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Detecting Seasonal Extent of Inundated Area of River Body in Banyuasin Regency Using Radar Data of Sentinel-1A

  • Fathoni UsmanEmail author
  • Erwin Ibrahim
Conference paper
Part of the Lecture Notes in Civil Engineering book series (LNCE, volume 53)

Abstract

Analysis of Synthetic Aperture Radar (SAR) data from satellite imagery has become preferable by enhancement of its technology, reliable temporal and spatial resolution, computational power and extensive area of coverage. This paper presents the use of SAR data to map the extent of the inundated area of the river as the primary access way in Banyuasin Regency for water transportation. In this study, 1-year data set of Ground Range Detected (GRD) type radar data from Sentinel-1A’s mission was used and analysed by using the Science Toolbox Exploitation Platform (SNAP) toolbox. Related factors on a diurnal tide of river water level and climate which controls the inundated area located mostly within lowland and the biannual season were taken into consideration in the selection of the data set. It is found that the extended inundated area of water body clearly detected. The enhanced polarisation index (VHdB*) which was developed have given a better insight for a response on the extent of inundated area and siltation caused by erosion and sedimentation.

Keywords

Sentinel-1 Synthetic aperture radar Inundated area 

References

  1. 1.
    BPS-Statistics of Banyuasin Regency (2018) Banyuasin Regency in figures 2018. Badan Pusat StatistikGoogle Scholar
  2. 2.
    Komite Nasional Keselamatan Transportasi (2018) Laporan Investigasi Kecelakaan Pelayaran: Tenggelamnya kapal Awet Muda di Hilir Sungai Banyuasin, Sumatera Selatan. In FINAL KNKT.18.01.02.03Google Scholar
  3. 3.
    Faturachman D, Mustafa S (2012) Sea transportation accident analysis in Indonesia. Procedia Soc Behav Sci 40:616–621CrossRefGoogle Scholar
  4. 4.
    Schlaffer S, Matgen P, Hollaus M, Wagner W (2015) Flood detection from multi-temporal SAR data using harmonic analysis and change detection. Int J Appl Earth Obs Geoinf 38:15–24CrossRefGoogle Scholar
  5. 5.
    Bioresita F, Puissant A, Stumpf A, Malet JP (2018) A method for automatic and rapid mapping of water surfaces from Sentinel-1 imagery. Remote Sens 10(217):1–17Google Scholar
  6. 6.
    Li Y, Martinisa S, Planka S, Ludwig R (2018) An automatic change detection approach for rapid flood mapping in Sentinel-1 SAR data. Int J Appl Earth Obs Geoinf 73:123–135CrossRefGoogle Scholar
  7. 7.
    Steinhausena MJ, Wagnerc PD, Narasimhand D, Waske B (2018) Combining Sentinel-1 and Sentinel-2 data for improved land use and land cover mapping of monsoon regions. Int J Appl Earth Obs Geoinf 73:595–604CrossRefGoogle Scholar
  8. 8.
    Anusha N, Bharathi B (2019) Flood detection and flood mapping using multi-temporal synthetic aperture radar and optical data. Egypt J Remote Sens Space Sci, In PressGoogle Scholar
  9. 9.
    Whyte A, Ferentinos KP, Petropoulos GP (2018) A new synergistic approach for monitoring wetlands using Sentinels-1 and 2 data with object-based machine learning algorithms. Environ Model Softw 104:40–54CrossRefGoogle Scholar
  10. 10.
    Veloso A, Mermoz S, Bouvet A, Toan TL, Planells M, Dejoux JF, Ceschia E (2017) Understanding the temporal behavior of crops using Sentinel-1 and Sentinel-2-like data for agricultural applications. Remote Sens Environ 199:415–426CrossRefGoogle Scholar
  11. 11.
    Wood M, de Jong SM, Straatsma MW (2018) Locating flood embankments using SAR time series: a proof of concept. Int J Appl Earth Obs Geoinf 70(September 2017), 72–83CrossRefGoogle Scholar
  12. 12.
    Muldiyatno F, Djunarsjah E, Adrianto D, Pranowo WS (2017) Kajian Awal Perubahan Muka Air Sungai untuk Penentuan Datum Peta (Studi Kasus Sungai Musi Palembang). Jurnal Chart Datum 01(02):36–42Google Scholar
  13. 13.
    Hasibuan RD, Surbakti H, Sitep R (2015) Tidal analysis using least squares method and determination of the return period of tidal using Gumbel method in Boom Baru and Tanjung Buyut. Maspari J 1(7):35–48Google Scholar
  14. 14.
    Surbakti H (2015) Prediksi Pasang Surut Tanjung Buyut 2015. Available at https://surbakti77.wordpress.com/category/data/
  15. 15.
    ESA (2019) The Sentinel Application Platform (SNAP), a common architecture for all Sentinel Toolboxes being jointly developed by Brockmann Consult, Array Systems Computing and C-S. Downloadable on http://step.esa.int/main/download/. European Space Agency (ESA)
  16. 16.
    CEOS (2018) A Layman’s interpretation guide to L-band and C-band synthetic aperture radar data version 2.0. Committee on Earth Observation Satellite System Engineering Office (SEO)Google Scholar
  17. 17.
    Huang W, DeVries B, Huang C, Lang MW, Jones JW, Creed IF, Carroll ML (2018) Automated extraction of surface water extent from Sentinel-1 data. Remote Sens 10(797):1–18Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Institute of Energy Infrastructure, Universiti Tenaga NasionalKajangMalaysia
  2. 2.Regional Development Planning Agency and Research Development, Banyuasin Regency Jalan Lingkar Sekojo, Banyuasin RegencySouth SumatraIndonesia

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