.

Kiker, Clyde F., J. Walter Milon, & Alan W Hodges. (2001) “South Florida: The

Reality of Change and the Prospects for Sustainability: Adaptive Learning for

Science-Based Policy: the Everglades Restoration.” Ecological Economics, 37

(3), 403-416.

Klemas, Victor. (2011) “Remote Sensing of Sea Surface Salinity: An Overview with

Case Studies.” Journal of Coastal Research, 27 (5), 830-838.

Koebel, Joseph W., Jr. (1995) “An Historical Perspective on the Kissimmee River

Restoration Project.” Restoration Ecology, 3 (3), 149–159.

Kranzer, Bonnie. (2003) “Everglades Restoration: Interactions of Population and

Environment.” Population and Environment, 24 (6), 455-484.

Lagerloef, Gary S.E., Calvin T. Swift and David M. Le Vine. (1995) “Sea Surface

Salinity: The Next Remote Sensing Challenge.” Oceanography, 8 (2), 44-50.

Lark, R. M. (2000) “Regression Analysis with Spatially Autocorrelated Error:

Simulation Studies and Application to Mapping of Soil Organic Matter.”

International Journal of Geographical Information Science, 14 (3), 247-264.

Lavery, P., C. Pattiaratchi, A. Wyllie, and P. Hick. (1993) “Water Quality Monitoring in

Estuarine Waters using the Landsat Thematic Mapper.” Remote Sensing of

Environment, 46, 268-280.

Lerner, Robert M. and James P. Hollinger. (1977) “Analysis of Microwave Radiometric

Measurements from Skylab.” Remote Sensing of the Environment, 6, 251-269.

LeVine, D. M., M. Kao, A. B. Tanner, C. T. Swift and A. Griffis. (1990) “Initial Results

in the Development of a Synthetic Aperture Microwave Radiometer.” IEEE

Transactions on Geoscience and Remote Sensing, 28, 614-619.

Liu, Yansui, Md Anisul Islam, and Jay Gao. (2003) “Quantification of Shallow Water

Quality Parameters by means of Remote Sensing.” Progress in Physical

Geography, 27 (1), 24–43.

Maie, Nagamitsu, Joseph N. Boyer, Chenyong Yang and Rudolf Jaffé. (2006) “Spatial,

Geomorphological, and Seasonal Variability of CDOM in Estuaries of the Florida

Coastal Everglades.” Hydrobiologia, 569, 135–150.

Mayo, M., A. Gitelson, Y. Z. Yacobi, and Z. Ben-Avraham. (1995) “Chlorophyll

Distribution in Lake Kinneret Determined from Landsat Thematic Mapper Data.”

International Journal of Remote Sensing, 16 (1), 175-182.

McPherson, B.F. and Halley, R.B. and National Water-Quality Assessment Program

(U.S.) and Geological Survey (U.S.). (1996). The South Florida Environment: A

Region Under Stress. The United States Government Printing Office.

Melesse, Assefa M., Jayachandran Krishnaswamy, and Keqi Zhang. (2008) “Modeling

Coastal Eutrophication at Florida Bay Using Neural Networks.” Journal of

Coastal Research, 24 (2B), 190-196.

Miller, Jerry. (1995) “Remote Sensing of Salinity in the Coastal Zone.” Oceanography,

8 (2), 51.

Montague, Clay L and Janet A. Leyi. (1993) “A Possible Effect of Salinity Fluctuation

on Abundance of Benthic Vegetation and Associated Fauna in Northeastern

Florida Bay.” Estuaries, 16 (4), 703-717.

Mulholland, Patrick J., G. Ronnie Best, Charles C. Coutant, George M. Hornberger, Judy

L. Meyer, Peter J. Robinson, John R. Stenberg, R. Eugene Turner, Francisco

Vera-Herrera, and Robert G. Wetzel. (1997) “Effects of Climate Change on

Freshwater Ecosystems of the South-Eastern United States and the Gulf Coast of

Mexico.” Hydrological Processes. 11, 949-970.

Nas, Bilgehan and Ali Berktay. (2010) “Groundwater Quality Mapping in Urban

Groundwater Using GIS.” Environmental Monitoring and Assessment, 160, 215-

227.

Nuttle, W.K., Fourqurean, J.W., Cosby, B.J., Zieman, J.C., and Robblee, M.B. (2000)

“The Influence of Net Freshwater Supply on Salinity in Florida Bay.” Water

Resources Research 36, 1805-1822. 52

Ozesmi, Stacy L. and Marvin E. Bauer. (2002) “Satellite Remote Sensing of Wetlands.”

Wetlands Ecology and Management 10: 381–402.

Palialexis, A., S. Georgakarakos, I. Karakassis, K. Lika, V.D. Valavanis. (2011) “Fish

Distribution Predictions from Different Points of View: Comparing Associative

Neural Networks, Geostatistics and Regression Models.” Hydrobiologia, 670,

165-188.

Parsons, Michael, Quay Dortch, R. Eugene Turner, and Nancy Rabalais. (1999).

“Salinity History of Coastal Marshes Reconstructed from Diatom Remains.”

Estuaries, 22 (4), 1078-1089.

Perry, William. (2004) “Elements of South Florida’s Comprehensive Everglades

Restoration Plan.” Exotoxicology, 13, 185-193.

Rand, Gary M. and Pamela M. Bachman. (2008) “South Florida Ecosystems.”

Ecotoxicology, 17, 565-568.

Rand, Gary M. and Piero R. Gardinali. (2004) “South Florida Ecosystems.”

Ecotoxicology, 13, 179-184.

Redfield, Garth W. (2000) “Ecological Research for Aquatic Science and

Environmental Restoration in South Florida.” Ecological Applications, 10 (4),

990-1005.

Restrepo, J. I., D. Garces, A. Montoya, N. Restrepo, and J. Giddings. (2006)

“Simulation of Water Levels and Water Diversions in a Subtropical Coastal

Wetland.” Journal of Coastal Research, 22 (2), 339–349.

Richter, Brian D., Ruth Mathews, David L. Harrison, and Robert Wigington. (2003)

“Ecologically Sustainable Water Management: Managing River Flows for

Ecological Integrity.” Ecological Applications, 13 (1), 206-224.

Ritchie, Jerry C., Paul V. Zimba, and James H. Everitt. (2003) “Remote Sensing

Techniques to Assess Water Quality.” Photogrammetric Engineering & Remote

Sensing, 69 (6), 695–704.

Rudnick, D. T., Z. Chen, D. L. Childers, J. N. Boyer, and T. D. Fontaine, III. (1999)

“Phosphorus and Nitrogen Inputs to Florida Bay: The Importance of the

Everglades Watershed.” Estuaries, 22 (2), 398-416.

Satula, M., J.W. Day, J. Cable, and D. Rudnick. (2001). “Hydrological and Nutrient

Budgets of Freshwater and Estuarine Wetlands of Taylor Slough in Southern

Everglades, Florida (U.S.A.).” Biogeochemistry, 56 (3), 287-310.

Sawaya, Kali E., Leif G. Olmanson, Nathan J. Heinert, Patrick L. Brezonik, and Marvin

E. Bauer. (2003) “Extending Satellite Remote Sensing to Local Scales: Land and

Water Resource Monitoring Using High-Resolution Imagery.” Remote Sensing of

the Environment, 88, 144-156.

Scholl, David W. (1963). “Sedimentation in modern coastal swamps, southwestern

Florida.” AAPG Bulletin, 47 (8), 1581 – 1603.

Secor, David, Mary Christman, Frank Curriero, David Jasinski, Elgin Perry, Steven

Preston, Ken Reckhow, and Mark Trice. (2006) “The Cumulative Frequency

Diagram Method for Determining Water Quality Attainment: Report of the

Chesapeake Bay Program STAC Panel to Review Chesapeake Bay Program 54

Analytical Tools.” STAC Publication 06 - 9 October 2006. (Available:

http://www.chesapeake.org/stac/Pubs/CFD_STAC_Final.pdf).

Sklar, F. H., H. C. Fitz, Y. Wu, R. Van Zee, and C. McVoy. (2001) “The Design of

Ecological Landscape Models for Everglades Restoration.” Ecological

Economics, 37, 379–401.

Song, Kaishan, Zongming Wang, John Blackwell, Bai Zhang, Fang Li, Yuanzhi Zhang,

and Guangjia Jiang. (2011) “Water Quality Monitoring Using Landsat Themate

Mapper Data with Empirical Algorithms in Chagan Lake, China.” Journal of

Applied Remote Sensing, 5 (1), 053506.

Stumpf, Richard P., Kristine Holderied, and Mark Sinclair. (2003) “Determination of

Water Depth with High-Resolution Satellite Imagery over Variable Bottom

Types.” Limnology and Oceanography, 48 (1), 547-556.

Swart, Peter K., Genevieve F. Healy, Richard E. Dodge, Philip Kramer, J. Harold

Hudson, Robert B. Halley, and Michael B. Robblee. (1996) “The Stable Oxygen

and Carbon Isotopic Record from a Coral Growing in Florida Bay: a 160 Year

Record of Climatic and Anthropogenic Influence.” Palaeogeography,

Palaeoclimatology, Palaeoecology, 123, 219-237.

Topliss, B. J., J.F.R. Gower, J.A. Helbig, A. W. Isenor, and I. Rubinstein. (2002) “Sea

Surface Salinity from Space: A Canadian Perspective.” The Canadian Space

Agency. Earth and Environmental Applications Program Report, March 2002,

74p.

Urquhart, Erin A., Benjamin F. Zaitchik, Matthew J. Hoffman, Seth D. Guikema, Erick

F. Geiger. (2012) “Remotely Sensed Estimates of Surface Salinity in the

Chesapeake Bay: A Statistical Approach.” Remote Sensing of the Environment,

123, 522-531.

Vuille, M., and M. F. Baumgartner. (1993) “Hydrological Investigation in the North

Chilean Altiplano using Landsat -MSS and -TM Data.” Geocarto International, 3,

35-45.

Wang, Lillian T. and Tracy L. DeLiberty. (2005) “Landsat Atmospheric Correction: The

Good, the Bad, and the Ugly.” University of Delaware, Department of

Geography, USA. gis. esri. com/library/userconf/proc05/papers/pap1560. pdf

16 June 2012.

Xie, Zhixiao, Caiyun Zhang, and Leonard Berry. (2013) “Geographically Weighted

Modeling of Surface Salinity in Florida Bay using Landsat TM Data.” Remote

Sensing Letters, 4 (1), 76-84.

Zhang, C., Z. Xie, C. Roberts, L. Berry, and G. Chen. (2012) “Salinity Assessment in

Northeast Florida Bay using Landsat TM data.” Southeastern Geographer, in

press.