Investigating small unoccupied aerial systems (sUAS) multispectral imagery for total suspended solids and turbidity monitoring in small streams

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dc.contributor.authorPrior, Elizabeth M.en
dc.contributor.authorO'Donnell, Frances C.en
dc.contributor.authorBrodbeck, Christianen
dc.contributor.authorRunion, G. Bretten
dc.contributor.authorShepherd, Stephanie L.en
dc.contributor.departmentBiological Systems Engineeringen
dc.date.accessioned2021-03-11T13:44:54Zen
dc.date.available2021-03-11T13:44:54Zen
dc.date.issued2021-01-02en
dc.description.abstractSmall unoccupied aerial systems (sUAS) are increasingly used for field data collection and remote sensing purposes. Their ease of use, ability to carry sensors, low cost, precise manoeuvrability and navigation makes them versatile tools. The goal of this study is to investigate if sUAS multispectral imagery can be utilized to measure turbidity and total suspended solids (TSS) of small streams. sUAS multispectral imagery and water samples at varying depths were collected before and after rain events on three sampling dates in 2019 from Moores Creek in Lanett, Alabama (AL), United States of America (USA), which was restored in 2017. The water samples were processed for TSS and turbidity and related to pixel values from the multispectral imagery. Linear regression was used to develop models for TSS and turbidity. The models were then tested on Moores Mill Creek in Chewacla State Park, AL, USA. For Lanett, TSS and turbidity regression models for low flows had coefficients of determination (R-2) values of 0.77 and 0.78, respectively. During high flows, different single bands and band ratios were required for comparableR(2)values, suggesting separate models may be needed for high and low flow events. When the Lanett models were applied to Chewacla State Park, predicted TSS and turbidity were not comparable to measured values indicating that location-specific models may be required. Future research should incorporate depth as a variable since streambed visibility likely impacts results, along with other modelling and data analysis methods, such as machine learning.en
dc.description.adminPublic domain – authored by a U.S. government employeeen
dc.description.notesThis work was supported by the Auburn University Office of Undergraduate Research; Virginia Tech Interdisciplinary Graduate Education Program in Remote Sensing; Auburn University Samuel Ginn College of Engineering 100+ Women Strong.en
dc.description.sponsorshipAuburn University Office of Undergraduate Research; Virginia Tech Interdisciplinary Graduate Education Program in Remote Sensing; Auburn University Samuel Ginn College of Engineering 100+ Women Strongen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1080/01431161.2020.1798546en
dc.identifier.eissn1366-5901en
dc.identifier.issn0143-1161en
dc.identifier.issue1en
dc.identifier.urihttp://hdl.handle.net/10919/102658en
dc.identifier.volume42en
dc.language.isoenen
dc.rightsPublic Domainen
dc.rights.urihttp://creativecommons.org/publicdomain/mark/1.0/en
dc.titleInvestigating small unoccupied aerial systems (sUAS) multispectral imagery for total suspended solids and turbidity monitoring in small streamsen
dc.title.serialInternational Journal of Remote Sensingen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.dcmitypeStillImageen

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