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• 1.JRS.18.024513.full type article assertion.
• 1.JRS.18.024513.full type FAIRDigitalObject assertion.
• 1.JRS.18.024513.full label "Benthic river algae mapping using hyperspectral imagery from unoccupied aerial vehicles" assertion.
• 1.JRS.18.024513.full comment "The increasing prevalence of nuisance benthic algal blooms in freshwater systems has led to water quality monitoring programs based on the presence and abundance of algae. Large blooms of the nuisance filamentous algae, Cladophora glomerata, have become common in the waters of the Upper Clark Fork River in western Montana. To aid in the understanding of algal growth dynamics, unoccupied aerial vehicle (UAV)-based hyperspectral images were gathered at three field sites along the length of the river throughout the growing season of 2021. Select regions within images covering the spectral range of 400 to 850 nm were labeled based on a combination of professional judgment and spectral profiles and used to train a random forest classifier to identify benthic algal growth across several classes, including benthic growth dominated by Cladophora (Clado), benthic growth dominated by growth forms other than Cladophora (non-Clado), and areas below a visually detectable threshold of benthic growth (bare substrate). After classification, images were stitched together to produce spatial distribution maps of each river reach while also calculating the average percent cover for each reach, achieving an accuracy of approximately 99% relative to manually labeled images. Results of this analysis showed strong variability across each reach, both temporally (up to 40%) and spatially (up to 46%), indicating that UAV-based imaging with high-spatial resolution could augment and therefore improve traditional measurement techniques that are spatially limited, such as spot sampling. Major findings: The study successfully utilized UAV-based hyperspectral imaging and a random forest classification model to map benthic algae in the Upper Clark Fork River with 99% accuracy, effectively distinguishing Cladophora from other growth forms and bare substrate. The researchers discovered significant spatial and temporal variability in algal coverage, with levels fluctuating by over 40% within short river reaches, demonstrating that high-resolution remote sensing provides a more comprehensive and accurate assessment of ecosystem health than traditional spot-sampling." assertion.
• 1.JRS.18.024513.full creator 0000-0002-5258-5472 assertion.
• 1.JRS.18.024513.full creator 0000-0003-1056-1269 assertion.
• 1.JRS.18.024513.full subject c_6498 assertion.
• 1.JRS.18.024513.full language en assertion.
• 1.JRS.18.024513.full publisher 045k5vt03 assertion.
• 1.JRS.18.024513.full startDate "2023-07-31" assertion.
• 1.JRS.18.024513.full endDate "2024-06-13" assertion.
• 1.JRS.18.024513.full hasMetadata RAhjVniFrnUdQr_lRZUACCocinFF3BrIkcTjhWA9qM_YA assertion.
• 1.JRS.18.024513.full contactPoint "joseph.shaw@montana.edu" assertion.
• 1.JRS.18.024513.full funder 02w0trx84 assertion.