detection of physical shoreline indicators in a object ... · the aim of this study is to test the...

Detection of physical shoreline indicators in a object-based classification approach

Study case: Island of Schiermonnikoog, The Netherlands

María Virginia Méndez Alves March, 2007

Detection of physical shoreline indicators in an object-based classification approaches.

Study area: Island of Schiermonnikoog, The Netherlands

by

María Virginia Méndez Alves

Thesis submitted to the International Institute for Geo-information Science and Earth Observation in partial fulfillment of the requirements for the degree of Master of Science in Geo-information Science and Earth Observation, Specialization: Geo Hazard Prof. Dr. V.G. Jetten (Chair) Prof. Dr. S. de Jong (External examiner) Dr. H.M.A. v.d Werff (Supervisor) Drs. M.C.J Damen (Supervisor)

INTERNATIONAL INSTITUTE FOR GEO-INFORMATION SCIENCE AND EARTH OBSERVATION ENSCHEDE, THE NETHERLANDS

Disclaimer This document describes work undertaken as part of a programme of study at the International Institute for Geo-information Science and Earth Observation. All views and opinions expressed therein remain the sole responsibility of the author, and do not necessarily represent those of the institute.

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Abstract

The analysis of a shoreline, the contact zone between a body of water and land, implies

addressing the dynamic nature of such a boundary in time and space. High level of uncertainties, in position (natural variability), measurement technique and interpretation, affect the accuracy of shoreline mapping. Definition of shoreline indicators (natural coastal features that represent the shoreline position) should meet as much as possible criteria of objectivity, in order to enable repeatability of remote sensing of shoreline features and to improve shoreline mapping techniques.

The aim of this study is to test the suitability of object-based classification techniques to detect and map shoreline indicators on the North sandy beach of the island of Schiermonnikoog, The Netherlands. A hyperspectral AHS image was used in combination with field observations and laboratory analysis to study the possibility of discriminating physical beach compartments.

This research has identified spectral characteristics across the beach land-water interface. Strong relationships quantified between reflectance and water content provides an insight to the definition of the shoreline indicators. Regarding this, an endmember selection took place based on sand wetness. In this selection, spectral brightness was the dominant aspect. Albedo differences are considered as the spectral signature of the 4 surface sand cover are: dry sand, moist sand, wet sand and saturated sand. With this spectral characterization, a class separability test was carried out with Minimum distance to class, pixel-based classifier, which proved that sand moisture content can be used to define these 4 water line features: previous high water line, high water line, instantaneous water line and low water line.

To map these boundaries, an object-based edge detection algorithm called “rotation variant template matching” was applied. The RTM method has failed in 1 of the 4 boundaries that were expected to be detected. From the results of the 3 detected boundaries, it is reasonable to suggest that higher moisture content contributed to the marginal definition of the indicator. Consequently, the ability to detect shoreline indicator will decrease seaward. An important implication has the timing image acquisition will hardly determinate the possibility of located physical water lines.

An image definition of shoreline indicator is proposed in this research. The purpose of the object-based approach was to optimize the accuracy and robustness, which mean good localization and discrimination of incorrect positions.

Optimization of shoreline mapping method has been achieved by using reliable features to detect, which results in a better performance than the common mapping methods.

This study concludes that, by carefully defining shoreline indicators, shoreline boundaries can be mapped and that the method we developed is able to decrease the level of uncertainty in shoreline mapping.

Key words: shoreline indicators, boundary, spectral characterization, object-based, soil

moisture, sandy beach.

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Acknowledgements

First that all, I will like to thank beaches and wave around the world" they are the most be

interesting and unpredictable environments " I really appreciate all of you. Literature review has proved that there are many people and circumstances that intervene in the

normal development of the MSc activities; work environment, fellows, teachers, friends and weather!!! To all of you thanks, you have given me the best lessons. It has been nice to interchange all the international peculiarities.

There are some special persons to thank. Harald; thank you for all your help, contribution and support, even in moments of mental inertia, which were plenty.

My three parents, a mother with energy enough to keep this world working by herself , and two fathers, both in different way had showing me, what is this all about!!. Elizabeth, Luis y Santo, thanks you to show me how to be happy.

Truly love to my bothers (Ale & Vlady) and sisters (Carolina & MG), in especially to Dabelucha my best friend, well until Daniela grows up, I can fell in her attitude.

My indispensable Dennis, you really make more smoothly this life transition by making the hard work of being there for every sunny day that I miss, and for every joke that I remember from my misfits friends, truly love for you, and keep working!!

And finally, Grande IYA!! Without you this will be not possible :-). Konsera pasampüra

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Table of contents

1. Framework of research.....................................................................................................................1 1.1. Introduction.............................................................................................................................1 1.2. Background .............................................................................................................................2

1.2.1. Natural shoreline variability...............................................................................................2 1.2.2. Shoreline indicators............................................................................................................2 1.2.3. Advances in shoreline mapping..........................................................................................3

1.3. Approach of the research problem..........................................................................................4 1.3.1. General objective................................................................................................................4 1.3.2. Research questions .............................................................................................................5

1.4. Methodology...........................................................................................................................5 1.4.1. Study area ...........................................................................................................................5 1.4.2. Data collection....................................................................................................................5 1.4.3. Methods and techniques .....................................................................................................6

2. Characterization of the beach features .............................................................................................7 2.1. Coastal setting of Schiermonnikoog .......................................................................................7

2.1.1. Morphological features of the north coast of Schiermonnikoog........................................7 2.1.2. Shoreline variability on the northern beach of Schiermonnikoog......................................8

2.2. Beach setting at the test site....................................................................................................8 2.2.1. Beach profile ......................................................................................................................8 2.2.2. Beach soil sampling............................................................................................................9 2.2.3. Beach survey ....................................................................................................................10

2.3. Beach field characterization .................................................................................................10 2.3.1. Vegetated area ..................................................................................................................10 2.3.2. Bare soil area (Non-Vegetated) ........................................................................................11

2.4. Shoreline indicators identified in the study area ..................................................................12 2.5. Chapter summary ..................................................................................................................12

3. Detection of beach features............................................................................................................13 3.1. Laboratory setting.................................................................................................................13 3.2. Laboratory spectrometry.......................................................................................................13 3.3. Artificial wetting experiment................................................................................................14 3.4. Laboratory beach characterization........................................................................................14

3.4.1. Vegetated areas.................................................................................................................16 3.4.2. Bare soil area (non-vegetated)..........................................................................................16

3.5. Shoreline features identified in the laboratory .....................................................................18 3.6. Chapter summary ..................................................................................................................18

4. Classification of shoreline indicators.............................................................................................19 4.1. Image data aquisition ............................................................................................................19

4.1.1. Data set .............................................................................................................................19 4.1.2. Pre-processing ..................................................................................................................19

4.2. Spectral processing ...............................................................................................................20 4.3. Spectral characterization of shoreline indicators..................................................................20

4.3.1. Vegetated beach area........................................................................................................20

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4.3.2. Bare soil beach area......................................................................................................... 21 4.3.3. Shoreline indicator endmembers ..................................................................................... 22

4.4. Pixel-based classification for shoreline indicator ................................................................ 23 4.5. Chapter summry................................................................................................................... 24

5. Delineation of shoreline indicators boundaries............................................................................. 25 5.1. Object-based classification technique.................................................................................. 25 5.2. Rotation-variante template matching for boundary delineation .......................................... 25

5.2.1. Algorithm......................................................................................................................... 25 5.2.2. Application ...................................................................................................................... 26

5.3. Shoreline indicator boundaries ............................................................................................ 30 5.4. Chapter summary ................................................................................................................. 30

6. Mapping shoreline boundaries on the Northern beach of Schiermonnikoog................................ 31 6.1. Suitability of the RTM object-based techniques to map shoreline indicators ..................... 31 6.2. Comparison of the RTM results with other data sets .......................................................... 31 6.3. Boundary condition assumptions and uncertainties in shoreline position........................... 32 6.4. Shoreline indicators defined ................................................................................................ 33 6.5. Conclusions.......................................................................................................................... 34

Glossary................................................................................................................................................. 36 Appendix ............................................................................................................................................... 37

A.- MHWL from LIDAR .................................................................................................................. 37 B.- Rotation varaiant template matching[28] .................................................................................... 38 C.- Examples from the resuts of RTM for Northern beach of Schiermonnikoog............................. 39

7. References ..................................................................................................................................... 40

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List of figures

Figure 1-1: An example of the spatial relationship between the commonly used shoreline indicators [12] .......... 3 Figure 1-2: An example of the Tidal Datum used in Australia [6] ......................................................................... 3 Figure 1-3: Study area, Schiermonnikoog Island ................................................................................................... 5 Figure 1-4: Beach profile showing backshore, foreshore and nearshore [12] ....................................................... 5 Figure 1-5: Flowchart of the methodological steps ................................................................................................ 6 Figure 2-1: (a) Subset of the north beach of Schiermonnikoog (bands 9/4/3 from AHS). (b)� Test site. (c) Across shore from the dunes test site................................................................................................................................... 9 Figure 2-2: Beach profile and sampling scene on the test site................................................................................ 9 Figure 2-3: Schiermonnikoog, references plane measurement site, NAP. HWL and LWL data considered for the surveyed delineation. Source: Ministerie van Verkeer en Waterstaat. www.getij.nl ............................................. 10 Figure 2-4: GPS surveyed delineation at LW and HW.......................................................................................... 10 Figure 2-5: Pictures from the point sample collection. Zones A (top and dune scarp), Zone B (densely vegetated area beach flat) and Zone C (irregular aeolian accumulation of sand, sparsely vegetated) ................................ 11 Figure 2-6: Pictures from the point sample collection. Zones D (wet backshore with presence of shell), Zone E (dry backshore), and Zone F (inter-tidal zone) ..................................................................................................... 11 Figure 2-7: Shoreline indicator identify in the field.............................................................................................. 12 Figure 3-1: Soil sample compositional content..................................................................................................... 14 Figure 3-2: water content in % of the individual 38 soil samples......................................................................... 15 Figure 3-3: laboratory spectra of the beach soil at the original field conditions ................................................. 15 Figure 3-4: Bare soil spectra at the original conditions. VIS, NIR, SWIR to discern the albedo changes ........... 16 Figure 3-5: Artificial wetting test. Foreshore (zones F) reflectance at different water contents .......................... 17 Figure 3-6: Quantify relation between reflectance and water content.................................................................. 17 Figure 3-7: Artificial wetting experiment used for the class selection depending of water content...................... 18 Figure 4-1: Linear tendency equation obtained in the artificial wetting experiment for 3 different wavelengths 21 Figure 4-2: Sand moisture % classification applied to the test site subset ........................................................... 22 Figure 4-3: Bare soil end member spectra............................................................................................................ 22 Figure 4-4: MDC at band 6, 11 and 21 in combination with the vegetated areas (NDVI). The arrows are showing how the % of water content can be related to different SI ...................................................................... 23 Figure 5-1:Orientations of the moving template of the RTM algorithm [28] ....................................................... 26 Figure 5-2: Application of RTM to the band 6 (601nm). Subset of bare soil at the test site ................................. 27 Figure 5-3: Application of RTM to the band 11 (746nm). Subset of bare soil at the test site ............................... 27 Figure 5-4: Application of RTM to the band 21 (1622nm). Subset of bare soil at the test site ............................. 27 Figure 5-5: Color key, for the template image output........................................................................................... 27 Figure 5-6: Across beach profile of the template image at the test site. DN values for Band 6............................ 28 Figure 5-7: Across beach profile of the template image at the test site. DN values for Band 11.......................... 29 Figure 5-8: Across beach profile of the template image at the test site. DN values for Band 21.......................... 29 Figure 6-1: RTM extracted HWL and LWL are compared with GPS surveyed water line and the MHWL extracted from LIDAR ........................................................................................................................................... 32

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List of tables

Table 4-1: Data acquisition weather condition. 19 June, 2005, Schiermonnikoog .............................................. 19 Table 4-2: Shoreline indicators related with the endmembers .............................................................................. 22 Table 6-1: A summary of the shoreline indicator definition achieved................................................................... 33

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List of Acronyms

AHS Airborne Hyperspectral System DN Digital number value DTM Digital Terrain Model HWL High water line IWL Instantaneous water line MDC Minimum distance to class MHW Mean high water MHWL Mean high water line MLW Mean Low Water MHWL Mean High Water Line NAP Normaal Amsterdams Peil NOS National Ocean service (U.S.A) LIDAR Light Detection and Ranging LWL Low water line PHWL Previous high water line RTM Rotation variant template matching

DETECTION OF PHYSICAL SHORELINE INDICATORS IN A OBJECT-BASED CLASSIFICATION APPROACH

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1. Framework of research

1.1. Introduction

The shoreline, as the zone of contact between a body of water and land, is an interface that continuously changes through time and space* [1, 2]. Analysis of shoreline variability, erosion and accretion trends is fundamental to a broad range of investigations undertaken by coastal scientists, coastal engineers, and coastal managers.

For practical purposes, coastal investigators have defined shoreline indicators as a feature that represents its position, such as the vegetation line, water line, and wet-dry sand line between the physical features, or, for example, mean high water line (MHWL) as a mathematical measure [3].

In this way, shoreline definition and delineation depends on the selected shoreline indicator [4]. However, the interpretation of these indicators tends to be subjective.

Nowadays, among the existing techniques used to objectively map shoreline indicators, digital terrain models combined with local tidal datum [2] and digital image-processing and classification methods appear. An example is a neural network classification which has been used to distinguish between water and sand [5]. In the same way, a supervised threshold classification technique has been applied to determine the boundary between dry and wet sand [6]. The use of multi-scale, multi-sensor and multi-temporal [7-10] as well as video images [11] for the estimation of shoreline detection and coastal variability has demonstrated its valuable contribution.

Despite the advances made in coastal remote sensing, literature shows that there is still a major problem to be solved. “It is necessary to criticism that the prevailing visual shoreline detection techniques are overly reliant upon opportunist data collection and subjective interpretation” [6].

It is in this sense that the proposed research takes place. This document begins with a general background that summarizes the 3 principal points (shoreline variability, indicator and mapping techniques) that guide the investigation to the problem and objectives. This is continued by a number of correlated steps, which start with the identification of shoreline indicators present in the study area. From here, distinguishable properties between beaches features were determined in the laboratory. Spectral characteristics related to physical parameters were quantified. The capability of airborne remote sensing data to identify shoreline features were tested in image classification approach. Pixel-based and object-based techniques were applied and the detection of shoreline indicator boundaries was carried out. This investigation concluded giving an overview of how the particular condition related to the environment and the techniques applied are affecting the definition and detection of the shoreline indicator boundaries.

Note: in order to avoid any coastal terminology discrepancy, the vocabulary used in this research makes reference to Coastal Engineering Manual [12]. For consulting purposed a Glossary in alphabetic order has be added at the end of investigation.

* Through time, because cross-shore and alongshore sediment movement in the littoral zone, and trough space because of the dynamic nature of the water level.

DETECTION OF PHYSICAL SHORELINE INDICATOR IN A OBJECT-BASED CLASSIFICATION APPROACH

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1.2. Background

1.2.1. Natural shoreline variability

The features and phenomena that reduce the accuracy of defining the shoreline position in a given year are known as positional uncertainty [13]. This is related to the natural variability of shorelines in time and space.

The location of a shoreline indicator is influenced by a couple of factors that are constantly interacting. The largest uncertainty is often a result of the natural short time scale (seasonal event) variability of both beach morphology and the level reached by water on the beach. Large astronomical tidal fluctuations, low beach slopes, wave energy, migrating swash bars, seasonal beach recovery, sediment transported direction, and changes in wave run up [14].

Field surveys have shown that shoreline variability decreases as one moves landward and upward on the beach profile. The upper shoreline features provide a more reliable measure of shoreline change than those at a lower elevation or more seaward proxy that are subject to a higher frequency and larger magnitude of changes. In addition, field investigations have found that these changes can be minimized by only using data that has been acquired in summer and spring [3].

These uncertainties focusing on the nature of the shoreline position will be evaluated in a remote sensing approach, which provide rapid and quantitative synoptic data [15]. The moment of acquisition plays an important role since the condition in which the image was collected has an influence on our perception of the shoreline position; but, of course, this is also related to the availability of data, processes and techniques applied [13].

1.2.2. Shoreline indicators

Certain beach characteristics may require the use of a specific shoreline indicator [3]. It is therefore, critical to choose the indicator that will accurately represent the shoreline.

A number of shoreline indicators have been reported in different papers [3, 6, 14, 16, 17] and possible shorelines proxies have been suggested. Two types of shoreline indicators could be identified, the physical beach morphological features and the non-morphological beach features [4];

� Morphological are those physical shorelines such as berm crest, scarp edge, vegetation line, dune crest, etc (fig.1.1);

� Non-morphological are those related to water line as wetted boundary, wet-dry boundary, wet sand line, etc (fig.1.1);

� Mathematical are those associated to water levels such as high water line HWL, mean high water line MHWL, mean lower water line MLWL, derivate from the local tidal datum (fig.1.2).

Recently, a third category of shoreline indicator has begun to be reported in the literature. This category is based on the application of image processing techniques to extract shoreline indicators from digital coastal images [11]. In this research, the product of morphological indicators and non-morphological water line features is considered as physical boundary indicators, while the mathematical boundary indicators are defined by water level shoreline indicators only.

An ideal shoreline indicator should be easily identified in the field and in remote sensing image data. Most of the coastal researchers and agencies in the United States use the HWL because this shoreline indicator is visible in the field and can be interpreted from images [3]. The HWL is one of the best available sources for the compilation of nautical charts [4]. HWL includes all land not covered by mean tidal range. It represents the line of permanent emersion of the land area. Seaward of


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this line is an area of alternative emersion and immersion. Seaward of the low-water line (LWL) is the area of permanent emersion [4]. It is however not a morphological feature but instead it is ephemeral (line in the sand) which is sensitive to short-term fluctuation in wave and tide conditions.

The National Ocean Service (NOS) defines a shoreline as Mean High Water Line (MHWL), and MHWL is defined as a datum where the surface elevation is determined by averaging the heights of the water to equal interval of time, [4] usually hourly.

Figure 1-1: An example of the spatial relationship between the commonly used shoreline indicators [12]

Figure 1-2: An example of the Tidal Datum used in Australia [6]

1.2.3. Advances in shoreline mapping

In addition to the positional uncertainty (section 1.2.1) there is a measurement uncertainty, which is related to process and techniques applied to an image, for instance geo-rectification and onscreen manual delineation of the shoreline features [13].

New methodologies for determining shoreline position have been developed [3]. LIDAR-based shoreline mapping has proven to be successful not only as a mathematically derived shoreline, but


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also to automatically extract shorelines without any visual interpretation [18]. Moreover, a new method based on merging topographic and bathymetric information has been developed to create a high accuracy DTM and, consequently, to estimate different water levels for the determination of the coastline position [19].

Detailed reviews of shoreline mapping techniques can be found in the Journal of Coastal Research [20], which presents an interesting overview of shoreline mapping and change analysis related to the technical and practical considerations, such as spatial and temporal coverage and accuracy, difficulties with measurement of a consistently defined shoreline.

At the same time, the possibilities of airborne hyperspectral data for studying coastal dynamics are starting to be explored. The ability to map shorelines using compact airborne spectrographic imager (CASI) data has been explored. However, the application of remote sensing has had limited success [15].

Advanced classification techniques are showing developments in image processing. For instance, object-oriented image analyses produce efficient classification results, which improve the traditional pixel-based image analysis classification [21-24]. A delineation of coastline from polarimetric SAR imager was investigated using an edge algorithm to find a continuous shoreline [25]. An integrated technique of multi-temporal hyperspectral data and LIDAR data has proven to be successful when studying sand dynamics of the Belgian coast [26]; in this research, a spectral angle mapper was applied to distinguish between sand types and was subsequently combined with a LIDAR-derived DTM to define erosion-sedimentation and transport mechanisms.

1.3. Approach of the research problem

Shoreline detection often has a high level of uncertainty [6], because there is a natural variability to deal with, and because the process and techniques applied tend to be vague and ambiguous, which has an adverse outcome of inherent subjectivity [27].

To analyze shoreline variability and trends, a functional definition of the ‘‘shoreline’’ is required. Therefore, an accurate assessment of shoreline detection is related to the consistency of the shoreline definition, and of the measurement techniques used to provide data that meet this definition [2], so that an apparent change in shoreline is not purely an expression of inconsistencies in the method of the chosen definition [2].

Considering the advantages of high-resolution detection techniques and a proper selection of shoreline indicators, the challenge is to integrate spectral and spatial data with knowledge of shoreline dynamics into the mapping process, in order to improve our ability to accurately and objectively determine shoreline position.

1.3.1. General objective

The aim of this investigation is to test the suitability of object-based classification techniques to detect and map shoreline indicators by using airborne hyperspectral data.

Specific objectives are: 1. To identify and characterize shoreline indicators present in the study area. 2. To investigate the usefulness of spectral information to detect shoreline indicators. 3. To investigate the usefulness of pixel-based techniques to build up a class separability

between shoreline indicators. 4. To investigate the usefulness of object-based techniques to map shoreline indicator

boundaries.


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1.3.2. Research questions

1.1 Which shoreline indicators can be identified in the field? 1 1.2 Which physical characteristics are suitable to define these shoreline features?

2 2.1 Which spectral characteristics are suitable to establish a functional definition of shoreline features?

3 3.1 Which shoreline indicators can be detected in the hyperspectral image? 4.1 Are the results of the measurement techniques in consistency with the definition of the shoreline indicators? 4.2 What are the strengths and limitations of the proxy shoreline indicators boundaries?

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4.3 What is the contribution of the technique in the capability to map shoreline indicators?

1.4. Methodology

1.4.1. Study area

The study takes place on the island of Schiermonnikoog (fig. 1.3), which is one of the barrier islands on the Northern part of the Netherlands. Special emphasis is given to the North sandy beach which comprises backshore, foreshore and shoreface (fig.1.4), where the dynamic shoreline is present.

Figure 1-3: Study area, Schiermonnikoog Island

Figure 1-4: Beach profile showing backshore, foreshore and nearshore [12]

1.4.2. Data collection

In order to characterize the beach profile, field data were required, such as soil samples, a beach GPS survey and field observation of the beach features. With this input, laboratory work was performed to determine soil attributes and spectral soil signature. Parallel to this, an airborne hyperspectral AHS scene from 2005 was acquired (see section 4.1.1) to apply the classification


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technique, as well a digital elevation model obtained by LIDAR, 2000 (appendix A), to be used as references for the classification results.

1.4.3. Methods and techniques

There are a number of methodological steps that together permit to map shoreline indicators boundaries. The method is summarized in figure 1.5. These involved the selection of spectral endmembers of different shoreline features coming from a laboratory study. After that, two different classification approaches of the AHS image took place, to identify the features that represent the shoreline.

A minimum distance to class (MDC) classifier was used in a pixel-based approach. This classification typically uses individual pixels whether it is a component of a phenomenon or not, and indicate the relative coverage of the class in the area represented by each pixel [21].

The second method is an object-based approach. This process of classifying image objects rather than considering individual pixels summarizes information from a collection of pixels that compose the image object. In addition to spectral information, each object also contains information regarding the texture, size, shape, and context of the surrounding image objects [21]. Consequently, a supervised edge detection algorithm was applied to the image. This algorithm interpreted the spectral signal and the combination of expert knowledge and spatial statistics to derive spatial and spectral information and finally indicate whether a boundary is present or not [28].

LIDAR data is used to create digital terrain models, from which a tidal datum indicator can be determined and the mathematical shoreline can be extracted. This, together with the shoreline surveyed GPS observation will be used as a reference, for the output obtained from the classification.

Detection of beach features

Classification of shoreline indicator

Delineation of shoreline indicator

boundaries

Mapping shoreline boundaries from the north coast of Schiermonnikoog

Lab

orat

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data Beach soil content

Beach soil reflectance

Airb

orne

hyp

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AH

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Characterization of beach features

Fiel

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Data collection

Data Analysis

GPS survey

Soil samples collection

Field ObservationBea

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Results

Bea

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Artificial wetting experiment

Imag

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Spatial and spectral subset

Geometric Correction RS- Dutch system

Spectral beach characterization

Spectral SIcharacterization

Imag

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Object-based classification

Rotation variant template matching

NDVI analysis

Sand moist % analysis

Shoreline indicators endmembers

Shoreline Indicator boundaries

S.I presents in the study area

Delineated water linesRepresentative beach samples

��

Minimum distance to class means

Classification of SI (zones)

Detection of beach features

Classification of shoreline indicator

Delineation of shoreline indicator

boundaries

Mapping shoreline boundaries from the north coast of Schiermonnikoog

Lab

orat

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data Beach soil content

Beach soil reflectance

Airb

orne

hyp

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AH

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Characterization of beach features

Fiel

d w

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dat

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Data collection

Data Analysis

GPS survey

Soil samples collection

Field ObservationBea

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Results

Bea

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Artificial wetting experiment

Imag

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Imag

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Spectral beach characterization

Spectral SIcharacterization

Imag

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Object-based classification

Rotation variant template matching

NDVI analysis

Sand moist % analysis

Shoreline indicators endmembers

Shoreline Indicator boundaries

S.I presents in the study area

Delineated water linesRepresentative beach samples

��

Minimum distance to class means

Classification of SI (zones)

Figure 1-5: Flowchart of the methodological steps


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2. Characterization of the beach features

In defining the shoreline the first step is to identify which shoreline indicators are present in the study area, as starting point to establish the particular properties and characteristics of those features.

Field work was undertaken to identify discernible indicators, with the purpose to build up a meaningful relationship among the physical environment and remotely sensed data.

To characterize features that are suitable to define the shoreline, the analysis consists of several phases: collection of soil samples, determination of soil attributes using laboratory analyses, collection of spectral data from these samples using a laboratory spectrometer and collection of spectral data from airborne hyperspectral images.

This chapter covers a general overview of the coastal environment of Schiermonnikoog and is followed by analysis of the field data.

2.1. Coastal setting of Schiermonnikoog

Schiermonnikoog is one of the barrier islands between the North Sea and the inter-tidal Wadden Sea. This island is characterized by large tidal flats, intersected by numerous creeks and channels.

The island is about 39 km2, with a maximum length of 16 km and width of 4 km formed by the deposition of sediments from the Rhine River. Due to the coastal processes, Schiermonnikoog has moved to the East and has grown in length [29].

The Northern shore of the island consists of dunes (strandline ridges) and an extensive beach plain. In the south-southeast are mainly includes salt marshes, tidal flats with creeks and channels. Schiermonnikoog is a dynamical tidal area [29], in which geomorphological processes such as erosion, transport and sedimentation of sandy material are causing significant changes along the coast.

The study area is located in the Northern part of Schiermonnikoog; a more detailed morphological description is given below.

2.1.1. Morphological features of the north coast of Schiermonnikoog

A wide sandy beach and an extensive zone of dunes dominate the entire Northern coast. These principal geomorphological features are divided into two types; high dunes (5-20 m) from high amplitude relief have a parabolic shape, especially in the north-western side, and the low dunes (2-5 m) have a rounded top. Non calcareous sandy soils are characteristic of the old dunes, which are well drained and non loamy to slightly loamy sandy. The young dunes contain more calcareous sandy soils with moderately fine to fine sand [29].

The strandline ridge (Rijkswaterstaat dune) is the first protection line after the beach. It was artificially formed by planting of marrow grass (amophila arenaria) with the aim of protecting the island from wind erosion. As a continuation of the western high dunes, the heights range from 1 - 5 meters, extended parallel to the beach towards the east.

The most extensive landform of the island is the flat beach area at the North Sea side which reaches 500 m wide from the berm to the low tide. This beach plain begins with an area with some


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hummocks (5cm) developed, so-called green-beaches, and is followed by a sandy area. The sand is fine (150-200 µm) and homogenizes with shells [29].

The slope of the beach profile is very low (0-2 %) but almost all the elements of the beach system can be depicted, namely: low –high tidal plain, through and berm [29].

The sediment feed is from the waves, which rework material brought by transversal currents and alongshore currents. Transportation is made by saltation load as a result of the above mentioned fine particle size of the sand [29].

2.1.2. Shoreline variability on the northern beach of Schiermonnikoog

The shoreline in the region has historically been defined as a horizontally referenced feature that represents the average of high tide height [14]. The following description explains the principal factors intervening in the variability of the island, in order to present an impression of natural uncertainties that can affect the shoreline detection.

2.1.2.1. Wind and wave processes

The wind mostly blows from a South-Southwest direction causing an alongshore current in Eastward direction [30].

The main direction of the incoming waves is caused by the dominating wind direction, although those waves formed in the very shallow water (inter-tidal zone) are consequences of the wind direction arriving from the North Atlantic [30].

2.1.2.2. Tidal range

Schiermonnikoog has a mean tidal range of 2.29 m [30].There appears to be an annual cycle of the tidal range, given that during the summer the tide is roughly 15 centimeters higher compared to the tidal range during winter. This is probably an effect of the expansion of the seawater during the summer, and the tidal range will be considerably larger during the summer than during the winter. However, it seems that after 1983 the tidal range starts decreasing [30].

2.2. Beach setting at the test site

The fieldwork took place on September 27th and 28 th, 2006. Both were clear, dry days in the fieldwork area, so those sample collections have the same conditions as when the image was acquired (section 4.1.1).

The test site is located in the North-eastern part of the island of Schiermonnikoog (fig. 2.1). This area is formed by crescent dunes, which is one of the most copious relief forms towards the eastern side of the island, as well as an extremely wide beach. The area has been selected since it represents, at a relatively short distance, the typical beach profile of the northern coast.

Since the field work had to be carried out in two days, all field observations on the shoreline and the sample collection were taken along three transects. Furthermore, general observations were taken in relation to the prevalent coastal dynamics.

2.2.1. Beach profile

The profile is characterized by a beach flat, approximately 600 m wide. The profile starts at the top of the semi artificial dune and ends at the low tide zone. Along the profile, three large zones can be distinguished; upland (dune), backshore (vegetated and non-vegetated) and foreshore.


9

In this flat beach the backshore is only covered during extreme high water, which gives time for the sand to dry and to be blown inland, feeding on dune build up.

(b)

(a)

(c)

Figure 2-1: (a) Subset of the north beach of Schiermonnikoog (bands 9/4/3 from AHS). (b)� Test site. (c)

Across shore from the dunes test site

2.2.2. Beach soil sampling

On September 27th (2006), from 13:00 until 17:00 (local time) sediment samples were acquired. 3 transects composed of 38 samples each, were taken from the top 4 cm of the beach soil. Individual sample were stored in plastic hermetic containers and then saved into plastic bags, in order to preserve as much as possible the same condition as the field.

The sediment sampling procedure was done by collecting samples along the profile; such as dune crest, backshore, high water, mid-tide and low water.

Transects A, B and C extend from the dune seawards to foreshore and they are separated from each other by 30 m (fig. 2.3). Samples were collected at a 30 m interval in the first 400 m and at 4 m intervals in the last 100 m along each transect. Each point was geo-referenced using a GPS with a margin of error of 3-6m.

Figure 2.2 presents the beach in a schematic way profile and the sampling scheme at the test site, which will be described in detail in the beach characterization (section 2.3.1).

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30 m 158 m 93 m 80 m 109 m 180 m Figure 2-2: Beach profile and sampling scene on the test site


10

2.2.3. Beach survey

In addition to soil sampling, a GPS survey was carried out at the test site to delineate discernible shoreline features. Delineation of the water lines was done at different tidal stages by using the tide prediction table for the island (fig. 2.3). In the field the E-TREX Garmin GPS (∼ 5m accuracy) was used to record the positions.

Height of tide (cm) LWLW

HWHW

Height of tide (cm) LWLW

HWHW

LW-116 cm19:55

HW114 cm13:45

LW-121 cm07:40

HW108 cm01:35

ExtremeWater levelTime

LW-116 cm19:55

HW114 cm13:45

LW-121 cm07:40

HW108 cm01:35

ExtremeWater levelTime

Sept

embe

r 2

7, 2

006

Figure 2-3: The predict water level, conditions for day 1. Schiermonnikoog, references plane measurement

site, NAP [31] The shorelines surveyed were conducted by moving GPS

over waterline. At the day of the measurements the tidal vertical amplitude was approximately 235 cm. The ebb and flood tides have a time difference of ± 6 hours.

Figure 2.4 shows both water GPS surveyed water lines, LWL and HWL. From the delineation of the two water lines it was observed that the horizontal differences between low and high water is approximately 176 m, increasing a third part of the beach plain. This may be because of the attenuated slope.

During the field work period, the waves were of 1.2 m height in average, considered calm conditions. West winds are dominant and represent 58% of the direction.

Figure 2-4: GPS surveyed delineation at LW and HW

2.3. Beach field characterization

2.3.1. Vegetated area

This is the starting point of the transects. This zone covers approximately 180 m from the top of the dune to the end of the vegetated beach plain. The vegetation coverage decreases seawards and could be divided into three zones depicts in figure 2.5.

Zone A- Dune. This zone is part of a younger dune, and consist of a semi-stable crescent dune that it is still being build up. The top has an irregular surface covered by grass followed by the dune scarp.

Zone B- Densely Vegetated. This zone has an extension of approximately 158 m. The sand is almost entirely covered by vegetation. Some irregular surfaces (aeolian accumulation) with topographic amplitude up to 40 cm are covered by grass.

Zone C- Sparsely Vegetated. The presence of vegetation in this zone is low. This zone extends 93 m, and consists of aeolian accumulation of sand with topographic amplitude up to 20 cm.

Low water line delineated at 07:40

High water line delineated at 13:50

Low water line delineated at 07:40

High water line delineated at 13:50


11

Zone A Zone B Zone CZone A Zone B Zone C

Figure 2-5: Pictures from the point sample collection. Zones A (top and dune scarp), Zone B (densely vegetated area beach flat) and Zone C (irregular aeolian accumulation of sand, sparsely vegetated)

2.3.2. Bare soil area (Non-Vegetated)

The backshore reaches 400 m from the edge of the vegetated area to the high tidal area, three zones are depicts in figure 2.6.

Zone D Zone E Zone FZone D Zone E Zone F

Figure 2-6: Pictures from the point sample collection. Zones D (wet backshore with presence of shell),

Zone E (dry backshore), and Zone F (inter-tidal zone) Zone D- Beach plain 1. This zone is the beginning of the bare soil (non-vegetated) beach face

and covers over 80m. It is flat area composed by moist compact sand with a great presence of sea shells.

Zone E- Beach plain 2. This zone covers more than 109 m of beach face before it reaches the foreshore. Transportation of sand can be observed all over the beach. Sparsely aeolian accumulation of sand migrating across de beach can be seen, a flat area mainly covered by dry sand material. At the level of a previous high tide there are various biological remains, such a seashells, and crab shell and seaweed.


12

Zone F- Foreshore. The morphology consists of an inter-tidal bar, which is intersected in different locations by rip channels, and a trough. The foreshore reaches more than 170 m from the high tide to the low tidal line. This zone forms the land-water interface with contrasting dry, wet and saturated wet sand. The foreshore is constituted by a gentle sloping plain, interrupted by low sand bars that are emerging in the incoming low tide.

In between ebb tide and flood tide, the troughs between the bars become visible. In this period, the troughs are filled up with water and intermediate mobile rips appear in the rid-runnel of the inter-tidal zone.

2.4. Shoreline indicators identified in the study area

A number of shoreline indicators have been identified in the field (fig. 2.7). There are two shoreline morphological features related to the vegetated area; the semi-stable dune vegetation line that coincides with the dune scarp and the edge of the active vegetation line related to the so-called green-beaches.

Based on non-morphological shoreline indicators, 4 water lines were recognized. In the backshore area the previous high tide was recognized. Continuing seaward at the foreshore interface, almost immediately appear the high water line, logically followed by the instantaneous water line and finally the low water line. These features may migrate several to tens of meters a day, since they depend on the temporal variability and rate of change.

Low water line

High water linePrevious high water line

Active vegetation line

Dune line

Water line

Figure 2-7: Shoreline indicator identify in the field

2.5. Chapter summary

With the beach features characterization, beach compartments (or zones) were recognized and indicators were found to be related with these zones. Sand samples are reflecting the typical beach profile of the northern coast of Schiermonnikoog.

LWL and HWL were delineated from a GPS surveyed in order to be used as data set for comparison purposes.

The task of the next chapters is to find a way to objectively characterize such features and to determine which of the shoreline indicators identified in the field can be detected and mapped in a hyperspectral image.


13

3. Detection of beach features

In chapter 2, the importance of identifying shoreline indicators in the field was explained. The next step is to detect indicators that have been characterized based on those field observations. For this reason a laboratory analysis has been undertaken to quantify moisture content of the beach soil, as well as organic matter and carbonate content. Furthermore, spectral measures were taken to find all factors that influence reflectance responses.

3.1. Laboratory setting

From the 38 samples that represent one transect on the profile, 6 composite samples were conformed (conserving the original field conditions). These are directly related to the 6 compartments than had been characterized in the beach zones A to F (fig. 2.2). To these composite samples as well as to the individual ones, a number of measurements were applied.

The first test was an oven-dry experiment. The samples were exposed to different temperatures in order to determine the principal components of the beach soil.

Water content was determined by weighing the sample in the original condition and weighing it again after oven drying at temperatures of 40ºC and 105ºC, in order to extract the relative and the absolute quantity of water lost. At this point, moisture content from mass measurements was expressed in percentage.

This test was applied to the 38 individual samples as well as to the 6 composite samples, which in addition were exposed to temperatures of 450ºC and 600ºC to determine the amount of organic matter and carbonate per compartment.

3.2. Laboratory spectrometry

Parallel to the beach soil content experiments, a spectral characterization took place. Reflectance measurements have been acquired in a laboratory, since it is a controlled environment. The whole process was conducted using a Field Spec® Pro (Analytical Spectral Device) spectroradiometer. This instrument has three spectrometers (VNIR, SWIR1, and SWIR2) with a contiguous spectral range from 350 to 2500 nm.

All samples were placed into round aluminum cups (3 cm thick), which had been previously weighed. After that, reflectance spectra were measured using a contact probe with an internal light source, using the same position of the sensor to measure the sample plate.

Spectral signatures that describe the beach scene were obtained by repetitively scanning the sediment surface. From each sample, 5 reflectance measurements were taken, and averaged to obtain a spectral reflectance signature of each sample.

Spectra have been collected not only of the 38 samples that represent the entire beach profile, but also of the 6 composite samples that remained in the original field conditions. Spectra from dry samples were taken, too in order to determine the reflectance of the beach with no water present.


14

3.3. Artificial wetting experiment

An experiment was carried out to quantify the relationship between the reflectance of the sand and different moisture content. The artificial wetting model consisted of spectral measurements that were collected repeatedly until the soil mass did not change any more, meeting the dry condition.

First, water was added to the oven-dried composite samples until a thin layer (1 mm) of water was visible. Then the water layer was removed to get the soil near to saturation, and starting from these conditions, measurements were collected. Then the samples were dried at 40ºC (simulating a sunny, dry day) for 1 hour in 7 stages. Every 1 hour, spectral measurements were taken, as well as mass measurements.

Volumetric water content was considered instead of mass in this research. This is the most appropriate measurement for analyzing reflectance since photon interaction depends on volume of substances [32]. From the following equation the volumetric water content was calculated.

3.4. Laboratory beach characterization

The main contribution of this lab-methodological step was to prove whether the laboratory spectra contain useful information for discriminating the compartments that make up the beach area.

In the previous chapter, six zones were characterized. To support this description and to build a more objective definition of these zones, the results from the laboratory soil samples are subsequently analyzed.

The results from the content and spectral laboratory experiments are now combined in order to characterize the beach profile, bearing in mind that the shape of the spectrum is influenced by composition of the beach material. Figure 3.1 shows the results from the beach soil content experiment.

0.11 0.550.06

9.72

0.50 0.12

5.65

0.40 0.07

8.12

0.600.12

0.540.42 0.08

13.70

0.37 0.09

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

1 2 3 4 5 6

Water content Carbonate content Organic content

Zone A Zone B Zone C Zone D Zone E Zone F

Vegetated area Bare soil (non-vegetated area)

% % %

0.11 0.550.06

9.72

0.50 0.12

5.65

0.40 0.07

8.12

0.600.12

0.540.42 0.08

13.70

0.37 0.09

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

1 2 3 4 5 6

Water content Carbonate content Organic content

Zone A Zone B Zone C Zone D Zone E Zone F

Vegetated area Bare soil (non-vegetated area)

% % %

Figure 3-1: Soil sample compositional content

The outcome confirms that the carbonate content and organic matter content is less then 1 %. This low presence can be assumed to be insignificant, and the main contribution can be related to the water content.

m, is the measured mass of the soil sample mo, is the initial (dry) mass (3.1) �b, is the soil bulk density �w, is the density of water

� = (m –mo ) / �w

mo / �b


15

Considering that the study area has a mineralogical homogeneous surface with only a minimal variation of grain size [29], it is assumed that spectral measurements are mainly affected by moisture content. All samples are composed of well sorted fine sand with a low occurrence of organic matter and carbonate content.

An analysis done on the 38 individual samples collected along the profile demonstrates the relation of water content to the different zones A to F (fig. 3.2).

0

5

10

15

20

25

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38

� � ��

0

5

10

15

20

25

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38

� � ��

Figure 3-2: water content in % of the individual 38 soil samples

As was expected, high water content is present in foreshore (F) which is followed by the vegetated zone (B), while the beach face (E) has lower values of water content. These results explain the behavior of spectral variations within these zones.

The shapes of six spectral signatures from the composite samples on field conditions can be seen in figure 3.3.

Zone AZone E

Zone C

Zone B Zone D

Zone F

Wavelength (nm)

Ref

lect

ance

0.5

0.4

0.3

0.2

0.1

500 1000 1500 2000 2500

Zone AZone E

Zone C

Zone B Zone D

Zone F

Wavelength (nm)

Ref

lect

ance

0.5

0.4

0.3

0.2

0.1

500 1000 1500 2000 2500

Figure 3-3: laboratory spectra of the beach soil at the original field conditions

Prominent absorptions at around 1450 and 1950 nm wavelengths in most soil spectra are attributed to water and hydroxyl ions. Occasionally, weaker water absorptions also occurred at 970, 1200, and 1770 nm [32].

The spectral signatures shows essentially the same shape, with the exception of some small absorption features which may be related to the presence of vegetation and sensor calibration, but meaningless to the objective of this research. Spectra from the dry samples were examined to determinate the reflectance of the beach with no water content and show the same curves, which makes it even more clear that there are not significant compositional differences between the sand samples. The most important differences then are related to the brightness, where the water content is playing the main role in the reflectance response.


16

3.4.1. Vegetated areas

These areas have a vegetation cover that decreases seawards, and can be differentiated by dune and beach plain. The analysis of this zone takes place in chapter 4, since an NDVI approach is used to extract this beach feature. Nevertheless, as part of a soil beach characterization, the samples collected in the vegetated area have been analyzed. For instance, soil samples collected on dune crests that are covered by herbs (zone A), are showing the brightest signature between 0.4 and 0.5 reflectance. This can be explained not only by the dryness of the zone but also can be influenced by the presence of carbonate content (0.55%) and a better sorted fine sand selection (aeolian transportation).

Zone C has the third brightest signature of the beach profile between, 0.3 and 0.4 reflectance. Followed by zone B, the more densely vegetated which has larger values of 0.12 % of organic matter content and 9.72 % of water content, giving a reflectance response between 0.2 and 0.3. Even when these areas are vegetated (for image classification the soil will be not discerned) this laboratory analysis is already offering some distinguishing points, which help to characterize and to support the next image classification approach.

3.4.2. Bare soil area (non-vegetated)

The bare soil areas were analyzed into more detail and with an emphasis on the spectral-albedo differences. It is important to establish that there is a significant brightness differences, between a dry spectral signature from zone E and a wet spectral signature from zone F. This zone F, foreshore is the wettest area with high water content (13.2%) and has consequently the darkest spectral signature of the profile; followed by zone D, which is a moist zone (8.2%), at the beginning of the beach face with. This zone D has the larger carbonate content from the samples as well as organic matter is observed, probably related to the transition zone between the vegetated and non vegetated area and with a notable presence of sea shells. And finally zone E which as the second brightness signature of the profile but represents the first zone from the bare area (beach plain).

Figure 3.4 summarizes the effect of soil moisture on reflectance and makes the changes in brightness for changing water content clearly visible.

Zone E

Zone D

Zone F

Wavelength (nm)

Ref

lect

ance

0.5

0.4

0.3

0.2

0.1600 800 16001000 1200 1400400

600 (VIS)

750 (NIR)

1600 (SWIR)

Zone E

Zone D

Zone F

Wavelength (nm)

Ref

lect

ance

0.5

0.4

0.3

0.2

0.1600 800 16001000 1200 1400400

600 (VIS)

750 (NIR)

1600 (SWIR)

Figure 3-4: Bare soil spectra at the original conditions. VIS, NIR, SWIR to discern the albedo changes

To highlights from this experiment (see section 3.3), once soil water is sufficiently absorbed, additional water has little effect on reflectance, since the signatures collected from a thin layer and in saturated samples gave minimum differences.

On the other hand, something that was clearly established was that the reflectance decreases with increasing moisture, as demonstrated by measured spectra in figure 3.5, where an example can be seen of the spectral measurement recorded from zone F in the foreshore after oven-drying the


17

saturated sand samples 7 times. To be noted are the typical shapes that are convex between 500-1300 nm and that dips at 1459 nm. These dips are so-called water absorption bands and are caused by the presence of soil moisture.

Wavelength (nm)

Ref

lect

ance

0.5

0.4

0.3

0.2

0.1

600 800 16001000 1200 1400

(VIS)750

(NIR) (SWIR)

Saturated

Oven-dried 1

Oven-dried 2

Oven-dried 3

Oven-dried 4Oven-dried 5Oven-dried 6Oven-dried 7

Wavelength (nm)

Ref

lect

ance

0.5

0.4

0.3

0.2

0.1

600 800 16001000 1200 1400

(VIS)750

(NIR) (SWIR)Wavelength (nm)

Ref

lect

ance

0.5

0.4

0.3

0.2

0.1

600 800 16001000 1200 1400

(VIS)750

(NIR) (SWIR)

Saturated

Oven-dried 1

Oven-dried 2

Oven-dried 3

Oven-dried 4Oven-dried 5Oven-dried 6Oven-dried 7

Figure 3-5: Artificial wetting test. Foreshore (zones F) reflectance at different water contents Once it was possible to observe the rate of change as a dependence of soil moist levels, then the

next step was to express this relation as a function of the volumetric water content, by using wavelength dependent values as those coming from the reflectance at 600 nm, 750 nm and 1600 nm.

The observed changes in soil reflectance revealed a linear response between the increasing of brightness and the decreasing volumetric water content. Figure 3.6 presents these changes at the wavelength mentioned.

Figure 3-6: Quantify relation between reflectance and water content

The three regions VIS, NIR and SWIR show the same tendency. Due to the variability the wet and dry reflectance are reduced between 0.14 and 0.37 at 600 nm. While wet and dry reflectance varied between 0.14 and 0.51 at 1600 nm, this larger range of sensitivity in the SWIR can be


18

attributed to the strong absorption of water in this region, combined with a high SWIR reflectance of dry sand, this can be potentially important in the image classification approach.

3.5. Shoreline features identified in the laboratory

Analysis of the responses of the reflectance values under different water content proved to have linear tendency (fig. 3.6). In this way, the lab analyses were used to investigate possibilities for differentiation of sediment types that were related to shoreline features.

To take advantage of the spectral correlation of reflectance with water content, the training set results from the artificial wetting model were used to determine shoreline compartment classes directly depending on water content. An extensive analysis of the behavior of the reflectance responses was done, and a practical physical differentiation was established.

The lab-spectral selection for endmembers was defined after extensively studies of the spectral reaction under different water conditions. Figure 3.7 presents the volumetric water content, for the different stages of the sand during the experiment, in which 4 classes of sand moisture were recognized.

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

1 2 3 4 5 6 7 8Zone F

Saturated water content (>>>> 40%)

Wet Sand (30 %)

Moist Sand (20 %)

Dry Sand (< 10%)

Vol

umet

ric

wat

erco

nten

t

Sa.WC 1h 2h 3h 4h 5h 6h 7h

0.00

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0.10

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1 2 3 4 5 6 7 8Zone F

Saturated water content (>>>> 40%)

Wet Sand (30 %)

Moist Sand (20 %)

Dry Sand (< 10%)

Vol

umet

ric

wat

erco

nten

t

Sa.WC 1h 2h 3h 4h 5h 6h 7hSa.WC 1h 2h 3h 4h 5h 6h 7h

Figure 3-7: Artificial wetting experiment used for the class selection depending of water content


The results confirm that discriminating spectral responses of different areas over the beach is possible in the laboratory. The characterization reveals homogeneous compositional sand beach (samples) with only variations in water content, what gave the bases to the spectral characterization.

The simple moist reflectance model presented here, which required only dry soil reflectance as input, demonstrated the potential of the control study of moisture conditions, since it was possible to quantify the strong influence of moisture on spectral reflectance.

Spectral characteristics related to physical parameters were quantified. In this sense reflectance responses were related to levels of water content, where brightness was playing the main role. This characterization allows the establishment of 4 endmember representing the beach features to be used in the followed classification approaches.

The laboratory study was intended to be a prerequisite for the use of hyperspectral image, in order to identify previously features with specific spectral signatures presents. In this sense the follow methodology had contributed to develop a physically definition of shoreline indicators.


19

4. Classification of shoreline indicators

Chapter 3 concluded that a spectral characterization of the beach profile at the test site is possible in the lab. The question that remains is whether the shoreline features can reliably be detected by airborne hyperspectral remote sensing. In this chapter, the transition from the field and laboratory data to image data is shown, being the first step in the processing and analysis of the image. Here the suitability of high spectral resolution remote sensing to detect and classify shoreline features is investigated.

4.1. Image data aquisition

4.1.1. Data set

An imaging spectroscopy flight campaign with the AHS sensor was undertaken in the study area, on the 19th June of 2005 by the Flemish Institute for Technological Research (VITO). The island of Schiermonnikoog is covered by three separate strips, of which only one needs to be used for studying the Northern sandy beach.

The airborne hyperspectral image has a spatial resolution of 3.4 meters and a spectral range from 455 to 12.227 nm. The AHS sensor has 80 channels with an almost contiguous coverage across the wavelengths 0.455-2.500 (VNIR-SWIR) and also collects broad band data in the 3 to 5 and 8 to 12 micrometer region (TIR) [33].

The image has a North-East orientation and it was acquired during an incoming LW. On average, waves were 0.37 m high, which can be considered a calm weather condition. Northern winds are dominant and records of wave directions show that waves have an overall Northern direction [31]. Table 4.1 presents the weather conditions during the flight period.

Table 4-1: Data acquisition weather condition. 19 June, 2005, Schiermonnikoog Water level / Tide (NAP) Wave height Wave direction

Observation time

Water height

Extremes WL

Observation time

Significant height

Observation time

Average direction in degree

10:20:00 57 cm LW:130cm

(02:16) 09:00 40 cm 09:00 50

10:30:00 51 cm HW: 85cm

(08:50) 10:00 37 cm 10:00 33

10:40:00 45 cm LW:120cm

(14:35) 11:00 30 cm 11:00 20

Hyp

ersp

ectr

al im

age

acqu

isiti

on

time:

10:

29

Float level meter - Schiermonnikoog south

HW:101cm (21:06)

Wave-measuring buoy - Schiermonnikoog North

Source: Ministerie van Verkeer en Waterstaat, www.getij.nl

4.1.2. Pre-processing

The hyperspectral image was radiometrically, geometrically and atmospherically corrected by VITO. In a previous research study that used the same data set [35, 36], it was pointed out that during the flight there was an accumulation of ice and dirt that resulted in a linear degradation of the sensor optics. For this reason only the first 21 bands of the image can be used.


20

The pre-processing report of VITO mentions that atmospheric correction involved the transformation of at-sensor radiance to surface reflectance by MODTRAN 4. A direct geo-referencing method was undertaken and projected in UTM WGS-84 [35, 36].

For this research effort, processing was needed after the geometric, radiometric and atmospheric calibration to re-project the image to the Rijks Driekhoeksmeting (RD) systems as well as for making spectral and spatial subsets. The spectral subset consisted of the first 21 bands, which cover the EM spectrum from 445 nm until the 1622 nm band. These include the visible, the red edge, near infrared and parts of the mid infrared regions. A spatial subset was needed for focus on the entire North Sea beach area and the test site. The software used to analyze and classify the image was ENVI 4.2.

4.2. Spectral processing

In a hyperspectral image, the high number of spectral bands with narrow bandwidths allows a better separation of spectral absorption features than multi-spectral imagery. On the other hand, the contiguous bands tend to be highly correlated and sometimes do not provide more useful information than one band that can represent similarly correlated ones. It is wise to select those spectral bands that effectively provide the critical classification information and, at the same time, reduce the processing time, analysis, storage, and transmission requirements for optimum quality maps [23].

In coastal research studies, the three wavelength regions VIS, NIR and SWIR have been used for analysis and classification of the coastal environment [8]. The visible wavelengths (600 nm) have been used to study sediment concentration in turbid water. Its optical properties have been used to detect a boundary between wet and dry regions of the beach [8, 37]. Near infra red (750 nm) has been used to enhance the contrast between water and land, and is useful for delineating water bodies and coastal lines. The short wave infrared (1600 nm) has been used for soil moisture measurements [37]. In the AHS hyperspectral image, these wavelengths are represented by band 6 (601 nm), band 11 (746 nm) and band 21 (1622 nm).

Other important consideration on the classification process is to ensure that the image data are comparable with lab-spectral data. Image-derived spectra were contrasted with the lab-derived spectra and no significant differences were found, assuming that this does not affect the relationship and that are comparable; thus, avoiding requirement for any pre-processing technique such as normalization of data for uniform.

4.3. Spectral characterization of shoreline indicators

4.3.1. Vegetated beach area

In the field it was possible to distinguish the dune vegetation and the beach plain vegetation (morphological features). In the image this differentiation was sought by applying a Normalized Difference Vegetation Index (NDVI) analysis.

Measuring the vegetation by NDVI implies the use of the wavelengths and intensity of visible and near-infrared light reflected to quantify the concentrations of green vegetation [38]. In order to keep working on the same spectral range, the red band used was band 6 (600 nm) and for NIR band 11 (750 nm). The output values indicate the amount of green vegetation present in the image, where higher NDVI values indicate more green vegetation.


21

With respect to this analysis, the discrimination between the vegetation in dune and the vegetated beach plain was not possible. This discrimination is in fact a research study in itself, as is mentioned in [8, 37], where the type of the vegetation of the island was studied in depth.

However, since the main idea is to be able to distinguish vegetation area (as shoreline feature) from the bare soil area, the NDVI was found to be useful enough for this propose. Thus, with the separation of vegetated area from bare soil, the edge of the active vegetation line can be determined.

Parallel to this analysis, the NDVI was used for masking purposes to solvent the water layer problem. Values for NDVI were interpreted in that pixels having negative values (partially) were assumed to contain water and in this way the threshold of the shallow water areas could be established.

4.3.2. Bare soil beach area

In the bare soil area, the obvious difference between the morphologic features in the image is a result of variations in brightness, which is caused by sand wetness, but also as a result of spectral differences between water and sand. Such differences can be distinguished on the inter-tidal bars, which are intersected by rip channels, and a trough (section 2.3.2). Dry sand appears light in an image, while wet sand appears darker in the foreshore (fig. 2.1).

Similar as to what was performed in the laboratory experiment, hardly any compositional difference can be detected between the beach surface cover. These results confirm that in the image, the differences between the sand samples are related to moisture content.

The effect of soil moisture is mainly seen by changes in albedo when measured in a laboratory. In this context, the goal of the experiment was to develop a simple classification approach from different sand wetness covers.

In order to get a view of the spatial behavior of the sand moist content, a classification was applied to the 3 bands (600, 750, and 1600), by using the linear tendency obtained in chapter 3.

Figure 4.1 shows the three tendency lines (regression equation) that were used to calculate different degrees of moistures at different reflection response values. Reflectance values show changes every 5% of water content determined and this relation was used to establish a hard classification. Figure 4.2 illustrates the outcome for the tree bands selected.

The idea of this classification was to show the differentiation that can be made between the very dry, high reflective, sandy material that occurs mainly in backshore and the wet sand from the foreshore.

Figure 4-1: Linear tendency equation obtained in the artificial wetting experiment for 3 different

wavelengths


22

40% of water content

10 %

15 %

20 %

25 %

30 %

35 %

SaturatedSea

5 % of water content

Band 6: 600nm Band 11: 750nm

Band 21:1600nm40% of water content

10 %

15 %

20 %

25 %

30 %

35 %

SaturatedSea



10 %

15 %

20 %

25 %

30 %

35 %

SaturatedSea


10 %

15 %

20 %

25 %

30 %

35 %

SaturatedSea


10 %

15 %

20 %

25 %

30 %

35 %

SaturatedSea


Band 6: 600nm Band 11: 750nm

Band 21:1600nm

Figure 4-2: Sand moisture % classification applied to the test site subset

4.3.3. Shoreline indicator endmembers

In section 3.4 a sand wetness differentiation was performed. The combination of water–sand allows classifying the spectral measurements into four groups: dry sand, moist sand, wet sand and saturated sand. In this way bare soil endmembers were selected, by using the spectral signatures obtained in the artificial wetting experiment. The mean spectral signatures for these 4 classes represent the endmembers of the bare soil area (fig 4.3).

Wavelength (nm)

Ref

lect

ance

0.5

0.4

0.3

0.2

0.1

600 800 16001000 1200 1400

Moist sand

Wet sand

Saturate sand

Dry sand

Wavelength (nm)

Ref

lect

ance

0.5

0.4

0.3

0.2

0.1

600 800 16001000 1200 1400

Moist sand

Wet sand

Saturate sand

Dry sand

Figure 4-3: Bare soil end member spectra

The definition of the endmember will allow the classification and delineation of the shoreline features. Table 4.2 shows this relationship.

Table 4-2: Shoreline indicators related with the endmembers Bare soil

Area Endmember

selection Water content

Boundary relationship between classes

Shoreline indicators related

Backshore Dry sand 0-10 % edge between dry and moist Previous high water line Moist sand 10-20 % edge between moist and wet High water line Wet sand 20-30 % edge between wet and saturated Instantaneous water line Foreshore

Saturate Sand 30-40 % edge between saturated and sea Low water line


23

4.4. Pixel-based classification for shoreline indicator

In a pixel-based approach a supervised clustering algorithm was applied to partition the feature space into a number of clusters. Thus, the pixel level in this research will use the spatial arrangement of the edge features in its local neighborhood to classify each pixel.

A supervised classification of the image allows the identification of shoreline features in the image. The outputs will show the extension and distribution of the classes established (table 4.2).

In order to apply a classification algorithm which does not ignore albedo, a minimum distance to class means (MDC) approach was used. The algorithm is sensitive to brightness as well as spectral absorption features. The MDC classification uses the mean vector of each endmember and calculates in feature space the Euclidean distance between an image pixel and the mean of reference pixels [28].

The MDC classification was applied to the three wavelengths (VIR, NIF and SWIF) so as to discern the different outputs in the distribution of the classes at the study area.

The pixel-based MDC classifier was chosen as a suitable classification algorithm as spectral differences are likely to be found in intensity rather than in spectral absorption features

For the classification vegetated areas were masked with the help of the NDVI analysis. The results of the MDC classification are given in figure 4.4, where for visualization purposes the vegetated area was overlaid, in order to be able to observe all the beach compartments defined.

Sea Dry sandSaturated sand Wet sand Moist sand Vegetated area

Low water line

High water line

Previous high water line

Instantaneous water line

Band 6 VIS

Band 11 SWIR

Band 21 NIR


Sea Dry sandSaturated sand Wet sand Moist sand Vegetated areaSea Dry sandSaturated sand Wet sand Moist sand Vegetated area

Low water line

High water line



Band 6 VIS

Band 11 SWIR

Band 21 NIR


Figure 4-4: MDC at band 6, 11 and 21 in combination with the vegetated areas (NDVI). The arrows are

showing how the % of water content can be related to different SI Results show that the shape of beach compartments changed remarkably in the 3 bands. This

demonstrates the difficulty to obtain constant water line in time and space; but even so the edges between the compartments are showing the same logical spatial relationship.


24

4.5. Chapter summry

Giving the exceptional atmospheric condition for the acquisition of the hyperspectral AHS, brightness differences between spectral laboratory data and spectral image has comparable ranges. The use of specific spectral range and brightness differences reduce the complexity spectral analysis.

The soil samples that have been analyzed in the laboratory were used for defining classification endmembers of different shoreline features. Physical differentiations (water content) were translated into spectral definition of shoreline indicators: PHWL, HWL, IWL and LWL.

Classification results show that wet and saturated spectra soil occur in mixture over the foreshore and that in consequence the ability to detect shoreline indicator decrease seaward. The MDC classification allows distinguished between beach compartments, but is not useful to map a boundary.


25

5. Delineation of shoreline indicators boundaries

The steps of finding an object (detection) and placing it into a corresponding region (classification) were undertaken in chapter 4, where it was concluded that a pixel-based method can be used for classification of beach compartments but not for the delineation of boundaries. Delineation, as in the placement of boundaries between regions [39] is now considered.

This chapter presents an object-based approach, in order to meet the robustness, but also to meet the grade of automation and transparency in the delineation of shoreline indicator boundaries.

5.1. Object-based classification technique

A conventional per-pixel approach includes only spectral information provided by single pixel and is therefore hampered by the lack of a spatial component [9].

An object-based classification follows the hypothesis that neighboring image elements belong to the same class. In this process of classifying, image objects are considering spectral similarities dependently from their occurrence, but also considered contextual and shape information. The spectral and spatial attributes are used to assign the object to a specific classification category, paralleling somewhat the human visual cognitive process [40].

Object detection and delineation relies heavily on a variety of assumptions about the scene, the object in it and the image processing [39]. The ultimate goal of object detection is to produce an automated system which can achieve qualitatively and quantitatively robust performance on an image.

5.2. Rotation-variante template matching for boundary delineation

Supervised boundary detection has been widely used via edge operators [41], and was recently improved to work with multispectral and hyperspectral imagery. Edge and line extraction only succeed when object boundaries have good contrast with their surrounding [39].

An interesting edge detection algorithm is the “rotation variant template matching” (RTM) algorithm. As a contrast to other template matching algorithms, the RTM algorithm was designed to be rotationally variant [28]. The rotation variance indicates whether there is a boundary between spectrally contrasting surface cover or not, which makes it ideal for the purpose of this research.

5.2.1. Algorithm

The algorithm matches the spectral and spatial knowledge of the object, by moving and rotating an image template over a remote sensing image. The template is a miniature image that has a one-dimensional design (3x1 pixels) consisting of two contrasting spectral signatures [28].

A statistical fit of the template is calculated for every position and orientation. The spectral fit of the template is expressed in Euclidean distance, intensity difference or vector angle, while the spatial fit is expressed by mean and variance in spectral fit measured over all orientations [28].

From the different template orientation, we can obtain the variance in spectral fit, which can be used for interpretation of the spectral signature. The rotation variance indicates the presence or absence of a crisp boundary between the two spectral signatures in the template. For the purpose of


26

finding crisp boundaries, the value of the template centre pixel is ignored in the RTM calculations (fig. 5.1) [28].

Figure 5-1:Orientations of the moving template of the RTM algorithm [28]

The RTM algorithm only indicates a relative fit and not an absolute fit of a template. The

output consists of an image with a template match measurements [28]. A detailed description of the equations and measurements obtained by the RTM can be found in appendix B.

5.2.2. Application

The interpretation of the algorithm output mainly depends on the theoretical knowledge that was used to design the template. Once the interpretation is defined, the template match is calculated, and measurements can be derived and interpreted [28].

From the rotation, the variance could be interpreted as follows: � Marginal template fit represented low match values and indicate that no signature is present; � Better template fit represented relatively high match values and indicate the presence of only

one signature; � Good template fit reflects a high match because both signatures are present; and � Optimal template fit has the highest match where contrasting signatures meet and form a

boundary [28]. In this research, the algorithm is used for the detection of boundaries between specific beach

zones, such boundaries here are considered in terms of edges between beach compartments. In order to map a boundary between two contrasting spectra, it is necessary to know that both spectral signatures used in the template are present, and whether these spectral signatures form a crisp or a gradual boundary [28].

The applied spectral matching method was the intensity difference [41] since this method is sensitive to brightness differences. The classification was applied by matching 3x1 pixel templates with 4 pairs of spectral contrasting endmembers that form the edge of the following shoreline indicators (see table 4.2):

� Dry sand signature – moist sand signature: edge for the previous water line (PHWL) � Moist sand signature – wet sand signature: edge for the high water line (HWL) � Wet sand signature – saturated sand signature: edge for the instantaneous water line (IWL) � Saturated sand signature – Sea signature: edge for the low water line (LWL)

The template matching was applied to the three spectral bands 6, 11 and 21 (as selected in chapter 3), and the results were analyzed by combining three RTM measures in a color composite image.

This color-composite image was made from the rotation variance, the optimal templates fit and the mean spectral variance to show the algorithm results (figs. 5.2, 5.3, 5.4). Moreover a profile (figs. 5.6, 5.7, 5.8) is presented for a better understanding these outputs.


27

(a) Previous water line:dry– moist signature

(b) High water line: moist–wet signature

(c) Instantaneous water line:wet– saturate signature

(d) Low water line:saturated– sea signature




(d) Low water line:saturated– sea signature

Figure 5-2: Application of RTM to the band 6 (601nm). Subset of bare soil at the test site




(d) Low water line: saturated–sea signature















→ Marginal template fit , no signature is present

→ Better template fit, only one signature is present→ Good template fit, both signatures are present→ Optimal template fit, boundary- contrasting signatures

→ Marginal template fit , no signature is present

→ Better template fit, only one signature is present→ Good template fit, both signatures are present→ Optimal template fit, boundary- contrasting signatures Figure 5-5: Color key, for the template image output

5.2.2.1. RTM application to band 6 (601 nm)

Figure 5.2 shows the template match for the 4 spectral contrasting signatures found at the 600 nm wavelength. High matches are present in all output, giving the nice red boundary line for the different shoreline indicators. This red line shows the optimal template fit, which can be seen in all contrasting signatures used. In the case of the wet–saturated signature (fig. 5.2c), the boundary is less clear and is surrounded by black and dark blue tones which means that both signatures were actively present, however form a gradual boundary.

This good template fit tends to decrease as one moves away from the defined boundary, passing form a lighter blue tone that indicates the presence of only one signature, and finishing in a marginal


28

template fit which is represented in greenish tones. This last one indicates that no signature was found. For example in fig. 5.2a, where the dry and the moist spectral signatures show green tones seawards, as expected, and in fig. 5.2d, where the saturated–sea signature is used in the template and consequently those pixels that belong to dry sand emerge as green, too.

Across beach, profiles were made in order to show the magnitude of the peaks, which represent the quality of the identification. The gradients in the graph for optimal fit can be found as peaks (fig. 5.6). The high DN values reproduce the crisp boundaries.

PHWL (fig 5.2a) results in an intermittent line; this fact is exemplified as a double peak. In addition those peaks do not represent the higher DN values from that profile, since a bigger value was found at the position of the HWL, even when the contrasting signature was used in the template to find the PHWL.

Contrarily, the HWL appears highlighted in the profile with the highest DN values, as well as IWL but a little bit less abrupt (figs 5.2b and 5.2c). A particular shape in the gradients of the profile was observed for the LWL boundary (fig 5.2d) where sequential smaller peaks are preceding an underlying peak, giving an impression of a transition, since the image was acquired during an incoming low tide.

DN

val

ues (

*10-3

) 108642

10 8 6 4 2


Instantaneous water line8 6 4 2

High water line10 8 6 4 2

6 4 2

Low water line

DN

val

ues (

*10-3

) 108642

10 8 6 4 2

10 8 6 4 2


Instantaneous water line8 6 4 2

8 6 4 2

High water line10 8 6 4 2

10 8 6 4 2

6 4 2

6 4 2

Low water line

Figure 5-6: Across beach profile of the template image at the test site. DN values for Band 6

5.2.2.2. RTM application to band 11 (746 nm)

The outputs of the template match offer similar responses in the wavelength of 746 nm. The instantaneous water line and the low water line (figs.5.3c and 5.3d, respectively) seem to have a higher fit, since the red boundary appears more clearly defined in the image.

Even when the shape of the areas under different color responses had changed, the 4 boundaries from the indicator, represented in red, preserved their characteristic form.

Boundaries were verified by examining the DN values, as seen in figure 5.7. In this case, the highlighted peaks are the high and low water line with the highest DN values between the profiles. Again a particular form in the profile was observed, but this time at the IWL, where smaller peaks are surrounding a significant high peak, which probably is a fuzzy transition.

The boundary of the previous water line (fig 5.3a) presents again an unclear outcome. Even when this time there is a remarkable peak, another peak is erroneously identified. These other high DN values are related to the HWL, just as happened in the band 6, were the classification had given the same false anomaly.


29

DN

val

ues (

*10-3

)8 6 4 2

12 10

8 6 4 2

10 8 6 4 2

8 6 4 2



High water line

Low water line

DN

val

ues (

*10-3

)8 6 4 2

8 6 4 2

12 10

8 6 4 2

12 10

8 6 4 2

10 8 6 4 2

10 8 6 4 2

8 6 4 2

8 6 4 2



High water line

Low water line


5.2.2.3. RTM application to Band 21 (1622 nm)

Once more the RTM was able to find boundaries. The contrasting spectral signatures delineated in this case were just three from the four expected shoreline indicators under investigation. Figure 5.4a shows that the PHWL is not recognized at all, since the red boundaries being delineated coincide with the position of HWL. This become clearer in the vertical profile (fig. 5.8) in which the peak that is supposed to represent the DN values for PHWL is meaningless.

Considering the overall profiles, DN values here at the wavelength of 1622 nm reach the highest values. Distinguished differences are related to the areas under different color responses, as tone blue and green. The boundary of the high water line shows again a discernible high match peak, whether the IWL and LWL evidence remarkable DN values but again surrounded by sequential smaller peaks. These can be visualized in figure 5.4d as aligned red boundaries in the inter-tidal zones.

DN

val

ues (

*10-3

)

8 6 4 2

12 10

8 6 4 2

10 8 6 4 2

8 6 4 2



High water line

Low water line

DN

val

ues (

*10-3

)

8 6 4 2

8 6 4 2

12 10

8 6 4 2

12 10

8 6 4 2

10 8 6 4 2

10 8 6 4 2

8 6 4 2

8 6 4 2



High water line

Low water line



30

5.3. Shoreline indicator boundaries

The rotation variance measure is able to detect boundaries in the 3 wavelengths that have been tested. HWL was optimally recognized in all three applications. The RTM algorithm could detect a crisp boundary for this particular shoreline indicator in a clear way.

With regard to the IWL and LWL, the rotating of the template had provided additional information, since a distinction could be made between crisp boundaries (red tones) and the surrounding areas (dark tones). Both water line indicators imply the detection of fuzzy boundaries where spectral and therefore spatial information are demonstrating the presence of a transitional zone.

On other hand, the classification is giving false anomalies for the detection of the PHWL boundary, since it is hardly detected in the three bands that were tested. This can be interpreted as a response to a weak endmember representation.

In the appendix C can be found 4 examples from the set 12 RTM outputs, from the North beach of Schiermonnikoog


Results show that the algorithm detects the boundaries that were defined in the templates. In this way, the Rotation variant template matching was proven to be suitable to detect and map shoreline indicators.

From the behavior of the detected boundaries, it is reasonable to suggest that higher moisture content contributed to the marginal definition of the indicators. Consequently, the ability to detect shoreline indicator will decrease seaward.

The RTM algorithm only indicates a relative fit and not an absolute fit of a template. The measures of the RTM algorithm should be relatively insensitive to spectral inconsistencies unless the different spectra used in the template have a different sensitivity. This is, however, a general problem faced in remote sensing and not solely a drawback of the RTM algorithm [28].


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6. Mapping shoreline boundaries on the Northern beach of Schiermonnikoog

6.1. Suitability of the RTM object-based techniques to map shoreline indicators

Accuracy and interpretation errors are determined from shoreline extraction and repeatability experiments [40]. By comparing the RTM output of the 3 selected image bands, each boundary indicator was analyzed to find the level of repeatability. By analyzing the series of profiles (figs. 5.6, 5.7 and 5.8), the repeatability of each indictor was tested. Spatial variations between profiles were observed. Many of those variations, such as irregular peaks, are due to the intrinsic variability of the indicator that is defined.

In the case of HWL, the analysis confirms that it is as a stable shoreline indicator, seeing that the positions detected on 3 bands is as good as on similar, at least, as no visual differences can be found. This stability is related to a less sensitive to tidal stage.

IWL and LWL are determined by the time of image acquisition, which was an incoming low tide. The different consecutive lines are caused by the emergence of the inter-tidal bars surrounded by troughs and separated by rips. Both water lines present the same range of horizontal spatial variability, which means that the degree of fuzziness seems to be related to the transition to a lower water level.

Contrarily, PHWL has the principal weakness. The RTM was not successful in detecting this boundary. It is likely that in its definition, the presence of other spectral properties needs to be considered. However, in the laboratory analysis no particular difference was found that gave it another physical characterization.

6.2. Comparison of the RTM results with other data sets

Considering the GPS shoreline surveyed and presented in section 2.2.3, as well as the water level’s mathematical boundary that can be extracted from LIDAR (appendix A), a data set comparison was performed.

The three results for HWL were visually compared with the MHWL extracted from the 0 m contour line of LIDAR [2], which is the result of a specific tidal Datum, in this case NAP. From this evaluation, it can be considered that the proposed method gives results in good agreement with LIDAR. The small differences can be associated with changing and irregular nearshore topography, but even more importantly with discrepancies in it definitions, since LWL and HWL can physically be seen as different boundaries between sand wetness, and the MHWL is a (mathematically calculated) water level.

The GPS surveyed shorelines yields a set of beach contour features at low and high tide basis. Since the GPS survey was not done at the moment of image acquisition, those two lines cannot be considered as a ground truth shoreline and are thus only visually compared. This visual evaluation is giving again high degrees of conformity. Figure 6.1 shows the relative position of these two indicators


32

for the three data sets. At the desired location the RTM HWL matches with the others, whereas LWL reflect again the horizontal variably at the same inter-tidal zone range.

HWL extracted from RTM LWL extracted from RTM MHWL extracted from LIDAR HWL and LWL from GPS surveyHWL extracted from RTMHWL extracted from RTM LWL extracted from RTMLWL extracted from RTM MHWL extracted from LIDARMHWL extracted from LIDAR HWL and LWL from GPS surveyHWL and LWL from GPS survey Figure 6-1: RTM extracted HWL and LWL are compared with GPS surveyed water line and the MHWL

extracted from LIDAR The actual shoreline location is unknown. To quantify by comparing the results of the different

data sets will add uncertainties, considering that the position of these indicator are related to a number of particular assumptions. GPS survey and LIDAR accuracy will need to be estimated, as well as the spatial resolution effect and image pre-processing of the AHS. A visual comparison seems then more objective and completes the purpose of demonstrating the confidence of the outputs obtained.

6.3. Boundary condition assumptions and uncertainties in shoreline position

A shoreline is a moving boundary and the key problem in coastline research is how to treat this boundary [42]. Shoreline position is under influence of long term trends (years), cyclical variations (months), random variations (hours), extreme events (hours) among the natural uncertainties, but also measurement uncertainties related to procedures, and interpretation. Its position must be properly accounted for to ensure that definition of shoreline indicators used is comparable, avoiding the possibility that an arbitrary shoreline offset is introduced [40]. The most important factors that play an intervenient role in the accuracy of the shoreline mapping are explained below.

The coastal condition of the Northern beach of Schiermonnikoog has been considered. In the long term an important component is the beach slope. Since the beach plain is very flat (section 2.3), there is a great influence on the horizontal variability of shoreline indicators. In addition, seasonal variations (section 2.1.2), alter the trend of shoreline with an increased tidal range in summer and decreased range in winter. This is important since the comparison in time will be more consistent between data sets of the same season. Another point is random variation, which is the typical local alongshore fluctuations such as wind and wave conditions.

The image acquisition date was June 2005, while the field data was collected in September 2006. Both conditions are considered and it was assumed that date discrepancy between the ground data and image acquisition will not affect the relationship, since both are considered to have similar seasonal variability and it was verified that weather conditions were similar, too (section 4.1.1).


33

6.4. Shoreline indicators defined

Non-morphological indicators related to water line were identified in both field and image setting. This was not the case for the two morphological indicators (vegetated dune and vegetated beach plain), which could only be identified in the field. However, an edge of the active vegetated area was determined with the application of NDVI analysis (section 4.3.1).

The following table summarizes the results of previous chapters and shows an overview of the principal advantages and disadvantages of the boundary indicators detected in this research study.

Table 6-1: A summary of the shoreline indicator definition achieved

Beach Feature

Shoreline Boundary Indicators

Field definition Image definition* Comments

Mor

phol

o-gi

cal

feat

ure Seaward

active vegetation line

Distinct edge between the vegetated and non-vegetated beach areas

NDVI, edge of the vegetated area

� Case specific, where vegetation is present. � May not show the dynamic of interest

Previous high water line (highest elevation tide)

A line interpreted from a difference in color in the dry beach, shell and sea grass were found

Contrasting edge between dry sand (0-10% wc**) and moist sand (10-20% wc) beach surface

� May not be clearly visible, in field and on image � Affected by wind erosion, weather condition. � Represent only elevated water conditions during spring and storms

High water line (High tide)

A line or mark left upon tide flats, or alongshore objects indicating the elevation of high water. Maximum run-up, the last high tide (day)

Contrasting edge between moist sand (10-20% wc) and wet sand (20-30% wc) beach surface

� Clearly visible, in field and on image. � Objective and repeatable shoreline (scientifically valid). � Affected by wind / wave condition at the time

Instantaneous water line (ordinary water line at specific time)

It is the interface between edge of the water and the beach at one instant in time. It is in the swash zone and is related to the stage of the tide and run-up

Contrasting edge between wet sand (20-30% wc) and saturated sand (30-40% wc) beach surface

� The position of this line cannot be used in quantitative analysis of shoreline position since it is time specific. � Highly sensitive to tidal stage � Affected by wind / wave conditions at the time

Non

-Mor

phol

ogic

al. W

ater

line

feat

ures

Low water line (Low tide)

Darker beach sand, where inter tidal zone ends. Lower level reached by wave, relative to still-water level

Contrasting edge between saturated sand (30-40% wc) beach surface and the shallow sea water

� Affected by wind / wave condition at the time. � Dependent on time of acquisition (low tide stages)

* Based on contrasting edge between two different reflectance surface cover (degrees sand wetness) ** wc: water content


34

6.5. Conclusions

RQ* 1.1 At the test site two morphological shoreline features were identified (dune vegetation

line and beach vegetation line), as well as 4 non-morphological water lines indicators (the previous high tide, the high water line, the instantaneous water line and f the low water line).

1.2 In the analysis of the beach profile no significant compositional differences between the sand samples were found, and the physical characterization of the shoreline feature is completely related to soil water content (sand wetness conditions)

2.1 Definition of the shoreline indicators were established with the quantified relation between the reflectance and water content.

Brightness was the dominant aspect to distinguish spectral signatures. Endmembers were selected to represent 4 types of sand surfaces: dry sand (0-10 %), moist sand (10-20 %), wet sand (20-30 %) and saturated sand (30-40 %), which are related to shoreline features.

3.1 The capability of the AHS hyperspectral image to detect shoreline indicator was assessed with a MDC pixel-based technique. The four discernible surface covers associated to shoreline features were detected.

This method does not map shoreline indicator boundaries, but has proven that the sand moisture content has the ability to define these 4 water line features; PHWL (edge between dry sand and moist sand), HWL (edge between moist sand and wet sand), IWL (edge between wet sand and saturated sand) and LWL (edge between saturated sand and the shallow sea water)

4.1 The definition of the shoreline indicator can actually be achieved by applying the Rotation variant template matching algorithm.

The degree of the moisture content in the beach profile appears as a print of the movement water lines. In this sense the reliability of this technique to detect contrasting spectral signatures by using the endmembers, forms an ideal combination to indicate whether a boundary is present or not

The RTM method has failed in only in 1 of 4 boundaries that were expected to be detected. In this sense further effort has to be undertaken in the definition (selection of the spectral signature) that will be able to represent PHWL.

4.2 Previous studies only considered the possibility to define dry and wet pixels. This research has studied the composite reflectance signature of the land-water boundary, more over has achieve to establish physical definition to 4 water lines.

The possibility to locate physical water lines is related to the water content at the time of the image acquisition, where the degree of underestimation could be related to the transition low tide.

The behavior of the detected boundaries indicates that higher moisture content contributes to weaker definition of the indicator. Consequently, the ability to detect shoreline indicator will decrease seaward.

* Research questions, section 1.3.2


35

4.3

Even when is not possible to optimally recognized a crispy boundary the method still

help to define the gradual interface. The result of template match explains the behavior of a transitional boundary (black and dark blue tones where both signatures are actively present).

The technique developed is least susceptible to variability in the definition of shoreline indicator can actually be achieved. This diminishes the possibility that an arbitrary shoreline offset is introduced for wrong application in measurement techniques

The RTM algorithm only indicates a relative fit and not an absolute fit of a template and the measures should be relatively insensitive to spectral inconsistencies. This contributes to make of this method a less scenario specific approach.

The spectral complexity of the image was minimized by using specific spectral ranges and analyzing brightness differences only, which opens the door for the applicability of this spectral feature characterization to multi-spectral broadband sensors.

An image definition of shoreline indicator is proposed in this research. The purpose of the

object-based approach was to optimize the accuracy and robustness, which mean good localization and discrimination of incorrect positions.

Optimization of the shoreline mapping methods has been achieved by giving reliable feature definitions to be detected, which results in a better performance than the common mapping methods.

It is a practical contribution based on the application of image processing techniques to extract proxy shoreline features from digital images.

Some significant remarks should be made concerning to the success of this precious work. It has to be considered the limitation of the temporal differences between the image and the reference data collection. It is important to remark too, that in this research no calibration was needed to compare lab spectra with the image spectra. Care must be taken in other cases and may consider the normalization of the image data set. That is why more generic application of the classification will be susceptible to atmospheric corrections techniques employed and would need to be re-evaluated appropriately [26].

In future research effort could be focused in a depth understanding of the role of all variables on the composite reflectance signature of the land-water boundary. Further studies on laboratory type analyses can be used to infer the complexities of real-world interactions, are considered to be necessary. However, spectral measurements are required from the field to validate laboratory results, as well as to extend the experiment to link the temporal, spectral and spatial domains.

In other hand could be interesting to use this method in a similar environment as for example one of the Frisian island, to validate the applicability of the method.

.


36

Glossary

Backshore- Beach plain: that part of the beach that is usually dry, being reached only by the highest tides, and by extension, a narrow, strip of relatively flat coast bordering the sea.

Foreshore: that part of shore which lies between high and low water mark at ordinary tide. Flood tide: period of tide between low water and the succeeding high water; a rising tide. Inter-tidal zone: area between the mean higher high water line. High Water Line: In strictness, the intersection of the plane of means high water with the

shore. The shoreline delineated on the nautical charts of the National Ocean Service is an approximation of the high water line. For specific occurrences, the highest elevation on the shore reached during a storm or rising tide, including meteorological effects.

Low Water Line: The line where the established low water datum intersects the shore. The plane of reference that constitutes the Low water datum differs in different regions.

Mean Low Water Line: The average height of the low waters over a 19-year period. For shorter periods of observations, corrections are applied to eliminate known variations and reduce the results to the equivalent of a mean 19-year value.

Mean High Water Line: The average height of the high waters over a 19-year period. For shorter periods of observations, corrections are applied to eliminate known variations and reduce the results to the equivalent of a mean 19-year value. All low water heights are included in the average where the type of tide is either semidiurnal or mixed.

Rip: a body of water made rough by waves meeting an opposing current, particularly a tidal current; often found where tidal currents are converging and sinking.

Rip channel: a channel cut by seaward flow of rip current. Rip runnel: beach topography consisting of sand bars that have welded to the shore during the

recovery stage after a storm. At low tide, water ponds in the runnels and flows seaward through gaps in the ridge

Run-up is the maximum elevation of wave up-rush above still-water level. Scarp edge: an almost vertical slope along the beach caused by erosion by wave action. It may

vary in height from a few cm to a meter or so, depending on wave action and the nature and composition of the beach.

Tidal Datum: horizontal plane to which soundings, ground elevations, or water surface elevations are referred. The plane is called a tidal datum when defined by a certain phase of the tide. It is important to realize that the tidal datum are local datum defined by the gravitational attraction of water with the moon and sun, as well as the non-astronomical factor such as the configuration of the costal line, local depth of the water, ocean floor topography, and other hydrographic and meteorological influences[4].

Tidal range: difference in height between consecutive high and low (or higher high and lower low) waters.

Though: lowest part of a waveform between successive crests. Also, that part of a wave below still-water level.

Extracted from the Glossary of coastal terminology of Coastal Engineering Manual [12].


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Appendix

A.- MHWL from LIDAR

The transformation of inter-tidal zone elevation data from the datum should produce a MHW shoreline, it being the zero elevation point[2]. It quality will depend of the resolution and accuracy of the elevation data. Airborne topographic LIDAR data is obvious choice for obtained high resolution elevation data.

Actual Height model of the Netherlands (AHN) is a detailed elevation model of the whole country using Airborne Laser Altimetry. Its in flat or soft topography, such as beaches and grass-fields applies a standard deviation of 15 cm [43]. To determine the elevation NAP that is the national sea level.


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B.- Rotation varaiant template matching[28]


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C.- Examples from the resuts of RTM for Northern beach of Schiermonnikoog


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References

1. Short, A., Handbook of beach and shoreface morphodynamics. 1999, Chichester etc.: Wiley & Sons. 379 p.

2. Parker, B.B., The difficulties in measuring a consistently defined shoreline - The problem of vertical referencing. Journal of Coastal Research, 2003: p. 44-56.

3. Leatherman, S.P., Shoreline change mapping and management along the US East Coast. Journal of Coastal Research, 2003: p. 5-13.

4. Graham, D., M. Sault, and C.J. Bailey, National Ocean Service shoreline - Past, present, and future. Journal of Coastal Research, 2003: p. 14-32.

5. Kinstong, K.S., et al., Artificial neural network correction of remotely sensed sandbard location. Marine geology, 2000. 169: p. 137-160.

6. Boak, E.H. and I.L. Turner, Shoreline definition and detection: A review. Journal of Coastal Research, 2005. 21(4): p. 688-703.

7. Eleveld, M.A., P.D. Jungerius, and J.L. van Genderen, Exploring coastal morphodynamics of Ameland, the Netherlands, with remote sensing techniques and dynamic modelling in GIS. 1999, ITC: Enschede. p. 225 p.

8. Applications of satellite and airborne image data to coastal management : UNESCO BILKO remote sensing distance learning module 7. 1999, Paris ; Enschede: UNESCO ; ITC.

9. Wright, D.J. and D.J. Bartlett, Marine and coastal geographical information systems. Research Monographs in GIS series. 1999, London etc.: Taylor & Francis. 320 p.

10. Woodroffe, C.D. and R.A. Furness, Coastal GIS 2003 : an integrated approach to Australian coastal issues : proceedings of a workshop held at the University of Wollongong, 7-8 July 2003. Wollongong papers on maritime policy. 2003, Wollongong: University of Wollongong. 553 p.

11. Aarninkhof, S.G.J., et al., A video-based technique for mapping intertidal beach bathymetry. Coastal Engineering, 2003. 49(4): p. 275-289.

12. Engineers, U.S.A.C.o., Coastal Engineering Manual, ed. U.S.A.C.o.E. Engineer Manual 1110-2-1100. Vol. Coastal classification and morphology. 2002, Washington, D.C.

13. Allan, J.C., P.D. Komar, and G.R. Priest, Shoreline variability on the high-energy Oregon coast and its usefulness in erosion-hazard assessments. Journal of Coastal Research, 2003: p. 83-105.

14. Ruggiero, P., G.M. Kaminsky, and G. Gelfenbaum, Linking proxy-based and datum-based shorelines on a high-energy coastline: Implications for shoreline change analyses. Journal of Coastal Research, 2003: p. 57-82.

15. Rainey, M.P., et al., Mapping intertidal estuarine sediment grain size distributions through airborne remote sensing. Remote Sensing of Environment, 2003. 86(4): p. 480-490.

16. Hess, K.W., Tidal datums and tide coordination. Journal of Coastal Research, 2003: p. 33-43. 17. Pajak, M.J. and S. Leatherman, The high water line as shoreline indicator. Journal of Coastal

Research, 2002. 18(2): p. 329-337. 18. Robertson, W.V., et al., Mapping shoreline position using airborne laser altimetry. Journal of

Coastal Research, 2004. 20(3): p. 884-892. 19. Wozencraft, J. and D. Millar, Airborne lidar and integrated technologies for coastal mapping

and nautical charting. Marine Technology Society Journal, 2005. 39(3): p. 27-35. 20. Byrnes, M.R., M. Crowell, and C. Fowler, Shoreline mapping and change analysis : technical

considerations and management implications. CERF : Journal of Coastal research : special issue. 2003, Palm Beach: Coastal Education & Research Foundation (CERF). 215.


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21. James D. Hurd, et al., Tidal wetland classification from Landsat imagery using an integrated pixel-based and object-based classification approach. ASPRS 2006 Annual conference. Definiens.The Image Intelligence Company, 2006.

22. Gamanya, R., P. De Maeyer, and M. De Dapper, Precison change detection: based on knowledge-based and object-oriented satellite image analysis in central Zimbabw. ASPRS 2006 Annual conference. Definiens.The Image Intelligence Company, 2006.

23. Miller, R.L., C.E. Del Castillo, and B.A. McKee, Remote sensing of coastal aquatic environments : technologies, techniques and applications. Remote sensing and digital image processing. 2005, Dordrecht: Springer. 345.

24. De Jong, S.M. and F.D. van der Meer, Remote sensing image analysis : including the spatial domain. Remote sensing and digital image processing. 2004, Dordrecht etc.: Kluwer Academic. 359 p.

25. Yu, Y. and S.T. Acton, Automated delineation of coastline from polarimetric SAR imagery. International Journal of Remote Sensing, 2004. 25(17): p. 3423-3438.

26. Deronde, B., et al., Sand dynamics along the Belgian coast based on airborne hyperspectral data and LIDAR data. EARSeL eProceedings 3, 1/2004, 2004.

27. Moore, L.J., Shoreline mapping techniques. Journal of Coastal Research, 2000. 16(1): p. 111-124.

28. van der Werff, H.M.A., et al., Knowledge based remote sensing of complex objects : recognition of spectral and spatial patterns resulting from natural hydrocarbon seepages, in ITC Dissertation;131. 2006, Utrecht University. ITC: Utrecht. Enschede. p. 138.

29. Abilio, I., landscape of Schiermonnikoog and Inhaca Island : an applied geomorphological comparison study for morphoconservation and land management using aerial photographs and satellite images. 1989, ITC: Enschede. p. 116.

30. Hollebrandse, Temporal development of the tidal range in the southern North Sea, in Faculty of Civil Engineering and Geosciences. 2005, Delft University of Technology: Delft, The Netherlands. p. 181 p.

31. Ministerie van Verkeer en Waterstaat. www.getij.nl 32. Lobell, D.B. and G.P. Asner, Moisture effects on soil reflectance. Soil Science Society of

America Journal, 2002. 66(3): p. 722-727. 33. Imaging Spectroscopy Flight Campaigns, http://campaigns.vgt.vito.be/quicklook2005.htm 35. Mulatu Mesgano, H., Land covers change detection using expert system and hyperspectral

imagery in the island of Schiermonnikoog, the Netherlands. 2006, ITC: Enschede. p. 60. 36. Banskota, A., Estimating leaf area index of salt marsh vegetation using airborne

hyperspectral data. 2006, ITC: Enschede. p. 39. 37. Edwards, A.J.e., Remote sensing handbook for tropical coastal management. Coastal

management sourcebooks;3. 2000, Paris: United Nations Educational Scientific and Cultural Organization (UNESCO). 316.

38. ENVI User's guide 2001, Boulder: Research Systems. 948. 39. Shufelt, J., Geometric constraints for object detection and delineation. Kluwer international

series in engineering and computer science SECS;530. 2000, Dordrecht etc.: Kluwer Academic. 265.

40. Hurd, J.D., et al., Tidal wetland classification from Landsat imagery using an integrated pixel-based and object-based classification approach. ASPRS 2006 Annual conference. Definiens.The Image Intelligence Company, 2006.

41. Bakker, W.H. and K.S. Schmidt, Hyperspectral edge filtering for measuring homogeneity of surface cover types. ISPRS journal of photogrammetry and remote sensing : official publication of the international society of photogrammetry and remote sensing - ISPRS, 2002. 56(4).

42. Zou, Z.L., Discussion of 'Moving shoreline boundary condition for nearshore models' by Prasad and Svendsen. Coastal Engineering, 2006. 53(5-6): p. 543-544.

43. Actueel Hoogtebestand Nederland, http://www.ahn.nl/english.php.

detection of physical shoreline indicators in a object ... · the aim of this study is to test the...

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