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Description
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Floods are among the most threatening and impacting environmental hazards. Their costs in terms of human lives, infrastructure damage or loss, and agricultural impact can be enormous and continue to increase due to climate change. Investigating effects and extents of flood events in short times after occurrence is of utmost importance in order to quantify damage, organize rescue measures, determine insurance refunds, and calibrate prediction models for risk assessment and management. In the last years, remote sensing is proving to be a strong aid in this direction by providing large amounts of data of the Earth’s surface at low to null costs. The increasing number of spacecraft and sensors available calls for the use of sophisticated procedures and algorithms to extract useful information from such large datasets. In [1], several examples of precise tools for investigating the effects of inundations were presented. Since then, improvements in technology, data availability, and processing power have occurred. This Special Issue is a collection of six articles and one technical note, which provide a wide overview of recent advances in these fields. The papers deal with various aspects of flood monitoring by using diverse sensors such as backpack-mounted 3-D optical cameras, airborne LiDAR, GNSS reflectometry, and spaceborne synthetic aperture radar (SAR) data analysis from multiple sensors and wavelengths. Test sites are located in various parts of the world, including China, Japan, Philippines, Mozambique, Iran, UK, Greece, and Turkey. The volume represents, therefore, a useful survey of methods to improve the performance of techniques concerning remote sensing of floods in the mapping phase, including the assessment of post-disaster flood damage, integration of observed and predicted flood impacts, and evaluation of flood prevention measures such as levees. (2023-07-04)
***This entry has been automatically imported via Infodoc(ASO) CSV by LIST harvest scripts. Please refer to https://doi.org/10.3390/w14030364 for the original and latest version of the dataset and data downloads*** (2025-09-02)
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