气候变化领域集成服务门户(CCPortal): Automatic Detection of Regional Snow Avalanches with Scattering and Interference of C-band SAR Data

CCPortal

DOI	10.3390/rs12172781
	Automatic Detection of Regional Snow Avalanches with Scattering and Interference of C-band SAR Data
	Yang, Jinming; Li, Chengzhi; Li, Lanhai; Ding, Jianli; Zhang, Run; Han, Tao; Liu, Yang
通讯作者	Liu, Y (通讯作者)
发表日期	2020
EISSN	2072-4292
卷号	12 期号:17
英文摘要	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.
关键词	PRINCIPAL COMPONENT ANALYSIS RISK-ASSESSMENT HAZARD
英文关键词	Sentinel-1A SLC; scattering and interference characteristics of avalanches; principal component analysis; support vector machine; machine learning; snow avalanche mapping
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000571142100001
来源期刊	REMOTE SENSING
来源机构	中国科学院青藏高原研究所
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/259798
推荐引用方式 GB/T 7714	Yang, Jinming,Li, Chengzhi,Li, Lanhai,et al. Automatic Detection of Regional Snow Avalanches with Scattering and Interference of C-band SAR Data[J]. 中国科学院青藏高原研究所,2020,12(17).
APA	Yang, Jinming.,Li, Chengzhi.,Li, Lanhai.,Ding, Jianli.,Zhang, Run.,...&Liu, Yang.(2020).Automatic Detection of Regional Snow Avalanches with Scattering and Interference of C-band SAR Data.REMOTE SENSING,12(17).
MLA	Yang, Jinming,et al."Automatic Detection of Regional Snow Avalanches with Scattering and Interference of C-band SAR Data".REMOTE SENSING 12.17(2020).