Small scale landslide detection using Sentinel-1 interferometric SAR coherence
Journal
Remote Sensing
Date Issued
May 2020
DOI
10.3390/rs12101560
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%.
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%.
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