Abstract
Synthetic aperture radar (SAR) images have been used to map flooded areas with great success. Flooded areas are often identified by detecting changes between a pair of images recorded before and after a certain flood. During the 2018 Western Japan Floods, the change detection method generated significant misclassifications for agricultural targets. To evaluate whether such a situation could be repeated in future events, this paper examines and identifies the causes of the misclassifications. We concluded that the errors occurred because of the following. (i) The use of only a single pair of SAR images from before and after the floods. (ii) The unawareness of the dynamics of the backscattering intensity through time in agricultural areas. (iii) The effect of the wavelength on agricultural targets. Furthermore, it is highly probable that such conditions might occur in future events. Our conclusions are supported by a field survey of 35 paddy fields located within the misclassified area and the analysis of Sentinel-1 time series data. In addition, in this paper, we propose a new parameter, which we named “conditional coherence”, that can be of help to overcome the referred issue. The new parameter is based on the physical mechanism of the backscattering on flooded and non-flooded agricultural targets. The performance of the conditional coherence as an input of discriminant functions to identify flooded and non-flooded agricultural targets is reported as well.
Highlights
Floods are natural phenomena that can perturb ecosystems and societies [1,2]
After removing pixels whose intensity at the post-event image was lower than −16 dB, the remaining pixels consist of those located in the paths between paddy fields and/or the infrastructure nearby
Solid evidence from field survey and Sentinel-1 Synthetic aperture radar (SAR) time series data demonstrated that this flaw might occur in future events
Summary
Microwave remote sensing is extremely useful for post-disaster analysis of floods for two reasons: First, microwaves can penetrate clouds, which are almost certainly present during heavy rainfall events. There are several methods to trace the flooded area from microwave remote sensing. Schumann and Moller [3] provided a comprehensive review of the role of microwave remote sensing on flood inundations. Their study focuses on the different potential conditions, such as mapping inundation at floodplains, coastal shorelines, wetlands, forest, and urban areas. Nakmuenwai et al [4] proposed a framework to identify flood-based water areas along the Chao Phraya River basin of central Thailand, an area were floods occur almost every year. Their study established an inventory of permanent waterbodies within the Chao Phraya River basin. For an arbitrary SAR image, local thresholds are computed using the inventory of waterbodies as references. Liu and Yamazaki [5]
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