Abstract
Flood disasters have a huge effect on human life, the economy, and the ecosystem. Quickly extracting the spatial extent of flooding is necessary for disaster analysis and rescue planning. Thus, extensive studies have utilized optical or radar data for the extraction of water distribution and monitoring of flood events. As the quality of detected flood inundation coverage by optical images is degraded by cloud cover, the current data products derived from optical sensors cannot meet the needs of rapid flood-range monitoring. The presented study proposes an adaptive thresholding method for extracting water coverage (AT-EWC) regarding rapid flooding from Sentinel-1 synthetic aperture radar (SAR) data with the assistance of prior information from Landsat data. Our method follows three major steps. First, applying the dynamic surface water extent (DSWE) algorithm to Landsat data acquired from the year 2000 to 2016, the distribution probability of water and non-water is calculated through the Google Earth Engine platform. Then, current water coverage is extracted from Sentinel-1 data. Specifically, the persistent water and non-water datasets are used to automatically determine the type of image histogram. Finally, the inundated areas are calculated by combining the persistent water and non-water datasets and the current water coverage as derived from the above two steps. This approach is fast and fully automated for flood detection. In the classification results from the WeiFang and Ji’An sites, the overall classification accuracy of water and land detection reached 95–97%. Our approach is fully automatic. In particular, the proposed algorithm outperforms the traditional method over small water bodies (inland watersheds with few lakes) and makes up for the low temporal resolution of existing water products.
Highlights
Flood disasters are high-frequency events with a wide influence, causing serious impacts on ecological environments, human societies, and economies
The results show that the metrics obtained using our proposed thresholds for the Ji’An site results show that the metrics obtained using our proposed thresholds for the Ji’An site perform well (Table 2)
The differences in the other three evaluation metrics images of a 20 km × 20 km block, the classification results of our method were significantly better than those using the Otsu method, especially in the two sets of comparison results for medium and low groups
Summary
Flood disasters are high-frequency events with a wide influence, causing serious impacts on ecological environments, human societies, and economies. Disaster assessment is based on the inundation range of the flood disaster. As an advanced and widely used technology, remote sensing is eminently suitable for assessing flood coverage [2]. In remote-sensing images, floods are characterized by clear boundaries. Many recent studies have extracted flooded coverage from satellite data [3,4]. Satellite-based remote sensing has become an important part of earth observation. Spectral and temporal resolution, it has been developed on multiple platforms and sensors [5]. Among these developments, multispectral, thermal and radar data are the main supports for flood coverage extraction [6,7].
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