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摘要： 泥石流物源的信息获取目前主要依靠野外勘查测量和目视解译提取，存在耗时费力、覆盖范围有限、主观性强等问题。遥感因其快速、大范围、高精度监测的特点为泥石流物源识别提供了更为可靠的方式。基于Sentinel 2影像和ALOS地形数据，根据物源区光谱特征和地形特征差异，采用面向对象的分类方法进行物源识别，实现了树正寨流域地震前后泥石流崩滑物源、沟道物源和坡面物源的遥感精细识别。实验结果表明：①基于无人机和Google Earth高分辨率影像选取验证样本发现，采用该方法的树正寨流域物源识别精度分别为震前85.71%，震后88.34%，对应的Kappa系数分别为0.77和0.76；②相比于传统基于像元的遥感分类方法，该方法震前和震后分类精度分别高出14.28%和22.70%，尤其对于小面积的崩滑单体识别有着更优秀的表现；③地震前后由于地震诱发崩塌滑坡等灾害导致物源总储量由1.85×106 m3增至3.99×106 m3，主要物源类型是崩滑物源，占比70.80%。总体而言，实验为泥石流物源的判识提供了基于高分辨率遥感影像观测的自动识别方法，判识结果也将为泥石流灾害防治及风险评估提供重要的科学支撑。 关键词： 遥感;  面向对象方法;  泥石流;  物源识别;  九寨沟地震     Abstract: At present， the quantification of debris flow material sources is mainly depended on field survey， which is time-consuming， with limited spatial coverage and strong subjectivity. Comparatively， remote sensing-based detection method provides a more reliable way for extracting areas of debris flow material sources because of its characteristics of frequent observation， large scale coverage and high precision. In this study， we developed an object-oriented classification method to extract the source area based on Sentinel 2 image and ALOS digital elevation model data， according to the spectral and topographic characteristics of the source area. Compared with visual interpretation method， this method was automatically conducted and can identify the type difference of the material sources. Take the Shuzheng village basin as a case study， the method precisely extracted the three key sources for debris flow （slump-mass sources， gully sediments sources and slope wash sources） before and after the earthquake. The results show that： （1） Based on the validation sample points collected from high-resolution images of UAV and Google Earth， the material sources extraction accuracy of the proposed method is 85.71% before the earthquake and 88.34% after the earthquake， and the corresponding Kappa coefficients are 0.77 and 0.76 respectively. （2） Compared with the pixel-based remote sensing classification method， the accuracy of the proposed method before and after the earthquake is 14.28% and 22.70% higher， and it has a better performance， especially for the recognition of small areas of slump-mass. （3） Before and after the earthquake， due to disasters such as collapses and landslides， the total source reserves increased from 1.85 million m3 to 3.99 million m3. The main source type is the slump-mass source， accounting for 70.80%. In general， this study provides a semi-automatic extraction method based on high-resolution remote sensing image for the extraction of debris flow sources， which will provide important scientific support for debris flow disaster prevention and risk assessment.

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