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<p>An accurate assessment of how spectral and spatial resolution influence coastal mapping remains a critical challenge for shallow-water monitoring. This study evaluates and compares hyperspectral (97 bands), multispectral (8 bands), and RGB (3 bands) data, with different spatial resolutions (10&#xa0;cm and 2&#xa0;m), to determine the most suitable spectral–spatial configuration for shallow-water mapping. For this, the effects of spectral and spatial resolution are isolated by simulating 8-band multispectral and 3-band RGB configurations at 10&#xa0;cm and 2&#xa0;m from a single 97-band hyperspectral drone dataset. This allows for comparisons between different resolutions without considering changes in temporal, atmospheric, water column, or image capture, among others acquisition-related factors. A comprehensive methodology for processing was developed using empirical and machine learning models for bathymetry estimation (Sigmoid, Subspace-KNN) and benthic mapping (SVM, FNN). The developed framework was applied at an urban sandy beach sheltered by a natural reef with rich marine biodiversity (Las Canteras beach, Gran Canaria, Spain). Results show that hyperspectral data achieved the highest accuracy (MAE, 0.15&#xa0;m; accuracy, 94%), while multispectral data offered an excellent balance between resolution and performance (MAE, 0.16&#xa0;m; accuracy, 93%). RGB data was acceptable for bathymetry but unreliable for benthic classification in complex habitats (MAE, 0.24&#xa0;m; accuracy, 83%). Subspace-KNN outperformed empirical models for bathymetry, and FNN improved substrate discrimination. In addition, a comparative analysis between 2016 and 2023 imagery, comparing real WorldView-2 imagery (2016; 2&#xa0;m and 8 bands), and drone imagery with the same resolutions emulated (2023; 2&#xa0;m and 8 bands), suggests an approximate 7,200&#xa0;m² reduction in marine vegetation that may be influenced with anthropogenic pressures and thermal increase. This approach provides a reproducible and adaptable tool for sustainable coastal management.</p>

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Spatial-spectral resolution analysis using drone hyperspectral and satellite multispectral imagery for shallow coastal water monitoring

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月22日

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  • A. Mederos-Barrera,
  • F. Eugenio,
  • J. Marcello

摘要

An accurate assessment of how spectral and spatial resolution influence coastal mapping remains a critical challenge for shallow-water monitoring. This study evaluates and compares hyperspectral (97 bands), multispectral (8 bands), and RGB (3 bands) data, with different spatial resolutions (10 cm and 2 m), to determine the most suitable spectral–spatial configuration for shallow-water mapping. For this, the effects of spectral and spatial resolution are isolated by simulating 8-band multispectral and 3-band RGB configurations at 10 cm and 2 m from a single 97-band hyperspectral drone dataset. This allows for comparisons between different resolutions without considering changes in temporal, atmospheric, water column, or image capture, among others acquisition-related factors. A comprehensive methodology for processing was developed using empirical and machine learning models for bathymetry estimation (Sigmoid, Subspace-KNN) and benthic mapping (SVM, FNN). The developed framework was applied at an urban sandy beach sheltered by a natural reef with rich marine biodiversity (Las Canteras beach, Gran Canaria, Spain). Results show that hyperspectral data achieved the highest accuracy (MAE, 0.15 m; accuracy, 94%), while multispectral data offered an excellent balance between resolution and performance (MAE, 0.16 m; accuracy, 93%). RGB data was acceptable for bathymetry but unreliable for benthic classification in complex habitats (MAE, 0.24 m; accuracy, 83%). Subspace-KNN outperformed empirical models for bathymetry, and FNN improved substrate discrimination. In addition, a comparative analysis between 2016 and 2023 imagery, comparing real WorldView-2 imagery (2016; 2 m and 8 bands), and drone imagery with the same resolutions emulated (2023; 2 m and 8 bands), suggests an approximate 7,200 m² reduction in marine vegetation that may be influenced with anthropogenic pressures and thermal increase. This approach provides a reproducible and adaptable tool for sustainable coastal management.