Paper
27 November 2024 Flood disaster inundation analysis based on HAND model under extreme rainfall conditions
Bingzhen Wu, Zeyuan Ye, Weidong Lei
Author Affiliations +
Proceedings Volume 13402, International Conference on Remote Sensing, Mapping, and Geographic Information Systems (RSMG 2024); 1340233 (2024) https://doi.org/10.1117/12.3048887
Event: International Conference on Remote Sensing, Mapping, and Geographic Information Systems (RSMG 2024), 2024, Zhengzhou, China
Abstract
With the change of climate, extreme weather in many places in the world is becoming more and more frequent and intense. As one of the countries with frequent flood disasters in the world, it is urgent to do a good job in risk prevention under extreme weather conditions. The current study is focused on the flood disaster under extreme rainfall conditions in Fogang-Qingcheng-Qingqing-Yingde administrative division of Qingyuan City. Based on multi-source data, the flood inundation range and water depth distribution of 30m resolution in the study area were evaluated and mapped rapidly, on a large scale and with high resolution by HAND model. And compared with the remote sensing monitoring results of inundation during the flood of Beijiang No. 2. The results show that the spatial distribution of inundation area predicted by HAND model is in good agreement with the remote sensing monitoring results, but the predicted inundation range is more complete. Combined with satellite images, a relatively complete maximum inundation area can be quickly obtained. The inundation mapping of flood disaster under extreme rainfall conditions based on HAND model is feasible and reasonable. It provides powerful theoretical support for disaster prevention under flood disaster situation caused by extreme rainfall.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Bingzhen Wu, Zeyuan Ye, and Weidong Lei "Flood disaster inundation analysis based on HAND model under extreme rainfall conditions", Proc. SPIE 13402, International Conference on Remote Sensing, Mapping, and Geographic Information Systems (RSMG 2024), 1340233 (27 November 2024); https://doi.org/10.1117/12.3048887
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KEYWORDS
Floods

Data modeling

Rain

Remote sensing

Satellites

Satellite imaging

Analytical research

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