An Application of Sentinel-1, Sentinel-2, and GNSS Data for Landslide Susceptibility Mapping

Omid Ghorbanzadeh,Khalil Didehban,Bakhtiar Feizizadeh,Thomas Blaschke,Hamid Rasouli,Khalil Kamran

doi:10.3390/ijgi9100561

Abstract

In this study, we used Sentinel-1 and Sentinel-2 data to delineate post-earthquake landslides within an object-based image analysis (OBIA). We used our resulting landslide inventory map for training the data-driven model of the frequency ratio (FR) for landslide susceptibility modelling and mapping considering eleven conditioning factors of soil type, slope angle, distance to roads, distance to rivers, rainfall, normalised difference vegetation index (NDVI), aspect, altitude, distance to faults, land cover, and lithology. A fuzzy analytic hierarchy process (FAHP) also was used for the susceptibility mapping using expert knowledge. Then, we integrated the data-driven model of the FR with the knowledge-based model of the FAHP to reduce the associated uncertainty in each approach. We validated our resulting landslide inventory map based on 30% of the global positioning system (GPS) points of an extensive field survey in the study area. The remaining 70% of the GPS points were used to validate the performance of the applied models and the resulting landslide susceptibility maps using the receiver operating characteristic (ROC) curves. Our resulting landslide inventory map got a precision of 94% and the AUCs (area under the curve) of the susceptibility maps showed 83%, 89%, and 96% for the F-AHP, FR, and the integrated model, respectively. The introduced methodology in this study can be used in the application of remote sensing data for landslide inventory and susceptibility mapping in other areas where earthquakes are considered as the main landslide-triggered factor.

Highlights

Natural mass-movement hazards, such as landslides, cause extensive damage to the environment and social infrastructure, severely affecting urban development and land use
We carried out fieldwork and prepared a global positioning system (GPS)-point dataset of landslide occurrences, which we randomly divided into two sections to validate the training dataset produced by the integrated model and the final resulting landslide susceptibility mapping (LSM)
We used the following methodology to produce the LSM in this study: first, we used an frequency ratio (FR) model to calculate the weights of classes that are necessary to aggregate LSM conditioning factors

Summary

Introduction

Natural mass-movement hazards, such as landslides, cause extensive damage to the environment and social infrastructure, severely affecting urban development and land use. Landslides are known as the downslope mass movements of earth, debris, and rock, due to gravity impact [1]. They always happen in variety of sizes and shapes and are triggered by different criteria, such as rainfall, earthquakes, slope erosion, and volcanic eruptions [2]. Researchers have applied landslide susceptibility mapping (LSM) as a practical analysis approach to obtain better insights into this phenomenon [3]. LSM is the best way to identify areas with potential slope instability. In this regard, remote sensing and geographic information system (GIS) data were extensively used for LSM studies [4]

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