Applied Sciences / Assessment of Landslide-Induced Geomorphological Changes in Hítardalur Valley, Iceland, Using Sentinel-1 and Sentinel-2 Data

Landslide mapping and analysis are essential aspects of hazard and risk analysis. Landslides can block rivers and create landslide-dammed lakes, which pose a significant risk for downstream areas. In this research, we used an object-based image analysis approach to map geomorphological features and...

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Bibliographic Details
Published in:Applied Sciences
Main Authors: Dabiri, Zahra, Hölbling, Daniel, Abad, Lorena, Helgason, Jón Kristinn, Þorsteinn Sæmundsson, Tiede, Dirk
Format: Article in Journal/Newspaper
Language:English
Published: MDPI 2020
Subjects:
Online Access:https://resolver.obvsg.at/urn:nbn:at:at-ubs:3-18513
https://doi.org/10.3390/app10175848
https://eplus.uni-salzburg.at/doi/10.3390/app10175848
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Summary:Landslide mapping and analysis are essential aspects of hazard and risk analysis. Landslides can block rivers and create landslide-dammed lakes, which pose a significant risk for downstream areas. In this research, we used an object-based image analysis approach to map geomorphological features and related changes and assess the applicability of Sentinel-1 data for the fast creation of post-event digital elevation models (DEMs) for landslide volume estimation. We investigated the Hítardalur landslide, which occurred on the 7 July 2018 in western Iceland, along with the geomorphological changes induced by this landslide, using optical and synthetic aperture radar data from Sentinel-2 and Sentinel-1. The results show that there were no considerable changes in the landslide area between 2018 and 2019. However, the landslide-dammed lake area shrunk between 2018 and 2019. Moreover, the Hítará river diverted its course as a result of the landslide. The DEMs, generated by ascending and descending flight directions and three orbits, and the subsequent volume estimation revealed that—without further post-processing—the results need to be interpreted with care since several factors influence the DEM generation from Sentinel-1 imagery. Zahra Dabiri, Daniel Hölbling, Lorena Abad, Jón Kristinn Helgason, Þorsteinn Sæmundsson and Dirk Tiede FWF-P29461-N29