FIELD STUDY ON THE NEARSHORE SEDIMENT PROCESSAROUND THE TENRYU ESTUARY USING IMAGE ANALYSIS
Haijiang Liu1, Yoshimitsu Tajima1 and Shinji Sato1
A field investigation, based on the shore-based video technique, was conducted toestimate the temporal and spatial variation of the nearshore sediment process at Tenryu
estuary, Japan. Image analysis with a new boundary detection technique and an improved
rectification approach was used on the recorded images. A case study, by focusing on thetime period from Jul to Sep, 2007 when significant typhoon and flood visited the relevant
region, was carried out to reveal the qualitative and quantitative insights into thecorresponding morphodynamic behaviors.

INTRODUCTION

The nearshore zone is of significant social importance for human beings.
Investigation on the present state of beaches and coastlines, together withpredicting future situations in terms of a variety of timescales, is critical forunderstanding the physical mechanism and providing the suitable managementof our precious coastal resources.
At the same time, such field research activities are embarrassed by thehostile field weather conditions. In-situ survey methods provide excellent databut require significant man power, major logistical commitments and often lackspatiotemporal resolution to resolve processes of interest. In the last two
decades, being an indirect measurement, shore-based video monitoring
techniques are applied to interpret the remotely sensed information in terms of
relevant hydrodynamic and morphological parameters. Among those, theadvanced ARGUS system developed at Oregon State University (Lippmann andHolman, 1989) enables the monitoring of various nearshore features atspatiotemporal scales of direct management and research interests (meterskilometers and hours-days). Recently, the ARGUS station was used as a basic
tool for the CoastView Program. Through it, comprehensive studies have been
conducted to bridge the communications gap between research outcomes andproducts of value to managers. A series of CoastView methodologies, called
video-derived Coastal State Indicators (CSIs), has been developed anddelivered in a timely manner to managers with a ground truth. The “frame ofreference” approach developed by CoastView has been fully summarized andreviewed through a special issue in Coastal Engineering (2007, 54(6-7), 461576).
Up to now, in total, 26 ARGUS stations were constructed around the world(Aarninkhof, 2003). However, only a few of them pay attention to the estuary
1

Department of Civil Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo,113-8656, Japan

1

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area where the spatiotemporal variation of the morphodynamic behavior israther significant. The ARGUS station established at Teignmouth, UK isoverlooking a dynamic estuarine inlet with an ebb tidal delta sandbar system.
The relative inlet morphodynamics has been studied by Siegle et al. (2007)
through coupling video imaging and numerical modeling. In Japan, a series of
Typhoons, together with the corresponding river floods, attacks the coastal
region frequently. Such energetic period complicates the situation for our
understanding and management. At the same time, during these extreme events,
dramatic local morphology change may occur in an instant mode at a certain
place that we can not predict beforehand. Comprehensive investigation isneeded to reinforce our knowledge on the estuary morphodynamics.
In this study, by applying the shore-based video technique, a field
investigation was conducted to focus on the dynamic behavior at the Tenryu
estuary, Japan. Attentions concentrate over a period from Jul to Sep, 2007,
when two extreme Typhoons visited this region. Three field cameras were used
to acquire the real-time images. Image analysis was used for data processing.
Two methodologies were considered here, boundary detection and image
rectification. Results are illuminated with respect to the temporal variation andspatial deformation of the river mouth sand spit area. Discussions are specified
from both the qualitative and quantitative viewpoints.
STUDY AREA AND RESEARCH APPROACH

In this study, the target area is the Tenryu estuary, which is the conjoint
region of Tenryu River and Enshunada Coast and located at the west of
Shizuoka Prefecture, Japan (Figure 1). Along the west side of river mouth, asand spit exists. This region has been suffered from coastal erosion induced byvarious natural and anthropogenic reasons. Detailed descriptions can be foundin Liu et al. (2007). The sand spit morphology and the relevant nearshore
bathymetry are rather dynamic with respect to the co-actions from riverdischarge, daily wave climate and tidal currents. Simultaneously, intermittent
extreme events, e.g., typhoons and floods, can cause significant deformation on
the topography in an instant mode. This further embarrasses our understandingon the physical phenomena. A qualitative and quantitative insight into themorphodynamic behavior of this area is required.
Taking into account that shore-based video technique, as a cost-efficientmethod, has the availability to quantify nearshore bathymetry with highresolution in time and space, similar to ARGUS system, field cameras were
installed at the both sides of the Tenryu River mouth (Figure 1). On the westside, one camera was fixed on the top of a vent tower at Seien (thereafter referto as C1) to look at the sand spit region; on the east side, two cameras were set
up on the top of a lighthouse at Kaketsuka (thereafter refer to as C2 and C3,
respectively) to monitor the sand spit and estuary entrance. The field camera
system mainly composes of three components: camera (for data recording),
Terastation (for data storage) and UPS (for power protection). Image recording

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starts from 5 am to 7 pm everyday. Truecolor images were recorded with JEPG
compression for storage. The image has a 1280×960 pixel resolution.

Seien

TenryuRiver

Shizuoka Pref.
Japan

C1
Kaketsuka

Sand spit
C2
N

Enshunada Coast, Pacific Ocean

C3

Figure 1. Tenryu Estuary and camera locations, together with their schematicrecording azimuths (broken lines).

Different from ARGUS system, which is based on hourly image collection
with three different types, i.e., a single snapshot, a 10-min time-exposure and a
10-min variance image (Holman and Stanley, 2007), we recorded images with atime interval of 1.2 s or 10 s. Such a short time interval is considered here
because change during typhoon or flood can be in a rather fast pattern, evenshorter than one-hour scale. From the recorded images, it is found that thecollapse of river mouth sand spit on Jul 15, 2007 was in a quick mode (timescale of minute). Complex deformation process on the relevant morphology can
only be observed from the continuously recorded images. Detailed recording
can facilitate us to clarify insight into the physical mechanism and has a greatvalue to the subsequent investigation. However, such process may be escapedfrom the hourly based ARGUS image. In ARGUS system, pixel time series bysampling intensity variations at 2 Hz at one or several representative locations
were applied to study nearshore features, e.g., wave period, wave angle, phasespeed, wave runup and longshore current (Holman and Stanley, 2007).
Nevertheless, it is very difficult for us to define the characteristic points during
extreme event since the most dynamic area, such as the sand spit collapsing spot,
is somewhat unpredicted both in time and space resolutions. Considering these,
the whole image recording was conducted in this study.
IMAGE ANALYSIS

In this section, approaches used for image analysis are specified, which
include two sub-topics, i.e., boundary detection to extract the sand spit profile
and image rectification/merging to obtain the clear and whole sand spit region.
Details are described in the following subsections. To do inter-comparison forthe sand spit profile on different days, recorded images at the same tidal level

4(T.P. equals to zero, in which T.P. stands for the average water level of Tokyo
Bay, a standard datum level in Japan) were considered here. Time exposure
images (10-min averaged image) were used to outline the natural estuary
modulations and remove any moving objects’ influence.
Boundary Detection

Figure 2 presents a typical time exposure image recorded from Camera C1
at Seien. The whole sand spit area can be observed in this figure although thecamera-far field is blurry. Boundary detection is trying to find this sand spit
profile to analyze the spatiotemporal morphodynamic behavior of this region.
Considering the shoreline detection in ARGUS system, there are four typicalshoreline mapping methods, i.e., the “Shore Line Intensity Maximum” model(SLIM model), the “Color Channel Divergence” model (CCD model), the“Artificial Neural Net” model (ANN model) and the “Pixel Intensity
Clustering” model (PIC model). These methods are based on various
mechanisms to delineate a shoreline. Detailed descriptions and performance of
these models are summarized in Plant et al. (2007). In this study, the sand spit isenclosed by two different circumstances, the south side facing wave-dominant
ocean and the north side facing current-dominant river. Here, to determine thesand spit profile, various techniques were introduced. Figure 3 shows the flow
chart for boundary detection procedure. Through this procedure, the original
truecolor image is converted into a binary image (black and white, two-color
image), which is much easier to deal with in terms of further digital image
processing, e.g., boundary detection.

Figure 2. A typical time exposure image recorded from Camera C1 at Seien at 9:19 am
Jul 2, 2007. Solid line box presents the region of interest.

An RGB image composes of three color matrixes, where each color pixel isa triplet corresponding to the red, green and blue components. It can be viewed
as a ‘stack’ of three gray-scale images. Considering that most of the digital

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image processing is for grayscale images. In this study, we first convert thetruecolor image into grayscale image as shown in Figure 4(a). Figure 4(b)illustrates the grayscale image intensity histogram. There are several intensity
peaks along the gray scale, which correspond to different features in the image.
The lowest intensity peak comes from trees since they are dark color in Figure4(a), and the highest three peaks are ascribed to the sky considering that it isbright. However, for our interests, we are trying to separate sand spit area fromwater region. As shown in Figure 4(b), these two features are related to thesmall middle peaks with relatively indistinct intensity values. To extract suchblurred components in one image, pre-processing is needed. First of all, regionof interest (ROI) was selected as shown in Figure 2 using a solid line box.
Considering only this area facilitates our further procedure because much more
important information, but much less image region, is included in this part.
Another typical feature of the recorded image is non-uniform background
intensity. As we can see from Figure 4(a), middle part of the image is somewhatbright; whereas, two sides of the image are dark. To remove such image
background inhomogeneity, intensity in the sky region is considered sincebrightness in this area is more uniform than other regions. With a view to thelarge intensity value in sky region, the negative image was taken into account as
illustrated in Figure 4(c). A representative line, i.e., solid line in Figure 4(c),was selected to delineate the background intensity information. Figure 4(d)demonstrates the cross-image background intensity distribution. Intensity
inhomogeneity can be easily found in this image with middle part being small.
To mathematically represent this characteristic, cubic curve fitting was applied
as shown by the equation in Figure 4(d).
Truecolor image
Region of Interest
Background Intensity

Grayscale image‘Uniform’ BG image
Bimodal image

Spatial filter
De-noised image
Thresholding
Binary image
Boundary detection
Figure 3. Flow chart for boundary detection procedure in image analysis.

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14000

(a)
Pixel Number

12000

8000

Tree

(b)

Water

Grass

Sky

10000

San

6000
4000
2000
0
0

50

100

150

200

250

Intensity
110
105
3

Intensity100

2

y = 5e-009*x + 5.3e-005*x - 0.071*x + 1e+002

95
90
85
80

(c)

75
70
0

(d)
200

400

600

800

1000

1200

Row

Figure 4. Image processing for an example image in Figure 2. (a) Grayscale image (b)Corresponding intensity histogram. (c) Negative image. Solid line presents a
representative line to remove the non-uniform background (d) Background intensity
distribution along the representative line.

Applying the curve fitting equation in the region of interest, a ‘uniform’background (BG) image can be obtained as illustrated in Figure 5(a). This
brings a relatively BG-homogeneous image for the subsequent processing. In
this image, considering the land area, there still exist significant intensity
differences between grass and bald land parts, which cause difficulties withrespect to defining a suitable threshold value for the final binary image. Such
image still includes several intensity histogram peaks. Taking into account thebald land intensity information, we further modified the ‘uniform’ BG image
into a bimodal image, whose intensity histogram only includes two main peaks,
i.e., one related to land area and the other related to water area. To further
remove the local inhomogeneity, a small disk-type spatial filter was used to theobtained bimodal image. Figure 5(b) shows the filtered bimodal image. In thisimage, there exists clear intensity difference between water and land area.
Simultaneously, intensity distribution is rather uniform within either land or
water region, which facilitates searching on the threshold value for binary
image. In the next step, either Otsu (1979) method or Gonzalez and Woods(2008) method was introduced to select the global threshold value inapplications of image segmentation. Finally, the binary image was obtained as7shown in Figure 5(c). Further binary image processing by applying theapproach proposed by Gonzalez and Woods (2008) presents the detected landboundary as shown using a solid line in Figure 5(d) together with the original
truecolor image. In general, satisfactory results can be obtained both for theseaside and riverside of the sand spit, especially in the camera-near field. In thecamera-far field, however, there exist some boundary detection errors. This isowing to the less pixel information in the recorded image. Such problem will besolved using the merged panoramic rectified image after considering therecorded images on both sides of the estuary, as specified in the following
subsection.

(a)

(b)

(c)

(d)

Figure 5. Image processing for an example image in Figure 2. (a) ‘Uniform’ BG image
within ROI. (b) De-noised bimodal image within ROI. (c) Binary image. (d) Detectedboundary (solid line) in the original image.

Image Rectification/Merging

Quantification of image features requires accurate geo-referencing of
oblique video data and a good understanding of the cross-camera variation of
pixel resolutions. These aspects are described through the relationship between
image coordinate and the corresponding real world location. Holland et al.
(1997) have established such relationship for application with the ARGUS
system. In this study, similar approach was applied for image rectification to get
the plan-view image of the whole sand spit area. However, considering that our8cameras are installed at a relatively low elevation, but far from the target site
due to field limitations, weight factors were introduced to improve the rectified
image quality by enhancing the role of camera-far-field ground control points(GCP). It is known that moving of one pixel in a recorded image represents
different real-world distance, that is, it may only be equal to a few meters in thecamera-near field; whereas, such one-pixel shift can induce tens of hundreds
meters difference in the camera-far field. This means image pixel plays adifferent role with respect to the camera calibration with camera far-field GCPs
being more important. Taking these into account, the following horizontal andvertical weight functions, wu and wv, were introduced,

wu =cross−shore distance (m)

wv =

1
2

2

⎛ ∂v ⎞ ⎛ ∂v ⎞⎜ ⎟ +⎜ ⎟⎝ ∂x ⎠ ⎝ ∂y ⎠2

⎛ ∂u ⎞ ⎛ ∂u ⎞⎜ ⎟ +⎜ ⎟⎝ ∂x ⎠ ⎝ ∂y ⎠
1
2

(1)

(2)

without weight function

400

cross−shore distance (m)800with weight function

400

800

400

800
longshore distance (m)1200

Figure 6. Comparison between the rectified image (C1) without and with weightfunction. Solid lines represent the synchronously recorded shoreline using GPS.

90

Tenryu River
C2
C1
C3

1000m

Enshunada

1600m

Figure 7. Merged, plan-view time exposure image of Tenryu Estuary from all threecameras. Images were recorded at 8 am, Jul 5, 2007.

where u, v are image coordinates, and x, y are the real-world coordinates. Figure
6 presents a comparison between the rectified image (camera C1) with andwithout weight function. Improvement can be easily observed after considering
the synchronously recorded shoreline position using GPS.
However, due to less pixel information in the camera-far field, rectified
image does not show a clear feature even after introduction of weight function.
At the same time, as pointed out in the last subsection, boundary detection
could not perform well in the camera-far field from the single camera-recorded
images. Merged, plan-view image after three cameras (C1, C2 and C3) was
introduced as presented in Figure 7. The whole sand spit area and river mouth
entrance, as well as the nearby ocean surface, were monitored with an adequate
resolution. Several interesting characteristics were revealed from Figure 7. A
smooth white band, being an excellent proxy for the underlying, submerged
sand bar topography (Lippmann and Holman, 1989), can be clearly
distinguished in image. Wave blocking phenomena, due to the convergence ofriver current and ocean wave, can be observed just in front of the river entrance
through a nearly closed white circle. In the following discussions, such merged,
plan-view image will be applied.
RESULTS AND DISCUSSIONS

Applying the above-mentioned image analysis approaches, a case study
was carried out to investigate the topography modification at Tenryu estuaryfrom Jul to Sep, 2007. During this period, a severe typhoon, No 4, with asignificant wave height over 7 m, attacked the research area on Jul 15.
Accompanying with it, a serious river flooding occurred. The river mouth sandspit partially collapsed owing to these extreme events. Afterwards, sand spit
was gradually recovered. During the recovery process, another typhoon, No 9,with a significant wave height around 6 m, visited this region on Sep 6. All
morphodynamic processes were fully recorded by field cameras, which present
the firsthand information for study on the whole physical procedure. In this10
section, by focusing on the sand spit area, discussion on the processed images iscarried out from both the qualitative and quantitative viewpoints.
Qualitative Discussions

Figure 8 presents qualitative results on the sand spit profile in the period of
pre-, during- and post-Typhoon. Figure 8(a) shows an example of the merged,
plan-view binary image recorded on Jul 16, just after typhoon attacking. In theimage, white area demonstrates the of-the-day sand spit profile in the region ofinterest specified in Figure 2. Whilst, all other regions are described as blackcolor. Applying the boundary detection technique mentioned previously, sandspit profile was extracted for qualitative analysis.
0

0
100

Cross-shore Distance (m)Tenryu RiverN

Enshunada
900m

(b

400
500
600

900
0

0
100

Accretion

300
400
500
600
700

Accretion

800

Erosion
200

400

600

800

(c)

200

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600

800

1000

1200

Alongshore Distance (m)

1400

1600

(d

1200

1400

1600

1400

1600

200
300
400
500
600

Recovery

700

Rotation

800
900
0

1000

Alongshore Distance (m)0200Collapse

700

100

Cross-shore Distance (m)Cross-shore Distance (m)

300

800

1600m
Binary image on Jul 16

(a)

200

900
0

200

400

600

800

1000

1200

Alongshore Distance (m)Figure 8. Qualitative discussion on the sand spit profile. (a) Merged, plan-view binary
image for sand spit on Jul 16. (b) Sand spit profiles on Jul 2 (solid line), 14 (dashedline) and 16 (dotted line). (c) Sand spit profiles on Jul 16 (dotted line), 20 (dashed line)
and 27 (solid line). (d) Sand spit profiles on Jul 2 (solid line), 16 (dashed line), Sep 2(dotted line) and 8 (dash-dotted line).

Figure 8(b) illustrates the original sand spit profile (Jul 2), just before andafter typhoon, No 4 (Jul 14 and 16, respectively). Looking at results on Jul 2
and 14, erosion process occurred at the tip of sand spit, which made river
entrance wide to allow the increasing river discharge. Just after typhoon on Jul
16, significant erosion was observed at the tip region. At the same time, the11middle part of sand spit collapsed, which separated the original sand spit into
two parts, a remaining sand spit in the west and an isolated island in the east.
These two processes, i.e., tip erosion and middle collapse, occurred
simultaneously to meet the significant river discharge from upstream flood (amaximum river discharge about 8.7×103 m3/s). It is found the strong river flow
played an important role on the sand spit failure, which moved the relevant
sediments to the Enshunada Coast in an instant mode. On the other hand, therecovery process was dominated by the daily wave climate. Figure 8(c) presents
the recovering process in July. It is found that deposition took place first at the
tip of the isolated island. Sediment accumulation was in the south-east direction,
instead of east direction of the original profile. This is ascribed to the strong
flows following river flooding. Comparing profiles on Jul 20 and 27, generationin the tip region is rather gradual. Nevertheless, offshore directional accretion
also occurred along the seaward of the remaining sand spit area. The reason
behind such recovery process was revealed through the measured bathymetry
data in Aug, 2007. After Typhoon 4, a large amount of sediments was depositedin front of the cut and tip subaqueous areas. These sands were gradually, but
firstly, sent back through the daily wave actions, and emerged in thecorresponding region. Figure 8(d) includes another two sand spit profiles on Sep
2 and 8. On Sep 2, the seaside of collapse region was closed and the sand spit
was re-connected as a whole unit. At the same time, a remaining riverside cut on
the north part still existed where it can not be affected by the normal daily
waves. On Sep 6, typhoon 9 visited the target area. Overtopping occurred owing
to the extreme wave height. Looking at sand spit profile on Sep 8, it is clear thatthe riverside cut was fully refilled after typhoon 9 due to the deposited
sediments carried by the overtopping waves. As for typhoon 9, thecorresponding river discharge (7.9×102 m3/s) is much smaller than that oftyphoon 4. Therefore, the enlarged river entrance (after typhoon 4) candischarge such amount of upstream flux. Sand spit did not collapse again to
increase the outflow since there is no significant water head difference between
the riverside and the seaside. Maximum head difference was 0.53 m for typhoon
9, whereas it was much larger for typhoon 4 with a value of 2.77 m. Anotherinteresting phenomenon is the rotation of sand spit head after typhoon 9. Certain
part of seaside sediments at the sand spit head was carried to the riverside due tohigh waves after comparing profiles recorded on Sep 2 and 8.
Quantitative Discussions

Figure 9 presents quantitative analysis on the temporal variation of the sandspit area from Jul to Sep, 2007.The area was estimated through the white part in
the merged, plan-view images (Figure 8a). In Figure 9, data shortage wascaused by the recorded image ambiguity or some technical problems related tothe camera system. It is observed that the whole sand spit covered an areaaround 2×105 m2 before typhoon 4. During typhoon, almost the whole area wassubmerged with an emerged area only about 1×104 m2 on Sep 15. This is
ascribed to the high river water level owing to the significant river discharge,

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not the real sand spit erosion. In general, the river entrance water level is equal
to tide level. However, water level at the river entrance was T.P. 2.4 m on Sep
15 when tide level was T.P. 0 m then. After typhoon 4, the whole sand spit area
was about 1.5×105 m2. Total loss of the sand spit area between pre- and posttyphoon 4 was about 5×104 m2. From the qualitative analysis, it is clear that thedecrease on sand spit area comes from two regions, the tip erosion/retreatment
and the middle part collapse. After typhoon 4, the recovery process can be
distinguished into two stages. Within July, recovery is in a fast phase as shownusing a dashed line in Figure 9. Afterwards, recovery is in a slow/stable phaseillustrated by a solid line. Sudden lose on Sep 6 was ascribed to the overtopping
effect from typhoon 9. The newly refilled parts, since they were relatively low,
were temporally covered by water owing to the significant wave setup (about
1.5 m) from the large wave height (near 6 m).
5

2.5

x 10

07/10
2

Sand Spit Area (m )

2

07/14

07/31

08/11

09/02 09/08

1.5

1

09/06
0.5

07/15
0

Date

Figure 9. Quantitative analysis on the temporal variation of the sand spit area from
Jul to Sep, 2007.

CONCLUSIONS

A field investigation, based on the shore-based video technique, wasconducted to estimate the temporal and spatial variation of the nearshore
sediment process at Tenryu estuary, Japan. A case study, by focusing on thetime period from Jul to Sep, 2007 when significant typhoon and flood passed
through the relevant region, was carried out to reveal the qualitative andquantitative insights into the corresponding morphodynamic behaviors. Mainconclusions were summarized as follows,
1. A boundary detection technique was established by converting thetruecolor image into binary image. It can figure out both the shoreline
position and riverside water-land interface.
2. By introducing a weight function to enhance the roles of camera-far field
ground control points, image rectification technique was improved.

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3.

4.

5.

Qualitative analysis on the sand spit profile demonstrates that during
typhoon 4, sand spit collapse occurred in an instant mode, and was
induced by the strong river flow; whereas, the subsequent recovery
process was ascribed to the daily wave climate in a gradual mode.
Seaside to riverside recovery can be observed after typhoon No 4. This
process starts from the cut and tip regions first. During the recovery, wave
overtopping can help to refill the riverside erosion.
Quantitative discussion on the sand spit area shows that total loss of sandspit area before and after typhoon 4 was about 5×104 m2. The recovery
went though a two-stage process, a fast phase in July and a slow/stablephase afterwards.

ACKNOWLEDGMENTS

We want to thank Dr. T. Takagawa, the University of Tokyo, for helps
during field survey. This study is a part of Tenryu-Enshunada Project
financially supported by the Japan Science and Technology Agency (JST).
REFERNCES

Aarninkhof, S.G.J., 2003. Nearshore bathymetry derived from video imagery.
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Gonzalez, R.C., and R.E., Woods, 2008. Digital image processing. 3rd ed.,Prentice Hall, Upper Saddle River, NJ, 954 pp.
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Holman, R.A., and J., Stanley, 2007. The history and technical capabilities ofArgus. Coastal Engineering, 54(6-7), 477-491.
Lippmann, T.C., and R.A., Holman, 1989. Quantification of sand bar
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Liu, H., S., Sato, Y., Tajima, G., Huang, and H., Fujiwara, 2007. Investigationon the historical evolution of alluvial deposits along the Tenryu River andthe Enshu Coast. Proceedings of the 4th Asian and Pacific Coastal
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KEYWORDS – ICCE 2008
PAPER TITLE: FIELD STUDY ON THE NEARSHORE SEDIMENTPROCESS AROUND THE TENRYU ESTUARY USING IMAGE ANALYSIS
Authors: Haijiang LIU, Yoshimitsu TAJIMA and Shinji SATO
Abstract number: 630
Field study
Nearshore sediment process
Tenryu estuary
Image analysis
Shoreline detection
Image rectification
Sand spit erosion
Sand spit collapse