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Comparison of Ratioing and RCNA Methods in the Detection of Flooded Areas Using Sentinel 2 Imagery (Case Study: Tulun, Russia)

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  • Helena Maria Fernandez

    (Department of Civil Engineering, Institute of Engineering, University of Algarve, Campus da Penha, 8005-139 Faro, Portugal
    Centre for Research in Tourism, Sustainability and Well-Being (CinTurs), Universidade do Algarve, 8005-139 Faro, Portugal)

  • Fernando Granja-Martins

    (Department of Civil Engineering, Institute of Engineering, University of Algarve, Campus da Penha, 8005-139 Faro, Portugal
    Centre for Research in Tourism, Sustainability and Well-Being (CinTurs), Universidade do Algarve, 8005-139 Faro, Portugal)

  • Olga Dziuba

    (Faculty of Sciences and Technology, University of Algarve, Campus de Gambelas, 8005-139 Faro, Portugal)

  • David A. B. Pereira

    (Department of Civil Engineering, Institute of Engineering, University of Algarve, Campus da Penha, 8005-139 Faro, Portugal)

  • Jorge M. G. P. Isidoro

    (Department of Civil Engineering, Institute of Engineering, University of Algarve, Campus da Penha, 8005-139 Faro, Portugal
    MARE—Marine and Environmental Sciences Centre/ARNET—Aquatic Research NETwork, Rua da Matemática, 49, 3004-517 Coimbra, Portugal)

Abstract

Climate change and natural disasters caused by hydrological, meteorological, and climatic phenomena have a significant impact on cities. Russia, a continental country with a vast territory of complex geographic–ecological environments and highly variable climatic conditions, is subject to substantial and frequent natural disasters. On 29 June 2019, an extreme precipitation event occurred in the city of Tulun in the Irkutsk oblast, Russian Federation, which caused flooding due to the increase in the water level of the Iya River that passes through the city, leaving many infrastructures destroyed and thousands of people affected. This study aims to determine the flooded areas in the city of Tulun based on two change detection methods: Radiometric Rotation Controlled by No-change Axis ( RCNA ) and Ratioing , using Sentinel 2 images obtained before the event (19 June 2019) and during the flood peak (29 June 2019). The results obtained by the two methodologies were compared through cross-classification, and a 98% similarity was found in the classification of the areas. The study was validated based on photointerpretation of Google Earth images. The methodology presented proved to be useful for the automatic precession of flooded areas in a straightforward, but rigorous, manner. This allows stakeholders to efficiently manage areas that are buffeted by flooding episodes.

Suggested Citation

  • Helena Maria Fernandez & Fernando Granja-Martins & Olga Dziuba & David A. B. Pereira & Jorge M. G. P. Isidoro, 2023. "Comparison of Ratioing and RCNA Methods in the Detection of Flooded Areas Using Sentinel 2 Imagery (Case Study: Tulun, Russia)," Sustainability, MDPI, vol. 15(13), pages 1-15, June.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:13:p:10233-:d:1181406
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    References listed on IDEAS

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    1. Omid Seyedashraf & Andrea Bottacin-Busolin & Julien J. Harou, 2021. "Many-Objective Optimization of Sustainable Drainage Systems in Urban Areas with Different Surface Slopes," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(8), pages 2449-2464, June.
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