Review of U.S. Tide-Coordinated Shoreline
Thesis
Presented in the Partial Fulfillment of the Requirements for the Degree Master of Science in the Graduate School of The Ohio State University
By
Anuchit Sukcharoenpong, B.E.
Graduate Program in Geodetic Science and Surveying
The Ohio State University
2010
Thesis Committee:
Dr. Rongxing Li, Advisor Dr. Christopher Parrish Dr. Alper Yilmaz
Copyright by
Anuchit Sukcharoenpong
2010
ABSTRACT
Shoreline mapping is of critical importance for coastal communities. It supports nautical charting, coastal zone management, legal boundary determination, analyzing shoreline erosion and other threats of climate change, and a host of other applications. A tide- coordinated shoreline is representative of the shoreline at certain tidal stage, e.g., Mean
Lower-Low Water (MLLW) or Mean High Water (MHW). Since the establishment of the Survey of the Coast in 1807, mapping of tide-coordinated shorelines has been a challenging operation. In the past, analog devices, such as plane tables and telemeter rods, had been used in mapping the tide-coordinated shoreline for nautical chart production. Although shoreline mapping from such techniques provided high-quality shoreline, the procedure requires massive amount of time, manpower and budget. The standard method to map tide-coordinated shorelines was later shifted to aerial photogrammetry in 1927. Advantages of conducting coastal surveys using aerial photogrammetry over the conventional ground survey include enabling surveying of large areas in a short time, while maintaining good accuracy. Today, approaches to tide- coordinated shoreline involve recent advances in technologies and methods, including global positioning system (GPS), light detection and ranging (LIDAR), satellite imagery, and auto-feature extraction from imagery. Additionally, LIDAR has currently begun to be utilized in NOAA’s national shoreline production for MHW shoreline, along with
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MLLW shoreline from photogrammetric compilation deriving tidally-referenced aerial imagery. This thesis discusses coastal mapping and surveying work focused on approaches to tide-coordinated shoreline. A brief history of shoreline mapping in the
United States, dating back to the early era of U.S. coastal survey, is presented as well as current standard procedures of tide-coordinated shoreline mapping, including quality of work and efficiency of the work process. Definitions of a shoreline and some of its variations, usually misinterpreted and misused, are addressed to give a basic understanding. Implementations of modern technologies and methods in derivation of tide-coordinated shoreline will then be reviewed and discussed regarding the accuracy and efficiency of the shoreline products. Finally, trends and developments of tide- coordinated shoreline mapping approaches regarding advancements of technologies in the future are analyzed and discussed in the thesis.
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Dedicated to:
Dad, Mum, Toey, Tae, and Onn
ACKNOWLEDGEMENTS
First of all, I would like to express my sincere gratitude to Dr. Rongxing Li, my advisor, for his patience, understanding, and supportive suggestions throughout my studies. His guidance led me to the successful completion of this thesis.
I am grateful to Dr. Christopher Parrish for sharing many valuable advices and knowledge in this thesis. I also want to thank Dr. Parrish for coming to Columbus and serving on my thesis examination committee.
I appreciate Dr. Alper Yilmaz for his participation on the thesis examination committee.
I am thankful to all my friends in Columbus and back in Thailand for their encouragement during my research.
I thank all of my colleagues in the OSU Mapping and GIS Lab, especially Dr. Bo Wu, Dr. Liang Cheng, Dr. Ernie Liu, and I-Chieh Lee, for their help during this research.
Lastly, I would like to thank my family for their endless support and encouragement, which made this thesis possible.
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VITA
January 19, 1982 …………………………. Born, Bangkok, Thailand
2006……………………………………….. Bachelor of Engineering
Survey Engineering
Chulalongkorn University
Bangkok, Thailand
FIELDS OF STUDY
Graduate Program: Geodetic Science and Surveying
Department: Civil and Environmental Engineering and Geodetic Science
Concentration: Mapping and Geographic Information System
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TABLE OF CONTENTS
Page
Abstract………………………………………………………………………………… ii
Dedication……………………………………………………………………………… iv
Acknowledgements…………………………………………………………………….. v
Vita……………………………………………………………………………………... vi
List of Figures………………………………………………………………………….. xi
List of Tables…………………………………………………………………………... xv
Chapters:
1 Introduction …...... 1
1.1 Introduction ………………………...... ………………………………………. 1
1.2 Organization of the thesis…………………………...……...…………………. 3
2 Definitions and Reviews of Available Shoreline Data…………………..………… 4
2.1 Definitions……..…………………………………………………………….... 4
2.1.1 Shorelines/ Coastlines………………………………………………...… 5
2.1.2 Blufflines/ Bluff lines………………………………………………..….. 5
2.1.3 Instantaneous Shorelines………………………………………………... 6
2.1.4 Tide-Coordinated Shorelines……………………………………………. 6
2.2 Tide-coordinated shorelines and instantaneous shorelines…………………… 7
2.3 Tidal Datum and Shoreline……………………………………………………. 9
2.3.1 Mean Sea Level (MSL)…………………………………………………. 12
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2.3.2 Mean Low Water (MLW)………………………………………………. 12
2.3.3 Mean High Water (MHW)……………………………………………… 13
2.4 Review of available shoreline data in the United States……………………… 13
2.4.1 NOAA National Shorelines……………………………………………... 15
2.4.2 NOAA Composite Shoreline……………………………………………. 17
2.4.3 USGS Vector Shorelines………………………………………………... 19
2.4.4 NOAA Office of Coast Survey (OCS) Shorelines……………………… 22
2.4.5 NOAA Medium Resolution Shoreline………………………………….. 25
2.4.6 Prototype Global Shoreline Data………………………………………... 27
2.4.7 World Vector Shorelines………………………………………………... 28
3 Tides and Shorelines……………………………………………………………….. 31
3.1 Observation of tides…………………………………………………………… 31
3.2 Available data from tide stations……………………………………………… 34
3.3 Water level from satellite altimetry…………………………………………… 38
3.4 Available products from satellite altimetry…………………………………… 40
3.4.1 Aviso sea surface height products………………………………………. 41
3.4.1.1 Ssalto/Duacs (Map of) Sea Level Anomalies & geostrophic velocity anomalies………………………………………………. 41
3.4.1.2 Ssalto/Duacs Map of Absolute Dynamic Topography & absolute geostrophic velocities (MADT)……………………….. 42
3.4.1.3 Ssalto/Duacs MSLA Monthly mean and Climatology Gridded Sea level anomalies……………………………………. 42
3.4.2 Sea Surface Height Anomaly (NASA/PO.DAAC)……………………... 43
3.4.3 The Ocean Surface Topography Mission (OSTM)/Jason-2…………….. 44
3.5 Digital modeling of water surface…………………………………………….. 44
3.5.1 Classification of ocean models………………………………………….. 46
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3.5.2 Ocean models…………………………………………………………… 49
3.5.2.1 Modular Ocean Model (MOM)…………………………………. 49
3.5.2.2 Princeton Ocean Model (POM)…………………………………. 51
3.5.2.3 MIT General Circulation Model (MITgcm)…………………….. 52
4 Reviews of Tide-Coordinated Shoreline…………………………………………… 55
4.1 U.S. tide-coordinated shoreline……………………………………………….. 55
4.2 NOAA’s standard procedure to achieve tide-coordinated shoreline from aerial photogrammetry………………………………………………………... 58
4.2.1 Project design and planning…………………………………………….. 59
4.2.1.1 Tide coordination……………………………………………….. 59
4.2.1.2 Flight conditions………………………………………………… 65
4.2.1.3 Ground photo control…………………………………………… 66
4.2.2 Field operations…………………………………………………………. 67
4.2.3 Data processing…………………………………………………………. 69
4.2.4 Aerotriangulation………………………………………………………... 70
4.2.5 Feature compilation……………………………………………………... 72
4.2.6 Project completion………………………………………………………. 74
4.3 New technologies implemented in NOAA’s shoreline mapping……………... 75
4.4 Tide-coordinated shoreline researches at the Ohio State University…………... 78
4.4.1 Digital tide-coordinated shoreline………...... 78
4.4.2 Review of shoreline mapping research at the Ohio State University…… 83
4.4.2.1 Instantaneous shoreline from aerial and satellite imagery……… 83
4.4.2.2 Implementation of instantaneous shorelines to derive tide-coordinated shoreline...... 100
4.4.2.3 Research on digital models and implementation to derive tide-coordinated shoreline……...... 111
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4.5 Review of recent approaches to achieve tide-coordinated shoreline…………. 122
4.5.1 Tide-coordinated shoreline from ground survey………………………... 122
4.5.2 Tide coordinated shoreline from airborne sensors…………………...... 126
4.5.3 Tide-coordinated shoreline from space-borne sensors………………….. 132
4.6 Discussion…………………………………………………………………...... 140
5 Future improvement of tide-coordinated shoreline mapping………………………. 148
5.1 Tide-coordinated shoreline from conventional aerial photogrammetry……...... 148
5.2 Tide-coordinated shoreline from digital models………………………...... 149
5.2.1 GPS survey…………………………………………………………...... 150
5.2.2 Implementation of satellite imagery…………………………………...... 151
5.2.3 LIDAR…………………………………………………………………... 154
5.2.4 VDatum…………………………………………………………………. 155
5.3 Modeling tide-coordinated shoreline………………………………………….. 157
5.4 Discussion…………………………………………………………………….. 159
6 Conclusion…………………………………………………………………………. 161
References……………………………………………………………………………… 165
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LIST OF FIGURES
Figure Page
2.1 Semidiurnal, diurnal and mixed type of tides ………………………………...... 10
2.2 Illustration of principle tidal datums used in the United States …………………. 11
2.3 NOAA Shoreline Data Explorer’s user interface ………………………………... 15
2.4 Control points (red triangles) are represented as line features in NOAA’s Composite Shoreline …………………………………………………………….. 18
2.5 Graphical user interface of the shoreline explorer ………………………………. 19
2.6 Change analysis has been studied for USGS Vector Shorelines ………………... 21
2.7 Analysis report provided by USGS ………………………………………………21
2.8 NOAA ENC Direct web GIS ……………………………………………………. 22
2.9 Multiple layers of data are available for NOAA OCS Shoreline ……………….. 23
2.10 Land area (red) and marsh areas (green) from EVS shorelines …………………. 24
2.11 Differences between merged vector shorelines and individual shorelines from coastal maps ……………………………………………………………………... 25
2.12 Combining land use data with shoreline data …………………………………… 26
2.13 Illustration of shoreline regions …………………………………………………. 27
2.14 Shorelines from region 15 and 17 ……………………………………………….. 28
2.15 National Geophysical Data Center (NGCD) Coastline Extractor ………………. 29
2.16 Java Map applet for NGCD Coastline Extractor ………………………………... 29
2.17 Illustration of shorelines plot from Matlab ……………………………………… 30
3.1 An old tide station and its components ………………………………………….. 32
3.2 A current tide station …………………………………………………………….. 33
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3.3 Observed data are transmitted via GOES satellite to NOAA headquarters promptly after data are collected …………………………………………………34
3.4 ODIN graphical user interface …………………………………………………... 35
3.5 Illustration of satellite altimetry principle ………………………………………..39
3.6 Illustration of a geopotential (z) coordinate model using 30 levels ……………... 48
3.7 Illustration of terrain-following (sigma) coordinate discretization with 20 levels 48
3.8 Illustration of isopycnic coordinate discretization with 20 layers ………………. 49
3.9 Illustration of WRAPPER components …………………………………………. 54
4.1 Tide windows ……………………………………………………………………. 60
4.2 Tide zones in lower Chesapeake Bay …………………………………………… 61
4.3 Flight time for April 24, 2002, in lower Chesapeake Bay ………………………. 64
4.4 Instantaneous shorelines at different time and tide-coordinated shoreline ……… 79
4.5 Digital tide-coordinated shoreline from CTM and WSM process flowchart …… 81
4.6 Error distribution of ground coordinates computed from vendor-provide RFs from ground control point with GPS survey …………………………………….. 86
4.7 Matched points along shoreline in Sheldon Marsh ……………………………… 88
4.8 Candidate polygons and refined shoreline ………………………………………. 89
4.9 Relationship of convergent angle from different azimuths and elevations of satellite images …………………………………………………………………... 91
4.10 Illustration of image exposures and footprints from different data source ……… 92
4.11 Workflow of the integration process ……………………………………………..93
4.12 Overlaying of extracted 3D shoreline and LIDAR bathymetry ………………… 95
4.13 Workflow of shoreline extraction process ………………………………………. 97
4.14 Shoreline comparison method ………………………………………………….. 98
4.15 A small dock in the bluff area where maximum error occurred ………………... 98
4.16 Blufflines segments at different times ………………………………………...... 101
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4.17 Transformation of a bluffline segment into another corresponding segment ….. 101
4.18 Implementation of snake model to derive tide-coordinated shoreline ………….. 104
4.19 Simulated instantaneous shorelines …………………………………………….. 106
4.20 Segmentation of the simulated shorelines ……………………………………….108
4.21 Deformation of snake shoreline from simulated straight shorelines and simulated water surface …………………………………………………………. 109
4.22 Deformation of snake shoreline from simulated straight shorelines and actual water surface ………………………………………………………………110
4.23 Tide-coordinated shoreline from historical shorelines and simulated water surface …………………………………………………………………….. 111
4.24 GLFS daily forecasting cycle …………………………………………………….112
4.25 Diagram of GLFS components ………………………………………………….. 113
4.26 Illustration of GPS buoy deployment near water gauge station ………………… 115
4.27 Location of T/P altimetry tracks and 15 gauge stations in Lake Erie …………… 116
4.28 Shoreline extraction procedure ………………………………………………….. 118
4.29 Illustration of intersection of CTM with water level ……………………………. 119
4.30 Predicted and actual tested errors in area with a lot of man-made constructions .. 122
4.31 Illustration of kinematic surveying with stop-and-go and GPS receiver mounted on a vehicle for Galveston Island State Park …………………………. 124
4.32 Detail of SWASH operation …………………………………………………….. 125
4.33 USGS-NASA Airborne Lidar Processing System (ALPS) ……………………... 128
4.34 Four steps of quantifying differences between contoured shoreline and orthoimages digitized shoreline …………………………………………………. 130
4.35 Flow chart of the applied segmentation-based image processing algorithms ….. 131
4.36 Diagram of water-line method ………………………………………………….. 134
4.37 Reflex of light from the sun to satellite ……………………………………….... 138
4.38 Flow chart of creating tide-coordinated shoreline at MSL ……………………... 140
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5.1 Estimated uncertainties cooperate with transformation between reference datums …………………………………………………………………………… 156
5.2 Illustration of current seaports around the U.S. with availability of VDatum ….. 157
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LIST OF TABLES
Table Page
2.1 Available Shoreline data from NOAA …………………………………………... 14
2.2 Important fields which appears in the attribute table …………………………..... 16
2.3 Important fields with explanations in the attribute table ………………………... 18
2.4 Example of available shoreline for Florida shorelines and its change analysis data ……………………………………………………………………... 20
3.1 An example of extracted verified water level of Cleveland, OH tide station …… 38
4.1 Accuracy from refinement result of first and second method …………………… 87
4.2 Summary of the presented approaches ………………………………………...... 143
4.3 Minimum requirement standards of hydrographic surveys for shoreline positioning and other navigation aids excerpted from IHO (2008) ……………... 146
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CHAPER 1 – INTRODUCTION
1.1 Introduction
Coastal zones, where land meets water and submerges, are one of the most important regions on the planet Earth. The beauty of beaches, waves, sand dunes are some of the reasons that millions of people come to these regions over each year. They provide recreational opportunities and increase land value in coastal areas. These regions are critically important from an ecological perspective. Seas and oceans are also the major source of nutrients for the world's population. Furthermore, there are still a lot of natural resources that have yet to be exploited under the ocean. Aforementioned benefits from ocean and coastal areas are examples to explain why coastal zones are valuable and profitable to humanity.
Land lost from coastal erosion and environmental degradation from human activities has long been a major concern for developing and developed maritime nations. Many coastal zones in the United States are in the process of being altered and destroyed by natural hazards and overpopulation (Timothy, 2002). As a result, several acts have been legislated to control and manage the use of land and provide sustainable development for coastal areas. Today, there are major federal organizations working on coastal environment and shore erosion issues, such as National Oceanic and Atmospheric
1
Administration's (NOAA) National Ocean Service (NOS), U.S. Army Corps of Engineers
(USACE), and International Hydrographic Organization (IHO).
Shoreline or coastline is an important feature for communities, as it represents and divides the ownership between public (States) and private. By definition, shoreline, in general, is the line of contact between land and water (Shalowitz, 1962). As the tide changes over time, shoreline position also changes with respect to shore profiles and tidal levels. This kind of shoreline is sometimes called "instantaneous shoreline" (Li et al.,
2002) because it represents a certain state of the shoreline at an instant of time.
Instantaneous shoreline itself does not have any specific meaning for mapping purposes, unless it is measured at a certain tide level, such as mean low water level or mean water level. Therefore, it is important to understand the nature of shorelines and discuss innovative methods to map shorelines that have meaning and are practical.
Tide-coordinated shoreline, on the other hand, is a representative shoreline which has been introduced to be used as a legal boundary of land delineated by the trace of the tide and linked with a specific phase of the tide; for example, mean lower low water (MLLW) and mean high water (MHW) (Shalowitz, 1962). In the past, analog devices, such as plane tables and telemeter rods, had been used in mapping the tide-coordinated shoreline for nautical chart production. Although the accuracy and quality of the surveying products were high, the work process was excessively demanding, and it took too much time complete only a short section of coastline. With recent advances in remote sensing technologies, satellite imagery, laser altimetry and GPS data can be utilized in mapping of tide-coordinated shoreline to realize the potential of more efficient and economical shoreline mapping techniques.
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This thesis discusses coastal mapping and surveying work focused on approaches to tide- coordinated shoreline. A brief history of shoreline mapping in the United States, dating back to the early era of U.S. coastal survey, is presented as well as current standard procedures of tide-coordinated shoreline mapping, including quality of work and efficiency of the work process. Definitions of a shoreline and some of its variations, usually misinterpreted and misused, are addressed to give a basic understanding.
Implementations of modern technologies and methods in derivation of tide-coordinated shoreline will then be reviewed and discussed regarding the accuracy and efficiency of the shoreline products. Finally, trends and developments of tide-coordinated shoreline mapping approaches regarding advancements of technologies in the future are analyzed and discussed in the thesis.
1.2 Organization of the thesis
This thesis is organized into 6 chapters. The first chapter addresses the importance of coastal areas, and the need for shoreline mapping. Chapter 2 presents definitions of shorelines and a review of available shoreline datasets in the United States. Chapter 3 discusses tidal information available and its applications to shoreline mapping. Chapter 4 reviews recent approaches to tide-coordinated shoreline and relevant research. A brief history of coastal survey and current standard procedure to derive tide-coordinated shoreline are also presented. Chapter 5 discusses future developments of methods to derive tide-coordinated shoreline with respect to advancements in remote sensing technologies. Finally, chapter 6 concludes the topics and issues concerned and discussed in the thesis.
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CHAPTER 2 – DEFINITIONS AND REVIEWS OF AVAILABLE
SHORELINE DATA
Attempts to survey and map the shoreline of the United States can be traced back to
1807. At that time, analog devices such as plane tables were the main tools to carry out coastal surveying and mapping tasks (Shalowitz, 1964). The resulting products have sufficiently high level of accuracy but the procedure requires massive amount of time, manpower and budget. Therefore, the efficiency of early age coastal survey is low and cannot be performed in these days. Owing to the advancement of technologies, achieving shoreline mapping at a desired accuracy with a minimum time and cost is now applicable.
2.1 Definitions
As the main aim of this thesis is to discuss and analyze possibilities of such shoreline mapping methods, the following present definitions of shorelines and its variations which should be clarified before readers proceed to latter chapters.
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2.1.1 Shorelines/ Coastlines
The exact definition of shoreline sometimes varies depending on the application.
Shoreline on Digital Line Graphs is defined by The U.S. Geological Survey (USGS) as a line of contact, naturally occurred, between the land and a body of water (Graham, Sault, and Bailey, 2003). Shalowitz (1962) also similarly defines shoreline and coastline as the line of contact between land and sea surface, and these terms are used synonymously between each other in the Coast Survey. Although coastline and coast line are intuitively similar, coast line is theoretically the intersection line of mean low water level and shore in the Coast Survey (Shalowitz, 1962).
2.1.2 Blufflines/ Bluff lines
Blufflines are the lines of feeder bluffs, which are a coastal features that have a steep, wide front facing towards the water along a coast. They are generally the intersection of the elevated horizontal land surface with the sloping surface facing the water (Carter et al., 1981). Bluffline is an important coastal feature long utilized in many forms of shoreline applications, such as shoreline mapping and coastal change detection and prediction, as they can be indicated easier than shoreline through aerial and satellite images and are not subject to short-term change due to the rising or falling of the tide.
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2.1.3 Instantaneous Shorelines
The instantaneous shorelines are the lines where land and water touch at one instant in time (Boak and Turner, 2005). These shorelines generally are from intersections at the instant of data acquisition. For example, shorelines extracted from satellite images are instantaneous shorelines. The shoreline has relatively little meaning as the water level is not referenced with any tidal datum (Li et al., 2002). Therefore, it cannot directly be implemented in applications unless sea surface reaches the height of any tidal datum at an instant of data acquisition.
2.1.4 Tide-Coordinated Shorelines
A definition of tide-coordinated shoreline in NOAA/NOS perspective may be derived from a definition of Tide-coordinated photography. In Graham et al. (2003), Tide- coordinated photography is defined as:
"Tide-coordinated photography means that the actual observed water levels were within the NOS guidelines while tide-predicted is based solely on tide predictions taken from tide tables. These guidelines were established to allow the aircraft sufficient time to acquire photographs within a zoned area."
To extrapolate beyond the photogrammetric case, the term "tide-coordinated" means that the shoreline was mapped and tidally referenced by acquiring the source data within a specified window, which is defined based on actual water level observations, and the shoreline is based on a specific tidal datum (personal communication, Dr. Christopher
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Parrish, NOAA National Geodetic Survey, 2010). Li et al. (2002) also defines tide- coordinated shorelines as shorelines extracted at a desired water level or tidal datum. This type of shoreline is referenced to a certain tidal datum or water level, such as mean lower- low water (MLLW) and mean high water (MHW), and is employed in coastal and mapping applications. Shorelines on nautical charts generally are tide-coordinated shorelines as shorelines in those charts normally are referenced to one or more tidal datums. Li et al. (2002) categorized tide-coordinated shorelines into two types depending on method of shoreline creation: 1) Physical tide-coordinated shorelines and 2) Digital tide-coordinated shorelines. Differences between the two shorelines are defined by the methodology of deriving the shoreline. Physical tide-coordinated shoreline is obtained from directly observable sources. For instance, shoreline digitized from tide-coordinated aerial images is a physical tide-coordinated shoreline. On the other hand, digital tide- coordinated shoreline is derived from indirect sources. Intersecting coastal terrain model or elevation model with the desired tidal datum is one way to derive digital tide- coordinated shoreline.
2.2 Tide-coordinated shorelines and instantaneous shorelines
Tide-coordinated shorelines are conventionally extracted from aerial photographs taken when the tide reached a desired level (Li et al., 2002). The National Oceanic and
Atmospheric Administration (NOAA) delineates tide-coordinated shorelines from tide- coordinated aerial stereo photography (Woolard et al., 2003). Hence, tide-coordinated shorelines obtained from this method are called physical tide-coordinated shorelines (Li
7 et al., 2002). This method for extracting tide-coordinated shoreline is operationally challenging as images of shorelines at an instant which tide reaches a desired level need to be taken. Therefore, the operation requires a thorough plan and also is weather dependent.
Nowadays, satellite imagery has improved to the point that the satellite images are almost as good as aerial images in terms of spatial resolution, and images can be taken sequentially over a short amount of time (Li et al., 2002). These shorelines are easy and inexpensive to acquire compared to tide-coordinated shorelines from aerial photogrammetry. Moreover, methods and procedures to achieve fine resolution from satellite imagery have widely been studied (Mattar et al., 2003; Di et al., 2003b;
Dellepiane et al., 2004; Foody et al., 2005). Nevertheless, shorelines extracted from those images are instantaneous shorelines which, as discussed before, are not applicable without linking to a tidal reference. Several studies have utilized a set of height- referenced instantaneous shorelines to generate intertidal elevation model (Hoja et al.,
2000; Mason et al., 1995, 1997, 1998, and 2001). Researchers at the GIS and Mapping
Laboratory at the Ohio State University also believe there are connections between instantaneous shorelines and tide-coordinated shorelines that can be expressed in mathematical terms as proposed in Li et al. (2002). This method of deriving digital tide- coordinated shoreline may exploit benefits of low-cost instantaneous shorelines and the derived tide-coordinated shorelines will be called digital tide-coordinated shorelines.
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2.3 Tidal Datum and Shoreline
Acquiring shorelines from applications requires references as mentioned in
“instantaneous and tide-coordinated shorelines”, section 2.2. Tidal datums, which are vertical datums serving as a reference level plane, are used as a link between shorelines and intended water level. Tidal datums are obtained by defining the observed phases of the tide, such as high water level or mean water level. In some places (i.e. along Pacific coast of the U.S.), there are differences between two consecutive high tides and low tides in each tidal day. The difference of two high and two low tides is called diurnal inequality, and it categorizes tides into diurnal tides, semidiurnal tides and mixed tides.
Diurnal tides are tides that exhibit only one high tide and one low tide for each tidal day.
These tides have a period of around one tidal day. Semidiurnal tides exhibit two high tides and two low tides for each tidal day. These tides have a period of around half a tidal day, and they are the predominant type of tides over the world. Finally, mixed tides exhibit large difference in the height reached by either two consecutive high tides or two consecutive low tides, or both, for each tidal day. These tides have a tidal cycle of around half a day. Figure 2.1 illustrates different type of tides observed from gauge stations around the United States during January 15-16, 1961 (Shalowitz, 1962).
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Figure 2.1 Semidiurnal, diurnal and mixed type of tides (Shalowitz, 1962)
In the United States, several tidal datums are utilized to establish a zone for privately owned land and state owned land. There is also an extension of tidal datum referenced shorelines to delineate a zone for territorial sea. Figure 2.2 shows applications of tidal datum referenced shorelines used in the United States.
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Figure 2. 2 Illustration of principle tidal datums used in the United States (NOAA, 2000)
Tidal datums have also widely been utilized in many fields , including both private and publ ic organizations. Mean lower low water is employed by NOAA as the reference datum for sounding in hydrographic surveys and nautical charts. The National Ocean
Service (NOS) defines high -water line in the charts from mean high water datum over 19 - year perio d for the United States. Any feature completely surrounded by the high -water line is acknowledged as an island (Reed, 2000) . Following are commonly used tidal datums.
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2.3.1 Mean Sea Level (MSL)
Mean Sea Level is the average height of all stages of sea surface observed over a period of 19 years. It is usually calculated from hourly water level readings. A standard height developed by averaging all heights of sea surface is called National Tidal Datum Epoch
(NTDE) (NOAA, 2010i). The present NTDE is calculated from tide observations at several gauge stations in the US and Canada over the 1983-2001 epoch (19 years), and is currently one of vertical datums officially used for the NOAA level net in the United
States.
2.3.2 Mean Low Water (MLW)
Mean low water is the average height of low water level observed over the nineteen-year tidal cycle period. Computing the average includes all heights of low water for the place where either semidiurnal or mixed tides exist. For the place where diurnal tides predominantly dominate, heights of only lower-low tides are used in computing the average for time when semidiurnal tides occur. Mean lower low water (MLLW) and mean higher low water (MHLW) are variations of mean low water which compute from the specific lower low or higher low tides (Shalowitz 1962).
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2.3.3 Mean High Water (MHW)
Mean high water is the average height of high water level observed over the nineteen- year tidal cycle period. Determining mean high water consist all high tides in computation of the average for the place where semidiurnal or mixed tides exist. For places that exhibit where diurnal tides, only heights of the higher high tides are used when semidiurnal tides occur. Mean higher high water (MHHW) and mean lower high water (MLHW) are variations of mean high water in which the average is determined from either higher high or lower high tides (Shalowitz, 1962).
2.4 Review of available shoreline data in the United States
The National Oceanic and Atmospheric Administration (NOAA) is the public organization responsible for distributing shoreline data for shorelines in the United States and its territories. Shoreline data is available to be accessed and obtained via the internet through the NOAA shoreline website (http://shoreline.noaa.gov/). The data is stored as a vector data, mostly in an ESRI shapefile format (.shp). Shoreline files are organized into
7 types with different map scales and coverage. Table 2.1 represents available shoreline data which can be accessed for free from NOAA shoreline website.
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Source (Dirived Tidal Shoreline Potential Applications Scale Coverage from) Datum
Shoreline Change Local NOAA T-sheets Continental NOAA Analysis, Nautical Chart MHW and U.S. + AK, National Production, Cartographic and 1:5,000 – georeferenced HI, PR, VI, Shoreline Representation, and MLLW 1:20,000 aerial photos Great Lakes Boundary Determination
Local NOAA Cartographic Continental Composite Representation and NOAA T-sheets MHW 1:5,000 – U.S. + HI Shoreline Boundary Determination 1:20,000
U.S. Gulf of Shoreline Change Local USGS Mexico, Analysis, Cartographic Vector NOAA T-sheets Southeast MHW Representation, and 1:5,000 – Shorelines Atlantic, and Boundary Determination 1:20,000 California
National Continental Cartographic MHW NOAA OCS NOAA Nautical U.S. + AK, Representation and and Shorelines 1:10,000 – Charts HI, PR, VI, Boundary Determination MLLW 1:80,000 Great Lakes
NOAA National Cartographic Medium NOAA Nautical Continental MHW or Representation and Resolution 1:70,000 Charts U.S. only MHHW Boundary Determination Shoreline average
Global Prototype Cartographic LANDSAT Global Representation and World-wide HWL 1:75,000 and GeoCover Shoreline Boundary Determination smaller
Defense Mapping World Cartographic Global Agency (DMA) Vector Representation and World-wide MHW Digital Shoreline Boundary Determination 1:250,000 Landmass Blanking data
Table 2.1 Available Shoreline data from NOAA (excerpted from http://shoreline.noaa.gov/data/index.html)
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2.4.1 NOAA National Shorelines
NOAA National Shorelines were derived from NOAA NOS raster T-sheets and georeferenced aerial photographs since 1985 (NOAA, 2010h). The coverage of the data is over the continental U.S., and Hawaii, part of Alaska, Puerto Rico and the U.S. Virgin
Islands. However, shorelines of the Great Lakes are only partially created. The tidal datums to which shorelines are referenced to are either mean high water (MHW) or mean lower-low water (MLLW). Digital shoreline data can be accessed by using NOAA
Shoreline Data Explorer. Users need to browse through the map and select the desired area of shoreline before downloading the vector data of shorelines.
Figure 2.3 NOAA Shoreline Data Explorer’s user interface
The download package is compressed into a .zip file which contains point features and line features in separate layers. Besides shoreline data for the line feature file, it also
15 includes streets and area polygons. Point features are used to indicate important points, such as the names of harbors and locations of lighthouses. NOAA National Shorelines uses Coastal Cartographic Object Attribute Source Table (C-Coast) standard to explain point and line features in attribute tables. Important fields in attribute tables, which appear in NOAA National Shorelines, are explained in Table 2.2.
Field name Explanations A - AERIAL PHOTOGRAPHY - Film emulsion DATA_SOURC D - DIGITAL PHOTOGRAPHY - Scanned or from digital camera M - MULTIPLE SOURCES - Other sources SRC_DATE Date of source imagery HOR_ACC Horizontal positional accuracy reported in meters INFORM Additional Information ATTRIBUTE Attributes of an object as explained in C-COAST. CLASS Class of an object as explained in C-COAST. Extracting methods from the source EX_METH M - Compiled from monoscopic image S - Compiled from stereoscopic image Extracting techniques from the source A - ANALOG PLOTTER EXTRACT_TE B - ANALYTICAL PLOTTER P - PLANETABLE S - SOFTCOPY
Table 2.2 Important fields which appears in the attribute table (derived from the
metadata)
Additional information regarding sources and date of creation can be found in the metadata file, accessible through icon under the icon allowing download of the shoreline data.
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2.4.2 NOAA Composite Shoreline
NOAA Composite Shoreline is high-resolution vector shoreline data derived from collections of NOAA T-sheets from various creation dates. The measured benchmarks show an average accuracy of 3.06 meters which exceeds the accuracy of 1:24,000-scale topographic maps of the U.S. Geological Survey. The coverage of shoreline data is over the Continental U.S. and Hawaii. Shorelines are referenced to the North American Datum of 1983 (NAD83) as a horizontal datum and Mean high water (MHW) as a tidal datum.
The composite shoreline does not have a data explorer or any user interface for users to access to any particular part of coastal zone. Shoreline data is available as a whole package with 200 megabyte file size and is downloadable via NOAA Shoreline website
(http://shoreline.noaa.gov/data/datasheets/noaa_composite.zip).
The available shoreline package file is composed of only line vectors showing point features like control points in polylines as illustrated in Figure 2.4. There are explanations about the types of shoreline features, such as man-made constructions and state boundaries. Table 2.3 explains supplementary information appearing in the attribute data.
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Figure 2.4 Control points (red triangles) are represented as line features in NOAA’s
Composite Shoreline
Fields Explanations
F_NAME Shoreline feature name (ie. Control point, jetty, shoreline)
INDEX_ Project number
T_SHEET Number of source map
SURVEYDATE Date of conducted survey for each t-sheet
Date the scanned raster t-sheets were digitized to create vector GISDATE data
SCALE Source map's scale
Table 2.3 Important fields with explanations in the attribute table
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2.4.3 USGS Vector Shorelines
The purpose of the USGS Vector Shoreline creation is to support shoreline change analysis applications. Shoreline data was compiled from several sources from different times. Shorelines are catagorized into 1) historic shorelines, digitized from scanned and georeferenced NOAA T-Sheets 2) modern shorelines, derived from LIDAR data. USGS
Vector Shoreline uses NAD83 as a horizontal datum and MHW as a tidal datum for all of shoreline data. Shoreline explorer browser is provided for users to browse and view project zones of shorelines over the Internet.
Figure 2.5 Graphical user interface of the shoreline explorer
Vector shorelines are available in separate zones: U.S. Southeast Atlantic, U.S. Gulf of
Mexico and California Coast. There is no Great Lakes shoreline data available. Shoreline data generally comes with 9 different shorelines: 4 shorelines from different years of the
19 derived sources and 5 extensions of shoreline analysis. Table 2.4 shows an example of provided shorelines and additional data of Florida.
Shoreline Description
fl1855_1895 Vector shoreline derived from 1855-1895 source data
fl1926_1953 Vector shoreline derived from 1926-1953 source data
fl1976_1979 Vector shoreline derived from 1976-1979 source data
fl1998_2001 Vector shoreline derived from 1998-2001 source data
fl_baseline Offshore baseline for generating shore-normal transects
Shore-normal transects with associated long-term rates of shoreline fl_transects_lt change
Shore-normal transects with associated short-term rates of shoreline fl_transects_st change
fl_intersects Transect/Shoreline intersection positions (point)
Fl_nourish Alongshore vector showing spatial extents of beach nourishments
Table 2.4 Example of available shoreline for Florida shorelines and its change analysis data
Shorelines from different periods have been analyzed and information such as transect lines and points of intersection between shorelines and transect lines is included, as shown in figure 2.6. There are also shoreline change analysis reports, studied by the
United State Geological Survey (USGS), available for each coastal zone.
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Figure 2.6 Change analysis has been studied for USGS Vector Shorelines
Figure 2.7 Analysis report provided by USGS
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2.4.4 NOAA Office of Coast Survey (OCS) Shorelines
NOAA OCS vector shorelines are derived from large-scale NOAA nautical charts. The shorelines are available in two types: extracted vector shoreline (EVS) and electronic navigational chart (ENC). The EVS does not reference to a tidal datum, while the ENC shoreline is tidally referenced to the mean high water (MHW). Both shorelines use the
North American Datum of 1983 (NAD83) as a horizontal datum (NOAA, 2010j). ENC shorelines can be accessed through “NOAA ENC Direct”, the web GIS portal for users to preview and choose interested area to download. Figure 2.8 illustrates the graphical user interface of NOAA ENC Direct.
Figure 2.8 NOAA ENC Direct web GIS
Users can acquire shoreline data by browsing to areas of interested and submitting a request using button. The shorelines are offered in several formats, such as
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ESRI shapefile, Google KML and AutoCAD. The ENC aims for cartographic representation as it provides huge set data layers for creating coastal maps which users can select from the ENC GIS DATA pane on the right.
Figure 2.9 Multiple layers of data are available for NOAA OCS Shoreline
EVS shorelines do not have a graphical user interface, so users need to submit a query form to find shoreline data. The original source of the coastal map is available for download and preview in some areas. Alternatively, merged vector shorelines, derived from MHW charts that cover the continental U.S., Hawaii, Alaska and U.S. territories can also be obtained. EVS shorelines offer two types of data 1) the land area (red tint) on nautical charts and 2) marsh areas (green tint) as shown in figure 2.10.
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Figure 2.10 Land area (red) and marsh areas (green) from EVS shorelines
The merged vector shorelines provide only the land area, and show some differences from the individual shorelines derived from coastal maps. Figure 2.11 demonstrates the differences between the merged vector shoreline and the individual shorelines derived from coastal maps. The individual EVS does not have any explanation regarding date of survey or type of land feature. On the other hand, the merged vector shoreline has more detail and better explanations of shorelines such as date, scale, and number of derived charts.
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Figure 2.11 Differences between merged vector shorelines and individual shorelines from coastal maps
2.4.5 NOAA Medium Resolution Shoreline
Shorelines were compiled from NOAA National Ocean Service (NOS) nautical charts aimed to be integrated with a Geographic Information Systems (GIS). The shorelines provide data only for the continental U.S. Therefore, it excludes Alaska, Hawaii, Puerto
Rico and other territories of the U.S. The data were derived from several charts with different revision dates and scales. Shorelines from older sources using the North
American Datums of 1902 and 1927 as the horizontal datum were converted to NAD83.
Shorelines and complementary data are downloadable through NOAA’s Coastal
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Geospatial Data Project website. Vector shoreline data is provided in 6 regions, which are
North Atlantic, Middle Atlantic, South Atlantic, Pacific, Gulf of Mexico and Great Lakes, and combined shorelines including all shorelines mentioned above.
The shoreline data does not carry much metadata except number, date of revision, scale and reference datum of the derived charts. The unique feature of NOAA Medium
Resolution Shoreline are the useful additional charts, such as land use and salinity zones.
GIS analysis can easily be performed with a detailed land use and other data as shown in
Figure 2.12.
Figure 2.12 Combining land use data with shoreline data
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2.4.6 Prototype Global Shoreline Data
The Shoreline data is derived from orthorectified satellite images of NASA LANDSAT
GeoCover at the high water level. The project is still in a prototype state and the National
Geospatial-Intelligence Agency (NGA), responsible for the project, will initiate a new version of the World Vector Shoreline after the effort progresses. The coverage of the project is worldwide with the gaps of about 10% of the overall data set due to the screening of cloud, ice and snow in some part of the world. However there are no
LANDSAT images of the polar regions available, thus the coverage is from approximately 60 degrees south to 80 degrees north latitude. Shorelines are divided into
28 regions as illustrated in Figure 2.13. Region number 29 is not available at the moment.
Figure 2.13 Illustration of shoreline regions (NGA, 2010)
Only shorelines included in NGA Prototype Global Shoreline Data. Attributes provide information on accuracy and deriving sources at the moment. The average accuracy of overall shorelines is reported to be approximately 50 meters. Shorelines from connected regions are gap free as illustrated in Figure 2.14.
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Figure 2.14 Shorelines from region 15 and 17
2.4.7 World Vector Shorelines
World Vector Shoreline (WVS) project was originally aimed for military operations. The shoreline data is available at 1:250,000 scale, accessed through the National Geophysical
Data Center (NGCD) Coastline Extractor website.
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Figure 2.15: National Geophysical Data Center (NGCD) Coastline Extractor
Users need to fill in boundaries in latitude/longitude of the interested area, or otherwise use a provided Java Map applet to help choose latitude/longitude of the desired area.
Figure 2.16 Java Map applet for NGCD Coastline Extractor
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There are 4 formats of the generated shoreline data: Mapgen, Arc/Info Ungenerate,
Matlab and Splus format. All formats of shoreline data requested from The NGCD
Coastline Extractor website returns in plain ASCII .dat file. Shoreline data contains two columns of longitude and latitude in decimal degrees for points along shorelines.
Therefore, there is no additional metadata to explain shorelines, such as type of shorelines and accuracy of the derived sources. The maximum error of WVS is reported to be less than 500 meters for 90% of shorelines.
Figure 2.17 Illustration of shorelines plot from Matlab
Arcview cannot directly read the extracted shoreline file. Instructions for converting
Arc/Info Ungenerate format to ESRI shapefile can be found in NOAA’s World Vector
Shorelines website (http://shoreline.noaa.gov/data/datasheets/wvs.html).
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CHAPTER 3 – TIDES AND SHORELINES
Understanding and exploiting observed information of tides is critical for shoreline work, since the changes of shorelines are mainly influenced by tides. Tides affect shorelines in many aspects, for instance, short- term change due to rising and falling of water level or long-term shorelines erosion caused by tidal waves and sediment transport by tides.
Tides, in general, are mainly created by celestial activities of the Earth, the moon and the sun, resulting in rising and falling of water level on the earth. Changes in tides are continuous and differ from place to place on the Earth, and those changes generally are periodic. Studies of tides may have been started 2000 years ago since the philosophers of
Greece discovered that changes of water level might relate to celestial bodies (Reddy
2001). In this chapter, tide observations and tidal data from tide stations around the
United States and from satellite altimetry are discussed including implementations of tidal data in digital water surface model to create tide-coordinated shoreline.
3.1 Observation of tides
In the United States, tides along the coasts have been observed and predicted since the early 1800s (NOAA, 2010g). In the early age, a recorder with a float in a stilling well was used for measuring tides. The tides observed from the water level sensor are prevented
31 from the fluctuations of water around the stilling well. Figure 3.1 illustrates a typical tide station with a recording unit, a stilling well, and a float hung by a wire.
Figure 3.1 An old tide station and its components (NOAA, 2010g)
However, there were drawbacks with the old tide measuring systems, as they produced recording errors and required a lot of maintenance. Moreover, users could not access the observed data in timely manner since it required weeks to provide the data after the measured tide was collected (NOAA, 2010g).
The current tidal recording systems have been developed to overcome the problems associated with the old tide measuring systems. Advanced acoustics and electronics are integrated in the new tide stations. Instead of using a float to measure water level, an acoustic signal is sent down a sounding tube to measure the reflect time and determine the water level. Figure 3.2 shows the new tide station and its components.
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Figure 3.2 A current tide station (NOAA, 2010g)
The new system also integrates sensors to record oceanographic and meteorological conditions which include speed and direction of water and wind, barometric pressure and temperature of water and air. Furthermore, tide stations use Geostationary Operational
Environmental Satellite (GOES) to transmit the observed data to NOAA headquarters
(NOAA, 2010g).
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Figure 3.3 Observed data are transmitted via GOES satellite to NOAA headquarters promptly after data are collected (NOAA, 2010g)
3.2 Available data from tide stations
NOAA collects the observed data from tide stations around the United States and distributes the raw and processed data through NOAA’s Tides and Currents website
(http://tidesandcurrents.noaa.gov/). NOAA provides a web graphical user interface which is called Observational Data Interactive Navigational (ODIN) for users to access tidal data for each tide station.
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Figure 3.4 ODIN graphical user interface (NOAA, 2010k)
For each tide station, there are several pieces of information regarding water level and meteorological conditions provided as follows:
1. Station Information – provides station location (latitude/longitude), how to access
the station and brief tidal characteristic at the station
2. Preliminary Data – raw observed water level is presented in a plot which shows
observed tides in red line, predicted tides in blue line, and the difference between
the observed and predicted tides in green line. Tide data can be viewed in table
format.
3. Verified Data – shows processed tide data in a plot which has blue line as
predicted water level, red line as observed water level, and green line as the
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difference between the observed and predicted tides. Data can also be viewed in
table format.
4. Tide Predictions – presents predicted water level in a plot. Predicted tide can be
downloaded in text (.txt) format or XML format. However, some stations, such as
tide stations in the Great Lakes do not provide prediction data.
5. Meteorological Observations – measured meteorological parameters are presented
in three plots: 1) wind speed, direction, and gusts, 2) air and water temperature,
and the 3) barometric pressure. Observed data can be viewed in table form instead
of plots.
6. Bench Mark Sheets - shows information of tidal bench mark sheets for the
selected station. A tidal bench mark is usually an inscribed metal disk which is
used as a reference for the height of tide gauge and tidal datums. The bench mark
sheets present descriptions, elevations, and locations of a station’s bench marks
(NOAA, 2010g).
7. Datums – provides datums associated with the selected station.
8. Harmonic Constituents – presents harmonic constituents for the selected station.
Harmonic constituents are the elements in mathematical expressions which are
used to perform tidal prediction at a certain location.
9. Sea Level Trends – provides four plots of trends (NOAA, 2010l):
1) Mean Sea Level Trend plot shows the monthly mean sea level without the
regular seasonal fluctuations.
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2) The average seasonal cycle of mean sea level plot which is caused by
regular fluctuations in temperatures, salinities, winds, atmospheric
pressures, and currents.
3) The inter-annual variation of monthly mean sea level and the 5-month
running average plot presents the variations created by irregular
fluctuations in temperatures, salinities, winds, atmospheric pressures, and
currents.
4) The inter-annual variation since 1980 plot. The plot is similar to the inter-
annual variation plot (3) but the plot starts at year 1980.
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Station Verified water Date Time ID level (m)
9063063 20091231 0:00 173.938 9063063 20091231 0:06 173.933 9063063 20091231 0:12 173.921 9063063 20091231 0:18 173.959 9063063 20091231 0:24 173.924 9063063 20091231 0:30 173.928 9063063 20091231 0:36 173.919 9063063 20091231 0:42 173.907 9063063 20091231 0:48 173.898 9063063 20091231 0:54 173.909 9063063 20091231 1:00 173.905 9063063 20091231 1:06 173.881 9063063 20091231 1:12 173.91 9063063 20091231 1:18 173.927 9063063 20091231 1:24 173.951 9063063 20091231 1:30 173.952 9063063 20091231 1:36 173.946 9063063 20091231 1:42 173.947 9063063 20091231 1:48 173.953 9063063 20091231 1:54 173.936
Table 3.1 An example of extracted verified water level of Cleveland, OH tide station; the data is observed every 6 minutes at the local time and the vertical datum used is IGLD
1985.
3.3 Water level from satellite altimetry
Satellite altimetry can be used to measure the level of the water surface. In principle, satellite altimetry measures the travel time of radar pulse from the satellite antenna to the
38 surface and back to a receiver on the satellite and estimates the distance between the satellite and the target which reflected the radar pulse.
Figure 3.5 Illustration of satellite altimetry principle (Altimetry.info, 2010)
To determine elevation of the pulse-reflected surface, the altitude of the satellite must be known. The satellite’s altitude is the distance of the satellite relative to an arbitrary reference such as the reference ellipsoid. The satellite’s orbit has to be precisely determined as to measure its altitude. There are many techniques to estimate the orbits which can yield an accuracy of 1 to 2 centimeters (Rosmorduc et al., 2009).
The radar pulse sent from a satellite is an electromagnetic wave, having a velocity of light, therefore, as the pulse travel through the atmosphere, its speed is decreased by water vapor or by ionization. The distance from the reflected pulse has to be corrected to
39 remove these phenomena. The accuracy of the corrected distance is generally within 2 centimeters (AVISO, 2010).
Finally, the elevation of the reflected surface can be determined by substituting satellite’s altitude with the corrected distance. However, for the water surface of the ocean, there are factors which need to be considered: 1) gravity variations due to mass and density differences on the seafloor. The denser rock zones on the seabed generally distort estimated elevation by tens of meters. 2) The ocean circulation, composed of a permanent stationary component, such as circulation due to Earth's rotation and permanent winds, and a highly variable component from wind, seasonal variations, et cetera. The average error yielded from the effects is within one meter (Rosmorduc et al., 2009).
3.4 Available products from satellite altimetry
Altimetry can not only measure elevation of the surface reflected by a radar pulse, but can also be used to determine various environmental conditions such as temperature, wind speed and wave height. In this section, some of available altimetry data relating to water level are discussed.
40
3.4.1 Aviso sea surface height products
Aviso (www.aviso.oceanobs.com) has been distributing altimetry data from
Topex/Poseidon and ERS satellites since 1992. The products are available in one global package and also available locally in following specific regions: European shelves,
Mediterranean Sea, Black Sea and Gulf of Mexico. The sea surface height products can be classified into: (1) Gridded products and (2) Along-track products. Grid products provide data in the form of gridded map. Usually, these data are multi-mission which combine altimetric data from more than one satellite. Note that Ssalto/Duacs products are intercalibrated at bridged points by determining two datasets (from different satellites or different tracks), so the data may be homogeneous at a given point. The following sections present some available altimetry products and their descriptions (AVISO, 2010).
3.4.1.1 Ssalto/Duacs (Map of) Sea Level Anomalies & geostrophic velocity anomalies
The product provides both gridded and along-track data of sea surface level and corresponding geostrophic velocity anomalies. The data surface elevation data is computed with respect to a seven-year mean. Near-real time and delayed time data are offered for both gridded and along-track products. There are two levels of resolution: (1) fine resolution (1/3°x1/3°, Mercator grid) or coarse resolution (1°x1°, Mercator grid) for gridded data. There is also a 1/4°x1/4° resampled Cartesian grid version derived from
Mercator gridded product which is for users who are not accustomed to Mercator grid. A mapping error file is provided with geostrophic velocity anomalies for gridded merged data.
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3.4.1.2 Ssalto/Duacs Map of Absolute Dynamic Topography & absolute geostrophic velocities (MADT)
The product is provided in near-real time and in delayed time formats for both along- track and gridded data. The containing data is sea surface level above geoid datum and relating absolute geostrophic velocities. For gridded data, only 1/3°x1/3° resolution
Mercator grid maps and a version of resampled 1/4°x1/4° Cartesian grid are available.
3.4.1.3 Ssalto/Duacs MSLA Monthly mean and Climatology Gridded Sea level anomalies
The product provides multi-mission gridded sea surface level regarding seven-year mean data (from 1993 to 1999). The available data also contains the seasonal variability, with annual cycle included. The product is distributed in delayed time at fine resolution
(1/3°x1/3°, Mercator grid). There are three types of data available:
- Monthly averaged MSLA. The data is generated monthly by averaging weekly
maps of delayed-time sea level anomalies. Aviso provides one file and one map
every month since December 1992.
- Seasonal mean of MSLA. The data is created each season by averaging weekly
maps of delayed-time sea level anomalies. The data is released one file and one
map per season.
- Climatological monthly MSLA. The map is generated monthly by averaging
weekly maps of delayed-time sea level anomalies. Aviso provides one file and
one map every month since December 1992.
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3.4.2 Sea Surface Height Anomaly (NASA/PO.DAAC)
The product provides in along-track and gridded format, including following information: sea surface levels above mean sea surface, significant wave height, inverted barometer, sigma naught, total electron content, ocean depth, and mean sea surface. The provided sea surface height was corrected for atmospheric effects, effects due to electromagnetic bias, and other contributions (PO.DAAC, 2010a). The data is available from Jason-1 and
Topex/Poseidon satellites.
Jason-1 Sea Surface Height Anomaly (J1SSHA) product provides data from January, 15
2002 to now. Jason-1satellite has 127 orbits/cycle with a period of 112 minutes/orbit.
Along track measurement resolution is roughly 1 second and 6 km. On the other hand, a
TOPEX/POSEIDON Sea Surface Height Anomaly (TPSSHA) product offers data from
September, 22 1992 to October, 8 2005. The satellite has 127 orbits/cycle with a period of 112 minutes/orbit. Similarly, along track resolution is approximately 1 second and 6 km.
Products from both satellites have a temporal resolution of about 10 days. Each cycle of data includes a maximum of 254 pass files (Rosmorduc et al, 2009). Each pass file contains data records of 11 parameters mentioned earlier (PO.DAAC, 2010b): record time (days and milliseconds), latitude, longitude, sea surface height anomaly, significant wave height, inverse barometer, sigma naught, total electron content of the atmosphere, depth of the ocean, and mean sea surface. J1SSHA and TPSSHA data are compatible as the records have the same number of header, number of records, and sizes of data.
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3.4.3 The Ocean Surface Topography Mission (OSTM)/Jason-2
The OSTM/Jason-2 mission was launched on June 20, 2008 to extend observations of climate records from TOPEX/Poseidon (1992) and Jason-1 (2001) missions. The main goals of the mission are: to establish a global multidecadal climate record, and to study the relationship between climate change and ocean circulation. Several developments, regarding altimeter tracking modes, radiometer antenna design, on-board navigator, and
DORIS design, have been made to overcome weaknesses on the Jason-1 and to improve precision and coverage of the observation (Lambin et al. 2010).
Four organizations, NOAA, EUMESAT (European Organisation for the Exploitation of
Meteorological Satellites), CNES (Centre National d’Etudes Spatiales), and NASA, are responsible for operations during the mission. Several products from the OSTM/Jason-2 mission are similar to those available from Jason-1. Each completed cycle of OSTM operation takes about 10 days (for 254 ascending and descending passes) (PO.DAAC,
2010c). Altimetric data provided by OSTM/Jason-2 includes: Precise Orbits, Altimeter
Range, Geoid, Mean Sea Surface, Mean Dynamic Topography, Geophysical Corrections,
Tides, etc (Dumont et al., 2008). Products of OSTM/Jason-2 mission are currently available for publicly use (PO.DAAC, 2010c; AVISO, 2010b)
3.5 Digital modeling of water surface
Modeling of water surface can be obtained utilizing hydrodynamic models or ocean models. The hydrodynamic models have been developed to serve the study of lake, coastal and ocean circulation (Velissariou, 2009). Study of water circulation is beneficial
44 to environmental management. The developments of pollution, such as spilt oil and hazardous materials from offshore drilling in the ocean, can be determined using the circulation models (Blumberg and Mellor, 1987). Water surface models can be utilized to generate digital shorelines by intersecting the digital models of a coastal terrain model and a water surface model. Li et al. (2002) described a procedure of creating digital shorelines from digital models and investigated the quality of resulting shorelines.
To model the ocean, the relationship of many processes need to be considered. These processes have different spatial and time scales, which can vary from the smallest scales, like saltwater intrusion and centimeters of viscous boundary layers, to surface waves generated by wind or wave breakings, tides, turbulence from Coriolis force, to the large scale circulation of the ocean. Hence, it is not really possible to represent the model with a high degree of realism since all these processes can interact with each other (Dupont,
2001). For model initialization process, the three dimensional surface of the bottom boundary (ocean floor) is required and the volume of water needs to be determined.
Bathymetry data usually is used for ocean floor model and the volume of water can be estimated from water levels obtained from gauge stations or satellite altimetry. Moreover, meteorological data such as air temperature, cloud cover, dew point temperature and wind speed and direction are necessary depending on the applied ocean model for simulation (spin up) session. Generally, results from the model may be as following: 3D circulation velocities, model of water surface heights, water surface fluctuations with respect to a desired mean water level, water temperature and turbulence kinetic energy
(Velissariou, 2009).
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3.5.1 Classification of ocean models
Ocean circulation modeling can roughly be classified into 4 classes which are characterized by spatial discretization (finite difference, finite element, and finite volume) and vertical coordinate discretization (geopotential, isopycnic, sigma, and hybrid)
(Ocean-Modeling.org, 2010).
For spatial discretization, finite difference (FD) model is popular and has been widely utilized in ocean modeling by many institutions for its simplicity in implementation
(Ocean-Modeling.org, 2010). The concept of finite difference is to represent a shoreline by Cartesian-like grids. The discretization formulates the boundary of the water by introducing a sequence of steps (grids) along the shore. The finite element (FE) model generally uses meshed triangular elements and most of the models are based on Galerkin formulation (Dupont, 2001). FE model provides advantages with flexibility in geometrical representation. However, the finite element model basically requires special treatment as it is prone to having a stability problem in fluid mechanics which was called the Ladyzhenskaya, Babouska and Brezzi (LBB) stability condition and is more expensive computationally as compared to the finite different model (Dupont, 2001).
Lastly, the finite volume (FV) model segments the domain (ocean or lake water) into a number of control volumes (cells or elements). Cells can embed in or intersect the boundaries and results in shaved or lopped cells to fit the boundary. The processes or property fluxes are determined normal to the sides of the cell. This method of spatial discretization can handle complicated geometry while preserving computational simplicity like finite different scheme. Furthermore, FV model still has the ability to
46 decompose the domain with parallel computers, where each vertical column of ocean can be assigned to one processing unit (Marshall et al, 1997).
For vertical coordinate discretization, the basis of currently used numerical models are the hydrostatic primitive equations (HPEs) derived in geopotential (z or height) coordinates. In general, this discretization is called “box-concept”, where water in the ocean is divided into rectangular boxes. Figure 3.6 illustrates different type of geopotential coordinates. The first sub-figure (a) is equidistant geopotential spacing and the second (b) has an increased resolution near the water surface for better resolving of thermocline processes. Alternatively, terrain-following (σ or sigma) or isopycnic (ρ) coordinates can be used in to represent vertical coordinate. The terrain-following coordinate treats water depth as the interval [0,1] which the lowest coordinate level represents the ocean floor. Sigma coordinate representation has advantages in handling benthic processes. Figure 3.7 represents two examples of terrain-following coordinate model. The isopycnic coordinate (Figure 3.8) utilizes a system of adaptive constant- density layers and evaluate layer spacing as a prognostic quantity. This discretization results in improved representation of thermohaline fronts (Ocean-Modeling.org, 2010).
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Figure 3.6 Illustration of a geopotential (z) coordinate model using 30 levels: Left (a) equidistant grid spacing: Right (b) discretization with higher resolution near the water surface (Ocean-Modeling.org, 2010)
Figure 3.7 Illustration of terrain-following (sigma) coordinate discretization with 20 levels: Left (a) standard (equidistant) sigma coordinate: Right (b) higher resolution near the sea surface (Ocean-Modeling.org, 2010)
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Figure 3.8 Illustration of isopycnic coordinate discretization with 20 layers: Left (a) approximately equidistant in density: Right (b) a less uniform vertical discretization
(Ocean-Modeling.org, 2010)
3.5.2 Ocean models
There are, as mentioned before, several types of ocean models with different characteristics and versatilities over different kind of problems. This section provides summary of representative ocean models which have been widely utilized in coastal and oceanographic community.
3.5.2.1 Modular Ocean Model (MOM)
MOM originated from numerical implementation performed at the Geophysical Fluid
Dynamics Laboratory (GFDL) by Kirk Bryan and Mike Cox during the 1960’s-1980’s.
The model is based on hydrostatic primitive equations for numerical representation of the ocean model. The main algorithm and software engineering utilized in MOM are
49 provided by NOAA’s GFDL. The latest version of the model is MOM4.1 (MOM4p1) which was release in December 2007 (as of 04/20/10). MOM4 is coded within GFDL's
Flexible Modeling System (FMS) using FORTRAN90. Source code of the model is available for free to use at http://www.gfdl.noaa.gov/fms.
The current version of MOM provides many features and tools for regional and coastal applications, some are presented as the follows.
- There are up to 6 types of vertical coordinate discretizations supported in MOM4p1
which are as follows: (1) Geopotential coordinate, (2) Quasi-horizontal rescaled
height coordinate, (3) Depth based terrain following coordinate (sigma), (4) Pressure
coordinate, (5) Quasi-horizontal rescaled pressure coordinate, and (6) Pressure based
terrain following coordinate.
- The standard spherical coordinates and the ''tripolar'' grid are used in MOM4 for
horizontal coordinates.
- Two time-stepping schemes for model simulation are supported: (1) leap-frog and (2)
predictor-corrector.
- The model has a capability of forcing the free ocean surface using tidal forcing
influences from the various lunar and solar components.
- MOM4 allows the problem domain to have open boundaries in any of the north,
south, east, or west directions. The model comes with numbers of new options for
radiating conditions.
- Parallel computation is supported for multi-processing units. The output will be
produced one per processer and “mppnccombine” tool can be used to join the output
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files together. The tool is available at http://data1.gfdl.noaa.gov/~arl/pubrel/o
/mom4p1/src/postprocessing/mppnccombine.c.
More information on MOM4 can be found in an online manual provided by NOAA’s
GFDL at http://data1.gfdl.noaa.gov/~arl/pubrel/o/mom4p1/src/mom4p1/doc/mom4_ manual.html.
3.5.2.2 Princeton Ocean Model (POM)
Princeton ocean model is a numerical ocean model developed by Blumberg and Mellor in 1987 (Blumberg and Mellor, 1987). POM provides a powerful but simple ocean modeling code, simulating extensive types of problems in many institutions such as the
Atmospheric and Oceanic Sciences Program of Princeton University, the Geophysical
Fluid Dynamics Laboratory of NOAA and Dynalysis of Princeton (Mellor, 2003). The oceanographic problems include circulation and mixing processes in rivers, estuaries, shelf and slope, lakes, semi-enclosed seas and open and global ocean. Following are some highlighted characteristics of the current version of POM (Mellor, 2003):
- The model is capable of having vertical mixing coefficients, as the model integrates
the second moment turbulence closure sub-mode.
- The model uses sigma coordinate model for vertical coordinate discretization.
- The horizontal grid of the model uses curvilinear orthogonal coordinates and an
Arakawa “C-grid" finite difference scheme.
- The model uses different types of horizontal and vertical time differencing schemes.
The horizontal uses explicit while the vertical uses implicit. Using implicit time
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differencing allows model to have fine resolution in the surface and bottom of the
ocean by excluding time constraints for the vertical coordinate.
- POM is a free surface and a split time step ocean model. The model provides an
external mode, which is two-dimensional and uses a short time step based on the
Courant-Friedrichs-Lewy (CFL) condition and the external wave velocity. The
internal mode of the model is three-dimensional which uses a long time step based on
CFL the condition and the internal wave velocity.
- Complete thermodynamics have been embedded in the model.
The current source code of the model is called pom08.f, released on 04/18/2008. The program is coded in standard FORTRAN 77 and is available at http://www.aos.princeton
.edu/WWWPUBLIC/PROFS/waddownload.html. Output of the program is in netCDF format. More information of POM can be found at POM’s official website
(http://www.aos.princeton.edu/WWWPUBLIC/htdocs.pom/index.html).
3.5.2.3 MIT General Circulation Model (MITgcm)
MITgcm is a numerical model designed for modeling and studying atmospheric and oceanographic phenomena. The model includes non-hydrostatic formulation to simulate fluid phenomena. Fluid isomorphisms is embedded which enables the model to simulate flow in both the atmosphere and ocean using only one hydrodynamical kernel. Key characteristics of MITgcm can be outlined as follows:
- The model uses one hydrodynamical kernel which can be used to study both
atmospheric and oceanic phenomena.
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- The model includes a non-hydrostatic formulation, used to study wide-range of scale
for fluid phenomena.
- The model is implemented on finite volume discretization. It is an intuitive
discretization, capable of handling irregular geometries by employing orthogonal
curvilinear grids and shaved cells.
- The model is designed to work efficiently on multi platforms.
MITgcm’s source code is available online with free access. The software architecture implemented in MITgcm is called the WRAPPER (Wrappable Application Parallel
Programming Environment Resource). All written numerical and support code in
MITgcm is follows certain rules and conventions to be compatible with the WRAPPER infrastructure. Figure 3.9 illustrates how the WRAPPER interprets code from different architectures of operating systems and hardware platforms.
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Figure 3.9 Illustration of WRAPPER components; the WRAPPER works like an interpreter for input code to be utilized in several hardware and programming environments (MITgcm, 2010)
More information of MITgcm can be found in online documentation which gives an expansive description of the based equations utilized in the model, the numerical algorithms, tutorials and program codes used in the model. The online document of the latest version can be found at http://mitgcm.org/public/r2_manual/latest/.
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CHAPTER 4 – REVIEW OF TIDE-COORDINATED
SHORELINE
In this chapter, methods for achieving tide-coordinated shoreline in the U.S. are presented, starting from the early works of U.S. Coast and Geodetic Survey to the modern approaches. Review of recent tide-coordinated shoreline studies regarding results and efficiencies, including possibilities for further development, are also discussed.
4.1 U.S. tide-coordinated shoreline
The beginning era of the United States Coast and Geodetic Survey can be dated back to the early 19th century when only 16 states, and some interior territories, comprised the
Republic. At that time, inland transportation was difficult, so offshore freighting was the preferred method to perform commerce between the states and for international trading.
However, shipwrecks were typical as the result of the lack of decent charts. Although there were some charts and guides for mariners, they were poor in quality and incomplete. Hence, it increased the cost of products and insurance rates, which were not encouraging economic indications for a growing country (Wraight and Roberts, 1957).
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The first real coastal surveys were ordered in 1806 by the United States government for
North Carolina and portions of Louisiana. On February 10, 1807, the U.S. Congress, with the lead of President Thomas Jefferson, started a resolution for a “Survey of the Coast”.
The act of 1807 resulted in an initiation of surveys of the coasts of the United States including islands and shoals within twenty leagues (roughly 60 miles) from the shores of the United States. The act was the beginning of the Coast and Geodetic Survey
(Shalowitz, 1964). The Coast and Geodetic Survey was combined into a part of the
Environmental Sciences Services Administration (ESSA) from 1965 to 1970, which later reorganized into the National Oceanic and Atmospheric Administration (NOAA) in 1970
(NOAA, 2010b).
At that time, ground survey using analog devices such as plane tables and leveling rods was the only option to perform a coastal survey. The first topographic sheet (T-Sheet
No.1,) derived using plane table, was conducted in 1834 including the northern shore of
Great South Bay, Long Island (Wainwright, 1922). The charts created from plane table mapping have a good accuracy and high detail in topographic features. In 1893, photographic survey using a phototheodolite was introduced (Graham et al., 2003). The instrument has an ability to capture images while measuring horizontal and vertical angles.
The standard method to perform coastal surveying was shifted to aerial photogrammetry in 1927 (Graham et al., 2003; Li et al., 2002; Smith, 1981). The pilot mission of aerial photogrammetric survey was executed in 1919 as an experimental mission in Atlantic,
New Jersey (Graham, Sault, and Bailey 2003). There were advantages to conducting coastal surveys using aerial photogrammetry over the conventional ground survey using a
56 plane table, as aerial photogrammetric surveying enables surveying of large areas in a short time, while maintaining good accuracy. Guidelines for achieving good quality images were established by NGS for tide-coordinated shoreline interpretation from aerial photogrammetric surveys including weather condition, position of the sun, overlapping coverage of images, and camera alignment (Graham et al., 2003). To achieve tide- coordinated shoreline, tide-referenced images are required. The images can be obtained by scheduling flights when tides in an interested area are close to the desired level. Flight schedule planning to match the estimated water level for each section of project area can be done using tide zoning and an estimated period of time which the predicted water level is within a desired tidal datum is called tide window (Hess, 2004).
Nowadays, advances in remote sensing technologies and the global positioning system
(GPS) enable mapping of tide-coordinated shoreline to be accomplished in less time with comparable accuracy. Interferometric Synthetic Aperture Radar (IfSAR/InSAR) and laser altrimetry, or LIDAR (Light Detection And Ranging) are implemented in many studies to generate a Digital Elevation Model (DEM) and extract tidally-referenced shoreline.
These sensor systems have advantages over optical sensors, such as aerial cameras, as their sensor systems are active, creating their own illumination source. Hence, shoreline mapping operations can be performed day or night with more flexibility regarding weather constraints (Graham et al., 2003). Satellite imagery has also been considered to have the potential of producing accurate nautical chart. Although tide coordination is nearly impossible to be performed using satellite imagery, it has potential to provide an efficient and inexpensive solution for shoreline mapping and coastal survey.
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4.2 NOAA’s standard procedure to achieve tide-coordinated shoreline from aerial photogrammetry
NOS provides guidelines currently in use for conducting tide-coordinated photogrammetry operation. As discussed earlier, shorelines appearing in nautical charts cannot be obtained at any arbitrary water level. Aerial photogrammetric surveys or other type of surveys to map shoreline must be performed at the known stage of tide when water level is within the tolerance limits (Hess, 2004; Graham et al, 2003). Generally, the tolerance is between ±0.3 feet (0.091 meters) for coasts where water levels show less than or equal to 5 feet (1.5 meters) of tidal ranges and within 10% of the tidal range for coasts that exhibit more than 5 feet of tidal ranges (Graham et al., 2003).
From aerial photographs, shorelines were originally extracted using analog instruments before the change to analytical instruments. Analytical photogrammetry is based on mathematical computation of measurements on aerial images and calibration information to create a mathematical stereo model (Thompson, 1966). Using such methods, systematic errors such as lens/film distortions, and atmospheric refraction can be minimized. Owing to advances in computer technology, the working environment of aerial photogrammetry evolved to the digital photogrammetric workstation, currently comprised of personal computers running softcopy photogrammetry software (Graham et al., 2003).
In general, aerial photogrammetry for shoreline mapping may consist of two phases. The first phase of the project includes design and planning, acquisition, and data processing.
The second phase contains aerotriangulation, feature compilation and final review. There
58 are differences in requirements between coastal survey and topographic survey utilizing aerial photogrammetry. NOAA specifies procedures and requirements for shoreline mapping in “SCOPE OF WORK FOR SHORELINE MAPPING (V. 13B, 2008)” (scope of work 2008 manual), which is published as a reference to support NOAA’s nautical chart production. The following sections summarize key elements applying to both film and digital aerial photogrammetric survey for shoreline mapping.
4.2.1 Project design and planning
The project design and planning phase includes flight planning, weather and visibility determination, tide coordination planning, and planning for photo control. Flight planning should consider overlap and sidelap, coverage area, and flying height. For aerial photography, sidelap should be more than 30 percent and overlap (endlap) should be more than 60 percent (Leigh and Hale, 2008).
4.2.1.1 Tide coordination
Tide coordination is an important element which distinguishes coastal survey from generic aerial surveys. Tide coordination in aerial images is crucial, especially if those images are used in nautical chart production. Generally, establishing tide observation stations, in addition to the existing NOS tide stations in the surveying region, used to be the practical way to perform tide-controlled photogrammetry. Due to budgetary concerns, installing additional tide stations was replaced by scheduling operation periods based on
59 the prediction of tides from tide tables (Hess, 2004; Graham et al., 2003). The period of time when water level falls within the tolerance (±0.3 feet) of the desired tidal datum
(MHW or MLLW) is called the tide window (Hess, 2004). The flight crew is expected to be acquiring images over the region at that period of time.
Figure 4.1 Tide windows (Hess, 2004)
Tide zoning is used to determine water level for the areas between tide stations. The method divides a region into zones and assumes each area has a fixed phase and magnitude of water level with respect to water level observation of a close tide station
(Hess, 2004). Estimating water level for a region distant from tide stations can be done using discrete tide zoning, Tidal Constituent and Residual Interpolation (TCARI), and ocean circulation model (Hess, 2004). The National Ocean Service (NOS) is currently utilizing primarily discrete tidal zoning method (National Ocean Service, 2010). The method defines a new zone for every change in tidal mean range of at least 0.06 meters and every 0.3 hours of tidal time progression (time lag) as a minimum requirement
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(National Ocean Service, 2010). The total water level, h, in any tidal zone, i, can be determined as the follow (Hess, 2004):
͜$ʚͨʛ Ɣ $% ͜%ʚͨ Ǝ $% ʛ (4.1)
Where ͜% is amplitude of water level over MLLW datum where the tide station is in the zone ( j); $% is a range factor between zone i and j; and $% is the time difference (time lag) between zone i and j.
Figure 4.2 Tide zones in lower Chesapeake Bay; Τ is time lag in minutes and ρ is range factor (Hess, 2004)
The Tidal Constituent and Residual Interpolation or TCARI model was designed by Dr.
Kurt Hess of the NOS Office of Coast Survey (OCS) to spatially interpolate tidal datums,
61 harmonic constituents, and residual of the difference between predicted and observed water levels (Cisternelli et al., 2007; Hess, 2004; National Ocean Service, 2010). The model utilizes astronomical tide prediction, harmonic analysis, and spatial interpolation requiring information from tide stations during the operation and also information from historical tide data (Hess et al., 2004). The model should work best for areas in where quality tidal data exists provided by many tide stations (National Ocean Service, 2010).
The method was specifically developed for the interpolation of the area where land is separated from the nearby tide stations (Hess, 2004). The basic form of TCARI model is
(Hess et al., 2004):
ℎ = − + ͤ (4.2) where is the astronomical tide; is the residual of non-tidal effect; and is the ͂ ͤ difference between MSL and MLLW. Detailed information on the TCARI model can be ͂ found in Hess et al. (2004). Cisternelli et al. (2007) compared generated tide curves from the discrete tidal zoning method and the TCARI model with an established tide station at
Rappahannock Light in Chesapeake Bay. The result showed that TCARI performed slightly better than the discrete tidal zoning method, which may conclude that the interpolation of residual used in TCARI can reflect the non-tidal effect better than the extrapolation from one tide station used in the discrete tidal zoning method.
In NGS’ Coastal Mapping Program (CMP), either TCARI grids or discrete zoning are requested from CO-OPS for which is available. However, TCARI is preferable, since it allows utilizing Pydro to perform robust tide planning (personal communication, Dr.
Christopher Parrish, NOAA National Geodetic Survey, 2010). Pydro is a software
62 package developed by NOAA for integrating various data sources in several file formats into one georeferenced interface. Marking, noting, and attributing are supported in the software to facilitate decision making and reporting (NOAA, 2010c).
Other methods such as numerical circulation modeling can also be used to provide tide correction information. However, the approach requires extensive calculation time
(months to years) to yield acceptable results, unlike TCARI or discrete tidal zoning approaches which generally perform faster (on the order of months) (Hess, 2004; Hess et al., 2004).
Finally, a map of flight times for the planning purposes can be created as the result of combining information of tidal zones and predicted tide windows.
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Figure 4.3 Flight time for April 24, 2002, in lower Chesapeake Bay (Hess, 2004).
However, for some regions where tidal ranges are small or influence from non-tidal factors make prediction of tide inapplicable or inaccurate, the way to acquire tide- coordinated images, within tolerance, is to conduct real-time monitoring of water levels at tide stations. There are two approaches to perform tide-coordinated photogrammetry in these certain areas (Leigh and Hale, 2008):
1. Real-time physical monitoring: One or more tide stations in the surveying area are
observed during the time of flight operation. This approach requires a member/
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members of the surveying crew to be positioned at the tide station and keep
contact with the flight crew via cell phone or radio. The tide monitoring crew will
continuously inform the flight crew the real-time water level and when to start
and stop taking images.
2. Real-time online monitoring of NOAA tide station: Water level and additional
information observed at tide station can be monitored online at NOAA CO-OPS
website. Flight crew can use internet connection to obtain real-time stage of the
tide and determine when to start and stop taking images. Tidal information from
tide stations are provided by NOAA’s CO-OPS website: http://tidesandcurrents.
noaa.gov/.
4.2.1.2 Flight conditions
NOAA has provided requirements for flight condition for shoreline mapping in attachment C of Scope Of Work (SOW) 2008, version 13 B. The SOW specifies constraints regarding weather, solar altitude, and time of year to obtain good quality aerial images which can be summarized as follows (Leigh and Hale, 2008):
- There shall be no clouds or shadow of clouds appearing in the images.
- High and thin overcast clouds over the flying height are allowed if they do not
degrade image quality.
- Black and white panchromatic films must not be acquired in a solid overcast
condition.
- The area of survey must not be covered by smoke, haze, water, snow, ice sleet,
etc. during the flight.
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- The minimum requirement for visibility during the time of image acquisition is 10
miles. The level of visibility is indicated by looking at an object in the direction to
the sun. It is a distance at which a tree crown can be clearly identified.
- The angle of the sun should be at least 30 degrees above the horizon and ideally
between 40 and 60 degrees above the horizon. In mountainous area or areas, with
steep terrain and tall trees, the minimum angle of the sun must be raised to avoid
overlaying shadows on the surveying area.
- Bright spots should be kept at minimum and removed if applicable. These
reflected spots can interfere with important features in the images.
The purpose of these requirements is to obtain good quality of aerial images which facilitates object identification and shoreline delineation for the post-flight process.
4.2.1.3 Ground photo control
Ground photo control is used to determine the mathematical relationship between photo coordinate system and ground coordinate system. Ground photo control may be categorized into ground control points and check points. Ground control points are used in aerotriangulation to establish the relationship, while check points must not be used in aerotriangulation but serve as independent accuracy check for solution from aerotriangulation. Four or more check points are required for NOAA’s shoreline mapping projects utilizing either film or digital photogrammetry and the number of ground control points depends on the size of the project. Adequate number and good spatial distribution of ground control points are, nevertheless, required for NOAA’s shoreline mapping
66 project (Leigh and Hale, 2008). For more detailed information on photo control planning, the SOW suggests Manual of Photogrammetry, Fifth Edition (McGlone, 2004).
4.2.2 Field operations
Field operations consist of two elements: photographic operation and ground survey operation. NOAA has specified several regulations to ensure accurate products from aerial photogrammetric survey in the coastal area. Some of the requirements which relate to aerial surveys may include 3D positioning of an aircraft, ground base stations, and camera orientation specifications. Other requirements for surveying equipment and calibration regarding cameras, surveying instruments and aircrafts can also be found in the scope of work manual from NOAA. The following summarizes requirements for field operations from the manual.
Image exposure requirements consist of specifications and allowable tolerances for tilt, crab, overlap, and sidelap of exposures as the followings (Leigh and Hale, 2008):
- The tilt of a camera at an instant of any exposure must not exceed ± 3 degrees and
the average tilt for all aerial images in the project must not exceed ± 1 degree.
- An airplane crab shall be compensated by the camera which must not exceed ± 5
degrees for resulting error. The error is determined from an average line of the
flight. Any two consecutive image exposures must also not exceed ± 5 degrees.
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- The overlap of any successive exposures should be 60 percent with + 5 degrees to
– 2 degrees for allowable tolerance. 80 percent overlap should be used when
taking images over open water or rugged terrain areas.
- Sidelap between any consecutive and connected flight line should be 30 percent.
Airborne positioning using Kinematic GPS (KGPS) techniques during the flight are required for all exposures in the project.
- An accuracy of measured offset between the nodal point of aerial camera and the
phase center of GPS antenna must be within ± 0.02 meters.
- The overall accuracy of 3D position of camera’s nodal point determined from the
GPS system must not exceed 0.5 meters relative to the National Spatial Reference
System (NSRS).
- KGPS configuration must be set to receive both L1 and L2 frequencies with a
data collection rate of 1 second or better.
- The maximum Position Dilution Of Precision (PDOP) must be less than 7 for all
GPS data collected during the mission.
- If the Inertial Measurement Unit (IMU) is used, the accuracy of absolute
orientation must be within 25 arc-seconds.
- Minimum number of two dual-frequency GPS ground receivers within 200
kilometers from the aircraft during the flight must be achieved to support KGPS
airborne positioning. Moreover, at least one ground station must be within 100
kilometers from the aircraft during the flight.
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- The National Continuously Operating Reference Stations (CORS) should be
utilized as a ground station, if possible. The aircraft should fly over CORS if it is
used as a ground station to improve GPS positioning accuracy.
- The ground stations should be on the different side of the project area and the
distance between ground stations must not be less than 50 kilometers.
Ground surveying for photo control points can be accomplished using GPS or conventional survey, with the result of 0.1 and 0.2 meters in horizontal accuracy and vertical accuracy, respectively (Leigh and Hale, 2008). If the GPS survey is utilized, measuring positions should be performed by linking to CORS. Ground surveying should be referenced to NSRS for both horizontal and vertical positions. At least two NSRS stations, which are 50 kilometers or further apart, must be tied to via the connections to
CORS in the project. For more detail about ground surveying procedures and requirements, see attachment P of the scope of work manual.
4.2.3 Data processing
Data processing deals with verification, reduction, and manipulation of data from GPS,
IMU, tide station, ground survey, etc. Several restrictions for data processing from each method have been defined in the SOW. Following are examples of instructions for data processing (Leigh and Hale, 2008):
- The data processing software for ground control survey must be NGS approved
software.
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- On-line Positioning User Service (OPUS: http://www.ngs.noaa.gov/OPUS/)
should be used if CORS data is used. Standard techniques including adjustment
computation are required for non-CORS survey.
- Verified water level data from NOAA tide stations is required to determine if the
acquired aerial image can be accepted by comparing the verified water level with
the predicted water level. In general, verified water level data is available within 1
month for primary and subordinate tide stations at CO-OPS web site.
4.2.4 Aerotriangulation
Aerotriangulation processes are required for all aerial photographs, including color and
B&W images used in NOAA’s shoreline mapping projects (Leigh and Hale, 2008).
Aerotriangulation, or block adjustment, combines processes of spatial intersection and resection of conjugate rays of image points, each point indicated in two or more overlapping images, to simultaneously define object space coordinates of those points and orientation parameters of aerial images (USACE, 2002, Mikhail et al., 2001). The process extends a sparse network of ground surveyed horizontal and vertical control to the unknown ground points over a large block of aerial photographs utilizing mathematical models (USACE, 2002; Leigh and Hale, 2008). Bundle block adjustment is the preferred method of triangulation, since it yields accurate and flexible solution which allows integrations of supplementary navigational or geometric information (Mikhail et al., 2001). Details on bundle adjustment and its computational algorithms can be found in
Chapter 5 of Mikhail et al. (2001). Performing aerotriangulation for aerial images in
70 coastal survey project has several issues associated with shoreline mapping, as pointed out in the SOW. Following summarizes issues for aerotriangulation in shoreline mapping:
- Flight lines for shoreline mapping projects are normally designed to follow the
curves of the coasts by having several short strips parallel to the shorelines. Each
small segment is intersected with the others at both ends, for which the angle may
be as steep as 20 degrees.
- Since most areas in aerial images may be covered by water, proper areas to
measure ground points may be difficult to find. Moreover, some coastal land
regions, such as Alaska, may be covered by thick forest. Therefore, well
distributed tie points may be impossible to achieve and results in a poor solution
from aerotriangulation
- Sun glare from water may cause an overall underexposed image. It is a result
from overcompensating camera exposure over high intensity areas, making other
areas of an image dark and difficult to measure ground points. Hence, sun angle
determination is necessary to avoid such issue and has been specified as a
requirement in flight planning for shoreline mapping projects.
The aerotriangulation solution is required to be assessed for accuracy. The horizontal accuracy of final block adjustment must not exceed half of the allowable final accuracy of the project at a confidence interval of 95% (Leigh and Hale, 2008).
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4.2.5 Feature compilation
Feature compilation proceeds from aerotriangulated imagery, using either digital or high quality analytical or softcopy photogrammetric systems. Several factors need to be considered during the compilation process, such as level of detail for features relating to map scale, tide-coordination area, and vertical and horizontal accuracy requirement.
General guidelines of feature compilation are specified in the scope of work 2008 manual, which can be summarized as follows (Leigh and Hale, 2008):
- Fixed and permanent features visible in the aerial images, such as man-made and
natural shorelines, port infrastructures, and landmarks, need to be compiled.
These features facilitate marine navigation and usually appear on NOAA’s
nautical charts.
- However, features in restricted areas such as a landward area within active
military reservation must not be compiled. Exemplification may be made for
some mapping projects.
- The compilation should be performed within neat limits of a stereo model. The
neat limits define a rectangular area between consecutive principle points
expanding to the half of each sidelap area.
- Features outside the fiducial marks area should not be compiled for film aerial
images, since the error from image distortion is generally high at the edge of the
stereomodel.
- Coastal Cartographic Object Attribute Source Table (C-COAST) must be used for
feature attribution. For more detail of C-COAST, please refer to attachment F in
the SOW.
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- The compilation scale in any portion of project area is required to be two times
the chart scale that includes the compilation portion. However, the compilation
scale must be within 1:20,000 to 1:2,500.
Since shoreline is one of the most important features in coastal mapping, there are numerous guidelines and restrictions specifically defined for shoreline delineation in the
SOW. These requirements and procedure guidelines are to aid compiler achieving good quality shoreline data. In general, there are also several issues associated with the dynamic nature of the shoreline in the shoreline delineation process. The most important which the compiler needs to keep in mind, is that the delineated shoreline must never be broken or forked into two lines and two shorelines cannot be merged into one (Leigh and
Hale, 2008). This is to maintain clean topology of shoreline data. When exact position of shoreline cannot be determined, “Approximate” modifier needs to be added into shoreline attribute. This may due to overcastting shadows or obscurity from overhanging cliffs, bridges, and tall building. The use of approximate shoreline should be minimized.
Moreover, in order to delineate tide-coordinated shoreline (MHW, MLLW), information on the actual tide stage and water level is important. Ideally, tide-coordinated shoreline is delineated from the virtual intersection line of land and water level at exact desire tide level. However, shorelines in aerial images cannot typically be easy to identify and water level often times is influenced by the wave action and differences in predicted and actual tide (Leigh and Hale, 2008). Black and white infrared imagery can alleviate the issue of gradual change or transparent in brightness of land/water intersection in aerial images due to shallow water or wave run-up effect which makes the exact position of shoreline hard
73 to be identified. Furthermore, delineating tide coordinated shoreline in the middle of run- up and retreat limits of water line instead of virtual shoreline may minimize effect from wave action which normally causes rapid changes in shoreline position (Leigh and Hale,
2008). Therefore, experience, training, and knowledge are necessary for the compiler to be able to accurately interpret tide-coordinated shoreline.
4.2.6 Project completion
Final review and chart production are then performed after the compilation of features.
Final review may consist of followings actions (Leigh and Hale, 2008):
- Check all manually recorded data
- Compare the compilation data with aerial photographs and largest scale nautical
charts in the same coverage area
- Review reports and documents created in the project
The purpose of project review is to evaluate completeness and accuracy of the products in the project prior to the production of nautical chart. These procedures are similar to those in topographic aerial survey and do not have specific requirements from NOAA for shoreline mapping project.
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4.3 New technologies implemented in NOAA’s shoreline mapping
Currently, NGS is experimenting with new technologies and new methodologies to map the shoreline using three airborne sensor technologies: InSAR, LIDAR, and hyperspectral imaging, including AVIRIS (Airborne Visible and Infrared Imaging System) (NGS,
2010). Among the technologies which have been investigated, LIDAR holds much potential and promise for collecting accurate elevation model data (White, 2007; Woolard et al., 2003) and has been applied in several shoreline mapping research. LIDAR is an active remote sensing technology which implements laser ranging combined with onboard GPS and inertial measurement unit (IMU) to generate high-resolution digital elevation model data (Parrish et al 2004). NOAA’s NGS and several collaborative partners have been developing standard procedures for shoreline mapping utilizing
LIDAR for the past decades (White et al., 2010).
Advantages of utilizing LIDAR over aerial photogrammetry include increased flexibility in data acquisition and a more automated shoreline extraction process. In general, LIDAR system is not limited to light conditions and sun’s angle restriction. Clouds are permitted during the flight if they are above an aircraft. Tide-coordination and 8 miles of visibility are still required for NOAA shoreline mapping (Leigh and Hale, 2008). Water level, at much lower than the referenced tidal datum, is preferred for LIDAR shoreline mapping using topographic LIDAR systems, since it provides good continuation of LIDAR data across the shoreline being mapped. However, topo/bathy LIDAR system, such as USGS
EAARL, can overcome such problems as they are able to map both submerged and exposed land across the intertidal zone (personal communication, Dr. Christopher
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Parrish, NOAA National Geodetic Survey, 2010). Therefore, tide windows for LIDAR acquisition are broader than tide windows in aerial photogrammetry.
Currently, NOAA has begun to utilize LIDAR to map MHW shorelines, and with the combination of aerial photogrammetry to map MLLW shorelines for national shoreline program (White et al., 2010). For MHW shoreline mapping using LIDAR system, orthomosaics from digital photogrammetry at MHW are still required for assigning feature attributes. Several requirements for utilizing LIDAR system in shoreline mapping have been specified by NOAA to ensure good quality of LIDAR data, utilized to extract
MHW shoreline. For flight planning, the overlap of flight lines must not be less than 25% and flying height during data acquisition must not result in more than 1 meter of point spacing for LIDAR data (White et al., 2010; Leigh and Hale, 2008). Both L1 and L2 carrier phases with one second collection interval, and PDOP/VDOP less than 3 are required during acquisition period to yield good positioning accuracy of LIDAR sensor from KGPS survey (Leigh and Hale, 2008). More details on requirements for LIDAR survey can be found in Appendix Y of the scope of work manual 2008. LAS (ASPRS
LIDAR data format standard) data from LIDAR survey are then cleaned by removing outliers and noise (White et al., 2010).
VDatum (vertical datum transformation tool) is another essential component for LIDAR shoreline mapping. NOAA developed a tool called VDatum (vertical datum transformation tool) for vertical datum transformation. Hydrodynamic model is necessary for VDatum to forming relationship between vertical datums (Graham, Sault, and Bailey
2003). VDatum was first applied in NOS/NOAA and USGS’s joint demonstration project in Tampa Bay using a version of Princeton Ocean Model (POM) as a hydrodynamic
76 model (Parker, 2003). VDatum was also implemented in NOAA’s tested project in
Shilshole Bay, Washington to combine topographic and hydrographic LIDAR data
(Woolard et al. 2003).
In NOAA shoreline mapping procedure, VDatum is implemented to transform LIDAR data from ellipsoidal datum (NAD83: CORS96) from onboard GPS to tidal datum
(MHW), used to extract tide-coordinated shoreline (White et al., 2010). LIDAR data is first transformed to orthometric heights (NAVD88) using hybrid geoid model GEOID09 and then to local mean sea level height from the use of modeled TSS (Topography of the
Sea Surface) grids. LIDAR data is then finally transformed to MHW tidal datum using hydrodynamic circulation models and TCARI method (White et al., 2010).
After LIDAR data is transformed to MHW datum, a Triangular Irregular Network (TIN) is created using an excursion filter for the limit of 3 meters for any side of a triangle
(White, 2007; White et al., 2010). A regular grid of DEM consequently is generated from
TIN through Delaunay triangulation techniques using planar interpolation (White, 2007).
Finally, MHW shoreline is extracted utilizing linear interpolation and is attributed by comparing with orthomosaics and aerial imagery (White et al., 2010).
Similar methods to derive tide-coordinated shoreline using LIDAR system may be found in Stockdon et al. (2002), Robertson et al. (2004), and Liu et al. (2007), which are discussed later in 4.5.2.
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4.4 Tide-coordinated shoreline research at the Ohio State University
Interest in coastal and shoreline mapping has been going on for more than a decade in
Mapping and GIS Laboratory of Department of Civil and Environmental Engineering and
Geodetic Science at the Ohio State University. Many investigations, including conference papers, journal publications, Master’s theses, and Ph.D. dissertations have been related to shoreline mapping with an aim toward achieving tide-coordinated shoreline. In general, concepts and frameworks of shoreline research may be divided into two approaches: utilization of digital models, and shoreline extraction from aerial/satellite imagery. The first approach deals with implementation of different data source to generate digital models, such as Coastal Terrain Model (CTM) and Water Surface Model (WSM), and development of methods to improve digital model accuracy. Similarly, research of shoreline extraction from imagery includes improving accuracy of shoreline extracted from aerial/satellite imagery and development of automatic approaches to extract shoreline.
4.4.1 Digital tide-coordinated shoreline
Li et al. (2002) introduced digital tide-coordinated shoreline (DTS), conceptualizing the two approaches to obtain tide-coordinated shoreline mentioned above. Compared to the conventional method of deriving tide-coordinated shoreline from aerial photogrammetry by directly delineating shorelines from tide-coordinated aerial images, digital tide- coordinated shoreline can be achieved from a set of instantaneous shorelines or the intersection of CTM and WSM. Digital tide-coordinated shoreline from instantaneous
78 shoreline approach utilizes the relationship between tide-coordinated shoreline and instantaneous shoreline. Theoretically, since instantaneous shoreline is a line of intersection between land and water at an instance of time, it carries information from the contour shape of terrain (beach) at a certain water level and a certain time. Hence, with a number of instantaneous shorelines, the dynamic nature of shoreline may be modeled into the form of mathematical functions and then tide-coordinated shoreline can be derived.
Figure 4.4 Instantaneous shorelines at different time and tide-coordinated shoreline (Li et al., 2002)
From figure 4.4, by introducing piece-wise polynomials, general functions of shoreline
̀ʚ͒, ͓, ͔ʛ at a particular piece can be can be decomposed by (Li et al., 2002):
͒ Ɣ ̀ʚ͕ͤ, ͕ͥ, ͕ͦ, … , ͕), ͧ, ͨʛ
(4.3) ͓ Ɣ ̀ʚ͖ͤ, ͖ͥ, ͖ͦ, … , ͖), ͧ, ͨʛ
͔ Ɣ ̀ʚ͗ͤ, ͗ͥ, ͗ͦ, … , ͗), ͧ, ͨʛ
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Where t is time variable, s is length parameter which starts at the beginning of shoreline, and are temporal polynomial coefficients which can be expressed ͤ ℎ , ͥ ℎ ,…, )(ℎ) as polynomials function at water level h by (Li et al., 2002): ͕ ʚ ʛ ͕ ʚ ʛ ͗
ͤ ͤ ͤ ͦ ͤ = ͤ + ͥ ℎ + ͦ ℎ +. ..
ͤ ͤ ͤ ͦ (4.4) ͕ͤ = ͤ + ͥ ℎ + ͦ ℎ + ⋯
ͤ ͤ ͤ ͦ ͖ͤ = ͤ + ͥ ℎ + ͦ ℎ + ⋯
However, the general shoreline function may vary depending on characteristics of the coastal area. Coefficients from polynomial orders may also differ according to shoreline topography. The consistent criteria for splitting shoreline into pieces needs to be examined in order to robustly parameterize polynomial coefficients. From instantaneous shoreline position (X, Y, Z) with observed water level from gauge station at time t, the polynomial coefficients can then be estimated by least square adjustment. This method of generating tide-coordinated shoreline is expected to yield better result than simplified models such as End-Point Rate (EPR) method or Linear Regression (LR) method.
Another approach is to utilize digital models of CTM and WSM to generate tide- coordinated shoreline. The CTM is the narrow zone along shore which consists of coast and near-shore bathymetry. CTM may be obtained by merging topographic data from aerial/satellite stereo imagery, LIDAR data, and bathymetric data. This process requires georeferencing and has to be done on the same horizontal and vertical reference system.
The WSM, representing the surface of water, can be produced using hydrodynamic
80 models as discussed in chapter 3. Digital tide-coordinated shoreline can then be achieved from the intersection of CTM and WSM.
Figure 4.5 Digital tide-coordinated shoreline from CTM and WSM process flowchart (Li et al., 2002)
Figure 4.5 represents the workflow to generate digital tide-coordinated shoreline utilizing CTM and WSM, conducted in the study. After merging topographic data,
LIDAR data, and near-shore bathymetric data, refining of the resulting CTM was performed, and the subtraction of WSM from CTM was made. The resulting shoreline of the digital model subtraction, represented by grid points with a value of 0, was smoothed and spikes/small shoreline segments were removed. A thematic image of land and water interaction was created by performing a classification of the differential values of the elevation and bathymetry and delineateding resulting grid points into the image. A clump image to group land, water, and land-water interaction areas was then created and refined to assist in shoreline identification. Finally, the shoreline in the raster image was
81 vectorized and manual inspection/editing was performed to create a vector shoreline that can be implemented in other applications.
The second approach (DTS from CTM and WSM) was researched to examine the potential of the resulting digital tide-coordinated shoreline. The study was performed on a
11 km long section of Lake Erie shore. A digital terrain model was created from NOAA’s aerial stereo tide-coordinated images at MLLW and combined with bathymetry from
Ohio Department of Natural Resources (ODNR) to generate a CTM. A WSM was obtained utilizing Great Lakes Forecasting System (GLFS) (Bedford and Schwab, 1991).
However, the implemented WSM did not represent MLLW, so the achieved shorelines were not tide-coordinated shoreline. To estimate the accuracy of the digital tide- coordinated shoreline, both accuracies of CTM and WSM are required to be considered.
The digital terrain model from aerial stereo image had an accuracy of 2.1 meters and the bathymetric data yielded an accuracy of about 40 meters. The study combined the overlapping data using normalized weight of 2/3 for digital terrain model and 1/3 for bathymetric data. Thus, CTM accuracy of the study ranges from 2.1 to 13.4 meters depending on the area where the elevation data was generated. Integrating LIDAR data is expected to improve overall accuracy of the CTM as some bathy or topo/bathy LIDAR systems has ability to penetrate shallow water and yields better accuracy than the utilized bathymetric data. The digital shoreline obtained from this method was estimated to have an accuracy of 2-13 meters for standard deviation.
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4.4.2 Review of shoreline mapping research at the Ohio State University
This section discusses researches conducted at the Ohio State University, related to shoreline mapping and methods toward obtaining of tide-coordinated shoreline.
4.4.2.1 Instantaneous shoreline from aerial and satellite imagery
Shoreline mapping from high accuracy satellite images has been examined in many studies here at OSU to exploit the full potential of satellite imagery. Methods to increase the geopositioning accuracy of IKONOS Geo stereo images by improving the Rational
Function (RF) of the sensor model has been developed and discussed in Di et al. (2001,
2003c), Li et al. (2003), and Wang et al. (2005). The studies examined Rational
Functions provided with IKONOS satellite images, instead of rigorous sensor models.
The difference between rigorous sensor model and Rational Function is the rigorous sensor model is a physical model that expresses the geometry of an image, while RF takes form of ratio of two polynomials to transform points between image and object spaces. An example of rigorous sensor model that is frequently used to perform transformation between image and object spaces are collinearity equations, composed of interior and exterior orientation parameters. Rational Functions can be categorized into upward RF and downward RF. A general form of upward RF can be expressed as followings (Di et al., 2001):
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, , = ͥ , , ͦ (4.5) ͊ ʚ͒ ͓ ͔ʛ ͬ ͊ ʚ͒, ͓, ͔ʛ = ͧ ͨ , , ͊ ʚ͒ ͓ ͔ʛ ͭ While the downward RF which is the inverse͊ ʚ ͒form͓ ͔ canʛ be shown as (Di et al., 2001):
, , = ͥ ͦ( , , ) (4.6) ͊ ʚͬ ͭ ͔ʛ ͒ ͊ ͬ, ͭ, ͔ = ͧ ͨ , , ͊ ʚͬ ͭ ͔ʛ ͓ The ratios perform transformation between͊ ʚ ͬ X,ͭ Y,͔ʛ and Z ground coordinates and x, y image coordinates. Noted that both image and ground coordinates are normalized to [-1,
1]. The studies used third order polynomials for P(X,Y,Z), producing 20 coefficients for each polynomial. Example of third order upward RF is as the follows (Li et al., 2003):
ͦ ͦ , , = ͤ + ͥ + ͦ + ͧ + ͨ + ͩ + ͪ + ͫ + ͬ + ͦ ͧ ͦ ͦ ͦ ͦ ͧ ͭ + ͥͤ + ͥͥ + ͥͦ + ͥͧ + ͥͨ + ͥͩ + ͥͪ + ͊ʚ͒ ͓ͦ ͔ʛ ͕ ͦ ͕ ͒ ͕ͧ ͓ ͕ ͔ ͕ ͒ ͕ ͓͒ ͕ ͔͒ ͕ ͓ ͕ ͓͔ ͥͫ + ͥͬ + ͥͭ (4.7) ͕ ͔ ͕ ͒ ͕ ͒ ͓ ͕ ͒ ͔ ͕ ͓͒ ͕ ͓͔͒ ͕ ͔͒ ͕ ͓ Di et al. (2001) experimented on the performance of upward and downward RFs with ͕ ͓ ͔ ͕ ͓͔ ͕ ͔ simulated IKONOS images, and the result showed that upward RF gives a very slight advantage over downward RF. The differences between upward and downward RFs are smaller than 10 ͯͫ for positional RMS errors. The study also implied that RFs can be
84 accurately implemented with the proper polynomial order when no rigorous sensor model available.
Li et al. (2003) applied two methods to improve the accuracy of ground coordinates using
RFs: refinement of vendor provided RF and refinement of ground coordinates from vendor provided RF. The first method improves RFs provided with IKONOS images using numerous ground control points. Since errors of ground coordinates computed from provided RFs indicate a systematic error, refinement of provided RFs can be done only once and the resulting RF corrections can be applied to other products that use the same set of images. The systematic error appears mostly in the West-East direction as shown in
Figure 4.6. The method requires a large number of ground control points (GCPs) in order to solve for the coefficients existing in a total of 78 unknowns. Therefore, at least 39 ground control points for each image are needed. However, there were 10 GPS control points available so bundle adjustment of NGS aerial stereo images was performed to the selected tie points. Ground coordinates of 57 tie points were calculated and used as the control points to solve RF coefficients. 52 control points were selected for each IKONOS image and the remaining 5 were used as check points. Refined RF coefficients were then computed using least-squares adjustment using provided RF coefficients as initial values to speed up the computation. Once refined RF coefficients were obtained, ground coordinates of checkpoints were estimated utilizing the refined coefficients and compared with coordinates from the bundle adjustment to evaluate the accuracy of the method.
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Figure 4.6 Error distribution of ground coordinates computed from vendor-provide RFs from ground control point with GPS survey (Li et al., 2003)
The second method utilized linear transformation to improve ground coordinates computed from vendor-provided RFs. Although this approach uses fewer GCPs, the refinement process is required for any different output. First, second and third-order of polynomials were studied for the ground coordinate transformation. First order polynomials were the most efficient, as the errors between computed and actual ground coordinates of GCPs appeared to be linearly distributed. In the transformation and accuracy assessment processes, 9 ground control points with 45 check points were used in first stereo pair and 8 ground control points with 49 check points were used in the second stereo pair. Ground coordinates of the check points were estimated from aerial triangulation. The GCPs were used to determine transformation parameters and the
86 checks points were used to compare with coordinates computed from vendor provided
RFs. Table 4.1 shows that both methods can improve positional accuracy to about 2-4 meters from vendor-provided RFs which yielded maximum error in one stereo image pair of up to 16 meters in the study.
RMS errors (m) Method stereo pair X Y Z 1 2.489 4.404 0.746 First 2 1.863 4.124 4.318 1 1.342 1.051 1.632 Second 2 0.991 0.787 1.513
Table 4.1 Accuracy from refinement result of first and second method (Li et al., 2003)
Li et al. (2003) derived 3-D shoreline from RF refined IKONOS stereo images using semi-automatic approach. An image-matching technique was applied to assist in conjugate point matching, a hard operation for low texture objects like a shoreline. In the study, the shoreline was manually digitized using the first image, and area-based matching using normalized correlation coefficients was performed using the second image. Since y-parallaxes of conjugate points in the data set were small, with a maximum value of 3 pixels, the area-based matching method was successfully performed. After the matched conjugate points along the shoreline were detected, the 3-D coordinates of the shoreline were then computed utilizing upward RF, refined by employing GCPs. Figure
4.7 illustrates shoreline with matched points in Sheldon Marsh area, Lake Erie. The method of 3-D shoreline extraction was performed on 1-meter resolution IKONOS panchromatic stereo images.
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Figure 4.7 Matched points along shoreline in Sheldon Marsh (Li et al., 2003)
Di et al. (2003c) performed an automatic approach of shoreline extraction using image processing methods on 1-meter resolution panchromatic and 4-meter resolution multispectral IKONOS stereo images. Mean shift segmentation was applied on satellite images to segment an image into homogeneous regions, where water area was identified and initial shoreline obtained. Once images were segmented, raster images were converted into vector format, each region represented as a polygon. The largest region was identified as a water region and initial shoreline was recognized as a boundary between water region and other regions. Since initial shoreline contained error due to influence of trees, jetties, and spray shadows, the refinement procedure of initial shoreline was performed. Adjacent polygons to the initial shoreline were detected and
88 used as candidate polygons to determine refined shoreline. Figure 4.8 shows candidate polygons, adjacent to initial shoreline, and the refined shoreline. Only 10 percent of candidates were manually selected in the refinement process and most portions of the shoreline remained from the initial shoreline. Therefore, the approach to delineate shoreline is semi-automatic with little human interaction. After refined shorelines were obtained from both stereo images, 3D shoreline extraction method from Li et al. (2003) was applied. Accuracy of 3D shoreline was estimated to be 2-3 meters for 1-m
Panchromatic IKONOS images and about 8.5 meters for 4-m multispectral images.
Figure 4.8 Candidate polygons (left column) and refined shoreline (right column) (Di et al., 2003c)
Integration of satellite images from different sources and integration of satellite images with aerial images have been studied in Li et al. (2007, 2008) and Zhou (2007) to
89 improve geopositioning accuracy of ground objects and extracted shorelines. Li et al.
(2007) experimented with the possibility of integrating IKONOS and QuickBird satellite images. Refinement of Rational Functions for IKONOS and QuickBird images was performed and different combinations of integration were studied. A pair of IKONOS
Geo Reference Pro images and a pair of QuickBird Basic images were used in the experiment. Both IKONOS (acquired September, 2003) and QuickBird (acquired July,
2004) satellite images were Panchromatic with an approximate resolution of 1 meter and
0.76 meters respectively. Ground control points and affine transformation (first degree polynomial) were applied to improve results from vendor-provided Rational Functions.
Four ground control points and 16 check points from aerial triangulation were utilized in the refinement process. Combining images from different satellite orbits yields different azimuths and elevation angles for the convergent angle, illustrated in figure 4.9 and the relationship of the convergent angle ( δ), azimuths ( θ), and elevations angles ( α) can be expressed as:
cos = sin ͥ sin ͦ + cos ͥ cos ͦ cos( ͦ − ͥ) (4.8)
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Figure 4.9 Relationship of convergent angle from different azimuths and elevations of satellite images (Li et al., 2007).
Li et al. (2008) further studied multi-source data integration, including aerial images in the experiment. IKONOS and QuickBird satellite images from the previous work (Li et al., 2007) and aerial images, obtained in February 1998 of the same region (south Tampa
Bay, FL), were utilized in the integration. Figure 4.10 illustrates the footprints of different data sources in the study area.
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Figure 4.10 Illustration of image exposures and footprints from different data source (Li et al., 2008)
Eleven GPS-measured GCPs, with a maximum error of 0.014 m, 0.017 m, and 0.028 m for X, Y, and Z direction respectively, were utilized as 5 GCPs and 6 checkpoints in aerial bundle adjustment to determine exterior orientation parameters for each aerial images. Some tie points from the bundle adjustment process were used as check points in
92 the integration process. RFs for aerial images were then estimated to accommodate the integration with satellite images.
Integration of aerial and satellite images was divided in to two approaches. The first approach included all IKONOS and QuickBird stereo images with all 24 aerial images, and the second approach used all satellite images, but with one image or a stereo pair of aerial images. Different combinations of IKONOS, QuickBird, and aerial images were utilized for both approaches. Overall process of satellite and aerial images integration is shown in Figure 4.11.
Figure 4.11 Workflow of the integration process (Li et al., 2008)
Results from the study showed that overall of combinations in first approach are better than combinations in the second approach. Geopositioning accuracy from using only aerial images yielded the best accuracy which is about 0.1 meters and 0.33 meters for horizontal and vertical RMSE. Integration of satellite and aerial images did not provide a
93 better result, and the best combination of satellite and aerial images was using 24 aerial images with stereo pair of IKONOS images or QuickBird images. The 24 aerial images with IKONOS stereo images combination gave 0.132 m, 0.172 m, and 0.385 m for X, Y, and Z RMSE while the combination of QuickBird and aerial images gave 0.248 m, 0.125 m, and 0.346 m for X, Y, and Z RMSE. However, the combination of all aerial images,
IKONOS images, and QuickBird images did not show improvement over aerial images +
QuickBird or aerial images + IKONOS combinations. The result of all data source integration was 0.272 m, 0.191 m, and 0.359 m for X, Y, and Z RMSE.
Extraction of 3-D shoreline was also performed in Li et al. (2008) and Zhou (2007).
Instantaneous shorelines were extracted from IKONOS and QuickBird stereo images.
Vertical accuracies of extracted shorelines were determined by comparison with water level from nearby gauge stations and water-penetrating LIDAR data from NASA’s
EAARL (Experimental Advanced Airborne Research LIDAR) system. The LIDAR bathymetry was overlaid with vector shorelines from IKONOS and QuickBird stereo images as shown in Figure 4.12.
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Figure 4.12 Overlaying of extracted 3D shoreline and LIDAR bathymetry (Li et al.,
2008)
The result showed that the shorelines were virtually well overlaid around -0.3 m and -0.2 m interval. Water level observations from three nearest gauge stations were used to compare the extracted shorelines. Average elevations from the comparison were +0.5 m and -0.2 m, respectively, for shorelines from IKONOS and QuickBird. The study concluded that vertical accuracies of derived shorelines compared well with the two types of coastal data (gauge station and LIDAR) and fell within vertical uncertainty of high resolution satellite imagery.
Lee et al. (2009) integrated LIDAR data with aerial orthophotos to extract shoreline.
Mean-shift algorithm and extended convex hull algorithm (Sampath and Shan, 2007) were utilized for LIDAR point segmentation and boundary tracing. The study did not
95 register the LIDAR data with the orthophotos, because both data were obtained simultaneously. Data used in the mean-shift algorithm were the three-dimensional
LIDAR data (X, Y, Z) position, color information (R, G, B) from orthophotos at the
LIDAR point positions, and calculated point density (PD). Utilizing mean-shift algorithm with the mentioned data enabled classification of points on the ground and on the water surface. Extended convex hull algorithm was then applied in the boundary determination process of the classified points. The result was a shoreline that separated LIDAR points on the ground and points on the water surface.
Lee et al. (2010) integrated satellite imagery and used near-infrared intensity data for classification of LIDAR points. Determination of normal vector direction (ND) and normal vector direction variation (NV) of the LIDAR points were added to facilitate mean-shift LIDAR classification. To perform mean shift segmentation for the LIDAR point cloud, color values from R, G, and B bands from orthophotos, and near-infrared (N-
IR) intensity value form orthophotos and satellite images were allocated to each LIDAR point. Parameter training process was done and mean-shift filtering was applied to
Elevation (Z) and RGB information for LIDAR data to minimize the number of segments. Point density (PD), normal vector direction, and normal vector direction variation was then calculated for each LIDAR point. All parameters (X,Y,Z,R,G,B,N-
IR,PD,ND,NV) were used in segmentation process and the determined parameters from the training process were applied to group the generated segments into land and water groups. Finally, shoreline was obtained as the boundary between land and water segments. Figure 4.13 illustrates workflow of shoreline extraction procedure.
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Figure 4.13 Workflow of shoreline extraction process (Lee et al., 2010)
LIDAR data, aerial orthophotos, and QuickBird satellite images were acquired at different times. The orthophotos used in the study have a resolution of 1 foot with an accuracy of approximately 5 feet. The LIDAR data has horizontal and vertical accuracy of around 1 foot with the average point spacing of about 7 feet. The satellite image has a resolution of about 0.7 meters. Although the datasets were obtained at different periods, near-infrared intensity information from satellite image was utilized in the process, not to increase the accuracy of the outcome, but to make the solution of classification robust.
The extracted shorelines were compared, using the procedure shown in figure 4.14, with the manually digitized shorelines from the orthophotos. The result showed the accuracy of the extracted shoreline is about 1.53 meters RMSE, with maximum error of 8.24 meters, occurring in an area where effects from LIDAR elevation and point density dominated, and included part of a dock in the extracted shoreline. Figure 4.15 illustrates an area where maximum error occurred. Although the approach presented in the study extracts instantaneous shoreline from aerial images or aerial orthophotos with the assistance of LIDAR data and satellite images, shoreline extracted from this approach can
97 be tide-coordinated if the aerial image dataset is acquired at the desired water level
(MHW, MLLW, etc.).
Figure 4.14 Shoreline comparison method (Lee et al., 2010)
Figure 4.15 A small dock in the bluff area where maximum error occurred (Lee et al.,
2010)
Integration of aerial images and LIDAR data was also implemented for bluffline extraction in Liu et al. (2009). Blufflines (bluff top) were derived using ATM (Airborne
Terrain Mapper) LIDAR collected in 1998 and aerial orthoimages collected in 2000.
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Each dataset utilized different methods to indicate bluff top. Blufflines from LIDAR and orthoimages were then matched together and refined 3D blufflines were generated.
For bluffline extraction from LIDAR data, median filtering was first applied to LIDAR data to remove noise, while edges of objects were preserved. Historical shoreline was introduced as a reference for setting up transects along shore and elevation profiles for each transect were determined. Finally, trees and objects which cause rapid changes in slope near bluff top were detected and eliminated before bluff top and bluff toe were identified from the slope profile. Blufflines extracted from LIDAR data comprised of 3D coordinates.
Image processing methods were employed to derive highly accurate 2D blufflines from aerial orthoimages. Mean shift segmentation was first applied to orthoimages to distinguish water body and land. In addition, bluff toe was also separated from the bluff face after the segmentation. A surface reconstruction method was then performed on the segmented image (Kovesi, 2003). The method decreases noise and enhances linear features in the image. Blufflines were obtained by manually connecting linear bluff features detected in the reconstructed image.
To integrate blufflines from different datasets (aerial orthoimage and DEM), Iterative
Closest Point (ICP) algorithm (Besl and McKay, 1992) was implemented to perform registration between the blufflines. Points on blufflines derived from orthoimages were treated as fixed points during the registration while points from LIDAR blufflines were used to refine the initial position with ICP algorithm. Elevations of refined blufflines were obtained from Z coordinates of LIDAR data at the refined points.
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The blufflines from orthoimages and LIDAR data integration were compared with blufflines manually digitized from orthoimages. Z-coordinates of the digitized blufflines were also obtained from LIDAR data. The average differences between refined bluffines and manually digitized blufflines are 1.36 m for bluff top and 0.9 m for bluff toe. In conclusion, the method to derive blufflines integrating multi data sources is capble of producing blufflines which are more accurate than blufflines derived from each data source. Minimal human interaction involved means the method provides efficient solution for coastal applications.
4.4.2.2 Implementation of instantaneous shorelines to derive tide-coordinated shoreline
As discussed in Li et al. (2002), some empirical models such as EPR or LR models can be implemented to achieve tide-coordinated shoreline with a set of instantaneous shorelines as input. Although the main focus in implementing those models in shoreline mapping is for shoreline change and erosion prediction, they can also be applied for the generation of tide-coordinated shoreline. Basically, historical shorelines are used and predicted shorelines can be obtained with a given time in the future, or between epochs of historical shorelines. To implement these models in a tide-coordinated shoreline mapping application, instantaneous shorelines are utilized instead of historical shorelines, and water level is the variable used to determine tide-coordinated shorelines. Srivastava
(2005) and Srivastava et al. (2005) introduced a least-squares method for shoreline modeling and shoreline change prediction which was studied on bluffline erosion the
100 southern coast of Lake Erie, Ohio. Blufflines at different times are divided into an equivalent number of segments, each segment represented with a straight line as shown in
Figure 4.16. Each segment is assigned with other corresponding bluffline segments at different times.
Figure 4.16 Blufflines segments at different times (Srivastava, 2005).
Figure 4.17 Transformation of a bluffline segment into another corresponding segment
(Srivastava, 2005).
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The parameters of segment transformation, which consists of translation (T), rotation (R), and scale change (S), are determined using a least-square approach. From figure 4.17, transformation equation of a segment can be represented as (Srivastava, 2005);
ͥ ͧ ͥ ͧ = ∗ + (4.9) ͦ ͨ ͬ ͬ ͦ ͨ ͭ /v ͭ /u Ʈͬ Ʋ ͍ ͌ Ʈͬ Ʋ ͎ Where; ͭ ͭ
ͥ − ͧ 0 0 0 0 v v ͥ − ͧ 0 0 0 0 ǭʚ3uͯ3vʛ ͮʚ4uͯ4vʛ = , = , = v v (4.10) ͥ − ͧ 0 0 cos sin ǭʚ3wͯ3xʛ ͮʚ4wͯ4xʛ ͬͥ − ͬͧ 0 0 −sin cos ͭ ͭ ͎ ʮ ʯ ͌ Ʈ Ʋ ͍ Ƭ ư ͬ ͬ Transformationͭ ͭ parameters are expressed in polynomials forms for different blufflines. If m is the number of blufflines ( ), k = m(m-1)/2 is the number of combinations ͥ, ͦ,…, ( where a pair of segments can be selected to determine transformation parameters. For ͨ ͨ ͨ example, polynomials form of rotation angle ( ) parameter for a pair of time p and q of j th segment of bluffline can be expressed as (Srivastava, 2005);
% & &ͯ$ͯͥ +ͯ, = $Ͱͥ $ͯͥ + − , (4.11)
With all combination of bluffline pairs,∑ ͕ the Ƴ followͨ ͨingƷ matrix form can be expressed
(Srivastava, 2005);
&ͯͦ &ͯͧ ͥͯͦ ͦ − ͥ ͦ − ͥ ⋯ 1 ͥ &ͯͦ &ͯͧ ͥͯͧ ͧ − ͥ ͧ − ͥ ⋯ 1 ͦ … = ⋯ ⋯ ⋯ ⋯ … (4.12) ʚͨ ͨ ʛ &ͯͦ ʚͨ ͨ ʛ&ͯͧ ͕… +ͯ, ˥ − − ⋯ 1 ˨ ˥ ˨ ʚͨ, ͨ+ʛ ʚͨ, ͨ+ʛ ˥ ͕ ˨ ˦ … ˩ ˦ ⋯ ⋯ ⋯ ⋯˩ ˦ &ͯͥ˩ ˦ ˩ ˦ ˩ ˦ ˩ ˦ ˩ ˦ ˩ ˦ ˩ Ƴͨ ͨ Ʒ Ƴ102ͨ ͨ Ʒ ˧ ˪ ˧ ˪ ˧͕ ˪
The matrix form represents Y = AX and can be solved using least-squares method. The solution is solved by X = AɑA ͯͥ(AɑY).
The method was tested onʚ a 15ʛ km long of coast of southern Lake Erie. The result was compared with predicted shoreline using ODNR’s traditional erosion rates method. The historical blufflines from years 1973, 1990, and 1994 were used to predict blufflines of year 2000. Both predicted blufflines from least-squares and erosion rates methods were compared to orthophoto-digitized blufflines of the same year (2000). The result yielded similar average error of about 5 meters for both methods, but least-squares method showed a significantly lower maximum error (23.86 m to 40.54 m). However, erosion rates method showed better accuracy when tested by error analysis approach developed by Ali (2003). The analysis determines positional quality of linear features, including distortion factor, generalization factor, bias factor, and fuzziness factor. The overall quality factors which exclude fuzziness factor of least-squares method and erosion rates method are 1.928 and 1.134 respectively. Another concept of utilizing instantaneous shorelines to derive tide-coordinated shorelines was proposed in Li et al. (2005, 2006). A snake model, also known as an active contour model (Kass et al., 1987), was introduced to model dynamic motions of instantaneous shoreline segments. As discussed in 4.4.1, an instantaneous shoreline can be represented by a set of polynomials expressed with mathematical functions as (Li et al., 2005);
/ / / / / = ( ͤ, ͥ, ͦ,…, ), , )
/ / / / (4.13) ͒/ = ̀ ͕ͤ, ͕ͥ, ͕ͦ,…, ͕), ͧ, ͨ
/ / / / ͓/ =̀ ʚ͖ ͤ,͖ͥ,͖ͦ,…, ͖), ͧ, ͨʛ 103 ͔ ̀ ʚ͗ ͗ ͗ ͗ ͧ ͨʛ Where s is as arc length of an entire shoreline which is normalized as 0 ≤ s ≤ 1
Figure 4.18 illustrates implementation of the snake model to achieve a tide-coordinated shoreline from instantaneous shorelines. Instantaneous shorelines at , , and along ͥ ͦ ͧ with complementary observation, such as water levels from WSM and gauge stations ͨ ͨ ͨ were utilized in the model. A piece of tide-coordinated shoreline C(s) is influenced by internal energy and external energy, making the shoreline piece deformed.
Instantaneous Instantaneous Instantaneous shoreline at time t 1 shoreline at time t 3 shoreline at time t 2
External force Tide-coordinated shoreline/snake-line i P C(s)
Tide gauges
Protection structure
Figure 4.18 Implementation of snake model to derive tide-coordinated shoreline (Li et al.,
2005)
The internal energy consists of two control parameters, and , which $)/ [ , , ] α β can be expressed as (Li et al., 2005); ̿ ̽ʚͧʛ ͧ ͨ
ͦ v ͦ i ʚ.ʛ i ʚ.ʛ (4.14) = + v 2 $)/ i. i.
̿ ƴ ʚͧʛ ʺ ʺ ʚͧʛ ʺ ʺ ƸƟ
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The first-order term, α, forces a curve to behave like a membrane, the second-order term,
β, will force a curve to act like a thin plate (Kass et al. 1987).
The external force may be derived from water level at gauge stations, 3/ [ , , ] meteorological information, and coastal morphology. Li et al. (2006) applied distances ̿ ̽ʚͧʛ ͧ ͨ between the initial tide-coordinated shoreline and historical/instantaneous shorelines as external forces for the snake model. The external energy is as follows (Li et al., 2006);
(4.15) = 1/ /ͯ/ĝ 3/ %Ͱͥ $Ͱͥ $
The distance /ͯ/ĝ is the distance̿ between∑ /# ∑ point ͘of tide-coordinated shoreline at time $
and /# point of the shoreline at time . Moreover, there are also other constraints, ͘ % ͝ gauge stations, man-made structures, and water surface model, that had been applied to ͨ ͝ ͨ the shoreline snake in order to control its deformation.
Total energy from both internal and external will be minimized to deform the piece of tide-coordinated shoreline C(s). The internal and external force terms can be expressed in total energy computation as (Li et al., 2006);
ͦ v ͦ = ͥ + ͥ ͥ i ʚ.ʛ + i ʚ.ʛ → (4.16) .) & ͤ 3/ ͦ ͤ i. i.v
A̿ preliminaryȄ ̀ experimentƳ̽ʚͧʛƷͧ͘ ofȄ applyingƴ ʚͧʛ ʺ polynomialsʺ ʚͧʛ ʺ into ʺ shorelineƸ ͧ͘ ͢͡͝ segments was performed in Li et al. (2005). The study adapted the representation of shoreline segments to 2D environment.
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Y coordinate position of a shoreline segment at time t is considered to be a function of X, thus ͓/ Ɣ ̀ʚ͒/ʛ. First and second-order polynomials were used to formulate ̀ʚ͒/ʛ, which can be shown as follows (Li et al., 2005):
/ / First order polynomial: ͓/ Ɣ ͕ͥ͒/ ƍ ͕ͤ (4.17)
/ ͦ / / Second order polynomial: ͓/ Ɣ ͕ͦ͒/ ƍ ͕ͥ͒/ ƍ ͕ͤ (4.18)
The coefficients of a function for each shoreline segment can be determined by applying a curve-fitting algorithm on the vertices along the segment.
Five simulated instantaneous shorelines were generated from intersections of CTM and randomly selected water levels.
Figure 4.19 Simulated instantaneous shorelines (Li et al., 2005).
Experiments were conducted with two types of shoreline segmentation methods to determine suitable solutions for dividing a shoreline into segments. The first scheme was
106 to separate the shoreline into equal lengths. Four lengths, 1000 m, 200 m, 100 m, and 50 m, were selected to be representative lengths in the study. Hypothesis tests were performed on each segment’s residuals mean ( µ) and standard deviation ( σ) from curve- fitting for both first and second-order polynomials. Following represents the tested hypotheses (Li et al., 2005);
| and | ͤ ∶ = ͤ ͥ ∶ ≠ ͤ ͤ ∶ = ͤ ͥ ∶ > ͤ
The tested͂ mean ( µ0) and standard͂ deviation ( σ0)͂ were selected at µ0͂ = 0 m. and σ0 = ± 15 m in the study. The hypotheses were tested at 95% level of significance. The result indicated that the tests of mean were accepted at all segment’s length, while the tests of standard deviations were rejected at lengths longer than 200 m. for both degrees of polynomials. Therefore, the length of shoreline segments should be 100 m. or shorter for dividing shoreline with fixed length.
The second shoreline division scheme was to segment the shoreline with variable length depending on RMSE test of shoreline segments. The shoreline is equally divided into
(n+1) meter-long segments, where n is the number of polynomial degree used. The curve fitting will be applied to the vertices along shoreline start from the beginning. An RMSE from fitting is calculated and checked if it exceeds a predetermined threshold. If the fitting RMSE is lower than the threshold, the next consecutive vertex is included and a fitting RMSE will be recalculate. The process of including vertices continues until the last vertex introduced yields a larger RMSE than the tolerance, and all vertices, excluding the last one, are allocated as one segment. The end of the recently formed segment will be assigned as the beginning vertex of the next segment and the procedure is executed until
107 the last vertex of the shoreline. The algorithm performance of the shoreline segmentation is more stable than the first method. RMSE threshold can be defined, so the quality of polynomials fitting can be controlled. Figure 4.20 illustrates shoreline segmentation using variable-length scheme.
Figure 4.20 Segmentation of the simulated shorelines (Li et al., 2005)
From Figure 4.20, shorelines extracted at different water levels were divided into an equal number of segments, in which each segment is fitted with a polynomial.
Correlations between polynomial coefficients from corresponding shoreline segments and water level changes were calculated, in order to study if there is any relationship between them. From the result, only zero-order coefficients of both first and second-order polynomials showed high correlations with changes in water level. The high correlations in zero-order coefficients may reflect the fact that Y coordinate of shoreline segments increased as water level increased. Therefore, zero-order coefficients would generally increase, because shoreline segments were expressed by the Y =F(X) function.
Further experiments to determine the potential of snake-based tide-coordinated shoreline implementation were conducted in Li et al. (2006). The first experiment used several
108 artificially generated straight shorelines and simulated water surface. An initial tide- coordinated shoreline (yellow line) moved to the resulting tide-coordinated shoreline (red line), corresponding to the simulated water surface (an inclined gridded surface) as shown in Figure 4.21. The average distance between resulting shorelines and closest points on the water surface is 0.012 m.
a. Case 1 b. Case 2
Historical lines Initial line s Result ing line s
Figure 4.21 Deformation of snake shoreline from simulated straight shorelines and simulated water surface (Li et al., 2006)
The second experiment applied artificially generated straight shorelines and simulated
MLLW water surface of Lake Erie during 1999 to 2001. Figure 4.22 shows the result from second experiment that used actual water surface instead of simulated water surface.
The average distance between resulting shorelines and closest points on the water surface for the second experiment is 0.037 m.
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a. Case 1 b. Case 2
Historical lines Initial line s Res ult ing line s
Figure 4.22 Deformation of snake shoreline from simulated straight shorelines and actual water surface (Li et al., 2006)
The last experiment employed real historical shorelines from 1979, 1990, 1997, and 2001 combined with the actual water surface used in the second experiment (Figure 4.23). The average distance between resulting shorelines and closest points on the water surface for the third experiment is less than 0.0234 m. Therefore, the results from the experiments show that implementation of snake model to derive tide-coordinated shoreline is applicable, as the snake shoreline could converge to the given water surface. However, there should be further experiments to compare with actual tide-coordinated shoreline.
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Water
Land
Figure 4.23 Tide-coordinated shoreline from historical shorelines and simulated water surface (Li et al., 2006)
4.4.2.3 Research on digital models and implementation to derive tide-coordinated shoreline
The Great Lakes Forecasting System (GLFS) (Schwab and Bedford, 1999) was a collaborative project between OSU and NOAA’s Great Lakes Environmental Research
Laboratory (GRERL). GLFS is a coastal forecasting system that utilizes a three dimensional circulation model and a parametric wave prediction model to observe and forecast meteorological conditions such as temperature, water level, and waves for the
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Great Lakes. The system was designed to perform a nowcast and 2 days forecasts as shown in Figure 4.24.
Figure 4.24 GLFS daily forecasting cycle (Schwab and Bedford, 1999).
The nowcast function presents the current observed meteorological information, used as the initial parameters for the forecast computation. The initial input data for the forecasting model were (Schwab and Bedford, 1999): 1) water levels, current, and temperature conditions, 2) last 24 hours of meteorological data, 3) forecasts of inflow and outflow boundary conditions, and 4) 48 hours forecasts of wind stress and heat flux on water surface. The entire lake basin was modeled and analyzed to provide forecasts of water surface conditions such as temperature, current, and water level for the next 48 hours. Figure 4.25 illustrates components of GLFS system.
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Figure 4.25 Diagram of GLFS components (Schwab and Bedford, 1999).
Two types of models were implemented in GLFS: a wave model and a numerical circulation model. The wave model used in the system was a parametric model that derived wave heights and directions. The circulation model was implemented to predict water level, temperature, and velocity distribution. It was adapted from Princeton Ocean
Model (POM) (Blumberg and Mellor, 1987) to perform specifically for the Great Lakes.
Therefore, the model inherits several key characteristics from POM such as using
Arakawa “C-grid” and implementing based on sigma ( σ) scheme for vertical coordinate.
GLFS was an operation maintained by OSU from 1994 to 2004 (GRERL, 2010). Great
Lakes Operational Forecast System (GLOFS), a descendant of GLFS, has currently been operated by NOAA’s NOS to provide short-term forecasts of water currents, water temperatures and water levels of the Great Lakes (NOAA, 2010a). Another adaption of
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GLFS is called Great Lakes Coastal Forecast System (GLCFS). GLCFS is a workstation version of GLFS, performing semi-operationally at GRERL office for all Great Lakes since 2002 (NOAA, 2010). Results of GLCFS’s nowcasts and forecasts can publicly be accessed at GLERL’s GLCFS website: http://www.glerl.noaa.gov/res/glcfs/.
Integration of multiple data sources for coastal monitoring and management applications has been proposed and studied in many studies. For example, water level reading from gauge stations along the shore may be integrated with satellite altimetry data in order to obtain water surface level. However, water level information provided by NOAA’s gauge stations usually refers to North American Vertical Datum (NAVD) 1988 and
International Great Lakes Datum (IGLD) 1985 which are orthometric datums that utilize the geoid, while other data sources, such as LIDAR and satellite altimetry, rely on ellipsoidal datums. Cheng et al. (2008) developed a method to accurately link water level between datums. The study was a joint research between National Chung Cheng
University and Ohio State University. A floating GPS buoy was implemented near two gauge stations in Lake Erie to simultaneously collect GPS data and water gauge readings.
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Figure 4.26 Illustration of GPS buoy deployment near water gauge station (Cheng et al.,
2008)
Water gauge observations were converted from IGLD85 to NAVD88. By obtaining both orthometic height (H) and ellipsoidal height (h), geoid height (N) can be determined and used to link water levels from gauge reading and satellite altimetry into a same reference system. Three tracks of TOPEX/POSEIDON (T/P) satellite altimetry from 1999 to 2001 were compared and combined with water level from 15 water gauge stations across Lake
Erie.
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Figure 4.27 Location of T/P altimetry tracks and 15 gauge stations in Lake Erie (Cheng et al., 2008)
Results showed good agreement in comparing water level from T/P and gauge stations.
Difference in mean water level was 2.5 ± 3.2 cm with a correlation of 0.994. Kriging interpolation was applied to generate water surface from 15 water gauge stations. The interpolated water surface was compared with the water surface from GLFS. Mean difference of 2.7 cm was presented in the comparison. However, the mean difference between interpolated water surface and GLFS water surface increased to 7.2 ± 7.2 cm with a maximum of 28.2 cm when the water gauge observations were incorporated with
T/P data in the interpolation. The large disagreement was assumed to be caused by either geoid model error or insufficient meteorological information sampling for GLFS to model water surface since the maximum difference occurred in the middle of the lake.
Since transformation of heights between reference systems utilizing NOAA’s VDatum is limited to the U.S. and some countries in North America, the approach of linking water
116 level from gauge stations with multiple data sources could prove to be useful for applications in other coastal regions around the world.
Li et al. (2010) performed an accuracy assessment of shoreline derived from intersection of digital models using prior-accuracy analysis method. In contrast to conventional posterior methods that determine quality of resulting shoreline with a reference shoreline, the prior-accuracy method is able to determine uncertainties of extracted shoreline bases on qualities of input data sources. The process to extract shoreline in the study was similar to the method mentioned in Li et al. (2002). First, an Inverse Distance Weighted
(IDW) interpolation method was implemented to generate a regular grid DEM from
LIDAR point cloud. The interpolated elevation of a DEM point is calculated from neighboring LIDAR points, represented as (Li et al., 2010);
5Ĝ ͥ = $ v / $ v (4.19) Ĝ Ĝ where is a elevation of LIDARͮ point used∑ in ∑ the calculation and is the distance $ $ between that LIDAR point and the interpolated point. The DEM from LIDAR data was ͮ ͘ then merged with bathymetry data to generate a CTM, and shoreline was finally extracted by intersecting CTM with water level observed at gauge stations. Figure 4.28 shows the process of shoreline derivation.
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Figure 4 .28: Shoreline extract ion procedure (Li et al., 2010)
To e stimate the error of the extracted shoreline, the method of prior -accuracy analysis based on First Order Variance analysis (FOVA) was applied to estimate error propagation from multiple data sources used to derive shoreline. Error variances in both vertica l and horizontal directions were assumed to be presented with no correlation between LIDAR points and propagated in to the generated DEM through IDW interpolation process. Thus, the error propagation from IDW interpolation to DEM can be expressed as (Li et al.,
2010);
ͦ ͦ ͦ ͦ $ ͦ ͦ ͦ ͧ # ͦ ͨ 1 (4.20) $ $ $ $ $ 1 $ ͮ Ǝ ͮ $ 1 $ 1 Ɣ 2 Ƶȕ ƹ . ȕ ʦ ʧ . ƍ Ƶȕ ƹ . ȕ . ͘ ͘ ͘ ͘ CTM was obtained from combining the generated DEM with bathymetric data. IDW interpolation was again appl ied to calculate CTM grid, considered to be a seaward extension of the DEM. For the areas where DEM and bathymetry overlap, elevation of any given CTM grid point was computed as (Li et al., 2011);
av 5 ͮ av 5 ͮ Ɣ ýþĆ ýþĆ ĕĔħěĬ ĕĔħěĬ (4.21) av ͮav ýþĆ ĕĔħěĬ
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LIDAR data and bathymetry were assumed to be unrelated and elevation error variance of the CTM can be estimated as (Li et al., 2010);
ͦ ͥ 5 Ɣ (4.22) üčĆ av ͮav ýþĆ ĕĔħěĬ
Horizontal error of CTM was expected to be equal to DEM horizontal error. Water levels used in the intersection with CTM were collected from NOAA’s Tides and Currents website (http://tidesandcurrents.noaa.gov/). To intersect gridded CTM with a height value, points along shoreline require interpolation as shown in Figure 4.29.
Figure 4.29 Illustration of intersection of CTM with water level at cross section along x- axis (left) and top view (right) (Li et al., 2010)
According to Figure 4.29, coordinates of points on the shoreline can be estimated as (Li et al., 2010);
͒.' Ɣ ͬͥ ƍ ∆ͬ
(4.23) ͓.' Ɣ ͭͥ ƍ ∆ͭ
͔.' Ɣ ͜
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Where;
∆ = #ͯ#u . − #vͯ#u ͦ ͥ (4.24) ℎ − ℎ ͥ ∆ ͬ= ʚ.ͬ ͦ −ͬ ʛͥ ℎͦ − ℎͥ
Errors of the extracted shoreline ͭwere propagatedʚͭ fromͭ ʛCTM and observed water level.
Therefore, horizontal error variances and covariance of errors in X and Y direction can be expressed as follows (Li et al., 2010);
∆ ͦ ∆ ͦ ∆ ͦ ͦ ͦ i 3 ͦ i 3 ͦ i 3 ͦ = u + + + Ħğ 3 i#u #u i#v #v i# #
∆ ͦ ∆ ͦ ∆ ͦ ͦ ͦ ʠ ʡ ͦ ʠ ʡ ͦ ʠ ʡ ͦ (4.25) Ħğ = 4u + #u + #v + # ℎͥ ℎͦ ℎ
∆ "∆ͭ ∆ " ∆ͭ ∆" ͭ∆ ƴ Ƹ ͦ ƴ Ƹ ͦ ƴ Ƹ ͦ Ħğ Ħğ = " #u + " #v + " # ℎͥ ℎͥ ℎͦ ℎͦ ℎ ℎ " ͬ " ͭ " ͬ " ͭ " ͬ " ͭ The error variances ͦ and ͦ are from the calculated CTM vertical error variances, #u " #v" " " " " while ͦ is an error variance from water level measurement, the same as vertical error # variance ͦ of the extracted shoreline. The horizontal error variances ͦ ͦ ͦ and Ħğ 3u, 3v, 4u ͦ inherited from the CTM horizontal error and the horizontal error variance of the 4v shoreline is ͦ ͦ . Ħğ + Ħğ
The study utilizedǯ LIDAR data, bathymetry, and water level from a gauge station in
Lake Erie in Lake County, Ohio. The 1-m grid DEM was interpolated from 0.25-point per square meter point density LIDAR data obtained from NOAA’s Coastal Service
Center (CSC) using IDW interpolation. The interpolated DEM was then merged with the
120 bathymetry from National Geophysical Data Center (NGDC) using IDW interpolation to generate CTM. Extracted shoreline as a result of CTM and water level intersection was compared with a reference shoreline delineated from a QuickBird satellite image in panchromatic mode, noting that the water level utilized in the intersection was at the same level as the water level during the acquisition of the satellite image. The proposed prior-accuracy analysis approach to determine extracted shoreline accuracy was performed, and the result showed that there were considerable differences between predicted and tested accuracies (1.58 m. to 3.55 m. in average error and 1.9 m. to 2.49 m. in standard deviation for predicted and tested result, respectively). Researchers examined and explained the differences as: 1) large differences were observed in low slope areas because the prior-accuracy method was sensitive to slight change in slope, and might cause inaccuracy in the estimation, 2) sudden change in slope of made-made areas showed rapid changes (Figure 4.30) in accuracy estimation of shoreline position, 3) the predicted and tested errors were observed to be most compatibility in high slope areas, such as bluffs. Therefore, the approach of error estimation is suitable for bluff areas as the predicted error is more accurate than those in flat slope and man-made shore areas.
However, this prior-accuracy analysis approach provides an advantage over the conventional posterior methods, as it enables the possibility of deciding which data sources can be integrated to provide optimal solutions.
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Figure 4.30 Predicted and actual tested errors in area with a lot of man-made constructions (Li et al., 2010)
4.5 Review of recent approaches to achieve tide-coordinated shoreline
Generally, the term “tide-coordinated shoreline” is not widely recognized and not applied in most coastal survey and shoreline mapping researches. Only mean high water level
(MHW) and mean lower-low water level (MLLW) often times are used as representative shorelines. The thesis will acknowledge such shorelines as tide-coordinated shorelines since the methods which are applicable for those shorelines practically can be applied in obtaining tide-coordinated shoreline.
4.5.1 Tide-coordinated shoreline from ground survey
The closest modern method to the conventional shoreline mapping by staff leveling may be the method of ground survey using GPS to track shoreline position. The approach of shoreline position determination using GPS survey yields accurate results, used as a
122 reference shoreline in many research. To increase effectiveness over the conventional levelling survey method, GPS receivers have been integrated on the all-terrain vehicle to track shoreline position while the vehicle runs along the beach (Shaw and Allen, 1995).
However, this method of tracing the shoreline normally marks physical appearance of the high water line, apparent wet-dry transition and deb;ris drifted up on the beach, rather than true position of the mean high water line applicable for making charts (Shalowitz
1964; Shaw and Allen, 1995; Li 1997; Graham, Sault, and Bailey 2003; Pajak and
Leatherman 2002). Creating a shore profile, similar to the generation of a DEM is an option to solve the limitation. Morton et al. (1993) utilized Kinematic GPS survey to monitor beach changes for a 2 kilometer segment of Galveston Island State Park. The beach profile was surveyed using conventional theodolite survey, stop-and-go kinematic survey, and fully kinematic survey with GPS antenna mounted on a vehicle. Although the accuracy of the GPS survey is comparable to conventional surveys using a theodolite, the amount of time spent on data acquisition and data processing did not show a significant improvement considering modern coastal surveys with airborne and spaceborne sensors.
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Figure 4.31 Illustration of kinematic surveying with stop-and-go and GPS receiver mounted on a vehicle for Galveston Island State Park (Morton et al., 1993)
The USGS developed SWASH (Surveying Wide-Area Shorelines) which is a vehicle- based system, with integrated GPS and orientation measuring unit, for measuring shoreline position including heading, pitch and roll angles of the vehicle. The project was initiated in 1998 and is still being applied to shoreline change study on North Carolina and Massachusetts beaches. As illustrated in figure 4.31, the system is mounted on a six- wheel, all-terrain vehicle, traveling along the shore, measuring horizontal position, vertical position and beach slope. Shoreline position is determined by extrapolating the observed position and beach slope and computing the contour’s intersection with the desired vertical datum, MHW, in the research. SWASH system can measure more than
70 kilometers of shoreline data within one period of low tide, and is less expensive compared to the traditional tide-coordinated shoreline mapping.
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Figure 4.32 Detail of SWASH operation (USGS, 2010b)
The National Park Service of U.S. Department of the Interior implemented SWASH in a
Coastal Shoreline Change project. The test result of SWASH-defined shoreline and referenced shoreline showed differences in shoreline positioning error of about ± 1.6 meters at a 95% confidence interval (National Park Service, 2010). The test also showed that the positional accuracy of shoreline would increase if the SWASH vehicle deviated further from the track that estimated to be the real desired shoreline on the beach. The derived shoreline from SWASH has a high accuracy which can be used as a reference shoreline for comparison with shorelines derived from different techniques. Stockdon et
125 al. (2002) implemented SWASH derived shoreline to investigate the resulting accuracy of
LIDAR derived shoreline accuracy using contouring method.
4.5.2 Tide coordinated shoreline from airborne sensors
As mentioned earlier, shoreline mapping from tide-coordinated aerial photography is currently the most widely-used method to map the National Shoreline in the National
Oceanic and Atmospheric Administration (NOAA) (Woolard et al. 2003). Shorelines delineated from aerial orthophotos can have an accuracy of about 2.6 meters (1 σ) (Li et al. 2001). However, there are limitations for this method, especially in severe weather conditions and large tidal ranges areas, which yields difficulties in planning for data acquisition at the time of tide reaching the desired level (Woolard et al. 2003).
SAR system is an active remote sensing technology that makes it capable of operating in at night time, and the system mostly has no weather constraint. Moreover, Interferometric
SAR (InSAR), which uses two or more SAR images to generate maps of the surface, is also capable of creating DEM. The Defence Research Establishment Ottawa (DREO) of
Canada employed polarimetric SAR images, acquired from an airborne platform, to extract the mean high water line (Yeremy et al., 2001). The study aimed to perform an assessment in preparation for the launch of RADARSAT2, which has a similar polarimetric mode to that of a SAR image, in 2004. A shore slope and a water-level referenced instantaneous shoreline were used to determine position of tide-coordinated shoreline. The study implemented polarimetric C-band data from Canada’s Convair CV-
580 airborne SAR system, with a spatial resolution of about 4 meters in slant range and
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0.4 meters in heading direction. Polarimetric SAR provides four channels of backscattered electric signal, measuring the velocity of the reflected object and making it similar to an along-track InSAR (Yeremy et al., 2001). The feature helped differentiate land regions (stationary object) and sea regions (moving object). A shore slope used to estimate tide-coordinated shoreline such as MHWL, is extracted from one polarimetric
SAR image. The method to determine shoreline slopes was estimated by the tilt of the reflected surface perpendicular to the SAR’s line-of-sight (LOS), by measuring the offsets from a model polarization response. GPS survey was performed at the time of
SAR image acquisition to determine reference shorelines. The accuracy of extracted shorelines from SAR image and estimated MHWL in mean error varies from 0.2 to 10.4 meters and from 6.3 to 7.8 meters respectively.
LIDAR has been utilized in coastal mapping to generate coastal terrain model (CTM) and extract tide-coordinated shoreline in several studies. To perform tide-coordinated shoreline mapping using airborne LIDAR, elevation data must be obtained at the time the tide falls significantly below MLLW for only elevation of land (Hess, 2004) or combining elevation data of the area above the water with bathymetry data to create CTM and extracting tide-coordinated shorelines (Woolard et al. 2003; Irish and Lillycrop 1999;
Li et al., 2010; Li et al., 2002). Some LIDAR systems, such as NASA’s EAARL
(Experimental Advanced Airborne Research Lidar) and USACE’s SHOALS (Scanning
Hydrographic Operational Airborne Lidar Survey), have water penetration capability that makes them able to map shallow-water bathymetry. Hence, shoreline mapping operation using LIDAR system can be performed with less restriction of tidal stages. Currently, some national agencies have been implementing LIDAR system to generate DEM in their
127 coastal mapping and monitoring programs. Aside from commercial topographic LIDAR systems used in NOAA’s national shoreline mapping operation mentioned earlier,
USACE also developed and utilizes Optech SHOALS LIDAR system for their National
Coastal Mapping Program (USACE, 2010). NASA’s EAARL is employed along with
USGS-NASA developed ALPS (Airborne Lidar Processing System) in USGS’s Coastal and Marine Geology Program (CMGP) (USGS, 2010c).
Figure 4.33 USGS-NASA Airborne Lidar Processing System (ALPS) (USGS, 2010d)
Since the data collection process of LIDAR systems includes positional information sent from onboard GPS, application of tide-coordinated shoreline mapping requires a transformation tool (VDatum) that can convert data from an ellipsoid datum (GPS), to an orthometric datum (NAVD88), then to a tidal datum (MLLW, MSL, MHW, etc.).
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Stockdon et al. (2002) developed a method for determining shoreline position using shoreline profile from collected airborne LIDAR data and specified vertical datum.
LIDAR data implemented in the research was from NASA’s Airborne Topographic
Mapper (ATM). The data has vertical accuracy of about and 0.15 meters, and the density of LIDAR data is approximately 1 point per 2 square meters. Cross-profiles of the shore were generated alongshore (every 20 and 10 meters in the research) from the irregularly spaced LIDAR data. Linear regression was employed to fit LIDAR points at an elevation of ± 0.5 meters around the specified vertical datum. Horizontal position of each cross- profile was then determined at the elevation of specified vertical datum. The extracted shoreline was compared to shorelines produced from ground survey using GPS and inclinometer-equipped all-terrain vehicle (SWASH) at the MHW datum. The referenced shoreline has a horizontal accuracy of about ± 1.6 meters at a 95% confidence interval.
The comparison of the two shorelines showed an RMS difference of 2.9 meters with an average offset of 2.12 meters.
Robertson et al. (2004) investigated the accuracy of mapped shoreline using a contouring method with airborne LIDAR and digitized shoreline from simultaneously acquired orthoimages. The LIDAR data used has an approximate point density of 1 point per square meter with the horizontal accuracy of around 0.2 meters and vertical accuracy of about 0.1 meters. A 0.5 meter resolution gridded DEM was then interpolated from
LIDAR data. The orthoimages, used in shoreline accuracy comparison, have a spatial resolution of 0.2 meters per pixel and estimated positional error of 0.4 meters. Shorelines were generated by contouring DEM with the level of observed water level from a nearby gauge station. In this research, visible high water line (HWL) was digitized from the
129 orthoimages and compared with the shorelines from contouring method. High water
(HW), mean high water (MHW), and mean higher-high water (MHHW) heights were used to contour DEM. The horizontal differences between the orthoimage delineated shoreline and DEM contouring shorelines were examined using GIS approach as illustrated in figure 4.34. The result showed that mean differences for HWL are less than
6 meters for the comparison of the two shorelines.
Figure 4.34 four steps of quantifying differences between contoured shoreline (dashed line) and orthoimages digitized shoreline (solid line) (Robertson et al., 2004)
Liu et al. (2007) used the data from LIDAR system to extract tide coordinated shorelines in an automated manner. The research implemented a segmentation-based image
130 processing method for shoreline extraction. Digital elevation model (DEM) from LIDAR data was segmented into a binary image, comprised of land and water pixels, by intersecting the DEM with a tidal datum plane. The tidal datum plane used is a plane with a constant level of the tidal datum (MHW, MLLW) derived from tidal data of a representative tide station. LIDAR data utilized to generate DEM has a horizontal accuracy of ± 0.8 m and a vertical accuracy of ± 0.15 m on bare ground with a point- cloud spacing of 1–2 m. The LIDAR DEM has a 1 m spacing grid. A sequence of image processing algorithms was then applied to achieve vector shoreline. The process of extracting shoreline is shown in figure 4.35.
Figure 4.35 Flow chart of the applied segmentation-based image processing algorithms
(Liu et al., 2007)
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The research also investigated the vertical error of LIDAR measurement, and the Monte
Carlo technique was performed to examine the uncertainty level in tidal datum determination on the shoreline extraction process. The result showed that the accuracy of the horizontal position of derived shoreline is within 4.5 meters at the 95% confidence level.
4.5.3 Tide-coordinated shoreline from space-borne sensors
Although it is not realistic to always schedule times to acquire satellite images at the time of tide reached the desired tidal level such as MLLW, there may be possibilities of obtaining tide-coordinated shoreline from instantaneous shoreline extracted from satellite images as discussed in chapter 2.
Hoja et al. (2000) extracted shoreline from SAR images of the European remote sensing satellites (ERS-1/2). Instantaneous shoreline was extracted from ERS-2 SAR images with the known water level from a tide gauge station. The extraction of instantaneous shorelines was based on a method using a combination of wavelet and an active contour
(snake) model. The wavelet methods enhance edges in SAR images allowing efficient solving of edge detection problems, and the snake algorithm vectorized shorelines using the energy function. The study selected a small test area and assumed a constant elevation of extracted instantaneous shoreline. Digital elevation model was interpolated from instantaneous shorelines combined with the observed water level from tide a gauge station using a Gaussian shape function on a 12.5 m resolution grid. Generated shoreline and DEM from this method’ was compared and showed good agreement with shoreline
132 extracted at the same water level from vessel mounted echo sounder DEM, and DEM generated from airborne IfSAR.
Mason et al. (1995) also presented similar approach to generate DEM from a set of instantaneous shorelines, where tidal stages were known, using a method called water- line from Koopmans and Wang (1995). The water-line method can quickly construct an inter-tidal DEM for the large areas with an iterative procedure to frequently monitor the generated DEM to detect changes. The method performed as illustrated in figure 4-36.
Shoreline was delineated from SAR image from ERS-1 satellite and water levels at the acquisition time were determined from 1.2 km grid hydrodynamic model. The method to semi-automatically determine shoreline position using an active contour model was presented in Mason and Davenport (1996). The automatic shoreline delineation yielded about 90% virtually correct. A gridded DEM can then be interpolated from the set of water-level referenced shorelines implementing universal block kriging as a spatiotemporal interpolator (Mason et al. 1998). An accuracy of the inter-tidal DEMs can be determined using error analysis as explained in Mason et al. (2001). The interpolated
DEMs have an average estimated standard deviation for vertical accuracy of about 20 cm,
26.8 cm, and 32.1 cm for beach slope of 1:500 (flat), 1:100, and 1:30 (steep), respectively
(Mason et al., 2001). The research, however, did not extract and test tide-coordinated shoreline from the generated inter-tidal DEM.
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Figure 4.36 Diagram of water-line method (Mason et al., 1997)
Improvement to this approach can be achieved from s more accurate delineation of shoreline and the hydrodynamic modeling (Mason et al., 1997). However, determining water level using hydrodynamic model at the time of image acquisition may not be necessary in the U.S., since there are tide stations that provide tide observations throughout the U.S. coasts as presented in chapter 3. Image processing techniques such as shoreline mapping at sub-pixel scale from a soft image classification (Foody et al., 2005)
134 and determination of SAR interferometry coherence (Schwäbisch et al., 1997; Mattar et al., 2001, 2003; Dellepiane et al., 2004) show promise in improving the accuracy of the extracted shoreline. Di et al. (2003a) extracted high-resolution 3-D shoreline from stereo images of IKONOS satellite. The rational function (RF) model of 1-meter resolution
IKONOS satellite images was refined to improve imaging geometry and yielded highly accurate 3-D shoreline. Di et al. (2003c) presented an automatic approach with little human interaction for extracting shoreline from IKONOS images using mean shift segmentation algorithm. The result showed an accuracy of 2-3 meters and 8.5 meters can be achieved from IKONOS stereo images of 1-meter resolution panchromatic and 4- meter resolution multispectral respectively.
Alternatively, satellite imagery is also capable of creating a DEM and bathymetric data of shallow water area at low tides. High spatial resolution satellite stereo images, such as
IKONOS images, have been implemented in creating DEM in many researches.
Sophisticated image matching techniques and sensor model refinements needs to be employed to achieve good result of DEM. Toutin (2004) performed accuracy tests for
DEMs generated from several satellite stereo-image sensors (SPOT-5, EROS-A,
IKONOS-II, and QuickBird). The research indicated that an accuracy of within 2.5 meters of linear errors with 68% level of confidence can be achieved for a bare ground.
On the other hand, bathymetry map derived from satellite sensor has also been studied since 1970s using Landsat Multispactral Scanner (Landsat MSS) (Benny and Dawson,
1983). Nowadays, spatial resolution of satellite sensors has improved significantly. For example, IKONOS and Quickbird satellites provide multispectral images that have a spatial resolution of 4 meters and 2.4 meters respectively. In general, applications of
135 bathymetric mapping using passive satellite sensors can be concentrated on 0.45-0.65 micrometers of electromagnetic spectrum for the ability to penetrate water (Muslim and
Foody, 2008). The depth of the water may be estimated from the reflectance of light accommodating with simple linear regression models and measurements from gauge stations (Fonstad and Marcus, 2005). There are several techniques developed to accurately determine bathymetry from satellite sensors (Muslim and Foody, 2008;
Lyzenka, 1985; Lyzenka et al., 2006).
Muslim and Foody (2008) generated both topographic (DEM) and bathymetric information from IKONOS imagery, and extracted tide coordinated shoreline for a coast located at Kuala Terengganu, Malaysia. Bathymetric data was derived from band 2 of 4- meter resolution multispectral IKONOS images. IKONOS image diginal numbers (DN) was converted to planetary reflectance values using (Muslim and Foody, 2008);
v _ Œ = (4.26) + Œ ΑΝΡ ʚWĦʛ
Where; = planetary reflectance + = spectral radiance at sensor’s aperture Z = band dependent mean solar exoatmospheric irradiance Z ͆ = solar zenith angle . ͍̿͏͈ = earth-sun distance, in astronomical units
͘
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The spectral radiance, , was calculated using published calibration coefficient and Z image bandwidth as (Fleming, 2001); ͆
(4.27) Z = /(( /10)/ ℎ)
The depth information͆ of 90 ͈̾ training͕͙͚̽̽ͣ͠ points and 25̼͕ͫͨ͘͘͢͝ accuracy check points along the shoreline, seaward, were used to determine parameters of the model for generating the bathymetry. Water depth of training points and check points was measured using vessel- mounted echo-sounding equipment and was referenced to a datum via a temporary benchmark, which was stationed at a marine jetty. The method Benny and Downson
(1983) used to determine depth from spectral radiance of image pixels was implemented in the study. Water depth at point x can be estimated as (Muslim and Foody, 2008);
ΚΜ ʚ īͯ ėʛͯΚΜ ʚ tͯ ėʛ (4.28) ℎ = ɒ 3 ͯ&Ƴͥͮ *. ʚ ʛƷ
Where; = radiance from͙̾ͤͨ shallow water ͤ = radiance from deep water ͆ = radiance from water depth at point x (measured point) 3 ͆ ′ = sun’s angle underwater (Figure 4.37) ͆ = constant ̿
and were estimated from histogram of reflectance of water area in the image. The ͤ ͟ reflectance value from the lower part of the histogram was used as and the upper part ͆ ͆ was used as . Parameter was calculated from water and air reflexive index and solar ͤ ′ ͆ elevation angle (E). Constant was determined as a slope of a regression line from the ͆ ̿ plot of logarithm of depth against logarithm of reflectance obtained from 90 training ͟ 137 points. Although, the RMS error of water depth of the bathymetric map was 0.87 meters, the error increased with the depth of water. Therefore, the large error number did not deliver a poor result, since only shallow area was considered for generating the shoreline.
Figure 4.37 Reflex of light from the sun to satellite (Benny and Dawson, 1983)
DEM was derived from stereo images of 1-meter resolution panchromatic-sharpened
IKONOS images. The stereo images used in DEM generation were acquired 4 months after the IKONOS image used in the bathymetry. Errors and biases of the IKONOS
138 stereo imagery were corrected by establishing 62 high precision GCPs which were surveyed using differential global positioning system (DGPS) technique. The RMS errors of DEM for x, y and z coordinates were estimated to be 0.921 m, 0.782 m, and 1.349 m, respectively.
The 3-D model of the coastal area or the coastal terrain model (CTM) was created by merging the digital elevation model of the land area with the bathymetric information.
The mean sea level (MSL) was used as a representative water level of tide-coordinated shoreline. The 4-meter resolution bathymetric map was rescaled to 1-meter resolution to assist the merging process with the DEM. Interpolation was performed to fill the empty area where there was no data. The water levels, at the time of acquisition of satellite images, were determined from a tide table created from the harmonic analysis. The accuracy of tide-coordinated shoreline generated by intersecting CTM with the water at
MSL was tested with the shoreline surveyed at MSL using DGPS, performed between the first and second satellite image acquisition time. The result showed RMS error of the generated tide-coordinated shoreline to be 1.80 meters, 90% of errors were within 2.8 meters. Hence, the resulting tide-coordinated shoreline has a good accuracy that can accommodate creation of a large-scale map (1:2,500).
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Figure 4.38 Flow chart of creating tide-coordinated shoreline at MSL (Muslim and
Foody, 2008)
4.6 Discussion
So far, several representative approaches, which do not include all approaches to obtain tide-coordinated shoreline, have been presented. Table 4.2 represents a summary of the presented tide-coordinated shoreline approaches. There are, however, some studies
(Morton et al., 1993; Hoja et al., 2000; Mason et al., 1995, 1997, 1998, 2001), that did not specifically aim to achieve tide-coordinated shoreline, also included in the table because the approaches are related or can be applied and further developed for tide- coordinated shoreline mapping.
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Information about spatial accuracy shown in Table 4.2 may come from different determination methods (RMS error, linear error, and mean error). Moreover, each research used a different method to compare the derived tide-coordinated shoreline with the reference shoreline, and each different reference shoreline also has its own different accuracy. Another factor affecting the accuracy of the mapped shoreline is the characteristic of the study areas. Basically, with similar vertical error in water level determination, the area which has a shallower slope generally yields larger error in horizontal position of derived shoreline. Therefore, the accuracies presented in the table only aim to show readers an approximate overview of each approach to obtain tide- coordinated shoreline, so the numbers should not be directly compared with each other.
From ground surveys using plane table to shoreline delineation using LIDAR and satellite imagery, shoreline mapping has developed over time to provide more efficient methodologies as technologies have progressed and are replaced with better inventions
Recent studies show that tide-coordinated shoreline mapping has focused on deriving digital models that are either from direct approaches, such as DEM and bathymetry from topo/bathy LIDAR system, or indirect approaches, such as generating inter-tidal DEM from instantaneous shorelines. Each method has its own distinctive merits over the others. For instance, LIDAR survey may require larger budget than utilizing satellite images to derive tide-coordinated shoreline, but it provides direct measurement of coastal geometry, and thus accurate mapping can be achieved with fewer procedures. On the other hand, ground survey using GPS techniques may produce the most accurate product of shoreline mapping, but it is impossible to implement such method for a large coverage area of project. There are, however, other potential methods to obtain tide-coordinated
141 shoreline such as video measurement of shoreline (Aarninkhof et al., 2003), and modeling dynamic shoreline by mathematical expression (Li et al., 2002, 2005, 2006).
Several methods, such as applying snake model to tide-coordinated shoreline and extracting tide-coordinated shoreline from WSM and CTM, are currently being developed at the OSU to achieve more robust and accurate solution of tide-coordinated shoreline mapping. The next chapter will discuss potential development of tide- coordinated shoreline mapping with respect to advancements in technology.
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Tide -coordinated Technique Referenced Citation Data source shoreline Comments implemented shoreline accuracy Beach profiles generated from Stop-and-go GPS techniques has Unknown GPS KGPS and vehicle Morton et al., comparable accuracy with the geodetic survey N/A N/A mounted GPS 1993 conventional theodolite survey equipment receiver but showed slight improvement for time spent. Magellen/Ashtech SWASH - vehicle ADU2 array for The system can determine mounted GPS ± 1.6 m at a 95% Ground survey survey Ground measuring more than 70 km of shoreline receiver with yaw, USGS, 2010 Unknown confidence oreintations and data within one period of low roll, and pitch interval Ashtech Z-Surveyor tide. measuring unit GPS receiver 14 The study was the pilot project 3 Canada's Convair Airborne Yeremy et al., GPS ground 6.3 m to7.8 m for for the launch of CV-580 airborne polarimetric SAR 2001 survey mean difference RADARSAT2 which has SAR similar attributes. Straightforward method of NASA’s airborne SWASH 2.9 m. for RMSE contouring of DEM to extract contouring method Stockdon et al., topographic mapper extracted tide- and 2.12 m for tide-coordinated shoreline is from LIDAR 2002 (ATM) LIDAR coordinated average implemented. However, DEM system shoreline difference. manual processes may still be required.
Airborne sensor Airborne sensor Used similar approach to Digitized Stockdon et al. (2002) but with contouring method shoreline from Less than 6 m for Robertson et al., Optech 1210 ALTM the time of data acquisition from LIDAR simutaneously mean difference 2004 LIDAR system was at ordinary high tide. DEM acquire of HWL Hence, the extracted shoreline orthoimage from orthoimage was HWL. Continued
Table 4.2 Summary of the presented approaches
Table 4.2 Continue
Shoreline from NASA’s airborne The accuracy of extracted tide - ± 4.5 m at a 95% LIDAR DEM with topographic mapper coordinated shoreline was Liu et al., 2007 N/A confidence image processing (ATM) LIDAR determined using Monte Carlo interval techniques system simulation technique. Tide -coordi nated approx. 0.5 m for Optech ALTM 3100 NOAA's standard procedure to shoreline from White et al., Topcon Laser- RMSE and 0.2 m LIDAR system and delineate National shoreline LIDAR and 2007, 2010 Zone transects for std. deviation Vdatum (MHW) VDATUM (bias removed) CTM was generated using Tide-coordinated Shoreline IDW interpolation of LIDAR shoreline from 3.55 m in LIDAR from delineated from DEM and bathymetry. The CTM (LIDAR average error and NOAA’s CSC and QuickBird study also developed prior- DEM + Li et al., 2010 2.49 m in bathymetry from satellite image in accuracy analysis based on Bathymetry) and standard NGDC panchromatic First Order Variance analysis Water gauge deviation mode (FOVA) to estimate accuracy observation
14 of the extracted shoreline . 4 DEM from NOAA's 2-13 m for Underdevelopment project. A Tide-coordinated stereo aerial images, standard water surface model is shoreline from Li et al., 2002 ODNR's bathymetry, N/A deviation implemented instead of water CTM and WSM and WSM from (estimated) level from gauge station GLFS records.
Mean shift 1.55 m in RMSE Manually segmentation with OGRIP orthoimages, and 8.24 m Mean shift segmentation digitized LIDAR DEM, Lee et al., 2009, OGRIP LiDAR maximum error technique and convex hull shoreline from satellite images, 2010 dataset,and for overall study algorithm helped delineating the used and orthoimages to QuickBird image area (Lee et al. shoreline over conventional orthoimage extract shoreline 2010) manually digitized shoreline. Continued
Table 4.2 Continue
Intertidal DEM generated from Intertidal DEM instantaneous shoreline can interpolated from compare well with bathymetry water level ESR-1/2 SAR Hoja et al., 2000 N/A N/A from vessel mounted echo referenced imagery sounder and DEM from instantaneous airborne InSAR of the AeS1 shoreline sensor.
Similar approach to Hoja et al. Intertidal DEM Mason et al., (2000) but used universal from water-line 1995, 1997, ESR-1 SAR imagery N/A N/A block kriging to interpolate method from ESR- 1998, 2001 DEM from multi temporal 1 SAR image dataset. The resulting tide -coordinated shoreline has a jagged shaped RMSE 1.80 m as it was determined by
14 DEM generated Muslim and Ground survey and ± 4.5 m at a delineating shoreline between
5 from IKONOS IKONOS imagery
sensor Satellite-borne Foody, 2008 using DGPS 90% confidence pixels of land and sea. Sub- imagery interval pixel mapping techniques may improve appearance of the shoreline. An active contour model A set of (Snake) is implemented to Snake-based tide- instantaneous deform tide-coordinated Li et al., 2005, coordinated shorelines from N/A N/A shoreline from instantaneous 2006 shoreline satellite/aerial shorelines and additional images information. The method is being developed at OSU.
NOAA’s NOS implements applicable standards and procedures for hydrographic surveys, such as for Order 1 surveys based on the International Hydrographic
Organization’s (IHO) Standards for Hydrographic Surveys, Special Publication 44, Fifth
Edition, February 2008 (OCS, 2010). The standards for shoreline/coastline survey horizontal accuracies vary from 10 meters for special order survey to 20 meters for first order (a/b) surveys at the 95% confident interval. The standards for shoreline position and coastal features are also identical to Standards for Nautical Charting Hydrographic
Surveys from Federal Geographic Data Committee (FGDC) (FGDC, 2005).
Special order First order Second order Coastal features surveys (a/b) surveys surveys Fixed aids to navigation and topography significant 2 meters 2 meters 5 meters to navigation.
Shoreline/Coastline and topography less significant 10 meters 20 meters 20meters to navigation
Mean position of floating 10 meters 10 meters 10 meters aids to navigation
Table 4.3 Minimum requirement standards of hydrographic surveys for shoreline positioning and other navigation aids excerpted from IHO (2008)
The current National Map Accuracy Standards defined by The American Society for
Photogrammetry and Remote Sensing (ASPRS) is implemented by The U.S. Geological
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Survey (USGS) to produce topographic maps in 1:250,000 and larger scale. The horizontal accuracy standard of USGS 7.5-minute quadrangle topographic map, which is the best known USGS map (USGS, 2010a), requires 90% of positioning tests to be within
1/50 th of an inch. Hence, the 12.2 meters of horizontal accuracy must be achieve at
1:24,000 scale of USGS 7.5-minute quadrangle map (USGS, 1999). Therefore, although approaches to obtain tide-coordinated shoreline presented in this chapter were not performed with systematical tests for horizontal accuracy, they certainly show a lot of promise, the results of achieved tide-coordinated shoreline are possibly accurate enough to meet these minimum standards.
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CHAPTER 5 – FUTURE IMPROVEMENT OF TIDE
COORDINATED SHORELINE MAPPING
The previous chapter discussed approaches and methods to achieve tide-coordinated shoreline. Many methods generate digital elevation model prior to extracting tide- coordinated shoreline. Accuracy of tidal datum is also another factor that affects the quality of the extracted shoreline. The current technology of tide observation enables tides to be monitored as frequent as every 6 minutes for over 175 tide stations around the
United States. In addition, with integration of VDatum, it results in the generation of highly accurate estimation of tidal datum, and thus distributes minor effect when compared to error from elevation model. There are also proposed methods of tide- coordinated shoreline modeling from Li et al. (2002, 2005, and 2006). This chapter discusses about future improvements of tide-coordinated shoreline with respect to techniques, data sources, and efficiencies to tide-coordinated shorelines.
5.1 Tide-coordinated shoreline from conventional aerial photogrammetry
Aerial photogrammetry and photogrammetric mapping techniques were introduced to replace ground surveys utilizing plane tables since around 1927 (Graham et al., 2003).
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Although tide-coordinated shorelines achieved from analog devices were high in quality, the operation was labor intensive and time consuming that makes it inapplicable to cover a large project in timely manner. Shoreline mapping from tidal-referenced photographs has been developed over time by implementing better technologies, such as GPS/IMU devices and digital aerial cameras, as well as regulating standards and instructions for conducting shoreline survey using aerial photogrammetry. Although, current coastal mapping projects still utilize aerial photogrammetry as a primary method to delineate tide-coordinated shoreline, several advanced technologies, such as LIDAR and satellite imagery, have been experimented to be an option for aerial photogrammetry in the past decade. As mentioned in the last chapter, LIDAR has currently begun to be implemented in NOAA’s national shoreline delineation for MHW shoreline (Parrish et al., 2010). As a result, aerial photogrammetry may be associated with or integrated into other technologies in shoreline mapping in the near future due to several constraints to obtain tide-referenced photographs and higher cost in surveying operation comparing to implementation of other advance technologies.
5.2 Tide-coordinated shoreline from digital models
Extracting tide-coordinated shoreline from elevation model is currently the preferred and widely utilized solution. The elevation model could be obtained from several approaches such as LIDAR data, GPS survey, or set of instantaneous shorelines. Hence, accuracy of resulting tide-coordinated shoreline is influenced by accuracy of data source which used
149 to create the elevation model. Following discusses different data sources which have been utilized to derive tide-coordinated shoreline from digital models.
5.2.1 GPS survey
Compared to its predecessor, conventional surveys using theodolites, GPS methods, such as real time kinematic, differential, or stop-and-go GPS, facilitate ground surveying tasks to be done while maintaining good accuracy. However, the approach to tide-coordinated shoreline implementing GPS survey is still unable to deliver timely mapping products.
For example, stop-and-go GPS required a rover GPS to spend about 30 seconds (El-
Rabbany, 2002) for each stop. If a project area is 10 m by 2 km along shore and elevation data needs to be collected every 2 meters, the surveying process requires roughly 42 hours only for GPS data collection. Although recent inventions, such as laser level integrated GPS which was utilized in Parrish et al. (2010), can reduce working time with centimeter-level precision, it is not efficient enough to be a main method to carry out shoreline mapping for a large area.
Integrating GPS with an IMU on an all-terrain vehicle (ATV) like SWASH project from
USGS seems to have more promise than conventional GPS survey. The setup yields better efficiency as the elevation model along the beach can be collected much faster (70 km. in one low tide period) while maintain high accuracy. One thing that needs to be considered for this approach, is the accuracy of the elevation model degrades significantly for the points further from the vehicle path. Therefore, it is necessary to
150 determine the driving course of vehicle before an actual operation, or multiple observation paths may be performed to avoid such problem.
As mentioned, mapping with GPS is capable of producing a highly accurate shoreline.
However, the derived shoreline products may be too accurate for some coastal applications. Consequently, development of GPS survey approaches to obtain tide- coordinated shoreline may point to automation of data collection rather than improvement of sensors to acquire better positional accuracy and they will serve as methods to produce accurate reference tide-coordinated shoreline for researching purposes. Additionally, many beaches may not be accessible by ATV or by foot.
5.2.2 Implementation of satellite imagery
Satellite imagery has become the main source for mapping and navigation applications owing to its continuous operation and improving image resolution. Both imaging sensors and methods have been developing for decades since the launch of U.S. Explorer VI
Earth satellite in 1959 (NASA, 2010). There are currently several satellites in operation for earth observation and imaging, whereas spatial resolution of some imaging sensors has already reached and surpassed 0.5 meters mark of spatial resolution. For example,
GeoEye-1 and IKONOS can produce respectively 0.41 m. and 0.82 m. spatial resolution at nadir in panchromatic mode (GeoEye, 2010). DigitalGlobe's WorldView-2 satellite can also capture images at 0.46 meters in resolution at the nadir for panchromatic mode
(DigitalGlobe, 2010b).
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Achieving inter-tidal DEM from satellite imagery may be accomplished by two approaches: DEM from instantaneous shorelines and DEM from satellite stereo images.
The first approach requires numerous satellite images with the known water level at image acquisitions. Water level can be determined by comparing the time when images were taken with water level reading at tide gauge station. This method has several drawbacks, making it unattractive when compared with other approaches. Instantaneous shoreline generally is susceptible to displacement from ideal position due to wind and wave force. As a result, although water level was determined from a gauge station, shoreline position in satellite images does not represent the position where land and water should intersect. Moreover, in order to create an elevation model for inter-tidal tide area, a set of instantaneous shorelines should be obtained within a short period of time, since shore may be subject to erosion or change. Lastly, this approach may not be an outstanding choice in term of efficiency because it requires numerous vectorized instantaneous shorelines from satellite images, and yields the elevation model of small inter-tidal area as a final result. Hence, although the method costs less than obtaining tide-coordinated shoreline form aerial photogrammetric survey or LIDAR survey, it is probably most suitable for national agencies that have unlimited accessibility to satellite images.
The second method generates DEM from satellite stereo images. Several methods, such as image matching and improvement of ground positional accuracy, are generally required to obtain a good relationship between image coordinates and ground coordinates. Increasing satellite image accuracy can be done using numerous ground control points to refine Rational Functions (RFs) instead of satellite physical model as
152 performed in Li et al. (2003) and Di et al. (2001, 2003c). Muslim and Foody (2008) also applied an equivalent method to improve ground accuracy by refining Rational
Polynomial Coefficient (RPC) model prior to generating an elevation model from satellite stereo images. Since the time of satellite image acquisition cannot generally be arranged, incorporation of bathymetry is required to achieve a complete coverage of the intertidal area used to extract tide-coordinated shoreline. Satellite imagery is also capable of estimating water depth and creating bathymetry as shown in Muslim and Foody
(2008). However, the method of determining water depth, which utilizes statistical information based on light reflectance, requires training samples of surface elevation under water at different locations. The training samples were obtained by manually determining water depth with a sonar device. This compulsory process hinders the method, as sea floor elevations are measured point by point. Since the depth measuring device is vessel-mounted, implementing multibeam echosounder could be a better solution to obtain bathymetry for merging with the upland DEM. Instead of using satellite imagery, hyperspectral imagery is also capable of deriving bathymetry (Bachmann et al.,
2010; Lee et al., 1999). Observing both satellite orbits and tidal stages may provide time windows which the satellite moves over the mapping area when tide falls much lower than MLLW level. Hence, the DEM generated from satellite images at the very low tide should be sufficient to derive tide-coordinated shoreline without combining with bathymetry.
Advancement of satellite imagery certainly benefits tide-coordinated shoreline mapping.
Firstly, improving spatial resolution means image matching should provide more accurate results and thus it yields better quality of DEM from satellite stereo images.
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Instantaneous shorelines extracted from satellite images will also have less spatial error, as advanced sensors bring higher image resolution. Moreover, since objects in the image have finer texture, automated delineation of shoreline could be able to be utilized more effectively, especially for areas with many man-made features that usually give poor results for automated shoreline extraction. Integration of several data sources will also benefit from the improvement. As shown in (Li et al., 2007, 2008), the accuracies from combining an aerial image with satellite images are not better than the aerial image alone, which has the highest accuracy. In general, positional accuracy of the result deteriorates from the best included data source depending on the configuration and quality of other combining sources. Hence, increasing image resolution and spatial accuracy for satellite imagery shall improve the overall result of the extracted shoreline from multi sources integration. Finally, there are possibilities of satellite images to be available at a little to no cost in fine resolution due to development of sensors technologies. Therefore, approaches to tide-coordinated shoreline utilizing satellite imagery holds much potential to progress in the future.
5.2.3 LIDAR
As discussed, LIDAR yields better weather constraint flexibilities over the aerial photogrammetry. The LIDAR derived tide-coordinated shoreline accuracy is comparable to conventional method and also is better than most of developing tide-coordinated shoreline deriving approaches as shown in table 4.2.
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LIDAR technology receives a lot of interest from many communities. The Center for
LIDAR Information Coordination and Knowledge (CLICK) was established by USGS to publicly provide information and access to dataset of LIDAR technology (USGS, 2010e).
In addition, an effort to constitute a national LIDAR dataset, which is concerned about availability and standards of LIDAR data over the United States, has been initiated by
USGS and cooperative national agencies (Stoker et al., 2008). Similar to satellite imagery, LIDAR sensor technology is still in the developing stage to yield finer and more accurate elevation measurement. Furthermore, some technical problems regarding to systematical bias and errors have been addressed and currently being studied (Burman,
2000, Schenk, 2001, Csanyi et al., 2005). In addition, a limitation of bathymetry LIDAR in shallow water (0-4 m depth) was addressed and utilization of hyperspectral imagery for bathymetry recovery was studied in Bachmann et al. (2010). Therefore, shoreline mapping using LIDAR system can produce better outcome, since there is still a capability for the technology to develop.
5.2.4 VDatum
VDatum is an important element in deriving tide-coordinated shoreline from digital models as it facilitates integration of vertical data from several sources. The tool is very useful for shoreline delineation using LIDAR and bathymetry, and has been utilized in the mapping of NOAA’s National Shoreline for MHW shoreline (White et al., 2010).
Implementing VDatum requires attention to possible uncertainties that follow the conversion of coordinates between vertical datums. Practically, utilizing different geoid
155 models yields different error for coordinate transformation on the same location (Cheng et al., 2008). F igure 5 .1 illustrates estimated errors from the International Terrestrial
Reference Frame of year XX (ITRFxx) to other reference datums including a tidal datum
(i.e., MLLW and MHW).
Figure 5.1 Estimated uncertainties cooperate with transformation betwe en reference datums (NOAA, 2010e)
The current version of VDatum supports transformation for the many seaports around the
United States, as shown in Figure 5 .2. NOAA also plans to expand availability of
VDatum to cover most coasts around the U.S., Puerto R ico, and Hawaii by 2013 (NOAA,
2010f).
156
Figure 5.2 Illustration of current seaports around the U.S. with avail ability of VDatum
(NOAA, 2010f)
5.3 Modeling tide-coordinated shoreline
Modeling tide-coordinated shoreline is a non -mainstream approach to deri ve tide- coordinated shoreline and has been studied in the Mapping and GIS laboratory at the
Ohio State University. In Li et al. (2002), dynamic movement of shorelines was represented with mathematical functions. Shoreline in the study area would be segment ed into pieces and shoreline positions (x, y, and z) of each segment were expressed as a set of polynomials with respect to time or water level. Once coefficients in polynomials were computed using numbers of instantaneous shorelines, position of tide -coordinated shoreline at a given water level can be determined. The method to model shorelines was also experimented in Li et al. (2005, 2006) by applying an active contour model (snake model) to derive a tide -coordinated shoreline. Snake-based shoreline utili zes internal and external energies to deform a snake shoreline to a position where overall energy is
157 minimized. Given the input of water level at a desired tidal datum, tide-coordinated shoreline can be derived.
Utilization of the novel concepts to modelling shoreline to represent and derive tide- coordinated shoreline possibly gives advantages over other methods, such as LIDAR and aerial photogrammetry. The retrieval or deriving process to tide-coordinated shoreline could be executed in less time once polynomial coefficients and supplemental components (water level at gauge stations and meteorological information for snake method) are obtained. Moreover, the approaches require less space for data storage and the update process of the model should also cost less than LIDAR and aerial photogrammetry methods. However, the two approaches to model shorelines mentioned above are currently in the developing stage and there are still some points that need to be realized in order to implement these models in actual applications. First, a robust solution for shoreline segmentation to provide consistent result should be developed. In addition, the degree of the polynomial to represent each shoreline segment could be examined further if higher degree yields better fitting for different types of shorelines or coastal morphologies (i.e. man-made construction, complicated bluff area, and general beach). In general, the length of shoreline segments is related to the degree of polynomials. For instance, if a shoreline was to segmented into longer pieces than those used in the experiment (Li et al., 2006), polynomial degree of higher than 2 should be applied to represent shoreline segments. Finally, the processes to divide shoreline and to determine the degree of polynomial should be automated or should involve little amount of human interaction to make the approach of shoreline modeling efficient and more attractive than other methods.
158
5.4 Discussion
There are demands to frequently update the map of a shoreline, as the position of tide- coordinated shorelines changes over time either by changes of coastal morphology (i.e. erosion and man-made construction) or by changes of sea level due to global warming.
Therefore, the need to search for efficient methodologies to conduct mapping of tide- coordinated shoreline will not cease. Current implementation, limitations, and potential development of approaches to tide-coordinated shoreline were discussed in this chapter.
Overall, most of the methods to derive tide-coordinated shoreline utilizing digital models show possibilities to perform better regarding to the coming development in sensors/instruments. In addition, satellite imagery and laser altimetry such as LIDAR currently have received a lot of interest as their solutions to achieve tide-coordinated shoreline are promising. On the other hand, approaches to model tide-coordinated shorelines proposed by Ohio State University’s Mapping and GIS laboratory may breach the novel technique to study and represent shorelines and their dynamic behavior.
Based on this review and comparisons of results, a promising area of future research is the use of satellite imagery, because of its improving capabilities in terms of spatial resolution, accuracy, and availability of hyperspectral imagery. Moreover, utilizing satellite imagery would yield better efficiency over using observation from ground and airborne sensors since utilizing satellite images does not require additional data collecting operations. However, in order to fully utilize satellite imagery for federal shoreline mapping programs, such as NOAA National Shoreline, robust/rigorous methods to derive tide-coordinated shoreline need to be realized. In addition, as utilization of a single source of data may not be sufficient to derive tide-coordinated shoreline, approaches to
159 integrate images from different satellites and consider differences in spatial resolution and accuracy from each sensor are also necessary.
160
CHAPTER 6 - CONCLUSION
This thesis discusses how important coastal regions are to human societies and how much endeavor people have made to study and preserve them. Shorelines serve as an essential feature to coastal communities, as they are used to indicate private/public boundaries and also are utilized in national maritime determination. By definition, shorelines or coastlines are the line of contact between land and water (Shalowitz, 1962). However, shoreline positions change continuously, either in short-term or in long-term, due to influences from several factors, such as tidal phenomena, meteorological conditions, and global warming. Tide-coordinated shorelines, on the other hand, are shorelines at a specific phase of the tide such as MHW and MLLW, which have been utilized as representatives of coastlines in production of nautical charts.
Mapping of tide-coordinated shoreline is a critical task for nautical charting, coastal area monitoring and management, and many coastal applications. In the early age of the Coast and Geodetic Survey, plane tables and staff leveling techniques were the main instruments for conducting shoreline mapping. Although ground surveys using analog devices were able to provide high-quality shoreline, the task was time consuming and labor intensive, resulting in its low productivity and poor efficiency. Aerial photogrammetry was then introduced in 1927 and yielded much development for coastal
161 mapping operations (Graham et al., 2003). Tide-coordinated shoreline mapping from aerial photogrammetry provides many advantages over shoreline mapping from ground surveys. The method utilizes tidally referenced aerial photographs which are obtained by scheduling flights when tides in an interested area are close to the desired level.
Recent progresses in tide-coordinated shoreline mapping involve advanced methods and technologies. Light detection and ranging (LIDAR), satellite imagery, and auto-feature extraction from imagery are examples which have been implemented in coastal mapping applications. NOAA’s National Geodetic Survey (NGS) has begun utilizing LIDAR data to map MHW shoreline in the production of NOAA’s national shoreline, while MLLW shoreline mapping still implements photogrammetric compilation from tide-coordinated aerial imagery. Many of recent approaches to tide-coordinated shoreline focused on deriving digital models, then extracting tide-coordinated shoreline with a desired tidal stage. Digital models were derived either from direct or indirect methods. Direct methods to derive elevation models include DEM from topo/bathy LIDAR systems and satellite imagery, and indirect methods include inter-tidal DEM from instantaneous shorelines.
However, there are methods to map tide-coordinated shoreline, such as modeling of tide- coordinated shoreline as mathematical expressions (Li et al., 2002, 2005, and 2006), conducted at the Ohio State University.
Tide-coordinated shoreline mapping research from the Mapping and GIS Laboratory at the Ohio State University can be distinguished by two approaches: mapping and utilization of instantaneous shorelines and implementation of digital models. The first approach deals with improving accuracy and efficiency of methods to extract instantaneous shorelines from satellite and aerial imagery as well as implementation of
162 instantaneous shorelines to derive tide-coordinated shorelines. The second approach emphasizes studies of coastal terrain model (DEM + near shore bathymetry), water surface model, and the method to estimate accuracy of tide-coordinated shorelines derived from the digital models.
Reviews of approaches to tide-coordinated shorelines show that several methods are capable of deriving accurate tide-coordinated shorelines with respect to standards of hydrographic surveys for shoreline positioning and other navigation aids (2008), published by the International from Hydrographic Organization (IHO). Conducting GPS ground surveys can produce highly an accurate tide-coordinated shoreline, but the approach is not suitable for shoreline mapping for large area projects. Hence, the applications are suitable for deriving high-quality ground truth of tide-coordinated shoreline for research purposes or being an alternative method when performing aerial photogrammetry is not applicable. Satellite imagery, on the other hand, may provide inferior spatial resolution and positional accuracy, but deriving tide-coordinated shorelines using satellite images can be achieved with a much lower budget than shoreline mapping from aerial photogrammetry. Therefore, each method delivers different level of accuracy and efficiency, making it suitable for certain applications.
Advancements in remote sensing methods, especially improvements of imaging sensors and laser altimetry, greatly benefit developments to approaches to derive tide-coordinated shoreline. For instance, availability of sub-meter resolution satellite images for commercial use would improve quality of inter-tidal elevation models from instantaneous shorelines and DEM from satellite stereo images. Higher spatial resolution yields finer texture of objects in an image and thus facilitates in accurate image matching processes
163 for automatic shoreline delineation and DEM generation. Moreover, airborne LIDAR and satellite altimetry technologies are still progressing in both sensors and implementation methodologies. In all, development of tide-coordinated shoreline mapping in the future is sound and promising, with possibilities to provide more accurate and efficient products of tide-coordinated shorelines.
164
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