Automatic Detection of Regional Snow Avalanches with Scattering and Interference of C-band SAR Data

Abstract

Avalanche disasters are extremely destructive and catastrophic, often causing serious casualties, economic losses and surface erosion. However, far too little attention has been paid to utilizing remote sensing mapping avalanches quickly and automatically to mitigate calamity. Such endeavors are limited by formidable natural conditions, human subjective judgement and insufficient understanding of avalanches, so they have been incomplete and inaccurate. This paper presents an objective and widely serviceable method for regional auto-detection using the scattering and interference characteristics of avalanches extracted from Sentinel-1 SLC images. Six indices are established to distinguish avalanches from surrounding undisturbed snow. The active avalanche belts in Kizilkeya and Aktep of the Western TianShan Mountains in China lend urgency to this research. Implementation found that smaller avalanches can be consistently identified more accurately in descending images. Specifically, 281 and 311 avalanches were detected in the ascending and descending of Kizilkeya, respectively. The corresponding numbers on Aktep are 104 and 114, respectively. The resolution area of single avalanche detection can reach 0.09 km2. The performance of the model was excellent in all cases (areas under the curve are 0.831 and 0.940 in descending and ascending of Kizilkeya, respectively; and 0.807 and 0.938 of Aktep, respectively). Overall, the evaluation of statistical indices are POD > 0.75, FAR < 0.34, FOM < 0.13 and TSS > 0.75. The results indicate that the performance of the innovation proposed in this paper, which employs multivariate comprehensive descriptions of avalanche characteristics to actualize regional automatic detection, can be more objective, accurate, applicable and robust to a certain extent. The latest and more complete avalanche inventory generated by this design can effectively assist in addressing the increasingly severe avalanche disasters and improving public awareness of avalanches in alpine areas.

Characteristics and hazards of different snow avalanche types in a continental snow climate region in the Central Tianshan Mountains

“…The average value of the slope angle in the starting zone of the avalanche is 37.2°, and two-thirds of avalanches occur on slopes with inclinations between 36.0° and 42.0° (Hao et al, 2018). The avalanche paths in the Kunes River Valley in the Central Tianshan Mountains were identified and described in detail by Yang et al (2020). The Tianshan Station for Snow Cover and Avalanche Research, Chinese Academy of Sciences (TSSAR; 43°16′N, 84°24′E) was established to perform observations of avalanches and snow cover in this area.…”

“…Along the Kunes River Valley, numerous avalanches were reported by the local transport administration each winter, as they caused disturbances to road traffic of the main transportation corridors across the Tianshan Mountains (Hao et al, 2018). Although several studies have been undertaken to investigate the distribution of snow avalanche activity in the central zone of the Tianshan Mountains (Hu et al, 1992;Hao et al, 2018;Yang et al, 2020), avalanche prevention strategies have not yet been developed due to a lack of avalanche hazard assessments. Avalanche hazard assessments are an effective means of avalanche disaster prevention and management (Wastl et al, 2011;Schweizer et al, 2020).…”

Snow Avalanche Frequency Estimation (SAFE): 32 years of remote hazard monitoring in Afghanistan

“…Backscatter values provide information on terrain roughness and any change indicates that a mass movement or a significant erosion event occurred in a given area. This technology seems very promising for avalanche detection (Eckerstorfer et al, 2017;Malnes et al, 2015;Martinez-Vazquez and Fortuny-Guasch, 2008;Schaffhauser et al, 2008;Tompkin and Leinss, 2021;Yang et al, 2020). However, the acquisition of frequent radar images is too recent to use this technique to detect historical avalanches.…”

Snow avalanche susceptibility assessment based on ensemble machine learning model in the central Shaluli Mountain

“…In recent years, with the rapid development of artificial intelligence technology, machine learning has been widely used in landslide susceptibility evaluation, earthquake prediction, rainfall correction, constitutive models, and groundwater storage change prediction (Youssef et al, 2016;Ghorbani Nejad et al, 2017;Hao et al, 2017;Choubin et al, 2019a;Choubin et al, 2019b;Li et al, 2021;Xiong et al, 2021;Xi et al, 2022). Some studies have tried to apply machine learning algorithms to the automatic detection of regional avalanches (Techel et al, 2015;Yang et al, 2020), avalanche transport material susceptibility evaluation (Choubin et al, 2020), and avalanche susceptibility mapping (Rahmati et al, 2019;Mosavi et al, 2020;Wen et al, 2022). In this study, based on remote sensing interpretation and field survey, a learning sample library is constructed, and a model combining machine learning and traditional statistical methods is used to explore avalanche susceptibility mapping under different combinations of methods, which can provide an important reference for regional disaster risk prediction.…”