Refinement of Landslide Susceptibility Map Using Persistent Scatterer Interferometry in Areas of Intense Mining Activities in the Karst Region of Southwest China

Chaoyong Shen,Zhongke Feng,Chou Xie,Haoran Fang,Yu Zhu,Honglin Bai,Kai Wang,Binbin Zhao,Hongwei Li,Panpan Chen,Wenhao Ou,Abdul Mannan

doi:10.3390/rs11232821

Abstract

Incremental vertical ground movements due to coal mining can increase landslide susceptibility greatly in a short time and have thus triggered a large number of geological disasters, especially in the Karst Region, where a lot of steep slopes are on fractured rocks. Therefore, the landslide susceptibility maps (LSM) in Karst Region should be updated regularly. This paper presents an efficient methodology to update and refine LSM by using Persistent Scatterer Interferometry (PSI) data directly. First, an original LSM was produced by using a support vector machine (SVM) algorithm, and the distribution of coal mining was considered a crucial factor to generate the LSM. Then, the Permanent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR) technique was implemented to retrieve displacement time-series. Finally, the landslide displacement map, produced by the PSInSAR analysis, was projected to the direction of the steepest slope and resampled to the same cell in the LSM, to update the original LSM. This methodology is illustrated with the case study of Bijie in the Karst Region of Southwest China, wherein the ascending RADARSAT-2 and descending Sentinel-1 datasets are processed for the period of 2017–2019. The results show that the susceptibility degree increased in 56.41 km2 of the study area, and 80 percent of the increased susceptibility degree was caused by coal mining. The comparison between original and refined LSM in two specific areas revealed that the proposed method can produce more-reliable landslide susceptibility maps in areas of intense mining activities in the Karst Region.

Highlights

Landslides constitute a serious source of danger, causing environmental damage and substantial human and financial losses
The confusion matrix includes the information of the model classification accuracy, which identifies true positives (TN), false negatives (FN), false positives (FP), and true negatives (TN)
We evaluated the geometrical visibility of the area of interest based on the R-indices, using the approach proposed by Notti, with respect to the morphology and the acquisition geometry of the available satellite system

Summary

Introduction

Landslides constitute a serious source of danger, causing environmental damage and substantial human and financial losses. Landslide susceptibility mapping can provide information about the spatial distribution of the probability of regional slope instability [1,2], which is the first and the most important step in landslide hazard assessment, in order to take effective measures for landslide mitigation. The weights of evidence [3,4], artificial neural network [5,6], random forest [7–9], and support vector machine [10,11] models have been used for the elaboration of landslide susceptibility or hazard maps [12]. The major impact factors that can affect landslide susceptibility include terrain, geology, geomorphology, and land cover [13]. These factors seldom undergo great changes in the span of one or two years; landslide risk increases significantly in areas of intense human activity. To increase the reliability of the LSM, some dynamic information, such as regional displacement information produced by Interferometric Synthetic Aperture Radar (InSAR) data, should be included for the dynamic updating of LSMs

Results

Discussion

Conclusion