Flood mapping based on open-source remote sensing data using an efficient band combination system

Entin Hidayah; Tedy Pranadiarso; Gusfan Halik; Indarto Indarto; Wei-Koon Lee; Mokhammad Farid Maruf

doi:10.3986/AGS.10598

Authors

Entin Hidayah University of Jember, Faculty of Engineering, Department of Civil Engineering, Jember, Indonesia https://orcid.org/0000-0002-1233-6850
Tedy Pranadiarso University of Jember, Faculty of Engineering, Department of Civil Engineering, Jember, Indonesia https://orcid.org/0000-0003-2740-848X
Gusfan Halik University of Jember, Faculty of Engineering, Department of Civil Engineering, Jember, Indonesia https://orcid.org/0000-0002-5447-1268
Indarto Indarto University of Jember, Faculty of Agricultural Technology, Department of Agricultural Engineering, Jember, Indonesia https://orcid.org/0000-0001-6319-6731
Wei-Koon Lee University Teknologi Mara, School of Civil Engineering, Shah Alam, Malaysia https://orcid.org/0000-0003-3910-4870
Mokhammad Farid Maruf University of Jember, Faculty of Engineering, Department of Civil Engineering, Jember, Indonesia https://orcid.org/0000-0002-5715-935X

DOI:

https://doi.org/10.3986/AGS.10598

Keywords:

rapid flood mapping, Sentinel-2, Modified Normalized Difference Water Index, Flood Inundation Extraction Index, kappa coefficient, accuracy, Indonesia

Abstract

Flood mapping is an essential component of planning flood mitigation. The availability of remote sensing data makes rapid flood mapping possible. This article develops an accurate method for rapid flood mapping using satellite imagery. Sentinel-2 imagery was tested by acquiring data before and after a flood event in a lowland area. Flooding extraction was performed using the newly developed Flood Inundation Extraction Index (FIEI) and compared to the Modified Normalized Difference Water Index (MNDWI), the most commonly used index. Based on the choice of threshold, the results are divided into flooded and non-flooded areas. Evaluation of the performance accuracy based on the total and kappa coefficients showed that the FIEI approach is more accurate than the MNDWI approach.

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Flood mapping based on open-source remote sensing data using an efficient band combination system

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