In this study, we used Sentinel-1 and Sentinel-2 data to delineate post-earthquake landslides within an object-based image analysis (OBIA). We used our resulting landslide inventory map for training the data-driven model of the frequency ratio (FR) for landslide susceptibility modelling and mapping considering eleven conditioning factors of soil type, slope angle, distance to roads, distance to rivers, rainfall, normalised difference vegetation index (NDVI), aspect, altitude, distance to faults, land cover, and lithology. A fuzzy analytic hierarchy process (FAHP) also was used for the susceptibility mapping using expert knowledge. Then, we integrated the data-driven model of the FR with the knowledge-based model of the FAHP to reduce the associated uncertainty in each approach. We validated our resulting landslide inventory map based on 30% of the global positioning system (GPS) points of an extensive field survey in the study area. The remaining 70% of the GPS points were used to validate the performance of the applied models and the resulting landslide susceptibility maps using the receiver operating characteristic (ROC) curves. Our resulting landslide inventory map got a precision of 94% and the AUCs (area under the curve) of the susceptibility maps showed 83%, 89%, and 96% for the F-AHP, FR, and the integrated model, respectively. The introduced methodology in this study can be used in the application of remote sensing data for landslide inventory and susceptibility mapping in other areas where earthquakes are considered as the main landslide-triggered factor.

中文翻译：

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Sentinel-1，Sentinel-2和GNSS数据在滑坡敏感性地图中的应用

在这项研究中，我们使用了Sentinel-1和Sentinel-2数据在基于对象的图像分析（OBIA）中描绘了地震后的滑坡。我们使用生成的滑坡清单图来训练频率比（FR）的数据驱动模型，以进行滑坡敏感性建模和制图，并考虑了11种条件因素，包括土壤类型，坡度，距道路的距离，距河流的距离，降雨，归一化差异植被指数（NDVI），纵横比，高度，到断层的距离，土地覆盖和岩性。还使用专家知识将模糊分析层次过程（FAHP）用于磁化率映射。然后，我们将FR的数据驱动模型与FAHP的基于知识的模型集成在一起，以减少每种方法的相关不确定性。我们根据研究区域进行的广泛野外调查的全球定位系统（GPS）点的30％，验证了生成的滑坡清单。剩下的70％的GPS点用于使用接收器工作特征（ROC）曲线来验证所应用模型的性能以及所产生的滑坡敏感性图。我们生成的滑坡清单图的精度为94％，而敏感性图的AUC（F-AHP，FR和集成模型分别为83％，89％和96％）。本研究中介绍的方法可用于遥感数据在其他地震被认为是主要滑坡触发因素的地区的滑坡清单和敏感性地图绘制中的应用。