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Using multispectral drones to predict water quality in a subtropical estuary.
Anna B Giles1, Rogger E Correa1,2, Isaac R Santos1,3
1National Marine Science Centre, Southern Cross University, Coffs Harbour, Australia.
Environmental Technology
|November 2, 2022
Summary
Drone multispectral imagery effectively predicts water quality parameters like chlorophyll in intermittently closed and opened lakes. However, temporal variations require dynamic, site-specific models for accurate monitoring.
Area of Science:
- Environmental Science
- Remote Sensing
- Water Resource Management
Background:
- Drones are increasingly vital for high-resolution earth system observations.
- Monitoring water quality in intermittently closed and opened lakes (ICOLs) presents unique challenges due to dynamic conditions.
Purpose of the Study:
- To assess the efficacy of drone-based multispectral imagery in predicting key water quality parameters in an ICOLL.
- To identify specific spectral bands and ratios correlated with water quality indicators.
Main Methods:
- Conducted three water quality sampling campaigns measuring parameters like chlorophyll, dissolved oxygen, and turbidity.
- Utilized DistilM statistical analyses to identify key spectral bands and performed linear correlations with water quality data.
Main Results:
- Significant correlations (R² > 0.4) were found for eleven water quality parameters, with chlorophyll showing the strongest link to the green band (R² = 0.72).
- The most influential spectral bands varied monthly, highlighting temporal dynamics.
- Predictive band importance differed significantly across campaigns and water quality variables.
Conclusions:
- Drone-based multispectral sensors provide valuable 'snapshot' predictions for parameters like chlorophyll, bluegreen algae, and dissolved oxygen.
- A single predictive model is insufficient for dynamic estuarine systems due to temporal variations in water parameters.
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