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Monitoring Biochemical Oxygen Demand (BOD) Changes During a Massive Fish Kill Using Multitemporal Landsat-8 Satellite Images in Maninjau Lake, Indonesia

Arif Rohman¹, Adam Irwansyah Fauzi^1,*, Nesya Hafiza Ardani¹, Muhammad Ulin Nuha¹, Redho Surya Perdana¹, Rian Nurtyawan², Aynaz Lotfata³

1Department of Geomatics Engineering, Faculty of Regional and Infrastructure Technology, Institut Teknologi Sumatera, Terusan Ryacudu Street, 35365, Lampung Selatan, Indonesia

2Department of Geodesy Engineering, National Institute of Technology, 23 PH.H. Mustofa Street, 40124, Bandung, Indonesia

3School of Veterinary Medicine, Department of Veterinary Pathology, University of California, Davis, United State of America

*) Correspondence: adam.fauzi@gt.itera.ac.id

Maninjau Lake is one of Indonesia's lakes used for hydroelectric power plants, tourism, and fish farming activities. Some activities around the lake cause pollution, leading to massive fish kills. Therefore, it is necessary to monitor water quality regularly. One of the critical water quality parameters is biochemical oxygen demand (BOD). This study aimed to analyze BOD changes using a remote sensing approach during massive fish kills in Maninjau Lake, Indonesia. Multi-temporal Landsat-8 satellite images are processed to estimate the BOD level based on Wang’s Algorithm. The estimated BOD value is then validated using in situ data measurements. The results show that the average BOD concentration in Lake Maninjau was 1.85 mg/L with R²of 0.8334, and the standard error was 0.076 between the estimated BOD and the in-situ data. Furthermore, the average concentration of BOD obtained on 23^rd August 2017, 13^th December 2017, 30^th January 2018, 19^th March 2018, and 7^th July 2018 are 4.96 mg/L, 4.82 mg/L, 5.31 mg/L, 6.94 mg/L, and 6.60 mg/L, respectively. Increased BOD concentration in January 2018 indicates moderate pollution in the waters. BOD concentration increases after the massive fish kills due to the fish decaying across the lake.

1. Introduction

Indonesia is an archipelago. Its water ecosystems are larger than its terrestrial ecosystems. Indonesian waters are composed of both freshwater and marine ecosystems. Its ecosystems are estimated at 14 million hectares, consisting of lakes, reservoirs, and swamps. These freshwater ecosystems are used for clean water, fisheries, agriculture, and tourism (Soeprobowati, 2015). However, a number of activities around the freshwater ecosystems, e.g., unappropriated industry and agriculture, deforestation around the watershed, and excessive fish farming lead to eutrophication, sedimentation, and oxygen depletion (Jamaldi, 2017).

In Indonesia, there are 15 priority lakes in urgent need of conservation. One of them is Maninjau Lake (Jamaldi, 2017; Ibrahim et al., 2021). Maninjau Lake has long been used for hydroelectric power, tourism, and fish farming (Putri, 2017; Subehi et al., 2020). Fish farming activities around the lake cause nutrient pollution due to excessive nitrogen and phosphorus concentration (Henny et al., 2016). Excessive nitrogen and phosphorus can cause eutrophication and a lack of dissolved oxygen, leading to massive fish kill (De Maisonneuve et al., 2019).

The first massive fish kill occurred in Maninjau Lake in 1997, with 950 tons of dead fish due to warer pollution caused a lack of oxygen supply(LIPI, 2020; Nasution et al., 2020). In 2018, 160 tons of massive fish kill and a financial loss of around IDR 3.75 billion (equivalent to USD 250 thousand). This second massive fish kill was caused by extreme weather that caused the fish’s food sources to rise to the water's surface, resulting in reduced oxygen (Liputan6.com, 2018). The fish’s food was forced to the surface] by upwelling (Pitcher, 2021). A vicious cycle took hold. Frequent massive fish kill due to extreme weather decreased water quality (Aileen et al., 2021; Kragh et al., 2020). Therefore, regular water quality monitoring is needed to mitigate the causes and effects of these massive fish kills.

Water quality monitoring involves the collection and evaluation of the water samples to determine the chemical, physical, and biological quality of water bodies and the causes of changes in water quality (Abdullah et al., 2017; Banda et al., 2020; Bozorg-Haddad et al., 2021). Water quality monitoring can be undertaken using a variety of techniques. The conventional method is to analyze water samples from the field. This method can produce accurate data but it takes time, effort, and it is not affordable (Hajigholizadeh et al., 2021). Water quality monitoring has been carried out in Maninjau Lake using such conventional methods. However, this approach has limitations, i.e., a challenge in data sampling\. Remote sensing technology can be a solution to this problem, providing continuous monitoring of water quality parameters. Some developing countries have used remote sensing methods for water quality monitoring since the 1970s (Gholizadeh et al., 2016).      

Remote sensing is a potential way to use technology to continuously monitor water quality parameters in lakes using an empirical algorithm  (Mushtaq et al., 2016). Abdullah, et al. (2017) used Landsat 8 satellite imaging to monitor and assess water quality in the lake and then compared the results with field data. The water quality parameters monitored are temperature, pH, Total Dissolved Solids (TDS), Electrical Conductivity (EC), Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD), Nitrate Nitrogen (NO3N), Phosphate (PO4), Total Phosphorous (TP), Temperature, Total Suspended Solid (TSS), and Turbidity. One of the most critical parameters is BOD.  BOD indicates oxygen-aerobic microorganisms that are needed to decompose all suspended substances. The BOD measures the amount of oxygen needed to oxidize or degrade the polluted material contained in the water (Erfina, 2021; Tamyiz, 2015).

Remote sensing helps predicting and estimating   the condition causing the massive fish kill. One of the parameters that can be estimated through satellite images is temperature. Research conducted by Marti-Cardona et al. (2008, 2019) explained that the surface temperature map produced using MODIS satellite imagery shows that upwelling occurs after a wind event affects the waters, causing massive fish kills. Meanwhile, the study by Bonansea et al. (2019) using Landsat imagery 7 and 8 explains that low water surface temperatures could increase fungi breeding in the waters that attack the fish. Changes in surface temperature can affect dissolved oxygen levels. Low levels of dissolved oxygen can increase BOD concentrations. The higher the BOD concentration, the higher the level of pollution that occurs, reducing oxygen levels and decreasing aquatic habitats (Apelabi et al., 2021; Vigiak et al., 2019).

Water quality monitoring can be conducted using various parameters, including BOD. However, the remote sensing-based approach to monitor BOD in the case of fish kill is limited. Therefore, this study aimed to analyze BOD changes using remote sensing during massive fish kill in Maninjau Lake, Indonesia, 2018. It used spectral values obtained from Landsat 8 satellite images to estimate the BOD level based on an empirical equation (Wang et al., 2004, Yang et al. 2022). The results have been validated using in-situ data from the Environment Office of Agam Regency.

2. Research Methods

The research has been undertaken in Maninjau Lake, West Sumatra, Indonesia (Figure 1). Maninjau Lake is a volcanic lake located at an altitude of 461.5 m above sea level with an area of 9,737.5 hectares and a maximum depth of 165 m (Lukman et al., 2013). Lake Maninjau is used for hydroelectric power production, tourism, and fish farming in Floating Net Cages (KJA), which began in 1990 (Subehi et al., 2020). The number of floating net cages (KJA) in Lake Maninjau has continued to increase from 16,380 plots in 2014 to 17,417 plots in 2021. This has caused mass fish mortality and other environmental damage.

The primary data used in this study is the Landsat 8 satellite images acquired on 23^rd August 2017, 13^th December 2017, 30^th January 2018, 19^th March 2018, 7^th July 2018, and 29^th October 2018 obtained from the United States Geological Survey (USGS). BOD's in situ measurement data conducted on 24^th October 2018 has been used for validation. This field data was obtained from six sampling locations maintained by the Environmental Service of Agam Regency with, Bayur, Batang River, Middle Lake, Tanjung Sani, Tampang River, and Muko-muko Hydroelectric Power Plant. Data from field measurements of BOD concentrations in Maninjau Lake are presented in Table 1.

In Landsat-8 image processing, digital number values are converted to reflectance using ToA (Top of Atmosphere) correction (Equation 1) and solar correction (Equation 2) (USGS, 2019). In Equation 1. ρλ՛ is ToA reflectance without correction of the sun's zenith angle, MP is the reflectance multiplicative scaling factor, Qcal is pixel value (digital number), and P is the reflectance additive scaling factor. In addition, ρλ is a ToA reflectance that correct the sun's zenith angle, and θ_SE is the sun's elevation angle.

Figure 1. Research Area.

Table 1. Field Data for BOD Concentration on 24^th October 2018

 

The BOD level is estimated using empirical equations adapted from Wang et al. (2004). The formula used for this estimation is shown in Equation 3. B2, B3, and B4 reflect the TOA of bands 2, 3, and 4 of Landsat-8.

The determination coefficient (R²) is used to evaluate the fitness between estimated BOD and in situ data. Equation 4 is used for this validation. R² is the coefficient of determination_, n is the number of samples_, X is the independent variable, and Y is the dependent variable.

3. Results and Discussion

3.1. Concentration of BOD using Remote Sensing

Using the results of the BOD level obtained using the Wang Algorithm, statistical tests were conducted between estimated BOD and in situ data, as presented in Table 2. Table 2 shows that the R² is 0.8334. This means that Wang's algorithm can explain BOD levels up to 83.34%, and the remaining 16.66% can be explained by other free variables not included in the study. This shows that the relationship between estimated BOD and in situ BOD is strongly indicated by the R²  ≥ 0.8.

Table 2. Statistical Analysis of Estimated and In Situ BOD.

On October 29, 2018, the water body recorded a maximum biochemical oxygen demand (BOD) level of 2.225 mg/L, indicating a higher level of organic pollutants. Conversely, the lowest BOD level measured on the same date was 1.823 mg/L, indicating a comparatively cleaner condition. On average, the BOD level for that day was 1.85 mg/L, suggesting a moderate level of organic pollution in the water. The spatial BOD concentration map is presented in Figure 2.

Figure 2. Map of BOD Concentration in Maninjau Lake estimated using Landsat-8 Imagery.

3.2. Monitoring BOD Changes During a Massive Fish Kill

The period of BOD changes tracking using Landsat-8 Images covered the time of a massive fish kill on 4^th February 2018. The BOD level was investigated before and after the massive fish kill event. The pre-event involves two-period images, i.e., one taken on 23^rd August 2017 and one taken on 13^th December 2017, while the post-event involves two-period images, i.e., one taken on 19^th March 2018 and one taken on 7^th July 2018. Figure 3 depicts spatiotemporal changes of BOD during the massive fish kill. From Fig. 3, we can observe that the BOD level on 23^rd August 2017 ranged from 4.159 mg/L to 22.448 mg/L with an average concentration of 4.96 mg/L. On 13^th December 2017, the BOD level ranged from 4.136 mg/L to 22.678 mg/L, with 4.82 mg/L. According to the average concentration of BOD, the pollution level in Maninjau Lake before the massive fish kill is classified as mild. When the massive fish kill occurred, BOD concentrations on 30^th January 2018 ranged from 4.495 to 13.409 mg/L with an average of approximately 5.31 mg/L. In this period, the average BOD level is higher than before the massive fish kill, consequently increasing the status of the pollution to moderately polluted.

Figure 3. Spatio-temporal of BOD Changes During Massive Fish Kill in Maninjau Lake in 2018. Note: (a) 23 August 2017, (b) 13 December 2017, (c) 30 January 2018, (d) 19 March 2018, (e) 7 July 2018.

After the massive fish kill event, the BOD concentrations on 19^th March 2018 ranged from 5.479 mg/L to 10.648 mg/L with an average concentration of 6.94 mg/L. The BOD concentration on 7^th July 2018 ranged from 4.382 mg/L to 40.834 mg/L with an average concentration of 6.60 mg/L. The increasing concentration of BOD in Maninjau Lake was driven by the numerous fish carcasses left after the fish kill. The decay of organic matter in the waters can cause dissolved oxygen levels to decrease (Mubarak et al., 2010). A decrease in dissolved oxygen levels affects the increase in BOD concentration in the waters (Tamyiz, 2015). This finding is the same as that of several previous studies, which showed that a high BOD value indicates a low dissolved oxygen content in the waters. A high BOD value can indicate a massive fish kill due to a lack of oxygen (Daroni et al., 2020). Figure 4. illustrates the trend of average BOD concentration during the massive fish kill.

Figure 4. The trend of Average BOD Concentrations During Massive Fish Kill in Maninjau Lake.

3.3. Discussion

The BOD concentration estimated using Landsat-8 imagery shows valid data, as illustrated by R² reaching 0.833. Furthermore, the BOD concentration changes analysis undertaken during the massive fish kill shows that the average concentration of BOD in five-period data, i.e., 23^rd August 2017, 13^th December 2017, 30^th January 2018, 19^th March 2018, and 7^th July 2018 are 4.96 mg/L, 4.82 mg/L, 5.31 mg/L, 6.94 mg/L, and 6.60 mg/L. The increase in BOD before the mass fish die-off indicates light pollution in Lake Maninjau caused by various factors such as extreme weather or temperature changes and fish feed deposits. Increasing temperatures in the waters will cause an increase in BOD due to increased oxygen consumption by organisms in the waters (Daroini &; Arisandi, 2020; Putri et al., 2019). At the same time, the increase in BOD due to the remnants of fish feed that settle on the bottom of the waters must be broken down by microorganisms. This decomposition process tends to produce organic substances that can increase the concentration of BOD This increase can (Ayuniar &; Hidayat, 2018). be controlled by having the righty type and the right amount of fish feed (Salmin, 2005; Ulum et al., 2020).

The mass death of fish in 2018 was caused by extreme weather, resulting in fish feed residues rising to the lake's surface and consequent reduced oxygen (Liputan6.com, 2018). Increasing temperatures in the waters will cause an increase in BOD because increased amounts of oxygen are consumed by organisms in the waters (Daroini & Arisandi, 2020; Putri et al., 2019). Increased temperatures also cause fish feed residues to rise to the surface due to upwelling. Thisoccurs because the surface water temperature decreases and the specific gravity increases. The surface water will drop, pressing down the inner water (Maulana et al., 2016). Increased loads of organic compounds and nutrients, as well as the characteristics of lakes with very high wind speeds and tropical temperatures, can affect the spread of organic compounds in lakes (Komala et al., 2019). The content of organic compounds in water affects the demand for oxygen (BOD) in waters, so the higher the organic compounds, the higher the BOD value (Daroini & Arisandi, 2020). BOD concentrations increased in January 2018 after a mass fish death, indicating moderate water pollution. BOD concentrations increase after massive fish deaths because of the decay of dead fish, lowering dissolved oxygen levels (Ndahawali, 2016; Salmin, 2005).

The spatial modeling of water quality parameters is still underutilized as a technique due to validation limitations. Validation aims to ensure that the data generated from remote sensing is accurate and trustworthy. This is done by comparing remote sensing or satellite imagery results with field data. This study used Landsat-8 satellite imagery data and field measurement data. A limited number of samples of field measurement data were used, and this sort of lack of samples can generally affect the accuracy of satellite image classifications (Danoedoro, 2015). In addition, the field data were acquired with satellite image data at different times due to the varying percentages of cloud cover in Landsat-8 satellite images. Based on the analysis of changes in BOD concentration during mass mortality of fish influenced by cloud cover, which is still contained in the results of the satellite image process. In addition, water column correction should be used in pre-processing to extract information in water areas. The correction of the water column can be done using the lyzenga method 1978, 1981 and n 2006 and the DOS (Dark Object Subtraction) method  (Inamdar et al., 2022; Purwanto et al., 2019; Cui et al., 2014; Hafizt et al., 2015).

Further research can explore the utilization of remote sensing in estimating chlorophyll (Laili et al., 2015; Hafeez et al., 2019), temperature (Sunder et al., 2020; Abdelmalik, 2018), dissolved oxygen (Wagle et al., 2019), total suspended solids (Abdullah et al., 2017a; Sukojo et al., 2018) and other parameters. In addition, future research can use hyperspectral sensors with more channels to acquire spectral resolution. With more bands, hyperspectral sensors can be used for separation, spectral measurement, and image formatting (Ayundyahrini et al., 2020). Thus, hyperspectral sensors can be used for water quality monitoring (Bresciani et al., 2022) and can also be integrated with UAVs for monitoring (Cai et al., 2022).

4. Conclusion

Lake Maninjau is one of the 15 priority lakes in Indonesia. The lake is used for hydroelectric power generation, tourism, and fish farming. Some activities around the lake cause pollution, leading to massive fish deaths.  This study tried to analyze BOD changes using a remote sensing approach during the mass mortality of fish in Lake Maninjau in 2018 using Landsat-8 satellite imagery and the wang algorithm.  It found that R ²was 0.8334, and the standard error was 0.076 between the BOD estimate using the wang algorithm and the in-situ data.  Based on these results, we can conclude that there was an increase in BOD which caused the mass death of fish due to moderate pollution in the waters. This pollution shows that Lake Maninjau requires additional attention to maintain water quality and ensure the sustainability of the ecosystem. Thus, the algorithm of this study can be used to monitor the dynamics of BOD levels that significantly affect water quality. Furthermore, in this era of geospatial research, the use of water quality parameters should be explored using hyperspectral sensors, drone vehicles, and early warning system development based on integrating remote sensing and field data. We encourage the use of remote sensing approaches integrated with field data to support the monitoring of water quality in Indonesian territory, and recommend that lakes that have a crucial role in the community have evenly- distributed water quality monitoring stations that are continuous with data accessible online so that they can be easily synchronized with satellite image data.

Acknowledgements

The authors are grateful to acknowledge the support from the Environmental Service of Agam Regency. We also thank the anonymous reviewers whose critical and constructive comments helped us prepare an improved and more precise version of this paper. All persons and institutes who kindly made their data available for this research are acknowledged.

Author Contributions

Conceptualization: Arif Rohman, Adam Irwansyah Fauzi; methodology:  Arif Rohman, Adam Irwansyah Fauzi, Nesya Hafiza Ardani; investigation: Arif Rohman, Adam Irwansyah Fauzi; writing—original draft preparation: Arif Rohman, Adam Irwansyah Fauzi, Nesya Hafiza Ardani; writing—review and editing: Arif Rohman, Adam Irwansyah Fauzi, Nesya Hafiza Ardani, Muhammad Ulin Nuha, Redho Surya Perdana, Aynaz Lotfata; visualization: Arif Rohman, Nesya Hafiza Ardani, Aynaz Lotfata. . All authors have read and agreed to the published version of the manuscript.

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