<%BANNER%>

Using remote sensing and geographic information systems for flood vulnerability mapping of the C-111 basin in south Miam...

University of Florida Institutional Repository
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USING REMOTE SENSING AND GEOGRA PHIC INFORMATION SYSTEMS FOR FLOOD VULNERABILITY MAPPING OF THE C-111 BASIN IN SOUTH MIAMIDADE COUNTY By WILLIAM ANDREW WEBB A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT FOR THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2006

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Copyright 2006 by William Andrew Webb

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This paper is dedicated to my pare nts Frank R. Webb and Brenda Y. Webb.

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iv ACKNOWLEDGMENTS I would like to acknowledge my departme nt professors Dr. Wendy Graham, Dr. Carol Lehtola and Dr. Jack Jordan for their guidance and hard work in this project. I would like to thank Dr. Clint Slatton for provi ding his expertise in Airborne Laser Swath Mapping. I would like to thank Don Pybas fo r his contribution and cooperation. I would like to thank Charles Brown for his assi stance during the development process.

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v TABLE OF CONTENTS page ACKNOWLEDGMENTS.................................................................................................iv LIST OF TABLES............................................................................................................vii LIST OF FIGURES...........................................................................................................ix GLOSSARY OF TERMS.................................................................................................xii ABSTRACT....................................................................................................................... xv CHAPTER 1 INTRODUCTION........................................................................................................1 Background...................................................................................................................1 Flood Management.......................................................................................................1 Objectives..................................................................................................................... 3 Project Area..................................................................................................................4 2 LITERATURE REVIEW.............................................................................................7 Active and Passive Remote Sensing.............................................................................7 Spectral Signature of Water..........................................................................................8 Sensor Performance......................................................................................................9 Normalized Differential Vegetation Index (NDVI)...................................................10 Water Detection..........................................................................................................11 Cloud Detection..........................................................................................................12 Airborne Laser Swath Mapping..................................................................................13 ALSM Accuracy.........................................................................................................15 ALSM Point Removal................................................................................................16 ALSM Applications....................................................................................................18 Geographic Information Systems...............................................................................20 Spatial Modeling.........................................................................................................21 Inundation Mapping with GIS....................................................................................24 3 DATA RESOURCES AND METHODOLOGY.......................................................26

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vi Introduction.................................................................................................................26 Surface Water Data.....................................................................................................26 Digital Elevation Model Construction........................................................................27 Landsat 7 Enhanced Thematic Mapper......................................................................28 Vegetative Index Methodology..................................................................................28 Unsupervised Classification.......................................................................................29 Bare Earth Modeling...................................................................................................30 Aerial Color Infrared Analysis...................................................................................31 Ground Control Point Analysis..................................................................................32 Topographic Spatial Modeling...................................................................................34 Surface Water Elevation Map Methodology..............................................................35 Surface Water Elevation Map Interpolation...............................................................37 Surface Water Inundation Map Methodology............................................................39 4 RESULTS AND DISCUSSION.................................................................................49 Cloud Detection..........................................................................................................49 Vegetation Index Two and Vegetation Index Three...................................................51 NDVI..........................................................................................................................5 5 Topographic Analysis.................................................................................................60 Classified ALSM DEM..............................................................................................61 Surface Water Elevation Map Analysis......................................................................68 Surface Water Inundation Map...................................................................................93 5 CONCLUSION.........................................................................................................114 Image Analysis.........................................................................................................114 Bare Earth Modeling.................................................................................................114 SWEM......................................................................................................................115 SWIM.......................................................................................................................115 Conclusion................................................................................................................116 6 RECOMMENDATIONS FOR FUTURE STUDIES...............................................117 LIST OF REFERENCES.................................................................................................119 BIOGRAPHICAL SKETCH...........................................................................................123

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vii LIST OF TABLES Table page 2.1 Specifications of a comm ercial Lidar system..............................................................13 3.1 Area 2 static GPS points..............................................................................................39 3.2 Inverse distance weighting sear ch parameters for topography....................................40 3.3 Global polynomial search parameters for topography.................................................41 3.4 Local polynomial search parameters for topography...................................................41 3.5 Radial based function search parameters for topography............................................41 3.6 Kriging search parameters for topography..................................................................42 3.7 Projection Parameters for ALSM.................................................................................42 3.8 Universal kriging search parameters for SWEM.........................................................43 3.9 Simple kriging search parameters for SWEM.............................................................44 3.10 Ordinary Kriging search parameters for SWEM.......................................................45 3.11 Disjunctive kriging search parameters for SWEM....................................................47 3.12 Universal Kriging for SWEM....................................................................................48 4.1 Radial based functions statistics for topography.......................................................106 4.2 Inverse distance weighting statistics for topography.................................................106 4.3 Global polynomial statistics for topography..............................................................107 4.4 Local polynomial sta tistics for topography................................................................107 4.5 Kriging statistics for topography...............................................................................108 4.6 Universal Kriging statistics for SWEM.....................................................................108 4.7 Universal kriging statis tics for SWEM, 10/12-22/1999............................................108

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viii 4.8 Simple kriging statistics for SWEM, 10/12-22/1999.................................................109 4.9 Ordinary kriging statistics for SWEM.......................................................................109 4.10 Disjunctive Kriging statistics for SWEM................................................................111 4.11 SWEM surface water values for October 12-22, 1999............................................112 4.12 Vulnerability index classes used for SWIM............................................................113 4.13 Calculated inundation statis tics for the study area...................................................113

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ix LIST OF FIGURES Figure page 1.1 Map of Miami-Dade Count y and the project area.........................................................4 1.2 Map of the study area.....................................................................................................5 2.1 Illustration of a Lidar infrared beam............................................................................14 3.1 Color infrared aerial photos of the study area..............................................................33 3.2 Map of measurement sites...........................................................................................36 4.1 Map of the Frog Pond with Band 8..............................................................................50 4.2 Vegetation index two map of south Florida, October 16, 1999...................................52 4.3 Vegetation index two map of the study area, October 16, 1999..................................54 4.4. October 16, 1999, NDVI map of the south Florida....................................................56 4.5. October 16, 1999, NDVI map of the study area.........................................................58 4.6 Planar view of the NAD 27 study area DEM..............................................................62 4.7 Graph of surface water elevation values......................................................................70 4.8 SWEM....................................................................................................................... ...71 4.9 Prediction error for SWEM..........................................................................................82 4.10 SWIM...................................................................................................................... ...95

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xii GLOSSARY OF TERMS ACIR Aerial Color Infrared ACIR is aerial color infrared imagery that is not referenced with a coordinate system. ALSM Airborne Laser Swath Mapping ALSM is a mapping technology that uses a laser to map land or bathymetric topography. BEM Bare earth model A bare earth model is a DEM with artifact or unwanted points removed. DEM Digital Elevation Model A DEM is a 3D representation of a surface than may be represented with raster cells or a TIN. Deterministic Interpolation Deterministic interpolation uses deterministic functions to predict values of a spatially distributed field at unmeasured locations. DOQQ Digital Orthographic Quarter Quadrangle A DOQQ is similar to an aerial photograph except it is referenced with a coordinate system and is used for general GIS mapping applications. DSM Digital Surface Model A DSM is a 3D representation of a surface with objects and man made features removed. DTM Digital Terrain Model A DTM is a 3D representation of a surface that uses a TIN to connect points. FEMA Federal Emergency Management Agency FEMA is the disaster management and relief agency of the federal government. GIS Geographic Information Systems GIS is software that captu res, stores, retrieves, manipulates and displays geographically referenced spatial tabular data.

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xiii Glossary of terms continued GPS Global Positioning Systems GPS is a constellation of 24 satellites that provides latitudinal and longitudinal data collected by a receiver. Kriging Kriging is geostatistical interpolation technique that uses the spatial correlation of a distributed field to predict its value of unmeasured locations. Landsat 7 ETM+ Landsat 7 Enhanced Thematic Mapper Landsat 7 ETM + is the seve nth USGS satellite in a series of satellites designed to capture environmental data with visible, near infrared, mid-infrared, low and high gain thermal sensor bands. Lidar Light Detection and Ranging Light Detection and Ranging is the enabling laser technology used for ALSM flight operations. NAD 83 North American Datum 1983 NAD 83 is the current horizontal datum used by the National Geodetic Survey. NAD 27 North American Datum 1927 NAD 27 is the predecessor horizontal datum to NAD 83. NDVI Normalized Differential Vegetation Index NDVI is a vegetative index th at is calculated as the difference between the red and near infrared bands divided by the sum of the red and near infrared bands. NGVD 29 National Geodetic Vertical Datum 1929 NGVD 29 is the predecessor vertical datum to NGVD 88. NGVD 88 National Geodetic Vertical Datum 1988 NGVD 88 is the current vertical datum used by the National Geodetic Survey. NPS National Park Service The National Park Service is controlled by the U.S. Department of Interior and is responsible for the management of all national parks. Raster A raster is a thematic map layer represented with a grid. Remote Sensing Remote sensing refers to the capture of data without a physical collection of the data.

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xiv Glossary of terms continued SCDS South Dade Conveyance System The SDCS is the southern extension of the Central and Southern Flood Control Project and is located in south Miami-Dade County. SFWMD South Florida Water Management District The SFWMD is one of five water management districts in Florida, and its district authority covers all of southeast Florida. SWEM Surface Water Elevation map The Surface Water Elevation Map is a representation of the surface water for elevation over project areas of interest. SWIM Surface Water Inundation Map The Surface Water Inundation Map is a representation of the surface water elevation measured in elevation above mean sea level with respect to NGVD 88. The Surface Water Inundation Map is the result of subtr acting land surface elevation grids from surface water elevation, and represents depth of water on the land surface. TIN Triangulated Irregular Network A TIN is a three dimensional representation of a surface created by using triangles to link points. USGS U.S. Geological Survey The USGS is a multi-disciplinary science organization that st udies biology, geography, geology, geospatial information, and water. Vector Vector is a thematic map layer represented by points, lines and polygons. VI2 Vegetative Index Two Vegetative Index Two is cal culated as the product of the green band and low gain thermal band divided by the high gain thermal band. VI3 Vegetative Index Three Vegetative Index Three is calculated as the low gain thermal band divided by the sum of the mid-infrared and red bands.

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xv Abstract of Thesis Presen ted to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science USING REMOTE SENSING AND GEOGRA PHIC INFORMATION SYSTEMS FOR FLOOD VULNERABILITY MAPPING OF THE C-111 BASIN IN SOUTH MIAMIDADE COUNTY By William Andrew Webb May 2006 Chair: Wendy D. Graham Major Department: Agricultural and Biological Engineering The hydrologic cycle of south Florida freque ntly produces rain events that include thunderstorms, tropical depressions a nd hurricanes. During 1999-2000, south MiamiDade was struck by two intens e rain events that severely inundated local agricultural operations for over a week. In the final asse ssment, agricultural losses sustained from these storms totaled to nearly $430 million. Flood hazard mapping has traditionally relied on paper maps that display the flood extent with only polygon boundaries. Unfortuna tely, paper maps are greatly limited in use, because they fail to show the exte nt, magnitude and duration of flooding. Recent advances in airborne lase r swath mapping, ALSM, and sate llite sensor technology have provided alternative types of data needed to more accurately map flood vulnerability. The general scope of this project is to improve mapping flood vulnerability in the southern C111 basin by combining a variety of remotely sensed data sets.

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xvi The procedure for mapping a severe flood condition following Hurricane Irene involved the combination of ALSM, Lands at7 ETM+ and Geographic Information Systems (GIS). Band 8, vegetation index two a nd vegetation index three derived from the Landsat 7 ETM+ image were useful fo r mapping cloud cover, and the normalized differential vegetation index (NDVI) was useful for mapping inundation produced by Hurricane Irene. The primary limitations of vegetation index maps include the 30 meter spatial resolution, and the obs truction of the spectral si gnature of water caused by vegetation and clouds. Project inundation maps created with regional surface water and airborne laser swath mapped (ALSM) data displayed the flood duration, magnitude and extent of the flood condition resulting from Hurricane Irene.

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1 CHAPTER 1 INTRODUCTION Background For nearly a century, south Miami-Dade ’s subtropical climate has provided a suitable environment for consistent annua l production of agricultural commodities. Agricultural production heavily depends upon th e regional climate that is characterized by a high mean annual rainfall, warm temp eratures and extremely mild winters. Hurricanes and tropical storms often produce fl ood conditions that can remain for weeks. During 1999-2000, south Miami-Dade was stru ck by two intense rain events. The first event, Hurricane Iren e, passed over South Florida on October 15, 1999 and the second event, the October 2000 No Name Event (NNE), struck almost one year later on October 4, 2000. The impact of both storms on the agricultural economy of south MiamiDade resulted in losses of nearly $430 million. Flood Management Flood control for south Florida became a fe deral priority in 1947 after back-toback hurricanes left most local communities and the newly created Everglades National Park (ENP) inundated for weeks. In 1948, C ongress authorized construction of the Central and Southern Florida Flood Control Project (CS&F) to regulate flooding and mitigate damage. The current system contai ns 1,800 miles of canals, 25 major pumping stations and other conveyance structures that stretch from Orlando to south Miami-Dade. The South Dade Conveyance System (SDCS) is the Miami-Dade County extension of the CS&F and is governed in a three part y agreement between ENP, the United States

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2 Army Corps of Engineers, and the South Florida Water Management District (South Florida Water Management Di strict 2000). Canals C-111 and L31W provide flood relief for agricultural lands and discharge water into Taylor Slough and Florida Bay. The frequency and magnitude of flood events in South Miami-Da de have increased the demand for high-resolution flood maps that are capable of displaying the extent, magnitude and duration of a specific flood event. In 1997, a University of Florida Hydrologic Sciences Task Force (HSTF) addressed the major issues surrounding flood management for agricultural areas in south Miami-Dade (Graham et al., 1997, pp.34), Flooding in the agricultural area has intensif ied in frequency, duration and depth . the lack of documentation concerning the negative impact of the experimental water deliveries has hindered progress by the USACOE and SFWMD to address these concerns. The hydrologic and geographic databases in the agricultural area east of the C-111 canal should be enhanced. Installation of additional monitoring stations, development of new geographic information, and further historical and statistical evaluations of the existing data bases is n ecessary to accurately assess the impact of canal operations on groundwater le vels in the agricultural area. A local-scale, event based hydrologic model is needed to define the risk of flooding to the agricultural community associated w ith alternative structural and operational plans for the C-111 project...such a mode l could be used to produce maps of flooding probability in the agri cultural area associated w ith alternative structural and operational plans for the C-111 project, which would allow local producers to better plan for the future.” The development of a multi-hazard database currently is the highest priority for the Department of Homeland Security and th e Federal Emergency Management Agency, FEMA (Lowe 2002). FEMA’s Multi-Hazard Flood Map Modernization initiative involves the expansion of the current geo sp atial hazard data base including the MultiHazard Flood Map Modernization. The moderni zation project is designed to produce a more accurate geospatial flood vulnerability da tabase that is accessible to the general public. Furthermore, the National Flood Insura nce Program has charged FEMA to head

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3 the Coordination of Surveying, Mapping, and Re lated Spatial Data Ac tivities. The action mandates that flood hazard mapping becomes FEMA’s top priority among all natural disasters. Part of the effort additionally in cludes the initiative to acquire geo-referenced spatial data and micro-topogr aphic Airborne Laser Swath Mapped data for advanced hydrologic models and maps. In 1999, FE MA and NASA sponsored ALSM flight operations of the C-111 basin and Everglad es National Park, ENP, for future flood mapping projects. Modern FEMA flood maps ar e required to meet the standard of a 5 meter spatial resolution (Maune 2001). Recently, FEMA and the Harris County Flood Control District developed the Tropical Storm Allison Recovery Project to assess flood vulnera bility in response to the aftermath of tropical storm Allison (www.hc fcd.org/tsarp.asp, April 2006). The project methodology featured the integration of ALSM and GIS for creating digital flood insurance rate maps, DFIRMS. Objectives The research objective of this project was to develop a method for assessing flood vulnerability in the C-111 basin by integra ting Landsat7 sensory da ta, regional surface water elevation data and air borne laser scanned topographi c data. Secondary objectives included the creation of a bare earth mode l for agricultural fields, modeling regional surface water elevation prediction grids and de tecting clouds with vegetative indices. The featured map product is the surface wate r inundation map (SWIM). SWIM is an inundation map capable of displaying the magn itude, duration and extent of flooding with a 3 meter spatial resolution. October 12-22, 1999, is the study period used for generating surface water grids and inundation maps.

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4 Project Area The study area lies in the southern area of the C-111 basin and is comprised of agricultural fields and prot ected wetland areas in ENP, Figure1.1. The Frog Pond is a fertile tract of land in the C-111 partitioned into twenty-two parcels and leased by the Federal government; however flood protecti on is not guaranteed for the Frog Pond. Parcels 14, 15, 16, 17, 18 and 19 are located with in the study area; however only 16 and 18 are completely displayed, (see Figure 1-2). The three major land cover types that dom inate the study area are wetland forest, wetland marsh and agricultural row crop fields Fiducial land features are permanent geomorphological features in the study area and they include the soil mound and the L-31W canal, Figure 1.2. The S175 culvert is also located on the L31W canal inside the study area. Figure 1.1 Map of Miami-Dade County and the project area.

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5 Figure 1.2 Map of the study area. Leased pa rcels are numbered 14-21. The S175 culvert is represented with a red circle on the L31W canal. The sub-surface hydrology of south Florida, including Miami-Dade, is characterized by an unconfined, highly perm eable system called the Biscayne Aquifer (Fetter 1998). The Biscayne aquifer is rech arged by precipitation, a nd water table levels fluctuate with the amount of precipitation. Belo w the Biscayne is a clastic semi-confining unit, the gray limestone aquifer and a lowe r clastic unit (Graham et al. 1997). Canals penetrate the most permeable part of the aquifer. The thickness and hydraulic conductivity of the Biscayne in the southern C-111 basin are approximately 46ft. and S175

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6 25,000 ft. /day, respectively (Graham et al. 1997). Water levels in th e Biscayne conform to the land surface with the highest levels o ccurring in the high elevation areas, and lowest levels in the low elevation areas.

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7 CHAPTER 2 LITERATURE REVIEW Photogrammetry is the science of analyzi ng photographs and images to determine the size, shape, and spatial attributes of th e features in an imag e acquired with remote sensing (Bethel and Cheng 1995). Remote sensi ng refers to the inferring of target or media characteristics by the reception of ener gy from the target or media. The energy may be electromagnetic, acoustic, subatomic particles, scattered energy originally transmitted from an active system sensor or originating from the sun. Active and Passive Remote Sensing All remote sensing applications use either active or passive sensors. Passive remote sensing is usually dependent on reflected solar illumination or the emission or transmission of black body radiation. Active re mote sensing involves sending a signal at a specific wavelength to the earth surface, de tecting a return signa l and assigning a pixel value to the received signal. Emitted energy, an earth surface feature, is op timally sensed in the near infrared to the far infrared bands and reflectance prope rties are optimally se nsed in the visible through the mid-infrared bands. For this reason, most passive sensor studies of planetary surfaces are conducted in the visible and in frared regions. Madden et al. (1998) used 1994 color infrared imagery to identify wetla nd vegetation in Everglades National Park. Doren, Rutchey and Welch (1998) used color in frared imagery to classify vegetation in the southern Everglades. Welch, Madden and Doren (1998) used color infrared imagery as ground control to classify vegetation in the Everglades.

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8 The major applications of visible and in frared remote sensing include detecting surface chemical compositions, vegetative cover and biological processes. Although, visible and color infrared sensory data are useful for environmental studies, wave matter interactions produce noise in the received signal. Spectral Signature of Water Variations in emitted and reflected radiat ion are used to measure, classify and verify the spectral signature characteristics of the land surface. Similar surfaces will share similar signature values within the electro magnetic spectrum for a specific wavelength, and different surfaces typically possess differe nt spectral signatures. Scatter, emittance, reflectance and absorption of specific bands produce a unique “sp ectral signature” or curve that is characteristic for a particular surface property. The remotely sensed spectral signature is related to an associated curv e obtained from laboratory measurements of wavelength versus reflectance for the visible and infrared regions of the electromagnetic spectrum for a library of materials. Water has a low spectral signature reflect ance in the visible and infrared region compared to all other major land cover types. Water, vegetation and exposed ground are the main ground cover types in the C-111, and the ability to rec ognize these ground cover types with remotely sensed images is de pendent on separating a nd distinguishing their spectral characteristics. Water with sediment and debris will produce a higher reflectance spectral signature than that of pure water. Albedo is the reflectivity of a surface, a nd water possesses a low albedo in the near infrared band. Vegetation possesses a high re flectance in the infrared spectrum due to plant microstructure. Vegetation has a re latively low reflectance in the red band compared with soil and turbid water, while wet soil and water have similar reflectance in

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9 the red band. The ratio of red and infrared bands is used to distinguish between water (pure and turbid) and land (vegetation and soil). Because of sediment and debris, flood water will produce a maximum reflectance peak in the red band. This signature is part icularly useful for flood detection; however, the presence of dense clouds may interfere with the signature of the land surface. No verifiable method can be expected to elimin ate cloud contamination to obtain visible and near infrared based flood information under thick cloud formation, (Sheng, Su and Xiao 1998). Active and passive signal errors are primarily attributed to abso rption or scatter of atmospheric noise components. Absorption is caused by the presence of water vapor and gases, while scatter is caus ed by the presence of vapor, gases, dust and atmospheric turbulence. Sensor Performance Four measures of sensor performance are used for determining the quality of the resolution of an image. These measures in clude spectral resolution, spatial resolution, radiometric resolution a nd temporal resolution. Spectral resolution refers to the specific wavelength intervals in the electromagnetic spectrum that the sensor records. A decrease in the wavelength interval results in an increase in the resolution of the image. Spatial resolution is the measure of the sma llest feature that a sensor can detect or the area on the ground represented by each pixe l for a nadir view. Nadir is the point diametrically opposed to the zen ith, which is the point in th e sky directly overhead. An azimuth is an arc from the horizon to the zenith Nadir can also be taken to mean "lowest point" in the sense that zenith can be taken to mean "highest point."

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10 Spatial resolution can also be described in the form of the instantaneous field of view (IFOV) or the measure of the cone angl e (radius) viewed by a single detector at a specific point in time. The scale at whic h an image is captured provides useful information about spatial reso lution, and spatial resolution ma y vary for different sensory bands in an image. For example, the panchromatic band of Landsat 7 possesses a 15 meter spatial resolution while the other bands possess a 30 meter reso lution. Objects that are smaller than the IFOV can be detected if they contrast strongly against the background of surrounding pixels. Conversely, obj ects larger than the pixel may not be detected if their reflectance does not dominate the surrounding pixels. Radiometric resolution or dynamic range is the number of possible data values in each band or the number of bits into which the remotely sensed energy is divided. For example, when the Landsat7 ETM+ sensor re cords the electromagnetic radiation in its IFOV, the total intensity of th e energy is divided into 256 br ightness values for 8 bit data. Data file values or digital numbers for 8 b it data range from 0 to 255 for each pixel. Temporal resolution is a measure of how often the sensor records imagery for a particular area, and for satellites, this is ge nerally defined by its path or orbital cycle. Normalized Differential Vegetation Index (NDVI) Vegetation indices are created by combin ing data from sensor bands into a specified algorithm. They are particularly us eful for identifying features by enhancing certain reflectance properties. NDVI is comm only used to visualize properties of land cover that are elusive with only raw band imagery. NDVI is most useful for mapping land c over including urban ar eas, water, soils, dying and healthy vegetation. A value near 1 repr esents high near infrared reflectivity and a value near –1 indicates str ong near infrared absorption. NDVI is calculated as the

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11 difference between near infrared and red di vided by the sum of red and near infrared. Melesse and Jordan (2003) calculated NDVI as 3 4 3 4 Band Band Band Band NDVI (1) Todd and Hoffer (1998) used mid-infrared with Landsat 5 near infrared data to map land surface moisture. NDVI increased with an increase in healthy vegetative cover. Inundated surfaces possess extremely low NDVI values, because of high infrared absorption and low infrared reflectance pr operties. The study investigated NDVI for targets with specific vegetation cover am ounts and varying soil backgrounds. Although vegetation indices were less sensitive to soil background, they were effective for determining vegetation biomass and vegetati on cover for small areas. The relationship between NDVI and vegetative land cover showed that NDVI was higher for moist soils than the drier soils at the same percent vegetation. NDVI increased substantially as moisture increased for the same vegetation cover. Water Detection Lunetta and Balog (1999) used multi tem poral Landsat 5 data for identifying wetland land cover including water bodies. The re sults showed that sensor data in the mid-infrared, Band 5, best discriminated be tween dry and wet areas. Frazier and Page (2000) successfully used visible and infrared bands from Landsat 5 to detect water bodies in the floodplain of the Murrumbi dgee River in central Australia. Song, Duanjun and Wesely (2003) researched the short wave spectral signature of water bodies. The signature of water is unique among signatures for most natural surfaces, because of its low reflectance thr oughout the electromagnetic spectrum. The reflectance of water bodies showed a decrea se in reflectance with an increase in

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12 wavelength. The signature of water produced a negative value with NDVI, and pixels in satellite images with the most negative NDVI values we re correlated with water bodies. The shape of the spectral su rface reflectance and its valu e in the red band greatly depended on the relative amounts of susp ended minerals, chlorophyll and dissolved organic matter in the water. Under clear water conditions, the re flectance was found to decrease linearly with wavelength. Cloud Detection Cloud contamination alters or sometimes completely obstructs the spectral signature of the land surface. The significan t difference in spectral reflectance between clouds and the earth makes the process of di stinguishing clouds from the Earth’s surface difficult due to the high variability in cloud expression. Sheng Su and Xiao (1998) used thermal, infrared and visible channels of Advanced Very High Resolution Radiometer (AVHRR) to distinguish cloud cover from la nd cover. The spatial variance of cloud top temperature was noted to be greater than that of the Earth’s surface, and the contextual feature of surface temperature was also used for cloud screening. Image analysis showed that cloud shadow caused a re duction in solar irradiance, and cloud shadow and water bodies were difficult to distinguish in the near infrared channel. Melesse and Jordan (2003) used visible, short-wave infrared and thermal infrared bands from Landsat 5 to develop two vege tative indices for de tecting clouds, cloud buildup and water in the Econ Ba sin, Florida. Clouds were detected and classified by using the simplified Plank constant to c onvert Band 6 digital number values to temperature. Image data was used to enhan ce dense clouds and urban features for visual analysis. For Landsat 7 ETM+, vegetative i ndex two and vegetative index three are calculated as,

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13 7 6 2 2 Band Band Band VI (2) 3 5 6 3 Band Band Band VI (3) where Band 2 is the green band, Band 6 is the thermal infrared band, Band 7 is the middle infrared band and Band 3 is the red band. These indices were found to be effective for mapping dense cl oud cover and partial clouds. Airborne Laser Swath Mapping Airborne Laser Swath Mapping (ALSM), or light detection and ranging, Lidar, remote sensing utilizes a laser, detector, scanning system and Global Positioning Systems for topographic mapping. The complete pr ocess involves plan ning, collection, processing, filtering and edit ing echo points from the return signal data. Elevation post spacing is a function of flying he ight, speed, pulse rate and scan angle. Specifications of a commercial ALSM system generally describe laser, scanning, GPS, INS and flying operations and information concerning error and delivery (Table 2.2). Table 2.1 Specifications of a commercial Lidar system. Specification Typical Value Wavelength 1,064 m Pulse Repetition Rate 5 – 33 kHz (50 kHz max) Pulse Energy 100s J Pulse Width 10 ns Beam Divergence 0.25 – 2 mrad Scan Angle 40 (75 Maximum) Scan Rate 25 – 40 Hz Scan Pattern Zig-Zag, Parallel, Elliptical, Sinusoidal GPS Frequency 1 – 2 times per second INS Frequency 50 (200 maximum) Operating Altitudes 100 – 1,000 m (6,000 m max) Footprint 0.25 – 2 m (from 1,000 m) Multiple Elevation Capture 1 – 5 Grid Spacing 0.5 – 2m Vertical RMSE 15+ cm

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14 Table 2.1 Continued Horizontal RMSE 10 – 100 cm Post-Processing Software Proprietary Topographic ALSM lasers use an infrared light beam (1064 nm) that is invisible, absorbed by water and strongly reflected by he althy vegetation and concrete. The laser is sent at a narrow dispersion angle (0.3 rad), and laser spot size or f ootprint is determined by flying height. The infrared beam reflects stro ngly off healthy vegetation, concrete and dry soils, however any presence of water wi ll absorb and warp the beam path. It is important to note that birds and other airbor ne objects will reflect the infrared beam and produce an exceptionally high elevation value. Figure 2.1 shows how the beam reflects off of and penetrates a tree canopy to produce an elevation point. Figure 2.1 Illustration of a Lidar infrared beam The actual beam diameter is smaller than what is shown in the figure. Sour ce http://earthobservatory.nasa.gov Intensity is the measure of the energy re flected from an object. Detecting return intensity involves recording the reflected or return beam energy from the earth surface. Objects possessing high reflectivity properties show a higher return energy than objects

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15 possessing low reflectivity prope rties. Objects such as metal roofs and sand possess high reflectivity values, while water and black ta r pavement possess low reflectivity values. Different sensors have been developed to record multiple returns and reflected intensity. Multiple return signals occur when part of a distended beam strikes an above ground object and the remaining portion stri kes the ground. When this occurs, the recorded signal will then display multiple elevation values from a single pulse. The above ground signal is the “first re turn” and the ground signal is the “last return ”. Multiple returns are found in high, de nse canopy areas, because th e first and middle returns provide elevations for the top and intermittent growth. The last return usually reflects from the ground, however extremely dens e canopy will prevent full penetration. Kraus and Pfeifer (1998) noted if the b eam strikes canopy or branches then the measured ground elevation value might be overes timated. This can lead to an asymmetric distribution error of laser sca nner points. The research result s emphasized the necessity to remove vegetation without deleting ground poi nts for areas possessi ng low penetration rates. ALSM Accuracy Post processing of ALSM data is perfor med to satisfy two requirements for product delivery. The first requirement derives accurate results based on GPS stations to provide a frame of reference for the airborne operati on (Maune 2001). The second requirement is to solve a bare earth conditi on by removing irrelevant points, and this is accomplished with automatic or manual post processing me thods. Automatic processing uses software algorithms to view neighborhood points and weigh them before removal. Manual processing is necessary, because automate d algorithms may produce anomalies not characteristic of the bare earth condition. Occasionally, some apparent data anomalies

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16 appear in files and the analyst may revi ew aerial photography, digital imagery or videotape to identify anomalies (Maune 2001). Few empirical studies exist for assessing th e accuracy of digita l elevation models created with ALSM. Under ideal conditions, absolute vertical accuracy for grass and pavement may be within 15 centimeters, but ve rtical accuracy cannot be obtained within 10 centimeters (Maune 2001). Daniels (2001) evaluated datum conversion issues and accuracy of ALSM by comparison of real time kinetic GPS sample points and lidar spot elevations. Base station, local orthometric heig ht and regional offset corrections used to isolate potential datum offsets in lidar were necessary for mapping dynamic geomorphological surfaces. Hodgson et al. (2003) found elevation root mean square error with ALSM was 33 centimeters for low grass and 153 centimeters for shrub/scrub land cover. In general, vertical errors with low gra ss and high grass were much smaller than in areas of heavy vegetation canopies. Hodgson and Bresnahan (2004) also noted that variation in land surface elevation was strongly correlated with a change in vegetation. Root mean square error values ranged from a low of 17-19 centimeters for low grass and pavement. Shrestha et al. (2000) performed an accuracy assessment for surveying and mapping applications with ALSM. The results s howed that elevation values for bare earth ground were accurate to within +/5 – 10 centimeters. The authors noted that ALSM technology was an innovative approach for high resolution flood plain and drainage mapping. ALSM Point Removal Automated post processing of ALSM data attempts to model a bare ground condition by using software to identify a nd remove artifacts. Automated methods for

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17 point removal are based on neighborhood operators that iteratively identify the lowest points within a defined search neighborhood. Th e operator then adds them to a candidate set of ground returns. Subseque nt iterations select the candida te set by adding returns that are low or exhibit some angular deflection from a surface modeled by the current candidate set of points. The details of search neighborhood operators and parameters vary by lidar mapping vendor. Generally, the analyst will examine a candidate set of ground returns to further improve the accuracy of labeling features. Th e procedure also requires an analysis of small areas as a three dimensional cloud of ALSM points overlain on available digital orthophotography. Thus, the process of point removal may contain errors, because removal is both adaptive and subjective. Krabill et al. (2000) used ALSM to study changes in beach morphology. The research showed that post processing rema ined problematic for removing artifacts including near ground vegetation. Okagawa (200 2) assessed multiple automatic filters to extract artifacts from digital surface m odels. The author concluded that image information was indispensable for identifyi ng artifacts during post processing. Kampa and Slatton (2004) used a multisca le filter to segment bare ground from artifact points in ALSM data. To compute the mean square erro r for performance, the adaptive filter was initially applied to simulated ground data. The ground surface was distinguishable from artifact points for a point density of tw elve points per 25 square meter grids. Raber et al. (2002) used an adaptive filte r to minimize the overall error by applying different vegetation point removal parame ters based on vegetation type. The study involved extracting vegetation land cover t ype information using only ALSM multiple

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18 return data. The study showed that land cove r information could be used adaptively in ALSM vegetation point removal for the pr oduction of accurate el evation models. Land cover observations involved analysis of colo r infrared imagery, gr ound control points and vegetation land cover. Histogram analysis showed that monoculture canopies were characterized by a dampened bimodal histogram A statistical analysis further showed that among all land cover types, low and high grass possessed the lowest mean absolute error values. Huising and Pereira (1998) studied bare earth modeling and found that separating dense vegetation from bare ground was a protra cted process. The authors observed that manual filtering may be better than automated, however more time is required for post processing large areas. The manual method was found to be ideal for filtering vegetation and other artifacts in small areas. The aut hors concluded that us ing only topography data compounded the problem, and aerial photogr aphy was determined essential for classifying land features. The accuracy of elev ation measurements was related to the laser system and terrain geometry, and flat terrain and low grass areas were used to estimate accuracy. ALSM Applications Persson, Holmgren and Soderman (2002) us ed ALSM to detect individual trees by estimating height crown closure and stem volume. The study used the lowest laser reflection points to derive ba re earth DTMs. The study further noted that return intensity and type return pulse data provided more in formation about tree structure. Hodgson et al. (2003) also used orthophotography and ALSM surface cover height to map impervious land surfaces.

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19 Multiple remotely sensed data sets may be used to separate vegetative height from a theoretical bare earth condition. Pope scu and Wynne (2004) combined lidar and multispectral data to accurately estimate plot level tree height by focusing on the individual tree level. Combining small footpr int airborne lidar data in conjunction with spatially coincident optical data was found to help accurately pr edict tree heights of interest for forest inventory and asse ssment. The study recommended that project methodology can be applied to process lidar data for vegetation rem oval, and individual tree location. Popescu and Wynne (2003) developed anal ysis and processing techniques to facilitate the use of small foot print ALSM for estimating plot level tree height. This was accomplished by measuring individual trees id entifiable on a three dimensional ALSM elevation model. The study used the combin ation of ALSM and multi-spectral optical data fusion to differentiate between forest types and improve the estimation of average plot heights for pines. The research demonstr ated that small foot print ALSM, used in conjunction with spatially coincident optical data, was accurately able to predict the tree heights of interest for forest inventory and assessment. Hopkinson et al. (2004) used ALSM to map snowpack depth under forested canopies. Snow pack distribution patterns we re mapped by subtracting a bare earth DEM grid from a peak snowpack DEM grid. Snow pack depth was used to predict water availability and flood levels during the warm ing period. The study also found that a high proportion of last pulse returns led to an overestimation of ground elevation. The study recommended a further assessment of type dependent elevation offsets for improving elevation and snow depth estimation.

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20 Brock et al. (2001) used ALSM to r ecognize and map surfaces that provide accurate low variability topographic measuremen ts. These features were termed fiducial and were used as reference base line features for mapping morphology. Fiducial features are naturally occurring bald earth features su ch as beaches, bare dunes and ice sheets. The process for separating dense plants of less th an 10 centimeters was difficult based solely on passive spectral signatures of ALSM. Th e presence of vegetation increases the difference between ALSM and ground survey el evations from a minimum of 0.26 meters over bare sand to values near 0.40 meters for all vegetation classes. Of the four defined vegetation classes consisting of mono, spar se, medium and dense, sparse vegetation possessed the highest variance between ALSM and coordinate survey elevations. Evans et al. (2001) used sampling theory to map individual trees and estimate tree height. Small foot print lida r failed to yield ground return s in areas dominated by dense vegetation canopy. Renslow and Gibson (2002) developed bare earth models from ALSM and high resolution aerial photography to assist th e decision making pro cess for increasing services for utility companies. This wa s accomplished by mapping fast track utility corridors using bare ea rth models. Heinzer et.al (2002) us ed ALSM and aerial images to model inundation, velocity a nd steady state flow of water. Interestingly, bare earth models were interpolated from group points; however buildings we re reinserted to display realistic stru ctural definitions. Geographic Information Systems Geographic Information Systems, GIS, is software designed to create maps by capturing, storing, retrieving, manipulati ng and displaying geographically referenced

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21 spatial tabular data (www.usgs.gov, April 2006) The three types of spatial data common in GIS are points, arcs (lines) and polygons (areas). GIS thematic map layers can display topological relationshi ps between mapped features. Topology refers to recording the sp atial relationship betw een points, arcs and polygons. A coverage is a GIS da ta file that display topol ogy, however some GIS data files such as shape files do not display topological relationships. Metadata files list important parameters de scribing attributes of remotely sensed and GIS data products. Metadata typically incl udes the coordinate system, period of data capture and ancillary information pertinent for mapping applications with other data sets. Spatial Modeling There are two classes of interpolation, deterministic and geo-statistical. Deterministic methods such as inverse dist ance weighting, splines, and radial based functions are directly based on an interpolat or that uses the surrounding measured values or mathematical formulas applied to those valu es. Geostatistical models, such as kriging, predict values by accounting for the probabi listic spatial relati onship among neighboring points. Kriging is able to predict esti mation errors and is often preferred over deterministic methods. The surface calculated using inverse distan ce weighting depends on the selection of a power value and the neighborhood search stra tegy. For inverse distance weighting the maximum and minimum values in the interp olated surface can only occur at sample points. The output surface is se nsitive to clustering and the pr esence of outliers. Inverse distance weighting assumes that the surface is being influenced by the local variation, which can be captured throughout the neighborhood.

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22 The local polynomial method is a modera tely quick and smooth deterministic interpolator. It is more fl exible that the global polynom ial method; however there are more parameter decisions. There is no a ssessment of prediction errors; however the method provides prediction surfaces that are comparable to kriging with measurement errors. Local polynomial methods do not allow a ny analysis of the sp atial autocorrelation of the data, thus it is less flexible and more automatic than kriging. The global polynomial method is also a quick and smooth deterministic interpolator. There are fewer decisions to ma ke regarding model parameters than for the local polynomial method. It is best used fo r surfaces that change slowly and gradually. There is no assessment of the predictions e rrors and this method may produce a surface that may be too smooth. Values at the edge of the data can have a significant impact on the interpolated surface. Radial based functions are moderately qui ck deterministic interpolators that are exact, and they are considerably more flexib le than inverse distan ce weighting, however there are more parameter decisions, and there is no assessment of prediction errors. The method provides prediction surfaces that are co mparable to the exact form of kriging. Radial based functions do not al low for analysis of the autocorrelation of the data, thus making it less flexible and more automatic than kriging. Radial base d functions are used for calculating smooth surfaces from a large num ber of data points, and are preferred for gently varying surfaces such as elevation. The radial ba sed function is inappropriate when there are large changes in the surface values within a short horizontal distance and/or when the sample data is prone to error or uncertainty.

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23 Ordinary kriging produces interpolated values by assuming a constant but unknown mean value, allowing a local influence fr om nearby neighboring values. Because the mean is unknown, there are few assumptions ab out the data. This makes ordinary kriging flexible but less powerful. Simple kriging produces interpolated va lues by assuming a constant but known mean value, allowing local influences due to nearby neighboring values. Because the mean is known it is slightly more powerful th an ordinary kriging but in some cases the selection of a mean va lue is not well known. Universal kriging produces interpolated va lues by assuming a trend surface with unknown coefficients in the model; however it allows local influences from nearby neighboring values. It is possible to overfit the trend surface, which fails to leave enough variation in the random errors to properly refl ect uncertainty in the model. It can be more powerful than ordinary kriging because it expl ains much of the variation in the data through a non-random trend surface. Disjunctive kriging considers f unctions of the data, rather than just the original data values themselves, and stronger assumptions are required. Disjunctive kriging assumes all data pairs come from a bivariate normal distributi on and the validity of these assumptions should be checked. A bivariat e normal distribution describes relative frequencies of occurrence in th e population of pairs of valu es. When this assumption is met, the functions of the data are indicator variables that transform the continuous data values to binary values base d on a decision threshold value. Doucette and Beard (2000) evaluated inverse distance weighting, splines and universal kriging as interpolators to fill gaps left by occlusions in digital elevation data.

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24 The results favored splines as a surface in terpolator, especially as terrain roughness increased. The study additionally found that altering the search radius parameter significantly impacts interpolation error statistic values. Selecting a best fit model depends on the assessment of several modeling statistics. In general, the best fit model is one that ha s the standardized mean error closest to zero, the lowest root mean squared prediction error, the average standard error nearest to the root mean squared prediction error and the standardized root mean squared prediction error closest to one (ESRI 2001). Inundation Mapping with GIS Previous flood mapping effo rts have used remote sensing and GIS to map the extent, duration and magnitude of floodi ng. Ball and Schaffranek (2000) used topographic and surface water grids to map wa ter depth in the southern Everglades. Temporal inundation patterns we re mapped and compared to hi storical and current water depths. A comparison to othe r hydroperiods was conducted to isolate temporal changes affected by anthropogenic influences of wate r management policy. To estimate water depth accuracy, computed depths were subtract ed from depths measured in the wetlands adjacent to the C-111 canal and in Taylor Slough in 1997 and 1999. Ball and Schaffranek (2000) employed a similar method to study water surface elevation and water depth for Taylor Slough in the southern Everglades. A GIS program was used to subtract topographic elevation grids from surface water elevation grids. The extremely low topographic relief of the southe rn Everglades produced significant spatial variability in surface water gradients. Furt hermore, the land surface elevation grid was calculated from interpolating global positioning systems (GPS) topographic data sponsored by the USGS, National Mapping Division. Daily surface water data was

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25 obtained from the SFWMD, and National Park Service (NPS) Everglades National Park. The research concluded that water depth and topographic accuracy were directly correlated to the spatial resolution and accura cy of input data. Project inundation grids were calculated with the sa me method; however an ALSM topographic grid was used instead of a GPS topographic grid. ALSM topographic grids produce inundation maps with a finer spatial resolution than GPS topographic grids.

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26 CHAPTER 3 DATA RESOURCES AND METHODOLOGY Introduction Project data sources include aerial color in frared imagery, Landsat7 EMT+ sensor data, surface water elevati on data and ALSM topographic data. All tables that are referenced can be found at the end of this chapter. Color infrared imagery was acquired from Land Boundary Information Systems (www.labins.com April 2006), Labins, and each image possessed a 1 meter spatial resolution. Color infrared imagery for the study area is found in qua drangle Royal Palm Ranger Station or Quadrangle 1205 S.W. and was obtained for 1994 and 1999. The primary use of color infrared imagery was to identify vegetation points in the NAD 27 and NAD 83 ALSM point data sets. Surface Water Data Surface water elevation values for regional canal stations and well monitoring sites were obtained from the South Florida Wate r Management District (SFWMD), U.S. Geological Survey (USGS) and National Pa rk Service (NPS). Su rface water elevation data covers the period during October 12-22, 1999. SFWMD canal stage elevations were recorded for both head and tail stage, and the mean between head and tail was used to create surface water grids. Surface water valu es were recorded in feet, and NGVD29 was used as the reference vertical datum for hydrologic and topographic data sets.

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27Digital Elevation Model Construction In 1999, FEMA and NASA sponsored 3001, a consulting firm, to conduct ALSM flight operations for the C-111 basin. ALSM files are arranged by flight area, and each flight area includes a cac he of files that pr ovide a variety of el evation data products. ALSM data was captured by single pulse return data, and the products included text files of x,y,z coordinates and processed DTM file s. All coordinate elevation values were recorded in feet, and text files were prepared for all first return raw data and automated filtered bare earth data. NAD 27 raw data valu es were recorded to either one-hundredth or one thousandth of a foot. All NAD 83 bare ea rth file data were recorded to one onehundred thousandth of a foot. Only accuraci es of hundredths of a foot should be considered for DEM analysis, because real time kinematic GPS values are only accurate to one one-hundredth of a foot. The post spac ing for points was 10 feet along the track direction and 23 feet across the track direc tion. Bare earth contour line DTMs were included for each area and elevation lines were categorized by one-foot intervals. These DTMs were not used in this study, becau se of their low vertical resolution. Data quality reports were prepared for al l areas and they included coordinates for flight area ground control points in addition to the methodology used to create the bare earth digital terrain models. These reports are commonly used to inform the client about the accuracy of ALSM data by a statistic al comparison between ground surveyed GPS points to associated ALSM points. Data quality reports for the study area listed a vertical accuracy of 15 centimeters, and bare earth DTM files were created using Delaunay triangulation (3001 1999). Bare earth files were created using proximal analysis to filter unwanted points according to the report; however no additional information was provided about the procedure.

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28Landsat 7 Enhanced Thematic Mapper Landsat 7 ETM + image data were used to detect inundated surfaces and dense clouds within the study area afte r Hurricane Irene. Landsat7 sc enes for October 16, 1999, and April 9, 2000, were obtained from the USGS and all data deliverables were stored on CD-ROM media and delivered as Geotiff files. For image processing, all Geotiff files required both importation in ERDAS Imagin e and exportation as an ERDAS Imagine image file. The first scene was captured on Oc tober 16, 1999, nine hours after Hurricane Irene passed over the C-111, and the second was captured on April 9, 2000 during the peak of the dry season. A bend in the L31W canal was used to de tect an offset between the October 16, 1999, and April 9, 2000, Landsat 7 ETM+ scen es. Band 8 was used from both scenes to locate the x and y values for associated pixe ls, and the offset was measured. The offset between associated pixels was 48 ft. north and 1 ft. east. Vegetative Index Methodology ERDAS Imagine is software that is speci fically designed to work with large georeferenced image data sets. NDVI map me thodology was initiated by creating a layer stack of red and infrared bands for both Landsat7 scenes. The ERDAS Imagine layer stack function combined sensor data, and th e NDVI function automatically created an NDVI image by separating Band 3 and Band 4 from the stack and subs tituting them into the NDVI equation. The ERDAS Imagine Spatial modeler extens ion selected the a ppropriate individual band layers in a composite layer stack and substituted them into their designated vegetative index equations. The Spatial Modeler tool was used to create vegetative index two and vegetative index three i ndices described in Chapter 2.

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29Unsupervised Classification An unsupervised classification divides pixe ls into classes based on their digital number value. ERDAS Imagine unsupervised classification was pe rformed on the three vegetative index images, and 30 classes were created for all vegetative index maps. All vegetative index unsupervised images were sc aled with Class 1 representing the lowest reflectance grouped values and class 30 re presenting the highest reflectance grouped values. ALSM Processing Topographic grids were created from NAD 83 / NGVD 29 and NAD 27 / NGVD 88 ALSM data. The procedure for creating a poi nt map theme from text data required a list of vertical, horizontal and elevation va lues. All text files were space delimited and consequently, no files could be opened by GI S software. Only tab and comma delimited formats are recognized by GIS software for im portation. Furthermore, all text files were too large to fit the 65,536 spreadsheet row entr y maximum. In response to this constraint, a quick and effective procedure was developed to convert text files into shape files. The conversion of text files into database tables was required for importation into GIS software. This initial step involved opening each text f ile with Microsoft Wordpad, and converting the native .xyz format to an ASCII text file. The ASCII text file was opened in a spreadsheet and saved as a data base file; however spreadsheet row entries were limited to only 65,536 displayed values The Find and Replace tool in Microsoft WordPad located the 65,536th value in the text file, and all values listed above the 65,536th value were selected and deleted. Th e altered file was saved under the original text file name, and displayed the 65,536th valu e as the first value in the spreadsheet. All cells were converted to a number format with six decimal places and the column

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30 containing coordinate values we re assigned X, Y, and Z field headings respective to their measurements. The process was applied to all files until the original text file was reduced to an acceptable size for one spreadsheet. The end result produced a series of spreadsheets with each representing one sub-area. Arcview 3.2 was used to develop a point shape file from the data base file. All database tables were imported into Arcvie w, and the Add Event function was used to display the table coordinate values as point s for all data base tables imported into Arcview. Arcview’s Geoprocessing tool was us ed to merge and convert sub area database files into a point shape file. The geoprocessing tool designates the tables to be merged and then exports the resultant thematic shape file to a known file di rectory. The offset coordinate value between NAD 83 and NAD 27 was 156235.73 ft. false east and 159.86 ft. false north. These values were later used to project surface wa ter data points from NAD 27 to NAD 83. Bare Earth Modeling The procedure for modeling a bare earth condition for the study area involved the manual removal of ALSM points that repr esent vegetation, fiducial features and structures. Point removal was based on the assumption that the st udy area possessed a flat topography and low elevation char acteristic of the C-111 basin. The steps utilized in the process included a cross-comparison between color infrared imagery, NAD 27 ALSM DEM, and ground control point elevations in the study area. Multiple interpolators and search parameters were tested fo r predicting grid elevation values. ALSM data was collected in NAD 83 and NGVD 88 datum; however the unfiltered data was placed in NAD 27 and NGVD 29. The measured difference between NGVD 29 and NGVD 88 first order benchmark elevation values is 1.5125 ft. Project inundation

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31 maps were placed in NGVD 88, because this wa s the vertical datum used in the data collection process. The proximal analysis method was used by 3001 to model the bare earth condition for NAD 83 data, however this method is not defined in the data quality report for Area 2. Furthermore, this method was not sufficient for flood mapping, because vegetation points were found in the bare ea rth model for the study area. The success of topographic bare earth models relies on th e accuracy of the estimated maximum bare earth elevation threshold used to remove points. Aerial Color Infrared Analysis The available Land Boundary Information System (Labins) aerial imagery covered the dates of December 27, 1994, Figure 3.1(a) and February 21, 1999, Figure 3.1(b). Aerial color infrared imagery was usef ul for identifying ve getation patterns and associated land features in ALSM maps. Healthy vegetation in images possessed a strong reflectivity in the infrared region of the el ectromagnetic spectrum, and was displayed as red. Although row crop vegetation reflected strongly as red, the individual boundaries varied between both images. In both images, dense tree canopies reflected the strongest and were easily distinguished from the surrounding land cover. In the 1999 image, wetland forest was characterized by variable red reflectivity values; however only high dense tree canopies were consistently reflect ed as red in the 1994 image. The soil mound seen in Figure 1.2, reflected as white in the 1994 image when leaf canopy was reduced, and was difficult to distinguish from th e surrounding land cover. In the 1999 aerial image, the soil mound was easily distinguished from the heterogeneous cover of healthy vegetation and exposed bare soil.

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32 In the 1999 aerial image row crop vegetation conformed to field boundaries, however in the 1994 aerial image not all ro w crop vegetation conformed to field boundaries. This dissimilarity was most not iceable in canopy patterns found in leased parcel 19. A semi-circular arc of vegetation can be seen in the southeast quadrant of the 1994 image, Figure 3.1(a). This was useful for identifying suspect vegetation patterns found in the raw ALSM topographic DEMs. Ground Control Point Analysis Although ground control points, GCP, were not in the study area, they were analyzed for determining the threshold valu e for estimating the bare earth condition, Table 3.1. The process for determining vege tation points was subjec tive. This depended on visual analysis and anal ysis of nearby ground control points in the NAD 27 DEM. The objective of the approach was the removal of vegetation points, while preserving points that represented roads and bare earth. Ba sed on this methodology, the value of 4.80 ft. was determined to be the maximum bare ea rth elevation value for the study area for NAD 27. Consequently, all points in the associated NAD 83 DEM th at exceeded the value of 3.29 ft. were also identified as vegetation a nd removed using the clip tool in Arcview. Recall that the difference between NAD 83 and NAD 27 maxi mum bare earth elevation values is 1.51 ft., and this is equal to the difference between NGVD 29 and NGVD 88 elevation values. Arcview’s query filter wa s used to remove elevation points that exceeded the designated maximum elevation thre shold value. The clip tool in Arcview was used to create a separate shape file cons isting only of points that were not deleted.

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33 A Figure 3.1 Color infrared aerial photos of th e study area. (A) 1994 co lor infrared aerial image (B) 1999 color infrared aerial im age. The study area is outlined in yellow. B

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34Topographic Spatial Modeling Multiple interpolators were used to develop prediction grids for NAD 27 and NAD 83 point shape files. These interpolators included inverse distance weighting, local polynomial, global polynomial, radial based functions, universal kriging, ordinary kriging, simple kriging and disjunctive krig ing. The root mean square error, RMSE, statistic was calculated by sequential dr opping of each observed elevation point and estimating it using the appropriate interpolation procedure. The RMSE was used to select the optimum interpolation met hod for surface water and topogr aphic grids. If two tests possessed an equal RMSE statistic, then the mean absolute error, was used as the next decision statistic. All prediction grids, including surface water, were exported as raster surfaces to be later used for calculating i nundation grids. All interpola tion methods were used to generate z prediction values for a test x a nd y location, the results of the different prediction methods showed that the predicte d values at the test location ranged from 2.9217 ft. to 2.9483 ft. The difference in z pr ediction values indicates that a small variation exists between pr edicted topographic grids using the various methods. Table 3.2 lists the search parameters for the inverse distance weighting method that were not set to a default value. The nei ghborhood method was used for all tests. The search ellipse used for the neighborhood s earch had major and minor semi-axes of 2,134.6 ft., and the anistropy factor was set to a value of 1. The x and y test prediction locations were 797,555.55 ft. and 394,614.36 ft. respectively. Table 3.3 lists the search parameters for the global polynomial method. Ta ble 3.4 lists the search parameters for local polynomial method. Table 3.5 lists the search parameters for radial based function tests.

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35 Table 3.6 lists the search parameters us ed for kriging methods. For all kriging methods, no transformation was applied, and no trend was assumed. The angle direction and tolerance were 15 and 45 and the band width was 6. The number of lags was 12, the search shape angle were 3 and 15 respec tively. The major and minor semi-axes were 6,306.9 ft. and 5,546.8 ft., and the anistropy fact or was 1.137. The test x and y locations were 797,555.95 ft. and 394,614.36 ft., and twen ty neighbors were used for the test prediction value. Surface Water Elevation Map Methodology The surface water elevation map (SWEM) was created to show the change in surface water elevation values over the st udy period, and was used to calculated inundation grids. All surface water data was acquired from the SFWMD, USGS, and the NPS. All surface water project data values were referenced with NAD 27 horizontal datum and NGVD 29 vertical datum. NGVD 29 surface water elevation values were converted to NGVD 88 by subtracti ng 1.51 ft from NGVD 29 values. REMO is the SFWMD internet data retrieval program that provided surface water elevation data. REMO hydrologic data was deli vered in text format and all data was converted to data base file format. All wate r elevation data was r ecorded in feet and referenced to NGVD29. Canal elevation measurements included head and tail measurements; however the mean value between head and tail was used to create surface water grids. SFWMD data sites included th e S175, S177, S178, FP, FP1, FP2 and S332D. FP, FP1 and FP2 are wells and the S175, S177, S178 and S332D are water control structures. Figure 3.2 shows in situ m easurement sites used to create SWEM. USGS provides maximum daily ground water elevations for monitoring stations in Miami-Dade. USGS maximum water elevati on data for G3355 was acquired through the

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36 USGS water resources internet link with the SFWMD, and USGS water resources for Miami-Dade. G3355 is located in the southeast corner of Figure 3.2. Figure 3.2 Map of measurement sites. USGS sponsors Tides and Inflows in th e Mangroves in the Everglades, TIME (time.er.usgs.gov). TIME provides telemetric surface water elevati on measurements in daily, hourly and fifteen minute intervals. TIME water monitoring wells NP112 and NP158 were used to create surface water grids. The locations NP112 and NP158 are shown in Figure 3.2. The procedure for developing the surface wa ter site point shape file began with transforming the Latitudinal and Longitudi nal coordinates from Degrees-MinutesSeconds to Data-Decimal-Degrees, DDD, Measurement Sites

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37 3600 60 Seconds Minutes Degrees DDD 1) The DMS coordinates were divided into separate spreadsheet fields, and the conversion for each coordinate was performed using cell formulas. Longitudinal coordinates were assigned nega tive values. The resultant sp read sheet file was exported as a tab delimited text file and assigned a .dat extension. The ERDAS Imagine vector tool was used to export the .dat files as Arcinfo coverages. The coverage file was opened in Ar cview, and the view properties were set to match the projection parameters of ALSM as defined in the meta-data report, Table 3.7. Finally, the coverage file was converted to a shape file with the same coordinates as the ALSM data. Surface Water Elevation Map Interpolation Tables 3.8, 3.9, 3.10 and 3.11 list the initia l search parameters for kriging tests used to estimate the surface water elevation. A de scription of these parameters is discussed below. For universal kriging no trend removal a nd no transformation were performed. A bandwidth of 6 ft. and the lag size and lag number were 2,721 ft. and 12. The major semiaxis and minor semi-axis for the neighborhood search ellipse were 30,000 ft. and 24,000 ft. The anisotropy factor was set to a default value of 1.25 for all tests. The x and y test prediction locations were 804,799 ft. and 389, 908 ft. The software’s default value for search neighbors was used, and the number of search neighbors was set to five for prediction. For disjunctive kriging, no transforma tion or trend removal was conducted. The direct method was used, and the major and minor ranges were 30,023 ft. and 12,607 ft.

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38 The search direction, partial sill and nugget were 64.9, 0.64504 ft. and 0.0221170 ft., respectively. The lag size and number were 2,721 and 12. The major and minor semiaxes were 30,023 ft. and 12,607 ft. The anisotropy factor was set to 2.3815. The x and y test location values were 804,799 ft. and 389,908 ft., and the bandwidth was set to 6 ft. For ordinary kriging, no tr ansformation or trend removal was conducted. The major and minor ranges were 30,705 ft. and 26,007 ft The angle direction, partial sill and nugget were 14.8, 1.1645 ft. and 0 ft., respectiv ely. The lag size and number were 2,721 ft. and 12, and the bandwidth was set to 6 ft. The major and minor semi-axes were 30,705 ft. and 26,007 ft. The anisotropy factor was 1. 1806, and the x and y test location values were 804,799 ft. and 389,908 ft. For simple kriging, no transformation was applied, and the mean threshold value not to be exceeded was 3.857 ft. The bandwidth was set to 6 ft., and the major and minor ranges were 30,091 ft. and 21,698 ft. The anisotro py factor was activated for all tests, and the nugget was 0.42352 ft. The lag size and numbe r were 2,721 ft. and 12 respectively. The search angle direction and partial sill were 20 and 0.61003 ft. The major and minor semi-axes were 30,705 ft. and 26,007 ft. The test x and y prediction locations were 804,799 ft. and 389,908 ft. Table 3.12 lists the search parameters for universal kriging for SWEM. The universal kriging major range was set to 30, 668 ft. and the minor range was set to 25,892 ft. The major semi-axis and minor semi-axi s were set to 30,000 ft. and 24,000 ft. The anisotropy factor was set to 1.25. The test prediction location was 804,799 ft. and 389,908 ft. The lag size and number were set to 2,721 ft. and 12 respectively. Eight neighbors were used for the search paramete rs. No trend removal and or transformation

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39 were conducted. The global influence, local in fluence, angle directi on, angle tolerance, search direction and were equal to 65%, 35% 15%, 35, 15 and 6 ft. respectively. Shape 3 was selected and the shape angle was set to 15. Surface Water Inundation Map Methodology The surface water inundation map, SWIM was calculated by subtracting ALSM topographic grid values from SWEM grid values. Surface water and topographic grids were exported as raster surfaces with a 3 mete r resolution. The raster math calculator in ArcGIS Spatial Analyst extension was used to subtract topographic grids from surface water grids, and the resultant inundation grid s also had a 3 meter spatial resolution. A value of 0 ft. in elevation was inserted into the L31W canal to prevent aliasing caused by interpolation. 3D ALSM DEMs were useful for determin ing elevation values that represented vegetation. Converting the rast er surface into a TIN created 3D TIN DEMs, and the TIN was imported into ArcGIS Scene. Table 3.1 Area 2 static GPS points used to determine the elevation filter. Z1 is the elevation of the GPS point, and Z2 is the measured ALSM elevation for that point. Z is the difference in elevation between Z1 and Z2. Test Id Z1 ft. X ft. Y ft. Z2 ft. Z ft. 663 4.50 640246.31394360.81 4.890 0.390 665 4.46 640248.90394361.10 4.890 0.430 729 4.72 640518.40392310.00 4.660 0.060 732 4.31 640707.30392308.60 4.300 0.010 733 4.29 640716.80392308.50 4.400 0.110 734 4.60 640728.50392308.40 4.400 0.200 735 4.61 640739.40392307.80 4.630 0.020 736 4.67 640752.00392306.70 4.660 0.010 737 4.64 640763.10392307.20 4.660 0.020 738 4.51 640774.10392305.30 4.760 0.250 739 4.66 640785.90392304.50 4.630 0.030 741 4.59 640495.50392311.90 4.860 0.270

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40 Table 3.1 Continued. Id Z1 ft. X ft. Y ft. Z2 ft. Z ft. 742 4.65 640484.10 392312.90 4.660 0.010 743 4.65 640472.00 392314.00 4.760 0.110 744 4.55 640460.70 392313.50 4.660 0.110 745 4.52 640449.80 392316.20 4.530 0.010 746 4.53 640438.80 392318.60 4.430 0.100 746 4.53 640438.80 392318.60 4.530 0.000 747 4.59 640427.50 392321.10 4.890 0.300 749 4.63 640402.80 392324.90 4.660 0.030 Table 3.2 Inverse distance weightin g search parameters for topography1. Test Id Power Shape Shape Angle Neighbors Prediction 1 2 3 15 60 2.9338 2 1.7682 3 15 60 2.9446 3 1.7682 1 15 15 2.9343 4 1.7682 2 15 60 2.9444 5 1.7682 4 15 120 2.9480 6 1.7682 4 10 120 2.9483 7 1.7682 1 10 15 2.9343 8 1.7682 2 10 60 2.9443 9 1.7682 3 10 60 2.9446 10 2 1 20 15 2.9343 11 2 2 20 60 2.9443 12 2 3 20 60 2.9449 13 2 4 20 120 2.9356 14 2.7365 1 20 15 2.9217 15 2.7365 2 20 60 2.9222 16 2.7365 3 20 60 2.9220 17 2.7365 4 20 120 2.9223 18 2.7365 4 15 120 2.9223 19 2.7365 3 15 60 2.9222 20 2.7365 2 15 60 2.9222 21 2.7365 1 15 15 2.9217 22 2.7365 1 10 15 2.9217 23 2.7365 2 10 60 2.9222 1 The shape angle is in degrees. Shape type refers to the search shape used for all interpolation tests. Shape 1 is an open circle and shape 2 is a circle divided by four perpendicular lines running north, south, east and west. Shape 3 is a circle divided by four perpendicular lines running northeast to southwest and northwest to southeast. Shape 4 is a circle divided by eight lines that possess the same directions as Shapes 2 and 3.

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41 Table 3.2 Continued Test Id Power Shape Shape Angle Neighbors Prediction 24 2.7365 3 10 60 2.9222 25 2.7365 4 10 120 2.9223 26 2.7365 4 5 120 2.9223 27 2.7365 3 5 60 2.9222 28 2.7365 2 5 60 2.9222 29 2.7365 1 5 15 2.9217 30 2.7365 1 0 15 2.9217 31 2.7365 2 0 60 2.9222 32 2.7365 3 0 60 2.9222 33 2.7365 4 0 120 2.9223 34 2.7365 4 25 120 2.9223 35 2.7365 3 25 60 2.9222 36 2.7365 2 25 60 2.9222 37 2.7365 1 25 15 2.9217 Table 3.3 Global polynomial sear ch parameters for topography. Test Id Power 1 2 2 2 Table 3.4 Local polynomial search parameters for topography. Test Id Global Influence (%) Local Influence (%) Power 1 10 90 1 2 15 85 1 3 20 80 1 4 25 75 1 5 30 70 1 6 0 100 1 7 10 90 2 8 20 80 2 9 25 75 2 10 30 70 2 Table 3.5 Radial based function search para meters for topography. SWT is spline with tension, MQ is multi-quadratic, CRS is completely regularized spline, IM is inverse multi-quadratic and TPS is thin plate spline. Test Id Kernal Function Parameter Shape Shape Angle Z Prediction Value ft. Neighbors 1 SWT 1.3715 3 15 2.9500 60 2 SWT 1.3715 1 15 2.9494 15 3 SWT 1.3715 2 15 2.9500 60

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42 Table 3.5 Continued Test Id Kernal Function Parameter Shape Shape Angle Z Prediction Value ft. Neighbors 4 SWT 1.3715 4 15 2.9500 64 5 SWT 1.3715 4 20 2.9499 64 6 SWT 1.3715 3 20 2.9504 32 7 SWT 1.3715 2 20 2.9500 32 8 SWT 1.3715 1 20 2.9468 8 9 SWT 1.3715 1 10 2.9468 8 10 SWT 1.3715 2 10 2.9498 32 11 SWT 1.3715 3 10 2.9504 32 12 SWT 1.3715 4 10 2.9500 64 13 SWT 1.3715 4 5 2.9500 64 14 SWT 1.3715 3 5 2.9505 32 15 SWT 1.3715 2 5 2.9500 32 16 SWT 1.3715 1 5 2.9468 8 17 MQ 0 1 5 2.9382 8 18 CRS 0.47662 1 5 2.9240 8 19 IM 7.3705 1 5 2.9248 8 20 TPS 1 e 20 1 5 2.9274 8 Table 3.6 Kriging search parameters for topography. Type Major Range Minor Range Direction Partial Sill Nugget Lag Number Z Prediction Value ft. OK 6306.9 5546.8 274.4 0.0153080.0406 532.08 3.0653 SK 1437.7 915.89 288.8 0.02606 0.0249 187.38 3.0558 UK 6040 6040 9.0 0.0000 0.013034532.08 3.0660 DK 1350.4 957.16 290.8 0.45212 0.41439 186.15 3.0569 Table 3.7 Projection Parameters for ALSM Source 3001 Area 2 Data Quality Report. Description NAD 27 NAD 83 Map Units Feet Feet Distance Units Feet Feet Standard Projection SP 27 Florida East SP 83 – Florida East Custom Projection Transverse Mercator UTM Central Meridian 81 00 00 81 00 00 Latitude of Orgin 24.3333 24.3333 Scale Factor 0.999942 0.999917 False Easting 500,000 200,000 False Northing 0 0

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43 Table 3.8 Universal kriging search parameters for SWEM. Test Id Global Local Angle Direction Angle Tolerance Nugget 1 20 80 15 45 0.011617 2 20 80 30 45 0.011617 3 30 70 30 45 0.017298 4 30 70 20 35 0.017298 5 30 70 0 30 0.017298 6 25 75 10 30 0.013573 7 25 75 20 30 0.013573 8 35 65 20 30 0.014003 9 40 60 10 30 0.007041 10 40 60 10 30 0.007041 11 45 55 25 30 0.000000 12 50 50 15 35 0.000000 13 55 45 15 35 0.000000 14 60 40 15 35 0.000000 15 65 35 15 35 0.000000 Table 3.8 Conyinued Test Id Major Range Minor Range Angle Direction Partial Sill Shape Shape Angle Z Prediction Value ft. 1 30332 23242 13.2 0.0081215 3 20 3.3100 2 30332 23242 13.2 0.0081215 2 15 3.3554 3 30453 24640 8.9 0.0151810 1 15 3.2981 4 30453 24640 8.9 0.0151810 4 25 3.3312 5 30453 24640 8.9 0.0151810 3 10 3.3214 6 30550 23285 13.3 0.0114650 3 10 3.3160 7 30550 23285 13.3 0.0114650 3 30 3.3245 8 30415 24626 11.8 0.0376450 3 15 3.1673 9 30526 25812 13.7 0.0774980 3 15 3.0834 10 30526 25812 13.7 0.0774980 3 30 3.0761 11 30569 25809 14.8 0.1291700 4 30 3.0526 12 30526 25864 15.7 0.1818600 3 15 3.0551 13 30628 25859 15.7 0.2500900 3 15 3.0550 14 30586 25890 14.9 0.3312100 3 15 3.0550 15 30668 25892 14.7 0.4212600 3 15 3.0549

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44 Table 3.9 Simple kriging search parameters for SWEM. Test Id Angle Direction Angle Tolerance Shape Shape Angle Neighbors Z Prediction Value ft. 1 0 35 1 30 5 3.3499 2 0 35 2 30 10 3.3380 3 0 35 3 30 9 3.3382 4 0 35 4 30 10 3.3380 5 15 35 1 30 10 3.3499 6 15 35 2 30 10 3.3380 7 15 35 3 30 9 3.3382 8 15 35 4 30 10 3.3380 9 15 35 1 20 5 3.3460 10 15 35 2 20 10 3.3380 11 15 35 3 20 8 3.3520 12 15 35 4 20 10 3.3380 13 15 35 1 10 5 3.3460 14 15 35 2 10 9 3.3500 15 15 35 3 10 8 3.3520 16 15 35 4 10 10 3.3380 17 20 50 1 15 5 3.3459 18 20 50 2 15 9 3.3459 19 20 50 3 15 8 3.3520 20 20 50 4 15 10 3.3380 21 20 50 1 35 5 3.3499 22 20 50 2 35 10 3.3380 23 20 50 3 35 9 3.3382 24 20 50 4 35 10 3.3380 25 15 35 1 20 5 3.3459 26 15 35 2 20 10 3.3380 27 15 35 3 20 8 3.3520 28 15 35 4 20 10 3.3380

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45 Table 3.10 Ordinary Kriging search parameters for SWEM. Test Id Angle Direction Angle ToleranceShape Shape Angle Neighbors Z Prediction Value ft. 1 15 45 1 15 5 3.0799 2 15 45 2 15 9 3.0588 3 15 45 3 15 8 3.0551 4 15 45 4 15 10 3.0532 5 15 45 1 20 5 3.0799 6 15 45 2 20 10 3.0532 7 15 45 3 20 8 3.0551 8 15 45 4 20 10 3.0532 9 15 45 1 15 5 3.0799 10 15 45 2 15 9 3.0588 11 15 45 3 15 8 3.0551 12 15 45 4 15 10 3.0532 13 15 45 1 25 5 3.0799 14 15 45 2 25 10 3.0532 15 15 45 3 25 8 3.0551 16 15 45 4 25 10 3.0532 17 20 70 1 25 5 3.0799 18 20 70 2 25 10 3.0532 19 20 70 3 25 8 3.0551 20 20 70 4 25 10 3.0532 21 20 35 1 25 5 3.0799 22 20 35 2 25 10 3.0532 23 20 35 3 25 8 3.0551 24 20 35 4 25 10 3.0532 25 20 35 1 10 5 3.0799 26 20 35 2 10 9 3.0588 27 20 35 3 10 8 3.0551 28 20 35 4 10 10 3.0532 29 10 60 1 15 5 3.0799 30 10 60 2 15 9 3.0588 31 10 60 3 15 8 3.0551 32 10 60 4 15 10 3.0532 33 10 50 1 15 5 3.0799 34 10 50 2 15 9 3.0588 35 10 50 3 15 8 3.0551 36 10 50 4 15 10 3.0532 37 10 50 1 15 5 3.0799 38 10 50 2 15 9 3.0588 39 10 50 3 15 8 3.0551 40 10 50 4 15 10 3.0532

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46 Table 3.10 Continued Test Id Angle Direction Angle Tolerance Shape Shape Angle Neighbors Z Prediction Value ft. 41 10 50 1 15 5 3.0799 42 10 50 2 15 9 3.0588 43 10 50 3 15 9 3.0551 44 10 50 4 15 10 3.0532 45 0 50 1 30 5 3.0757 46 0 50 2 30 10 3.0532 47 0 50 3 30 9 3.0488 48 0 50 4 30 10 3.0532 49 0 35 1 30 5 3.0757 50 0 35 2 30 10 3.0532 51 0 35 3 30 9 3.0488 52 0 35 4 30 10 3.0532

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47 Table 3.11 Disjunctive kriging search parameters for SWEM. D in the column heading is the distribution, PD is probability distribution and CD is cumulative distribution. Test Id D Angle Direction Angle Tolerance Shape Shape Angle Neighbors Z Prediction Value ft. 1 PD 15 45 3 20 5 3.1977 2 PD 30 45 2 15 5 3.1977 3 PD 30 45 1 15 5 3.2058 4 PD 20 35 4 25 5 3.1977 5 PD 0 30 3 10 5 3.1977 6 PD 10 30 3 10 5 3.1977 7 PD 20 30 3 30 5 3.2058 8 PD 20 30 3 15 5 3.1977 9 PD 10 30 3 15 5 3.1977 10 PD 10 30 3 30 5 3.2058 11 PD 25 30 4 30 5 3.1977 12 PD 15 35 3 15 5 3.1977 13 CD 15 45 3 15 5 3.1029 14 CD 15 45 4 20 5 3.1977 15 CD 20 40 1 10 5 3.2058 16 CD 20 40 4 10 5 3.1977 17 CD 20 40 1 20 5 3.2058 18 CD 20 40 1 5 5 3.2058 19 CD 20 40 1 30 5 3.2058 20 CD 20 40 2 35 6 3.1977 21 CD 20 40 3 35 5 3.2058 22 CD 20 40 1 35 6 3.2058 23 CD 20 40 4 35 6 3.1977 24 CD 20 40 1 0 5 3.2009 25 CD 15 40 1 20 5 3.2058 26 CD 15 40 2 20 6 3.1977 27 CD 15 40 3 20 6 3.1977 28 CD 15 40 4 20 6 3.1977 29 CD 15 45 1 15 5 3.2058 30 CD 15 45 2 15 6 3.1977 31 CD 15 45 4 15 6 3.1977 32 CD 15 45 1 30 5 3.2058 33 CD 15 45 2 30 6 3.1977 34 CD 15 45 3 30 5 3.2058 35 CD 15 45 1 10 5 3.2058 36 CD 15 45 2 10 6 3.1977 37 CD 15 45 3 10 6 3.1977 38 CD 15 45 4 10 6 3.1977 39 CD 15 45 1 20 5 3.2058 40 CD 15 45 2 20 6 3.1977 41 CD 15 45 3 20 6 3.1977

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48 Table 3.11 Continued Test Id D Angle Direction Angle ToleranceShape Shape Angle Neighbors Z Prediction Value ft. 42 CD 15 45 4 20 6 3.1977 43 CD 15 45 1 10 5 3.2058 44 CD 15 45 2 10 6 3.1977 45 CD 15 45 3 10 6 3.1977 46 CD 15 45 4 10 6 3.1977 47 CD 15 45 4 20 6 3.1977 48 CD 15 45 3 20 6 3.1977 49 CD 15 45 2 20 6 3.1977 50 CD 15 45 1 20 5 3.2058 Table 3.12 Universal Kriging for SWEM, Oc tober 12-22, 1999. All values represent surface water interpolation for NAD 83 and NGVD 88. Day Nugget ft. Partial Sill ft. Z Prediction Value ft. 10/12/1999 0.1965 0.32490 0.6102 10/13/1999 0.2296 0.30122 0.6224 10/14/1999 0.0000 0.61716 0.4183 10/15/1999 0.0000 0.54420 0.3928 10/16/1999 0.0000 0.42082 0.3454 10/17/1999 0.0000 0.35905 0.3190 10/18/1999 0.0000 0.33162 0.3037 10/19/1999 0.0000 0.36915 0.3235 10/20/1999 0.0000 0.35809 0.4177 10/21/1999 0.0426 0.43048 0.4304 10/22/1999 0.0049 0.54628 0.4031

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49 CHAPTER 4 RESULTS AND DISCUSSION Cloud Detection The initial step for flood analysis began with detecting clouds in the October 16, 1999, Landsat 7 ETM+ scene. Clouds can obstruc t the spectral signat ure of water bodies, therefore clouds were mapped in the study area. Clouds found within Hurricane Irene were used as a reference f eature to identify cloud classes in all maps of vegetative indices. Hurricane Irene is lo cated in the north east quadr ant of the October 16, 1999, Landsat 7 ETM+ scene. Vegetative index tw o, vegetative index th ree and Band 8 were used to map clouds in the study area. For an initial analysis, Band 8 was selected to identify clouds in the study area, because of its 15 meter spatial resolution. Clouds are clearly displayed as irregular shapes compri sed of white pixels, and each cloud possesses a shadow located northwest of the cloud shape, Figure 4.1(A). Three clouds are visible in the study area, however only two fu ll cloud shadows are visible, Figure 4.1(B). Clouds in the study ar ea were found to completely obstruct the ground signature and their shadows produced darker pixels on the land surface.

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50 A Figure 4.1 (A) Map of the Frog Pond with Band 8. Clouds appear as white irregular shaped features. (B) Zoom in of the study area with Band 8.

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51 B Figure 4.1 Vegetation Index Two and Vegetation Index Three Unsupervised classification of vegetati on index two and vegetation index three were also used to detect dense clouds and ve rify clouds identified with Band 8. As with Band 8, the cloud formation of Hurricane Iren e was used as the re ference feature to identify potential cloud classes. The clouds from Hurricane Irene were located in the northeast quadrant of the ve getative index two and vege tative index three maps. Classes 1-2 in vegetation index two were determined to be cloud classes and classes 3-8 were determined to be partial classes. Classes 3-7 were determined to be cloud classes in vegetation inde x three. Clouds that were id entified with Band 8 in the study area produced similar but not exact shapes with vegetation index two and

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52 vegetation index three. Vegeta tion index two uses more classes to map these clouds than vegetation index three, Figur e 4.2(A) and Figure 4.2(B). A Figure 4.2(A) Vegetation index two map of so uth Florida, October 16, 1999. Clouds from Hurricane Irene are most visible in the nor theast section with class 1 and class 2. (B) Vegetation index three map of south Florida, October 16, 1999. Clouds from Hurricane Irene are most visible in the northeast section with classes 3-7.

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53 B Figure 4.2

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54 A Figure 4.3(A) Vegetation index two map of the study area, October 16, 1999. All three clouds are visible in the study area ou tlined in red. The legend for Figure 4.2(A) applies to Figure 4.3(A). (B) Vegetation index three map of the study area, October 16, 1999. Clouds in the st udy area, outlined in yellow, are most visible with classes 15-30. The legend for Figure 4.2(B) applies to Figure 4.3(B).

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55 B Figure 4.3 The analysis of vegetative index two and vegetative i ndex three showed that the combination of high and low gain thermal ba nds was superior for mapping clouds. It is important to note that the lack of cloud cover displayed with vegetative index three could possibly lead to the incorrect assessment th at clouds do not exist in the study area. To conclude, three clouds were located in the study area, and their signature completely dominated the signature of the ground, however it is inconclusive whether or not cloud shadows prevented water detection. NDVI NDVI was useful for mapping water under both dry and severe flood conditions, and the Atlantic Ocean was the primary feat ure used to identify open water classes in

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56 both NDVI maps. Open water represents a severely inundated c ondition where only the spectral signature of water is visible; however this may also represent a condition where emergent canopy does not exceed inundation dept h. Atlantic Ocean open water classes were found to conform to the east coast of south Florida’s peni nsular land boundary. Open water classes were found to be clearly distinguishable and separated from land classes along the east coast boundary, Figure 4.4(A) and Figure 4.4(B). A Figure 4.4(A). October 16, 1999, NDVI map of the south Florid a. Open water classes are represented with blue. (B). April 9, 2000, NDVI map of south Florida. Classes 1-7 are open water and represented with blue.

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57 B Figure 4.4 To identify and map water classes in the study area for October 16, 1999, and April 9, 2000, only the NDVI classes found in th e Atlantic Ocean were used. The land boundary was clearly visible from open water in both images; however the increase of open water classes in the October 16, 1999, NDVI map showed the flood impact of Hurricane Irene. Classes 1 -7 were determined to be op en water in the April 9, 2000, NDVI map, and classes 1-17 were determined to be ope n water in the October 16, 1999, NDVI map. The October 16, 1999, NDVI map was expected to have more water classes due to the flood condition produced by Hurricane Irene. Fi gures 4.5(A) and Fi gure 4.5(B) display the coverage of water in the study area. Cl ouds from Hurricane are visible with classes 18-20; however clouds in the study area are not distinguis hable with classes 18-20.

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58 Because clouds are not be mapped in Oct ober 16, 1999, NDVI map, it is difficult to exactly determine the separation between ope n water and cloud pixels in the study area. The method used to determine an open wate r class was successful for separating clouds from water in the April 9, 2000, NDVI map; however this method is not adequate for separating clouds from water in the Oct ober 16, 1999, NDVI map. Despite this constraint, the October 16, 1999, NDVI map does display a larg e increase in the coverage of water classes that is not found in the April 9, 2000, NDVI map. A Figure 4.5(A). October 16, 1999, NDVI map of the study area outlined in yellow. Open water classes are represented with blue (B). April 9, 2000, NDVI map of the study area outlined in blue. Open water classes 1-7 are represented with blue.

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59 B Figure 4.5 High NDVI classes in the October 16 1999, NDVI map are found where the spectral signature of water is obstructed by the signature of canopy. These pixels were mostly found in areas where high and dense can opy exists. This is most visible in the wetland shrub/scrub areas and in the row cr op areas where high NDVI class pixels are located adjacent to NDVI water pixels. Although NDVI was determined to be useful for verifying the flood extent, several constraints became obvious during the analysis. First, the 30 meter spatial resolution of NDVI maps failed to distinguish vegeta tion from water where vegetation canopy exceeded ponded water depth. Second, clouds that were mapped with Band 8, vegetation index two and vegetation inde x three were not mapped with NDVI. Finally, NDVI maps

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60 could not display the duration, change in ma gnitude and extent of flooding for Hurricane Irene, due to the low frequency of available Landsat 7 ETM+ images. Topographic Analysis The procedure used to create bare eart h topographic grids involved bare earth modeling and spatial modeling. Bare earth modeling initially began by identifying ALSM vegetation and artifact points with color infrared imagery and DEMs, and then the points were removed. Point removal was based unde r the assumption that the topography in the C-111 basin is extremely flat and that a low variability exists between neighborhood elevation points. Spatial mode ling was employed to predict elevation values where large gaps were left from ALSM point removal. The spatial modeling procedure involved the use of multiple interpolators and assessment of the genera ted statistics. The optimum interpolation method was used to cr eate both NAD 27 and NAD 83 ALSM DEMs. The four deterministic interpolators that were used to create ALSM elevation grid surfaces are inverse distance weighting, globa l polynomial, local polynomial and radial based functions. The lowest root mean square value was used as the decision statistic for selecting the optimum test method, however several tests were found to possess the lowest value. To resolve this problem, the test that possessed a mean absolute error closest to zero was selected as the optimum method. The radial based function interpolator produ ced the overall lowest root mean square error values, and was selected as the best interpolation method for ALSM DEMs, Table 4.1. Tests 3, 12, 13 and 16 all produced the lowest root mean square value, 0.1351 ft., therefore the lowest mean erro r statistic among these tests was used to select the optimum search parameters. Because of its low mean error value, test 13 was selected as the

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61 optimum search parameter method for NAD 27 and NAD 83 ALSM data. Details for the other interpolators may be found in Tables 4.2, 4.3 and 4.4. The search parameters for test 13 were applied to ordinary kriging, universal kriging, disjunctive kriging and simple krigi ng interpolators. Simple kriging produced the lowest root mean square value, 0.14 33 ft., Table 4.5. Although geo-statistical interpolators are more rigorous than determin istic interpolators, they are not ideal for predicting topographic grids that possess a sign ificant variability in density with ALSM points (ESRI 2001). Furthermore, the high variation in point density within the DEM made analysis and interpretation of semi-variograms inconclusive. Classified ALSM DEM Classified ALSM DEMs were manually creat ed by assigning elevation values into a specified interval. An elevation interval of 0.2 ft. was used to separate vegetation from the bare ground between the el evations of 4-6 ft. for NAD 27. The legend for elevation in Figure 4.6(A) describes elevation intervals in feet. Elevation intervals that were above the maximum elevation threshold of 4.8 ft. we re represented with green, to represent vegetation. Three dimensional images of classified NAD 27 and NAD 83 DEMs were used to analyze the effect of point remova l, Figure 4.6(A)-(H). Th e classified TIN DEM clearly displayed field vegetation, fiducial fe atures and the S175 culvert. Except for part of the L31W canal, the NAD 83 classified TIN DEM did not map these features. This is attributed to large gaps pr oduced by point removal. The th ree dimensional views of the NAD 83 DEM in Figures 4.6 (E-H) show the effect of point removal. It is interesting to note that both NAD 27 DEMs show extremely false low elevation values east of the L31 W canal. This may be caused by scattering of the infrared laser beam, or a problem with pos t processing. Both NAD 27 DEMs also display

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62 extremely high elevation values that are not characteristic of the topography in the study area, and this was likely caused by the laser beam striking an object in the atmosphere. Figure 4.6(A) Planar view of the NAD 27 study area DEM. The legend applies to all three dimensional (3D) DEMs. (B) 3D southeasterly view of the study area using NAD 27 ALSM data. (C) 3D sout herly view of the study area using NAD 27 ALSM data. (D) 3D westerly view of the study area using NAD 27 ALSM data. (E) 3D southerly view of the study area using NAD 83 ALSM data. (F) 3D easterly view of the study area using NAD 83 ALSM data. (G)

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63 3D easterly view of the study area using NAD 83 ALSM data. (H) 3D westerly view of the study area using ALSM data. B Figure 4.6

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64 C Figure 4.6

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65 D Figure 4.6

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66 E Figure 4.6 F Figure 4.6

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67 G Figure 4.6

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68 H Figure 4.6 Surface Water Elevation Map Analysis The four geo-statistical inter polators that were used to predict elevation values for surface water elevation grids are universal kr iging, disjunctive kriging, simple kriging and ordinary kriging. The root mean square error served as the decision statistic for selection of the optimum inter polation method. If tw o tests shared an equal value, then the test with the mean error value closest to zero was selected as the optimum method. For universal kriging, test 15 produced th e overall lowest root mean square error value of 0.4701 ft. (see Table 4.6), and the sear ch parameters for test 15 were applied to create all surface water elevation maps, Table 4.7. These parameters were also used for October 16, 1999, NAD 27 surface water elevatio n data; however the root mean square error value was 0.02 ft. greater than that of NAD 83. Furthermore, NAD 27 possessed

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69 greater mean error and mean error values; how ever the root mean square standardized error and average standard e rror values for NAD 27 were le ss than NAD 83. Details for the other interpolators are shown in Tables 4.8, 4.9 and 4.10. Table 4.11 lists the surface water elevati on values for the study period, and Figure 4.7 shows a graph of surface water elevation da ta for the study period. An analysis of surface water data showed a sharp increase in elevation that was coincident with the impact of Hurricane Irene, and a gradual decrease associated with drainage. SWEM contours in Figures (A-H) app ear to show a directional fl ow towards the S332 and S178 pumping stations. The SFWMD (2000) reporte d that the S332 was operating at maximum capacity on October 14, 1999, however no specif ic information is provided for the other water control structures in the study area. Surface water elevation maps displayed a smooth transition between contour intervals; however discontinuities in the elevation intervals were more noticeable as the distan ce between stations increased, Figure 4.8 (AK). Elevation values are in feet NGVD 88. Prediction error maps for SWEM were produced, because universal kriging was selected as the interpolation method. Figure 4.9 (A-K) show universal kriging prediction error maps made from surface water elevati on maps. Several trends were noticed during the production of SWEM predic tion error maps. October 16, 1999 displayed the lowest prediction error, and an increase in prediction error existed for the remainder of the study period. Furthermore, SWEM prediction erro r for October 12, 1999, was observed to be the highest for the entire study period. Prediction e rror values are in feet. The low prediction error for October 16, 1999, is attributed to both high surface water elevation values and the low variability in values for stations throughout the study

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70 area. The increase in prediction error coincide s with an increase in the variability in elevation values between neighbor ing stations. This increase in variability is most likely due to the effect of water management and variable drainage rates for water control structures. Surface Water Elevation NGVD 880 1 2 3 4 5 6 1213141516171819202122DayElevation Ft. S178 NP 158 S175 FP1 FP2 S332 NP112 S177 FP G3355 Figure 4.7 Graph of surface water elevati on values. Values are in feet NGVD 88.

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71 Figure 4.8 (A) SWEM October 12, 1999. (B) SWEM October 13, 1999. (C) SWEM October 14, 1999. (D) SWEM Octobe r 15, 1999. (E) SWEM October 16, 1999. (F) SWEM October 17, 1999. (G) SWEM October 18, 1999. (H) SWEM October 19, 1999. (I) SWEM October 20, 1999. (J) SWEM October 21, 1999. (K) SWEM October 22, 1999. Elevation values are in feet NGVD 88.

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72 B Figure 4.8

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73 C Figure 4.8

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74 D Figure 4.8

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75 E Figure 4.8

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76 F Figure 4.8

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77 G Figure 4.8

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78 H Figure 4.8

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79 I Figure 4.8

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80 J Figure 4.8

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81 K Figure 4.8

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82 A Figure 4.9 (A) Prediction error for SWEM October 12, 1999. (B) Prediction error for SWEM October 13, 1999. (C) Predicti on error for SWEM October 14, 1999. (D) Prediction error for SWEM October 15, 1999. (E) Prediction error for SWEM October 16, 1999. (F) Predicti on error for SWEM October 17, 1999. (G) Prediction error for SWEM October 18, 1999. (H) Prediction error for SWEM October 19, 1999. (I) Prediction error for SWEM October 20, 1999. (J) Prediction error for SWEM Octo ber 21, 1999. (K) Prediction error for SWEM October 22, 1999. Legend error values are in feet.

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83 B Figure 4.9

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84 C Figure 4.9

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85 D Figure 4.9

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86 E Figure 4.9

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87 F Figure 4.9

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88 G Figure 4.9

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89 H Figure 4.9

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90 I Figure 4.9

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91 J Figure 4.9

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92 K Figure 4.9

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93 Surface Water Inundation Map The surface water inundation map, SWIM, was created by subtracting topographic grids from surface water grids. SWIM was a pplied to each day of the study period from October 12-22, 1999, Figure 4.10(A)-(L), and depth was characterized by five classes based on 0.25 ft. elevation intervals repr esenting water table elevation below ground surface, Table 4.12. Numerical values found in the SWIM legends represent no inundation in 0.25 ft intervals. For example, the interval, 0-0.25 ft., represents a water table 0-0.25 ft. below land surface. An an alysis of NAD 83 SWIM maps show a sharp increase in flooding that is coincident with Hurricane Irene. The maximum flood condition occurred on October 16, 1999, and inundation gradually decreased over the study period. Inundation contours were also observed to conform to the change in surface water elevation. On October 12, 1999, SWIM show s inundation in the L31W canal and portions of wetland and agricultural areas Inundation values in these areas are lik ely the result of a low topographic elevation and high surf ace water elevation at the S332 pumping station. The same reason may also explai n inundation patterns for October 13, 1999. The low surface water elevation value of the S178 pumping station and well G3355 significantly influenced inundation patterns in the southeast quadran t of the study area. Due to this, the first non-inunda ted area appear in the sout heast quadrant on October 21, 1999. Inundation statistics were calculated using ArcGIS 3D Analyst for October 16, 1999, Table 4.13. The SWIM NAD 83 inundation grid was used for the calculation, and values were calculated above a plane that was set to 0 ft. Th e Z-tolerance factor was set

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94 to 0.1 ft. The greatest percentage change in inundation volume occurs on October 15, 1999, and the largest volume tric value occurs on October 16, 1999. The calculated volume of water over the study area was f ound to decrease during October 17-22, 1999. The increase in volume for October 14, 1999, is likely due to either the increase in surface water elevation from conveyance opera tions or initial rainfall from Hurricane Irene. The most vulnerable areas inside the st udy area are those which have the highest magnitude of inundation and duration of floodi ng. SWIM classes 4 and 5 represent a high magnitude, and the areas remaining in cla ss 4 and class 5 on SWIM October 22, 1999, shows the greatest duration of flooding. Figure 4.10(K) shows the coverage of class 4 and class 5 areas for October 22, 1999, and these areas were determined to be the most vulnerable inside the study area. These areas coincide with low elevation areas in the ALSM DEM.

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95 Figure 4.10(A) SWIM October 12, 1999. (B) SWIM October 13, 1999. (C) SWIM October 14, 1999. (D) SWIM October 15, 1999. (E) SWIM October 16, 1999. (F) SWIM October 17, 1999. (G) SWIM October 18, 1999. (H) SWIM October 19, 1999. (I) SWIM October 20, 1999. (J) SWIM October 21, 1999. (K) SWIM October 22, 1999. Nume rical values are in feet.

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96 B Figure 4.10

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97 C Figure 4.10

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98 D Figure 4.10

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99 E Figure 4.10

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100 F Figure 4.10

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101 G Figure 4.10

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102 H Figure 4.10

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103 I Figure 4.10

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104 J Figure 4.10

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105 K Figure 4.10

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106 Table 4.1 Radial based functions statistics for topography. Test Id Mean Error Root Mean Square Error 1 -0.000441 0.1352 2 -0.000826 0.1360 3 -0.000431 0.1351 4 -0.000415 0.1352 5 -0.000419 0.1352 6 -0.000451 0.1353 7 -0.000472 0.1352 8 -0.001378 0.1373 9 -0.001378 0.1373 10 -0.000451 0.1352 11 -0.000468 0.1353 12 -0.000409 0.1351 13 -0.000403 0.1351 14 -0.000464 0.1353 15 -0.000432 0.1351 16 -0.001378 0.1373 17 -0.000737 0.1374 18 -0.001111 0.1376 19 -0.001252 0.1384 20 -0.001165 0.1914 Table 4.2 Inverse distance weight ing statistics for topography. Test Id Mean Error Root Mean Square Error 1 -0.001844 0.141 2 -0.001688 0.1418 3 -0.002222 0.1395 4 -0.001682 0.1417 5 -0.001571 0.1443 6 -0.001563 0.1443 7 -0.002222 0.1395 8 -0.001694 0.1416 9 -0.001675 0.1418 10 -0.002222 0.1395 11 -0.001677 0.1417 12 -0.001695 0.1418 13 -0.001803 0.1427 14 -0.002136 0.1406 15 -0.002068 0.1405 16 -0.002074 0.1405

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107 Continued Table 4.2. Test Id Mean Error Root Mean Square Error 17 -0.002134 0.1408 18 -0.002132 0.1408 19 -0.002074 0.1405 20 -0.00207 0.1405 21 -0.002136 0.1406 22 -0.002136 0.1406 23 -0.002076 0.1404 24 -0.002071 0.1405 25 -0.002129 0.1408 26 -0.002134 0.1408 27 -0.002069 0.1405 28 -0.002079 0.1404 29 -0.002136 0.1406 30 -0.002136 0.1406 31 -0.002088 0.1405 32 -0.00207 0.1405 33 -0.002137 0.1408 34 -0.002134 0.1408 35 -0.002074 0.1405 36 -0.002071 0.1405 37 -0.002136 0.1406 Table 4.3 Global polynomial statistics for topography. Table 4.4 Local polynomial statistics for topography. Test Id Mean Error Root Mean Square Error 1 0.0001813 0.1813 2 0.0001305 0.1665 3 0.0001559 0.1474 4 -0.0001782 0.1489 5 -0.000265 0.1507 6 0.0002789 0.1827 7 -0.0004779 0.1722 8 -0.0005851 0.1621 9 -0.0000333 0.1577 10 0.00007704 0.1610 Global Mean Error Root Mean Square Error 2 6.22E-07 0.2234 2 1.07E-06 0.2161

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108 Table 4.5 Kriging statistics for topography. ME is the mean error, RMSE is the root mean square error, MSE is the mean standa rdized error and RMSSE is the root mean square standard error. Type ME RMSE ASE MSE RMSSE OK 0.0004726 0.1458000 0.2068000 0.0022880 0.7046000 SK 0.0003122 0.1433000 0.1637000 0.0020840 0.8732000 UK 0.0005407 0.1462000 0.1170000 0.0046110 1.2490000 DK 0.0024980 0.1439000 0.1524000 0.0165800 0.9417000 Table 4.6 Universal Kriging statistics for SWEM. ME is the mean error, RMSE is the root mean square error, MSE is the mean standardized error and RMSSE is the root mean square standard error. Test Id ME RMSE ASE MSE RMSSE 1 0.0316 0.6499 0.1377 0.0110 4.4710 2 0.0577 0.6723 0.1377 0.2104 4.6320 3 0.1904 0.6005 0.1742 0.9627 3.2320 4 0.0892 0.6624 0.1716 0.3053 3.5910 5 0.0422 0.6288 0.1727 0.0638 3.4250 6 0.0434 0.6308 0.1528 0.0747 3.8730 7 0.0560 0.6321 0.1528 0.1614 3.8830 8 0.0723 0.5466 0.1954 0.1525 2.4730 9 0.0944 0.4942 0.2202 0.2057 1.8820 10 0.1275 0.5013 0.2185 0.3262 1.9100 11 0.1399 0.4778 0.2505 0.3217 1.5530 12 0.1087 0.4709 0.3000 0.1902 1.2840 13 0.1085 0.4705 0.3516 0.1621 1.0950 14 0.1085 0.4705 0.4045 0.1409 0.9517 15 0.1084 0.4701 0.4560 0.1249 0.8438 Table 4.7 Universal kriging st atistics for SWEM, 10/12-22/1999. ME is the mean error, RMSE is the root mean square error, MSE is the mean standardized error and RMSSE is the root mean square standard error. Day ME RMSE ASE MSE RMSSE Regression 10/12 -0.0536 0.7424 0.6619 -0.0694700 1.1140 .206x + 2.224 10/13 -0.0549 0.7493 0.6699 -0.0710300 1.1130 .191x + 2.234 10/14 -0.0007 0.6126 0.5520 0.0006252 1.0740 .499x + 1.569 10/15 0.0601 0.6005 0.5183 0.0516000 1.0380 .624x + 1.427 10/16 0.1084 0.4701 0.4558 0.1250000 0.8442 .685x + 1.325 10/17 0.0891 0.3556 0.4208 0.0915000 0.7339 .732x + 1.127 10/18 0.0842 0.2994 0.4049 0.1014000 0.6990 .758x +1.000 10/19 0.0647 0.3202 0.4267 0.0586000 0.7260 .760x + .955 10/20 0.0642 0.4841 0.4975 0.0554000 1.0070 .639x + 1.33 10/21 0.0625 0.5155 0.5231 0.0524000 1.0340 .642x + 1.273 10/22 0.0653 0.5153 0.5263 0.0631000 1.0610 .691x + 1.108

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109 Table 4.8 Simple kriging statistics for SW EM, 10/12-22/1999. ME is the mean error, RMSE is the root mean square error, MSE is the mean standardized error and RMSSE is the root mean square standard error. Test Id ME RMSE ASE MSE RMSSE 1 0.23820 0.7930 0.89760 0.21760 0.80370 2 0.24260 0.7845 0.89620 0.22300 0.79450 3 0.24330 0.7878 0.89620 0.22370 0.79770 4 0.24220 0.7846 0.89610 0.22260 0.79460 5 0.23820 0.7930 0.89760 0.21760 0.80370 6 0.24260 0.7845 0.89620 0.22300 0.79450 7 0.24330 0.7878 0.89620 0.22370 0.79770 8 0.24220 0.7846 0.89610 0.22260 0.79460 9 0.23480 0.7932 0.89710 0.21350 0.80410 10 0.24230 0.7844 0.89620 0.22270 0.79440 11 0.24340 0.7879 0.89620 0.22370 0.79780 12 0.24200 0.7845 0.89610 0.22240 0.79460 13 0.23480 0.7932 0.89710 0.21350 0.80410 14 0.24210 0.7844 0.89620 0.22240 0.79440 15 0.24250 0.7885 0.89620 0.22270 0.79860 16 0.24200 0.7845 0.89610 0.22240 0.79460 17 0.23480 0.7932 0.89710 0.21350 0.80410 18 0.24230 0.7844 0.89620 0.22270 0.79440 19 0.24340 0.7879 0.89620 0.22370 0.79780 20 0.24190 0.7846 0.89610 0.22220 0.79470 21 0.23820 0.7930 0.89760 0.21760 0.80370 22 0.24250 0.7845 0.89620 0.22290 0.79450 23 0.24350 0.7878 0.89620 0.22390 0.79770 24 0.24210 0.7845 0.89610 0.22250 0.79450 25 0.23480 0.7932 0.89710 0.21350 0.80410 26 0.24230 0.7844 0.89620 0.22270 0.79440 27 0.24340 0.7879 0.89620 0.22370 0.79780 28 0.24200 0.7845 0.89610 0.22240 0.79460 Table 4.9 Ordinary kriging statistics for SWEM ME is the mean error, RMSE is the root mean square error, MSE is the mean standardized error and RMSSE is the root mean square standard error. Test Id ME RMSE ASE MSE RMSSE 1 0.12450 0.4990 0.76020 0.09016 0.57330 2 0.11370 0.4765 0.75650 0.08454 0.51990 3 0.09540 0.4731 0.75680 0.05357 0.51600 4 0.09287 0.4758 0.75580 0.05081 0.52000 5 0.12450 0.4990 0.76020 0.09016 0.57330 6 0.11370 0.4765 0.75650 0.08454 0.51990

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110 Table 4.9 Continued Test Id ME RMSE ASE MSE RMSSE 7 0.09540 0.4731 0.75680 0.05357 0.51600 8 0.09290 0.4758 0.75580 0.05088 0.51990 9 0.12450 0.4990 0.76020 0.09016 0.57330 10 0.11370 0.4765 0.75650 0.08454 0.51990 11 0.09540 0.4731 0.75680 0.05357 0.51600 12 0.09287 0.4758 0.75580 0.05081 0.52000 13 0.12620 0.4991 0.76000 0.09286 0.57350 14 0.13260 0.4763 0.74980 0.09687 0.51490 15 0.09444 0.4735 0.75680 0.05198 0.51720 16 0.11920 0.4793 0.74920 0.07421 0.52300 17 0.12620 0.4991 0.76000 0.09286 0.57350 18 0.13260 0.4763 0.74980 0.09687 0.51490 19 0.09444 0.4735 0.75680 0.05198 0.51720 20 0.11920 0.4793 0.74920 0.07421 0.52300 21 0.12620 0.4991 0.76000 0.09286 0.57350 22 0.13260 0.4763 0.74980 0.09687 0.51490 23 0.09444 0.4735 0.75680 0.05198 0.51720 24 0.11920 0.4793 0.74920 0.07421 0.52300 25 0.12450 0.4990 0.76020 0.09016 0.57330 26 0.09870 0.4745 0.75650 0.05988 0.52230 27 0.12990 0.4732 0.75000 0.09259 0.51050 28 0.11150 0.4735 0.74900 0.06382 0.51540 29 0.12450 0.4990 0.76020 0.09016 0.57330 30 0.11370 0.4765 0.75650 0.08454 0.51990 31 0.09540 0.4731 0.75680 0.05357 0.51600 32 0.09287 0.4758 0.75580 0.05081 0.52000 33 0.12450 0.4990 0.76020 0.09016 0.57330 34 0.11370 0.4765 0.75650 0.08454 0.51990 35 0.09540 0.4731 0.75680 0.05357 0.51600 36 0.09287 0.4758 0.75580 0.05081 0.52000 37 0.12450 0.4990 0.76020 0.09016 0.57330 38 0.11370 0.4765 0.75650 0.08454 0.51990 39 0.09540 0.4731 0.75680 0.05357 0.51600 40 0.09287 0.4758 0.75580 0.05081 0.52000 41 0.12450 0.4990 0.76020 0.09016 0.57330 42 0.11370 0.4765 0.75650 0.08454 0.51990 43 0.09540 0.4731 0.75680 0.05357 0.51600 44 0.09287 0.4758 0.75580 0.05081 0.52000 45 0.12620 0.4991 0.76000 0.09286 0.57350 46 0.14040 0.4795 0.74990 0.10870 0.52170 47 0.09004 0.4730 0.75620 0.04613 0.51630

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111 Table 4.9 Continued Test Id ME RMSE ASE MSE RMSSE 48 0.11430 0.4790 0.7486 0.06760 0.52260 49 0.12620 0.4991 0.7600 0.09286 0.57350 50 0.14040 0.4795 0.7499 0.10870 0.52170 51 0.09004 0.4730 0.7562 0.04613 0.51630 52 0.11430 0.4790 0.7486 0.06760 0.52260 Table 4.10 Disjunctive Kriging statistics for SWEM. ME is the mean error, RMSE is the root mean square error, MSE is the mean standardized error and RMSSE is the root mean square standard error. Test Id ME RMSE ASE MSE RMSSE 1 0.31350 0.85500 0.59990 0.3775 1.15300 2 0.31330 0.85500 0.59990 0.3770 1.15300 3 0.29560 0.86910 0.60260 0.3436 1.17300 4 0.31280 0.85500 0.59990 0.3761 1.15300 5 0.31670 0.86340 0.60050 0.3798 1.16000 6 0.31670 0.86340 0.60050 0.3798 1.16000 7 0.31370 0.85500 0.59990 0.3778 1.15300 8 0.31350 0.85500 0.59990 0.3775 1.15300 9 0.31350 0.85500 0.59990 0.3775 1.15300 10 0.31370 0.85500 0.59990 0.3778 1.15300 11 0.31390 0.85500 0.59990 0.3781 1.15300 12 0.31350 0.85500 0.59990 0.3775 1.15300 13 0.28280 0.72070 0.59090 0.3065 0.92520 14 0.31280 0.85500 0.59990 0.3761 1.15300 15 0.29830 0.87000 0.60270 0.3487 1.17600 16 0.31050 0.84840 0.59980 0.3737 1.14500 17 0.30000 0.86820 0.60210 0.3521 1.17000 18 0.34190 0.87840 0.60600 0.4215 1.19300 19 0.29520 0.85940 0.60140 0.3466 1.16200 20 0.31370 0.85500 0.60000 0.3779 1.15300 21 0.30520 0.85440 0.60010 0.3643 1.15200 22 0.31370 0.85500 0.60000 0.3779 1.15300 23 0.31370 0.85500 0.60000 0.3779 1.15300 24 0.34220 0.87830 0.60600 0.4220 1.19200 25 0.30000 0.86820 0.60210 0.3521 1.17000 26 0.31270 0.85520 0.59990 0.3760 1.15300 27 0.31350 0.85500 0.59990 0.3775 1.15300 28 0.31280 0.85500 0.59990 0.3761 1.15300 29 0.29560 0.86910 0.60260 0.3436 1.17300

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112 Table 4.10 Continued Test Id ME RMSE ASE MSE RMSSE 30 0.31330 0.85500 0.59990 0.3770 1.15300 31 0.31280 0.85500 0.59900 0.3761 1.15300 32 0.29520 0.85940 0.60140 0.3466 1.16200 33 0.31360 0.85510 0.59990 0.3776 1.15300 34 0.31370 0.85500 0.59990 0.3778 1.15300 35 0.29830 0.87000 0.60270 0.3487 1.17600 36 0.28970 0.85280 0.60040 0.3403 1.15400 37 0.31670 0.86340 0.60050 0.3798 1.16000 38 0.31050 0.84840 0.59980 0.3737 1.14500 39 0.30000 0.86820 0.60210 0.3521 1.17000 40 0.31270 0.85520 0.59990 0.3760 1.15300 41 0.31350 0.85500 0.59990 0.3775 1.15300 42 0.31280 0.85500 0.59990 0.3761 1.15300 43 0.29830 0.87000 0.60270 0.3487 1.17600 44 0.28970 0.85280 0.60040 0.3403 1.15400 45 0.31670 0.86340 0.60050 0.3798 1.16000 46 0.31050 0.84840 0.59980 0.3737 1.14500 47 0.31280 0.85500 0.59990 0.3761 1.15300 48 0.31350 0.85500 0.59900 0.3775 1.15300 49 0.31270 0.85520 0.59990 0.3760 1.15300 50 0.30000 0.86820 0.60210 0.3521 1.17000 Table 4.11 SWEM surface water values for October 12-22, 1999. Elevation values are in feet NGVD 88 and ST is th e station description. ST 12 13 14 15 16 17 18 19 20 21 22 S178 1.44 1.41 1.24 1.391.651.912.081.931.75 1.58 1.44 NP158 2.79 2.75 2.97 3.713.963.983.963.893.81 3.73 3.68 S175 2.43 2.40 2.60 3.684.304.224.113.983.85 3.73 3.62 S178 1.44 1.41 1.24 1.391.651.912.081.931.75 1.58 1.44 NP158 2.79 2.75 2.97 3.713.963.983.963.893.81 3.73 3.68 S175 2.43 2.40 2.60 3.684.304.224.113.983.85 3.73 3.62 FP1 3.29 3.22 3.41 3.994.194.194.194.194.19 4.19 4.17 FP2 2.39 2.33 2.86 4.134.434.434.394.294.08 3.87 3.70 S332 3.90 3.90 3.96 4.474.814.804.734.654.57 4.51 4.43 NP112 3.95 3.94 4.03 5.104.754.644.574.503.84 3.80 3.85 S177 1.61 1.60 1.86 3.263.533.343.102.882.67 2.48 2.31 FP 2.84 2.77 3.27 4.194.644.614.534.424.32 4.23 4.14 G3355 2.49 2.46 2.44 2.422.312.252.162.092.01 1.93 1.85

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113 Table 4.12 Vulnerability index classes used for SWIM. Classes 1-5 represent the increase in inundation magnitude. Class Inundation Depth Interval in Feet 1 0.0 0.25 2 0.25 – 0.5 3 0.5 – 0.75 4 0.75 – 1.0 5 1.0 & Greater Table 4.13 Calculated inundation statistics for the study area. Day 2D Area ft.2 Surface Area ft.2 Volume Area ft.2 Volume ft.3 Volume % 12 1797974.14 1797975.00 42193.36 0.86 0.00 0.00% 13 1384624.66 1384625.48 37114.63 0.82 -5078.73 -12.04% 14 4746567.06 4746568.03 80282.80 0.97 43168.17 116.31% 15 36122841.00 36122843.492268912.29 2.49 2188629.49 2726.15% 16 36122841.00 36122843.494175404.91 2.49 1906492.62 84.03% 17 36122841.00 36122843.493916325.23 2.49 -259079.68 -6.20% 18 36122841.00 36122843.503523287.65 2.50 -393037.58 -10.04% 19 36122841.00 36122843.503062008.15 2.50 -461279.50 -13.09% 20 36122841.00 36122843.492653239.40 2.49 -408768.75 -13.35% 21 35687948.23 35687950.732269041.32 2.50 -384198.08 -14.48% 22 32926192.31 32926194.801895679.52 2.49 -373361.80 -16.45%

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114 CHAPTER 5 CONCLUSION The objective of this research was to deve lop flood maps that use ALSM, Landsat 7 ETM+ and regional surface water elevation data The combination of these data sources proved to be successful for mapping water produced by Hurricane Irene. Low elevation areas were found to be the most vulnerable to flooding, because of their high inundation magnitude and duration of flooding. Image Analysis Landsat7 ETM+ was effective for mapping the flood impact of Hurricane Irene, and detecting dense clouds. NDVI was determ ined to be useful for mapping water for October 16, 1999, and April 9, 2000, Lands at 7 ETM+ scenes. Although unsupervised classified NDVI maps were useful for mappi ng water, several constraints were observed after the mapping process. Vegetation ca nopy and clouds were found to prevent the detection of water, and second, the 30 meter re solution was too course to detect water at a high resolution. The presence of excess wate r after Hurricane Irene produced more NDVI water classes than were observed with the April 9, 2000, NDVI map. Because of this condition, classes 1 and 2 in both NDVI maps were determined to be pure open water and severely inundated. It is impor tant to note that this does not mean that higher NDVI class pixels were not flooded. Bare Earth Modeling Bare earth topographic modeling was depe ndent on detecting vegetation patterns visible with color infrared images. The ma ximum elevation thresh old filter used to

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115 remove vegetation and artifact points was e ffective; however gaps in the NAD 83 DEM produced an increase in uncertainty for inte rpolated surfaces. The radial based function interpolator produced the lowest root mean square value and was used to create topographic grids. Topographic grids displayed a flat and low elevation surface that is characteristic of the C-111’s topography. SWEM The surface water elevation map was useful for displaying the change in surface water elevation before and after Hurricane Ir ene, and universal krig ing was judged to be the best interpolator for surface water el evation grids. The maximum surface water elevation value for the S 175 culvert occurred on October 16, 1999, and a gradual decrease in elevation was observed for th e period following Hurricane Irene. SWEM maximum surface water values match reports from the SFWMD. SWIM SWIM was shown to be successful for displaying a severe inundation condition produced by Hurricane Irene. SWIM may al so be used to predict flooding for agricultural, environmental and urban areas insi de the flight area. Agricultural operation managers in the C-111 and Frog Pond may use SWIM to predict areas that may experience the greatest damage, and water managers may also determine which areas would required the greatest flood protection. Fo r example, if an area near a water control structure shows severe inundation, then mana gers may decide to increase the pumping or drainage capacity of that structure. A ny flood assessment performed by FEMA requires the ability to accurately map the most vulnerable areas, and the project methodology provided a suitable guide for determining thes e areas in the C-111 Basin. The method for GIS flood mapping may also be expanded to include other ALSM areas sponsored by

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116 FEMA; however these areas may not include satellite imagery that covers a specific event. Conclusion To conclude, the evolution of 3D flood mapping depends on the ability to integrate and interpret multiple remotely sensed data; however combining elevation data from different vertical and horizontal datums is not recommended. Ideally, all elevation data should be in NAD 83 and NGVD 88, and a ny conversion to NAD 27 and NGVD 29 is dependent on the conversion of first order benchmarks by the National Geodetic Survey. The conversion value for vertical datu ms was 1.5125 ft.; however NAD 83 and NGVD 88 are measured differently than NAD 27 and NGVD 88, and no relationship exists between these sets of datums. Converti ng ALSM elevation data to NAD 27 and NGVD 29 for the purpose of matching surface water el evation data may reduce the accuracy of ALSM data. Additionally, the latitudinal a nd longitudinal coordina tes of surface water sites in the study area were not measured w ith the same degree of accuracy as the ALSM data. ALSM clients should consider onl y using NAD 83 and NGVD 88 datums, because they are the only datums used for reference during ALSM data capture.

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117 CHAPTER 6 RECOMMENDATIONS FOR FUTURE STUDIES SWIM and NDVI maps were shown to be useful for flood detection; however the study area consists of only a small portion of the ALSM flight area. For a complete flood analysis, SWIM and NDVI maps should be made of the entire flight area. Combining the C-111 ALSM data with other Miami-Dade ALSM data will contribute to developing a comprehensive high resolution topographic DEM of MiamiDade and eventually, all of south Florida. It is important to note that NAD 83 and NGVD 88 datums should only be used for 3D mappi ng since they are global datums derived from Differential Global Positioning Systems (DGPS). SWIM methodology may be applied to ALSM coastal data sets acquired during 2001; however surface water data for al l SFWMD, USGS and NPS hydrologic monitoring sites should be reviewed. The pr ocedure for assimilating surface water data must consider anomalies that do not represent accurate surface water elevation values. For example, Robblee well was excluded, b ecause it possessed su rface water elevation measurements far below the expected range of values. Additionally, surface water site locations should be surveyed with DGPS to improve horizontal and vertical accuracy with NAD 83 and NGVD 88 datums. Integratin g Synthetic Aperature Radar (SAR) with ALSM would assist in distinguishing water from soil. Furthermore, this will improve separating trees with water and trees over dry land, and crops from trees. Future applications of SWIM for the f light area will require an improvement of bare earth models for other types of land c over. The bare earth procedure should involve

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118 point removal and spatial modeling with multiple interpolators; however a more variable surface should be expected for the entire flight area. The introduction of precipitation data will assist in the creation of high resolution water budgets. Precipitation values are provi ded by the SFWMD, USGS and NPS, and spatial modeling of precipitation will provide insight into th e distribution of precipitation for Hurricane Irene. NEXRAD radar precipita tion images measure hourly rainfall, and images are available for Hurricane Irene a nd other severe rain events. The primary limitation of NEXRAD is its coarse resoluti on and no definable coordinates are provided for GIS mapping applications. Future efforts should investigate other me thods of interpolation which may include inverse distance weighting, lo cal and global functions, krig ing and cokriging. Maximum surface water values for October 16, 1999, woul d be useful for cokriging because of the smooth surface that conforms to topographic reli ef. A variety of search parameters should be used to improve topographic and surface wa ter prediction grids. Finally, all metadata for each processed geo-spatial data set must be carefully reviewed to verify the accuracy of listed datums and projections.

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119 LIST OF REFERENCES Adams, J.C. and J.H Chandler. 2002. Ev aluation of LIDAR and Medium Scale Photogrammetry for Detecting Soft-Cliff Co astal Change. Photogrammetric Record 1799: 405-18. Ball, M.H., R.W. Schaffranek. 2000. Interrel ation of Everglades Hydrology and Florida Bay Dynamics to Ecosystem Processes and Restoration in Sout h Florida: Regional Simulation of Inundation Pattern s in the South Florida Everglades. Naples, FL: United States Geological Survey. Ball, M.H., R.W. Schaffranek. 2000. Water Surface Elevation and Water Depth Analyses Using a GIS Application. Naples, FL: United States Geological Survey, Brock, J.C., A.H. Sallenger, W.B. Krabill, R.N. Swift, C.W. Wright. 2001. Recognition of Fiducial Surfaces in Lidar Surveys of Coastal Topography. Photogrammetric Engineering and Remote Sensing 68: 1245-58. Bethel, J.L., W.F. Cheng. 1995. The Civil Engineering Users Manual. Boca Raton, FL: CRC Press. Daniels, R.C. 2000. Datum Conversion Issu es with Lidar Spot Elevation Data. Photogrammetric Engineering a nd Remote Sensing 67: 735-40. Doren, R.F., K. Rutchey, R. Welch. 1998. The Everglades: A Perspective on the Requirements and Applications for Ve getation Map and Database Products. Photogrammetric Engineering a nd Remote Sensing 65: 155-61. Douchette, P., K. Beard. 1999. Explori ng the Capability of Some GIS Surface Interpolators for DEM Gap Fill. Photogrammetric Engineering and Remote Sensing 66: 881-87. ESRI. 2001. ArcGIS Geostatistical Analyst: Statistical Tools for Data Exploration, Modeling and Advanced Surface Generation. Relands, CA: ESRI. Evans, D.L., S.D. Roberts, J.W. McCo mbs, R.L. Harrington. 2000. Detection of Regularly Spaced Targets in Small Footprint LIDAR Data: Research Issues for Considerations. Photogrammetric Engin eering and Remote Sensing 67: 1133-36. Fetter, C.W. 1998. Applied Hydrogeology. Oshkosh: University of Wisconsin.

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120 Frazier, P.S., K.J. Page. 1999. Water Body De tection and Delineation with Landsat TM Data. Photogrammetric Engineerin g and Remote Sensing 66: 1461-67. Graham, W.D., K.L. Campbell, J. Mossa, L. H. Motz, P.S.C. Rao, W.R. Wise. 1997. Water Management Issues Affecting the C-111 Basin, Dade County, Florida: Hydrologic Sciences Task Force Initial Assessment Repor t. Gainesville, FL: University of Florida. Harris County Flood District. April 2006. Heinzer, T., Michael Sebhat, Bruce Feinberg Dale Kerper. 2002. The Use of GIS to Manage LIDAR Elevation Data and Facil itate Integration with the MIKE21 2-D Hydraulic Model in a Flood Inundati on Decision Support System. April 2006. Hodgson, M.E., J.R. Jensen, J.A. Tullis, K.D. Riordan, C.M. Archer. 2003. Synergistic Use of Lidar and Color Aerial Photography for Mapping Urban Parcel Imperviousness. Photogrammetric Engineering a nd Remote Sensing 69: 973-980. Hodgson, M.E., P. Bresnahan. 2004. Accuracy of Airborne Lidar Derived Elevation: Empirical Assessment and Error Budget. P hotogrammetric Engineering and Remote Sensing 70: 331-339. Hopkinson, C., M. Sitar, L. Chasmer, P. Tr eitz. 2003. Mapping Snowpack Depth Beneath Forest Canopies Using Airborne Lidar. Photogrammetric Engineering and Remote Sensing 70: 323-330. Huising, E.J., L.M. Gomes Pereira. 1998. E rrors and Accuracy Estimates of Laser Data Acquired by Various Laser Scanning Sy stems for Topographic Applications. International Society of Photogrammetric E ngineering and Remote Sensing 53: 245-261. Kampa, K., K.C. Slatton, 2004. An Adaptive Multiscale Filter for Segmenting Vegetation in ALSM Data. Gainesville, FL: University of Florida, Department of Civil and Coastal Engineering and Department of Computer Science Engineering. Krabill, W.B., C.W. Wright, R.N. Swift, E.B. Fredrick, S.S. Manizade, J.K. Yungei, C.F. Martin, J.G. Sonntag, M. Duffy, W. Huls lander, J.C. Brock. 1999. Airborne Laser Mapping of Assateague National Seashore B each. Photogrammetric Engineering and Remote Sensing 66: 65-71. Kraus, K., N. Pfeifer. 1998. Determination of Terrain Models in Wooded Areas with Airborne Laser Scanner Data. International Society of Phot ogrammetric Engineering and Remote Sensing 53: 193-203. Lowe, A.S., 2002. The Federal Emergency Ma nagement Agency’s Multi-Hazard Flood Map Modernization and The National Map. Photogrammetric Engineering and Remote Sensing 69: 1133-35.

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121 Lunetta, R.S., M.E. Balogh. 1998. Applicati on of Multi-Temporal Landsat 5 TM Imagery for Wetland Identification. Photogrammetric Engineering and Remote Sensing 65: 130310. Lyon, J.G., D Yuan, R.S. Lunetta, C.D. El vidge. 1997. A Change Detection Experiment Using Vegetation Indices. P hotogrammetric Engineering an d Remote Sensing 64: 14350. Madden, M., K. Marguerite, D. Jones, L. V ilchek. 1998. Photointerpretation Key for the Everglades Vegetation Classi fication System. Photogrammetr ic Engineering and Remote Sensing 65: 171-77. Maune, D.M. 2001. The DEM Users Manual: Digital Elevation M odel and Technology and Applications. Arlington, VA: American Society of Photogrammetry and Remote Sensing. Melesse, A.M., J.D. Jordan. 2003. Spatially Distributed Watershed Mapping and Modeling: Thermal Maps and Vegetation I ndices to Enhance Land Cover and Surface Microclimate Mapping: Part 1. Jour nal of Spatial Hydrology 3: 1-29. Okagawa, M. 2001. Algorithm of Multiple Fi lter to Extract DSM from LIDAR data. 2001. Presentation at ESRI International Us er Conference. 23-25 July. San Diego, California. Persson, A., J. Holmgren, U. Soderman. 2001. Detecting and Measur ing Individual Trees Using an Airborne Laser Scanner. Photogram metric Engineering and Remote Sensing 68: 925-32. Peters, A.J., E.A. Walter-Shea, L. Ji, A. Vina, M. Hayes, M.D. Svoboda. 2001. Drought Monitoring with NDVI-Based Standardized Vegetation Index. Photogrammetric Engineering and Remote Sensing 68: 71-5. Popescu, S.C., and R.H. Wynne. 2004. Seeing the trees in the forest: Using Lidar and Multispectral Data Fusion with Local Filte ring and Variable Window Size for Estimating Tree Height. Photogrammetric Engineeri ng and Remote Sensing 71: 589-604. Popescu, S.C., R.H. Wynne, a nd J.A. Scrivani. 2004. Fusion of Small-footpr int Lidar and Multispectral Data to Estimate Plot-level Volume and Biomass in Deciduous and Pine Forests in Virginia, USA. Forestry Science 50: 551-65. Raber, G.T., J.R. Jensen, S.R. Schill, K. Schuckman. 2002. Creation of Digital Terrain Models Using an Adaptive Lidar Vegetation Point Removal Process. Photogrammetric Engineering and Remote Sensing 69: 1307-1315. Sheng, Y., Y. Su, Q. Xiao. 1998. Challenging the Cloud Contamination Problem in Flood Monitoring with NOAA/AVHRR Imagery. Phot ogrammetric Engineering and Remote Sensing 65: 191-98.

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122 Song, J., L. Duanjun, M.L. Wesely. 2002. A Simplified Atmospheric Correction Procedure for the Normalized Differen tial Vegetation Index. Photogrammetric Engineering and Remote Sensing 69: 521-528. Sorin, C.P., H.W. Randolph. 2003. Seeing th e Trees in the Forest: Using Lidar and Multispectral Data Fusion with Local Filte ring and Variable Window Size for Estimating Tree Height. Photogrammetric Engineer ing and Remote Sensing 70: 589-604. South Florida Water Manageme nt District. 2000. After Irene Assessment. West Palm Beach: South Florida Water Management District. Stow, D., L. Coulter, J. Kaiser, A. Hope, D. Service, K. Schutte, A. Walters. 2002. Irrigated Vegetation Assessment for Urban E nvironments. Photogrammetric Engineering and Remote Sensing 69: 381-390. Todd, S., R.M. Hoffer. 1997. Responses of Sp ectral Indices to Variat ions in Vegetation Cover and Soil Background. Photogrammetric Engineering and Remote Sensing 64: 91521. United States Geological Surv ey. April 2006. Welch, R., M. Madden, R.F. Doren. 1998. Mapping the Everglades. Photogrammetric Engineering and Remote Sensing 65: 163-170. Yodoyama, R., M. Shirasawa, R.J. Pike. 2001. Visualizing Topography by Openess: A New Application of Image Processing to Digital Elevation Mode ls. Photogrammetric Engineering and Remote Sensing 68: 257-265.

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123 BIOGRAPHICAL SKETCH William Webb is the son of Brenda Y. Webb and Frank R. Webb, and is the brother of Ron Webb, Ashleigh Webb and Hiliary Webb. The author graduated from the University of Florida in May 1999 with a bach elor’s degree in Agri cultural Operations Management. The author has completed the Hydrologic Sciences Academic Cluster and Remote Sensing and Geographic Inform ation Systems Curriculum certificates.


Permanent Link: http://ufdc.ufl.edu/UFE0014307/00001

Material Information

Title: Using remote sensing and geographic information systems for flood vulnerability mapping of the C-111 basin in south Miami-Dade County
Physical Description: Mixed Material
Language: English
Creator: Webb, William Andrew ( Dissertant )
Graham, Wendy D. ( Thesis advisor )
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2006
Copyright Date: 2006

Subjects

Subjects / Keywords: Agricultural and Biological Engineering thesis, M.S
Dissertations, Academic -- UF -- Agricultural and Biological Engineering
Genre: bibliography   ( marcgt )
theses   ( marcgt )
Spatial Coverage: United States--Florida-Miami

Notes

Abstract: The hydrologic cycle of south Florida frequently produces rain events that include thunderstorms, tropical depressions and hurricanes. During 1999-2000, south Miami-Dade was struck by two intense rain events that severely inundated local agricultural operations for over a week. In the final assessment, agricultural losses sustained from these storms totaled to nearly $430 million. Flood hazard mapping has traditionally relied on paper maps that display the flood extent with only polygon boundaries. Unfortunately, paper maps are greatly limited in use, because they fail to show the extent, magnitude and duration of flooding. Recent advances in airborne laser swath mapping, ALSM, and satellite sensor technology have provided alternative types of data needed to more accurately map flood vulnerability. The general scope of this project is to improve mapping flood vulnerability in the southern C-111 basin by combining a variety of remotely sensed data sets. The procedure for mapping a severe flood condition following Hurricane Irene involved the combination of ALSM, Landsat7 ETM+ and Geographic Information Systems (GIS). Band 8, vegetation index two and vegetation index three derived from the Landsat 7 ETM+ image were useful for mapping cloud cover, and the normalized differential vegetation index (NDVI) was useful for mapping inundation produced by Hurricane Irene. The primary limitations of vegetation index maps include the 30 meter spatial resolution, and the obstruction of the spectral signature of water caused by vegetation and clouds. Project inundation maps created with regional surface water and airborne laser swath mapped (ALSM) data displayed the flood duration, magnitude and extent of the flood condition resulting from Hurricane Irene.
Subject: SWEM, SWIM
General Note: Title from title page of source document.
General Note: Document formatted into pages; contains 137 pages.
General Note: Includes vita.
Thesis: Thesis (M.S.)--University of Florida, 2006.
Bibliography: Includes bibliographical references.
General Note: Text (Electronic thesis) in PDF format.

Record Information

Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
Resource Identifier: alephbibnum - 003589537
System ID: UFE0014307:00001

Permanent Link: http://ufdc.ufl.edu/UFE0014307/00001

Material Information

Title: Using remote sensing and geographic information systems for flood vulnerability mapping of the C-111 basin in south Miami-Dade County
Physical Description: Mixed Material
Language: English
Creator: Webb, William Andrew ( Dissertant )
Graham, Wendy D. ( Thesis advisor )
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2006
Copyright Date: 2006

Subjects

Subjects / Keywords: Agricultural and Biological Engineering thesis, M.S
Dissertations, Academic -- UF -- Agricultural and Biological Engineering
Genre: bibliography   ( marcgt )
theses   ( marcgt )
Spatial Coverage: United States--Florida-Miami

Notes

Abstract: The hydrologic cycle of south Florida frequently produces rain events that include thunderstorms, tropical depressions and hurricanes. During 1999-2000, south Miami-Dade was struck by two intense rain events that severely inundated local agricultural operations for over a week. In the final assessment, agricultural losses sustained from these storms totaled to nearly $430 million. Flood hazard mapping has traditionally relied on paper maps that display the flood extent with only polygon boundaries. Unfortunately, paper maps are greatly limited in use, because they fail to show the extent, magnitude and duration of flooding. Recent advances in airborne laser swath mapping, ALSM, and satellite sensor technology have provided alternative types of data needed to more accurately map flood vulnerability. The general scope of this project is to improve mapping flood vulnerability in the southern C-111 basin by combining a variety of remotely sensed data sets. The procedure for mapping a severe flood condition following Hurricane Irene involved the combination of ALSM, Landsat7 ETM+ and Geographic Information Systems (GIS). Band 8, vegetation index two and vegetation index three derived from the Landsat 7 ETM+ image were useful for mapping cloud cover, and the normalized differential vegetation index (NDVI) was useful for mapping inundation produced by Hurricane Irene. The primary limitations of vegetation index maps include the 30 meter spatial resolution, and the obstruction of the spectral signature of water caused by vegetation and clouds. Project inundation maps created with regional surface water and airborne laser swath mapped (ALSM) data displayed the flood duration, magnitude and extent of the flood condition resulting from Hurricane Irene.
Subject: SWEM, SWIM
General Note: Title from title page of source document.
General Note: Document formatted into pages; contains 137 pages.
General Note: Includes vita.
Thesis: Thesis (M.S.)--University of Florida, 2006.
Bibliography: Includes bibliographical references.
General Note: Text (Electronic thesis) in PDF format.

Record Information

Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
Resource Identifier: alephbibnum - 003589537
System ID: UFE0014307:00001


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USING REMOTE SENSING AND GEOGRAPHIC INFORMATION SYSTEMS FOR
FLOOD VULNERABILITY MAPPING OF THE C-111 BASIN IN SOUTH MIAMI-
DADE COUNTY














By

WILLIAM ANDREW WEBB


A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF
FLORIDA INT PARTIAL FULFILLMENT FOR THE REQUIREMENTS FOR THE
DEGREE OF MASTER OF SCIENCE

UNIVERSITY OF FLORIDA


2006

































Copyright 2006

by

William Andrew Webb


































This paper is dedicated to my parents Frank R. Webb and Brenda Y. Webb.
















ACKNOWLEDGMENTS

I would like to acknowledge my department professors Dr. Wendy Graham, Dr.

Carol Lehtola and Dr. Jack Jordan for their guidance and hard work in this proj ect. I

would like to thank Dr. Clint Slatton for providing his expertise in Airborne Laser Swath

Mapping. I would like to thank Don Pybas for his contribution and cooperation. I would

like to thank Charles Brown for his assistance during the development process.





















TABLE OF CONTENTS




page

ACKNOWLEDGMENT S .............. .................... iv


LI ST OF T ABLE S ................. .............. vii...___.....


LIST OF FIGURES .............. .................... ix


GLOSSARY OF TERMS ............ _...... ._ ..............xii...


AB S TRAC T ......_ ................. ............_........x


CHAPTER


1 INTRODUCTION ................. ...............1.......... ......


Background ................. ...............1.................
FI ood Management ................. ...............1.......... ......
Obj ectives ................. ...............3.......... ......
Proj ect Area ................. ...............4................

2 LITERATURE REVIEW .............. ...............7.....


Active and Passive Remote Sensing ................. ...............7............ ...
Spectral Si nature of Water............... ...............8.
Sensor Perform ance ................... ............ ............ ...............9.
Normalized Differential Vegetation Index (NDVI) .............. ....................1
Water Detection ................. ...............11.................
Cloud Detection ................. .......... ...............12.......
Airborne Laser Swath Mapping ................. ...............13................
ALSM Accuracy ........._..... ...._... ...............15.....
ALSM Point Removal .............. ...............16....
AL SM Applications ............._. ...._... ...............18....
Geographic Information Systems .............. ...............20....
Spatial M odeling.................... .............2
Inundation Mapping with GIS .............. ...............24....


3 DATA RESOURCES AND METHODOLOGY .............. ...............26....












Introducti on ................. ...............26___ .......
Surface Water Data ............... ... ........... ...............26...

Digital Elevation Model Construction................ .............2
Landsat 7 Enhanced Thematic Mapper .............. ...............28....
Vegetative Index Methodology .............. ...............28....
Unsupervised Classification .............. ...............29....
Bare Earth Modeling............... ...............30
Aerial Color Infrared Analysis .............. ...............31....
Ground Control Point Analysis .............. ...............32....
Topographic Spatial Modeling .............. ...............34....
Surface Water Elevation Map Methodology .............. ...............35....
Surface Water Elevation Map Interpolation ......._ ......... ___ ........._ ......37
Surface Water Inundation Map Methodology .............. ...............39....


4 RE SULT S AND DI SCU SSION ............... ...............4


Cloud Detection ............... ... .....___ ..... ....__ .............4

Vegetation Index Two and Vegetation Index Three ....._____ ........___ ..............51
NDVI .............. .. ...............55..

Topographic Analysis............... ...............60
Classified ALSM DEM .............. .. ...............61..
Surface Water Elevation Map Analysis............... ...............68
Surface Water Inundation Map ................. ...............93................


5 CONCLU SION................ .............11


Image Analysis ................. ...............114......... ......
Bare Earth Modeling ................. ...............114................
SW EM ................. ...............115......... ......
SW IM ................. ...............115......... ......
Conclusion ................ ...............116................


6 RECOMMENDATIONS FOR FUTURE STUDIES ................. ............ .........117


LIST OF REFERENCES ............__........_ ...............119....


BIOGRAPHICAL SKETCH .........._.... ...............123..__.........


















LIST OF TABLES


Table pg

2. 1 Specifications of a commercial Lidar system. .......................... ........13

3.1 Area 2 static GPS points. ............. ...............39.....

3.2 Inverse distance weighting search parameters for topography ................. ...............40

3.3 Global polynomial search parameters for topography ................. .......................41

3.4 Local polynomial search parameters for topography ................. ........................41

3.5 Radial based function search parameters for topography. ................... ...............4

3.6 Kriging search parameters for topography. ............. ...............42.....

3.7 Proj section Parameters for AL SM ................. ...............42........... .

3.8 Universal kriging search parameters for SWEM. .............. ...............43....

3.9 Simple kriging search parameters for SWEM. ............. ...............44.....

3.10 Ordinary Kriging search parameters for SWEM. ............. ...............45.....

3.11 Disjunctive kriging search parameters for SWEM. .................. ................4

3.12 Universal Kriging for SWEM ................. ...............48......____..

4.1 Radial based functions statistics for topography. ............. ...............106....

4.2 Inverse distance weighting statistics for topography. ................... ...............10

4.3 Global polynomial statistics for topography. ........_................. .............. ....107

4.4 Local polynomial statistics for topography ................. ...............107..............

4.5 Kriging statistics for topography. ............. ...............108....

4.6 Universal Kriging statistics for SWEM. .............. ...............108....

4.7 Universal kriging statistics for SWEM, 10/12-22/1999. ............. .....................0











4.8 Simple kriging statistics for SWEM, 10/12-22/1999. .............. ......__............109

4.9 Ordinary kriging statistics for SWEM. ............. ...............109....

4. 10 Disjunctive Kriging statistics for SWEM. ................ ...............111.............

4. 11 SWEM surface water values for October 12-22, 1999. ................ ............... .....112

4. 12 Vulnerability index classes used for SWIM. ................ .............................113

4. 13 Calculated inundation statistics for the study area ................. ................ ...._.113



















LIST OF FIGURES


Figure pg

1.1 Map of Miami-Dade County and the proj ect area. ......___ ..... ...__ ........._.......4

1.2 M ap of the study area. ..............._ ...............5......... ...

2. 1 Illustration of a Lidar infrared beam. ......._...._ ... ........ ......_...........1


3.1 Color infrared aerial photos of the study area. ...._.._.._ ..... .._._. ....._.... .....3

3.2 Map of measurement sites. ........._.._.. ....__. ...._.._ ....._.. ........._......36

4. 1 Map of the Frog Pond with Band 8. ................ ...._.._ ...............50. .

4.2 Vegetation index two map of south Florida, October 16, 1999. ........._.._... ...............52

4.3 Vegetation index two map of the study area, October 16, 1999. .............. ... ............54

4.4. October 16, 1999, NDVI map of the south Florida. ............. .....................5

4.5. October 16, 1999, NDVI map of the study area. ............. ...... ............... 5

4.6 Planar view of the NAD 27 study area DEM. ................ ...............62.............

4.7 Graph of surface water elevation values.. ............. ...............70.....

4.8 SW EM ................. ...............71................

4.9 Prediction error for SWEM ................. ...............82...............

4. 10 SW IM ................. ...............95._ ___.....






Pages
X -XI
Mis sing
F ro m
Or iginal
















GLOSSARY OF TERMS


ACIR


Aerial Color
Infrared

Airborne Laser
Swath Mapping

Bare earth
model

Digital
Elevation
Model

Determini sti c
Interpolation


Digital
Orthographic
Quarter
Quadrangle

Digital Surface
Model

Digital Terrain
Model

Federal
Emergency
Management
Agency

Geographic
Information
Systems


ACIR is aerial color infrared imagery that is not
referenced with a coordinate system.

ALSM is a mapping technology that uses a laser to
map land or bathymetric topography.

A bare earth model is a DEM with artifact or
unwanted points removed.

A DEM is a 3D representation of a surface than may
be represented with raster cells or a TIN.


Deterministic interpolation uses deterministic
functions to predict values of a spatially distributed
field at unmeasured locations.

A DOQQ is similar to an aerial photograph except it
is referenced with a coordinate system and is used for
general GIS mapping applications.


A DSM is a 3D representation of a surface with
objects and man made features removed.

A DTM is a 3D representation of a surface that uses a
TIN to connect points.

FEMA is the disaster management and relief agency
of the federal government.



GIS is software that captures, stores, retrieves,
manipulates and displays geographically referenced
spatial tabular data.


AL SM


BEM


DEM


DOQQ




DSM


DTM


FEMA




GIS










Glossary of terms continued
GPS Global
Positioning
Systems

Kriging


GPS is a constellation of 24 satellites that provides
latitudinal and longitudinal data collected by a
receiver.

Kriging is geostatistical interpolation technique that
uses the spatial correlation of a distributed field to
predict its value of unmeasured locations.

Landsat 7 ETM + is the seventh USGS satellite in a
series of satellites designed to capture environmental
data with visible, near infrared, mid-infrared, low and
high gain thermal sensor bands.

Light Detection and Ranging is the enabling laser
technology used for ALSM flight operations.

NAD 83 is the current horizontal datum used by the
National Geodetic Survey.
NAD 27 is the predecessor horizontal datum to NAD
83.

NDVI is a vegetative index that is calculated as the
difference between the red and near infrared bands
divided by the sum of the red and near infrared
bands.

NGVD 29 is the predecessor vertical datum to
NGVD 88.




NGVD 88 is the current vertical datum used by the
National Geodetic Survey.



The National Park Service is controlled by the U.S.
Department of Interior and is responsible for the
management of all national parks.

A raster is a thematic map layer represented with a
grid.

Remote sensing refers to the capture of data without a
physical collection of the data.


Landsat 7
ETM+


Landsat 7
Enhanced
Thematic
Mapper


Lidar


Light Detection
and Ranging

North American
Datum 1983
North American
Datum 1927

Normalized
Differential
Vegetation
Index


NAD 83

NAD 27


NDVI


NGVD 29 National
Geodetic
Vertical Datum
1929

NGVD 88 National
Geodetic
Vertical Datum
1988

NPS National Park
Service


Raster


Remote Sensing










Glossary of terms continued
SCDS South Dade
Conveyance
System

SFWMD South Florida
Water
Management
Di stri ct

SWEM Surface Water
Elevation map


SWIM Surface Water
Inundation Map







TIN Triangulated
Irregular
Network

USGS U.S. Geological
Survey


Vector


Vegetative
Index Two


Vegetative
Index Three


The SDCS is the southern extension of the Central
and Southern Flood Control Proj ect and is located in
south Miami-Dade County.

The SFWMD is one of five water management
districts in Florida, and its district authority covers all
of southeast Florida.


The Surface Water Elevation Map is a representation
of the surface water for elevation over proj ect areas
of interest.

The Surface Water Inundation Map is a
representation of the surface water elevation
measured in elevation above mean sea level with
respect to NGVD 88. The Surface Water Inundation
Map is the result of subtracting land surface elevation
grids from surface water elevation, and represents
depth of water on the land surface.

A TIN is a three dimensional representation of a
surface created by using triangles to link points.


The USGS is a multi-disciplinary science
organization that studies biology, geography,
geology, geospatial information, and water.

Vector is a thematic map layer represented by points,
lines and polygons.

Vegetative Index Two is calculated as the product of
the green band and low gain thermal band divided by
the high gain thermal band.

Vegetative Index Three is calculated as the low gain
thermal band divided by the sum of the mid-infrared
and red bands.
















Abstract of Thesis Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Master of Science

USING REMOTE SENSING AND GEOGRAPHIC INFORMATION SYSTEMS FOR
FLOOD VULNERABILITY MAPPING OF THE C-111 BASIN IN SOUTH MIAMI-
DADE COUNTY

By

William Andrew Webb

May 2006

Chair: Wendy D. Graham
Maj or Department: Agricultural and Biological Engineering

The hydrologic cycle of south Florida frequently produces rain events that include

thunderstorms, tropical depressions and hurricanes. During 1999-2000, south Miami-

Dade was struck by two intense rain events that severely inundated local agricultural

operations for over a week. In the final assessment, agricultural losses sustained from

these storms totaled to nearly $430 million.

Flood hazard mapping has traditionally relied on paper maps that display the flood

extent with only polygon boundaries. Unfortunately, paper maps are greatly limited in

use, because they fail to show the extent, magnitude and duration of flooding. Recent

advances in airborne laser swath mapping, ALSM, and satellite sensor technology have

provided alternative types of data needed to more accurately map flood vulnerability. The

general scope of this proj ect is to improve mapping flood vulnerability in the southern C-

1 11 basin by combining a variety of remotely sensed data sets.









The procedure for mapping a severe flood condition following Hurricane Irene

involved the combination of ALSM, Landsat7 ETM+ and Geographic Information

Systems (GIS). Band 8, vegetation index two and vegetation index three derived from the

Landsat 7 ETM+ image were useful for mapping cloud cover, and the normalized

differential vegetation index (NDVI) was useful for mapping inundation produced by

Hurricane Irene. The primary limitations of vegetation index maps include the 30 meter

spatial resolution, and the obstruction of the spectral signature of water caused by

vegetation and clouds. Proj ect inundation maps created with regional surface water and

airborne laser swath mapped (ALSM) data displayed the flood duration, magnitude and

extent of the flood condition resulting from Hurricane Irene.















CHAPTER 1
INTTRODUCTION

Background

For nearly a century, south Miami-Dade's subtropical climate has provided a

suitable environment for consistent annual production of agricultural commodities.

Agricultural production heavily depends upon the regional climate that is characterized

by a high mean annual rainfall, warm temperatures and extremely mild winters.

Hurricanes and tropical storms often produce flood conditions that can remain for weeks.

During 1999-2000, south Miami-Dade was struck by two intense rain events. The

first event, Hurricane Irene, passed over South Florida on October 15, 1999 and the

second event, the October 2000 No Name Event (NNE), struck almost one year later on

October 4, 2000. The impact of both storms on the agricultural economy of south Miami-

Dade resulted in losses of nearly $430 million.

Flood Management

Flood control for south Florida became a federal priority in 1947 after back-to-

back hurricanes left most local communities and the newly created Everglades National

Park (ENP) inundated for weeks. In 1948, Congress authorized construction of the

Central and Southern Florida Flood Control Proj ect (CS&F) to regulate flooding and

mitigate damage. The current system contains 1,800 miles of canals, 25 maj or pumping

stations and other conveyance structures that stretch from Orlando to south Miami-Dade.

The South Dade Conveyance System (SDCS) is the Miami-Dade County extension

of the CS&F and is governed in a three party agreement between ENP, the United States










Army Corps of Engineers, and the South Florida Water Management District (South

Florida Water Management District 2000). Canals C-111 and L31W provide flood relief

for agricultural lands and discharge water into Taylor Slough and Florida Bay.

The frequency and magnitude of flood events in South Miami-Dade have increased

the demand for high-resolution flood maps that are capable of displaying the extent,

magnitude and duration of a specific flood event. In 1997, a University of Florida

Hydrologic Sciences Task Force (HSTF) addressed the maj or issues surrounding flood

management for agricultural areas in south Miami-Dade (Graham et al., 1997, pp.34),

Flooding in the agricultural area has intensified in frequency, duration and depth ..
.the lack of documentation concerning the negative impact of the experimental
water deliveries has hindered progress by the USACOE and SFWMD to address
these concerns.

The hydrologic and geographic databases in the agricultural area east of the C- 111
canal should be enhanced. Installation of additional monitoring stations,
development of new geographic information, and further historical and statistical
evaluations of the existing data bases is necessary to accurately assess the impact of
canal operations on groundwater levels in the agricultural area.

A local-scale, event based hydrologic model is needed to define the risk of flooding
to the agricultural community associated with alternative structural and operational
plans for the C- 111 proj ect...such a model could be used to produce maps of
flooding probability in the agricultural area associated with alternative structural
and operational plans for the C- 111 proj ect, which would allow local producers to
better plan for the future."

The development of a multi-hazard database currently is the highest priority for the

Department of Homeland Security and the Federal Emergency Management Agency,

FEMA (Lowe 2002). FEMA' s Multi-Hazard Flood Map Modernization initiative

involves the expansion of the current geo spatial hazard data base including the Multi-

Hazard Flood Map Modernization. The modernization proj ect is designed to produce a

more accurate geospatial flood vulnerability database that is accessible to the general

public. Furthermore, the National Flood Insurance Program has charged FEMA to head









the Coordination of Surveying, Mapping, and Related Spatial Data Activities. The action

mandates that flood hazard mapping becomes FEMA' s top priority among all natural

disasters. Part of the effort additionally includes the initiative to acquire geo-referenced

spatial data and micro-topographic Airborne Laser Swath Mapped data for advanced

hydrologic models and maps. In 1999, FEMA and NASA sponsored ALSM flight

operations of the C- 111 basin and Everglades National Park, ENP, for future flood

mapping proj ects. Modern FEMA flood maps are required to meet the standard of a 5

meter spatial resolution (Maune 2001).

Recently, FEMA and the Harris County Flood Control District developed the

Tropical Storm Allison Recovery Proj ect to assess flood vulnerability in response to the

aftermath of tropical storm Allison (www.hcfcd.org/tsarp.asp, April 2006). The project

methodology featured the integration of ALSM and GIS for creating digital flood

insurance rate maps, DFIRMS.

Obj ectives

The research obj ective of this proj ect was to develop a method for assessing flood

vulnerability in the C-111 basin by integrating Landsat7 sensory data, regional surface

water elevation data and airborne laser scanned topographic data. Secondary objectives

included the creation of a bare earth model for agricultural fields, modeling regional

surface water elevation prediction grids and detecting clouds with vegetative indices. The

featured map product is the surface water inundation map (SWIM). SWIM is an

inundation map capable of displaying the magnitude, duration and extent of flooding with

a 3 meter spatial resolution. October 12-22, 1999, is the study period used for generating

surface water grids and inundation maps.










Proj ect Area

The study area lies in the southern area of the C- 111 basin and is comprised of

agricultural fields and protected wetland areas in ENP, Figurel.1. The Frog Pond is a

fertile tract of land in the C-111 partitioned into twenty-two parcels and leased by the

Federal government; however flood protection is not guaranteed for the Frog Pond.

Parcels 14, 15, 16, 17, 18 and 19 are located within the study area; however only 16 and

18 are completely displayed, (see Figure 1-2).

The three maj or land cover types that dominate the study area are wetland forest,

wetland marsh and agricultural row crop fields. Fiducial land features are permanent geo-

morphological features in the study area and they include the soil mound and the L-31W

canal, Figure 1.2. The S175 culvert is also located on the L31W canal inside the study

area.
















] Project Area
g Prog Pond
)'1 ~Streams and Canalsin
[ ] County Boundary




10 0 10 20 Miles


Figure 1.1 Map of Miami-Dade County and the project area.

















/ Roads
Study Area
0 114



21





,:II ~ ~ ~ i Wetland Forest~eln as
(I 'Canals


I I S175

0.6 0 0.6 1.2 Miles



Figure 1.2 Map of the study area. Leased parcels are numbered 14-21. The S175 culvert
is represented with a red circle on the L31W canal.

The sub-surface hydrology of south Florida, including Miami-Dade, is

characterized by an unconfined, highly permeable system called the Biscayne Aquifer

(Fetter 1998). The Biscayne aquifer is recharged by precipitation, and water table levels

fluctuate with the amount of precipitation. Below the Biscayne is a plastic semi-confining

unit, the gray limestone aquifer and a lower plastic unit (Graham et al. 1997). Canals

penetrate the most permeable part of the aquifer. The thickness and hydraulic

conductivity of the Biscayne in the southern C- 111 basin are approximately 46ft. and










25,000 ft. /day, respectively (Graham et al. 1997). Water levels in the Biscayne conform

to the land surface with the highest levels occurring in the high elevation areas, and

lowest levels in the low elevation areas.















CHAPTER 2
LITERATURE REVIEW

Photogrammetry is the science of analyzing photographs and images to determine

the size, shape, and spatial attributes of the features in an image acquired with remote

sensing (Bethel and Cheng 1995). Remote sensing refers to the inferring of target or

media characteristics by the reception of energy from the target or media. The energy

may be electromagnetic, acoustic, subatomic particles, scattered energy originally

transmitted from an active system sensor or originating from the sun.

Active and Passive Remote Sensing

All remote sensing applications use either active or passive sensors. Passive remote

sensing is usually dependent on reflected solar illumination or the emission or

transmission of black body radiation. Active remote sensing involves sending a signal at

a specific wavelength to the earth surface, detecting a return signal and assigning a pixel

value to the received signal.

Emitted energy, an earth surface feature, is optimally sensed in the near infrared to

the far infrared bands and reflectance properties are optimally sensed in the visible

through the mid-infrared bands. For this reason, most passive sensor studies of planetary

surfaces are conducted in the visible and infrared regions. Madden et al. (1998) used

1994 color infrared imagery to identify wetland vegetation in Everglades National Park.

Doren, Rutchey and Welch (1998) used color infrared imagery to classify vegetation in

the southern Everglades. Welch, Madden and Doren (1998) used color infrared imagery

as ground control to classify vegetation in the Everglades.









The maj or applications of visible and infrared remote sensing include detecting

surface chemical compositions, vegetative cover and biological processes. Although,

visible and color infrared sensory data are useful for environmental studies, wave matter

interactions produce noise in the received signal.

Spectral Signature of Water

Variations in emitted and reflected radiation are used to measure, classify and

verify the spectral signature characteristics of the land surface. Similar surfaces will share

similar signature values within the electromagnetic spectrum for a specific wavelength,

and different surfaces typically possess different spectral signatures. Scatter, emittance,

reflectance and absorption of specific bands produce a unique "spectral signature" or

curve that is characteristic for a particular surface property. The remotely sensed spectral

signature is related to an associated curve obtained from laboratory measurements of

wavelength versus reflectance for the visible and infrared regions of the electromagnetic

spectrum for a library of materials.

Water has a low spectral signature reflectance in the visible and infrared region

compared to all other major land cover types. Water, vegetation and exposed ground are

the main ground cover types in the C-111, and the ability to recognize these ground cover

types with remotely sensed images is dependent on separating and distinguishing their

spectral characteristics. Water with sediment and debris will produce a higher reflectance

spectral signature than that of pure water.

Albedo is the reflectivity of a surface, and water possesses a low albedo in the near

infrared band. Vegetation possesses a high reflectance in the infrared spectrum due to

plant microstructure. Vegetation has a relatively low reflectance in the red band

compared with soil and turbid water, while wet soil and water have similar reflectance in










the red band. The ratio of red and infrared bands is used to distinguish between water

(pure and turbid) and land (vegetation and soil).

Because of sediment and debris, flood water will produce a maximum reflectance

peak in the red band. This signature is particularly useful for flood detection; however,

the presence of dense clouds may interfere with the signature of the land surface. No

verifiable method can be expected to eliminate cloud contamination to obtain visible and

near infrared based flood information under thick cloud formation, (Sheng, Su and Xiao

1998).

Active and passive signal errors are primarily attributed to absorption or scatter of

atmospheric noise components. Absorption is caused by the presence of water vapor and

gases, while scatter is caused by the presence of vapor, gases, dust and atmospheric

turbulence.

Sensor Performance

Four measures of sensor performance are used for determining the quality of the

resolution of an image. These measures include spectral resolution, spatial resolution,

radiometric resolution and temporal resolution.

Spectral resolution refers to the specific wavelength intervals in the electromagnetic

spectrum that the sensor records. A decrease in the wavelength interval results in an

increase in the resolution of the image.

Spatial resolution is the measure of the smallest feature that a sensor can detect or

the area on the ground represented by each pixel for a nadir view. Nadir is the point

diametrically opposed to the zenith, which is the point in the sky directly overhead. An

azimuth is an arc from the horizon to the zenith. Nadir can also be taken to mean "lowest

point" in the sense that zenith can be taken to mean "highest point."










Spatial resolution can also be described in the form of the instantaneous field of

view (IFOV) or the measure of the cone angle (radius) viewed by a single detector at a

specific point in time. The scale at which an image is captured provides useful

information about spatial resolution, and spatial resolution may vary for different sensory

bands in an image. For example, the panchromatic band of Landsat 7 possesses a 15

meter spatial resolution while the other bands possess a 30 meter resolution. Obj ects that

are smaller than the IFOV can be detected if they contrast strongly against the

background of surrounding pixels. Conversely, obj ects larger than the pixel may not be

detected if their reflectance does not dominate the surrounding pixels.

Radiometric resolution or dynamic range is the number of possible data values in

each band or the number of bits into which the remotely sensed energy is divided. For

example, when the Landsat7 ETM+ sensor records the electromagnetic radiation in its

IFOV, the total intensity of the energy is divided into 256 brightness values for 8 bit data.

Data fie values or digital numbers for 8 bit data range from 0 to 255 for each pixel.

Temporal resolution is a measure of how often the sensor records imagery for a

particular area, and for satellites, this is generally defined by its path or orbital cycle.

Normalized Differential Vegetation Index (NDVI)

Vegetation indices are created by combining data from sensor bands into a

specified algorithm. They are particularly useful for identifying features by enhancing

certain reflectance properties. NDVI is commonly used to visualize properties of land

cover that are elusive with only raw band imagery.

NDVI is most useful for mapping land cover including urban areas, water, soils,

dying and healthy vegetation. A value near 1 represents high near infrared reflectivity and

a value near -1 indicates strong near infrared absorption. NDVI is calculated as the









difference between near infrared and red divided by the sum of red and near infrared.

Melesse and Jordan (2003) calculated NDVI as



Band 4 + Band3 (1)

Todd and Hoffer (1998) used mid-infrared with Landsat 5 near infrared data to map

land surface moisture. NDVI increased with an increase in healthy vegetative cover.

Inundated surfaces possess extremely low NDVI values, because of high infrared

absorption and low infrared reflectance properties. The study investigated NDVI for

targets with specific vegetation cover amounts and varying soil backgrounds. Although

vegetation indices were less sensitive to soil background, they were effective for

determining vegetation biomass and vegetation cover for small areas. The relationship

between NDVI and vegetative land cover showed that NDVI was higher for moist soils

than the drier soils at the same percent vegetation. NDVI increased substantially as

moisture increased for the same vegetation cover.

Water Detection

Lunetta and Balog (1999) used multi temporal Landsat 5 data for identifying

wetland land cover including water bodies. The results showed that sensor data in the

mid-infrared, Band 5, best discriminated between dry and wet areas. Frazier and Page

(2000) successfully used visible and infrared bands from Landsat 5 to detect water bodies

in the floodplain of the Murrumbidgee River in central Australia.

Song, Duanjun and Wesely (2003) researched the short wave spectral signature of

water bodies. The signature of water is unique among signatures for most natural

surfaces, because of its low reflectance throughout the electromagnetic spectrum. The

reflectance of water bodies showed a decrease in reflectance with an increase in









wavelength. The signature of water produced a negative value with NDVI, and pixels in

satellite images with the most negative NDVI values were correlated with water bodies.

The shape of the spectral surface reflectance and its value in the red band greatly

depended on the relative amounts of suspended minerals, chlorophyll and dissolved

organic matter in the water. Under clear water conditions, the reflectance was found to

decrease linearly with wavelength.

Cloud Detection

Cloud contamination alters or sometimes completely obstructs the spectral

signature of the land surface. The significant difference in spectral reflectance between

clouds and the earth makes the process of distinguishing clouds from the Earth's surface

difficult due to the high variability in cloud expression. Sheng Su and Xiao (1998) used

thermal, infrared and visible channels of Advanced Very High Resolution Radiometer

(AVHRR) to distinguish cloud cover from land cover. The spatial variance of cloud top

temperature was noted to be greater than that of the Earth' s surface, and the contextual

feature of surface temperature was also used for cloud screening. Image analysis showed

that cloud shadow caused a reduction in solar irradiance, and cloud shadow and water

bodies were difficult to distinguish in the near infrared channel.

Melesse and Jordan (2003) used visible, short-wave infrared and thermal infrared

bands from Landsat 5 to develop two vegetative indices for detecting clouds, cloud

buildup and water in the Econ Basin, Florida. Clouds were detected and classified by

using the simplified Plank constant to convert Band 6 digital number values to

temperature. Image data was used to enhance dense clouds and urban features for visual

analysis. For Landsat 7 ETM+, vegetative index two and vegetative index three are

calculated as,









BBBBBBBBBBBBBBBBBBBBan 2 B and 6
VI 2 = (2)
B a n d7B~~~~BBBBB~~~~BBBB

Band6
VI3 = (3)
Band 5~~~~BBBBB~~~~BBBB + Band3

where Band 2 is the green band, Band 6 is the thermal infrared band, Band 7 is the

middle infrared band and Band 3 is the red band. These indices were found to be

effective for mapping dense cloud cover and partial clouds.

Airborne Laser Swath Mapping

Airborne Laser Swath Mapping (ALSM), or light detection and ranging, Lidar,

remote sensing utilizes a laser, detector, scanning system and Global Positioning Systems

for topographic mapping. The complete process involves planning, collection,

processing, filtering and editing echo points from the return signal data. Elevation post

spacing is a function of flying height, speed, pulse rate and scan angle. Specifications of a

commercial ALSM system generally describe laser, scanning, GPS, INS and flying

operations and information concerning error and delivery (Table 2.2).

Table 2. 1 Specifications of a commercial Lidar system.
Specification Typical Value
Wavelength 1,064 Clm
Pulse Repetition Rate 5 33 k Pulse Energy 100s CIJ
Pulse Width 10 ns
Beam Divergence 0.25 2 mrad
Scan Angle 40 (75 o Maximum)
Scan Rate 25 40 Hz
Scan Pattemn Zig-Zag, Parallel, Elliptical, Sinusoidal
GPS Frequency 1 2 times per second
INS Frequency 50 (200 maximum)
Operating Altitudes 100 1,000 m (6,000 m max)
Footprint 0.25 2 m (from 1,000 m)
Multiple Elevation Capture 1 5
Grid Spacing 0.5 2m
Vertical RMSE 15+ cm









Table 2. 1 Continued
Horizontal RMSE 10 100 cm
Post-Processing Software Proprietary


Topographic ALSM lasers use an infrared light beam (1064 nm) that is invisible,

absorbed by water and strongly reflected by healthy vegetation and concrete. The laser is

sent at a narrow dispersion angle (0.3 Cprad), and laser spot size or footprint is determined

by flying height. The infrared beam reflects strongly off healthy vegetation, concrete and

dry soils, however any presence of water will absorb and warp the beam path. It is

important to note that birds and other airborne obj ects will reflect the infrared beam and

produce an exceptionally high elevation value. Figure 2.1 shows how the beam reflects

off of and penetrates a tree canopy to produce an elevation point.


figure z. 1 Illustration or a Liaar intrarea D~eam. Ine actual D~eam diameter Is smaller than
what i s shown in the figure. Source http://earthob servatory.nasa.gov

Intensity is the measure of the energy reflected from an obj ect. Detecting return

intensity involves recording the reflected or return beam energy from the earth surface.

Obj ects possessing high reflectivity properties show a higher return energy than objects










possessing low reflectivity properties. Obj ects such as metal roofs and sand possess high

reflectivity values, while water and black tar pavement possess low reflectivity values.

Different sensors have been developed to record multiple returns and reflected

intensity. Multiple return signals occur when part of a distended beam strikes an above

ground obj ect and the remaining portion strikes the ground. When this occurs, the

recorded signal will then display multiple elevation values from a single pulse. The above

ground signal is the "first return" and the ground signal is the "last return". Multiple

returns are found in high, dense canopy areas, because the first and middle returns

provide elevations for the top and intermittent growth. The last return usually reflects

from the ground, however extremely dense canopy will prevent full penetration.

Kraus and Pfeifer (1998) noted if the beam strikes canopy or branches then the

measured ground elevation value might be overestimated. This can lead to an asymmetric

distribution error of laser scanner points. The research results emphasized the necessity to

remove vegetation without deleting ground points for areas possessing low penetration

rates.

ALSM Accuracy

Post processing of ALSM data is performed to satisfy two requirements for product

delivery. The first requirement derives accurate results based on GPS stations to provide

a frame of reference for the airborne operation (Maune 2001). The second requirement is

to solve a bare earth condition by removing irrelevant points, and this is accomplished

with automatic or manual post processing methods. Automatic processing uses software

algorithms to view neighborhood points and weigh them before removal. Manual

processing is necessary, because automated algorithms may produce anomalies not

characteristic of the bare earth condition. Occasionally, some apparent data anomalies










appear in files and the analyst may review aerial photography, digital imagery or

videotape to identify anomalies (Maune 2001).

Few empirical studies exist for assessing the accuracy of digital elevation models

created with ALSM. Under ideal conditions, absolute vertical accuracy for grass and

pavement may be within 15 centimeters, but vertical accuracy cannot be obtained within

10 centimeters (Maune 2001). Daniels (2001) evaluated datum conversion issues and

accuracy of ALSM by comparison of real time kinetic GPS sample points and lidar spot

elevations. Base station, local orthometric height and regional offset corrections used to

isolate potential datum offsets in lidar were necessary for mapping dynamic geo-

morphological surfaces.

Hodgson et al. (2003) found elevation root mean square error with ALSM was 33

centimeters for low grass and 153 centimeters for shrub/scrub land cover. In general,

vertical errors with low grass and high grass were much smaller than in areas of heavy

vegetation canopies. Hodgson and Bresnahan (2004) also noted that variation in land

surface elevation was strongly correlated with a change in vegetation. Root mean square

error values ranged from a low of 17-19 centimeters for low grass and pavement.

Shrestha et al. (2000) performed an accuracy assessment for surveying and

mapping applications with ALSM. The results showed that elevation values for bare earth

ground were accurate to within +/- 5 10 centimeters. The authors noted that ALSM

technology was an innovative approach for high resolution flood plain and drainage

mapping.

ALSM Point Removal

Automated post processing of ALSM data attempts to model a bare ground

condition by using software to identify and remove artifacts. Automated methods for









point removal are based on neighborhood operators that iteratively identify the lowest

points within a defined search neighborhood. The operator then adds them to a candidate

set of ground returns. Subsequent iterations select the candidate set by adding returns that

are low or exhibit some angular deflection from a surface modeled by the current

candidate set of points.

The details of search neighborhood operators and parameters vary by lidar mapping

vendor. Generally, the analyst will examine a candidate set of ground returns to further

improve the accuracy of labeling features. The procedure also requires an analysis of

small areas as a three dimensional cloud of ALSM points overlain on available digital

orthophotography. Thus, the process of point removal may contain errors, because

removal is both adaptive and subj ective.

Krabill et al. (2000) used ALSM to study changes in beach morphology. The

research showed that post processing remained problematic for removing artifacts

including near ground vegetation. Okagawa (2002) assessed multiple automatic fi1ters to

extract artifacts from digital surface models. The author concluded that image

information was indispensable for identifying artifacts during post processing. Kampa

and Slatton (2004) used a multiscale filter to segment bare ground from artifact points in

ALSM data. To compute the mean square error for performance, the adaptive fi1ter was

initially applied to simulated ground data. The ground surface was distinguishable from

artifact points for a point density of twelve points per 25 square meter grids.

Raber et al. (2002) used an adaptive fi1ter to minimize the overall error by applying

different vegetation point removal parameters based on vegetation type. The study

involved extracting vegetation land cover type information using only ALSM multiple










return data. The study showed that land cover information could be used adaptively in

ALSM vegetation point removal for the production of accurate elevation models. Land

cover observations involved analysis of color infrared imagery, ground control points and

vegetation land cover. Histogram analysis showed that monoculture canopies were

characterized by a dampened bimodal histogram. A statistical analysis further showed

that among all land cover types, low and high grass possessed the lowest mean absolute

error values.

Huising and Pereira (1998) studied bare earth modeling and found that separating

dense vegetation from bare ground was a protracted process. The authors observed that

manual filtering may be better than automated, however more time is required for post

processing large areas. The manual method was found to be ideal for filtering vegetation

and other artifacts in small areas. The authors concluded that using only topography data

compounded the problem, and aerial photography was determined essential for

classifying land features. The accuracy of elevation measurements was related to the laser

system and terrain geometry, and flat terrain and low grass areas were used to estimate

accuracy.

ALSM Applications

Persson, Holmgren and Soderman (2002) used ALSM to detect individual trees by

estimating height crown closure and stem volume. The study used the lowest laser

reflection points to derive bare earth DTMs. The study further noted that return intensity

and type return pulse data provided more information about tree structure. Hodgson et al.

(2003) also used orthophotography and ALSM surface cover height to map impervious

land surfaces.









Multiple remotely sensed data sets may be used to separate vegetative height from

a theoretical bare earth condition. Popescu and Wynne (2004) combined lidar and

multispectral data to accurately estimate plot level tree height by focusing on the

individual tree level. Combining small footprint airborne lidar data in conjunction with

spatially coincident optical data was found to help accurately predict tree heights of

interest for forest inventory and assessment. The study recommended that proj ect

methodology can be applied to process lidar data for vegetation removal, and individual

tree location.

Popescu and Wynne (2003) developed analysis and processing techniques to

facilitate the use of small foot print ALSM for estimating plot level tree height. This was

accomplished by measuring individual trees identifiable on a three dimensional ALSM

elevation model. The study used the combination of ALSM and multi-spectral optical

data fusion to differentiate between forest types and improve the estimation of average

plot heights for pines. The research demonstrated that small foot print ALSM, used in

conjunction with spatially coincident optical data, was accurately able to predict the tree

heights of interest for forest inventory and assessment.

Hopkinson et al. (2004) used ALSM to map snowpack depth under forested

canopies. Snow pack distribution patterns were mapped by subtracting a bare earth DEM

grid from a peak snowpack DEM grid. Snow pack depth was used to predict water

availability and flood levels during the warming period. The study also found that a high

proportion of last pulse returns led to an overestimation of ground elevation. The study

recommended a further assessment of type dependent elevation offsets for improving

elevation and snow depth estimation.









Brock et al. (2001) used ALSM to recognize and map surfaces that provide

accurate low variability topographic measurements. These features were termed fiducial

and were used as reference base line features for mapping morphology. Fiducial features

are naturally occurring bald earth features such as beaches, bare dunes and ice sheets. The

process for separating dense plants of less than 10 centimeters was difficult based solely

on passive spectral signatures of ALSM. The presence of vegetation increases the

difference between ALSM and ground survey elevations from a minimum of 0.26 meters

over bare sand to values near 0.40 meters for all vegetation classes. Of the four defined

vegetation classes consisting of mono, sparse, medium and dense, sparse vegetation

possessed the highest variance between ALSM and coordinate survey elevations.

Evans et al. (2001) used sampling theory to map individual trees and estimate tree

height. Small foot print lidar failed to yield ground returns in areas dominated by dense

vegetation canopy.

Renslow and Gibson (2002) developed bare earth models from ALSM and high

resolution aerial photography to assist the decision making process for increasing

services for utility companies. This was accomplished by mapping fast track utility

corridors using bare earth models. Heinzer et.al (2002) used ALSM and aerial images to

model inundation, velocity and steady state flow of water. Interestingly, bare earth

models were interpolated from group points; however buildings were reinserted to

display realistic structural definitions.

Geographic Information Systems

Geographic Information Systems, GIS, is software designed to create maps by

capturing, storing, retrieving, manipulating and displaying geographically referenced










spatial tabular data (www.usgs.gov, April 2006). The three types of spatial data common

in GIS are points, arcs (lines) and polygons (areas).

GIS thematic map layers can display topological relationships between mapped

features. Topology refers to recording the spatial relationship between points, arcs and

polygons. A coverage is a GIS data Hile that display topology, however some GIS data

files such as shape files do not display topological relationships.

Metadata files list important parameters describing attributes of remotely sensed

and GIS data products. Metadata typically includes the coordinate system, period of data

capture and ancillary information pertinent for mapping applications with other data sets.

Spatial Modeling

There are two classes of interpolation, deterministic and geo-statistical.

Deterministic methods such as inverse distance weighting, splines, and radial based

functions are directly based on an interpolator that uses the surrounding measured values

or mathematical formulas applied to those values. Geostatistical models, such as kriging,

predict values by accounting for the probabilistic spatial relationship among neighboring

points. Kriging is able to predict estimation errors and is often preferred over

deterministic methods.

The surface calculated using inverse distance weighting depends on the selection of

a power value and the neighborhood search strategy. For inverse distance weighting the

maximum and minimum values in the interpolated surface can only occur at sample

points. The output surface is sensitive to clustering and the presence of outliers. Inverse

distance weighting assumes that the surface is being influenced by the local variation,

which can be captured throughout the neighborhood.









The local polynomial method is a moderately quick and smooth deterministic

interpolator. It is more flexible that the global polynomial method; however there are

more parameter decisions. There is no assessment of prediction errors; however the

method provides prediction surfaces that are comparable to kriging with measurement

errors. Local polynomial methods do not allow any analysis of the spatial autocorrelation

of the data, thus it is less flexible and more automatic than kriging.

The global polynomial method is also a quick and smooth deterministic

interpolator. There are fewer decisions to make regarding model parameters than for the

local polynomial method. It is best used for surfaces that change slowly and gradually.

There is no assessment of the predictions errors and this method may produce a surface

that may be too smooth. Values at the edge of the data can have a significant impact on

the interpolated surface.

Radial based functions are moderately quick deterministic interpolators that are

exact, and they are considerably more flexible than inverse distance weighting, however

there are more parameter decisions, and there is no assessment of prediction errors. The

method provides prediction surfaces that are comparable to the exact form of kriging.

Radial based functions do not allow for analysis of the autocorrelation of the data, thus

making it less flexible and more automatic than kriging. Radial based functions are used

for calculating smooth surfaces from a large number of data points, and are preferred for

gently varying surfaces such as elevation. The radial based function is inappropriate

when there are large changes in the surface values within a short horizontal distance

and/or when the sample data is prone to error or uncertainty.









Ordinary kriging produces interpolated values by assuming a constant but unknown

mean value, allowing a local influence from nearby neighboring values. Because the

mean is unknown, there are few assumptions about the data. This makes ordinary kriging

flexible but less powerful.

Simple kriging produces interpolated values by assuming a constant but known

mean value, allowing local influences due to nearby neighboring values. Because the

mean is known it is slightly more powerful than ordinary kriging but in some cases the

selection of a mean value is not well known.

Universal kriging produces interpolated values by assuming a trend surface with

unknown coefficients in the model; however it allows local influences from nearby

neighboring values. It is possible to overfit the trend surface, which fails to leave enough

variation in the random errors to properly reflect uncertainty in the model. It can be more

powerful than ordinary kriging because it explains much of the variation in the data

through a non-random trend surface.

Disjunctive kriging considers functions of the data, rather than just the original data

values themselves, and stronger assumptions are required. Disjunctive kriging assumes

all data pairs come from a bivariate normal distribution and the validity of these

assumptions should be checked. A bivariate normal distribution describes relative

frequencies of occurrence in the population of pairs of values. When this assumption is

met, the functions of the data are indicator variables that transform the continuous data

values to binary values based on a decision threshold value.

Doucette and Beard (2000) evaluated inverse distance weighting, splines and

universal kriging as interpolators to fill gaps left by occlusions in digital elevation data.









The results favored splines as a surface interpolator, especially as terrain roughness

increased. The study additionally found that altering the search radius parameter

significantly impacts interpolation error statistic values.

Selecting a best fit model depends on the assessment of several modeling statistics.

In general, the best fit model is one that has the standardized mean error closest to zero,

the lowest root mean squared prediction error, the average standard error nearest to the

root mean squared prediction error and the standardized root mean squared prediction

error closest to one (ESRI 2001).

Inundation Mapping with GIS

Previous flood mapping efforts have used remote sensing and GIS to map the

extent, duration and magnitude of flooding. Ball and Schaffranek (2000) used

topographic and surface water grids to map water depth in the southern Everglades.

Temporal inundation patterns were mapped and compared to historical and current water

depths. A comparison to other hydroperiods was conducted to isolate temporal changes

affected by anthropogenic influences of water management policy. To estimate water

depth accuracy, computed depths were subtracted from depths measured in the wetlands

adjacent to the C-111 canal and in Taylor Slough in 1997 and 1999.

Ball and Schaffranek (2000) employed a similar method to study water surface

elevation and water depth for Taylor Slough in the southern Everglades. A GIS program

was used to subtract topographic elevation grids from surface water elevation grids. The

extremely low topographic relief of the southern Everglades produced significant spatial

variability in surface water gradients. Furthermore, the land surface elevation grid was

calculated from interpolating global positioning systems (GPS) topographic data

sponsored by the USGS, National Mapping Division. Daily surface water data was









obtained from the SFWMD, and National Park Service (NPS) Everglades National Park.

The research concluded that water depth and topographic accuracy were directly

correlated to the spatial resolution and accuracy of input data. Proj ect inundation grids

were calculated with the same method; however an ALSM topographic grid was used

instead of a GPS topographic grid. ALSM topographic grids produce inundation maps

with a finer spatial resolution than GPS topographic grids.















CHAPTER 3
DATA RESOURCES AND METHODOLOGY

Introduction

Proj ect data sources include aerial color infrared imagery, Landsat7 EMT+ sensor

data, surface water elevation data and ALSM topographic data. All tables that are

referenced can be found at the end of this chapter.

Color infrared imagery was acquired from Land Boundary Information Systems

(www.1abins.com April 2006), Labins, and each image possessed a 1 meter spatial

resolution. Color infrared imagery for the study area is found in quadrangle Royal Palm

Ranger Station or Quadrangle 1205 S.W. and was obtained for 1994 and 1999. The

primary use of color infrared imagery was to identify vegetation points in the NAD 27

and NAD 83 ALSM point data sets.

Surface Water Data

Surface water elevation values for regional canal stations and well monitoring sites

were obtained from the South Florida Water Management District (SFWMD), U.S.

Geological Survey (USGS) and National Park Service (NPS). Surface water elevation

data covers the period during October 12-22, 1999. SFWMD canal stage elevations were

recorded for both head and tail stage, and the mean between head and tail was used to

create surface water grids. Surface water values were recorded in feet, and NGVD29 was

used as the reference vertical datum for hydrologic and topographic data sets.










Digital Elevation Model Construction

In 1999, FEMA and NASA sponsored 30010, a consulting firm, to conduct ALSM

flight operations for the C-111 basin. ALSM Hiles are arranged by flight area, and each

flight area includes a cache of Eiles that provide a variety of elevation data products.

ALSM data was captured by single pulse return data, and the products included text Hiles

of x,y,z coordinates and processed DTM files. All coordinate elevation values were

recorded in feet, and text Hiles were prepared for all first return raw data and automated

filtered bare earth data. NAD 27 raw data values were recorded to either one-hundredth

or one thousandth of a foot. All NAD 83 bare earth fie data were recorded to one one-

hundred thousandth of a foot. Only accuracies of hundredths of a foot should be

considered for DEM analysis, because real time kinematic GPS values are only accurate

to one one-hundredth of a foot. The post spacing for points was 10 feet along the track

direction and 23 feet across the track direction. Bare earth contour line DTMs were

included for each area and elevation lines were categorized by one-foot intervals. These

DTMs were not used in this study, because of their low vertical resolution.

Data quality reports were prepared for all areas and they included coordinates for

flight area ground control points in addition to the methodology used to create the bare

earth digital terrain models. These reports are commonly used to inform the client about

the accuracy of ALSM data by a statistical comparison between ground surveyed GPS

points to associated ALSM points. Data quality reports for the study area listed a vertical

accuracy of 15 centimeters, and bare earth DTM files were created using Delaunay

triangulation (3001 1999). Bare earth Hiles were created using proximal analysis to filter

unwanted points according to the report; however no additional information was provided

about the procedure.









Landsat 7 Enhanced Thematic Mapper

Landsat 7 ETM + image data were used to detect inundated surfaces and dense

clouds within the study area after Hurricane Irene. Landsat7 scenes for October 16, 1999,

and April 9, 2000, were obtained from the USGS, and all data deliverables were stored on

CD-ROM media and delivered as Geotiff files. For image processing, all Geotiff files

required both importation in ERDAS Imagine and exportation as an ERDAS Imagine

image file. The first scene was captured on October 16, 1999, nine hours after Hurricane

Irene passed over the C-111, and the second was captured on April 9, 2000 during the

peak of the dry season.

A bend in the L31W canal was used to detect an offset between the October 16,

1999, and April 9, 2000, Landsat 7 ETM+ scenes. Band 8 was used from both scenes to

locate the x and y values for associated pixels, and the offset was measured. The offset

between associated pixels was 48 ft. north and 1 ft. east.

Vegetative Index Methodology

ERDAS Imagine is software that is specifically designed to work with large geo-

referenced image data sets. NDVI map methodology was initiated by creating a layer

stack of red and infrared bands for both Landsat7 scenes. The ERDAS Imagine layer

stack function combined sensor data, and the NDVI function automatically created an

NDVI image by separating Band 3 and Band 4 from the stack and substituting them into

the NDVI equation.

The ERDAS Imagine Spatial modeler extension selected the appropriate individual

band layers in a composite layer stack and substituted them into their designated

vegetative index equations. The Spatial Modeler tool was used to create vegetative index

two and vegetative index three indices described in Chapter 2.










Unsupervised Classification

An unsupervised classification divides pixels into classes based on their digital

number value. ERDAS Imagine unsupervised classification was performed on the three

vegetative index images, and 30 classes were created for all vegetative index maps. All

vegetative index unsupervised images were scaled with Class 1 representing the lowest

reflectance grouped values and class 30 representing the highest reflectance grouped

values.

ALSM Processing

Topographic grids were created from NAD 83 / NGVD 29 and NAD 27 / NGVD

88 ALSM data. The procedure for creating a point map theme from text data required a

list of vertical, horizontal and elevation values. All text files were space delimited and

consequently, no files could be opened by GIS software. Only tab and comma delimited

formats are recognized by GIS software for importation. Furthermore, all text files were

too large to fit the 65,536 spreadsheet row entry maximum. In response to this constraint,

a quick and effective procedure was developed to convert text files into shape files.

The conversion of text files into database tables was required for importation into

GIS software. This initial step involved opening each text file with Microsoft Wordpad,

and converting the native .xyz format to an ASCII text file. The ASCII text file was

opened in a spreadsheet and saved as a database file; however spreadsheet row entries

were limited to only 65,536 displayed values. The Find and Replace tool in Microsoft

WordPad located the 65,536th value in the text file, and all values listed above the

65,536th value were selected and deleted. The altered file was saved under the original

text file name, and displayed the 65,536th value as the first value in the spreadsheet. All

cells were converted to a number format with six decimal places and the column









containing coordinate values were assigned X, Y, and Z field headings respective to their

measurements. The process was applied to all files until the original text file was reduced

to an acceptable size for one spreadsheet. The end result produced a series of

spreadsheets with each representing one sub-area.

Arcview 3.2 was used to develop a point shape file from the data base file. All

database tables were imported into Arcview, and the Add Event function was used to

display the table coordinate values as points for all data base tables imported into

Arcview. Areview' s Geoprocessing tool was used to merge and convert sub area database

files into a point shape file. The geoprocessing tool designates the tables to be merged

and then exports the resultant thematic shape file to a known file directory. The offset

coordinate value between NAD 83 and NAD 27 was 156235.73 ft. false east and 159.86

ft. false north. These values were later used to proj ect surface water data points from

NAD 27 to NAD 83.

Bare Earth Modeling

The procedure for modeling a bare earth condition for the study area involved the

manual removal of ALSM points that represent vegetation, fiducial features and

structures. Point removal was based on the assumption that the study area possessed a flat

topography and low elevation characteristic of the C- 111 basin. The steps utilized in the

process included a cross-comparison between color infrared imagery, NAD 27 ALSM

DEM, and ground control point elevations in the study area. Multiple interpolators and

search parameters were tested for predicting grid elevation values.

ALSM data was collected in NAD 83 and NGVD 88 datum; however the unfiltered

data was placed in NAD 27 and NGVD 29. The measured difference between NGVD 29

and NGVD 88 first order benchmark elevation values is 1.5 125 ft. Proj ect inundation










maps were placed in NGVD 88, because this was the vertical datum used in the data

collection process.

The proximal analysis method was used by 3001 C to model the bare earth

condition for NAD 83 data, however this method is not defined in the data quality report

for Area 2. Furthermore, this method was not sufficient for flood mapping, because

vegetation points were found in the bare earth model for the study area. The success of

topographic bare earth models relies on the accuracy of the estimated maximum bare

earth elevation threshold used to remove points.

Aerial Color Infrared Analysis

The available Land Boundary Information System (Labins) aerial imagery covered

the dates of December 27, 1994, Figure 3.1(a) and February 21, 1999, Figure 3.1(b).

Aerial color infrared imagery was useful for identifying vegetation patterns and

associated land features in ALSM maps. Healthy vegetation in images possessed a strong

reflectivity in the infrared region of the electromagnetic spectrum, and was displayed as

red. Although row crop vegetation reflected strongly as red, the individual boundaries

varied between both images. In both images, dense tree canopies reflected the strongest

and were easily distinguished from the surrounding land cover. In the 1999 image,

wetland forest was characterized by variable red reflectivity values; however only high

dense tree canopies were consistently reflected as red in the 1994 image. The soil mound

seen in Figure 1.2, reflected as white in the 1994 image when leaf canopy was reduced,

and was difficult to distinguish from the surrounding land cover. In the 1999 aerial

image, the soil mound was easily distinguished from the heterogeneous cover of healthy

vegetation and exposed bare soil.









In the 1999 aerial image row crop vegetation conformed to Hield boundaries,

however in the 1994 aerial image not all row crop vegetation conformed to Hield

boundaries. This dissimilarity was most noticeable in canopy patterns found in leased

parcel 19. A semi-circular arc of vegetation can be seen in the southeast quadrant of the

1994 image, Figure 3.1(a). This was useful for identifying suspect vegetation patterns

found in the raw ALSM topographic DEMs.

Ground Control Point Analysis

Although ground control points, GCP, were not in the study area, they were

analyzed for determining the threshold value for estimating the bare earth condition,

Table 3.1. The process for determining vegetation points was subjective. This depended

on visual analysis and analysis of nearby ground control points in the NAD 27 DEM. The

obj ective of the approach was the removal of vegetation points, while preserving points

that represented roads and bare earth. Based on this methodology, the value of 4.80 ft.

was determined to be the maximum bare earth elevation value for the study area for NAD

27. Consequently, all points in the associated NAD 83 DEM that exceeded the value of

3.29 ft. were also identified as vegetation and removed using the clip tool in Arcview.

Recall that the difference between NAD 83 and NAD 27 maximum bare earth elevation

values is 1.51 ft., and this is equal to the difference between NGVD 29 and NGVD 88

elevation values. Areview's query filter was used to remove elevation points that

exceeded the designated maximum elevation threshold value. The clip tool in Arcview

was used to create a separate shape Eile consisting only of points that were not deleted.














































































L


s A

Figure 3.1 Color infrared aerial photos of the study area. (A) 1994 color infrared aerial
image (B) 1999 color infrared aerial image. The study area is outlined in
yellow.


0.3 D


0 68 Miles










Topographic Spatial Modeling

Multiple interpolators were used to develop prediction grids for NAD 27 and NAD

83 point shape files. These interpolators included inverse distance weighting, local

polynomial, global polynomial, radial based functions, universal kriging, ordinary

kriging, simple kriging and disjunctive kriging. The root mean square error, RMSE,

statistic was calculated by sequential dropping of each observed elevation point and

estimating it using the appropriate interpolation procedure. The RMSE was used to select

the optimum interpolation method for surface water and topographic grids. If two tests

possessed an equal RMSE statistic, then the mean absolute error, was used as the next

decision statistic.

All prediction grids, including surface water, were exported as raster surfaces to be

later used for calculating inundation grids. All interpolation methods were used to

generate z prediction values for a test x and y location, the results of the different

prediction methods showed that the predicted values at the test location ranged from

2.9217 ft. to 2.9483 ft. The difference in z prediction values indicates that a small

variation exists between predicted topographic grids using the various methods.

Table 3.2 lists the search parameters for the inverse distance weighting method that

were not set to a default value. The neighborhood method was used for all tests. The

search ellipse used for the neighborhood search had maj or and minor semi-axes of

2, 134.6 ft., and the anistropy factor was set to a value of 1. The x and y test prediction

locations were 797,555.55 ft. and 394,614.36 ft. respectively. Table 3.3 lists the search

parameters for the global polynomial method. Table 3.4 lists the search parameters for

local polynomial method. Table 3.5 lists the search parameters for radial based function

tests.










Table 3.6 lists the search parameters used for kriging methods. For all kriging

methods, no transformation was applied, and no trend was assumed. The angle direction

and tolerance were 150 and 450 and the band width was 6. The number of lags was 12,

the search shape angle were 3 and 150 respectively. The major and minor semi-axes were

6,306.9 ft. and 5,546.8 ft., and the anistropy factor was 1.137. The test x and y locations

were 797,555.95 ft. and 394,614.36 ft., and twenty neighbors were used for the test

prediction value.

Surface Water Elevation Map Methodology

The surface water elevation map (SWEM) was created to show the change in

surface water elevation values over the study period, and was used to calculated

inundation grids. All surface water data was acquired from the SFWMD, USGS, and the

NPS. All surface water proj ect data values were referenced with NAD 27 horizontal

datum and NGVD 29 vertical datum. NGVD 29 surface water elevation values were

converted to NGVD 88 by subtracting 1.51 ft from NGVD 29 values.

REMO is the SFWMD internet data retrieval program that provided surface water

elevation data. REMO hydrologic data was delivered in text format and all data was

converted to data base file format. All water elevation data was recorded in feet and

referenced to NGVD29. Canal elevation measurements included head and tail

measurements; however the mean value between head and tail was used to create surface

water grids. SFWMD data sites included the S175, S177, S178, FP, FPl, FP2 and S332D.

FP, FP1 and FP2 are wells and the S175, S177, S178 and S332D are water control

structures. Figure 3.2 shows in situ measurement sites used to create SWEM.

USGS provides maximum daily ground water elevations for monitoring stations in

Miami-Dade. USGS maximum water elevation data for G3355 was acquired through the







































2 01 2 4 Miles


USGS water resources internet link with the SFWMD, and USGS water resources for

Miami-Dade. G33 55 is located in the southeast corner of Figure 3.2.


PPZ


Figure 3.2 Map of measurement sites.

USGS sponsors Tides and Inflows in the Mangroves in the Everglades, TIME

(time.er.usgs.gov). TIME provides telemetric surface water elevation measurements in

daily, hourly and fifteen minute intervals. TIME water monitoring wells NPl l2 and

NPl158 were used to create surface water grids. The locations NP ll2 and NPl158 are

shown in Figure 3.2.

The procedure for developing the surface water site point shape file began with

transforming the Latitudinal and Longitudinal coordinates from Degrees-Minutes-

Seconds to Data-Decimal-Degrees, DDD,


SMeasurement Sites


WE










,,=,,,,( Minutes Seconds
60 3600 1

The DMS coordinates were divided into separate spreadsheet fields, and the

conversion for each coordinate was performed using cell formulas. Longitudinal

coordinates were assigned negative values. The resultant spread sheet file was exported

as a tab delimited text file and assigned a .dat extension.

The ERDAS Imagine vector tool was used to export the .dat files as Arcinfo

coverages. The coverage file was opened in Arcview, and the view properties were set to

match the proj section parameters of ALSM as defined in the meta-data report, Table 3.7.

Finally, the coverage file was converted to a shape file with the same coordinates as the

ALSM data.

Surface Water Elevation Map Interpolation

Tables 3.8, 3.9, 3.10 and 3.11 list the initial search parameters for kriging tests used

to estimate the surface water elevation. A description of these parameters is discussed

below.

For universal kriging no trend removal and no transformation were performed. A

bandwidth of 6 ft. and the lag size and lag number were 2,721 ft. and 12. The maj or semi-

axis and minor semi-axis for the neighborhood search ellipse were 30,000 ft. and 24,000

ft. The anisotropy factor was set to a default value of 1.25 for all tests. The x and y test

prediction locations were 804,799 ft. and 389,908 ft. The software's default value for

search neighbors was used, and the number of search neighbors was set to five for

prediction.

For disjunctive kriging, no transformation or trend removal was conducted. The

direct method was used, and the maj or and minor ranges were 30,023 ft. and 12,607 ft.










The search direction, partial sill and nugget were 64.90, 0.64504 ft. and 0.0221170 ft.,

respectively. The lag size and number were 2,721 and 12. The major and minor semi-

axes were 30,023 ft. and 12,607 ft. The anisotropy factor was set to 2.3815. The x and y

test location values were 804,799 ft. and 389,908 ft., and the bandwidth was set to 6 ft.

For ordinary kriging, no transformation or trend removal was conducted. The maj or

and minor ranges were 30,705 ft. and 26,007 ft. The angle direction, partial sill and

nugget were 14.80, 1.1645 ft. and 0 ft., respectively. The lag size and number were 2,721

ft. and 12, and the bandwidth was set to 6 ft. The maj or and minor semi-axes were 30,705

ft. and 26,007 ft. The anisotropy factor was 1.1806, and the x and y test location values

were 804,799 ft. and 389,908 ft.

For simple kriging, no transformation was applied, and the mean threshold value

not to be exceeded was 3.857 ft. The bandwidth was set to 6 ft., and the maj or and minor

ranges were 30,091 ft. and 21,698 ft. The anisotropy factor was activated for all tests, and

the nugget was 0.423 52 ft. The lag size and number were 2,721 ft. and 12 respectively.

The search angle direction and partial sill were 200 and 0.61003 ft. The maj or and minor

semi-axes were 30,705 ft. and 26,007 ft. The test x and y prediction locations were

804,799 ft. and 389,908 ft.

Table 3.12 lists the search parameters for universal kriging for SWEM. The

universal kriging maj or range was set to 30,668 ft. and the minor range was set to 25,892

ft. The maj or semi-axis and minor semi-axis were set to 30,000 ft. and 24,000 ft. The

anisotropy factor was set to 1.25. The test prediction location was 804,799 ft. and

389,908 ft. The lag size and number were set to 2,721 ft. and 12 respectively. Eight

neighbors were used for the search parameters. No trend removal and or transformation










were conducted. The global influence, local influence, angle direction, angle tolerance,

search direction and were equal to 65%, 35%, 15%, 350, 150 and 6 ft. respectively. Shape

3 was selected and the shape angle was set to 150.

Surface Water Inundation Map Methodology

The surface water inundation map, SWIM, was calculated by subtracting ALSM

topographic grid values from SWEM grid values. Surface water and topographic grids

were exported as raster surfaces with a 3 meter resolution. The raster math calculator in

ArcGIS Spatial Analyst extension was used to subtract topographic grids from surface

water grids, and the resultant inundation grids also had a 3 meter spatial resolution. A

value of 0 ft. in elevation was inserted into the L31W canal to prevent aliasing caused by

interpolation.

3D ALSM DEMs were useful for determining elevation values that represented

vegetation. Converting the raster surface into a TIN created 3D TIN DEMs, and the TIN

was imported into ArcGIS Scene.

Table 3.1 Area 2 static GPS points used to determine the elevation filter. Zl is the
elevation of the GPS point, and Z2 is the measured ALSM elevation for that
point. AZ is the difference in elevation between Zl and Z2.
Test Id Z1 ft. X ft. Y ft. Z2 ft. aZ f t.
663 4.50 640246.31 394360.81 4.890 0.390
665 4.46 640248.90 394361.10 4.890 0.430
729 4.72 640518.40 392310.00 4.660 0.060
732 4.31 640707.30 392308.60 4.300 0.010
733 4.29 640716.80 392308.50 4.400 0.110
734 4.60 640728.50 392308.40 4.400 0.200
735 4.61 640739.40 392307.80 4.630 0.020
736 4.67 640752.00 392306.70 4.660 0.010
737 4.64 640763.10 392307.20 4.660 0.020
738 4.51 640774.10 392305.30 4.760 0.250
739 4.66 640785.90 392304.50 4.630 0.030
741 4.59 640495.50 392311.90 4.860 0.270










Table 3.1 Continued.


Id
742
743
744
745
746
746
747
749


Z1 ft.
4.65
4.65
4.55
4.52
4.53
4.53
4.59
4.63


X ft.
640484.10
640472.00
640460.70
640449.80
640438.80
640438.80
640427.50
640402.80


Y ft.
392312.90
392314.00
392313.50
392316.20
392318.60
392318.60
392321.10
392324.90


Z2 ft.
4.660
4.760
4.660
4.530
4.430
4.530
4.890
4.660


aZ ft.
0.010
0.110
0.110
0.010
0.100
0.000
0.300
0.030


Table 3.2 Inverse distance weighting search parameters for topography .


Test Id
1
2
3
4


Power
2
1.7682
1.7682
1.7682
1.7682
1.7682
1.7682
1.7682
1.7682
2
2
2
2
2.7365
2.7365
2.7365
2.7365
2.7365
2.7365
2.7365
2.7365
2.7365
2.7365


Shape
3
3
1
2
4
4
1
2
3
1
2
3
4
1
2
3
4
4
3
2
1
1
2


Shape Angle
15
15
15
15


Neighbors
60
60
15
60
120
120
15
60
60
15
60
60
120
15
60
60
120
120
60
60
15
15
60


Prediction
2.9338
2.9446
2.9343
2.9444
2.9480
2.9483
2.9343
2.9443
2.9446
2.9343
2.9443
2.9449
2.9356
2.9217
2.9222
2.9220
2.9223
2.9223
2.9222
2.9222
2.9217
2.9217
2.9222


SThe shape angle is in degrees. Shape type refers to the search shape used for all interpolation tests. Shape
1 is an open circle and shape 2 is a circle divided by four perpendicular lines running north, south, east and
west. Shape 3 is a circle divided by four perpendicular lines running northeast to southwest and northwest
to southeast. Shape 4 is a circle divided by eight lines that possess the same directions as Shapes 2 and 3.










Table 3.2 Continued
Test Id Power
24 2.7365
25 2.7365
26 2.7365
27 2.7365
28 2.7365
29 2.7365
30 2.7365
31 2.7365
32 2.7365
33 2.7365
34 2.7365
35 2.7365
36 2.7365
37 2.7365


Shape
3
4
4
3
2
1
1
2
3
4
4
3
2
1


Shape Angle
10


Neighbors
60
120
120
60
60


Prediction
2.9222
2.9223
2.9223
2.9222
2.9222
2.9217
2.9217
2.9222
2.9222
2.9223
2.9223
2.9222
2.9222
2.9217


120
60
60
15


Table 3.3 Global polynomial search parameters for topography.
Test Id Power


Table 3.4 Local polynomial search parameters for topography.
Test Id Global Influence (%) Local Influence (%) Power
1 10 90 1
2 15 85 1
3 20 80 1
4 25 75 1
5 30 70 1
6 0 100 1
7 10 90 2
8 20 80 2
9 25 75 2
10 30 70 2


Table 3.5 Radial based function search parameters for topography. SWT is spline with
tension, MQ is multi-quadratic, CRS is completely regularized spline, IM is
inverse multi-quadratic and TPS is thin plate spline.
Kernal Shape Z Prediction
Test Id Function Parameter Shape Angle Value ft. Neighbors
1 SWT 1.3715 3 15 2.9500 60
2 SWT 1.3715 1 15 2.9494 15
3 SWT 1.3715 2 15 2.9500 60












































Table 3.7 Proj section Parameters for ALSM. Source 30010 Area 2 Data Quality Report.
Description NAD 27 NAD 83
Map Units Feet Feet
Distance Units Feet Feet
Standard Proj section SP 27 Florida East SP 83 Florida East
Custom Proj section Transverse Mercator UTM
Central Meridian 81 00 00 81 00 00
Latitude of Orgin 24.3333 24.3333
Scale Factor 0.999942 0.999917
False Easting 500,000 200,000
False Northing 0 0


Table 3.5 Continued
Kernal
Test Id Function
4 SWT
5 SWT
6 SWT
7 SWT
8 SWT
9 SWT
10 SWT
11 SWT
12 SWT
13 SWT
14 SWT
15 SWT
16 SWT
17 MQ
18 CRS
19 IM
20 TPS


Shape
Angle
15
20
20
20
20
10
10
10
10
5
5
5
5
5
5
5
5


Z Prediction
Value ft.
2.9500
2.9499
2.9504
2.9500
2.9468
2.9468
2.9498
2.9504
2.9500
2.9500
2.9505
2.9500
2.9468
2.9382
2.9240
2.9248
2.9274


Parameter
1.3715
1.3715
1.3715
1.3715
1.3715
1.3715
1.3715
1.3715
1.3715
1.3715
1.3715
1.3715
1.3715
0
0.47662
7.3705
1 e 20


Shape
4
4
3
2
1
1
2
3
4
4
3
2
1
1
1
1
1


Neighbors
64
64
32
32
8
8
32
32
64
64
32
32
8
8
8
8
8


Table 3.6 Kriging search parameters for topography.
Maj or Minor Partial
Type Range Range Direction Sill Nugget
OK 6306.9 5546.8 274.4 0.015308 0.0406


Lag Z Prediction
Number Value ft.
532.08 3.0653


1437.7
6040
1350.4


915.89
6040
957.16


288.8
9.0
290.8


0.02606
0.0000
0.45212


0.0249
0.013034
0.41439


187.38
532.08
186.15


3.0558
3.0660
3.0569
































Table 3.8 Conyinued
Test Id Maj or Minor Angle Shape Z Prediction
Range Range Direction Partial Sill Shape Angle Value ft.


Table 3.8 Universal kriging search parameters for SWEM.


Angle
Direction


Angle
Tolerance
45
45
45
35
30
30
30
30
30
30
30
35
35
35
35


Test Id
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15


Global
20
20
30
30
30
25
25
35
40
40
45
50
55
60
65


Local
80
80
70
70
70
75
75
65
60
60
55
50
45
40
35


Nugget
0.011617
0.011617
0.017298
0.017298
0.017298
0.013573
0.013573
0.014003
0.007041
0.007041
0.000000
0.000000
0.000000
0.000000
0.000000


30332
30332
30453
30453
30453
30550
30550
30415
30526
30526
30569
30526
30628
30586
30668


23242
23242
24640
24640
24640
23285
23285
24626
25812
25812
25809
25864
25859
25890
25892


13.2
13.2
8.9
8.9
8.9
13.3
13.3
11.8
13.7
13.7
14.8
15.7
15.7
14.9
14.7


20
15
15
25
10
10
30
15
15
30
30
15
15
15
15


3.3100
3.3554
3.2981
3.3312
3.3214
3.3160
3.3245
3.1673
3.0834
3.0761
3.0526
3.0551
3.0550
3.0550
3.0549


0.0081215
0.0081215
0.0151810
0.0151810
0.0151810
0.0114650
0.0114650
0.0376450
0.0774980
0.0774980
0.1291700
0.1818600
0.2500900
0.3312100
0.4212600


3
2
1
4
3
3
3
3
3
3
4
3
3
3
3











Table 3.9 Simple kriging search parameters for SWEM.
Angle Angle Shape
Test Id Direction Tolerance Shape Angle
1 0 35 1 30
2 0 35 2 30
3 0 35 3 30
4 0 35 4 30
5 15 35 1 30
6 15 35 2 30
7 15 35 3 30


Z Prediction
Value ft.
3.3499
3.3380
3.3382
3.3380
3.3499
3.3380
3.3382
3.3380
3.3460
3.3380
3.3520
3.3380
3.3460
3.3500
3.3520
3.3380
3.3459
3.3459
3.3520
3.3380
3.3499
3.3380
3.3382
3.3380
3.3459
3.3380
3.3520
3.3380


Neighbors
5
10
9
10
10
10
9










Table 3.10 Ordinary Kriging search parameters for SWEM.


Angle
Direction
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
20
20
20
20
20
20
20
20
20
20
20
20
10
10
10
10
10
10
10
10
10
10
10
10


Angle
Tolerance
45
45
45
45
45
45
45
45
45
45
45
45
45
45
45
45
70
70
70
70
35
35
35
35
35
35
35
35
60
60
60
60
50
50
50
50
50
50
50
50


Shape
Angle
15
15
15
15
20
20
20
20
15
15
15
15
25
25
25
25
25
25
25
25
25
25
25
25
10
10
10
10
15
15
15
15
15
15
15
15
15
15
15
15


Z Prediction
Value ft.
3.0799
3.0588
3.0551
3.0532
3.0799
3.0532
3.0551
3.0532
3.0799
3.0588
3.0551
3.0532
3.0799
3.0532
3.0551
3.0532
3.0799
3.0532
3.0551
3.0532
3.0799
3.0532
3.0551
3.0532
3.0799
3.0588
3.0551
3.0532
3.0799
3.0588
3.0551
3.0532
3.0799
3.0588
3.0551
3.0532
3.0799
3.0588
3.0551
3.0532


Test Id
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40


Shape
1
2
3
4
1
2
3
4
1
2
3
4
1
2
3
4
1
2
3
4
1
2
3
4
1
2
3
4
1
2
3
4
1
2
3
4
1
2
3
4


Neighbors
5
9
8
10
5
10
8
10
5
9
8
10
5
10
8
10
5
10
8
10
5
10
8
10
5
9
8
10
5
9
8
10
5
9
8
10
5
9
8
10









Table 3.10 Continued
Angle
Test Id Direction
41 10
42 10
43 10
44 10
45 0
46 0
47 0
48 0
49 0
50 0
51 0
52 0


Angle
Tolerance
50
50
50
50
50
50
50
50
35
35
35
35


Shape
Angle
15
15
15
15
30
30
30
30
30
30
30
30


Z Prediction
Value ft.
3.0799
3.0588
3.0551
3.0532
3.0757
3.0532
3.0488
3.0532
3.0757
3.0532
3.0488
3.0532


Shape
1
2
3
4
1
2
3
4
1
2
3
4


Neighbors
5
9
9
10
5
10
9
10
5
10
9
10









Table 3.11 Disjunctive kriging search parameters for SWEM. D in the column heading is
the distribution, PD is probability distribution and CD is cumulative
di stributi on.
Angle Angle Shape Z Prediction
Test Id D Direction Tolerance Shape Angle Neighbors Value ft.
1 PD 15 45 3 20 5 3.1977
2 PD 30 45 2 15 5 3.1977
3 PD 30 45 1 15 5 3.2058
4 PD 20 35 4 25 5 3.1977
5 PD 0 30 3 10 5 3.1977
6 PD 10 30 3 10 5 3.1977
7 PD 20 30 3 30 5 3.2058
8 PD 20 30 3 15 5 3.1977
9 PD 10 30 3 15 5 3.1977
10 PD 10 30 3 30 5 3.2058
11 PD 25 30 4 30 5 3.1977
12 PD 15 35 3 15 5 3.1977
13 CD 15 45 3 15 5 3.1029
14 CD 15 45 4 20 5 3.1977
15 CD 20 40 1 10 5 3.2058
16 CD 20 40 4 10 5 3.1977
17 CD 20 40 1 20 5 3.2058
18 CD 20 40 1 5 5 3.2058
19 CD 20 40 1 30 5 3.2058
20 CD 20 40 2 35 6 3.1977
21 CD 20 40 3 35 5 3.2058
22 CD 20 40 1 35 6 3.2058
23 CD 20 40 4 35 6 3.1977
24 CD 20 40 1 0 5 3.2009
25 CD 15 40 1 20 5 3.2058
26 CD 15 40 2 20 6 3.1977
27 CD 15 40 3 20 6 3.1977
28 CD 15 40 4 20 6 3.1977
29 CD 15 45 1 15 5 3.2058
30 CD 15 45 2 15 6 3.1977
31 CD 15 45 4 15 6 3.1977
32 CD 15 45 1 30 5 3.2058
33 CD 15 45 2 30 6 3.1977
34 CD 15 45 3 30 5 3.2058
35 CD 15 45 1 10 5 3.2058
36 CD 15 45 2 10 6 3.1977
37 CD 15 45 3 10 6 3.1977
38 CD 15 45 4 10 6 3.1977
39 CD 15 45 1 20 5 3.2058
40 CD 15 45 2 20 6 3.1977
41 CD 15 45 3 20 6 3.1977





























Z Prediction Value ft.
0.6102
0.6224
0.4183
0.3928
0.3454
0.3190
0.3037
0.3235
0.4177
0.4304
0.4031


Table 3.11 Continued
Angle Angle Shape Z Prediction
Test Id D Direction Tolerance Shape Angle Neighbors Value ft.
42 CD 15 45 4 20 6 3.1977
43 CD 15 45 1 10 5 3.2058
44 CD 15 45 2 10 6 3.1977
45 CD 15 45 3 10 6 3.1977
46 CD 15 45 4 10 6 3.1977
47 CD 15 45 4 20 6 3.1977
48 CD 15 45 3 20 6 3.1977
49 CD 15 45 2 20 6 3.1977
50 CD 15 45 1 20 5 3.2058


Table 3.12 Universal Kriging for SWEM, October 12-22, 1999. All values represent
surface water interpolation for NAD 83 and NGVD 88.


Day
10/12/1999
10/13/1999
10/14/1999
10/15/1999
10/16/1999
10/17/1999
10/18/1999
10/19/1999
10/20/1999
10/21/1999
10/22/1999


Nugget ft.
0.1965
0.2296
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0426
0.0049


Partial Sill ft.
0.32490
0.30122
0.61716
0.54420
0.42082
0.35905
0.33162
0.36915
0.35809
0.43048
0.54628















CHAPTER 4
RESULTS AND DISCUSSION


Cloud Detection

The initial step for flood analysis began with detecting clouds in the October 16,

1999, Landsat 7 ETM+ scene. Clouds can obstruct the spectral signature of water bodies,

therefore clouds were mapped in the study area. Clouds found within Hurricane Irene

were used as a reference feature to identify cloud classes in all maps of vegetative

indices. Hurricane Irene is located in the north east quadrant of the October 16, 1999,

Landsat 7 ETM+ scene. Vegetative index two, vegetative index three and Band 8 were

used to map clouds in the study area. For an initial analysis, Band 8 was selected to

identify clouds in the study area, because of its 15 meter spatial resolution. Clouds are

clearly displayed as irregular shapes comprised of white pixels, and each cloud possesses

a shadow located northwest of the cloud shape, Figure 4. 1(A).

Three clouds are visible in the study area, however only two full cloud shadows are

visible, Figure 4.1(B). Clouds in the study area were found to completely obstruct the

ground signature and their shadows produced darker pixels on the land surface.






50















e~A
Figure~~~~~~~~~i: 4. A a fteFo odwt ad .Cod pera ht reua











Fig ~ hpe faures (B) Zoo in)M of the study area with Band 8. Cod pera ht reua




































Figure 4.1

Vegetation Index Two and Vegetation Index Three

Unsupervised classification of vegetation index two and vegetation index three

were also used to detect dense clouds and verify clouds identified with Band 8. As with

Band 8, the cloud formation of Hurricane Irene was used as the reference feature to

identify potential cloud classes. The clouds from Hurricane Irene were located in the

northeast quadrant of the vegetative index two and vegetative index three maps.

Classes 1-2 in vegetation index two were determined to be cloud classes and

classes 3-8 were determined to be partial classes. Classes 3-7 were determined to be

cloud classes in vegetation index three. Clouds that were identified with Band 8 in the

study area produced similar but not exact shapes with vegetation index two and


*C
s Clollds
I rl~i I II1 12
1 13

tt-~








52



vegetation index three. Vegetation index two uses more classes to map these clouds than


vegetation index three, Figure 4.2(A) and Figure 4.2(B).













SClass 1-2

Class 11-1
SClass 13-1
I~Class 15-1
Class 17-181
Class 19-20
Class 23-24
SClass 25-1

'-Class 26-2
SClass 27
.1~ Class 28













Figure 4.2(A) Vegetation index two map of south Florida, October 16, 1999. Clouds from
Hurricane Irene are most visible in the northeast section with class 1 and class

2. (B) Vegetation index three map of south Florida, October 16, 1999. Clouds
from Hurricane Irene are most visible in the northeast section with classes 3-7.

















































Figure 4.2


I _


Class 1
Class 2


SClass 4
SClass 5
Class 60
| Class ?
Class 83
SClass 9Q
Class 15-0




Clss1


50 0 50







































1 0 1 2 Miles


Figure 4.3(A) Vegetation index two map of the study area, October 16, 1999. All three
clouds are visible in the study area outlined in red. The legend for Figure
4.2(A) applies to Figure 4.3(A). (B) Vegetation index three map of the study
area, October 16, 1999. Clouds in the study area, outlined in yellow, are most
visible with classes 15-30. The legend for Figure 4.2(B) applies to Figure
4.3(B).





































I 0 1 2 Miles


Figure 4.3
The analysis of vegetative index two and vegetative index three showed that the

combination of high and low gain thermal bands was superior for mapping clouds. It is

important to note that the lack of cloud cover displayed with vegetative index three could

possibly lead to the incorrect assessment that clouds do not exist in the study area. To

conclude, three clouds were located in the study area, and their signature completely

dominated the signature of the ground, however it is inconclusive whether or not cloud

shadows prevented water detection.

NDVI

NDVI was useful for mapping water under both dry and severe flood conditions,

and the Atlantic Ocean was the primary feature used to identify open water classes in










both NDVI maps. Open water represents a severely inundated condition where only the

spectral signature of water is visible; however this may also represent a condition where

emergent canopy does not exceed inundation depth. Atlantic Ocean open water classes

were found to conform to the east coast of south Florida' s peninsular land boundary.

Open water classes were found to be clearly distinguishable and separated from land

classes along the east coast boundary, Figure 4.4(A) and Figure 4.4(B).










Class 1-17
C;illass 18
Class 19
SClass 20
IIclass 21
SClass 22
SClass 23
C...._ lass 24
SClass 26
Class 26
Class 29
Class 28
Class 27
Class 30





(1 0 10 20 Miles




Figure 4.4(A). October 16, 1999, NDVI map of the south Florida. Open water classes are
represented with blue. (B). April 9, 2000, NDVI map of south Florida. Classes
1-7 are open water and represented with blue.
















8-9

'12-13
S14-15
S16-17
118-19
20-21
S22-23
24-25
S26-27
28-30




200 0 2001 400 Miles~i
r............................. -B

Figure 4.4
To identify and map water classes in the study area for October 16, 1999, and April

9, 2000, only the NDVI classes found in the Atlantic Ocean were used. The land

boundary was clearly visible from open water in both images; however the increase of

open water classes in the October 16, 1999, NDVI map showed the flood impact of

Hurricane Irene.

Classes 1 -7 were determined to be open water in the April 9, 2000, NDVI map,

and classes 1-17 were determined to be open water in the October 16, 1999, NDVI map.

The October 16, 1999, NDVI map was expected to have more water classes due to the

flood condition produced by Hurricane Irene. Figures 4.5(A) and Figure 4.5(B) display

the coverage of water in the study area. Clouds from Hurricane are visible with classes

18-20; however clouds in the study area are not distinguishable with classes 18-20.









Because clouds are not be mapped in October 16, 1999, NDVI map, it is difficult to

exactly determine the separation between open water and cloud pixels in the study area.

The method used to determine an open water class was successful for separating clouds

from water in the April 9, 2000, NDVI map; however this method is not adequate for

separating clouds from water in the October 16, 1999, NDVI map. Despite this

constraint, the October 16, 1999, NDVI map does display a large increase in the coverage

of water classes that is not found in the April 9, 2000, NDVI map.

























~ 1 A

Figure 4.5(A). October 16, 1999, NDVI map of the study area outlined in yellow. Open
water classes are represented with blue. (B). April 9, 2000, NDVI map of the
study area outlined in blue. Open water classes 1-7 are represented with blue.

















5~~~ V Study Area



18-19


s 1 20-21
S.22-23
V I 26-27
28-30





2 0 2 4 Miles i


Figure 4.5
High NDVI classes in the October 16, 1999, NDVI map are found where the

spectral signature of water is obstructed by the signature of canopy. These pixels were

mostly found in areas where high and dense canopy exists. This is most visible in the

wetland shrub/scrub areas and in the row crop areas where high NDVI class pixels are

located adj acent to NDVI water pixels.

Although NDVI was determined to be useful for verifying the flood extent, several

constraints became obvious during the analysis. First, the 30 meter spatial resolution of

NDVI maps failed to distinguish vegetation from water where vegetation canopy

exceeded ponded water depth. Second, clouds that were mapped with Band 8, vegetation

index two and vegetation index three were not mapped with NDVI. Finally, NDVI maps









could not display the duration, change in magnitude and extent of flooding for Hurricane

Irene, due to the low frequency of available Landsat 7 ETM+ images.

Topographic Analysis

The procedure used to create bare earth topographic grids involved bare earth

modeling and spatial modeling. Bare earth modeling initially began by identifying ALSM

vegetation and artifact points with color infrared imagery and DEMs, and then the points

were removed. Point removal was based under the assumption that the topography in the

C-111 basin is extremely flat and that a low variability exists between neighborhood

elevation points. Spatial modeling was employed to predict elevation values where large

gaps were left from ALSM point removal. The spatial modeling procedure involved the

use of multiple interpolators and assessment of the generated statistics. The optimum

interpolation method was used to create both NAD 27 and NAD 83 ALSM DEMs.

The four deterministic interpolators that were used to create ALSM elevation grid

surfaces are inverse distance weighting, global polynomial, local polynomial and radial

based functions. The lowest root mean square value was used as the decision statistic for

selecting the optimum test method, however several tests were found to possess the

lowest value. To resolve this problem, the test that possessed a mean absolute error

closest to zero was selected as the optimum method.

The radial based function interpolator produced the overall lowest root mean square

error values, and was selected as the best interpolation method for ALSM DEMs, Table

4.1. Tests 3, 12, 13 and 16 all produced the lowest root mean square value, 0.1351 ft.,

therefore the lowest mean error statistic among these tests was used to select the optimum

search parameters. Because of its low mean error value, test 13 was selected as the









optimum search parameter method for NAD 27 and NAD 83 ALSM data. Details for the

other interpolators may be found in Tables 4.2, 4.3 and 4.4.

The search parameters for test 13 were applied to ordinary kriging, universal

kriging, disjunctive kriging and simple kriging interpolators. Simple kriging produced the

lowest root mean square value, 0.1433 ft., Table 4.5. Although geo-statistical

interpolators are more rigorous than deterministic interpolators, they are not ideal for

predicting topographic grids that possess a significant variability in density with ALSM

points (ESRI 2001). Furthermore, the high variation in point density within the DEM

made analysis and interpretation of semi-variograms inconclusive.

Classified ALSM DEM

Classified ALSM DEMs were manually created by assigning elevation values into

a specified interval. An elevation interval of 0.2 ft. was used to separate vegetation from

the bare ground between the elevations of 4-6 ft. for NAD 27. The legend for elevation in

Figure 4.6(A) describes elevation intervals in feet. Elevation intervals that were above the

maximum elevation threshold of 4.8 ft. were represented with green, to represent

vegetation. Three dimensional images of classified NAD 27 and NAD 83 DEMs were

used to analyze the effect of point removal, Figure 4.6(A)-(H). The classified TINT DEM

clearly displayed field vegetation, fiducial features and the S175 culvert. Except for part

of the L3 1W canal, the NAD 83 classified TINT DEM did not map these features. This is

attributed to large gaps produced by point removal. The three dimensional views of the

NAD 83 DEM in Figures 4.6 (E-H) show the effect of point removal.

It is interesting to note that both NAD 27 DEMs show extremely false low

elevation values east of the L3 1 W canal. This may be caused by scattering of the

infrared laser beam, or a problem with post processing. Both NAD 27 DEMs also display







62


extremely high elevation values that are not characteristic of the topography in the study

area, and this was likely caused by the laser beam striking an obj ect in the atmosphere.


LI--0( r0van


0 0.05 0 t 02 0.3 0.4




s


Figure 4.6(A) Planar view of the NAD 27 study area DEM. The legend applies to all
three dimensional (3D) DEMs. (B) 3D southeasterly view of the study area
using NAD 27 ALSM data. (C) 3D southerly view of the study area using
NAD 27 ALSM data. (D) 3D westerly view of the study area using NAD 27
ALSM data. (E) 3D southerly view of the study area using NAD 83 ALSM
data. (F) 3D easterly view of the study area using NAD 83 ALSM data. (G)










3D easterly view of the study area using NAD 83 ALSM data. (H 3D
westerly view of the study area using ALSM data.






















jLB


Figure 4.6














































Figure 4.6

























a-s
-~F~





~t' '~+:Ci .i'


Figure 4.6





































Figure 4.6


Figure 4.6








































Figure 4.6






































Figure 4.6
Surface Water Elevation Map Analysis

The four geo-statistical interpolators that were used to predict elevation values for

surface water elevation grids are universal kriging, disjunctive kriging, simple kriging

and ordinary kriging. The root mean square error served as the decision statistic for

selection of the optimum interpolation method. If two tests shared an equal value, then

the test with the mean error value closest to zero was selected as the optimum method.

For universal kriging, test 15 produced the overall lowest root mean square error

value of 0.4701 ft. (see Table 4.6), and the search parameters for test 15 were applied to

create all surface water elevation maps, Table 4.7. These parameters were also used for

October 16, 1999, NAD 27 surface water elevation data; however the root mean square

error value was 0.02 ft. greater than that of NAD 83. Furthermore, NAD 27 possessed


~b~i~jk~l~li~'










greater mean error and mean error values; however the root mean square standardized

error and average standard error values for NAD 27 were less than NAD 83. Details for

the other interpolators are shown in Tables 4.8, 4.9 and 4.10.

Table 4. 11 lists the surface water elevation values for the study period, and Figure

4.7 shows a graph of surface water elevation data for the study period. An analysis of

surface water data showed a sharp increase in elevation that was coincident with the

impact of Hurricane Irene, and a gradual decrease associated with drainage. SWEM

contours in Figures (A-H) appear to show a directional flow towards the S332 and S178

pumping stations. The SFWMD (2000) reported that the S332 was operating at maximum

capacity on October 14, 1999, however no specific information is provided for the other

water control structures in the study area. Surface water elevation maps displayed a

smooth transition between contour intervals; however discontinuities in the elevation

intervals were more noticeable as the distance between stations increased, Figure 4.8 (A-

K). Elevation values are in feet NGVD 88.

Prediction error maps for SWEM were produced, because universal kriging was

selected as the interpolation method. Figure 4.9 (A-K) show universal kriging prediction

error maps made from surface water elevation maps. Several trends were noticed during

the production of SWEM prediction error maps. October 16, 1999 displayed the lowest

prediction error, and an increase in prediction error existed for the remainder of the study

period. Furthermore, SWEM prediction error for October 12, 1999, was observed to be

the highest for the entire study period. Prediction error values are in feet.

The low prediction error for October 16, 1999, is attributed to both high surface

water elevation values and the low variability in values for stations throughout the study







70


area. The increase in prediction error coincides with an increase in the variability in

elevation values between neighboring stations. This increase in variability is most likely

due to the effect of water management and variable drainage rates for water control

structures.










6L








1, x~x

02


12 13 14 15 16 17
Day
-*-S178 -. NP 158
-a- FP2 -* S332 -
FP G3355


18 19 20 21 22


S175 FP1
- NP112 -- S177


Figure 4.7 Graph of surface water elevation values. Values are in feet NGVD 88.





FP2
*


cP112


5178


5177


G3355


SURFACE WATER STfES
L31 W
1 175-2

2-225


0 0.25 0.5 1 1.5 2


Mies


Figure 4.8 (A) SWEM October 12, 1999. (B) SWEM October 13, 1999. (C) SWEM
October 14, 1999. (D) SWEM October 15, 1999. (E) SWEM October 16,
1999. (F) SWEM October 17, 1999. (G) SWEM October 18, 1999. (H)
SWEM October 19, 1999. (I) SWEM October 20, 1999. (J) SWEM October
21, 1999. (K) SWEM October 22, 1999. Elevation values are in feet NGVD
88.

























FP2


Pli2


5178


S177


G3355


SSURFACE WATER STfES
-L31 W
1 1.75-2
S2-225
2225-25
2.5-2.75
1 275-3



335-375


Figure 4.8


0 0.25 0.5 1 1.5 2



Mies

























FP1


FP2


SilB


51i7


G63355

MIREACEWAERSITES

0 11 W2




03 25-7



14-425


Figure 4.8


0 0.25 0.5 1 1.5 2





S







































































































FP(


5178


G3355


SURFFACEWATERSITES




215- 5

150-275

03-325
325-35
1350-375

4-425
1425-45
m45-475


Figure 4.8


0 0.25 0.5 1 1.5 2




Mies




































FP1I




























G3355


L31 W
015-175

0 0.25 0.5 1 1.5 2 Z2






435-475
mars-s



E

Figure 4.8








76

































S178
















G33f55

SURFACEW~ATERSITES


0 0.25 0.5 1 1.5 2 ~-2

12275-



S 35

45-475


Figure 4.8






























FP(


G3355


I SURFACEWATERSITES


-2L 2.25

25-275


SI 3.5
3 5- 3.75

14-4.25
425-4.5


Figure 4.8


0 0.25 0.5 1 1.5 2







S





























FP(


5178


G3355


SUlRFACEVATERSITES



-2225





4-425


4925-45
4 5-475


Figure 4.8


0 0.25 0.5 1 1.5 2







S








79



































S'178

















G3355

SU RFACE WATER SITES
SL31 W

0 0.25 0.5 1 1.5 2 [ ]2-2.25
Miles M221-21

E ~~2 5-275 32 .
03-325

S 3 5 3.5
13 75 4

14.25-4.5



Figure 4.8








80

























FP1I


























G3355


L31 W
015-1 7
0 0.25 0.5 1 1.5 2 O r-







35-475





Figure 4.8









81























































G3355

SURFACEWATERSITES
L31W
0125-15
0 0.25 0.5 1 1.5 2
Miles 2-225

W E us.25


3 75-43
4325-4




K

Figure 4.8
















































G3355


*SURFACE WATER SITES
- 31 W

0.6 0.7

0.7 -0.8


Figure 4.9 (A) Prediction error for SWEM October 12, 1999. (B) Prediction error for
SWEM October 13, 1999. (C) Prediction error for SWEM October 14, 1999.
(D) Prediction error for SWEM October 15, 1999. (E) Prediction error for
SWEM October 16, 1999. (F) Prediction error for SWEM October 17, 1999.
(G) Prediction error for SWEM October 18, 1999. (H) Prediction error for
SWEM October 19, 1999. (I) Prediction error for SWEM October 20, 1999.
(J) Prediction error for SWEM October 21, 1999. (K) Prediction error for
SWEM October 22, 1999. Legend error values are in feet.


0 0 25 0 5 1 1 5 2


Mies








83















FP2




NP1IP
,, *5







NP1128





e 33325















0 0.25 0.5 1 1.5 2 SUFC WAE SIS


W E 0.5 -0.6






B

Figure 4.9






















FP(


G3355


SSURFACE WATER STiES
SL31 W
lo 0 .1
S0.1 0 2

10.3- 0 4
04-05
0.5 -0 6

0.7 -0 8


Figure 4.9


L,


0 0.25 0.5 1 1.5 2





S

























































































FP(


G3355


SSURFACE WATER SITES
SL1 W

S0-0.1
0.1-0.2
S0.2-03
S0.3-04
0.4-0.5


06i-0.7
S07-0.8


0 0.25 0.5 1 1.5 2






S


Figure 4.9