landslide monitoring using multi-temporal sar interferometry with advanced persistent scatterers identification methods and super high-spatial resolution terrasar-x images

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ID: 243843
2018
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Abstract
Landslides are one of the most common and dangerous threats in the world that generate considerable damage and economic losses. An efficient landslide monitoring tool is the Differential Synthetic Aperture Radar Interferometry (DInSAR) or Persistent Scatter Interferometry (PSI). However, landslides are usually located in mountainous areas and the area of interest can be partially or even heavily vegetated. The inherent temporal decorrelation that dramatically reduces the number of Persistent Scatters (PSs) of the scene limits in practice the application of this technique. Thus, it is crucial to be able to detect as much PSs as possible that can be usually embedded in decorrelated areas. High resolution imagery combined with efficient pixel selection methods can make possible the application of DInSAR techniques in landslide monitoring. In this paper, different strategies to identify PS Candidates (PSCs) have been employed together with 32 super high-spatial resolution (SHR) TerraSAR-X (TSX) images, staring-spotlight mode, to monitor the Canillo landslide (Andorra). The results show that advanced PSI strategies (i.e., the temporal sub-look coherence (TSC) and temporal phase coherence (TPC) methods) are able to obtain much more valid PSs than the classical amplitude dispersion (DA) method. In addition, the TPC method presents the best performance among all three full-resolution strategies employed. The SHR TSX data allows for obtaining much higher densities of PSs compared with a lower-spatial resolution SAR data set (Sentinel-1A in this study). Thanks to the huge amount of valid PSs obtained by the TPC method with SHR TSX images, the complexity of the structure of the Canillo landslide has been highlighted and three different slide units have been identified. The results of this study indicate that the TPC approach together with SHR SAR images can be a powerful tool to characterize displacement rates and extension of complex landslides in challenging areas.
Reference Key
zhao2018remotelandslide Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Feng Zhao;Jordi J. Mallorqui;Rubén Iglesias;Josep A. Gili;Jordi Corominas
Journal Journal of pharmacological sciences
Year 2018
DOI
10.3390/rs10060921
URL
http://www.mdpi.com/2072-4292/10/6/921
Keywords
psi

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