Authors: Wondimagegn Beshah*, Department of Geosciences, Mississippi State University, Padmanava Dash, Department of Geosciences, Mississippi State University, Lee Hathcock, Geosystems Research Institute and Northern Gulf Institute, Mississippi State University, Robert Moorhead, Geosystems Research Institute and Northern Gulf Institute, Mississippi State University
Topics: Remote Sensing, Water Resources and Hydrology, UAS / UAV
Keywords: remote sensing, UAS, UAV, image processing, water quality, suspended sediments, SPM, visualization tool
Session Type: Guided Poster
Presentation File: No File Uploaded
The Oysters in the Mississippi Sound are depleting because of a range of environmental and anthropogenic stressors such as suspended particulate matter (SPM). Remote sensing is useful in mapping the spatio-temporal distribution of SPM. The overarching objective of this research is to develop remote sensing algorithms for mapping SPM using Unmanned Aerial Systems (UASs) imagery and integrate the mapped images to a visualization tool. UAS imagery and water samples was collected by 71 flights during seven week-long trips in the months of March, May, June, July, and December 2018, and June and July, 2019 over the Henderson Point and Pass Christian Oyster Reefs, Mississippi, the largest oyster reef in the Mississippi Sound. A series of image pre-processing techniques were applied to the UAS imagery to correct for gain, exposure time, vignetting effects, and lens distortion. The processed data were converted into remote sensing reflectance using solar irradiance measured during the flights. Empirical and semi-analytical algorithms were developed using the UAS imagery and field data for generating SPM images. A visualization tool has been developed where the SPM images will be visualized as maps that could be used for time-series analyses. The SPM images generated will inform water managers, the fishery industry, and other stakeholders on variation of SPM over the largest oyster reef in the Mississippi Sound. The procedures developed for pre- and post-processing of UAS imagery will act as a blueprint for future research for exploring the potential of UAS remote sensing for water quality mapping.