Mapping Outburst Floods Using a Collaborative Learning Method Based on Temporally Dense Optical and SAR Data: A Case Study with the Baige Landslide Dam on the Jinsha River, Tibet

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.

Primary and potential secondary risks of landslide outburst floods

“…The Baige landslide is continuously deforming due to gravity and this resulted in landslide dams that breached on two closely spaced dates in 2018 (Fan et al 2020;Liu et al 2021;Yang et al 2021). The potentially unstable slope continues to erode is likely already blocking the Jinsha River again.…”

An Improved Method for the Evaluation and Local Multi-Scale Optimization of the Automatic Extraction of Slope Units in Complex Terrains

“…It is the first step for eliminating the unreasonable SUs result. The common error measures are constructed by regarding the assessment as a process of intersection (correct) or difference (wrong) pixel labeling [40,41]. Consequently, it cannot accurately distinguish the error originated from under-subdivision or over-subdivision.…”

A Novel Shipyard Production State Monitoring Method Based on Satellite Remote Sensing Images

“…With the development of sensor technology, the RS data have found extensive applications in disaster monitoring [2][3][4][5][6][7], precision agriculture [8,9], environmental risk assessment [10,11], and oil spill detection [12]. Furthermore, leveraging the repeated earth observations facilitated by satellites, multi-temporal RS data have been utilized to enhance land cover classification accuracy [13][14][15][16][17][18][19][20][21], object recognition [22][23][24], as well as disaster monitoring and assessment [25][26][27].…”

“…Figure 5. Evidence fusion flow chart, keyed as follows:P d : the SCS evidence composed of single-phase observation P todo as Formulas(3). P m : the MUCS evidence composed of single-phase observation P to mo as Formulas (4).…”