Fisheries Research Articles

Document Type

Article

Publication Date

12-5-2025

Journal Title

Remote Sens

Keywords

marine habitat, aquatic vegetation index, Sentinel-2, automatic image annotation, Google Earth Engine, Marine Stewardship Council, Ngari-Capes Marine Park, submerged aquatic vegetation, enhanced greenlip abalone fishery, Flinders Bay

Disciplines

Marine Biology

Abstract

Highlights

What are the main findings?
  • Four spectral indices were identified as important for the quantification of seagrass within and adjacent to the MSC-certified Western Australia Enhanced Greenlip Abalone Fishery. The Normalised Difference Aquatic Vegetation Index (NDAVI) and Depth Invariant Index of the blue and green bands were the most important indices.
  • Similar seagrass cover and distribution were observed inside and outside of the fishery area of operation.
What are the implication of the main finding?
  • The use of indices from free satellite products via Google Earth Engine workflows and automatic image annotation provides a rapidly repeatable method to support ecosystem-based fisheries management for this fishery.
  • These findings may have broader applications for ecosystem monitoring across moderately deep (< 20 m) fisheries and marine management areas.

Abstract

Understanding and monitoring benthic habitat distribution is essential for implementing ecosystem-based fisheries management (EBFM). Satellite remote sensing offers a rapid and cost-effective approach to marine habitat assessments; however, its application requires context-specific adjustment to account for environmental variability and differing study aims. As such, predictor variables must be tailored to the specific site and target habitat. This study uses Sentinel-2 Level 2A surface reflectance satellite imagery and stability selection via Random Forest Recursive Feature Elimination to assess the importance of remote sensing indices for mapping moderately deep (< 20 m) seagrass habitats in relation to the Marine Stewardship Council-certified Western Australia Enhanced Greenlip Abalone Fishery (WAEGAF). Of the seven indices tested, the Normalised Difference Aquatic Vegetation Index (NDAVI) and Depth Invariant Index for the blue and green bands were selected in the optimal model on every run. The kernelised NDAVI and Water-Adjusted Vegetation Index also scored highly (both 0.92) and were included in the final classification and regression models. Both models performed well and predicted a similar cover and distribution of seagrass within the fishery compared to the surrounding area, providing a baseline and supporting EBFM of the WAEGAF within the surrounding marine protected area. Importantly, the use of indices from freely accessible ready-to-use satellite products via Google Earth Engine workflows and expedited ground truth image annotation using highly accurate (0.96) automatic image annotation provides a rapidly repeatable method for delivering ecosystem information for this fishery.

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Digital Object Identifier (DOI)

https://doi.org/https://doi.org/10.3390/rs17243932