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<p>Accurately monitoring flood-affected areas is crucial for effective disaster risk management and land use planning. This study evaluates pixel accuracy in detecting flooded versus non-flooded areas on residential surfaces, riverbanks, and rice fields using various flood indexes derived from Sentinel-2 satellite data across two sides of the Wallace Line. The indexes are derived from field reflectance measurements. The approach involves analyzing spectral reflectance data from the Near Infrared (NIR) and Shortwave Infrared (SWIR) bands and validating the results with a confusion matrix. The model demonstrated high accuracy when applied in East Nusa Tenggara, with minimal spectral response variation of flooded areas across the two regions. This research achieved a detection accuracy exceeding 81.99% across various surface types in three flood-affected study areas. Specifically, Band 7 achieved the highest accuracy rate at 86.95%, followed by Band 6 (86.5%), Band 8A (86.48%), and Band 8 (84.65%). These results highlight the method’s effectiveness in distinguishing between flooded and non-flooded areas. Additionally, the study showed a low commission rate for Band 8A (0.86%) across the three locations, while Band 7 recorded the lowest omission error at 25.67%. These findings suggest that the biogeographical divide of the Wallace Line does not significantly affect the spectral characteristics of flooded surfaces in remote sensing.</p>

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Precision in Flood Detection Across Varying Land Types over Two Sides of the Wallace Line

  • 期刊论文

  • 发表日期：

    2025年8月14日

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  • Muhammad Priyatna,
  • Muhammad Rokhis Khomarudin,
  • Sastra Kusuma Wijaya,
  • Galdita Aruba Chulafak,
  • Gatot Nugroho,
  • Ahmad Maryanto,
  • Muhammad Arfin Hussein

摘要

Accurately monitoring flood-affected areas is crucial for effective disaster risk management and land use planning. This study evaluates pixel accuracy in detecting flooded versus non-flooded areas on residential surfaces, riverbanks, and rice fields using various flood indexes derived from Sentinel-2 satellite data across two sides of the Wallace Line. The indexes are derived from field reflectance measurements. The approach involves analyzing spectral reflectance data from the Near Infrared (NIR) and Shortwave Infrared (SWIR) bands and validating the results with a confusion matrix. The model demonstrated high accuracy when applied in East Nusa Tenggara, with minimal spectral response variation of flooded areas across the two regions. This research achieved a detection accuracy exceeding 81.99% across various surface types in three flood-affected study areas. Specifically, Band 7 achieved the highest accuracy rate at 86.95%, followed by Band 6 (86.5%), Band 8A (86.48%), and Band 8 (84.65%). These results highlight the method’s effectiveness in distinguishing between flooded and non-flooded areas. Additionally, the study showed a low commission rate for Band 8A (0.86%) across the three locations, while Band 7 recorded the lowest omission error at 25.67%. These findings suggest that the biogeographical divide of the Wallace Line does not significantly affect the spectral characteristics of flooded surfaces in remote sensing.