Mapping Coral Reef Habitats with ICESat-2 and Satellite Imagery: A Novel Spectral Unmixing Approach Compared to Machine Learning

Details

• Journal Title:
  Remote Sensing
• Personal Author:
  Trudeau, Gabrielle A. ; Lyon, Mark ; Lowell, Kim ; Dijkstra, Jennifer A.
• NOAA Program & Office:
  NOS (National Ocean Service) ; CoRIS (Coral Reef Information System)
• Description:
  Accurate, scalable mapping of coral reef habitats is essential for monitoring ecosystem health and detecting change over time. In this study, we introduce a novel mathematically based nonlinear spectral unmixing method for benthic habitat classification, which provides sub-pixel estimates of benthic composition, capturing the mixed benthic composition within individual pixels. We compare its performance against two machine learning approaches: semi-supervised K-Means clustering and AdaBoost decision trees. All models were applied to high-resolution PlanetScope satellite imagery and ICESat-2-derived terrain metrics. Models were trained using a ground truth dataset constructed from benthic photoquadrats collected at Heron Reef, Australia, with additional input features including band ratios, standardized band differences, and derived ICESat-2 metrics such as rugosity and slope. While AdaBoost achieved the highest overall accuracy (93.3%) and benefited most from ICESat-2 features, K-Means performed less well (85.9%) and declined when these metrics were included. The spectral unmixing method uniquely captured sub-pixel habitat abundance, offering a more nuanced and ecologically realistic view of reef composition despite lower discrete classification accuracy (64.8%). These findings highlight nonlinear spectral unmixing as a promising approach for fine-scale, transferable coral reef habitat mapping, especially in complex or heterogeneous reef environments.
• Source:
  Remote Sensing, 17(21), 3623
• DOI:
  https://doi.org/10.3390/rs17213623
• ISSN:
  2072-4292
• Format:
  pdf
• Publisher:
  MDPI AG
• 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-512:89f51bc0f1f6e8956d8586cfca3aeeddb80b99b9a1048642f4a8fd0f9e89a9af30c49e13515d6ba300e5d7ba57c30a116e24750e7f2b58d9ee8f365c388fea84
• Download URL:
  https://repository.library.noaa.gov/view/noaa/72226/noaa_72226_DS1.pdf
• File Type:
  [PDF - 27.16 MB ]