Urban Flood Disaster Monitoring Based on Remote Sensing and Social Media Data
Urban flood monitoring is crucial for disaster management and emergency response. Single data sources often have their own shortcomings,which are not conducive to the multi-dimensional and all-round analysis of complex urban flooding disasters. The GF-3 SAR images were combined,which were not affected by clouds and rain and had a large coverage,with real-time social media data to establish a flood monitoring method for the downtown area of Zhengzhou City during the "7·20" heavy rainstorm. Threshold segmentation and random forest were used to extract water bodies from GF-3 SAR images before and during the flood,the most accurate water body extraction results and flood monitoring results were obtained,and the effect of water body extraction in typical areas was analyzed. Using Python tools,the obtained social media data about urban waterlogging were processed,visualized and spatially analyzed. GF-3 SAR images and social media data flood monitoring results were combined to explore the complementary advantages of the two. The results showed that the overall extraction accuracy of SAR water bodies was in the order of random forest,Otsu threshold method,and water body index method. However,in some typical regional analyses,the extraction effect of random forest was lower than that of the other methods. The flood inundation range extracted based on SAR images was mainly concentrated in the urban fringe areas outside the Third Ring Road and around large water bodies. The flood information extracted based on social media data was mainly concentrated in the Third Ring Road,where the urban population was densely populated with buildings.
flood disasterGF-3threshold segmentationrandom forestsocial media data
万方数据
维普
