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<p>Benthic habitat mapping is an emerging discipline in the international marine field in recent years, providing an effective tool for marine spatial planning, marine ecological management, and decision-making applications. Seabed sediment classification is one of the main contents of seabed habitat mapping. In response to the impact of remote sensing imaging quality and the limitations of acoustic measurement range, where a single data source does not fully reflect the substrate type, we proposed a high-precision seabed habitat sediment classification method that integrates data from multiple sources. Based on WorldView-2 multi-spectral remote sensing image data and multibeam bathymetry data, constructed a random forests (RF) classifier with optimal feature selection. A seabed sediment classification experiment integrating optical remote sensing and acoustic remote sensing data was carried out in the shallow water area of Wuzhizhou Island, Hainan, South China. Different seabed sediment types, such as sand, seagrass, and coral reefs were effectively identified, with an overall classification accuracy of 92%. Experimental results show that RF matrix optimized by fusing multi-source remote sensing data for feature selection were better than the classification results of simple combinations of data sources, which improved the accuracy of seabed sediment classification. Therefore, the method proposed in this paper can be effectively applied to high-precision seabed sediment classification and habitat mapping around islands and reefs.</p>

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High-precision classification of benthic habitat sediments in shallow waters of islands by multi-source data

  • 期刊论文

  • 发表日期：

    2025年10月6日

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  • Qiuhua Tang,
  • Ningning Li,
  • Yujie Zhang,
  • Zhipeng Dong,
  • Yongling Zheng,
  • Jingjing Bao,
  • Jingyu Zhang

摘要

Benthic habitat mapping is an emerging discipline in the international marine field in recent years, providing an effective tool for marine spatial planning, marine ecological management, and decision-making applications. Seabed sediment classification is one of the main contents of seabed habitat mapping. In response to the impact of remote sensing imaging quality and the limitations of acoustic measurement range, where a single data source does not fully reflect the substrate type, we proposed a high-precision seabed habitat sediment classification method that integrates data from multiple sources. Based on WorldView-2 multi-spectral remote sensing image data and multibeam bathymetry data, constructed a random forests (RF) classifier with optimal feature selection. A seabed sediment classification experiment integrating optical remote sensing and acoustic remote sensing data was carried out in the shallow water area of Wuzhizhou Island, Hainan, South China. Different seabed sediment types, such as sand, seagrass, and coral reefs were effectively identified, with an overall classification accuracy of 92%. Experimental results show that RF matrix optimized by fusing multi-source remote sensing data for feature selection were better than the classification results of simple combinations of data sources, which improved the accuracy of seabed sediment classification. Therefore, the method proposed in this paper can be effectively applied to high-precision seabed sediment classification and habitat mapping around islands and reefs.