Day 4

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Paper title Application of Earth Observation and Artificial Intelligence for Mapping Spatial Distribution and Biomass of Estuarine and Coastal Macroalgal Blooms
Authors
  1. Sita Karki Irish Centre for High End Computing (ICHEC) Speaker
  2. Ricardo Bermejo Universidad de Cádiz
  3. Robert Wilkes Environmental Protection Agency (EPA)
  4. Michéal Mac Monagail National University of Ireland Galway, Galway
  5. Eve Daly National University of Ireland Galway, Galway
  6. Mark Healy National University of Ireland Galway, Galway
  7. Jenny Hanafin Irish Centre for High End Computing
  8. Alastair McKinstry Irish Centre for High End Computing
  9. Per-Erik Mellander Teagasc Agricultural Catchments Programme
  10. Owen Fenton Teagasc Agricultural Catchments Programme
  11. Liam Morrison National University of Ireland Galway
Form of presentation Poster
Topics
  • C1. AI and Data Analytics
    • C1.04 AI4EO applications for Land and Water
Abstract text The application of earth observation (EO) data sets and artificial intelligence was explored to develop EO-based monitoring of the algal blooms. Opportunistic macroalgal blooms have been an essential factor in determining the ecological status of coastal and estuarine areas in Ireland and across the world. A novel approach to map green algal cover using a Normalised Difference Vegetation Index (NDVI) was developed using EO data sets. Scenes from Sentinel-2A/B, Landsat-5 and Landsat-8 missions were processed for eight different estuarine areas of moderate, poor, and bad ecological status using European Union Water Framework Directive classification for transitional water bodies. Images acquired during low-tide conditions from 2010 to 2018 within 18 days of field surveys were considered for the investigation. The estimates of percentage coverage obtained from different EO data sources and field surveys were significantly correlated (R2= 0.94) with Cohen’s kappa coefficient of 0.69 ± 0.13. The results demonstrated that the NDVI-based methodology 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 posed additional constraints during the selection of the images. Considering these limitations, the findings showed that both Sentinel-2 and Landsat scenes could be used to estimate bloom coverage. Moreover, Landsat, because of its legacy program since the 1970s, can be utilized 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 and the combination of radar backscatter (Sentinel-1) and optical reflectance in the visible and near-infrared regions (Sentinel-2) to predict the biomass quantity. The ANN model based on multispectral imagery was suitable to estimate biomass quantity (R2=0.74). The model performance could be improved with the addition of more training samples over time. The developed methodology can be applied in other areas experiencing macroalgal blooms in a simple, cost-effective, and efficient way. Similarly, the technology can be replicated for other species of algae. The study has demonstrated that both the NDVI-based technique to map spatial coverage of macroalgal blooms and the ANN-based 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 datasets.