摘要
2013年4月20日,四川省雅安市芦山县境内发生MS 7.0级地震。为充分发挥遥感技术在地震灾害应急决策、救援及震后恢复重建中的作用,利用地震前后的多源遥感数据,基于遥感图像人机交互解译和野外现场考察,研究了芦山地震次生地质灾害的特征及其空间分布。遥感调查结果表明,芦山地震引发了1678处次生地质灾害,覆盖地表面积约8.354 km2,具有规模小且以崩塌、落石为主要灾害类型的特点。基于地震前的地形数据,研究了次生地质灾害的空间分布与高程、坡度的关系。对次生地质灾害分布特征的统计分析结果显示,95%的次生地质灾害分布在海拔750~1850 m之间;82.5%的次生地质灾害分布在地形坡度15毅~50毅之间,但随着坡度的增加,次生地质灾害发生率显著升高。在空间分布上,芦山地震次生地质灾害呈现显著的线性排列:或沿NE向发震断裂线性排布,或沿山脊和河谷线性排列。研究结果为芦山地震应急决策、救援及震后恢复重建提供了重要依据。
On April 20,2013,a catastrophic earthquake with MS 7. 0 occurred in Lushan County,Sichuan Province. Using the multi -source remote sensing data acquired before and after the earthquake, the authors analyzed the secondary geological disasters and their spatial distribution based on interactive visual interpretation and field survey. The remote sensing investigation results have shown that the earthquake has triggered 1 678 secondary geological disasters,covering an area of about 8. 354 km2 . The secondary geological disasters are characterized by smaller scale and dominance of collapse and rockfall types. Using the terrain data before the earthquake,the authors analyzed the relationship between the distribution of secondary geological disasters and the elevation and slope. Statistical and analytical results show that 95% of the secondary geological disasters are located in the area with the elevation between 750~1 850 m,and 82. 5% of the secondary geological disasters are located in the area with the slope between 15 o~ 50o. With the increasing slopes,however, the incidence of the secondary geological disasters increases significantly. The secondary geological disasters assume remarkable linear arrangements, with some distributed along the NE-trending seismogenic fault and the others along the mountain ridge and river valley. The results obtained by the authors provide some important information for the emergency decision-making,rescue and reconstruction after the earthquake.
出处
《国土资源遥感》
CSCD
北大核心
2014年第3期99-105,共7页
Remote Sensing for Land & Resources
关键词
芦山地震
次生地质灾害
遥感调查
空间分布
Lushan earthquake
secondary geological disaster
remote sensing survey
spatial distribution