Please use this identifier to cite or link to this item:
http://ir.mu.ac.ke:8080/jspui/handle/123456789/4030
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ouma, Yashon O. | - |
dc.contributor.author | Waga, M. | - |
dc.contributor.author | Okech, M. | - |
dc.contributor.author | Lavisa, O. | - |
dc.date.accessioned | 2021-01-27T09:14:37Z | - |
dc.date.available | 2021-01-27T09:14:37Z | - |
dc.date.issued | 2018-08 | - |
dc.identifier.uri | http://ir.mu.ac.ke:8080/jspui/handle/123456789/4030 | - |
dc.description.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. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Hindawi | en_US |
dc.subject | Water Quality | en_US |
dc.subject | Parametres | en_US |
dc.subject | Smartphone Sensor Apps | en_US |
dc.title | Estimation of reservoir bio-optical water quality parameters using smartphone sensor Apps and landsat ETM+: Review and comparative experimental results | en_US |
dc.type | Article | en_US |
Appears in Collections: | School of Engineering |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Yashon O. Ouma.etal.pdf | 12.36 MB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.