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Title: | Estimation of reservoir bio-optical water quality parameters using smartphone sensor Apps and landsat ETM+: Review and comparative experimental results |
Authors: | Ouma, Yashon O. Waga, M. Okech, M. Lavisa, O. |
Keywords: | Water Quality Parametres Smartphone Sensor Apps |
Issue Date: | Aug-2018 |
Publisher: | Hindawi |
Abstract: | This study presents a comparative evaluation of three real-time imaging-based approaches for the prediction of optically active water constituents as chlorophyll- a (Chl- a ), turbidity, suspended particulate matter (SPM), and reservoir water colour. The imaging models comprise of Landsat ETM+-visible and NIR (VNIR) data and EyeOnWater and HydroColor Smartphone sensor apps. To estimate the selected water quality parameters (WQP) from Landsat ETM+-VNIR, predictive models based on empirical relationships were developed. From the in situ measurements and the Landsat regression models, the results from the remote re fl ectances of ETM+ green, blue, and NIR independently yielded the best fi ts for the respective predictions of Chl- a , turbidity, and SPM. The concentration of Chl- a was derived from the Landsat ETM+ and HydroColor with respective Pearson correlation coe ffi cients r of 0.8977 and 0.8310. The degree of turbidity was determined from Landsat, EyeOnWater, and HydroColor with respective r values of 0.9628, 0.819, and 0.8405. From the same models, the retrieved SPM was regressed with the laboratory measurements with r value results of 0.6808, 0.7315, and 0.8637, respectively, from Landsat ETM+, EyeOnWater, and HydroColor. The empirical study results showed that the imaging models can be e ff ectively applied in the estimation of the physical WQP. |
URI: | http://ir.mu.ac.ke:8080/jspui/handle/123456789/4030 |
Appears in Collections: | School of Engineering |
Files in This Item:
File | Description | Size | Format | |
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Yashon O. Ouma.etal.pdf | 12.36 MB | Adobe PDF | View/Open |
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