Please use this identifier to cite or link to this item: https://ktisis.cut.ac.cy/handle/10488/18919
Title: Small scale landslide detection using Sentinel-1 interferometric SAR coherence
Authors: Tzouvaras, Marios 
Danezis, Chris 
Hadjimitsis, Diofantos G. 
Major Field of Science: Engineering and Technology
Field Category: Civil Engineering
Keywords: Coherence;Change detection;Landslides;Rainfall;CCD;SAR;Copernicus;Sentinel-1;Early warning systems
Issue Date: May-2020
Source: Remote Sensing, 2020, vol. 12, no. 10, articl. no. 1560
Volume: 12
Issue: 10
Project: ERATOSTHENES: Excellence Research Centre for Earth Surveillance and Space-Based Monitoring of the Environment 
Journal: Remote Sensing 
Abstract: Infrastructure is operational under normal circumstances and is designed to cope with common natural disruptions such as rainfall and snow. Natural hazards can lead to severe problems at the areas where such phenomena occur, but also at neighboring regions as they can make parts of a road network virtually impassable. Landslides are one of the most devastating natural hazards worldwide, triggered by various factors that can be monitored via ground-based and/or satellite-based techniques. Cyprus is in an area of high susceptibility to such phenomena. Currently, extensive field campaigns including geotechnical drilling investigations and geophysical excavations are conducted to monitor land movements, and, at the same time, determine the geological suitability of areas. Active satellite remote sensors, namely Synthetic Aperture Radar (SAR), have been widely used for detecting and monitoring landslides and other ground deformation phenomena using Earth Observation based techniques. This paper aims to demonstrate how the use of Copernicus open-access and freely distributed datasets along with the exploitation of the open-source processing software SNAP (Sentinel’s Application Platform), provided by the European Space Agency, can be used for landslide detection, as in the case study near Pissouri, where a landslide was triggered by heavy rainfall on 15 February 2019, which caused a major disturbance to everyday commuters since the motorway connecting the cities of Limassol and Paphos remained closed for more than a month. The Coherent Change Detection (CCD) methodology was applied successfully by detecting the phenomenon under study accurately, using two indicators (the coherence di erence and the normalized coherence di erence). Receiver Operating Characteristic (ROC) analysis was carried out to measure their performance with the coherence di erence having an overall accuracy of 93% and the normalized coherence di erence having an overall accuracy of 94.8% for detecting the landslide and non-landslide areas. The probability of landslide detection was 63.2% in the case of the coherence di erence and increased to 73.7% for the normalized coherence di erence, whereas the probability of false alarm for both indicators was approximately 1%.
Description: The authors would like to acknowledge the “CUT Open Access Author Fund” for covering the open access publication fees of the paper.
URI: https://ktisis.cut.ac.cy/handle/10488/18919
ISSN: 2072-4292
DOI: 10.3390/rs12101560
Rights: © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license
Type: Article
Affiliation : Cyprus University of Technology 
ERATOSTHENES Centre of Excellence 
Appears in Collections:Publications under the auspices of the EXCELSIOR H2020 Teaming Project/ERATOSTHENES Centre of Excellence

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