Measurement of suspended sediment concentration in open channel flows based on hyperspectral imagery from UAVs

Kwon, Siyoon; Shin, Jaehyun; Seo, Il Won; Noh, Hyoseob; Jung, Sung Hyun; You, Hojun

doi:10.1016/j.advwatres.2021.104076

Cited by 23 publications

(15 citation statements)

References 92 publications

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“…The empirical models designed in this study can be implemented in recently developed flow‐suspended sediment‐monitoring techniques to estimate Q t because the required input variables can be obtained by these techniques. For example, at the river scale, drone‐based remote‐sensing techniques have been applied to SSCs (Kwon et al., 2022a, 2022b; Kwon et al., 2023), bathymetry, and flows (Eltner et al., 2020; Legleiter & Harrison, 2019; Legleiter & Kinzel, 2021). ADCPs can be utilized for the simultaneous measurement of flow and suspended sediment (Noh et al., 2022, 2023; Son et al., 2021).…”

Section: Discussionmentioning

confidence: 99%

“…In particular, monitoring bed loads is costlier than monitoring suspending loads. Alternative methods to monitor suspended sediment have been proposed that utilize various equipment, such as optical sensors (Agrawal & Pottsmith, 2000) and hyperspectral cameras (Kwon et al., 2022a, 2022b), enabling high spatiotemporal resolution monitoring in the simplified monitoring process. Specifically, suspended loads can be easily calibrated with optical features using turbidity or reflectances, which are readily measured remotely.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Efficient Method of Estimating Suspended‐To‐Total Sediment Load Fraction in Natural Rivers

Noh,

Park,

Seo

2023

Water Resources Research

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Sediment transport load monitoring is important in civil and environmental engineering fields. Monitoring the total load is difficult, especially because of the cost of the bed load transport measurement. This study proposes estimation models for the suspended‐to‐total load fraction using dimensionless hydro‐morphological variables. Two prominent variable combinations were identified using the recursive feature elimination for support vector regression (SVR): (1) width‐to‐depth ratio, dimensionless particle size, flow Reynolds number, densimetric Froude number, and falling particle Reynolds number, and (2) flow Reynolds number, Froude number, and densimetric Froude number. The explicit relations between the suspended‐to‐total load fraction and the two combinations were revealed by two modern symbolic regression methods: multi‐gene genetic programming and Operon. The five‐variable SVR model showed the best performance. Clustering analyses using a self‐organizing map and Gaussian mixture model, respectively, identified the underlying relationships between dimensionless variables. Subsequently, the one‐at‐a‐time sensitivity of the input variables of the empirical models was investigated. The suspended‐to‐total load fraction is positively related to the flow Reynolds number and is inversely related to the densimetric Froude number. The models developed in this study are practical and easy to implement in other suspended sediment monitoring methods because they only require basic measurable hydro‐morphological variables, such as velocity, depth, width, and median bed material size.This article is protected by copyright. All rights reserved.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Efficient Method of Estimating Suspended‐To‐Total Sediment Load Fraction in Natural Rivers

Noh,

Park,

Seo

2023

Water Resources Research

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“…Furthermore, I λ can be used for optimal band selection using RFE with CV. Kwon et al (2022a) employed RFE for hyperspectral retrievals of suspended sediment concentration under optically complex conditions. That study highlighted that utilizing the optimal combination of bands through RFE is more efficient than using the entire set of bands in hyperspectral imagery.…”

Section: Random Forest (Rf)mentioning

confidence: 99%

“…Recent studies have significantly improved the ability to estimate depths up to 20 m using the advanced topographic laser altimeter system (ATLAS) onboard the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) (Xie et al, 2023;Chen et al, 2021). However, applying lidar in turbid waters remains challenging (Legleiter & Harrison, 2019;Chen et al, 2021).Compared to lidar, imaging spectroscopy highly depends on weather conditions, but this method is more versatile thanks to abundant spectral bands that allow it to measure various parameters in water environments at the same time, such as algal bloom, suspended sediment, organic matter, and water depth from one image (Kwon et al, 2022a(Kwon et al, , 2022b(Kwon et al, , 2023bLegleiter et al, 2022;Hestir et al, 2015). Recently, there has been an increase in satellites equipped with hyperspectral sensors, enabling their application over wide spatial areas.…”

Section: Introductionmentioning

confidence: 99%

Depth Mapping in Turbid and Deep Waters Using AVIRIS-NG Imagery: A Study in Wax Lake Delta, Louisiana, USA

Kwon,

Passalacqua,

Soloy

et al. 2024

Preprint

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Remote sensing has been widely applied to investigate fluvial processes, but depth retrievals face significant constraints in deep and turbid conditions. This study evaluates the potential for depth retrievals under such challenging conditions using NASA’s Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) imagery. We employ interpretable machine learning to construct a hyperspectral regressor for water depth and explore the spectral characteristics of deep and turbid waters in Wax Lake Delta (WLD), LA. The reflectance spectra of WLD show minor effects from depth differences due to turbidity. Nevertheless, a Random Forest with Recursive Feature Elimination (RF-RFE) effectively generalizes high and low turbid cases in a single model, achieving a R² of 0.94 ± 0.005. Moreover, this model shows a maximum detectable depth of approximately 30 m, outperforming other methods. A spectral analysis using Shapley additive explanations (SHAP) points out the importance of learning various spectral bands and non-linear relationships between depth and reflectance. Specifically, the short blue and Near-InfraRed (NIR) bands, with high attenuation coefficients, play a crucial role. This finding highlights the attenuation as the key process for deep-depth retrievals. The depth maps of WLD captured by this model distinctly represent the spatial distribution of deep river and shallow delta regions. However, the high dependency on short blue and NIR bands leads to discontinuous areas due to the noise sensitivity of these bands. This result highlights a drawback of remote sensing using empirical models. Future research will focus on correcting such discontinuities by integrating data from multiple remote sensing sources.

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“…This difficulty can be minimized by using other orbital sensors, increasing the likelihood of capturing images without clouds, as was done in other studies [15,18]. Another solution was proposed by researchers who used unmanned aircraft with onboard spectral sensors during field collections [44,45]. When using the proper method, this approach would reduce impacts from the atmosphere and give some freedom for planning field sampling.…”

Section: Comparisons To Previous Researchmentioning

confidence: 99%

Estimating Suspended Sediment Concentration Using Remote Sensing for the Teles Pires River, Brazil

et al. 2023

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Improving environmental sustainability involves measuring indices that show responses to different production processes and management types. Suspended sediment concentration (SSC) in water bodies is a parameter of great importance, as it is related to watercourse morphology, land use and occupation in river basins, and sediment transport and accumulation. Although already established, the methods used for acquiring such data in the field are costly. This hinders extrapolations along water bodies and reservoirs. Remote sensing is a feasible alternative to remedy these obstacles, as changes in suspended sediment concentrations are detectable by satellite images. Therefore, satellite image reflectance can be used to estimate SSC spatially and temporally. We used Sentinel-2 A and B imagery to estimate SSC for the Teles Pires River in Brazil’s Amazon. Sensor images used were matched to the same days as field sampling. Google Earth Engine (GEE), a tool that allows agility and flexibility, was used for data processing. Access to several data sources and processing robustness show that GEE can accurately estimate water quality parameters via remote sensing. The best SSC estimator was the reflectance of the B4 band corresponding to the red range of the visible spectrum, with the exponential model showing the best fit and accuracy.

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Measurement of suspended sediment concentration in open channel flows based on hyperspectral imagery from UAVs

Cited by 23 publications

References 92 publications

A Novel Efficient Method of Estimating Suspended‐To‐Total Sediment Load Fraction in Natural Rivers

A Novel Efficient Method of Estimating Suspended‐To‐Total Sediment Load Fraction in Natural Rivers

Depth Mapping in Turbid and Deep Waters Using AVIRIS-NG Imagery: A Study in Wax Lake Delta, Louisiana, USA

Estimating Suspended Sediment Concentration Using Remote Sensing for the Teles Pires River, Brazil

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