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<p>This study develops two spectral indices to improve spatial monitoring of mangrove ecosystems and industrial expansion in rapidly transforming coastal environments. The Mangrove Adaptive Spectral Index (MASI) enhances detection of mangroves at the land–water interface by resolving mixed vegetation–water reflectance that limits conventional indices. The Industrial Reflectance Anomaly Index (IRAI) improves the spatial delineation of industrial surfaces by exploiting their characteristic SWIR–NIR contrast while reducing confusion with soils and residential areas. Both indices were derived through iterative spectral testing and applied to Sentinel-2 MSI imagery (2020–2025) across four coastal sites in northern Java, Indonesia. MASI consistently achieved accuracies above 85%, frequently exceeding 90%, outperforming commonly used mangrove indices. IRAI also maintained accuracies above 85% and showed greater spatial stability than NDBI. These indices provide more reliable spatial information for monitoring mangrove integrity and industrial expansion in support of blue carbon assessment.</p>

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Adaptive spectral indices for mangrove and industrial area detection to strengthen blue carbon assessment in coastal regions

  • 期刊论文

  • 发表日期：

    2026年4月18日

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  • Dimas Danar Dewa,
  • Ugay Sugarmansyah,
  • Heri Apriyanto,
  • Sherafina Reni Cahayanti,
  • Nugroho Adi Sasongko,
  • Warseno Warseno,
  • Yanuar Iman Dwiananto,
  • Anissa Dewi Suryaningtyas,
  • Muhammad Abrar Rizqullah

摘要

This study develops two spectral indices to improve spatial monitoring of mangrove ecosystems and industrial expansion in rapidly transforming coastal environments. The Mangrove Adaptive Spectral Index (MASI) enhances detection of mangroves at the land–water interface by resolving mixed vegetation–water reflectance that limits conventional indices. The Industrial Reflectance Anomaly Index (IRAI) improves the spatial delineation of industrial surfaces by exploiting their characteristic SWIR–NIR contrast while reducing confusion with soils and residential areas. Both indices were derived through iterative spectral testing and applied to Sentinel-2 MSI imagery (2020–2025) across four coastal sites in northern Java, Indonesia. MASI consistently achieved accuracies above 85%, frequently exceeding 90%, outperforming commonly used mangrove indices. IRAI also maintained accuracies above 85% and showed greater spatial stability than NDBI. These indices provide more reliable spatial information for monitoring mangrove integrity and industrial expansion in support of blue carbon assessment.