Dynamic susceptibility mapping of slow-moving landslides using PSInSAR

ABSTRACT

A landslide susceptibility map (LSM) is a valuable tool for landslide assessment and land use management. This research proposes a landslide susceptibility dynamic map (DLSM) to increase LSM utility and update the predicted map in a time series. Slope units, as basic mapping units, were produced to define the landslide boundaries and simplify the mapping in the DLSM. The permanent scatterer interferometric synthetic aperture radar (PSInSAR) technique was used to estimate the line of sight velocity (V_los). This was then reprojected into the velocity in the steepest slope direction (V_slope) to avoid the influence of geometric distortion. The DLSM was produced by integrating, using slope unit aggregate values, the susceptibility (probability) of landsliding predicted by logistic regression based on eight spatial covariates and the V_slope predicted using the PSInSAR technique. The DLSM is a dynamically changing susceptibility map in which susceptibility is increased in certain months, particularly where surface velocity increases following the rainy season. The proportion of the study area classified with extremely high susceptibility increased from 22.2% to 44.8% after the rainy season. The DLSM, thus, potentially improves the prediction reliability for slow-moving landslides and, in particular, can help to avoid false negatives. The DLSM can be applied in areas for which radar data are available and can provide more reliable and readily interpretable results to decision-makers.

Acknowledgements

This research was funded by the National Natural Science Foundation of China (No.41372370), (No.41572274) and Zhejiang Provincial Natural Science Foundation of China (No. LQ02D020001). The authors thank the Changjiang Institute of Survey, Planning, Design and Research for providing the supporting data for this research.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [41372370,41572274]; Zhejiang Provincial Natural Science Foundation of China [LQ02D020001].