Abstract

Flash floods are the highest sediment transporting agent, but are inaccessible for in-situ sampling and have rarely been analyzed by remote sensing technology. Laboratory and field experiments were done to develop linear spectral unmixing (LSU) remote sensing model and evaluate its performance in simulating the suspended sediment concentration (SSC) in flash floods. The models were developed from continuous monitoring in the laboratory and the onsite spectral signature of river bed sediment deposits and flash floods in the Tekeze River and in its tributary, the Tsirare River. The Pearson correlation coefficient was used to determine the variability of correlations between reflectance and SSCs. The coefficient of determination (R2) and root mean square of error (RMSE) were used to evaluate the performance of the generated models. The results found that the Pearson correlation coefficient between SSCs and reflectance varied based on the level of the SSCs, geological colors, and grain sizes. The performance of the LSU model and empirical remote sensing approaches were computed to be R2 = 0.92, and RMSE = +/- 0.76 g/l in the Tsirare River and R2 = 0.91, and RMSE = +/- 0.73 g/l in the Tekeze River and R2 = 0.81, RMSE = +/- 2.65 g/l in the Tsirare river and R2 = 0.76, RMSE = +/- 10.87 g/l in the Tekeze River, respectively. Hence, the LSU approach of remote sensing was found to be relatively accurate in monitoring and modeling the variability of SSCs that could be applied to the upper Tekeze River basin. (C) 2019 International Research and Training Centre on Erosion and Sedimentation/the World Association for Sedimentation and Erosion Research. Published by Elsevier B.V. All rights reserved.

Keywords

Empirical remote sensing; Flash floods; Linear spectral unmixing; Suspended sediment concentration; Tekeze River

Published in

International Journal of Sediment Research
2020, Volume: 35, number: 1, pages: 79-90
Publisher: IRTCES

SLU Authors

• Bishop, Kevin

  • Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences
    

Sustainable Development Goals

SDG6 Clean water and sanitation


UKÄ Subject classification

Oceanography, Hydrology, Water Resources
Remote Sensing


Publication Identifiers

DOI: https://doi.org/10.1016/j.ijsrc.2019.07.007

Permanent link to this page (URI)

https://res.slu.se/id/publ/103343