## 5 Conclusions  
(1) Based on the bibliometric analysis, a statistical analysis was conducted on the theme, keywords, publication time, authors, and other factors of papers in landslide monitoring technology. It was found that comprehensive economic strength is an important factor that affects the progress of landslide hazard monitoring research in various countries. With the continuous strengthening of disaster prevention and reduction awareness, research in landslide monitoring technology has been increasingly valued by scholars from different countries in the past decade. According to the keyword analysis of the literature retrieved from the Web of Science core database, the deformation monitoring of landslides is a core element in the research of landslide hazards. Based on the high-frequency keywords of landslide monitoring technology from the Web of Science database, it can be concluded that the currently most widely used technologies in landslide monitoring are GNSS, InSAR, and GIS. Among them, the application frequency of GNSS and InSAR is more than twice that of GIS, indicating that 3S is still one of the most critical technologies in the field of landslide monitoring. In addition, due to the unique advantages of high precision, large scale, and full real-time monitoring, DFOS and InSAR will play an increasingly important role in landslide monitoring. It is necessary to accelerate future research and improvement in various advanced landslide monitoring technologies. After that, the impact of crucial control factors such as deformation, rainfall, and vibration on landslides can be accurately controlled so as to provide a vital identification basis for the early warning and prevention of landslide hazards. (2) The 'Space-Sky-Ground-Underground' comprehensive monitoring system of landslide proposed in this study comprehensively utilizes the advantages of different dimensional monitoring technologies. Through this comprehensive monitoring system, it is possible to overcome the limitations of a single monitoring technology in landslide hazard monitoring to further achieve comprehensive, high-precision, and continuous monitoring of landslides. To be specific, InSAR in space-based monitoring is easily affected by atmospheric visibility and vegetation cover; a UAV in sky-based monitoring is generally challenging to operate in harsh climates such as heavy rainfall and typhoons; three-dimensional laser scanning in ground-based monitoring is prone to errors in landslide monitoring with complex terrain structures; and DFOS in underground-based monitoring often has the problem of sensors exceeding the range and being easily damaged during large deformation of landslides. On this basis, advanced machine learning algorithms such as artificial neural networks, support vector machines, and random forests are combined to perform denoising, clustering, and other processes of analysis to improve the utilization rate of the massive monitoring data. Therefore, these can provide a reference basis for predicting the development trend of landslide hazards. (3) According to the on-site measurement results of the Xinpu landslide, it can be concluded that the temperature of the shallow soil is significantly affected by the external climate, and will change accordingly with seasonal changes. The deeper the rock and soil  
Figure 21: Landslide-deformation-monitoring layout and cloud map. inside the landslide, the less affected it is by sunlight. When it reaches a certain depth, the rock and soil are in a relatively constant temperature state. At this time, if a significant temperature anomaly is found in the area during monitoring, it can be determined preliminarily that there is a certain degree of relative sliding of the rock and soil nearby. Therefore, more attention should be paid to this area during subsequent monitoring.