Skip to main content
SpringerLink
Log in

Inundation extend mapping for multi-temporal SAR using automatic thresholding and change detection: a case study on Kosi river of India

  • Published:
Spatial Information Research Aims and scope Submit manuscript

This article has been updated

Abstract

The flood occurrence frequency has increased over the years due to climate change, and various state-of-the-art methods have been proposed for flood mapping using Synthetic Aperture Radar (SAR) data. However, whenever there are similarities in the radar backscatter values of permanent water bodies and sand areas, the riverine floods are generally ignored due to high computational complexity. This paper proposes a multi-source data fusion-based model for mapping the Kosi river floodplain areas in the Supaul district of Bihar, India, using both VV and VH bands of Sentinel-1 SAR imagery. The proposed model involves image pre-processing, classification, and post-processing of results to obtain the flood map. The combination of Otsu automatic threshold detection and change detection methods is used for reducing the overestimation of flooded pixels while identifying flood-prone areas. The post-processing involves the identification of high and low-confidence flood regions, riverine floods, generation of flood maps, and estimation of flooded areas. The impact of the flood on the nearby area is captured using multi-temporal images of the Supaul district. The pre-processing, visualizing, processing, and analysis of the results are carried out in Google Earth Engine. The proposed method is suitable for identifying flooding in both non-permanent and permanently low backscattering areas.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Data availability

The data presented in this article are publicly available on the Scihub Copernicus website at https://scihub.copernicus.eu/dhus/#/home.

Change history

  • 13 November 2023

    The original online version of this article was revised to remove the unnecessary text in the Abstract.

References

  1. Wikimedia Foundation. (2023). Floodplain. Retrieved 17 August 2023. from Wikipedia. https://en.wikipedia.org/wiki/Floodplain

  2. World Health Organization. (n.d.). Floods. Retrieved 7 May 2023. From World Health Organization. https://www.who.int/health-topics/floods#tab=tab_1

  3. Flood risk already affects 1.81 billion people. Climate change and unplanned urbanization could worsen exposure. World Bank Blogs. (n.d.). Retrieved 6 February 2023. From https://blogs.worldbank.org/climatechange/flood-risk-already-affects-181-billion-people-climate-change-and-unplanned

  4. Rentschler, J., Salhab, M., & Jafino, B. A. (2022). Flood exposure and poverty in 188 countries. Nature Communications, 13(1), 3527. https://doi.org/10.1038/s41467-022-30727-4

    Article  Google Scholar 

  5. Merwade, V., Cook, A., & Coonrod, J. (2008). GIS techniques for creating river terrain models for hydrodynamic modeling and flood inundation mapping. Environmental Modelling & Software, 23(10–11), 1300–1311. https://doi.org/10.1016/j.envsoft.2008.03.005

    Article  Google Scholar 

  6. Biswas, S., Mahajan, P., Sharma, A., Singh Baghel, D., & Nmims, I. (2018). Methodologies for flood hazard mapping-a review. NMIMS, MPSTME, SVNIT.

    Google Scholar 

  7. National Disaster Management Authority. Floods | NDMA, GoI. (n.d.). Retrieved 7 September 2023. From https://ndma.gov.in/Natural-Hazards/Floods

  8. Muñoz, D. F., Muñoz, P., Moftakhari, H., & Moradkhani, H. (2021). From local to regional compound flood mapping with deep learning and data fusion techniques. Science of the Total Environment, 782, 146927. https://doi.org/10.1088/1755-1315/37/1/012034

    Article  Google Scholar 

  9. DeVries, B., Huang, C., Armston, J., Huang, W., Jones, J. W., & Lang, M. W. (2020). Rapid and robust monitoring of flood events using Sentinel-1 and Landsat data on the Google Earth Engine. Remote Sensing of Environment, 240, 111664. https://doi.org/10.1016/j.rse.2020.111664

    Article  Google Scholar 

  10. Raj, A., & Minz, S. (2022). Spatial granule based clustering technique for hyperspectral images. In 2022 IEEE 2nd Mysore sub section international conference (MysuruCon) (pp. 1–6). IEEE. https://doi.org/10.1109/MysuruCon55714.2022.9972609

  11. Tripathi, G., Pandey, A. C., & Parida, B. R. (2022). Flood hazard and risk zonation in north Bihar using satellite-derived historical flood events and socio-economic data. Sustainability, 14(3), 1472. https://doi.org/10.3390/su14031472

    Article  Google Scholar 

  12. Tripathy, P., & Malladi, T. (2022). Global flood mapper: A novel Google Earth Engine application for rapid flood mapping using Sentinel-1 SAR. Natural Hazards, 114(2), 1341–1363. https://doi.org/10.1007/s11069-022-05428-2

    Article  Google Scholar 

  13. Mudashiru, R. B., Sabtu, N., Abustan, I., & Balogun, W. (2021). Flood hazard mapping methods: A review. Journal of Hydrology, 603, 126846. https://doi.org/10.1016/j.jhydrol.2021.126846

    Article  Google Scholar 

  14. Kumar, M., Singh, S. K., Kundu, A., Tyagi, K., Menon, J., Frederick, A., Raj, A., & Lal, D. (2022). GIS-based multi-criteria approach to delineate groundwater prospect zone and its sensitivity analysis. Applied Water Science, 12(4), 71. https://doi.org/10.1007/s13201-022-01585-8

    Article  Google Scholar 

  15. Vanama, V. S. K., Mandal, D., & Rao, Y. S. (2020). GEE4FLOOD: Rapid mapping of flood areas using temporal Sentinel-1 SAR images with Google Earth Engine cloud platform. Journal of Applied Remote Sensing, 14(3), 034505–034505. https://doi.org/10.1117/1.JRS.14.034505

    Article  Google Scholar 

  16. Bhatt, C. M., Srinivasa Rao, G., Manjushree, P., & Bhanumurthy, V. (2010). Space based disaster management of 2008 Kosi floods, North Bihar, India. Journal of the Indian Society of Remote Sensing, 38, 99–108. https://doi.org/10.1007/s12524-010-0015-9

    Article  Google Scholar 

  17. Kumari, A., Mayoor, M., Mahapatra, S., Singh, H., & Parhi, P. (2018). Flood risk monitoring of Koshi river basin in north plains of Bihar state of India, using standardized precipitation index. Int J Adv Innovative Res, 5(3), 21–30.

    Google Scholar 

  18. Jha, R. K., & Gundimeda, H. (2019). An integrated assessment of vulnerability to floods using composite index–A district level analysis for Bihar, India. International Journal of Disaster Risk Reduction, 35, 101074. https://doi.org/10.1016/j.ijdrr.2019.101074

    Article  Google Scholar 

  19. Modi, M., Kumar, R., Ravi Shankar, G., & Martha, T. R. (2014). Land cover change detection using object-based classification technique: A case study along the Kosi river, Bihar. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 40, 839–843. https://doi.org/10.5194/isprsarchives-XL-8-839-2014

    Article  Google Scholar 

  20. Purnamasayangsukasih, P. R., Norizah, K., Ismail, A. A., & Shamsudin, I. (2016). A review of uses of satellite imagery in monitoring mangrove forests. In IOP Conference series: Earth and environmental science (Vol. 37, No. 1, p. 012034). IOP Publishing. https://doi.org/10.1088/1755-1315/37/1/012034

  21. Manavalan, R. (2017). SAR image analysis techniques for flood area mapping-literature survey. Earth Science Informatics, 10(1), 1–14. https://doi.org/10.1007/s12145-016-0274-2

    Article  Google Scholar 

  22. Garg, R., Kumar, A., Bansal, N., Prateek, M., & Kumar, S. (2021). Semantic segmentation of PolSAR image data using advanced deep learning model. Scientific Reports, 11(1), 1–18. https://doi.org/10.1038/s41598-021-94422-y

    Article  Google Scholar 

  23. Raj, A., & Minz, S. (2021). Spatial rough k-means algorithm for unsupervised multi-spectral classification. In Information and communication technology for intelligent systems: Proceedings of ICTIS 2020, (Vol. 1 pp. 215–226). Springer Singapore. https://doi.org/10.1007/978-981-15-7078-0_20.

  24. Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1), 62–66. https://doi.org/10.1109/TSMC.1979.4310076

    Article  Google Scholar 

  25. Kittler, J., & Illingworth, J. (1986). Minimum error thresholding. Pattern Recognition, 19(1), 41–47. https://doi.org/10.1016/0031-3203(86)90030-0

    Article  Google Scholar 

  26. Martinis, S. (2017). Improving flood mapping in arid areas using Sentinel-1 time series data. In 2017 IEEE international geoscience and remote sensing symposium (IGARSS) (pp. 193–196). IEEE. https://doi.org/10.1109/IGARSS.2017.8126927.

  27. 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), 2073. https://doi.org/10.3390/rs12132073

    Article  Google Scholar 

  28. Google Earth Engine (GEE). Sentinel-1 SAR GRD: C-band Synthetic Aperture Radar Ground Range Detected, log scaling. Available at: https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S1_GRD

  29. Mascolo, L., Lopez-Sanchez, J. M., & Cloude, S. R. (2021). Thermal noise removal from polarimetric Sentinel-1 data. IEEE Geoscience and Remote Sensing Letters, 19, 1–5. https://doi.org/10.1109/LGRS.2021.3050921

    Article  Google Scholar 

  30. Filipponi, F. (2019). Sentinel-1 GRD preprocessing workflow. In International Electronic Conference on Remote Sensing (p. 11). MDPI. https://doi.org/10.3390/ECRS-3-06201.

  31. Mullissa, A., Vollrath, A., Odongo-Braun, C., Slagter, B., Balling, J., Gou, Y., Gorelick, N., & Reiche, J. (2021). Sentinel-1 sar backscatter analysis ready data preparation in Google Earth Engine. Remote Sensing, 13(10), 1954. https://doi.org/10.3390/rs13101954

    Article  Google Scholar 

  32. Yommy, A. S., Liu, R., & Wu, S. (2015). SAR image despeckling using refined Lee filter. In 2015 7th International conference on intelligent human-machine systems and cybernetics (Vol. 2, pp. 260–265). IEEE. https://doi.org/10.1109/IHMSC.2015.236

  33. Choi, H., & Jeong, J. (2019). Speckle noise reduction technique for SAR images using statistical characteristics of speckle noise and discrete wavelet transform. Remote Sensing, 11(10), 1184. https://doi.org/10.3390/rs11101184

    Article  Google Scholar 

  34. About hydrosheds. (n.d.). Retrieved 13 February 2023. From https://www.hydrosheds.org/about

  35. Bangira, T., Alfieri, S. M., Menenti, M., & Van Niekerk, A. (2019). Comparing thresholding with machine learning classifiers for mapping complex water. Remote Sensing, 11(11), 1351. https://doi.org/10.3390/rs11111351

    Article  Google Scholar 

  36. Tran, K. H., Menenti, M., & Jia, L. (2022). Surface water mapping and flood monitoring in the Mekong delta using Sentinel-1 SAR time series and Otsu threshold. Remote Sensing, 14(22), 5721. https://doi.org/10.3390/rs14225721

    Article  Google Scholar 

  37. Carreño Conde, F., & De Mata Muñoz, M. (2019). Flood monitoring based on the study of Sentinel-1 SAR images: The Ebro River case study. Water, 11(12), 2454. https://doi.org/10.3390/w11122454

    Article  Google Scholar 

  38. Martinis, S., & Rieke, C. (2015). Backscatter analysis using multi-temporal and multi-frequency SAR data in the context of flood mapping at River Saale. Germany. Remote Sensing, 7(6), 7732–7752. https://doi.org/10.3390/rs70607732

    Article  Google Scholar 

  39. Chini, M., Hostache, R., Giustarini, L., & Matgen, P. (2017). A hierarchical split-based approach for parametric thresholding of SAR images: Flood inundation as a test case. IEEE Transactions on Geoscience and Remote Sensing, 55(12), 6975–6988. https://doi.org/10.1109/TGRS.2017.2737664

    Article  Google Scholar 

  40. Landuyt, L., Van Wesemael, A., Schumann, G. J. P., Hostache, R., Verhoest, N. E., & Van Coillie, F. M. (2018). Flood mapping based on synthetic aperture radar: An assessment of established approaches. IEEE Transactions on Geoscience and Remote Sensing, 57(2), 722–739. https://doi.org/10.1109/TGRS.2018.2860054

    Article  Google Scholar 

Download references

Funding

This research received no external funding.

Author information

Author notes

  1. Tanupriya Choudhury

    Present address: Professor, CSE Dept., Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, Maharashtra, India

Authors and Affiliations

  1. School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, India

    Greetta Pinheiro, Aditya Raj & Sonajharia Minz

  2. Ex-Professor, SoCS, University of Petroleum and Energy Studies (UPES), Dehradun, 248007, India

    Tanupriya Choudhury

  3. Department of Geography, College of Social Sciences, Kyungpook National University, Daegu, South Korea

    Jung-Sup Um

Authors
  1. Greetta Pinheiro
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aditya Raj
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sonajharia Minz
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tanupriya Choudhury
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jung-Sup Um
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Aditya Raj.

Ethics declarations

Conflicts of interest

The authors have no relevant financial interests in the manuscript or other potential conflicts of interest to disclose.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The original online version of this article was revised to remove the unnecessary text in the Abstract.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pinheiro, G., Raj, A., Minz, S. et al. Inundation extend mapping for multi-temporal SAR using automatic thresholding and change detection: a case study on Kosi river of India. Spat. Inf. Res. (2023). https://doi.org/10.1007/s41324-023-00555-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s41324-023-00555-9

Keywords

Search

Navigation