Abstract
Outburst floods resulting from giant landslide dams can cause devastating damage to hundreds or thousands of kilometres of a river. Accurate and timely delineation of flood inundated areas is essential for disaster assessment and mitigation. There have been significant advances in flood mapping using remote sensing images in recent years, but little attention has been devoted to outburst flood mapping. The short-duration nature of these events and observation constraints from cloud cover have significantly challenged outburst flood mapping. This study used the outburst flood of the Baige landslide dam on the Jinsha River on 3 November 2018 as an example to propose a new flood mapping method that combines optical images from Sentinel-2, synthetic aperture radar (SAR) images from Sentinel-1 and a Digital Elevation Model (DEM). First, in the cloud-free region, a comparison of four spectral indexes calculated from time series of Sentinel-2 images indicated that the normalized difference vegetation index (NDVI) with the threshold of 0.15 provided the best separation flooded area. Subsequently, in the cloud-covered region, an analysis of dual-polarization RGB false color composites images and backscattering coefficient differences of Sentinel-1 SAR data were found an apparent response to ground roughness’s changes caused by the flood. We carried out the flood range prediction model based on the random forest algorithm. Training samples consisted of 13 feature vectors obtained from the Hue-Saturation-Value color space, backscattering coefficient differences/ratio, DEM data, and a label set from the flood range prepared from Sentinel-2 images. Finally, a field investigation and confusion matrix tested the prediction accuracy of the end-of-flood map. The overall accuracy and Kappa coefficient were 92.3%, 0.89 respectively. The full extent of the outburst floods was successfully obtained within five days of its occurrence. The multi-source data merging framework and the massive sample preparation method with SAR images proposed in this paper, provide a practical demonstration for similar machine learning applications using remote sensing.
Highlights
In mountainous areas with rugged terrain and narrow valleys, large-scale landslides often cause river blocking and produce severe explosive floods [1,2]
Through artificial visual optimization (Figure 7), we identified the thresholds of normalized difference vegetation index (NDVI) (0.15) and normalised difference water index (NDWI) (−0.19) to be most consistent with the flood scene photos
Given the challenges existing in mapping ephemeral outburst floods in cloud-covered areas, the random forest (RF) algorithm’s flood mapping framework was based on synthetic aperture radar (SAR) images
Summary
In mountainous areas with rugged terrain and narrow valleys, large-scale landslides often cause river blocking and produce severe explosive floods [1,2]. 2021, 13, 2205 along the river [3,4] As these hazards tend to occur in harsh environments, complex terrain, and scarce hydrological observation stations, there are very few recorded data on floods caused by landslide dams [5]. There is an urgent need for developing outburst floods mapping methods and enrich observation data for the scientific understanding of geohazard chains that can result from them. Remote sensing has become an essential tool for large-scale flood disaster monitoring and mapping. The launch of the Sentinel satellites series increased the availability of free high-quality remote sensing data [6]. Effective spectral indicators have been identified to distinguish open or stagnant waters such as lakes, rivers and coastlines [7]
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