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Urban flood risk assessment using Sentinel-1 on the google earth engine: A case study in Thai Nguyen city, Vietnam
The cloud computing architecture on which Google Earth Engine (GEE) is built efficiently handles satellite photos and keeps tabs on data sources. GEE has potential benefits and is being used more often in disaster management. This research aims to aid in flood risk management by developing a framewo...
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Published in: | Remote sensing applications 2023-08, Vol.31, p.100987, Article 100987 |
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Main Authors: | , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | The cloud computing architecture on which Google Earth Engine (GEE) is built efficiently handles satellite photos and keeps tabs on data sources. GEE has potential benefits and is being used more often in disaster management. This research aims to aid in flood risk management by developing a framework to rapidly quantify urban flood risk assessment utilizing Sentinel-1 on the GEE and socio-economic data using spatial analytic techniques in GIS. Specifically, this approach only evaluated the flood risk based on the flood events and their impact on social-economic aspects. The flood risk analysis results show that the impact of flood events in Thai Nguyen city is significant. Specifically, 33 educational institutions, 04 medical facilities, 116.46 ha of traffic road, 1406.42 ha of agricultural land, and 762.7 ha of residential land were affected by flood risk. The impact assessment maps provide detailed information on the quantities and locations of the affected areas. These assessments can assist local managers in preventing and minimizing damages, providing necessary information for implementing planning solutions, and appropriate land use in the future.
•Sentinel 1 and GEE for detecting the inundation areas.•Framework for evaluating flood consequences.•Recommendations of flood risk mitigation solutions for city government. |
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ISSN: | 2352-9385 2352-9385 |
DOI: | 10.1016/j.rsase.2023.100987 |