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On Oct. 10 and Nov. 3, 2018, two landslides, i.e., the "10.10" and "11.3" rockslides, occurred successively at Baige Village, Jiangda County, Tibetan Autonomous Region, P. R. China. The two landslides are located in the upper reaches of the Jinsha River and both dammed the river. Immediately since the slides, the authors have been working on the slides and help disaster reduction. Based on the data collected by April 2020, this paper is aimed at clarifying the geological condition of the slides and at explaining why the slides occurred and what the whole sliding process was. Conclusions are summarized as follows. First, the two landslides occurred in the suture belt of the Jinsha River and the rocks are composed of tectonic mélange slices of mainly gneiss intermingled with carboniferous slate and marble and with intruded serpentine and granite porphyry. The gneiss generally bears a schistosity plane with an averaged attitude of N47° W/47°, dipping into the slope. Secondly, long-term geomorphological evolution of the bank slope due to river incision contributed to the progressive slope deformation for the development of the "10.10" rockslide. No preferential joints exist in the slope, but alteration and weathering played important roles in its occurrences. Rainfall and earthquakes may also accelerate its deformation. Thirdly, the "10.10" rockslide is of high-speed wedge-like slope failure with a high-position and a high-shear outlet. Its sliding and deposition process demonstrate special features as initial speed, collision between debris, surging waterjet, and second slipping. Fourthly, the whole process of the "10.10" rockslide can be divided into 6 steps, i.e., startup of the major sliding and sliding resistance zones, sliding initiation of the trailing zone, formation of debris-eroded zones, collision of debris and triggering waterjet and mist, secondary slip of the landslide dam, and surface flush in the deposition area. The estimated speed may reach as high as 67 m/s. Fifthly, the "11.3" rockslide follows a different mode, i.e., wedge cleaving effect. And finally, the cracked zones still have the risk to constitute a potential landslide and to dam the river again. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]