NDLI: High-resolution remote sensing image disaster emergency investigation and analysis

Content Provider	IEEE Xplore Digital Library
Author	Dongjian Xue Zhengwei He Shu Tao
Copyright Year	2011
Description	Author affiliation: State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu, Sichuan, China (Dongjian Xue; Zhengwei He) \|\| Key Laboratory of Integrated Disaster Assessment and Risk Governance of the Ministry of Civil Affairs, Beijing, China (Shu Tao)
Abstract	A strong earthquake will cause a large number of residential damage, casualties, severely holding back regional economic development. Rapid extraction of information on disasters is critical to acquire the disaster index and disaster assessment, and emergency rapid response can be effective in reducing seismic disaster losses and casualties. Beijing April 14, 2010, Yushu County, Yushu Tibetan Autonomous Prefecture in Qinghai Province, 7.1 magnitude earthquake occurred, as a result of high elevation and strong rainfall after the earthquake, dangerous roads is difficult to walk, even most of the sections of interruption, so it is very difficult for the rescue and assessment. Based on the experience of “5.12” earthquake damage assessment in the paper, to earthquake-stricken areas as the study area of Yushu, a survey and analysis of damaged residential is carried out, to provide technical support for earthquake relief, and it is important reference to damage assessment, post-disaster reconstruction due to similar disasters in the future.
Starting Page	3195
Ending Page	3197
File Size	462282
Page Count	3
File Format	PDF
ISBN	9781424491728
e-ISBN	9781424491711
DOI	10.1109/RSETE.2011.5964993
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-06-24
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Economics Satellites Roads Disasters Earthquakes Buildings Investigation Remote sensing image Rivers Remote sensing
Content Type	Text
Resource Type	Article

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