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Flood risk maps are important references for disaster prevention and emergency response. The accuracy of risk maps is greatly affected by the resolutions of hazard and vulnerability maps. To determine the impact of map resolutions on flood risk analysis, a total of 12 risk maps were generated for Shanhua District, Taiwan, via the integration of hazard and vulnerability maps under two resolutions and three return periods. The hazard, vulnerability, and risk maps were classified into five levels according to flood depth, socio-economic indicators, and their products, respectively. The results show that the downscaling of hazard maps greatly increases the hit rate by 28% and decreases the false alarm rate by 53% in the flood risk analyses of households. In contrast, the downscaling of vulnerability maps only slightly increases the hit rate without an obvious decrease in the false alarm rate. To improve flood risk analysis under time and budget limitations, numerical downscaling of hazard maps should be given higher priority because it reduces the structural errors in hydraulic simulations that cannot be compensated for by the statistical downscaling of vulnerability maps. [ABSTRACT FROM AUTHOR]

Many people use smartphone cameras to record their living environments through captured images, and share aspects of their daily lives on social networks, such as Facebook, Instagram, and Twitter. These platforms provide volunteered geographic information (VGI), which enables the public to know where and when events occur. At the same time, image-based VGI can also indicate environmental changes and disaster conditions, such as flooding ranges and relative water levels. However, little image-based VGI has been applied for the quantification of flooding water levels because of the difficulty of identifying water lines in image-based VGI and linking them to detailed terrain models. In this study, flood detection has been achieved through image-based VGI obtained by smartphone cameras. Digital image processing and a photogrammetric method were presented to determine the water levels. In digital image processing, the random forest classification was applied to simplify ambient complexity and highlight certain aspects of flooding regions, and the HT-Canny method was used to detect the flooding line of the classified image-based VGI. Through the photogrammetric method and a fine-resolution digital elevation model based on the unmanned aerial vehicle mapping technique, the detected flooding lines were employed to determine water levels. Based on the results of image-based VGI experiments, the proposed approach identified water levels during an urban flood event in Taipei City for demonstration. Notably, classified images were produced using random forest supervised classification for a total of three classes with an average overall accuracy of 88.05%. The quantified water levels with a resolution of centimeters (<3-cm difference on average) can validate flood modeling so as to extend point-basis observations to area-basis estimations. Therefore, the limited performance of image-based VGI quantification has been improved to help in flood disasters. Consequently, the proposed approach using VGI images provides a reliable and effective flood-monitoring technique for disaster management authorities. [ABSTRACT FROM AUTHOR]