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.

Downloads

Download data is not yet available.

References

Bousbih, S., Zribi, M., Pelletier, C., Gorrab, A., Lili-Chabaane, Z., Baghdadi, N., Ben Aissa, N., Mougenot, B. 2019: Soil texture estimation using radar and optical data from Sentinel-1 and Sentinel-2. Remote Sensing 11-13. DOI: https://doi.org/10.3390/rs11131520 DOI: https://doi.org/10.3390/rs11131520

Chen, Z., Luo, J., Chen, N., Xu, R., Shen, G. 2019: RFim: A real-time inundation extent model for large floodplains based on remote sensing big data and water level observations. Remote Sensing 11-13. DOI: https://doi.org/10.3390/rs11131585 DOI: https://doi.org/10.3390/rs11131585

Ettehadi Osgouei, P., Kaya, S., Sertel, E., Alganci, U. 2019: Separating built-up areas from bare land in Mediterranean cities using Sentinel-2A imagery. Remote Sensing 11-3. DOI: https://doi.org/10.3390/rs11030345 DOI: https://doi.org/10.3390/rs11030345

European space agency 2015: Sentinel-2 user handbook. European Space Agency Standard Document 1-2.

Ferk, M., Ciglič, R., Komac, B., Lóczy, D. 2021: Management of small retention ponds and their impact on flood hazard prevention in the Slovenske Gorice Hills. Acta geographica Slovenica 61-1. DOI: https://doi.org/https://doi.org/10.3986/AGS.7675 DOI: https://doi.org/10.3986/AGS.7675

Feyisa, G. L., Meilby, H., Fensholt, R., Proud, S. R. 2014: Automated Water Extraction Index: A new technique for surface water mapping using Landsat imagery. Remote Sensing of Environment 140. DOI: https://doi.org/10.1016/j.rse.2013.08.029 DOI: https://doi.org/10.1016/j.rse.2013.08.029

Fisher, A., Flood, N., Danaher, T. 2016: Comparing Landsat water index methods for automated water classification in eastern Australia. Remote Sensing of Environment 175. DOI: https://doi.org/10.1016/j.rse.2015.12.055 DOI: https://doi.org/10.1016/j.rse.2015.12.055

Gašparovič, M., Klobučar, D. 2021: Mapping floods in low-land forest using Sentinel-1 and Sentinel-2 data and an object-based approach. Forests 12-5. DOI: https://doi.org/10.3390/f12050553 DOI: https://doi.org/10.3390/f12050553

Ghofrani, Z., Sposito, V., Faggian, R. 2019: Improving flood monitoring in rural areas using remote sensing. Water Practice and Technology 14-1. DOI: https://doi.org/10.2166/wpt.2018.118 DOI: https://doi.org/10.2166/wpt.2018.118

Huang, M., Jin, S. 2020: Rapid flood mapping and evaluation with a supervised classifier and change detection in Shouguang using Sentinel-1 SAR and Sentinel-2 optical data. Remote Sensing 12-13. DOI: https://doi.org/10.3390/rs12132073 DOI: https://doi.org/10.3390/rs12132073

Kaplan, G., Avdan, U. 2017: Mapping and monitoring wetlands using Sentinel-2 satellite imagery. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences IV-4/W4. DOI: https://doi.org/10.5194/isprs-annals-IV-4-W4-271-2017 DOI: https://doi.org/10.5194/isprs-annals-IV-4-W4-271-2017

Mahmood, S., Sajjad, A., Rahman, A.-ur. 2021: Cause and damage analysis of 2010 flood disaster in district Muzaffar Garh, Pakistan. Natural Hazards 107. DOI: https://doi.org/10.1007/s11069-021-04652-6 DOI: https://doi.org/10.1007/s11069-021-04652-6

Niu, L., Kaufmann, H., Xu, G., Zhang, G., Ji, C., He, Y. 2022: Triangle Water Index (TWI): An advanced approach for more accurate detection and delineation of water surfaces in Sentinel-2 data. Remote Sensing 14-21. DOI: https://doi.org/https://doi.org/10.3390/rs14215289 DOI: https://doi.org/10.3390/rs14215289

Niwas, R., Grewal, M. S., Khichar, M. L. 2015: Practical manual on Remote Sensing , GIS and Land Use Planning. Hisar.

Nurullatifah, T. 2021: Ribuan Warga Terdampak Banjir di Jember. Internet: https://akurat.co/ribuan-warga-terdampak-banjir-di-jember (25. 1. 2023).

Pena-Regueiro, J., Sebastiá-Frasquet, M.-T., Estornell, J., Aguilar-Maldonado, J. A. 2020: Sentinel-2 application to the surface characterization of small water bodies in wetlands. Water 12-5. DOI: https://doi.org/10.3390/w12051487 DOI: https://doi.org/10.3390/w12051487

Rahman, M., Ningsheng, C., Islam, M. M., Dewan, A., Iqbal, J., Washakh, R. M. A., Shufeng, T. 2019: Flood susceptibility assessment in Bangladesh Using machine learning and multi-criteria decision analysis. Earth Systems and Environment 3. DOI: https://doi.org/10.1007/s41748-019-00123-y DOI: https://doi.org/10.1007/s41748-019-00123-y

Ryu, J., Yoon, E. J., Park, C., Lee, D. K., Jeon, S. W. 2017: A flood risk assessment model for companies and criteria for governmental decision-making to minimize hazards. Sustainability 9-11. DOI: https://doi.org/10.3390/su9112005 DOI: https://doi.org/10.3390/su9112005

Sajjad, A., Lu, J., Chen, X., Chisenga, C., Mazhar, N., Nadeem, B. 2022: Riverine flood mapping and impact assessment using remote sensing technique: A case study of Chenab flood-2014 in Multan district, Punjab, Pakistan. Natural Hazards 110. DOI: https://doi.org/https://doi.org/10.1007/s11069-021-05033-9 DOI: https://doi.org/10.1007/s11069-021-05033-9

Sarp, G., Ozcelik, M. 2017: Water body extraction and change detection using time series: A case study of Lake Burdur, Turkey. Journal of Taibah University for Science 11-3. DOI: https://doi.org/10.1016/j.jtusci.2016.04.005 DOI: https://doi.org/10.1016/j.jtusci.2016.04.005

Sathianarayanan, M. 2018: Assessment of surface water dynamics using multiple water indices around Adama woreda, Ethiopia. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences 4-5. DOI: https://doi.org/10.5194/isprs-annals-IV-5-181-2018 DOI: https://doi.org/10.5194/isprs-annals-IV-5-181-2018

Sipelgas, L., Aavaste, A., Uiboupin, R. 2021: Mapping flood extent and frequency from Sentinel-1 imagery during the extremely warm winter of 2020 in boreal floodplains and forests. Remote Sensing 13-23. DOI: https://doi.org/10.3390/rs13234949 DOI: https://doi.org/10.3390/rs13234949

Sivanpillai, R., Jacobs, K. M., Mattilio, C. M., Piskorski, E. V. 2021: Rapid flood inundation mapping by differencing water indices from pre- and post-flood Landsat images. Frontiers of Earth Science 15. DOI: https://doi.org/10.1007/s11707-020-0818-0 DOI: https://doi.org/10.1007/s11707-020-0818-0

Suwarsono, Nugroho, J. T., Wiweka. 2013: Identification of inundated area using normalized difference water index (NDWI) on lowland region of Java island. International Journal of Remote Sensing and Earth Sciences 10-2. DOI: https://doi.org/10.30536/j.ijreses.2013.v10.a1850 DOI: https://doi.org/10.30536/j.ijreses.2013.v10.a1850

Wang, Y., Colby, J. D., Mulcahy, K. A. 2002: An efficient method for mapping flood extent in a coastal floodplain using Landsat TM and DEM data. International Journal of Remote Sensing 23-18. DOI: https://doi.org/10.1080/01431160110114484 DOI: https://doi.org/10.1080/01431160110114484

Watson, P. F., Petrie, A. 2010: Method agreement analysis: A review of correct methodology. Theriogenology 73-9. DOI: https://doi.org/10.1016/j.theriogenology.2010.01.003 DOI: https://doi.org/10.1016/j.theriogenology.2010.01.003

Xu, H. 2006: Modification of normalized difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing 27-14. DOI: https://doi.org/10.1080/01431160600589179 DOI: https://doi.org/10.1080/01431160600589179

Zhou, Y., Dong, J., Xiao, X., Xiao, T., Yang, Z., Zhao, G., Zou, Z., Qin, Y. 2017: Open surface water mapping algorithms: A comparison of water-related spectral indices and sensors. Water 9-4. DOI: https://doi.org/10.3390/w9040256Bousbih, S., Zribi, M., Pelletier, C., Gorrab, A., Lili-Chabaane, Z., Baghdadi, N., Aissa, N. Ben., Mougenot, B. 2019: Soil texture estimation using radar and optical data from Sentinel-1 and Sentinel-2. Remote Sensing. DOI: https://doi.org/10.3390/rs11131520 DOI: https://doi.org/10.3390/rs11131520