Abstract
Abstract. Change detection has been widely used in many flood-mapping algorithms using pairs of Synthetic Aperture Radar (SAR) intensity images as floodwater often leads to a substantial decrease of backscatter. However, limitations still exist in many areas, such as shadow, layover, urban areas and densely vegetated areas, where the SAR backscatter is not sufficiently impacted by floodwater-related surface changes. This study focuses on these so-called exclusion areas, i.e. areas where SAR does not allow detecting water based on change detection. Our approach considers both pixel-based time series analyses and object-based spatial analyses using 20m Sentinel-1 Interferometric Wide Swath data, including 922 Sentinel-1 tiles covering the River Severn basin (UK) and the Lake Maggiore area (Italy). The results show that our exclusion map presents a good agreement (∼63%) with reference data derived from different data sources and indicate that it may complement SAR-derived flood extent maps. Allowing to accurately identify potential misclassifications in flood extent mapping, our exclusion map provides valuable information for flood management and, in particular, flood forecasting and prediction.
Highlights
Flooding is a major hazard in both rural and urban areas worldwide, leading to significant human and economic losses
We propose an algorithm able to extract an “exclusion” map (EXmap), which is composed of all the pixels that cannot be classified as flooded or not using Synthetic Aperture Radar (SAR)-based change detection algorithms designed for bare soil and scarcely vegetated areas
When it comes to areas with extremely low backscatter over time, we propose to use a texture index, i.e. local Getis-Ord Gi (Gamba et al, 2011; Getis and Ord, 1992) since such areas are considered to be homogeneous with extremely low backscatter over time
Summary
Flooding is a major hazard in both rural and urban areas worldwide, leading to significant human and economic losses. Flood maps derived from historical and up-to-date data can be employed for the (re-)calibration and validation of hydraulic models (Di Baldassarre et al, 2009) Such maps can be assimilated into flood forecasting systems in order to improve near real-time model-based predictions (Hostache et al, 2018). There are some land cover classes where SAR can sense the surface but scattering variation caused by the presence of water is negligible when compared to the normal “unflooded” condition. Examples of such land cover classes are layover areas, dry sand, streets and building areas. Conventional change detection approaches fail in detecting floodwater
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