Monitoring Total Suspended Sediment Concentration in Spatiotemporal Domain over Teluk Lipat Utilizing Landsat 8 (OLI)

Abstract

Total suspended sediment (TSS) is a water quality parameter that is used to understand sediment transport, aquatic ecosystem health, and engineering problems. The majority of TSS in water bodies is due to natural and human factors such as brought by river runoff, coastal erosion, dredging activities, and waves. It is an important parameter that should be monitored periodically, particularly over the dynamic coastal region. This study aims to monitor spatiotemporal TSS concentration over Teluk Lipat, Malaysia. To date, there are two commonly used methods to monitor TSS concentration over wide water regions. Firstly, field sampling is known very expensive and time-consuming method. Secondly, the remote sensing technology that can monitor spatiotemporal TSS concentration freely. Although remote sensing technology could overcome these problems, universal empirical or semiempirical algorithms are still not available. Most of the developed algorithms are on a regional basis. To measure TSS concentration over the different regions, a new regional algorithm needs to develop. To do so, two field trip was conducted in the study area concurrent with the passing of Landsat 8. A total of 30 field samples were collected from 30 sampling points during the first field trip and 30 samples from 30 samplings from the second field trip. The samples were then analyzed using an established method to develop the TSS algorithm. The data obtained from the first field trip were then used to develop a regional TSS algorithm using the regression analysis technique. The developed algorithm was then validated by using data obtained from the second field trip. The results demonstrated that TSS in the study area is highly correlated with three Landsat 8 bands, namely green, near-infrared (NIR), and short-wavelength (SWIR) bands, with R2 = 0.79. The TSS map is constructed using the algorithm. Analyses of the image suggest that the highest TSSs are mainly observed along the coastal line and over the river mouth. It suggested that the main contributing factors over the study area are river runoff and wave splash.

Farming Practices and Environmental Quality of Integrated Mangrove-Shrimp Farming Systems in Tra Vinh Province, Vietnam

“…Higher TSS in the IMSF systems could be attributed to the decomposition of leaf litter fall (Cong and Khanh, 2022; Hai and Yakupitiyage, 2005). High TSS could augment nutrients in the aquatic ecosystems, as TSS could be used as an indicator for nutrient contamination and possibly raise ecological risks to aquatic life (Nurgiantoro and Jaelani, 2017; Park, 2007; Sa'ad et al ., 2021).…”

“…Higher TSS in the IMSF systems could be attributed to the decomposition of leaf litter fall (Cong and Khanh, 2022;Hai and Yakupitiyage, 2005). High TSS could augment nutrients in the aquatic ecosystems, as TSS could be used as an indicator for nutrient contamination and possibly raise ecological risks to aquatic life (Nurgiantoro and Jaelani, 2017;Park, 2007;Sa'ad et al, 2021). Total Fe concentrations in shrimp ponds and rivers were 3.50 fold and 1.16 fold, respectively, compared to the national standard, revealing a total Fe pollution status in these water bodies.…”

Towards a Paradigm Shift on Mapping Muddy Waters with Sentinel-2 Using Machine Learning

“…The presence of muddy water that acts as a contaminant in drinking water reservoirs can be explored by optical sensors and is mostly studied in open seas [13] and to a lesser extent in inland large water bodies (e.g., [14]). In particular, the focus is on parameter retrieval of water quality environmental proxies with respect to muddy waters, such as turbidity [15,16] or total suspended matter (TSM)/total suspended sediment (TSS) [17] rather than binary classification/detection (e.g., presence/absence) or multi-class classification (e.g., [18]). There is also research on the monitoring of spatiotemporal changes in TSM combined with retrieval [19].…”

“…One reason for the research focus being on parameters' (turbidity, TSM) retrieval is due to the huge local variations in physical processes in inland waters that make a global water quality estimator very difficult. Many of the mentioned studies focus only on local water bodies [13,17,[19][20][21][22] and the results of the monitoring cannot be used globally.…”

“…In addition, in some cases the spatial [13][14][15][17][18][19]22] and temporal [15,17] resolution is not sufficient for monitoring.…”