Shallow Water Depth Mapping Using Band Ratio

Shallow Water Depth Mapping Using Single Band and Band Ratio………….................................................................. (Prayogo & Basith)
651
SHALLOW WATER DEPTH MAPPING USING SINGLE BAND AND
BAND RATIO ON HIGH-RESOLUTION IMAGERY
(Pemetaan Kedalaman Perairan Dangkal Menggunakan Single Band dan Band Rasio Pada
Citra Resolusi Tinggi)
Luhur Moekti Prayogo, Abdul Basith
Department of Geodetic Engineering, Universitas Gadjah Mada, Yogyakarta, 55284, Indonesia
Jl. Grafika Bulaksumur No. 2, Senolowo, Sinduadi, Sleman, Daerah Istimewa Yogyakarta, 55281
E-mail: abd_basith@ugm.ac.id
ABSTRACT
One of the availabilities of remote sensing satellite imagery can be used as a provider of shallow sea
depth information using the Satellite-Derived Bathymetry (SDB) technique. This technique's main problem is
the variation in the bottom cover of waters such as coral reefs and seagrass, which distorts the spectral values.
The use of band ratios can normalize variations in bottom water cover. This study compares the single band
algorithm's accuracy with the band ratio depth data obtained by field survey around the port of Karimunjawa
Islands, Central Java. The image used in this study is high-resolution imagery, Worldview 3. Preprocessing
includes Sunglint correction to reduce the effect of sunglint in the waters and correction of depth data so that
the data are free from tides' influence. The bands used are red, green, blue, and Near-Infrared, which results
in 10 combinations. This study indicates that the band ratio method produces a smaller RMSE value than the
single band. The blue/green ratio makes the best depth values with an RMSE of 1.669 meters at a depth of
0-5 meters. In comparison, single-band use shows that the best estimation result is with an RMSE of 2.373
meters in the green band. This study shows that the band ratio method produces better depth estimates than
the single band method.
Keywords: satellite-derived bathymetry, single band, band ratio, Worldview 3, Karimunjawa
ABSTRAK
Ketersediaan citra satelit penginderaan jauh salah satunya dapat dimanfaatkan sebagai penyedia
informasi kedalaman laut dangkal menggunakan teknik Satellite-Derived Bathymetry (SDB). Permasalahan
utama dalam teknik ini adalah beragamnya tutupan dasar perairan seperti terumbu karang dan lamun
sehingga mendistorsi nilai spektral. Penggunaan band rasio memiliki kemampuan untuk menormalisasikan
variasi tutupan dasar perairan. Penelitian ini bertujuan untuk membandingkan akurasi yang dihasilkan
algoritma single band dengan band rasio. Data kedalaman diperoleh dengan survei lapangan di sekitar
pelabuhan Kepulauan Karimunjawa, Jawa Tengah. Citra yang digunakan dalam penelitian ini adalah citra
resolusi tinggi, Worldview 3. Preprocessing meliputi koreksi Sunglint untuk mengurangi efek kilap matahari
yang terjadi di perairan dan koreksi data kedalaman agar data terbebas dari pengaruh pasang surut air laut.
Band yang digunakan yaitu merah, hijau, biru, dan Near-Infrared yang menghasilkan 10 kombinasi. Hasil dari
penelitian ini menunjukkan bahwa metode band rasio menghasilkan nilai RMSE yang lebih kecil dibandingkan
dengan single band. Penggunaan band rasio biru/hijau menghasilkan nilai kedalaman terbaik dengan RMSE
sebesar 1,669 meter pada kedalaman 0-5 meter. Sedangkan penggunaan single band menunjukkan bahwa
hasil estimasi terbaik dengan RMSE sebesar 2,373 meter pada band hijau. Sehingga dari penelitian ini dapat
disimpulkan bahwa metode band rasio menghasilkan estimasi kedalaman lebih baik dibandingkan metode
single band.
Kata kunci: satellite-derived bathymetry, band tunggal, band rasio, Worldview 3, Karimunjawa
INTRODUCTION
Many industries are established in coastal areas because of easy sea transportation access,
which encourages economic growth (Hidayah et al., 2018). The existence of hydro-oceanographic
data, one of which is bathymetry, is an essential parameter in supporting transportation activities.
Conventional bathymetry measurement takes a relatively long time and is expensive, so it is
inefficient. New alternatives are needed in the provision of bathymetric data. The availability of
bathymetric data for all Indonesian coastal areas can be fulfilled, and indirectly, coastal areas'

Seminar Nasional Geomatika 2020: Informasi Geospasial untuk Inovasi Percepatan Pembangunan Berkelanjutan
652
economic development will increase. Technological advances are in line with advances in the remote
sensing, one of which is the SDB (Satellite-Derived Bathymetry) technique. The existence of images
of various resolutions and more complete image channels increase the SDB technique's accuracy.
This method is based on the principle of bands seen from satellites to estimate water depth. The
accuracy obtained by the SDB method ranges from 2-30 meters because it is influenced by the
spatial resolution ( Jégat et al., 2016). There are two popular methods in SDB studies, namely the
Analytical and Empirical methods. Based on Nuha (2019), the Analytical method is an SDB method
by extracting channels with high correlation by considering field parameters including brightness,
TSM (Total Suspended Matter). Simultaneously, the Empirical method is the extraction of the water
depth value by connecting the pixel value with the measured depth (Nuha, 2019).
Wicaksono (2015) research states that the Empirical method has a depth range influenced by
the depth data and the number of bands in the image used. However, the water conditions with
heterogeneous bottom cover and the single band will not be adequate, and the results will not be
optimal (Jupp, 1988; Wicaksono, 2015). Based on Hogrefe (2005); Stumpf (2003); Wicaksono
(2015) stated that the use of the band ratio could reduce the effect of reflection from aquatic objects
so that the resulting value is relatively constant even though the water object has a heterogeneous
base cover. SDB research using empirical methods has been carried out a lot but has not yet obtained
maximum results, as evidenced by the high RMSE value. Prayogo & Basith (2020) compare two
images in their research, namely Worldview 3 and Sentinel 2A to estimate shallow water depth in
the Karimunjawa Islands, Central Java. This study uses the Stumpf algorithm, where Worldview 3
imagery produces a better depth estimate than Sentinel 2A using the green/blue band ratio. The
RMSE value generated from the study was 1.53 meters.
Bergsma et al. (2019) produced an R2 value of 0.82 and RMSE 2.58 meters using Sentinel 2A
imagery. The methods used by Bergsma et al. (2019) are Radon Transform and Augmentation. The
research Manessa et al. (2018) using SOPT 6 high-resolution imagery produced the best accuracy
with an RMSE value of 1.09 meters and R2 of 0.45 in the Mejangan Islands. Furthermore, Bobsaid
et al. (2017) 's research resulted in an NMAE value of 25,777% using Landsat 8 imagery. While
using the Sentinel 2A imagery, the resulting NMAE value was 26,887%. Syaiful et al. (2019) research
resulted in an RMSE of 4.00 and a correlation of 0.6976 using the blue/green band ratio. Another
research was also conducted by Muzirafuti et al. (2020) with the Empirical method. This study uses
the Log-Band method on Quick Bird imagery. Meanwhile, the Object Base Image Analysis (OBIA)
method on the same image produces an RMSE value of 0.35 meters and a correlation of 0.91 meters.
The Empirical method was also carried out by Traganos et al. (2018), producing an RMSE value of
1.39 meters and R2 of 0.79 with Sentinel 2A imagery. In the same study, Traganos et al. (2018)
also used Google Earth imagery, which resulted in an RMSE of 1.67 meters and R2 of 0.9. Ledera et
al. (2019) has also conducted research using the HR400512 Electronic Nautical Chart (ENC) method
by adding vertical accuracy and position to Sentinel 2A, and Landsat 8 imageries carried out in
Hramina Bay.
Hence, this research aims to compare the performance of the single band and band ratio
methods on Worldview 3 imagery to provide depth data, especially in shallow waters using the
Empirical Method. This research is essential to do in order to determine the extent to which the use
of the single band and band ratio can produce a depth value that is close to the measured depth
value. The following is a map of the research location in Karimunjawa waters, Central Java:

653
Figure 1. Research locations around Karimunjawa Port with Google Earth imagery.
This research was conducted in the Karimunjawa Islands with a geographical position of 5°
52'56.01" S and 110°25'52.62" E, precisely around the port in March 2019. The location selection
was based on the consideration of shallow water conditions due to the limitations of satellite image
sensors in penetrating water objects. This study uses Worldview 3 imagery level ORStandard2A that
has been geometrically corrected and radiometric AComp. In-situ depth data measurements used
the Bathy-2010 SyQwest Single Beam Echosounder (SBES), and the Geodetic GPS mounted on the
survey ship to obtain real-time position information. Meanwhile, a tide master is used to obtain tide
data. Data collection was carried out on March 20, 2019, s.d. March 23, 2019. Furthermore, data
processing in this study uses various kinds of software. First, ArcGIS was used to create and display
maps. The second is ENVI, which functions for image processing, and the third is Microsoft Excel
2013 for statistical analysis.
METHOD
This chapter describes the research methodology, including image preprocessing, statistical
analysis to determine the depth extraction model, and accuracy testing.
Sunglint Correction
Sunglint is a sun flash effect that occurs when sunlight hits a surface such as water or a smooth
surface like a mirror. This correction aims to eliminate the effect of sunshine due to the angle of the
rays' angle, which is the same as its reflection, causing white spots on water objects. Syaiful et al.
(2019) stated that this correction aims to eliminate water waves' effects. Hochberg et al. (2003);
Hedley et al. (2005) have refined the Sunglint correction algorithm as Equation 1.
R'
i=Ri-bi (RNIR-Min NIR)……………………………………………………………………………………….……(1)
where:
R'i = the i channel value after being reduced
Ri = the initial i channel value
bi = the amount of regression slope

654
RNIR i :the channel value, and Min NIR is the minimum NIR channel value
Tide Correction
Tide correction aims to free the measured depth data from SBES recording from the influence
of tides. According to Setyawan et al. (2014), the algorithm for tide correction is as Equation 2.
D=dT-rt………………………………………………………………………………………………………..…………….(2)
where:
D = the measured depth
dT = the transducer corrected depth
rt = the tides' reduction value
Satellite Derived Bathymetry
The single band method uses the principle that the electromagnetic waves hitting the water
column will be attenuated. Bands with longer wavelengths are weakened more strongly than shorter
wavelengths (Bukata et al., 1995; Goodman et al., 2013; Wicaksono, 2015). Several parameters
that affect the single band method include selecting the band, the characteristics of the water column
attenuation and the variation in depth used (Wicaksono, 2015). The single band method produces
exponential modelling, so it is necessary to do linearization by modelling the log-transformed results
(Wicaksono, 2015).
The band ratio method in this study uses the Stumpf algorithm (Stumpf et al., 2003). The
principle of this method is to use a two-band ratio where if the ratio increases, the estimated depth
will also increase. If the depth continues to increase, the bands with a high absorption rate will
decrease (Irwanto, 2018). The equation of the Stumpf method is as Equation 3.
Z=m1 (
ln(nRw(λi))
ln(nRw(λj))
) -m0 ………………………………………………………………………………………………….(3)
where:
Z = the depth sought
m1 = the calibration coefficient
Rw (λij) = the reflectance of the extension of the wave
ln = the constant, and m0 is the depth correction (0)
The empirical method in the SDB technique utilizes the relationship between the measured
depth value and the spectral value from satellite images. Depth data were measured using SBES in
2019 along with tidal data for data correction. The amount of depth data used in this study was 218
data, with the following details:
Table 1. Number of depth samples.
Depth (meters) Number of Samples
0 – 5 25
5 – 10 31
10 – 15 82
15 – 20 50
20 - 25 30
The modelling stage begins with the Pearson product-moment correlation analysis to find the
value (r). The limit of significance value (r) on the number of samples (n) is used as a condition that
the correlation results can be continued for modelling using Regression analysis (Wicaksono, 2015).
This study uses a significance level of 95% so that a band with weak energy absorption will produce

655
the best estimation model. The Resultant Regression function's value is used to convert the image
pixel value to the depth value (Wicaksono, 2015).
Accuracy Test
The SDB Empirical Model is formed from the relationship between the image pixel value and the
measured depth. The equation used in the Root Mean Square Error (RMSE) accuracy test is as
Equation 4 (Walpole, 1968; Manessa et al., 2017):
RMSE= √∑ (At-Ft)
2
n
t=1
n
………………………………………………………………………………………………..…….(4)
where:
Ft = the measured depth of the survey results with SBES
At = the estimated depth value from the extraction of the image pixel value
n = the number of measured depth points
RESULT AND DISCUSSION
This chapter describes the results and discussion, divided into two groups: Preprocessing and
Processing data.
Preprocessing Data
Depth data consist of position data and tide data. Depth data were measured using the Bathy-
2010 SyQwest Single-Beam Echosounder (SBES). The research location is located around the port
of Karimunjawa, Central Java. Real-time positioning using the Global Navigation Satellite System
(GNSS) with the Trimble NET R9 geodetic type placed on the ship. The following is the result of the
bathymetric route around the Karimunjawa Port, which is shown in the Worldview 3 imagery as
Figure 1.
This study uses depth data from 0 to 25 meters. The depth is grouped into five groups with
ranges of 0-5 meters, 5-10 meters, 10-15 meters, 15-20 meters, and 20-25 meters. Details of depth
data are 25 data (0-5 meters), 31 data (5-10 meters), 82 data (10-15 meters), 50 data (15-20
meters) and 30 data (20-25 meters). Then, the acquisition of field tide data in the Karimunjawa port
area uses the Tide Master tool installed at the port. Tide measurements were carried out for 2 x 24
hours. This instrument's principle is to automatically measure sea-level changes with sensors
connected to a computer device. The tide data are used to correct the SBES depth value resulting
from the results so that the depth value is free from tides' influence.
Figure 2. Ship trajectory for data acquisition using SBES.

656
Finally, the correction made on image data are Sunglint correction. This correction is carried out
on Worldview 3 imagery to minimize sun flash in the water. The sunglint correction uses the RGB
ratios band with the Near-Infrared band. This correction begins with the creation of ROIs in the
areas affected by the sunglint effect. Then extract pixel values with ROIs and save in ASCII format.
Furthermore, the equation's value is applied to the image using ENVI software so that the sunglint
effect can be reduced.
Processing Data
Single Band
The first thing to do in the extraction of depth values is to make depth sampling in various
groups. Secondly, the depth sampling results are inputted in the ENVI software as vectors to be
converted as ROIs. Then the ROIs formed are converted into ASCII format. In principle, all bands
in the image can be used for depth estimation. However, only a few bands produce the best
correlation and small RMSE values. The single band method in this study uses four bands so that
there are four equations for the relationship between the depth and spectral values in the image.
The best correlation is the green band with an RMSE of 2.373 meters.
Band Ratio
Depth estimation using the band ratio method, the first is to make a depth sampling of each
group that functions as training data. The training data that have been inputted are then analyzed
to find a relationship between the partial value. This process is carried out on all band ratios formed
from the red, green, blue and Near-Infrared bands. This study's combination band ratio is
blue/green, blue/red, blue/Near-Infrared, green/red, green/Near-Infrared, and red/Near-Infrared.
Furthermore, the accuracy test is carried out by looking for the RMSE value generated from the
image extraction with measured depth. From the band ratio method used, the smallest RMSE is
obtained in the blue/green band ratio of 1.669 meters at a depth of 0-5 meters. The band ratio in
the Worldview 3 imagery results in a relatively small RMSE value compared to the single band. The
band ratio can reduce the bottom cover value of the waters to correlate the pixel value and the
measured depth. This study uses four bands, namely red, green, blue, and Near-Infrared. The four
bands produce six band ratios with different RMSE values.
Previous researchers have done SDB analysis using the Empirical method using the Stumpf
algorithm by comparing the Worldview 3 with Sentinel 2A. This study indicates that the green/blue
band ratio produces the best depth estimate with an RMSE of 1.526 meters (Prayogo & Basith,
2020). The RMSE value and the resulting band ratio differed between previous and present studies.
The formed model is influenced by the depth sample used (Prayogo & Basith, 2020).
CONCLUSION
From this research, the best equation for the single-band method is the green band with y: -
0.2633x + 2.3691. From this model, the green band produces the best estimation results with an
RMSE value of 2.373 meters at a depth of 0-5 meters. The best equation for the resulting band ratio
method is the blue/green band ratio with an RMSE of 1.669 meters at a depth of 0-5 meters. So it
can be concluded that the band ratio method produces a better depth estimate than the single-band
method.
ACKNOWLEDGEMENTS
The author would like to thank the Department of Geodesy Engineering and Universitas Gadjah
Mada for assisting in the form of funding for this research.

657
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