Abstract
Urban flooding is a serious natural hazard to many cities all over the world, which has dramatic impacts on the urban environment and human life. Urban flooding mapping has practical significance for the prevention and management of urban flood disasters. Remote sensing images with high temporal resolutions are widely used for urban flooding mapping, but have a limitation of relatively low spatial resolutions. In this study, a new method based on a generalized regression neural network (GRNN) is proposed to achieve improved accuracy in super-resolution mapping of urban flooding (SMUF) from remote sensing images. The GRNN-SMUF algorithm was proposed and then assessed using Landsat 5 and Landsat 8 images of Brisbane city in Australia and Wuhan city in China. Compared to three traditional methods, GRNN-SMUF mapped urban flooding more accurately according to both visual and quantitative assessments. The results of this study will improve the accuracy of urban flooding mapping using easily-available remote sensing images with medium-low spatial resolutions and will be propitious to the prevention and management of urban flood disasters.
Highlights
Urban flooding is the inundation of land or property in densely-populated areas usually caused by heavy rainfalls
The fraction image of urban flooding is the input to super-resolution mapping of urban flooding (SMUF), where fraction values stand for the proportion of flooding in mixed pixels
Thirty percent mixed pixels were randomly selected as training samples for standard back-propagation neural network (SBPNN)-SMUF, BRBPNN-SMUF and generalized regression neural network (GRNN)-SMUF
Summary
Urban flooding is the inundation of land or property in densely-populated areas usually caused by heavy rainfalls. Brisbane city of Australia and New York City of the United States have both experienced significant flood events in recent years. In January 2011, the Brisbane River flooded and inundated more than 20,000 houses [3]; in October 2012, Hurricane Sandy hit New York City and produced a major storm surge, which flooded much of the city [3]. It is a crucial task to effectively monitor and manage urban flooding and to ensure the resilience of cities. Urban flooding mapping has practical significance for the prevention and management of urban flood disasters. The mixed pixel issue, in which one pixel covers multiple types of land surfaces, commonly occurs in such images
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