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Persistent Scatterers Interferometry Hotspot and Cluster Analysis (PSI-HCA) for detection of extremely slow-moving landslides
- Lu, Ping, Casagli, Nicola, Catani, Filippo, Tofani, Veronica
- International journal of remote sensing 2012 v.33 no.2 pp. 466-489
- atmospheric disturbances, cluster analysis, landslides, synthetic aperture radar, watersheds, Italy
- The synthetic aperture radar (SAR) interferometry (InSAR) technique has already shown its importance in landslide mapping and monitoring applications. However, the usefulness of traditional differential InSAR applications is limited by disturbing factors such as temporal decorrelation and atmospheric disturbances. The Persistent Scatterers Interferometry (PSI) technique is a recently developed InSAR approach. It generates stable radar benchmarks (namely persistent scatterers, PSI point targets) using a multi-interferogram analysis of SAR images. The PSI technique has the advantage of reducing temporal decorrelation and atmospheric artefacts. The PSI technique is suitable for the investigation of extremely slow-moving landslides due to its capability to detect ground displacements with millimetre precision. However, the interpretation of PSI outputs is sometimes difficult for the large number of possible persistent scatterers (PSs). A new approach of PSI Hotspot and Cluster Analysis (PSI-HCA) is introduced here in order to develop a procedure for mapping landslides efficiently and automatically. This analysis has been performed on PSs in hilly and mountainous areas within the Arno river basin (Italy). The aim is to use PSs processed from 4 years (2003–2006) of Radarsat images for identifying areas preferentially affected by extremely slow-moving landslides. The Getis–Ord G ᵢ* statistic is applied in the study for the PSI-HCA approach. The velocity of PSs is used as weighting factor and the G ᵢ* index is calculated for each single point target. The results indicate that both high positive and low negative G ᵢ* values imply the clustering of potential mass movements. High positive values suggest the moving direction towards the sensor along the satellite line-of-sight (LOS), whereas low negative values imply the movement away from the sensor. Furthermore, the kernel function is used to estimate PS density based on these derived G ᵢ* values. The output is a hotspot map which highlights active mass movements. This spatial statistic approach of PSI-HCA is considered an effective way to extract useful information from PSs at a regional scale, thus providing an innovative approach for rapid mapping of extremely slow-moving landslides over large areas.