Linear spectral unmixing algorithm for modelling suspended sediment concentration of flash floods, upper Tekeze River, Ethiopia
| Autor: | Hagos G. Gebreslassie, Assefa M. Melesse, Kevin Bishop, Azage Gebreyohannes Gebremariam |
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| Rok vydání: | 2020 |
| Předmět: |
Hydrology
geography geography.geographical_feature_category Spectral signature Coefficient of determination Stratigraphy 0207 environmental engineering Drainage basin Sediment Sampling (statistics) Geology 02 engineering and technology 010501 environmental sciences 01 natural sciences Pearson product-moment correlation coefficient symbols.namesake Tributary symbols Flash flood Environmental science 020701 environmental engineering 0105 earth and related environmental sciences |
| Zdroj: | International Journal of Sediment Research. 35:79-90 |
| ISSN: | 1001-6279 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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