## 5 Discussion
### Future Landslide Identification Using InSAR Techniques  
In this study, a total of 81 landslides were identified in an area of 768.7 km\({}^{2}\) using only Sentinel-1 data, showing the high feasibility of InSAR analysis for landslide detection and enhancing the understanding of landslide development in the alpine forest region. As reported by previous studies, the performance of InSAR analysis, especially the deformation map, is influenced by the resolution of the SAR image. The higher the resolution of the SAR image, the more detailed the deformation characteristics of landslides that can be obtained. Thus, from the aspect of obtaining detailed landslide deformation characteristics, high-resolution SAR images are still required. With the development of remote sensing techniques, more commercial SAR satellites, such as COSMO-SkyMed and TerraSAR-X, have been launched to provide high-quality and high-resolution images. In future studies, such data sets can be adopted to perform integrated InSAR analyses with multi-resolution SAR images, which could provide more detailed deformation features as well as increase the detection rate [77; 78]. In addition, the combination of both ascending and descending orbit data sets can overcome the poor observation induced by the shadow and foreshortening effects of SAR images to increase the detection ratio of potential landslides and, therefore, improve the identification of potential landslides. Therefore, the combination of multi-sensor data with different paths would also be helpful for observations in all azimuths [57]. For landslide detection with InSAR techniques in alpine forest regions, some key points need to be stressed in future studies. The temporal coherence of SAR images is a critical factor for obtaining precise ground deformation and identifying potential landslides [54] because it is significantly affected by the growth and withering of vegetation and snow and ice melting in alpine forest regions. In order to achieve higher temporal coherence, relatively longer wave length SAR data should be used. Although the C-band Sentinel-1 SAR data have already been shown to be feasible for landslide detection and providing new understanding on landslide development in this study, L-band sensors, such as ALOS-2, would be favorable for future landslide studies in alpine forest areas [79]. Another factor affecting the coherence of SAR images is the landslide itself. Fast-evolving landslides, such as the Baige landslides along the Jinsha River, are widely distributed in alpine forest regions, especially in the eastern area of the Tibetan Plateau, because of the steep terrain conditions and historical tectonic movement [80; 81]. This type of landslide might not be detected through time-series analysis such as PS-InSAR and SBAS-InSAR because the coherence may not be sufficient [82]. Previous studies pointed out that traditional D-InSAR or Stacking-InSAR techniques are feasible tools for handling fast-evolving landslides [28]. Therefore, the combination of such InSAR techniques with time-series InSAR analysis can increase the detection ratio of potential landslides. Another interesting phenomenon worth noting is that the boundaries of some potential landslide sites determined by the InSAR technique differ from those identified in the field investigations. This is because the InSAR technique and field investigation determine