Version 1
: Received: 25 January 2022 / Approved: 27 January 2022 / Online: 27 January 2022 (17:01:13 CET)
How to cite:
Hayes, M.; Puckett, B.; Deaton, C.; Ridge, J. Estimating Dredge-Induced Turbidity using Drone Imagery. Preprints2022, 2022010424. https://doi.org/10.20944/preprints202201.0424.v1.
Hayes, M.; Puckett, B.; Deaton, C.; Ridge, J. Estimating Dredge-Induced Turbidity using Drone Imagery. Preprints 2022, 2022010424. https://doi.org/10.20944/preprints202201.0424.v1.
Cite as:
Hayes, M.; Puckett, B.; Deaton, C.; Ridge, J. Estimating Dredge-Induced Turbidity using Drone Imagery. Preprints2022, 2022010424. https://doi.org/10.20944/preprints202201.0424.v1.
Hayes, M.; Puckett, B.; Deaton, C.; Ridge, J. Estimating Dredge-Induced Turbidity using Drone Imagery. Preprints 2022, 2022010424. https://doi.org/10.20944/preprints202201.0424.v1.
Abstract
While maintenance dredging of port access channels is often required to maintain navigability, it can result in increased turbidity, sediment plumes, and associated reductions in water quality. Unoccupied aircraft systems (UAS, or drones) are increasingly applied to study water quality due to their high spatial and temporal resolutions. In this study, we investigated the use of drone imagery to monitor turbidity in the Morehead City Harbor, North Carolina, USA, during channel maintenance by hopper dredge. Drone flights were conducted concurrently with in-situ sampling during active dredging and post-dredging. Multispectral drone images were radiometrically calibrated, converted to reflectance and then turbidity using two separate processing methods and a single-band (red; 620nm-700nm) generic turbidity retrieval algorithm, and then compared to in-situ measurements. The method of using average reflectance to retrieve a single turbidity measurement per drone image produced agreeable results when compared to the in-situ measurements (R2 = 0.84). This method was then used to generate turbidity maps and extract surface plumes. While this could be considered a limited validation, the results indicate that realistic values can be obtained from drone imagery for low and high turbidity concentrations (1-72 FNU), making drones a viable option for monitoring surface turbidity associated with dredging.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
