The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B3-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 291–297, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-291-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 291–297, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-291-2021

  28 Jun 2021

28 Jun 2021

ASSESSMENT OF FLOODED AREAS CAUSED BY A DAM BREAK (SARDOBA DAM, UZBEKISTAN)

B. Tavus1, S. Kocaman1, and C. Gokceoglu2 B. Tavus et al.
  • 1Dept. of Geomatics Engineering, Hacettepe University, 06800 Beytepe Ankara, Turkey
  • 2Dept. of Geological Engineering, Hacettepe University, 06800 Beytepe Ankara, Turkey

Keywords: Dam Break, Flash Flood, Synthetic Aperture Radar, Optical Sensors, Earth Observation Data, Data Fusion, Disaster Assessment, Sardoba (Uzbekistan)

Abstract. Although dams are very useful engineering structures, they can have extremely harmful consequences if they fail. One example of these failures occurred in Sardoba Reservoir (Uzbekistan). On May 1, 2020, a part of an earthfill dam failed along the Sardoba Reservoir, and a large region with settlements and agricultural areas in Uzbekistan and Kazakhstan was flooded. Accurate mapping and monitoring of the flooded areas are crucial for the damage assessment and the mitigation efforts. Satellite Earth Observation datasets can serve for these purposes due to their greater availability with high spatial and temporal resolutions. However, the optical sensors have limitations for data acquisition due to the atmospheric conditions, particularly the cloud cover, which often severely affects the image usability when floods occur. The synthetic aperture radar sensors provide valuable information under all weather conditions, but their interpretation is relatively difficult. Therefore, a data fusion methodology is proposed here for the integrated use of Sentinel-1 and Sentinel-2 datasets using a set of features obtained from both. Four different feature combinations were evaluated using the random forest classifier. The pre-processing steps for the feature extraction are explained in detail and the results are discussed here. The proposed algorithm exhibits very high classification accuracy for the flooded areas and flooded vegetation classes. The method can be employed for the flash flood mapping at regional scale. In addition, the damage assessment especially for agricultural areas in the region is very important for accounting the economic losses and the resilience purposes.