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Title: Multi-depth suspended sediment estimation using high-resolution remote-sensing UAV in Maumee River, Ohio

Satellite remote-sensing has been widely used to map suspended sediment concentration (SSC) in waterbodies. Current development of the unmanned aerial vehicle (UAV) technology allows mapping of SSC at finer spatial resolution providing high flexibility in terms of cost and acquisition time. However, the technology is immature and transfer of empirical algorithms from existing remote-sensing technologies to UAV still has to be explored. Here, this study uses the MicaSense Sequoia sensor with four bands (green, red, red edge, and near-infrared [NIR]) mounted on-board a fixed-wing UAV to map SSC within the Maumee River in Ohio, USA, at multiple depth intervals (15, 61, 91, and 182 cm). The simple linear and stepwise regression models show the advantage of multiple bands and band ratios over single bands in mapping SSC. The findings show a limited performance of the Sequoia sensor when compared to field spectroradiometer measurements. In all cases but one, the adjusted coefficient of determination (R$$2\atop{adj}$$) values is lower for the UAV data. The regression equations become similar at and below a depth of 0–61 cm, and R$$2\atop{adj}$$ become constant at and below a depth of 0–91 cm. While the spectroradiometer-related equations are sensitive to a wider spectral range (from green at the surface to NIR wavelength at 182 cm depth), the UAV-related equations are insensitive to green spectrum and they include a narrower spectral range (from red to NIR) over all depth increments. Field spectroradiometer measurements exhibit a strong relationship with cumulative SSC at 182 cm depth (0–182 cm) (R$$2\atop{adj}$$ = 0.71) whereas UAV reflectance data show the best relationship with SSC at 91 cm (0–91 cm) (R$$2\atop{adj}$$ = 0.56) suggesting that ~91 cm may be an optimal depth for UAV under given conditions. In conclusion, the results show that UAVs can be a practical but somewhat limited tool to monitor SSC in small- to medium-sized rivers.
Authors:
ORCiD logo [1] ;  [2] ;  [2] ;  [2]
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Bowling Green State Univeristy, OH (United States)
  2. Bowling Green State Univeristy, OH (United States)
Publication Date:
Grant/Contract Number:
AC05-00OR22725
Type:
Accepted Manuscript
Journal Name:
International Journal of Remote Sensing
Additional Journal Information:
Journal Volume: 39; Journal Issue: 15-16; Journal ID: ISSN 0143-1161
Publisher:
Taylor & Francis
Research Org:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org:
USDOE
Country of Publication:
United States
Language:
English
Subject:
58 GEOSCIENCES; 47 OTHER INSTRUMENTATION
OSTI Identifier:
1489563

Larson, Matthew D., Simic Milas, Anita, Vincent, Robert K., and Evans, James E.. Multi-depth suspended sediment estimation using high-resolution remote-sensing UAV in Maumee River, Ohio. United States: N. p., Web. doi:10.1080/01431161.2018.1465616.
Larson, Matthew D., Simic Milas, Anita, Vincent, Robert K., & Evans, James E.. Multi-depth suspended sediment estimation using high-resolution remote-sensing UAV in Maumee River, Ohio. United States. doi:10.1080/01431161.2018.1465616.
Larson, Matthew D., Simic Milas, Anita, Vincent, Robert K., and Evans, James E.. 2018. "Multi-depth suspended sediment estimation using high-resolution remote-sensing UAV in Maumee River, Ohio". United States. doi:10.1080/01431161.2018.1465616.
@article{osti_1489563,
title = {Multi-depth suspended sediment estimation using high-resolution remote-sensing UAV in Maumee River, Ohio},
author = {Larson, Matthew D. and Simic Milas, Anita and Vincent, Robert K. and Evans, James E.},
abstractNote = {Satellite remote-sensing has been widely used to map suspended sediment concentration (SSC) in waterbodies. Current development of the unmanned aerial vehicle (UAV) technology allows mapping of SSC at finer spatial resolution providing high flexibility in terms of cost and acquisition time. However, the technology is immature and transfer of empirical algorithms from existing remote-sensing technologies to UAV still has to be explored. Here, this study uses the MicaSense Sequoia sensor with four bands (green, red, red edge, and near-infrared [NIR]) mounted on-board a fixed-wing UAV to map SSC within the Maumee River in Ohio, USA, at multiple depth intervals (15, 61, 91, and 182 cm). The simple linear and stepwise regression models show the advantage of multiple bands and band ratios over single bands in mapping SSC. The findings show a limited performance of the Sequoia sensor when compared to field spectroradiometer measurements. In all cases but one, the adjusted coefficient of determination (R$2\atop{adj}$) values is lower for the UAV data. The regression equations become similar at and below a depth of 0–61 cm, and R$2\atop{adj}$ become constant at and below a depth of 0–91 cm. While the spectroradiometer-related equations are sensitive to a wider spectral range (from green at the surface to NIR wavelength at 182 cm depth), the UAV-related equations are insensitive to green spectrum and they include a narrower spectral range (from red to NIR) over all depth increments. Field spectroradiometer measurements exhibit a strong relationship with cumulative SSC at 182 cm depth (0–182 cm) (R$2\atop{adj}$ = 0.71) whereas UAV reflectance data show the best relationship with SSC at 91 cm (0–91 cm) (R$2\atop{adj}$ = 0.56) suggesting that ~91 cm may be an optimal depth for UAV under given conditions. In conclusion, the results show that UAVs can be a practical but somewhat limited tool to monitor SSC in small- to medium-sized rivers.},
doi = {10.1080/01431161.2018.1465616},
journal = {International Journal of Remote Sensing},
number = 15-16,
volume = 39,
place = {United States},
year = {2018},
month = {4}
}