Detecting Flood Inundation using a Multi-Threshold Overlay Approach and Its Relationship with the Land Cover in Pontianak
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flood-prone, land cover, multi-threshold, google earth engine
Abstract
Pontianak is a lowland area prone to flooding driven by hydrological factors, topographical conditions, and land cover changes. This study analyzed flood inundation using a Multi-Threshold Overlay approach based on Sentinel-1 SAR imagery and examined its relationship with land cover classified from Sentinel-2 MSI imagery using the Random Forest algorithm on the Google Earth
Engine. This study assumes that the Multi-threshold Overlay approach improves flood detection reliability by identifying areas consistently detected across multiple threshold values. The relationship between flood inundation and land cover was analyzed using spatial overlay and regression analysis. The results showed that the land cover classification achieved an Overall Accuracy of 78.82% (κ = 0.706), while the flood inundation detection attained an Overall Accuracy of 85% (κ = 0.70). The Multi-Threshold Overlay approach identified 1,335.86 ha of flood inundation and generated consistent inundation areas across all threshold values (1.05–1.25). The consistent identification of inundated areas across multiple threshold values suggests that integrating threshold values can improve the reliability of flood inundation mapping. Overlay analysis indicated that infrastructure areas accounted for 49.8% of the total inundated area, followed by agricultural/plantation areas (26.0%) and vegetation cover (24.2%). Regression analysis further confirmed a strong positive relationship between land cover and flood inundation extent (R² = 0.875; r = 0.93). These findings suggest that the dominance of built-up areas increases the risk of flood inundation, highlighting the importance of infiltration space, sustainable drainage systems, and blue-green infrastructure in spatial planning.
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Copyright (c) 2026 Boy Rangga; Hervian Handika Sugasta, Doddy Cahyadi Saputra
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