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

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