Improved flood mapping for efficient policy design by fusion of Sentinel-1, Sentinel-2, and Landsat-9 imagery to identify population and infrastructure exposed to floods

• URL: http://arxiv.org/abs/2306.06074v1
• Date: Wed, 31 May 2023 20:46:06 GMT
• Title: Improved flood mapping for efficient policy design by fusion of Sentinel-1, Sentinel-2, and Landsat-9 imagery to identify population and infrastructure exposed to floods
• Authors: Usman Nazir, Muhammad Ahmad Waseem, Falak Sher Khan, Rabia Saeed, Syed Muhammad Hasan, Momin Uppal, Zubair Khalid
• Abstract summary: The application of optical and SAR imagery in tandem provides a means of extended availability and enhanced reliability of flood mapping. We propose a methodology to merge these two types of imagery into a common data space and demonstrate its use in the identification of affected populations and infrastructure for the 2022 floods in Pakistan.
• Score: 15.893084685925073
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A reliable yet inexpensive tool for the estimation of flood water spread is conducive for efficient disaster management. The application of optical and SAR imagery in tandem provides a means of extended availability and enhanced reliability of flood mapping. We propose a methodology to merge these two types of imagery into a common data space and demonstrate its use in the identification of affected populations and infrastructure for the 2022 floods in Pakistan. The merging of optical and SAR data provides us with improved observations in cloud-prone regions; that is then used to gain additional insights into flood mapping applications. The use of open source datasets from WorldPop and OSM for population and roads respectively makes the exercise globally replicable. The integration of flood maps with spatial data on population and infrastructure facilitates informed policy design. We have shown that within the top five flood-affected districts in Sindh province, Pakistan, the affected population accounts for 31 %, while the length of affected roads measures 1410.25 km out of a total of 7537.96 km.

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