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.
Highlights
Snow avalanche is among the most catastrophic natural disasters worldwide and seriously threatens the safety of residents, socioeconomic development and biodiversity [1,2,3,4,5]
This paper aims to mine the response characteristics of scattering and interference on Sentinel-1 SLC (Single Look Complex) images, to find a more robust and applicable regional automatic detection method on this basic, to support auxiliary decision-making for avalanche prevention
Correlated high-dimensional variables can be synthesized into linearly independent low-dimensional variables by principal component analysis, thereby revealing the most consequential elements and structures hidden in intricate data [40]
Summary
Snow avalanche (hereinafter “avalanche”) is among the most catastrophic natural disasters worldwide and seriously threatens the safety of residents, socioeconomic development and biodiversity [1,2,3,4,5]. From the perspective of geoscience, erosion from avalanches causes the hazard-bearing bodies increasingly fragile, reshaping the micro-geomorphology and changing the type and density of surface cover, these effects will be more conducive to its re-release, forming a vicious circle [6]. Frequent extreme snowfall events and the warming effect in mountainous areas have aggravated the avalanche hazard [7]. There is an urgent need for avalanche spatial distribution mapping to provide more efficient support in coping with increasingly harsh avalanche hazards. Some facts and data sets about avalanche activity are well-documented, providing a scientific basis for the rational use of mountain land and effective avalanche governance [8,9,10,11]. The result is that there is still a need to obtain accurate avalanche data on a regional scale to fill the huge data hole
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