Mapping Seaweed Tides from Space

Satellite scene of Dungarvan Bay in Co Waterford showing the Ulva biomass distribution. Photo: Sentinel 2 image from European Space Agency.
Apr 27 2021 Posted: 09:09 CDT

A study led by Dr Liam Morrison from the Ryan Institute in collaboration with Dr Sita Karki from ICHEC at NUI Galway, have used macroalgal blooms for the assessment of the ecological status of coastal and estuarine areas in Ireland. The use of earth observation data sets to map green algal cover based on a Vegetation Index index was explored. The study was published in the international journal Frontiers in Marine Science.

Several optical and radar satellite scenes from the European Space Agency and National Aeronautics and Space Administration missions were processed for eight different Irish estuaries (Clonakilty, Courtmacsherry, Lower Blackwater Estuary, Dungarvan, Bannow Bay, Tolka, Malahide and Rogerstown) of moderate, poor, and bad ecological status of estuaries and coastal lagoons. Satellite images acquired during low-tide conditions from 2010 to 2018 within 18 days of field surveys were considered. 

The estimates of percentage green algal blooms coverage obtained from different earth observation data sources and field surveys were significantly correlated in terms of temporal and spatial accuracy. The results showed that the adopted technique could be successfully applied to map the coverage of the blooms and to monitor estuarine areas in conjunction with other monitoring activities that involve field sampling and surveys. 

The combination of wide-spread cloud-coverage and high-tide conditions provided additional constraints during the image selection. The findings showed that the scenes of variable resolutions could be utilised to estimate bloom coverage. Moreover, Landsat, which is a legacy mission from NASA, can be utilised to reconstruct the blooms using historical archival data. 

Considering the importance of biomass for understanding the severity of algal accumulations, an Artificial Neural Network (ANN) model was trained using the in situ historical biomass samples collected by Environmental Protection Agency and those collected during previous research projects by the group. 

The model performance could be improved with the addition of more training samples. The developed methodology can be applied in other areas experiencing macroalgal blooms in a simple, cost-effective, and efficient way. 

The study has demonstrated that both the vegetation index-based technique to map spatial coverage of macroalgal blooms and the machine learning model to compute biomass have the potential to become an effective complementary tool for monitoring macroalgal blooms where the existing monitoring efforts can leverage the benefits of earth observation data sets. 

To read the full study in Frontiers in Marine Science, visit: https://www.frontiersin.org/articles/10.3389/fmars.2021.633128/full.

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