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

被引:16
|
作者
Yang, Jinming [1 ]
Li, Chengzhi [1 ,2 ]
Li, Lanhai [3 ,4 ,5 ]
Ding, Jianli [1 ,2 ]
Zhang, Run [6 ]
Han, Tao [7 ]
Liu, Yang [3 ,4 ,5 ]
机构
[1] Xinjiang Univ, Coll Resources & Environm Sci, Urumqi 830046, Peoples R China
[2] Xinjiang Univ, Inst Arid Ecol Environm, Urumqi 830046, Peoples R China
[3] Chinese Acad Sci, Xinjiang Inst Ecol & Geog, State Key Lab Desert & Oasis Ecol, Urumqi 830011, Peoples R China
[4] Chinese Acad Sci, Ili Stn Watershed Ecosyst Res, Xinyuan 835800, Peoples R China
[5] Chinese Acad Sci, Res Ctr Ecol & Environm Cent Asia, Urumqi 830011, Peoples R China
[6] Chinese Acad Sci, Inst Tibetan Plateau Res, Key Lab Tibetan Environm Changes & Land Surface P, Beijing 100101, Peoples R China
[7] Urumqi Publ Secur Bur, Seventh Detachment Special Police, Urumqi 830017, Peoples R China
基金
中国科学院西部之光基金; 中国国家自然科学基金;
关键词
Sentinel-1A SLC; scattering and interference characteristics of avalanches; principal component analysis; support vector machine; machine learning; snow avalanche mapping; PRINCIPAL COMPONENT ANALYSIS; RISK-ASSESSMENT; HAZARD;
D O I
10.3390/rs12172781
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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 km(2). 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.
引用
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页数:23
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