Can remote sensing (drones) find harmful algae blooms?

A harmful aquatic algae bloom is when there is sudden a rapid growth of algae. In the past these were more commonly referred to as red tides. These populations of algae in term then produce extracellular compounds that can cause harm and even death to humans and wildlife and fish. Shellfish that are subjected to HABs cause humans to get sick. In rare cases people can even die. HABs most commonly occur in the oceans and large freshwater lakes, such as the Great Lakes.

Often HABs occurs due to nutrient runoff but it is important to understand these have been occurring for millions of years. There are fossilized HABs and fossils of whales found in a proximity and quality near the fossilized HABs that is is theorized the HABs resulted in the death of the whales. Climate change is increasing the frequency and intensity of HABs so it is clear we need to develop tools to predict and mitigate risks with HABs.

I looked at research into using remote sensing to predict algal blooms of Karenia brevis in coastal Florida. This algal is responsible for the most red-tides in Florida. It produces a neurotoxin that kills aquatic life and makes shellfish toxic to humans. Researchers used a geographic information system (GIS) to develop a machine learning method to detect harmful algal blooms (HABs).

Karenia brevis Images source: Florida fish and wildlife resources department

Detecting HABs using remote sensing has many advantages as the amount of area able to be observed is far greater than the capacity of current field staff. This is particularly important in states with limited budgets and resources In addition, it is possible that a HAB goes undetected as not all coastal areas are frequently observed.

Images source: Florida fish and wildlife resources department

The researchers were able to develop a method that allowed them to detect HABs up to 8 days before they occurred which allows time for mitigation measures to be put in place. In addition, the model was 91 percent accurate in the detection of HABs. The model was also tested on the Arabian Gulf and there was 93 percent accurate in the detection of HABs. Previous methods in remote detection were less accurate but remote sensing has been used in detecting HABs since 1981.

One pitfall of using the above method is that the area that the blooms occupy can be measured but not the concentration of algal within the bloom. However, in terms of mitigating these blooms concentration of the algal seems to have minimal importance. Perhaps in the future drones would be able to provide water samples to a lab remotely.

Paper cited:

Hill, P. R., Kumar, A., Temimi, M., & Bull, D. R. (2020). HABNet: Machine Learning, Remote Sensing-Based Detection of Harmful Algal Blooms. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing13, 3229-3239.

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