中文摘要：

北京时间2013年4月20日上午8：02分，四川芦山县发生了7．0级强震，地震发生后中国科学院遥感与数字地球研究所迅速启动重大灾害应急响应，及时对灾情进行评估．通过0．6r11分辨率航空遥感影像解译的崩滑体分布情况，结合坡度、坡向、高程、岩性、构造、水系、植被覆盖及地震烈度8个评价因子信息，分别基于概率指数模型与信息量模型对地震重灾区芦山县、宝兴县进行了地质灾害敏感性评价．各因子分析的贡献度排序表明，距离构造350m以内对地质灾害影响最大，其次为较陡的坡度（40°以上）及距离主干河道较近区域．采用接收者操作特征（receiveroperatingcharacteristic，ROC）曲线对模型精度评价，表明信息量模型具有73．3％的精度，略高于概率指数模型的71．5％，二者的评价结果较吻合，均表明芦山县双石镇、太平镇、宝盛乡、龙门乡及宝兴县五龙乡、民治乡、永富乡等地具有较高的地质灾害敏感性．评估结果为震后地质灾害隐患点排查，预防二次灾害提供技术支持．

英文摘要：

A 7.0-magnitude earthquake hit Lushan County, Ya＇an, Sichuan, southwestern China at 8：02 am on April 20, 2013. The Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences immediately launched a major emergency response and conducted a rapid assessment of the situation after the earthquake. Based on the distribution of landslides interpreted from aerial remote sensing images with a 0.6-m resolution, we applied eight evaluation factors （slope, aspect, elevation, lithology, faults, hydrology, vegetation cover, and seismic intensity） to two models--the information index model and the probability model to assess the landslide susceptibility of the quake-hit Lushan and Baoxing counties. Analysis of the contribution of each evaluation factor indicates that the earthquake had the greatest impact in areas which were less than 350 meters from the fault, and the next most affected areas were those with a steep slope （40~ or more） close to the main river. An evaluation of the accuracy of the models using receiver operating characteristic （ROC） shows that the information index model has a slightly higher accuracy （73.3%） than the probability model （71.5%）. The two evaluation results are in good agreement and indicate that the towns of Shuangshi, Taiping, Baosheng, and Longmen in Lushan County and the townships of Wulong, Minzhi, and Yongfu in Baoxing County are highly susceptible to damage from geological disasters. These results can provide useful information for the post-earthquake geological disaster point investigation and help plan measures to prevent secondary disasters.