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Results of a PSInSAR™ project carried out by the Regional Agency for Environmental Protection (ARPA) in Piemonte Region (Northern Italy) are presented and discussed. A methodology is proposed for the interpretation of the PSInSAR™ data at the regional scale, easy to use by the public administrations and by civil protection authorities. Potential and limitations of the PSInSAR™ technique for ground movement detection on a regional scale and monitoring are then estimated in relationship with different geological processes and various geological environments.

The Permanent Scatterers (PS) technique is an advanced tool for processing series of interferometric SAR data aiming at millimetric precision ground deformation mapping. The approach is based on a joint time-space-acquisition geometry analysis that is carried out at individual point-wise radar targets. The aim of this paper is twofold: (1) describe the main issues related to the precision of PS products; (2) show preliminary PS results obtained using, instead of ESA-ERS scenes, data acquired by other spaceborne SAR platforms characterized by different acquisition parameters (namely RADARSAT and JERS). I.PRECSION ASSESMENT The estimation of the precision of PS output products (Line of Sight (LOS) displacement measurements, average deformation rate and PS elevation) is not an easy task to perform. The three main factors impacting on the precision of PS measurements are:1. atmospheric phase artifacts (tropospheric and ionospheric delays), creating the so-called Atmospheric Phase Screen (APS) superimposed on each SAR acquisition; 2. state vectors errors, creating the so-called "orbital fringes" (low order phase polynomials) on the differential interferograms; 3. temporal and geometrical decorrelation, affecting individual PS. A full statistical characterization of these contributions is not straightforward [3], here it will suffice to recall that tropospheric phase components are characterized by high spatial correlation, baseline errors and ionospheric effects usually generate phase pattern well fitted by means of a low order phase polynomial, and decorrelation phenomena give rise to phase noise that is uncorrelated both spatially and temporally. In the following, we will briefly discuss how important is the use of a multi-interferogram approach to improve DInSAR data quality and we will highlight the major statistical features of PS results.One of the first steps of PS Technique is the generation of N differential interferograms with respect to the same master acquisition [1] [2]. This operational strategy allows one to carry out the interferometric analysis in a simple, effective, and elegant mathematical framework, where the user can find useful tools also for accuracy and precision assessment. Problems related to optimum data selection and clustering (which data should I use? How many and what interferograms should I generate?) as well as the difficulties related to the correlation of partial results (most of the times data coming from different interferograms cannot be considered independent, since they have common images) are no more present. For each interferogram i, the phase can be modeled as:

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