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Open AccessArticle

Landslide Susceptibility Mapping of Karakorum Highway Combined with the Application of SBAS-InSAR Technology

by Fumeng Zhao 1,2, Xingmin Meng 1,2,*, Yi Zhang 1,2, Guan Chen 1,2, Xiaojun Su 1,2 and Dongxia Yue 1,2
Key Laboratory of West China’s Environmental System (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
Gansu Environmental Geology and Geohazards Engineering Research Centre, Lanzhou University, Lanzhou 730000, China
Author to whom correspondence should be addressed.
Sensors 2019, 19(12), 2685;
Received: 29 April 2019 / Revised: 7 June 2019 / Accepted: 12 June 2019 / Published: 14 June 2019
(This article belongs to the Special Issue SAR and Optical Data for Crustal Deformation Monitoring)
Geological conditions along the Karakorum Highway (KKH) promote the occurrence of frequent natural disasters, which pose a serious threat to its normal operation. Landslide susceptibility mapping (LSM) provides a basis for analyzing and evaluating the degree of landslide susceptibility of an area. However, there has been limited analysis of actual landslide activity processes in real-time. The SBAS-InSAR (Small Baseline Subsets-Interferometric Synthetic Aperture Radar) method can fully consider the current landslide susceptibility situation and, thus, it can be used to optimize the results of LSM. In this study, we compared the results of LSM using logistic regression and Random Forest models along the KKH. Both approaches produced a classification in terms of very low, low, moderate, high, and very high landslide susceptibility. The evaluation results of the two models revealed a high susceptibility of land sliding in the Gaizi Valley and the Tashkurgan Valley. The Receiver Operating Characteristic (ROC) curve and historical landslide verification points were used to compare the evaluation accuracy of the two models. The Area under Curve (AUC) value of the Random Forest model was 0.981, and 98.79% of the historical landslide points in the verification points fell within the range of high and very high landslide susceptibility degrees. The Random Forest evaluation results were found to be superior to those of the logistic regression and they were combined with the SBAS-InSAR results to conduct a new LSM. The results showed an increase in the landslide susceptibility degree for 2808 cells. We conclude that this optimized landslide susceptibility mapping can provide valuable decision support for disaster prevention and it also provides theoretical guidance for the maintenance and normal operation of KKH. View Full-Text
Keywords: KKH; landslide susceptibility mapping; logistic regression; random forest; SBAS-InSAR KKH; landslide susceptibility mapping; logistic regression; random forest; SBAS-InSAR
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Zhao, F.; Meng, X.; Zhang, Y.; Chen, G.; Su, X.; Yue, D. Landslide Susceptibility Mapping of Karakorum Highway Combined with the Application of SBAS-InSAR Technology. Sensors 2019, 19, 2685.

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