Comparing Salinity Models in Whitewater Bay Using Remote Sensing
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COMPARING SALINITY MODELS IN WHITEWATER BAY USING REMOTE SENSING by 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 Copyright by Donna Selch 2012 ii 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. iv 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. v 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 vi 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;