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• W2997305502 abstract "The purpose of this study was to create different statistically reliable predictive algorithms for trophic state or water quality for optical (total suspended solids (TSS), Secchi disk depth (SDD), and chlorophyll-a (Chl-a)) and non-optical (total phosphorus (TP) and total nitrogen (TN)) water quality variables or indicators in an oligotrophic system (Grand River Dam Authority (GRDA) Duck Creek Nursery Ponds) and a eutrophic system (City of Commerce, Oklahoma, Wastewater Lagoons) using remote sensing images from a small unmanned aerial system (sUAS) equipped with a multispectral imaging sensor. To develop these algorithms, two sets of data were acquired: (1) In-situ water quality measurements and (2) the spectral reflectance values from sUAS imagery. Reflectance values for each band were extracted under three scenarios: (1) Value to point extraction, (2) average value extraction around the stations, and (3) point extraction using kriged surfaces. Results indicate that multiple variable linear regression models in the visible portion of the electromagnetic spectrum best describe the relationship between TSS (R2 = 0.99, p-value = <0.01), SDD (R2 = 0.88, p-value = <0.01), Chl-a (R2 = 0.85, p-value = <0.01), TP (R2 = 0.98, p-value = <0.01) and TN (R2 = 0.98, p-value = <0.01). In addition, this study concluded that ordinary kriging does not improve the fit between the different water quality parameters and reflectance values." @default.
• W2997305502 created "2020-01-10" @default.
• W2997305502 creator A5018509074 @default.
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• W2997305502 date "2019-12-24" @default.
• W2997305502 modified "2023-10-01" @default.
• W2997305502 title "Prediction of Optical and Non-Optical Water Quality Parameters in Oligotrophic and Eutrophic Aquatic Systems Using a Small Unmanned Aerial System" @default.
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• W2997305502 doi "https://doi.org/10.3390/drones4010001" @default.
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