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

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.

Analisis Dataran Banjir Menggunakan Spatial Computing MNDWI Dengan Sentinel 2 di Danau Rawa Pening

“…Hasil penelitian oleh Ali et al (2021) mengkaji wilayah lahan basah di Kalimantan Selatan menggunakan Landsat 8 OLI menghasilkan overall accuracy sebesar 68.59% pada wilayah kajian. Terdapat kajian MNDWI pada kajian di wilayah Kabupaten Jember yang melakukan kajian pada beberapa kecamatannya oleh Hidayah et al (2022) hasil pengolahan data MNDWI setelah di uji akurasi menghasilkan nilai overall accuracy sebesar 75.47%. Hasil penelitian oleh Nugraha et al (2018) pada penelitian mengenai badan air di Sungai Yeh Unda Bali menggunakan Landsat 8, menghasilkan nilai overall accuracy mencapai 97.54%.…”