Urban Flood Disaster Monitoring Based on Remote Sensing and Social Media Data

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Urban Flood Disaster Monitoring Based on Remote Sensing and Social Media Data

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[1]ZHANG Hu,PAN Xiaoning,JIA Hao,et al.Urban Flood Disaster Monitoring Based on Remote Sensing and Social Media Data[J].Journal of Zhengzhou University (Engineering Science),2024,45(pre):2-.[doi:10. 13705 / j. issn. 1671-6833. 2025. 01. 011]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 45 Number of periods: 2024 pre Page number: 2- Column: Public date: 2024-12-31

Title:: Urban Flood Disaster Monitoring Based on Remote Sensing and Social Media Data

Author(s):: ZHANG Hu ; PAN Xiaoning; JIA Hao; XU Dehe; (College Of Surveying And Geographic Information, North China University Of Water Resources And Electric Power, Zhengzhou 450045,China)

Keywords:: flood disaster; Gaofen-3; threshold segmentation; random forest; social media date

CLC:: TP79

DOI:: 10. 13705 / j. issn. 1671-6833. 2025. 01. 011

Abstract:: Urban flood monitoring is critical for disaster management and emergency response. A single data source often has its own shortcomings, which is not conducive to the multi-dimensional and omni-directional analysis of complex urban flooding disasters. In this paper, we combine the GF-3 SAR images, which are not affected by clouds and rain and have 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" flooding incident: we use threshold segmentation and random forests to extract the water bodies before and during the disaster from the GF-3 SAR images, and we obtain the most accurate water body extraction and flood monitoring results. water body extraction and flood monitoring results, analyze the effect of water body extraction in typical areas; based on Python tools to obtain social media information on urban flooding, processing, visualization and spatial analysis; combined with GF-3 SAR images and social media data flood monitoring results to explore the complementary advantages of the two. The results show that:(1) the overall extraction effect of water bodies from SAR images: Random Forest is optimal, HV is second, HV is better than HH, and HH is better than the water body index, but the extraction effect of Random Forest is lower than that of the other methods in some typical regional analyses; (2) the extracted flood inundation range based on SAR images is mainly concentrated in the urban fringe areas outside the Third Ring Road with the surroundings of the large-scale water bodies; and the extracted flood information based on the social media data is mainly concentrated in the Third Ring Road, which is densely populated with buildings in the city

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Last Update: 2024-10-10