###### Abstract  
In order to identify giant ancient landslides more effectively and to quantify the risk of giant ancient landslides, this study takes a Lengqu River section located on the Qinghai-Tibet Plateau as an example and then uses the red relief image map (RRIM) method to enhance the digital elevation model (DEM) for topographic 2D visualization to identify giant ancient landslides. Then, the relationships between giant ancient landslides (GALs), resurgent GALs, the deposition of inactive GALs and the excess topography of hillslopes under 30\({}^{\circ}\) threshold are analyzed separately. A total of 54 GALs are identified at last by using the RRIM method; 77.75% of GALs are still located on excess topography, 68.38% of resurgent GALs occurred on excess topography, and 62.21% of the deposition of inactive GALs are on non-excess topography. The RRIM method provides a new way to identify giant ancient landslides. The excess topography provides an indication of the risk of new landslides through the destructive effect of GALs on the threshold hillslope, and the preliminary investigation of the quantitative relationship between the resurrection of GALs and excess topography also shows that there is a certain pattern between the resurrection of GALs and the excess topography under the natural state, so the excess topography has a certain indication of the generation of new landslides and secondary resurrection at the original GAL positions. Lengqu River section; giant ancient landslides; RRIM; excess topography; potential risk location +
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time phases [17]. The original DEM without any changes can only roughly discriminate landslide locations and elevations, for example, using the historical landslide morphology displayed by high-precision DEM combined with high-resolution remote sensing images to interpret landslides, but DEM cannot provide a realistic and detailed portrayal of the surface morphology. A DEM with enhanced mountain shadow visualization can observe certain landslide morphologies; however, the color contrast is not obvious and the details of the surface are not carved enough, which often leads to the misjudgment of landslides. Compared with the original DEM and hillshade, the sky view factor (SVF) is more detailed for landslide contours, but the specific structure of the landslide can still easily be misjudged due to the poor visual effect. Matching different time-phase DEMs to identify landslides often requires a consideration of the alignment accuracy of the two time-phase DEMs. In order to use the DEM more easily and effectively, it is possible to increase the utilization of the DEM by rendering the terrain in 2D visualization and by zooming terrain features to extract related objects. [PERSON] et al. 2002 proposed a DEM-based terrain 2D visualization parameter, the name of which is topographic openness, to visualize terrain features in 2002 [18]. Then, [PERSON] et al. 2008 proposed that RRIM can be synthesized from remote sensing images such as airborne light detection and ranging (LiDAR) and Shuttle Radar Topography Mission (SRTM) to visualize terrain and then used the visualized terrain map for the identification of geological hazards such as landslides [19], and topographic features such as fractures, deserts and landslides were identified in Mongolia and Japan by visualizing RRIM terrain transformations of the DEM [20]. Later, an assessment of the contribution of landslide interpretation in Turkey using 2D-visualized RRIM maps based on LiDAR data was made by [PERSON] 2019 [21]. This method is significant for the identification of geological hazards such as landslides and debris flows and can also provide support for laymen. All in all, the higher the DEM resolution, the more delicate the features [22; 23]. We will introduce the RRIM 2D visualization terrain method supporting visual interpretation and try to use a 12.5 m DEM for the effective identification of GALs based on the above research in this research.