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Abstract
Floodplain inundation plays a key role in riparian ecosystems. Remote sensing provides an advanced technology for detecting floodplain inundation, but the trade-off between the spatial and temporal resolutions of remotely sensed imagery is a well-known issue. Sub-pixel mapping is an effective way to mitigate the trade-off by improving the spatial resolution of image classification results while keeping their temporal resolution. It is therefore useful for improving the mapping of highly dynamic flood inundation using coarse-resolution images. However, traditional sub-pixel mapping algorithms have limitations on delineating the extent of floodplain inundation that reveals linear and complex characteristics. A modified pixel-swapping (DMPS) algorithm which is based on a digital elevation model (DEM) is thus developed in this study. It is built on the widely accepted pixel-swapping (PS) algorithm and one of its derivatives, the linearized pixel-swapping (LPS) algorithm. A Landsat image recording a significant flood inundation event in the Chowilla Floodplain of the Murray–Darling Basin in Australia was used as a case study. The results show that the DMPS algorithm outperformed the original PS and LPS algorithms both in accuracy and rationality of the resultant map. It improves the accuracy and the kappa coefficient by about 5% and 0.1, respectively, in comparison with the PS algorithm. The spatial pattern of inundation derived from the DMPS algorithm reveals fewer breakpoints and errors along the river channels. Moreover, it is observed that the DMPS algorithm is less sensitive to some critical parameters compared with the PS and LPS algorithms. It is hoped that the proposed DMPS algorithm will broaden the application of coarse-resolution sensors in floodplain inundation detection, which would thereby benefit the ecological studies in floodplains.
Acknowledgements
The authors wish to thank the China Scholarship Council for providing a scholarship to Chang Huang to support this research at CSIRO Land and Water. This work has been conducted under the auspices of the CSIRO Land and Water and Water for a Healthy Country National Research Flagship. The authors are grateful to the NSW Office of Environment and Heritage for providing lidar DEM data for this study. The authors also wish to thank their colleagues Susan Cuddy, Dr Catherine Ticehurst, and Dr Irina Emelyanova for initially reviewing this manuscript. Two anonymous reviewers are acknowledged for their helpful comments.
