E-catalog        

Библиогр.: с. 145-146

Flood disasters seriously threaten the survival and development of human beings. Monitoring the changes of water bodies during floods and estimating the affected area is essential for comprehensive and accurate analysis of disaster information. Recently, radar satellite data has been increasingly used for flood monitoring, since in this case, cloudiness is not an obstacle to estimating the flood area. In this paper Sentinel-1 ground range detected (GRD) data was selected to estimate the inundated area after the Xe-Pian Xe-Namnoy Dam breach in Laos at the end of July 2018. The flooded Hinlat area and the Xe-Pian Xe-Namnoy reservoir were selected as the study area for flood inundation extent monitoring, because this area is characterized by bare land, agricultural land, and residential land with complex topography and geomorphology. The study area is located in the Bolaven Plateau, is a highland region in southern Laos. One of the reasons for the flooding of the study area is an elevation difference between upper reaches and downstream of the river. Several reaches with a convex profile and knickpoints because of the geologic control when draining the plateau represent the undeveloped longitudinal profile of the Vang Ngao River. The main channel of the Vang Ngao River is dug into Mesozoic fluvial sandstones, which resist scouring by the flood. The eCognition software is used to organize the process of extracting information about the flood zone. The object-oriented approach and the threshold method are combined to extract information about the reservoir. First, SNAP software is used to pre-process Sentinel-1A SAR images. Then, the eCognition multi-scale segmentation method is used to determine the best segmentation scale, for iterative testing and comparative analysis of experimental results, taking into account the characteristics of the object and a priori knowledge. After sensitivity analysis of the flooded area image features and other features, the VH-polarized backscattered mean features were selected to construct a knowledge base for flooded area extraction to differentiate water and non-water bodies. At the same time, the modified bare soil index (MBI) and the terrain relief were combined to remove the influence of bare land and mountain shadow on the extraction results to achieve the 2018 dam collapse flood monitoring in Laos. Finally, the extent and area of the affected region were analyzed and the changes of water bodies before and after the disaster were mapped. The study shows that the monitoring results of Sentinel-1A SAR data are more consistent with the actual situation and have significant advantages in flood hazard monitoring and assessment, which can effectively carry out large-scale flood inundation extent monitoring.