Characterizing Coastline and Sediment Interaction in The North Coast of Java using Satellite Images

The large amount of sedimentation process caused rapid changes in the coastline presented by capes form in the coastal area. To characterize coastline and sediment interaction used coastline change analysis, hydrological and vegetation indices using Landsat imagery. Accordingly, we successfully characterized the Cirebon coastline into four categories: abrasion, accretion, abrasion to accretion, accretion to abrasion. The NSM range values -1.123,67 m – 1.026,19 m. The highest abrasion (EPR, LRR, WLR) is 33,18 m/y, 31,16 m/y and 31,16 m/y. The highest accretion (EPR, LRR, WLR) 30,3 m/y, 29,16 m/y, 29,16 m/y. The results of the hydrological index parameters show that humidity levels vary from drought (low humidity) to high humidity, but in the eastern part it includes the water surface. The results of the vegetation index parameter show that the vegetation level was dense, moderate to sparsely grown with vegetation, canopy cover varying from low to medium. Clay material (mud) has impermeable properties, so the study area is dominated by accretion because there is large supply sediment from rivers that enter the sea. Based on these data, it can be concluded that the characteristics of the beach are divided into muddy beaches, and coastal structures such as harbor.


Introduction
Sediments are the main material forming the morphology (topography and bathymetry) of the coastal area.Sediment originated from rock fragmentation due to weathering which can take place physically, chemically or biologically.Changes in the morphology of coastal areas can occur through erosion, transportation, and deposition mechanisms.Sedimentation in the coastal environment originates from the presence of sediments originating from the mainland and is basically the main factor in forming the coastal area.The displaced sediments are sediment that lies on the surface of the bottom of the waters [1].
Sedimentation in the coastal environment originates from the presence of sediments originating from the mainland and basically is the main factor in forming the coastal.Sedimentation is closely related to the geomorphic agents working in it.The main geomorphic agents that cause or influence the processes and dynamics of coastal waters are waves, currents and wind [2].In the coastal environment, sediments are dynamic which will have erosion, transportation and deposition on a spatial and temporal scale.The strong sedimentation process causes rapid changes in the coastline and causes capes and land to form in coastal areas.
Research about coastal characteristics is needed to be carried out in Cirebon Coastal Area to obtain information regarding the physical condition of coastline including the ongoing processes in coastal area.The study of the Cirebon Coastal sedimentation process was carried out in the context of collecting geological data and information on coastal and offshore waters using satellite imagery to determine coastline changes, accretion or abrasion processes and determine the characteristics of beach types.
Cirebon Coastal Area is defined as an area consisting of the Cirebon District and Cirebon City.This area is located on the north coast of Java Island, Indonesia.This area covers a shoreline of approximately 50 km long [3].The research area is located along the coastal area of, which is bordered by Indramayu District in the northwest (Figure 1).The surface geology of the Cirebon coastal area and its surroundings consists of alluvium (Qa) deposits.These sedimentary facies are increasingly towards the coast covered by coastal deposits (Qac) [4].Coastal deposits consist of silt from swamp deposits, silt, and gray clay containing mollusk shells which are deposited around the coast with a thickness of up to several meters.While alluvium deposits can be distinguished into gravel, sand and gray clay, which are deposited along the river floodplain with a thickness of approximately 5 m.
In the geological map of the West Java Muara-Cirebon Quaternary Sheet, Scale 1:50,000, which is based on the results of shallow drilling with a scale of 1:50,000, it is said that Qac belongs to the BM 1245 (2023) 012039 IOP Publishing doi:10.1088/1755-1315/1245/1/0120393 (beach on marine) facies, namely coastal bund deposits above nearshore/shallow sea deposits.It was also stated that Qa is included in the FM (floodplain on marine) facies, namely floodplain deposits above sediments near the coast [5].The repetition of sedimentation processes between linear clastic (sea and coast), fluviatile, and swamp deposits in the study area, has inspired that these deposits are related to basin filling and environmental changes.

Image data collection
The data used is Landsat satellite imagery which is considered to have the availability of long enough time series image data, free of charge and has good resolution (spatial, temporal, radiometric) (intermediate level).The satellite images used were taken in 1988, 2003 and 2022 using the Landsat 5 TM, Landsat 7 ETM+ and Landsat 8 OLI/TIRS satellites which can be downloaded from the website http://earthexplorer.usgs.gov/[6] (Table 1, Figure 2).In addition to using Landsat satellite imagery, geological maps and Quaternary geological maps of the study area are also used to support research results and discussion.

Research methodology
Coastal characteristics and sediment material in this study used 3 parameters, namely coastline changes, hydrological indices and vegetation indices.The following describes the method used in this study.

Image processing
Reflectance, radiance, and atmospheric conditions all affect the digital number (DN) values of Landsat data.Radiometric calibration employs algorithms and processes that improve Landsat data.This is done by converting the DN values of the data to spectral radiance (at the sensor), and then to reflectance (also at the sensor).This is followed by the removal of atmospheric effects, which are due to absorption and scattering, to perform atmospheric correction (reflectance at the surface) [7].
FLAASH (Fast Line-of-sight Atmospheric Analysis of Hypercubes) is a first-principles atmospheric correction tool that corrects wavelengths in the visible through near-infrared and shortwave infrared regions, up to 3 µm.FLAASH works with most hyperspectral and multispectral sensors [8].Water vapor and aerosol retrieval are only possible when the image contains bands in appropriate wavelength positions.FLAASH can correct images collected in either vertical (nadir) or slant-viewing geometries.Atmospheric correction results in this study (Figure 3).

Identifying land boundaries using BILKO method
Determination of the boundary between land and sea is done by utilizing the brightness value or Brightness Value (BV) of land and sea.For Landsat 5 and 7 required band 4 while Landsat 8 uses band 5 which is the infrared band [9].Infrared waves have a reflectance low to water and high reflectance against the mainland (Figure 4).This method requires a BV value for the lowest land and the highest oceanic BV values.The BV value is required in the separation of land and ocean [9], [10].The use of the BILKO formula is as follows (1) Where N is minimum BV value of Landsat 5 and 7 (30) and Landsat 8 (7000) imagery and ∅ is Band 4 (Landsat 5 dan 7) or Band 5 (Landsat 8) The results of applying the BILKO formula can be seen in Figure 4.The boundaries of land and sea are clearly visible before digitizing the coastline.

Figure 4.
The results of applying the BILKO formula to separate land and sea parts on Landsat to make it easier to digitize coastlines: Landsat-5 before applying the BILKO formula (a); Landsat 7 before applying the BILKO formula (b); LAndsat-8 before applying the BILKO formula (c) ); Landsat 5 after applying the BILKO formula (d); Landsat-7 after the application of the BILKO formula (e); Landsat-8 after the application of the BILKO formula (f) The results of the BILKO algorithm method show that the boundary between land and water body is clear in areas without cloud and fog.In the processed image, the land is black and the sea is white.Coastline digitation is done over the image satellite by applying the BILKO formula.The results of beach digitization carried out on Landsat imagery in 1988, 2003 and 2022 can be seen in Figure 5.

Analyzing digital land boundaries
The Digital Shoreline Analysis System (DSAS) v5 software is an add-in to Esri ArcGIS desktop (10.4-10.7+)that enables a user to calculate rate-of-change statistics from multiple historical shoreline positions [11].Digital Shoreline Analysis System (DSAS) is a remote sensing technology that can be used to automatically detect and calculate coastline changes in an area [12].DSAS generates transects that are cast perpendicular to the reference baseline at a user-specified spacing alongshore.DSAS v5 supports a baseline located anywhere like offshore, onshore, in the middle of the shoreline data (midshore), or a combination of baseline placements.There are no restrictions on where the reference baseline is drawn, it may be positioned completely to one side of the shoreline data or be placed between the historical shoreline positions.The result of DSAS analysis we can see Figure 6 and all the rate has been classified into 5 classes based on Table 2.  DSAS measures the distance between the baseline and each shoreline intersection along a transect, and combines date information, and positional uncertainty for each shoreline, to generate the following change metrics: Net Shoreline Movement (NSM), End Point Rate (EPR), Linear Regression Rate (LRR) and Weighted Linear Regression Rate (WLR) [11].The net shoreline movement (NSM) is the distance between the oldest and the youngest coastlines for each transect.The coastline change envelope (SCE) reports a distance (in meters), not a rate.The SCE value represents the greatest among all the coastlines that intersect.The end point rate (EPR) is calculated by dividing the distance of coastline movement by the time elapsed between the oldest and the most recent coastline.A linear regression rate-of-change statistic can be determined by fitting a least-squares regression line to all coastline points for a transect.In a weighted linear regression, the more reliable data are given greater emphasis or weight towards determining a best-fit line [14].
The highest negative NSM value was found to be -1.123,67m and the highest positive NSM was found to be 1.026,19 m (Figure 7).The highest abrasion EPR on the same transect with NSM is 33,18 m/y and the highest accretion EPR is 30,3 m/y (Figure 8).The highest abrasion LRR on the same transect with NSM and EPR is 31,16 m/y and the highest accretion EPR is 29,16 m/y (Figure 9).The highest abrasion WLR on the same transect with NSM, EPR and WLR is 31,16 m/y and the highest accretion EPR is 29,16 m/y (Figure 10).A visual representation of the rate of change (1988-2022) of all methods is shown in Figure 11.

Coastline change analysis
The results of the DSAS in the study area show that there are 4 processes that occur from the observations of coastline changes namely abrasion, accretion, abrasion to accretion and accretion to abrasion.The dominant process that occurs in the study area is accretion.This is due to the large supply of sediment from the rivers around the Cirebon Beach area such as the Bondet River, Ciberes River, Pemali River, Sukalila River so that it is possible that the dominant process is accretion rather than abrasion.The area of each process that occurs at the study site can be seen in Figure 12 which shows that the process at the study site is dominated by accretion with an area of 710.88 ha while abrasion is with an area of 213.51 ha.The process that begins with abrasion then accretion with an area of 170.28 ha while the process that begins with accretion then abrasion with an area of 71.9 ha.The Normalized Difference Water Index (NDWI) is used to highlight open water features in a satellite image, allowing a water body to "stand out" against the soil and vegetation [15].The NDWI equation looks like this: The modified normalized difference water index (MNDWI) uses green and SWIR bands for the enhancement of open water features.It also diminishes built-up area features that are often correlated with open water in other index [16].The NDWI equation looks like this: Table 3. NDWI and MNDWI value ranges and interpretations [17] Value Interpretation 0,2 -1 Water surface 0.0 -0,2 Flooding, humidity, -0,3-0 Moderate drought, non-aqueous surfaces -1-0,3 Drought, non-aqueous surfaces A total of four spectral reflectance bands are used in this technique.It is the ratio between the total spectral reflectance of two visible bands (i.e., green and red) to the total spectral reflectance of nearinfrared and Middle infrared band (MIR) [18].The WRI equation looks like this: The Normalized Difference Moisture Index (NDMI) detects moisture levels in vegetation using a combination of near-infrared (NIR) and short-wave infrared (SWIR) spectral bands.It is a reliable indicator of water stress in crops [19].The NDMI equation looks like this:

Table 4. NDMI value ranges and interpretations [17] Value
Interpretation -1 --0,8 Bare soil -0,8 --0,6 Almost absent canopy cover -0,6 --0,4 Very low canopy cover -0,4 --0,2 Low canopy cover, dry or very low canopy cover, wet -0,2 -0 Mid-low canopy cover, high water stress or low canopy cover, low water stress 0 -0,2 Average canopy cover, high water stress or mid-low canopy cover, low water stress 0,2 -0,4 Mid-high canopy cover, high water stress or average canopy cover, low water stress 0,4 -0,6 High canopy cover, no water stress 0,6 -0,8 Very high canopy cover, no water stress 0,8 -1 Total canopy cover, no water stress/waterlogging The results of hydrological indices parameters can be seen in Figure 13.The results of the hydrological index parameters along the Coastal Cirebon show that humidity levels vary from drought (low humidity) to high humidity, but in the eastern part it includes the water surface.This depends on the type of material that makes up the Cirebon coast which is divided into clay and sandy clay.Clay or mud material tends to have a high humidity level because it is impermeable while the humidity level of sandy clay is lower than clay (mud) because it has a greater permeability than clay.

Vegetation indices derived by Landsat-8 images
The vegetation indices is the amount of greenness of the vegetation obtained from digital signal processing of the brightness value data of several channels of satellite sensor data.In the following, several remote sensing vegetation indices that are often used are NDVI (Normalized Difference Vegetation Index), SAVI (Soil Adjusted Vegetation Index) and MSAVI (Modified Landsat Soil Adjusted Vegetation Index).
The Normalized Difference Vegetation Index (NDVI) measures the greenness and the density of the vegetation captured in a satellite image.Healthy vegetation has a very characteristic spectral reflectance curve which we can benefit from by calculating the difference between two bandsvisible red and nearinfrared.The NDVI is that difference expressed as a numberranging from -1 to 1 [20].The NDVI equation looks like this: Table 5. NDVI value ranges and interpretations [17] Value Interpretation <0,1 Bare soil 0,1 -0,2 Almost absent canopy cover 0,2 -0,3 Very low canopy cover 0,3 -0,4 Low canopy cover, low vigour or very low canopy cover, high vigour 0,4 -0,5 Mid-low canopy cover, low vigour or low canopy cover, high vigour 0,5 -0,6 Average canopy cover, low vigour or mid-low canopy cover, high vigour 0,6 -0,7 Mid-high canopy cover, low vigour or average canopy cover, high vigour 0,7 -0,8 High canopy cover, high vigour 0,8 -0,9 Very high canopy cover, very high vigour 0,9 -1 Total canopy cover, very high vigour Landsat Soil Adjusted Vegetation Index (SAVI) is used to correct Normalized Difference Vegetation Index (NDVI) for the influence of soil brightness in areas where vegetative cover is low.Landsat Surface Reflectance-derived SAVI is calculated as a ratio between the R and NIR values with a soil brightness correction factor (L) defined as 0.5 to accommodate most land cover types [21].The SAVI equation looks like this: Modified Landsat Soil Adjusted Vegetation Index (MSAVI) minimizes the effect of bare soil on the Soil Adjusted Vegetation Index (SAVI).MSAVI is calculated as a ratio between the R and NIR values with an inductive L function applied to maximize reduction of soil effects on the vegetation signal [22].The MSAVI equation looks like this: Table 6.SAVI and MSAVI value ranges and interpretations [17] Value Interpretation -1-0,2 Bare soil 0,2 -0,4 The seed germination stage 0,4 -0,6 The leaf development stage The results of vegetation indices parameters can be seen in Figure 14.The results of the vegetation index parameter along the Cirebon coast showed that the vegetation level was dense, moderate to sparsely grown with vegetation, canopy cover varying from low to medium.This depends on the type of vegetation that can grow on the Cirebon coastal material.Mangroves usually grow on clay material (mud) so that the vegetation is dense.In addition, land use will also affect the level of canopy cover and vegetation density.

Discussion
Determination of beach type characteristics based on shoreline change analysis, hydrological indices and vegetation indices is assisted with the Geological Map of the Cirebon Sheet [4] and the Geological Map of the Muara Quarter Sheet [5].
The study area, we know the morphological condition along the Cirebon Coastal, its formation is affected by fluvio-marine and marine.The original fluvio-marine is characterized by the formation of a delta because the material brought from the river is in greater supply than the sea waves and currents that carry the material.The original marine is characterized by the morphology of the coastal plains due to the very sloping topography which forms swamps, mangrove forests and beach sand.Based on the Geological Map of Cirebon scale 1:100.000[3], the dominant lithology in the study area is alluvium deposits consisting of gravel, sand, clay and coastal deposits consisting of silt from swamp deposits, silt and clay.In addition to alluvium deposits, there are also coastal deposits consisting of mud deposited in swampy areas, silt and gray clay containing mollusks shells, deposited along and around shore.
From the results of the DSAS analysis that has been carried out on the coastline, it can be grouped into 4 categories of processes that occur, namely abrasion, accretion, abrasion to accretion and accretion to abrasion.Based on previous study [23], the calculations of wave flux energy produce a coastal dynamic map show the dominant process that occurs in the Coastal Cirebon Area is accretion, especially in the eastern part.The process of strong accretion, especially at estuary, resulted in this area run into quite rapid shallowing and forming new coastlines.This research is relevant with previous study which shows that the study area is dominated by the accretion process.This is due to the supply of sediment material originating along the coast from the rivers around the Cirebon Beach area such as the Bondet River, Ciberes River, Pemali River, Sukalila River.
The results of the hydrological index parameters along the Coastal Cirebon show that humidity levels vary from drought (low humidity) to high humidity, but in the eastern part it includes the water surface.This depends on the type of material that makes up the Cirebon coast which is divided into clay and sandy clay.Clay or mud material tends to have a high humidity level because it is impermeable while the humidity level of sandy clay is lower than clay (mud) because it has a greater permeability than clay.
The results of the vegetation index parameter along the Cirebon Coast show that the vegetation level was dense, moderate to sparsely grown with vegetation, canopy cover varying from low to medium.This depends on the type of vegetation that can grow on the Cirebon coastal material.Mangroves usually grow on clay material (mud) so that the vegetation is dense.In addition, land use will also affect the level of canopy cover and vegetation density.
Clay material (mud) has impermeable properties so that the processes that occur are dominated by accretion due to the large supply of sediment from rivers that enter the sea.Sandy clay material has a greater permeability than clay and the process is dominated by abrasion because there is no large sediment supply from rivers and the influence of currents and waves from the sea is higher.
Based on these data, it can be concluded that the characteristics of the beach at the study area are divided muddy beaches which are dominated by fluvial processes and coastal structures such as harbor (Figure 15).

Conclusion
The research was conducted along the Cirebon coast using Landsat imagery data (Landsat-5, Landsat-7 and Landsat-8).The results of image optimization can be proven by the spectral profile which shows a lower blue band value than the green band.The BILKO method is used to describe coastlines that utilize NIR and SWIR bands to separate land and sea.The DSAS method is used to analyze the coastline and the rates of abrasion and accretion, knowing the dominant processes that occur along the coastline in the study area.The results of the DSAS in the study area show that there are 4 processes that occur from the observations of coastline changes namely abrasion, accretion, abrasion to accretion and accretion to abrasion.The dominant process that occurs in the study area is accretion.This is due to the large supply of sediment from the rivers around the Cirebon Beach area such as the Bondet River, Ciberes River, Pemali River, Sukalila River so that it is possible that the dominant process is accretion rather than abrasion.
The dominant lithology in the study area is clay (mud) and sandy clay.Clay material (mud) has impermeable properties so that the processes that occur are dominated by accretion due to the large supply of sediment from rivers that enter the sea.Sandy clay material has greater permeability than clay and the process is dominated by abrasion because there is no large sediment supply from rivers and the influence of currents and waves from the sea is higher.
Based on these data, it can be concluded that the characteristics of the beach at the study site are divided into muddy beaches which are dominated by fluvial processes and coastal structures such as harbor.

Figure 1 .
Figure 1.The Cirebon coastal is in Cirebon Regency and Cirebon City which are bordered by Indramayu Regency in the northwest and the purple line is the coastline that is the focus of this research

Figure 3 .
Figure 3. Atmospheric correction results using the FLAASH method are shown by the spectral profile of the blue band values lower than the green band on Landsat imagery: Landsat-5 with acquisition time of 14 September 1988 (a); Landsat-7 with acquisition time 19 January 2003 (b) and Landsat-8 with acquisition time 26 July 2022 (c)

Figure 5 .
Figure 5.The results of digitizing the coastline on Landsat imagery after applying the BILKO formula: digitizing the coastline on Landsat-5 with the acquisition date of September 14, 1988 (a); digitizing the coastline on Landsat-7 with acquisition date January 19, 2003 (b); digitizing the coastline on Landsat-8 with acquisition time 26 July 2022 (c) and combine digitization of the entire coastline in 1988, 2003 and 2022 (d)

Figure 6 .
Figure 6.DSAS analysis of the coastlines in 1988, 2003 and 2022 showing NSM maps with a range of values 2649--1159 with a positive NSM indicating accretion and a negative NSM value indicating abrasion (a); EPR map with value ranges divided into 5 classes (high abrasion <-5 -high accretion >5) (b); LRR map with value range divided into 5 classes (high abrasion <-5 -high accretion >5) (c) and WLR map with value range divided by 5 classes (high abrasion <-5 -high accretion >5) (d)

Figure 7 . 9 Figure 8 .Figure 9 .Figure 10 .Figure 11 .
Figure 7. Bar diagram showing the value of NSM on the coastline in 1988-2022 with interval transect 100 and we can see the highest value of NSM is 2.649 m (accretion) and the lowest is 1.159 m (abrasion)

Figure 12 .
Figure 12.The division of processes that occur is based on the results of DSAS analysis in 1988-2003 with dominant accretion than abrasion (a); 2003-2022 with dominant accretion than abrasion (b) and 1988-2023 with the result dominant accretion than abrasion (c)

Figure 15 .
Figure 15.Characteristics of the beach in the study area divided into muddy beaches which are dominated by fluvial processes and coastal structures such as harbor

Table 1 .
Details of Landsat data used in this study