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Remote Sensing
Volume 10
Issue 10
10.3390/rs10101503
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Article

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

by
Kyle T. Peterson
1,
Vasit Sagan
1,*,
Paheding Sidike
1,
Amanda L. Cox
2 and
Megan Martinez
2
1
Department of Earth and Atmospheric Sciences, Saint Louis University, Saint Louis, MO 63108, USA
2
Department of Civil Engineering, Parks College of Engineering, Aviation and Technology, Saint Louis University, Saint Louis, MO 63108, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(10), 1503; https://doi.org/10.3390/rs10101503
Received: 30 August 2018 / Revised: 12 September 2018 / Accepted: 17 September 2018 / Published: 20 September 2018
(This article belongs to the Special Issue Quantitative Remote Sensing of Land Surface Variables)
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Abstract

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. View Full-Text
Keywords: machine learning; water quality; suspended sediment; Landsat; extreme learning machine machine learning; water quality; suspended sediment; Landsat; extreme learning machine
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
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MDPI and ACS Style

Peterson, K.T.; Sagan, V.; Sidike, P.; Cox, A.L.; Martinez, M. Suspended Sediment Concentration Estimation from Landsat Imagery along the Lower Missouri and Middle Mississippi Rivers Using an Extreme Learning Machine. Remote Sens. 2018, 10, 1503. https://doi.org/10.3390/rs10101503

AMA Style

Peterson KT, Sagan V, Sidike P, Cox AL, Martinez M. Suspended Sediment Concentration Estimation from Landsat Imagery along the Lower Missouri and Middle Mississippi Rivers Using an Extreme Learning Machine. Remote Sensing. 2018; 10(10):1503. https://doi.org/10.3390/rs10101503

Chicago/Turabian Style

Peterson, Kyle T., Vasit Sagan, Paheding Sidike, Amanda L. Cox, and Megan Martinez. 2018. "Suspended Sediment Concentration Estimation from Landsat Imagery along the Lower Missouri and Middle Mississippi Rivers Using an Extreme Learning Machine" Remote Sensing 10, no. 10: 1503. https://doi.org/10.3390/rs10101503

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