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论文摘要

基于RS和GIS的震后汶川县移民选址方法

Site Selection for the Immigrants of Post earthquake Region of Wenchuan County Based on RS and GIS Technology

作者:黎小东(四川大学 水利水电学院;成都理工大学 地质灾害防治与地质环境保护国家重点实验室);杨武年(成都理工大学 地质灾害防治与地质环境保护国家重点实验室);罗智勇(成都理工大学 地质灾害防治与地质环境保护国家重点实验室);李红霞(四川大学 水利水电学院;四川大学 水力学与山区河流开发保护国家重点实验室);敖天其(四川大学 水利水电学院;四川大学 水力学与山区河流开发保护国家重点实验室)

Author:Li Xiaodong(School of Water Resources and Hydropower, Sichuan Univ.;Key Lab. of Geo-hazard Prevention and Geo-environment Protection,Chengdu Univ. of Technol.);Yang Wunian(Key Lab. of Geo-hazard Prevention and Geo-environment Protection,Chengdu Univ. of Technol.);Luo Zhiyong(Key Lab. of Geo-hazard Prevention and Geo-environment Protection,Chengdu Univ. of Technol.);Li Honghxia(School of Water Resources and Hydropower, Sichuan Univ.;State Key Lab. of Hydraulics and Mountain River Eng., Sichuan Univ.);Ao Tianqi(School of Water Resources and Hydropower, Sichuan Univ.;State Key Lab. of Hydraulics and Mountain River Eng., Sichuan Univ.)

收稿日期:2010-04-29          年卷(期)页码:2010,42(5):83-91

期刊名称:工程科学与技术

Journal Name:Advanced Engineering Sciences

关键字:地震;选址;遥感;地理信息系统

Key words:earthquakes; location; Remote Sensing(RS); Geographic Information System(GIS)

基金项目:国家自然基金面上资助项目(50979062);地质灾害防治与地质环境保护国家重点实验室"科技减灾、重建家园"开放研究基金资助项目(DZKJ_0806)

中文摘要

以震后汶川县为例,针对地震灾区的特殊性,考虑地震灾区的可持续发展和应对未来地震灾害的能力,通过筛选确定地质灾害、断裂构造、植被盖度、居民点、道路、水系、地形坡度、高程等8个评价指标因子,以遥感影像数据和数字化地形图为主要信息源,以GIS技术为主要支撑,完成了评价指标体系中各个评价指标因子的信息提取与专题地图制作,建立了研究区的移民搬迁选址模型,结果表明,高、中、低、不适宜区的比例分别为0.64%、24.94%、69.94%、4.48%,其分布情况和位置在图11中有明确体现,可以辅助汶川县灾后移民选址决策,同时为灾后重建的科学规划提供重要参考。

英文摘要

The selection of the immigrant location for Wenchuan people after the earthquake was carried out using RS and GIS. According to the particularity of this great earthquake, considering sustainable development and capability of responding to future disasters of the seismic disastrous area, eight factors including geological hazards, fault structure, vegetation cover, settlements, roads, water system, gradient, and elevation were considered as for the assessment. Information extraction of these factors and thematic map were carried out by Remote sensing image and digital topographic map, and finally the locating modeling for the research area was developed. The results suggested that the respective ratios of high-suitability area, medium-suitability area, Low-suitability area and non-suitability area are 0.64%, 24.94%, 69.94% and 4.48%, and the distribution of their Location is clear revealed in the figure 11, which could be used as a good reference for decision makers.

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