4.7 Article

Suspended Sediment Concentration Estimation along Turbid Water Outflow Using a Multispectral Camera on an Unmanned Aerial Vehicle

Journal

REMOTE SENSING
Volume 15, Issue 23, Pages -

Publisher

MDPI
DOI: 10.3390/rs15235540

Keywords

direct georeferencing; multiple linear regression; suspended sediment concentration; unmanned aerial vehicles measurement; water quality

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Optical remote sensing using unmanned aerial vehicles (UAVs) is proposed as an effective method for monitoring changes in marine environments, specifically the rapidly changing suspended sediment concentration (SSC). By developing an SSC algorithm and combining optical field measurements with water sample measurements, a wider SSC map can be generated for real-time monitoring of fluctuations in SSC.
Optical remote sensing using unmanned aerial vehicles (UAVs) is proposed to monitor changes in marine environments effectively. Optical measurements were performed using a UAV multispectral camera (RedEdge, five spectral wavelengths of 475, 560, 668, 717, and 842 nm) with high spatial (5 cm) and temporal (1 s) resolutions to monitor the rapidly changing suspended sediment concentration (SSC) in the Saemangeum coastal area on the western coast of Korea. To develop the SSC algorithm, optical field, and water sample measurements were obtained from outside (11 stations) and inside (three stations) regions separated by a seawall, accounting for 100 measurements from 2018 to 2020. Accordingly, the remote sensing reflectance (Rrs) was estimated at each sampling station and used to develop the SSC algorithm based on multiple linear regression. The algorithm reasonably estimated the SSC with an R2 and root mean square error of 0.83 and 4.27 (mg L-1), respectively. Continuous individual UAV measurements over the coastal area of Saemangeum were combined to generate a wider SSC map. For the UAV observational data, the atmospheric influence at each altitude was reduced to the surface altitude level using a relative atmospheric correction technique. The SSC map enabled front monitoring of SSC fluctuations caused by discharge water due to the sluice gate opening. These results demonstrated the usability of the UAV-based SSC algorithm and confirmed the possibility of monitoring rapid SSC fluctuations.

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