Coral reef detection using ICESat-2 and machine learning

Details

• Journal Title:
  Ecological Informatics
• Personal Author:
  Trudeau, Gabrielle A. ; Lowell, Kim ; Dijkstra, Jennifer A.
• NOAA Program & Office:
  NOS (National Ocean Service) ; CoRIS (Coral Reef Information System)
• Description:
  As anthropogenic impacts threaten natural habitats, effective monitoring strategies are crucial. Coral reefs, among the most vulnerable ecosystems, traditionally employ monitoring techniques that are labor-intensive and costly, prompting the exploration of remote sensing as a cost-effective alternative. Launched in October 2018, the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) provides high-resolution, high-frequency data, with its green laser offering unprecedented opportunities for bathymetric and coral reef applications. This study investigates the use of ICESat-2 data for atoll coral reef detection, utilizing Heron Island in the Great Barrier Reef, AU, and employing machine learning models. A binary logistic regression (BLR) model and convolutional neural network (CNN) were tested for determining coral reef presence, with the CNN outperforming the BLR in accuracy (85.4%), F1 score (43%), and false positive rate (13.1%). A challenge of the study included the difficulty of balancing false positive rates in predictive models to avoid over- or underestimations of reef extent. These obstacles were mitigated through the integration of algorithmically derived pseudo-rugosity and slope metrics as innovative proxies for seafloor complexity, significantly improving predictive performance. Feature importance analysis identified satellite-derived bathymetry (SDB) depth as the most critical predictor of coral presence, followed by pseudo-rugosity, slope, and various other depth measurements. This research establishes a new application of ICESat-2 data combined with advanced machine learning techniques as a promising method for efficient and cost-effective coral reef monitoring. Future work should refine algorithms and incorporate additional environmental variables to improve model performance across various reef types.
• Keywords:
  Coral Reefs And Islands Machine Learning Coral Reefs And Islands Machine Learning Coral Reefs Heron Island ICESat-2 Rugosity
• Source:
  Ecological Informatics 87 (2025) 103099
• DOI:
  https://doi.org/10.1016/j.ecoinf.2025.103099
• Format:
  PDF
• Document Type:
  Journal Article
• Funding:
  Grant no. NA20NOS4000196
• License:
  https://creativecommons.org/licenses/by/4.0/
• Rights Information:
  CC BY
• Compliance:
  Submitted
• Main Document Checksum:
  urn:sha-512:eb7c1bcaa0f7d71ee8550dd365dd5765d6ff394ed5831876c27467ec3c37e7d27c57451664eceea75f283e400d9c577cfd102e577e933bac7dbbf8c93f965f48
• Download URL:
  https://repository.library.noaa.gov/view/noaa/70094/noaa_70094_DS1.pdf
• File Type:
  [PDF - 5.35 MB ]