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This study aims to explore building education capacity to improve disaster management in public awareness. We can improve public awareness of disaster risks through public education on landslide disasters. The current education in disaster courses is mainly aimed at students and ambulance personnel. Those education methods focus on textbook teaching and professional training, and the contents are too professional. There is no effective communication channel between educators and the public. Thus, this paper proposes a visual representation method of landslide disasters for public education. We prepared a concept map to organize and represent bits of knowledge of landslide, instantiating nodes of concept map as animation, then used animation to publicize. The landslide on June 24, 2017 in Diexi, Maoxian was selected to conduct a case test analysis. One short animation created by the method and one brief disaster report summarized by that animation is produced for a questionnaire. We collected and analyzed the results of the questionnaire. The results show that people tend to learn about landslides by watching animation, and they answer more questions correctly in the questionnaire after watching animation than reading the report. So the proposed method in this paper can effectively construct the visual content of landslide disaster knowledge and quickly help the public understand the landslide disaster knowledge. [ABSTRACT FROM AUTHOR]

The pre-failure evolution of landslides is of great value for the analysis of landslide triggering factors and post-disaster stability assessment. At present, optical images are commonly employed to ana⁃ lyze the pre-failure evolution, but it is well-known that their data availability could be highly limited due to the presence of clouds. With the advance in radar remote sensing and interferometric synthetic aperture radar(InSAR), it could provide a new technical approach for landslide pre-failure detection. In this paper, the 2018 Nanyu landslide in Gansu Province is utilized to demonstrate the capability of InSAR to trace its pre-failure surface displacements using European Space Agency’s Sentinel-1 radar data with a temporal interval of 12 days in different tracks. The results show that the landslide began to deform in June of 2017, and the maximum cumulative deformation reached up to 77 mm in the 13 months before the occurrence of the landslide. The time series InSAR derive displacements and the rainfall data is consistent, suggesting that the rainfall should be one of the triggering factors for the landslide. The study demonstrates the potential of radar interferometry for landslide detection, which can provide insights on landslide triggering factors, landslide disaster prevention and mitigation, and even landslide monitoring and early warning work in the future. [ABSTRACT FROM AUTHOR]