Co-seismic landslide detection after M 7.4 earthquake on June 23, 2020, in Oaxaca,Mexico, based on rapid mapping method using high and medium resolution synthetic aperture radar (SAR) images |
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Authors: | Hernandez Norma Davila Pastrana Alexander Ariza Garcia Lizeth Caballero de Leon Juan Carlos Villagran Alvarez Antulio Zaragoza Morales Leobardo Dominguez Nemiga Xanat Antonio Posadas Gustavo Dominguez |
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Institution: | 1.Laboratory of Remote Sensing, Faculty of Geography, Autonomous University of Mexico State (UAEMEX), Cerro de Coatepec, Ciudad Universitaria, Estado de México, Toluca de Lerdo, Mexico ;2.National Autonomous University of Mexico (UNAM), National School of Higher Studies, ENES-Juriquilla, Boulevard Juriquilla 3001, Queretaro, Juriquilla, Mexico ;3.United Nations Platform for Space-based Information for Disaster Management and Emergency Response (UN-SPIDER), Vienna, Austria ;4.Departamento de Física, Facultad de Ciencias, Universidad Nacional Autónoma de México (UNAM), Mexico City, Mexico ;5.Department of Soil Dynamics and Gravitational Processes, National Center of Disaster Prevention (CENAPRED), Mexico City, Mexico ; |
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Abstract: | Landslides are the fourth most common natural disasters in the world, with Costa Rica and southern Mexico being the most affected regions of Central America (Froude and Petley, 2018). In this work, we propose a semi-automated method to detect earthquake-triggered landslides for rapid mapping after a disaster event using open Sentinel-1 data. We used high-resolution TerraSAR-X data and very high-resolution Spot-7 images to compare and evaluate the accuracy of landslide distribution maps generated from the semi-automated method, applied to the M 7.1 earthquake on June 23, 2017, in Oaxaca, Mexico. The outcomes showed better accuracy in descending orbits due to ‘windward-leeward’ physiographic conditions, with a 50.56% quality percentage. This shows a reasonably good capacity to detect co-seismic landslides. However, the breaching factor was also high because several features, such as bare soils and agricultural areas, were incorrectly identified as co-seismic landslides. Finally, this semi-automated method establishes a basis for future improvements in methodologies applied to construct rapid mapping inventories using medium SAR scales. |
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