As case study, the landslide of the Amyntaio’s mine in Greece at 10/6/2017 was selected. The landslide was rapidly and effectively detected through operational and automatic processes. Thus, rapid and reliable conclusions can be extracted for decision making and risk monitoring.
Operational, rapid and automatic landslide detection provides vital information for decision making and risk monitoring. Copernicus/ESA data can contribute to this topic as provide free use, efficient information, high temporal frequency, and big area cover. Multimodal approaches such a synergy of Sentinel 1 and 2 processes can lead to proper detection of landslide phenomena.
The main goal of this research is the rapid and automatic detection of large landslide phenomena using Sentinel 1 and 2 data applying several change detection algorithms.
Several change detection techniques were used to map the large landslide of the Amynteou mine. The used time series data from Copernicus program, i.e before the landslide and after the landslide, were: 2 SAR images type IW/SLC from Sentinel 1 (4/6/2017 and 10/6/2017) and 2 EO images type MSIL2A from Sentinel 2 (1/6/2017 and 21/6/2017). Concerning the Sentinel 1 data, change detection maps were extracted using the magnitude and the phase layer. On the other hand, concerning the Sentinel 2 data, change detection maps were extracted through direct image processing and machine learning using spectral information and annotated data. The landslide detection was performed via change detection techniques at ERDAS IMAGINE software 2016.1 (Imagine SAR Feature tool, Imagine SAR Interferometry tool, Imagine Objective tool, and Change Detection tools).
Used time series data from Sentinel 1 and 2
Change detection strategy and results using Sentinel 1 and Sentinel 2 data
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