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
Made available by Ohio Sea Grant as OHSU-TD-111 as a result of project #R/CE-010
Copyright by
Anuchit Sukcharoenpong
2010
Made available by Ohio Sea Grant as OHSU-TD-111 as a result of project #R/CE-010
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 tidecoordinated 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 tidecoordinated 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
Made available by Ohio Sea Grant as OHSU-TD-111 as a result of project #R/CE-010
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
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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 tidecoordinated 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), Tidecoordinated 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 tidecoordinated 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 lowerlow 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 tidecoordinated shoreline is derived from indirect sources. Intersecting coastal terrain model
or elevation model with the desired tidal datum is one way to derive digital tidecoordinated 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 tidecoordinated aerial stereo photography (Woolard et al., 2003). Hence, tide-coordinated
shorelines obtained from this method are called physical tide-coordinated shorelines (Li
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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 heightreferenced 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 tidecoordinated 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.
10
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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
public 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 period 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.
11
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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 nineteenyear 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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Shoreline
Potential Applications
NOAA
National
Shoreline
Shoreline Change
Analysis, Nautical Chart
Production, Cartographic
Representation, and
Boundary Determination
NOAA
Composite
Shoreline
Cartographic
Representation and
Boundary Determination
USGS
Vector
Shorelines
Shoreline Change
Analysis, Cartographic
Representation, and
Boundary Determination
NOAA OCS
Shorelines
Cartographic
Representation and
Boundary Determination
NOAA
Medium
Resolution
Shoreline
Cartographic
Representation and
Boundary Determination
Prototype
Global
Shoreline
Cartographic
Representation and
Boundary Determination
World
Vector
Shoreline
Cartographic
Representation and
Boundary Determination
Scale
Local
1:5,000 –
1:20,000
Source (Dirived
from)
Coverage
Tidal
Datum
NOAA T-sheets
and
georeferenced
aerial photos
Continental
U.S. + AK,
HI, PR, VI,
Great Lakes
MHW
and
MLLW
NOAA T-sheets
Continental
U.S. + HI
MHW
NOAA T-sheets
U.S. Gulf of
Mexico,
Southeast
Atlantic, and
California
MHW
NOAA Nautical
Charts
Continental
U.S. + AK,
HI, PR, VI,
Great Lakes
MHW
and
MLLW
NOAA Nautical
Charts
Continental
U.S. only
MHW or
MHHW
LANDSAT
GeoCover
World-wide
HWL
Defense
Mapping
Agency (DMA)
Digital
Landmass
Blanking data
World-wide
MHW
Local
1:5,000 –
1:20,000
Local
1:5,000 –
1:20,000
National
1:10,000 –
1:80,000
National
1:70,000
average
Global
1:75,000 and
smaller
Global
1:250,000
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
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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
HOR_ACC
INFORM
ATTRIBUTE
CLASS
EX_METH
Date of source imagery
Horizontal positional accuracy reported in meters
Additional Information
Attributes of an object as explained in C-COAST.
Class of an object as explained in C-COAST.
Extracting methods from the source
M - Compiled from monoscopic image
S - Compiled from stereoscopic image
EXTRACT_TE
Extracting techniques from the source
A - ANALOG PLOTTER
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
GISDATE
Date the scanned raster t-sheets were digitized to create vector
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
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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
fl_transects_lt
fl_transects_st
Shore-normal transects with associated long-term rates of shoreline
change
Shore-normal transects with associated short-term rates of shoreline
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
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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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Made available by Ohio Sea Grant as OHSU-TD-111 as a result of project #R/CE-010
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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Made available by Ohio Sea Grant as OHSU-TD-111 as a result of project #R/CE-010
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 interannual variation plot (3) but the plot starts at year 1980.
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Station
ID
Date
Time
Verified
level (m)
9063063
9063063
9063063
9063063
9063063
9063063
9063063
9063063
9063063
9063063
9063063
9063063
9063063
9063063
9063063
9063063
9063063
9063063
9063063
9063063
20091231
20091231
20091231
20091231
20091231
20091231
20091231
20091231
20091231
20091231
20091231
20091231
20091231
20091231
20091231
20091231
20091231
20091231
20091231
20091231
0:00
0:06
0:12
0:18
0:24
0:30
0:36
0:42
0:48
0:54
1:00
1:06
1:12
1:18
1:24
1:30
1:36
1:42
1:48
1:54
173.938
173.933
173.921
173.959
173.924
173.928
173.919
173.907
173.898
173.909
173.905
173.881
173.91
173.927
173.951
173.952
173.946
173.947
173.953
173.936
water
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
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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
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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.
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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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Made available by Ohio Sea Grant as OHSU-TD-111 as a result of project #R/CE-010
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 alongtrack 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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Made available by Ohio Sea Grant as OHSU-TD-111 as a result of project #R/CE-010
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
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Made available by Ohio Sea Grant as OHSU-TD-111 as a result of project #R/CE-010
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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Made available by Ohio Sea Grant as OHSU-TD-111 as a result of project #R/CE-010
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
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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 constantdensity 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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Made available by Ohio Sea Grant as OHSU-TD-111 as a result of project #R/CE-010
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
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Made available by Ohio Sea Grant as OHSU-TD-111 as a result of project #R/CE-010
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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Made available by Ohio Sea Grant as OHSU-TD-111 as a result of project #R/CE-010
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
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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 tidecoordinated 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
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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
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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):
௜ ௜௝ ௝ ௜௝ (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,
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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
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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 tidecoordinated 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
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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
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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
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to be identified. Furthermore, delineating tide coordinated shoreline in the middle of runup 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
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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 tidecoordinated shoreline can be achieved from a set of instantaneous shorelines or the
intersection of CTM and WSM. Digital tide-coordinated shoreline from instantaneous
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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
, , at a particular piece can be can be decomposed by (Li et al., 2002):
௑ ଴ , ଵ , ଶ , … , ௡ , , ௒ ଴ , ଵ , ଶ , … , ௡ , , (4.3)
௓ ଴ , ଵ , ଶ , … , ௡ , , 79
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Where t is time variable, s is length parameter which starts at the beginning of shoreline,
and ଴ ℎ, ଵ ℎ, … , ௡ (ℎ) are temporal polynomial coefficients which can be expressed
as polynomials function at water level h by (Li et al., 2002):
଴ = ଴଴ + ଵ଴ ℎ + ଶ଴ ℎଶ +. ..
଴ = ଴଴ + ଵ଴ ℎ + ଶ଴ ℎଶ + ⋯
(4.4)
଴ = ଴଴ + ଵ଴ ℎ + ଶ଴ ℎଶ + ⋯
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 tidecoordinated 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
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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
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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 tidecoordinated 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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=
ଵ , , ଶ , , =
ଷ , , ସ , , (4.5)
While the downward RF which is the inverse form can be shown as (Di et al., 2001):
=
ଵ , , ଶ (, , )
=
ଷ , , ସ , , (4.6)
The ratios perform transformation between X, Y, 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):
, , = ଴ + ଵ + ଶ + ଷ + ସ ଶ + ହ + ଺ + ଻ ଶ + ଼ +
ଽ ଶ + ଵ଴ ଷ + ଵଵ ଶ + ଵଶ ଶ + ଵଷ ଶ + ଵସ + ଵହ ଶ + ଵ଺ ଷ +
ଵ଻ ଶ + ଵ଼ ଶ + ଵଽ ଷ
(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 10ି଻ for positional RMS errors. The study also implied that RFs can be
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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
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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.
Method
First
Second
stereo pair
1
2
1
2
RMS errors (m)
X
Y
Z
2.489
4.404
0.746
1.863
4.124
4.318
1.342
1.051
1.632
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
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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
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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 ଵ sin ଶ + cos ଵ cos ଶ cos(ଶ − ଵ )
(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
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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
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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
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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 (NIR) 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,NIR,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
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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
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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);
ଵ
ଷ
ଵ
ଷ
= ∗ + ଶ
ସ
ଶ ௧
ସ ௧
మ
(4.9)
భ
Where;
ଵ − ଷ
0 0
0
ଵ − ଷ
0 0
0
=
, = ଵ − ଷ
0 0 cos ଵ − ଷ
0 0 − sin 0
ඥሺ௫ ି௫ ሻమାሺ௬ ି௬ ሻమ
0
, = భ మ మ భ మ మ
ඥሺ௫యି௫ర ሻ ାሺ௬యି௬రሻ
sin cos (4.10)
Transformation parameters are expressed in polynomials forms for different blufflines. If
m is the number of blufflines (ଵ , ଶ , … , ௠ ), 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 jth
segment of bluffline can be expressed as (Srivastava, 2005);
௞ି௜ିଵ
௝
௣ି௤ = ∑௞௜ୀଵ ௜ିଵ ௣ − ௤ (4.11)
With all combination of bluffline pairs, the following matrix form can be expressed
(Srivastava, 2005);
ଶ − ଵ ௞ିଶ
ଵିଶ
"
" %
− ଵ ௞ିଶ
! ଵିଷ $ ! ଷ
⋯
! … $=!
!௣ି௤ $ ! − ௞ିଶ
௤
௣
… #
⋯
ଶ − ଵ ௞ିଷ
ଷ − ଵ ௞ିଷ
⋯
௞ିଷ
௤ − ௣ ⋯
⋯
⋯
⋯
⋯
⋯
1
%
1$
⋯$
1$
⋯#
ଵ
" ଶ %
! … $
!
$
! … $
௞ିଵ #
(4.12)
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The matrix form represents Y = AX and can be solved using least-squares method. The
solution is solved by X = Aᇱ Aିଵ (Aᇱ Y).
The method was tested on a 15 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);
௧ = &௑ (଴௧ , ଵ௧ , ଶ௧ , … , ௡௧ , ', )
௧ = &௒ ଴௧ , ଵ௧ , ଶ௧ , … , ௡௧ , ', (4.13)
௧ = &௓ ଴௧ , ଵ௧ , ଶ௧ , … , ௡௧ , ', 103
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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 ଵ , ଶ , and ଷ 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
shoreline at time t1
Instantaneous
shoreline at time t3
Instantaneous
shoreline at time t2
External force
Tide-coordinated
shoreline/snake-line
C(s)
Pi
Tide gauges
Protection structure
Figure 4.18 Implementation of snake model to derive tide-coordinated shoreline (Li et al.,
2005)
The internal energy (௜௡௧ [) ', ', ] consists of two control parameters, α and β, which
can be expressed as (Li et al., 2005);
డ஼ሺ௦ሻ ଶ
(௜௡௧ = * ' +
డ௦
డమ஼ሺ௦ሻ
+ + ' +
డ௦మ
ଶ
+ ,-2
(4.14)
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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 (௘௫௧ [) ', ', ] may be derived from water level at gauge stations,
meteorological information, and coastal morphology. Li et al. (2006) applied distances
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);
௧ି௧ೕ
௃
(4.15)
(௘௫௧ = ∑௝ୀଵ ∑ூ௜ୀଵ 1/.௜
௧ି௧ೕ
The distance .௜
is the distance between / ௧௛ point of tide-coordinated shoreline at time
and / ௧௛ 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);
ଵ
ଵ
ଵ
డ஼ሺ௦ሻ ଶ
(௦௡௔௞௘ = 0଴ &௘௫௧ )'.' + ଶ 0଴ * ' +
డ௦
+ + ' +
డమ஼ሺ௦ሻ
డ௦మ
ଶ
+ , .' → 1/2
(4.16)
A preliminary experiment of applying polynomials into shoreline 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 ௧ ௧ . First and second-order polynomials were used to formulate ௧ ,
which can be shown as follows (Li et al., 2005):
First order polynomial: ௧ ଵ௧ ௧ ଴௧
(4.17)
Second order polynomial: ௧ ଶ௧ ௧ଶ ଵ௧ ௧ ଴௧
(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
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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 curvefitting for both first and second-order polynomials. Following represents the tested
hypotheses (Li et al., 2005);
଴ ∶ 3 = 3଴
| ଵ ∶ 3 ≠ 3଴
and
଴ ∶ 4 = 4଴
|
ଵ ∶ 4 > 4଴
The tested mean (µ0) and standard deviation (σ0) were selected at µ0 = 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
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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
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artificially generated straight shorelines and simulated water surface. An initial tidecoordinated 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
Historical lines
b. Case 2
Initial lines
Resulting lines
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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b. Case 2
a. Case 1
Historical lines
Initial lines
Resulting lines
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
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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);
5஽ாெ = ∑௜
௭೔
ௗ೔మ
/ ∑௜
ଵ
ௗ೔మ
(4.19)
where 5௜ is a elevation of LIDAR 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 extraction procedure (Li et al., 2010)
To estimate 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 vertical 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);
ଶ
ଶ ஽ாெ
ଶ
ଶ
1
௜ ஽ாெ
1
1
. ௛ଶ ଶ 2 ଶ . . ସ . ௩ଶ
ଷ
௜
௜
௜
௜
௜
௜
௜
௜
(4.20)
CTM was obtained from combining the generated DEM with bathymetric data. IDW
interpolation was again appl
applied
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);
‫ ݖ‬ൌ
షమ
షమ
ವಶಾ
ವಶಾ ್ೌ೟೓೤
್ೌ೟೓೤
షమ షమ
ವಶಾ
್ೌ೟೓೤
(4.21)
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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);
ߪ௭ଶ಴೅ಾ ൌ
ଵ
షమ ାఙ షమ
ఙವಶಾ
್ೌ೟೓೤
(4.22)
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 xaxis (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);
௦௟ ଵ ∆
௦௟ ଵ ∆
(4.23)
௦௟ 119
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Where;
∆ =
௛ି௛భ
௛మ ି௛భ
. ଶ − ଵ (4.24)
ℎ − ℎଵ
∆ =
. ଶ − ଵ ℎଶ − ℎଵ
Errors of the extracted shoreline were propagated from CTM 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);
డ∆௫ ଶ
డ∆௫ ଶ
డ∆௫ ଶ
4௑ଶೞ೗ = 4௫ଶభ + 6డ௛ 7 4௛ଶభ + 6డ௛ 7 4௛ଶమ + 6 డ௛ 7 4௛ଶ
భ
4௒ଶೞ೗
=
4௬ଶభ
4௑ೞ೗ ௒ೞ೗ =
మ
8∆ ଶ ଶ
8∆ ଶ ଶ
8∆ ଶ ଶ
+*
, 4௛భ + *
, 4௛మ + *
, 4௛
8ℎଵ
8ℎଶ
8ℎ
(4.25)
8∆ 8∆ ଶ
8∆ 8∆ ଶ
8∆ 8∆ ଶ
4௛భ +
4௛మ +
4
8ℎଵ 8ℎଵ
8ℎଶ 8ℎଶ
8ℎ 8ℎ ௛
The error variances 4௛ଶభ and 4௛ଶమ are from the calculated CTM vertical error variances,
while 4௛ଶ is an error variance from water level measurement, the same as vertical error
variance 4௓ଶೞ೗ of the extracted shoreline. The horizontal error variances 4௫ଶభ , 4௫ଶమ , 4௬ଶభ and
4௬ଶమ inherited from the CTM horizontal error and the horizontal error variance of the
shoreline is 94௑ଶೞ೗ + 4௒ଶೞ೗ .
The study utilized 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
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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
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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 vehiclebased 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 sixwheel, 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
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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 (1σ) (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 CV580 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
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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 crossprofile 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
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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
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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 pointcloud 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
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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 waterline 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)
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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 4meter 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
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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 4meter resolution multispectral IKONOS images. IKONOS image diginal numbers (DN)
was converted to planetary reflectance values using (Muslim and Foody, 2008);
ߩ௣ =
Where;
గ௅ഊ ௗమ
ாௌ௎ேഊ ୡ୭ୱሺఏೞ ሻ
(4.26)
:௣ = planetary reflectance
;ఒ = spectral radiance at sensor’s aperture
(<=ఒ = band dependent mean solar exoatmospheric irradiance
௦ = solar zenith angle
. = earth-sun distance, in astronomical units
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The spectral radiance, ;ఒ , was calculated using published calibration coefficient and
image bandwidth as (Fleming, 2001);
;ఒ = >=/(()?)@AB/10)/C2.D/.ℎ)
(4.27)
The depth information of 90 training points and 25 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 vesselmounted 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);
>AEℎ௫ =
Where;
୪୬ሺ௅ೣ ି௅೏ሻି୪୬ሺ௅బି௅೏ ሻ
ି௞൫ଵା௖௢௦௘௖ሺா ᇲ ሻ൯
(4.28)
;଴ = radiance from shallow water
;ௗ = radiance from deep water
;௫ = radiance from water depth at point x (measured point)
(′ = sun’s angle underwater (Figure 4.37)
F = 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 F was determined as a slope of a regression line from the
plot of logarithm of depth against logarithm of reflectance obtained from 90 training
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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
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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 tidecoordinated 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 tidecoordinated 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
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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 tidecoordinated shoreline mapping with respect to advancements in technology.
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Ground survey
Technique
implemented
143
Airborne sensor
Stop-and-go
KGPS and vehicle
mounted GPS
receiver
Citation
Data source
Tide-coordinated
shoreline
accuracy
Comments
N/A
N/A
Beach profiles generated from
GPS techniques has
comparable accuracy with the
conventional theodolite survey
but showed slight
improvement for time spent.
The system can determine
more than 70 km of shoreline
data within one period of low
tide.
Referenced
shoreline
Morton et al.,
1993
Unknown GPS
geodetic survey
equipment
SWASH - vehicle
mounted GPS
receiver with yaw,
roll, and pitch
measuring unit
USGS, 2010
Magellen/Ashtech
ADU2 array for
measuring
oreintations and
Ashtech Z-Surveyor
GPS receiver
Unknown
± 1.6 m at a 95%
confidence
interval
Airborne
polarimetric SAR
Yeremy et al.,
2001
Canada's Convair
CV-580 airborne
SAR
GPS ground
survey
6.3 m to7.8 m for
mean difference
contouring method
from LIDAR
DEM
Stockdon et al.,
2002
NASA’s airborne
topographic mapper
(ATM) LIDAR
system
SWASH
extracted tidecoordinated
shoreline
2.9 m. for RMSE
and 2.12 m for
average
difference.
Optech 1210 ALTM
LIDAR system
Digitized
shoreline from
simutaneously
acquire
orthoimage
Less than 6 m for
mean difference
of HWL
contouring method
from LIDAR
DEM
Robertson et al.,
2004
The study was the pilot project
for the launch of
RADARSAT2 which has
similar attributes.
Straightforward method of
contouring of DEM to extract
tide-coordinated shoreline is
implemented. However,
manual processes may still be
required.
Used similar approach to
Stockdon et al. (2002) but with
the time of data acquisition
was at ordinary high tide.
Hence, the extracted shoreline
from orthoimage was HWL.
Continued
Table 4.2 Summary of the presented approaches
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Table 4.2 Continue
Shoreline from
LIDAR DEM with
image processing
techniques
Tide-coordinated
shoreline from
LIDAR and
VDATUM
Tide-coordinated
shoreline from
CTM (LIDAR
DEM +
Bathymetry) and
Water gauge
observation
Liu et al., 2007
NASA’s airborne
topographic mapper
(ATM) LIDAR
system
The accuracy of extracted tidecoordinated shoreline was
determined using Monte Carlo
simulation technique.
White et al.,
2007, 2010
Optech ALTM 3100
LIDAR system and
Vdatum
Topcon LaserZone transects
approx. 0.5 m for
RMSE and 0.2 m
for std. deviation
(bias removed)
NOAA's standard procedure to
delineate National shoreline
(MHW)
Li et al., 2010
LIDAR from
NOAA’s CSC and
bathymetry from
NGDC
Shoreline
delineated from
QuickBird
satellite image in
panchromatic
mode
3.55 m in
average error and
2.49 m in
standard
deviation
CTM was generated using
IDW interpolation of LIDAR
DEM and bathymetry. The
study also developed prioraccuracy analysis based on
First Order Variance analysis
(FOVA) to estimate accuracy
of the extracted shoreline .
Li et al., 2002
DEM from NOAA's
stereo aerial images,
ODNR's bathymetry,
and WSM from
GLFS
N/A
2-13 m for
standard
deviation
(estimated)
Underdevelopment project. A
water surface model is
implemented instead of water
level from gauge station
records.
Lee et al., 2009,
2010
OGRIP orthoimages,
OGRIP LiDAR
dataset,and
QuickBird image
Manually
digitized
shoreline from
the used
orthoimage
1.55 m in RMSE
and 8.24 m
maximum error
for overall study
area (Lee et al.
2010)
Mean shift segmentation
technique and convex hull
algorithm helped delineating
shoreline over conventional
manually digitized shoreline.
144
N/A
± 4.5 m at a 95%
confidence
interval
Tide-coordinated
shoreline from
CTM and WSM
Mean shift
segmentation with
LIDAR DEM,
satellite images,
and orthoimages to
extract shoreline
Continued
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145
Satellite-borne sensor
Table 4.2 Continue
Intertidal DEM
interpolated from
water level
referenced
instantaneous
shoreline
Hoja et al., 2000
Intertidal DEM
from water-line
method from ESR1 SAR image
Mason et al.,
1995, 1997,
1998, 2001
DEM generated
from IKONOS
imagery
Snake-based tidecoordinated
shoreline
ESR-1/2 SAR
imagery
N/A
N/A
ESR-1 SAR imagery
N/A
N/A
Muslim and
Foody, 2008
IKONOS imagery
Ground survey
using DGPS
RMSE 1.80 m
and ± 4.5 m at a
90% confidence
interval
Li et al., 2005,
2006
A set of
instantaneous
shorelines from
satellite/aerial
images
N/A
N/A
Intertidal DEM generated from
instantaneous shoreline can
compare well with bathymetry
from vessel mounted echo
sounder and DEM from
airborne InSAR of the AeS1
sensor.
Similar approach to Hoja et al.
(2000) but used universal
block kriging to interpolate
DEM from multi temporal
dataset.
The resulting tide-coordinated
shoreline has a jagged shaped
as it was determined by
delineating shoreline between
pixels of land and sea. Subpixel mapping techniques may
improve appearance of the
shoreline.
An active contour model
(Snake) is implemented to
deform tide-coordinated
shoreline from instantaneous
shorelines and additional
information. The method is
being developed at OSU.
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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).
Coastal features
Special order
surveys
First order
(a/b) surveys
Second order
surveys
Fixed aids to navigation
and topography significant
to navigation.
2 meters
2 meters
5 meters
Shoreline/Coastline and
topography less significant
to navigation
10 meters
20 meters
20meters
Mean position of floating
aids to navigation
10 meters
10 meters
10 meters
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
1/50th 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 tidecoordinated 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 tidecoordinated 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
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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 (ElRabbany, 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
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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 tidecoordinated 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
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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
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models yields different error for coordinate transformation on the same location (Cheng
et al., 2008). Figure 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 between 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 Rico,
Rico, and Hawaii by 2013 (NOAA,
2010f).
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Figure 5.2 Illustration of current seaports around the U.S. with availability of VDatum
(NOAA, 2010f)
5.3 Modeling tide-coordinated shoreline
Modeling tide-coordinated shoreline is a non-mainstream approach to derive tidecoordinated 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 segmented
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 utilizes internal and
external energies to deform a snake shoreline to a position where overall energy is
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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 tidecoordinated 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.
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5.4 Discussion
There are demands to frequently update the map of a shoreline, as the position of tidecoordinated 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 tidecoordinated 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
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integrate images from different satellites and consider differences in spatial resolution
and accuracy from each sensor are also necessary.
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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
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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 tidecoordinated 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
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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
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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.
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