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Article

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

1
Department of Geoinformatics-Z _GIS, University of Salzburg, 5020 Salzburg, Austria
2
Department of Remote Sensing and GIS, University of Tabriz, Tabriz 5166616471, Iran
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(10), 561; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9100561
Received: 5 August 2020 / Revised: 10 September 2020 / Accepted: 22 September 2020 / Published: 27 September 2020
(This article belongs to the Special Issue Multi-Hazard Spatial Modelling and Mapping)
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. View Full-Text
Keywords: optical data; synthetic aperture radar (SAR); object-based image analysis (OBIA); frequency ratio (FR); fuzzy analytic hierarchy process (FAHP) optical data; synthetic aperture radar (SAR); object-based image analysis (OBIA); frequency ratio (FR); fuzzy analytic hierarchy process (FAHP)
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MDPI and ACS Style

Ghorbanzadeh, O.; Didehban, K.; Rasouli, H.; Kamran, K.V.; Feizizadeh, B.; Blaschke, T. An Application of Sentinel-1, Sentinel-2, and GNSS Data for Landslide Susceptibility Mapping. ISPRS Int. J. Geo-Inf. 2020, 9, 561. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9100561

AMA Style

Ghorbanzadeh O, Didehban K, Rasouli H, Kamran KV, Feizizadeh B, Blaschke T. An Application of Sentinel-1, Sentinel-2, and GNSS Data for Landslide Susceptibility Mapping. ISPRS International Journal of Geo-Information. 2020; 9(10):561. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9100561

Chicago/Turabian Style

Ghorbanzadeh, Omid, Khalil Didehban, Hamid Rasouli, Khalil V. Kamran, Bakhtiar Feizizadeh, and Thomas Blaschke. 2020. "An Application of Sentinel-1, Sentinel-2, and GNSS Data for Landslide Susceptibility Mapping" ISPRS International Journal of Geo-Information 9, no. 10: 561. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9100561

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