Monitoring Landslide Deformation Using Kalman Filtering

publication Evidence: high

Author Information

Author(s): Yao Jingchuan, Zhan Runqing, Guo Jiliang, Wang Wei, Yuan Muce, Li Guangyu, Zhang Bo, Zhang Rui

Primary Institution: China Academy of Railway Sciences Corporation Limited, Beijing, China

Can combining ascending and descending orbit InSAR data with Kalman filtering improve the temporal resolution and accuracy of landslide monitoring?

The proposed method enhances the temporal resolution of landslide monitoring to six days and improves the reliability of deformation data.

The method improved the temporal resolution of landslide monitoring to six days.
Two significant slips were detected in January 2016 and June 2021.
The study area is prone to geological disasters, impacting transportation infrastructure.
Kalman filtering was used to enhance the accuracy of deformation data.
Data from multiple satellite orbits were combined for better monitoring.
The study provides a scientific basis for disaster prevention and mitigation.
Significant differences in slip deformation rates were observed between different parts of the slope.
The research highlights the importance of accurate landslide monitoring.

This study shows how scientists can use special satellite data to better understand how landslides move over time, helping to keep people safe.

The study used time-series InSAR data from Sentinel-1 satellites and applied Kalman filtering to reconstruct landslide deformation.

The study is limited by the availability of satellite data and the inherent challenges of monitoring in complex terrain.

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