EGU2020-9090
https://doi.org/10.5194/egusphere-egu2020-9090
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Progressive landslide activity analysis and monitoring from Multi-temporal high-resolution geoinfomatic data sets

Kuo-Jen Chang1, Chih-Ming Tseng2, Ho-Hsuan Chang3, and Mei-Jen Huang1
Kuo-Jen Chang et al.
  • 1National Taipei University of Technology, Civil Engineering, Taipei, Taiwan (epidote@ntut.edu.tw)
  • 2Chang Jung Christian University, Dept. Land management and Development, Tainan, Taiwan (cmtseng@mail.cjcu.edu.tw)
  • 3Communication Engineering Department, I-Shou University, Kaohsiung, Taiwan (hhchang@isu.edu.tw)

Due to the high seismicity and high annual precipitation, numerous landslides have occurred and caused severe impact in Taiwan. In recent years, the remote sensing technology improves rapidly, providing a wide range of image, essential and precise geoinformation. The Small unmanned aircraft system (sUAS) has been widely used in landslide monitoring and geomorphic change detection. To access potential hazards we combine sUAS, field survey, terrestrial laser scanner (ground LiDAR) and UAS LiDAR for data acquisition. Based on the methods we construct multi-temporal high-resolution DTMs so as to access the activity and to monitoring the creeping landslides in Paolai village, southern Taiwan. The data set are qualified from 21 ground control points (GCPs) and 11 check points (CPs) based on real-time kinematic-global positioning system (RTK-GPS) and VBS RTK-GPS (e-GNSS). Since 2015, more than 10 geospatial datasets have been produced for an area between 5-80 Km2 with 8-12 cm spatial resolution. These datasets were then compared with the airborne LiDAR data to access the quality and interpretability of the data sets. Since 2017, we integrate UAS LiDAR to monitoring landslide area, and re-evaluate the data accuracy. Since 2018 we have integrate UAS LiDAR, terrestrial LiDAR, and photogrammetric point cloud for landslide study, to ensure no shadow effect of the dataset. The geomorphologic changes and landslide activities were quantified in Paolai area. The results of this study provide not only geoinfomatic datasets of the hazardous area, but also for essential geomorphologic information for other study, and for hazard mitigation and planning, as well.

How to cite: Chang, K.-J., Tseng, C.-M., Chang, H.-H., and Huang, M.-J.: Progressive landslide activity analysis and monitoring from Multi-temporal high-resolution geoinfomatic data sets, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9090, https://doi.org/10.5194/egusphere-egu2020-9090, 2020

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