第35回日本情報地質学会講演会 講演要旨集 033-034頁 2024年

講演要旨
空白Assessment and Prediction of Shoreline Change
Using Free and Open Source CoastSat & AMBUR Toolkits
Job THOMAS, Tatsuya NEMOTO and Venkatesh RAGHAVAN
Department of Geosciences, Graduate School of Science, Osaka Metropolitan University,
3-3-138 Sugimoto Sumiyoshi-ku, Osaka, 558-8585, Japan E-mail: sw23079s@st.omu.ac.jp
明朝(約 6.5mm)

Key words: Shoreline prediction, Shoreline change analysis, Transect method, Open source.

空白
1. Introduction

Table 1: Input data used in this study.

Coastal management requires a careful evaluation ofshoreline change dynamics, which is important toprotect the fragile near-shore environment. This problem
becomes particularly crucial due to the impact of climatechange and related factors. To understand the dynamics
of coastal change, it is important to derive accurate
shorelines, and this can be achieved with the help ofCoastSat, which extract shorelines from 30+ years ofsatellite imagery, providing cloud removal, highresolution coverage, tidal corrections, and a user-friendlyinterface. (Vos et al., 2019).
On the other hand, AMBUR (Analyzing Moving
Boundary Using R) provides time-series capabilities(Jackson Jr et al., 2012). It's new transect methodaddresses curved shoreline movements. In this study, weevaluate the efficacy of using CoastSat and AMBUR togain understandings on shoreline changes, erosion
patterns, and future shoreline disposition.

2. Materials and Methods
2.1 Data
The data used for this study are 4 shorelines from 1989,
2003. 2013 & 2023, where shoreline of 2023 was used forvalidation. These Shorelines were extracted from opticalsatellites, where beach slope and shoreline elevation
were calculated using DEM, and tidal data was obtained
from National Oceanographic Centre. Table 1 shows thedetailed description of the data and its sources.

Figure 1: Shoreline Extraction for 1989.

2.2 Methodology
The CoastSat toolkit extract shorelines and perform
tidal corrections, achieving around 10-meter accuracy. It
processes multispectral images from Sentinel-2 andLandsat via Google Earth Engine, applying cloudmasking and pansharpening. With the help of MNDWI,and Otsu's threshold (Otsu, 1979), it segments images
into land and water pixels, extracting shorelines withthe Marching Squares algorithm (Cipolletti et al., 2012).
Tidal correction, including tidal data, beach slope, andshoreline elevation, normalizes the shoreline positions tomean sea level, providing an accurate long-termshoreline analysis.
The AMBUR toolkit was used to calculate shorelinechange rates and predict future shorelines. It starts fromcollecting baselines and shorelines, then generates
transects at regular intervals. For this study 5 different

Figure 2: Transect Methods.

intervals of 1 m, 25 m, 50 m, 75 m, and 100 m were usedalong the shoreline. Two transect types are used:perpendicular and near transects shown in Figure 2.

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Table 2: Accuracy Assessment & Shoreline Change Rate

DITMMD

SD

MAE

1mP
N
PN
PN
PN
P
N

20.66
20.70
20.63
20.48
20.39
20.17
21.56
21.26
20.87
20.95

14.26
14.29
13.98
14.12
14.12
13.87
13.77
13.78
13.94
14.05

20.67
20.70
20.62
20.48
20.39
20.17
21.57
21.26
20.87
20.95

25 m
50 m
75 m
100 m

AR
(m/y)
2.36
3.01
0.89
2.98
0.88
2.98
0.80
0.92
0.84
0.92

ER
(m/y)
-3.95
-3.94
-3.95
-3.91
-3.95
-3.91
-3.66-3.74
-3.94
-3.91

D I: Distance Intervals, T M: Transect Methods, P: Perpendicular, N:Near, M D: Mean Distance, S D: Standard Deviation, MAE: Mean
Absolute Error, A R: Accretion Rate, E R: Erosion Rate. m: Meter, y: Year.

These transects help measure shoreline positions overtime. Shoreline positions are analyzed by calculatingintersection points and measuring distances from thebaseline. Statistical methods, including End Point Rate(EPR), Linear Regression Rate (LRR), and Weighted
Linear Regression (WLR), are used to calculate changerates. Predicting future shorelines depends on factors
like shoreline change rates calculated using statisticalmethods (EPR, LRR, WLR), Transect azimuth, Offshore
Correction values, latest shoreline, and the forecast
period (e.g., 10 years, 50 years) for which the future
shoreline position needs to be predicted.

3. Results and Conclusions
In this study, only the End Point Rate statistical method
was performed on the shorelines with different transects
intervals of 1 m, 25 m, 50 m, 75 m, and 100 m, using
both the perpendicular transect method and the neartransect method. Table 2 shows the accretion and erosion
rates at various distance intervals. It was observed thatthe near transect method generally yields higher
accretion rates as compared to the perpendiculartransect method, this is particularly noticeable at the 1m, 25 m, and 50 m intervals. The perpendicular transect
method tends to show slightly higher erosion rates at
shorter intervals and the near transect method, however,maintains higher and more consistent accretion rates
across all intervals. Figure 3 shows the frequency ofdistances within a specific range between the predicted
and observed shorelines for 1 m interval. The y-axis
ranges from 0 to 500, a bar reaching up to 100 means
there are 100 cases where the distance between thepredicted and observed shorelines fell within the range
fbb ’ wx-axis.
Shorelines for 2023 were forecasted using two transect
methods from 1989, 2003, and 2013 coastal data. Theresults were validated with the actual shoreline of 2023
derived from CoastSat as shown in Table 2. The accuracy
assessment provides understanding of shoreline
prediction for 2023 using near and perpendiculartransect methods. From the results, it can be observedthat the mean distances range from approximately 20.17
to 21.56 meters, indicating the average deviation of thedistances between the predicted and observed shorelines.
The standard deviations have values ranging from 13.77
to 14.29 meters, that suggest the variability of theFigure 3: Histogram Plot for 1 meter Perpendicular Transect &Near Transect.

prediction around the mean. Furthermore, the MAE
values provide additional measures of prediction
accuracy of 20.17 meters. These findings suggest thenear transect method better captures accretion trends,
while both methods reliably measure erosion rates.
Overall, the accuracy assessment allows for thecomparison of different transect methods and intervals,
aiding in the selection of the most suitable approach for
future shoreline prediction tasks.

References
Cipolletti, M. P., Delrieux, C. A., Perillo, G. M., andPiccolo, M. C. (2012) Super resolution bordersegmentation and measurement in remote sensing
images. Computers & Geosciences, vol.40, pp.87-96.
Jackson Jr, C. W., Alexander, C. R., and Bush, D. M.
(2012) Application of the AMBUR R package forspatio-temporal analysis of shoreline change: JekyllIsland, Georgia, USA. Computers & Geosciences,vol.41, pp.199-207.
Otsu, N. (1979) A -threshold - selection - method - fromgray-level histograms. IEEE –Transactions on
Systems, Man, and Cybernetics, vol.9(1), pp.62-66.
https://doi.org/10.1109/TSMC.1979.4310076
Vos, K., Splinter, K. D., Harley, M. D., Simmons, J. A.,
and Turner, I. L. (2019) CoastSat: A Google EarthEngine-enabled Python toolkit to extract shorelines
frompubliclyavailable
satellite
imagery.
Environmental Modelling & Software, vol.22, 104528.
Vos, K. (2018) CoastSat. GitHub Repository,https://github.com/kvos/CoastSat.