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INTERSEASONAL VARIABILITY IN THE ANALYSIS OF TOTAL SUSPENDED SOLIDS (TSS) IN SURABAYA COASTAL WATERS USING LANDSAT-8 SATELLITE DATA

The spatial and temporal capabilities of remote sensing data are very effective for monitoring the value of total suspended solids (TSS) in water using optical sensors. In this study, TSS observations were conducted in the west season, transition season 1, east season, and transition season 2 in 2018 and 2019. Landsat 8 image data were used, extracted into TSS values using a semi-analytic model developed in the Mahakam Delta, East Kalimantan, Indonesia. The TSS data obtained were then analysed for distribution patterns in each season. The sample points were randomly scattered throughout the study area. The TSS distribution pattern in the west season showed a high concentration spread over the coastal area to the off sea, while the pattern in the east season only showed a high concentration in the coastal areas. Transitional seasons 1 and 2 showed different patterns of TSS distribution in 2018 and 2019, with more varied values. The distribution of TSS is strongly influenced by the season. Observation of each cluster resulted in the conclusion that human activity and the rainfall rate can affect the concentration of TSS.


INTRODUCTION
Technological advances have completely changed human life and work. The digital world is increasingly helping people to do many things that were previously done manually in a more practical and effective way. Such advances have also taken place in earth observation satellite technology, which is used as a vehicle for remote sensing. The use of such technology can make work efficient both in terms of time and energy, allowing observation of large areas of study. Optical remote sensing systems are often used in monitoring natural resources and waters qulity. Satellite recording technology can record electromagnetic waves outside the range of visible waves, which is very useful in distinguishing the spectral pattern of objects being recorded; for example, land and water can be distinguished easily because they have spectral differences. The spectral pattern of water in visible waves can provide extensive effective information on matter in the air (Le, Li, Zha, Sun, Huang, & Zhang, 2011), meaning that water quality observation methods, such as total suspended solids (TSS), can be performed using digital images. Improved sensor spectra and various algorithms are continuously being developed, and TSS detection parameters can be determined more accurately and quickly. The use of Landsat-based sensors has been widely used in determining TSS parameters; for example, Arief et al. (2016)  Landsat-8 imagery to make TSS model estimation using a two-dimensional algorithm, with use of the green and red bands. In addition, Yanti et al. (2016) compared single Landsat-8 bands to map TSS distribution in Gajahmungkur Reservoir, Wonogiri City, Central Java, Indonesia, finding that the red band was the best to estimate TSS. Wang et al. (2017) and Jiyah et al. (2017) used QRLTSS model for TSS mapping, which used the red band and had a good ability to detect TSS with high dynamics. Using Landsat 5 TM, Landsat 7 ETM and Landsat 8, Parwati and Purwanto (2017) built a model based on one previously developed by Budhiman (2004), which also used the red band. Their results showed that TSS would increase if there was a change in land cover. Trisakti et al. (2015) also employed Landsat 8 to ascertain water clarity in Kerinci and Tondano Lakes, showing that Kerinci Lake (2m) had lower water clarity than Tondano Lake (2-3m).
TSS is the result of the reactions of heterogeneous materials, which then serve as the initial constituent material for sediment and inhibit the formation of organic substances in water (Tarigan & Edward, 2003). High TSS concentrations in water can cause other impacts, such as those found by Helfinalis et al. (2012), who demonstrated that high TSS concentrations can reduce the photosynthetic activity of marine biota, both micro and macro, which will result in decreased oxygen levels produced by plants, meaning fish will die due to a lack of oxygen. However, the level of TSS cannot determine what is happening in the water (Ainy et al., 2011;Domining et al., 2019). Waters that contain high TSS also tend to experience high sedimentation.
TSS concentration measurements can be used as a determining indicator of water quality.
The coast of Surabaya is a busy area. The various activities that are conducted there show the importance of these waters. This entails the monitoring of water quality, namely the TSS content. Monitoring TSS concentrations can show the level of pollution in Surabaya waters (Hariyanto & Krisananda, 2019;Hariyanto et al., 2014). Information on TSS concentration can be used as a parameter of water quality, so that action can be taken to prevent or deal with any water pollution that occurs. Pollution handling activities are conducted when the TSS content being monitored is at a dangerous level; namely, when the concentration is very high. Reducing TSS concentration can be achieved by avoided deforestation, especially of mangroves in the area around the water. Such concentration has been shown to decrease when there was an increase in the area of mangrove plants around the water (Parwati & Purwanto, 2017).
The territory of Indonesia is located in the tropics and is crossed by the equator. The pseudo motion of the sun crossing the equator cause the country experiences two seasons, namely the west season and the east season (Hutabarat, 2006). The west, or rainy season, occurs in December, January and February, while the east, or dry season occurs in June, July and August. The period between the west and east seasons, in March, April and May, is referred to as transitional season 1. While between the East Season to the West Season, in September, October and November, is referred to as transitional season 2 (Fadika et al., 2014).
In the west or rainy season, the TSS concentration (turbidity) will increase in the central area of the waters due to the high intensity of rain, which results in the process of material being moved from small rivers around the waters or from the coast to the middle of the water (deep water) (Risuana et al., 2017). Meanwhile, in the east or dry season, the TSS concentration will be low in the middle part of the water, but high in the coastal area. Baseline sediment resuspension is a major factor affecting TSS concentrations during the east season (Quang et al., 2017).
The aim of this study is to determine the distribution pattern of TSS content in each observation season in order to establish the related TSS value range that was also matched with rainfall rate data.

MATERIALS AND METHODOLOGY 2.1 Location and Data
The research location was the Surabaya coastal region, East Java Province, Indonesia. Geographically, Surabaya is located to the south of the equator, at -7°9'N to -7°21'N and stretches from 112°36' E to 112°57'E. The area of Surabaya City is 52,087 Ha, 63.45% or 33,048 Ha of which is land, with the remaining 36.55% or 19,039 Ha being sea. The focus of this research is on the waters of the City of Surabaya, with the study area divided into three observation clusters, namely cluster 1 (waters of Lamong Bay Port), cluster 2 (waters around Suramadu Bridge) and cluster 3 (Wonorejo Mangrove area), as shown in Figure 2-1.

Data standardisation
Landsat 8    The working stages in the research can be seen in the flow chart in Figure 2-2. The TSS information extraction process followed the stages described below. 1. The data collection required for the study was that of Landsat 8 imagery representing each season. At this stage, the number and distribution of the research sample points was also determined. 2. Performance of a cloud masking process. This was done manually using the QA channel on Landsat 8, which contains information about the quality of Landsat 8 images (USGS, 2013). The range of values in the QA channel detected as cloud will be eliminated, so the clouds in the scene image will disappear.
3. Image pre-processing, including radiometric correction, sun angle correction, and atmospheric correction using the DOS1 (Dark Object Substraction) method. This process was performed using the preprocessing tools in QGIS 3.14 to produce surface reflectance data, the bottom of atmosphere reflectance. 4. Performance of the process of separating land and sea by running the normalized difference water index (NDWI) algorithm (McFeeters, 1996) with the equation: A : reflectance Green band B : reflectance NIR band 5. Calculation of the TSS content using a semi-analytic model. This model was used as Afgatiani et al. (2020) showed that it produced lower error than the empirical model. The semi-analytic model of Budiman (2004) was employed, which has previously been used in the Porong River area (Budianto & Hariyanto, 2017; Indeswari et al, 2018) and has a high regression value, with the following formula: MPT (mg/L) = X * (exp (Y * Z)) (2-2) X : 8.1529 Y : 23.704 Z : reflectance Red band 6. Extraction of the TSS value with a predetermined sample point on all the images resulting from the MPT processing. The sample point MPT data were used in the process of evaluating the MPT value in each cluster. 7. The rainfall rate used was based on data from the JAXA Global Rainfall Watch (GSMaP). It was calculated according to each season. 8. Secondary data for validation were obtained from other papers and reports, as there are no in situ data. 9. Analysis of the TSS distribution data for each season, and analysis of the TSS sample data in each cluster by adding the rainfall rate and secondary data. By observing the variation in the TSS distribution in each season, it is clear that the distribution of TSS is strongly supported by the changing seasons, especially in the west and east seasons. In these seasons, the surface currents move in the direction of the monsoon wind. Surface currents in the west and east seasons are greatly influenced by the wind that blows over the season. In transition seasons 1 and 2, surface currents in the water are not dominated by the influence of the wind. This is because the wind speed above the sea level is too low. It is suspected that the surface currents that occur are caused by tidal influences (Fadika et al., 2014). Surface currents in water also affect the distribution and value of TSS; according to Fathiyah et al. (2017), strong surface currents affect the direction of TSS distribution, which will follow the direction of the sea surface currents. Krisna et al. (2012) also found that the direction of TSS distribution will be in the same direction as that of the wind in the area. Wind direction is also influenced by the seasons. Evaluation of the TSS values in each cluster was made by using six sample points. The selection of these took into account spatial distribution and avoided outlier values, as can be seen in Tables 3-1  This high TSS value was due to the relatively high activity at Lamong Bay Port; moreover, the port is also near the other big port of Tanjung Perak. This directly increases the activity in cluster 1 waters. High activity in the waters causes considerable material to be carried into the waters, resulting in a high TSS value (Handoyo et al., 2017).
Cluster 2 in the Suramadu Bridge area also shows a variation in TSS values from season to season, as can be seen in Figure 3-4. Evaluation of the TSS values of the selected six cluster 2 sample points is shown in Table 3-2. The selection of sample points in each cluster must be free from clouds in each season so that the TSS value selected represents the actual situation.
Starting from the west season 2018, the TSS value was quite high, in the range of 100-200 mg/L, while in transition season 1 2018, it dropped drastically. The west season has the highest TSS value. This period is the peak of the rainy season at Indonesia, especially in the southern equator regions, such as Sumatra, Java, Bali and Nusa Tenggara. The amount of rainfall in the season is generally above 250 mm/month (Mulyana, 2002). This means there will be increasingly more water containing residue running off the land, resulting in an increment in TSS. The transition season is a time when there is a change from the west to the east seasons. In this season, the TSS values are irregular due to the changing wind direction (Siregar et al., 2017).
In Meanwhile, for other seasons the TSS value was below 100 mg/L. In transition season 2 2019, four sample points had no TSS data because the areas were covered by cloud. Overall, the TSS value in cluster 2 was below that of cluster 1, which was because the activity in the waters around Surabaya Bridge was not as dense as that of Lamong Bay Port (cluster 1). Environmental factors such as wind speed, wind direction and tides have a relatively minor effect on increasing TSS concentrations compared to human activities in water areas (Noel et al., 1995). However, this paper does not include wind data.    Cluster 3 was located in the Wonorejo Mangrove area, which shows that the TSS value varies from season to season, as seen in Figure 3-5. Evaluation of the TSS values for the selected six sample points in cluster 2 are shown in Table 3 The monsoon season will affect rainfall. The rainfall data are shown in Figure 3-6, with the highest TSS values occurring in transition season 2 2018, with a concentration higher than 100 mg/L. On the other hand, in other seasons the value is below 100 mg/L. However, rainfall in this season tends to be high at the end of the period. Even though west and transition seasons 1 experience high rainfall with relatively high intensity, TSS levels are no higher than in transition season 2. Rainfall can affect TSS concentration; however, there is no correlation between first-flush loads of TSS and rainfall characteristics (He et al., 2010). Therefore, it is likely that the end of transition season 2 will be a seasonal first flush event. Seasonal first flush is the release of a bigger mass off contaminants or higher concentration, compared with storms later in the season (Strenstrom & Kayhanian, 2005). Rainfall can be a medium for the transport of pollutants from the surface (Shehane et al., 2005). TSS variation is influenced by seasons, and any season can affect rainfall.
Based on the TSS value sample points in cluster 3, it can be said that cluster 3 has a lower level when compared to cluster 1 and cluster 2. The waters of cluster 3 have activities that are not as busy as cluster 1 or cluster 2. The cluster 3 area is used by fishermen-small fishermen from the surrounding area, not the center of large activity. Hasyim et al. (2009)    TSS values with high concentrations will be scattered in the middle of the waters (deep waters), while in the east season they will only be seen in coastal areas. During transition seasons 1 and 2, the distribution of TSS is not influenced by seasons, only by tides, so that the distribution is more varied. Overall, TSS values in the west season will be higher than in the east season because of the higher rainfall.
As for the TSS values in the observation clusters, cluster 1 (Lamong Bay Port) shows different TSS distribution compared to the other locations. One of the factors that influences the distribution of TSS is seasonality.