## 3 Method  
### Research Objectives and Methodological Framework  
This study focuses on the identification and analysis of landslide hazards in loess regions by developing a systematic and integrated recognition methodology. Utilizing multi-source remote-sensing data and spatial-terrain information, the research undertook hazard identification, validation, and analysis. The primary research approach is outlined as follows (Figure 4):  
1. Development of an integrated interpretation method and indicator library for loess landslide hazards. By examining the typical remote-sensing characteristics of loess landslide hazards, this study integrates optical remote-sensing imagery features with InSAR ground deformation monitoring results, combined with terrain characteristics such as slope gradients and proximity to rivers. This integration establishes a comprehensive interpretation framework for identifying loess landslide hazards. Additionally, an interpretation indicator library was created, clearly defining typical recognition characteristics of various landslide hazards in remote-sensing imagery and deformation data, thus providing essential technical support for subsequent interpretation tasks;
2. Implementation of ground deformation monitoring and extraction of deformation information. Using SBAS-InSAR technology, Sentinel-1 SAR imagery data from 2022 to 2023 were processed to derive regional cumulative surface deformation and deformation rate maps. These maps revealed surface displacement patterns, offering crucial evidence for the identification and evaluation of landslide hazards [27];
3. There was the comprehensive interpretation of landslide hazards to achieve preliminary identification results. Based on the developed integrated interpretation method, the ground deformation results from SBAS-InSAR and high-resolution optical remote-sensing imagery data were combined to systematically identify potential landslide hazard areas, resulting in a preliminary distribution map of landslide hazards;
4. There was the validation of identified landslide hazard points to enhance accuracy. The initially identified hazard points were imported into the Google Earth platform for visual verification and correction using high-resolution satellite imagery. The areas erroneously identified were further excluded, thereby retaining authentic and reliable hazard points. The selected typical deformation areas were analyzed in-depth to explore the evolutionary processes and spatial characteristics of landslide hazards, ultimately producing a definitive dataset of landslide hazards for the central Tianshui region;
5. There was spatial distribution analysis of landslide hazards to inform disaster prevention and mitigation strategies. Based on interpretation outcomes, spatial analytical methods were applied to statistically summarize the distribution characteristics of hazard points. The study investigates spatial associations between landslide hazards and various influencing factors, including topography, proximity to rivers, geological types, and human activity intensity. Further analyses identify mechanisms and primary controlling factors of landslide hazards, providing a scientific basis and decision-making support for landslide disaster prevention and risk management in the central Tianshui area.