|
|||||
|
|
A remote sensing tool for near real-time monitoring of harmful algal blooms and turbidity in reservoirs |
|
| Authors: | Abhiram S P Pamula Hamed Gholizadeh Mark J Krzmarzick William E Mausbach David J Lampert |
| Institution: | 1. School of Civil and Environmental Engineering, Oklahoma State University, Stillwater, Oklahoma, USA
Contribution: Formal analysis, Software, Visualization, Writing - original draft;2. Department of Geography, Oklahoma State University, Stillwater, Oklahoma, USA Contribution: Methodology, Supervision, Validation, Writing - review & editing;3. School of Civil and Environmental Engineering, Oklahoma State University, Stillwater, Oklahoma, USA Contribution: Funding acquisition, Methodology, Project administration, Supervision, Validation, Writing - review & editing;4. Grand River Dam Authority, Langley, Oklahoma, USA Contribution: Data curation, Resources, Writing - review & editing;5. Department of Civil Architectural, and Environmental Engineering, Illinois Institute of Technology, Chicago, Illinois, USA |
| Abstract: | Harmful algal blooms (HABs) diminish the utility of reservoirs for drinking water supply, irrigation, recreation, and ecosystem service provision. HABs decrease water quality and are a significant health concern in surface water bodies. Near real-time monitoring of HABs in reservoirs and small water bodies is essential to understand the dynamics of turbidity and HAB formation. This study uses satellite imagery to remotely sense chlorophyll-a concentrations (chl-a), phycocyanin concentrations, and turbidity in two reservoirs, the Grand Lake O′ the Cherokees and Hudson Reservoir, OK, USA, to develop a tool for near real-time monitoring of HABs. Landsat-8 and Sentinel-2 imagery from 2013 to 2017 and from 2015 to 2020 were used to train and test three different models that include multiple regression, support vector regression (SVR), and random forest regression (RFR). Performance was assessed by comparing the three models to estimate chl-a, phycocyanin, and turbidity. The results showed that RFR achieved the best performance, with R2 values of 0.75, 0.82, and 0.79 for chl-a, turbidity, and phycocyanin, while multiple regression had R2 values of 0.29, 0.51, and 0.46 and SVR had R2 values of 0.58, 0.62, and 0.61 on the testing datasets, respectively. This paper examines the potential of the developed open-source satellite remote sensing tool for monitoring reservoirs in Oklahoma to assess spatial and temporal variations in surface water quality. |
| Keywords: | harmful algal blooms remote sensing machine learning water quality monitoring lakes inland water |