Retrieval of suspended sediment concentration of the Chilika Lake, India using Landsat-8 OLI satellite data

2021 ◽  
Vol 80 (8) ◽  
Author(s):  
Sujit Kumar Jally ◽  
Akhila Kumar Mishra ◽  
Sachikanta Balabantaray
Keyword(s):  
Suspended Sediment ◽  
Satellite Data ◽  
Suspended Sediment Concentration ◽  
Sediment Concentration ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Chilika Lake

Estimation of Trophic State Index of Chilika Lake using Landsat-8 OLI and LISS-III satellite data

2019 ◽  
Vol 35 (7) ◽  
pp. 759-780 ◽  
Author(s):  
Sujit Kumar Jally ◽  
Akhila Kumar Mishra ◽  
Sachikanta Balabantaray
Keyword(s):  
Satellite Data ◽  
Trophic State ◽  
Landsat 8 ◽  
Trophic State Index ◽  
Landsat 8 Oli ◽  
Chilika Lake ◽  
Liss Iii ◽  
State Index

scholarly journals Monitoring of seasonally variability and movement of suspended sediment concentration along Thiruvananthapuram coast using OLI sensor

2018 ◽  
Author(s):  
Bismay Ranjan Tripathy ◽  
Kaliraj Seenipandi ◽  
Haroon Sajjad ◽  
Pawan Kumar Joshi ◽  
Bhagwan Singh Chaudhary ◽  
...  
Keyword(s):  
Suspended Sediment ◽  
Suspended Sediment Concentration ◽  
Suspended Sediments ◽  
Analysis Data ◽  
Sediment Concentration ◽  
Landsat 8 ◽  
Wave Direction ◽  
High Concentration ◽  
Littoral Current ◽  
Lower Depth

Abstract. In the recent decades hydrologists, geologists, and stream ecologists have shown substantial interest in analyzing suspended sediments in water. Extracting information like suspended sediment concentration (SSC) in coastal waters is very important for assessment and monitoring of coastal settings and their effects on their ecology. This article demonstrates importance of Landsat 8 Operational Land Imager (OLI) for monitoring seasonal variation in SSC and movement (pre and post monsoon) along Thiruvananthapuram Coast in India. The data was converted into marine reflectance after correcting due atmospheric errors. SSC was extracted using spectral analysis data analysis. Movement of SSC was monitored using wave direction and significant wave height data. The results revealed that the SSC decreased rapidly with the increase in distance from the beach and depth of the seabed. Wave with higher frequency in deeper water caused sparely circulation of sediments and their concentration at the lower depth in high bathymetry. Thus, the suspended sediments were indirectly proportional to bathymetry and distance from the shoreline and directly proportion to wave direction and littoral current at off-shore. High concentration of sediments was found to be accumulated at shallow depth (

scholarly journals ALGORITHM TO CALCULATE SUSPENDED SEDIMENT CONCENTRATION USING LANDSAT 8 IMAGERY

2019 ◽  
Vol 17 (3) ◽  
Author(s):  
B HERNÁNDEZ-CRUZ ◽  
M VÁSQUEZ-ORTIZ ◽  
C CANET ◽  
J PRADO-MOLINA
Keyword(s):  
Suspended Sediment ◽  
Suspended Sediment Concentration ◽  
Sediment Concentration ◽  
Landsat 8

scholarly journals Suspended Sediment Concentration Estimation from Landsat Imagery along the Lower Missouri and Middle Mississippi Rivers Using an Extreme Learning Machine

2018 ◽  
Vol 10 (10) ◽  
pp. 1503 ◽  
Author(s):  
Kyle Peterson ◽  
Vasit Sagan ◽  
Paheding Sidike ◽  
Amanda Cox ◽  
Megan Martinez
Keyword(s):  
Neural Network ◽  
Suspended Sediment ◽  
Extreme Learning Machine ◽  
Suspended Sediment Concentration ◽  
Landsat Imagery ◽  
Sediment Concentration ◽  
Landsat 8 ◽  
Fluvial Systems ◽  
Spatial Coverage ◽  
Learning Machine

Monitoring and quantifying suspended sediment concentration (SSC) along major fluvial systems such as the Missouri and Mississippi Rivers provide crucial information for biological processes, hydraulic infrastructure, and navigation. Traditional monitoring based on in situ measurements lack the spatial coverage necessary for detailed analysis. This study developed a method for quantifying SSC based on Landsat imagery and corresponding SSC data obtained from United States Geological Survey monitoring stations from 1982 to present. The presented methodology first uses feature fusion based on canonical correlation analysis to extract pertinent spectral information, and then trains a predictive reflectance–SSC model using a feed-forward neural network (FFNN), a cascade forward neural network (CFNN), and an extreme learning machine (ELM). The trained models are then used to predict SSC along the Missouri–Mississippi River system. Results demonstrated that the ELM-based technique generated R2 > 0.9 for Landsat 4–5, Landsat 7, and Landsat 8 sensors and accurately predicted both relatively high and low SSC displaying little to no overfitting. The ELM model was then applied to Landsat images producing quantitative SSC maps. This study demonstrates the benefit of ELM over traditional modeling methods for the prediction of SSC based on satellite data and its potential to improve sediment transport and monitoring along large fluvial systems.

scholarly journals Estimation of Coastal Suspended Sediment Concentration using Satellite Data and Oceanic In-Situ Measurements

2011 ◽  
Vol 27 (6) ◽  
pp. 677-692 ◽  
Author(s):  
Min-Sun Lee ◽  
Kyung-Ae Park ◽  
Jong-Yul Chung ◽  
Yu-Hwan Ahn ◽  
Jeong-Eun Moon
Keyword(s):  
Suspended Sediment ◽  
Satellite Data ◽  
Suspended Sediment Concentration ◽  
Sediment Concentration ◽  
In Situ Measurements

Neural networks approached for modelling river suspended sediment concentration due to tropical storms

2013 ◽  
Vol 11 (4) ◽  
pp. 457-466
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Suspended Sediment ◽  
Suspended Sediment Concentration ◽  
Time Series Data ◽  
Water Discharge ◽  
Sediment Concentration ◽  
Series Data ◽  
Generalized Regression Neural Network ◽  
Event Based

Artificial neural networks are one of the advanced technologies employed in hydrology modelling. This paper investigates the potential of two algorithm networks, the feed forward backpropagation (BP) and generalized regression neural network (GRNN) in comparison with the classical regression for modelling the event-based suspended sediment concentration at Jiasian diversion weir in Southern Taiwan. For this study, the hourly time series data comprised of water discharge, turbidity and suspended sediment concentration during the storm events in the year of 2002 are taken into account in the models. The statistical performances comparison showed that both BP and GRNN are superior to the classical regression in the weir sediment modelling. Additionally, the turbidity was found to be a dominant input variable over the water discharge for suspended sediment concentration estimation. Statistically, both neural network models can be successfully applied for the event-based suspended sediment concentration modelling in the weir studied herein when few data are available.

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