## 4 Result
### Comprehensive Analysis of Remote-Sensing Interpretation Results  
Based on the comprehensive landslide hazard identification method proposed and integrated with the InSAR interpretation results, this study identified a total of 146 potential landslide hazard points. Subsequent validation using the Google Earth platform confirmed 127 of these points as actual landslide hazards, achieving an identification accuracy rate of 86.99%. Subsequently, an in-depth analysis was performed on these 127 confirmed hazard points from multiple perspectives. Optical remote-sensing imagery, characterized by nadir viewing geometry and daytime imaging capabilities, can clearly and intuitively depict surface morphological features and environmental changes. In contrast, SAR data provide all-weather, high-precision observations with heightened sensitivity to subtle ground deformation, making them particularly effective for early landslide hazard detection. Thus, inherent differences inevitably exist in the identification results obtained from these two remote-sensing methodologies. To further analyze and validate these differences, this study utilized online platforms to conduct systematic comparative analyses and on-site verification. A focused analysis was conducted on spatial-distribution patterns, subtle ground-deformation characteristics, and the respective advantages and discrepancies inherent to optical remote-sensing and InSAR techniques, particularly emphasizing the detection of concealed landslide hazards within the study area. Furthermore, through comprehensive field investigations and remote-sensing interpretations, this study extensively summarized the spatial distribution patterns and developmental characteristics of the identified landslide hazard points. Figure 6: Ascending-orbit SBAS-InSAR deformation map. The surface deformation velocity in the study area ranges from \(-93.68\) mm/a to \(88.12\) mm/a. Four typical deformation zones are highlighted in the lower part of the map, involving potential landslide hazards and anthropogenic activities. Through the above systematic analyses and cross-validation procedures, the differences between optical remote-sensing and InSAR identification results were classified into three typical scenarios, with their detailed characteristics systematically presented in Table 4.  
(1) General consistency between InSAR interpretation and optical remote-sensing results Among the identified landslide hazard points, 15 locations show general consistency between InSAR-derived deformation interpretation and the results of optical remote-sensing results. These two datasets exhibit a high degree of spatial overlap, and terrain-geological analysis further confirms their suitability for the research objectives. Landslide hazard point A was selected for detailed image analysis. By integrating InSAR deformation monitoring, optical remote-sensing interpretation, and validation using the online Google Earth platform, a comprehensive assessment of deformation characteristics, geological conditions, and slope stability was conducted. Based on the InSAR deformation-monitoring results (Table 4(b)), the boundary of the landslide could be reliably delineated. The deformation in the middle section of the slope is particularly prominent, characterized by significant ground displacement. However, due to signal decorrelation effects in areas of dense vegetation or complex terrain, certain regions exhibit interpretation gaps. The monitoring data indicate a maximum deformation rate of \(-23.6\) mm/a, suggesting the landslide is currently in a steady deformation phase. The optical remote-sensing imagery (Table 4(a)) clearly shows an active landslide  
Figure 7: Descending-orbit SBAS-InSAR deformation map. The surface deformation velocity in the study area ranges from \(-48.66\) mm/a to \(41.19\) mm/a. Four typical deformation zones are marked in the lower part of the map, involving landslide-prone areas and engineering activities.