a b s t r a c t Persistent Scatterer Interferometry (PSI) is a powerful remote sensing technique able to measure and monitor displacements of the Earth's surface over time. Specifically, PSI is a radar-based technique that belongs to the group of differential interferometric Synthetic Aperture Radar (SAR). This paper provides a review of such PSI technique. It firstly recalls the basic principles of SAR interferometry, differential SAR interferometry and PSI. Then, a review of the main PSI algorithms proposed in the literature is provided, describing the main approaches and the most important works devoted to single aspects of PSI. A central part of this paper is devoted to the discussion of different characteristics and technical aspects of PSI, e.g. SAR data availability, maximum deformation rates, deformation time series, thermal expansion component of PSI observations, etc. The paper then goes through the most important PSI validation activities, which have provided valuable inputs for the PSI development and its acceptability at scientific, technical and commercial level. This is followed by a description of the main PSI applications developed in the last fifteen years. The paper concludes with a discussion of the main open PSI problems and the associated future research lines.

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This paper describes a new approach to Persistent Scatterer Interferometry (PSI) data processing and analysis, which is implemented in the PSI chain of the Geomatics (PSIG) Division of CTTC. This approach includes three main processing blocks. In the first one, a set of correctly unwrapped and temporally ordered phases are derived, which are computed on Persistent Scatterers (PSs) that cover homogeneously the area of interest. The key element of this block is given by the so-called Cousin PSs (CPSs), which are PSs characterized by a moderate spatial phase variation that ensures a correct phase unwrapping. This block makes use of flexible tools to check the consistency of phase unwrapping and guarantee a uniform CPS coverage. In the second block, the above phases are used to estimate the atmospheric phase screen. The third block is used to derive the PS deformation velocity and time series. Its key tool is a new 2+1D phase unwrapping algorithm. The procedure offers different tools to globally control the quality of the processing steps. The PSIG procedure has been successfully tested over urban, rural and vegetated areas using X-band PSI data. Its performance is illustrated using 28 TerraSAR-X StripMap images over the metropolitan area of Barcelona.

Wildfires have major effects on forest dynamics, succession and the carbon cycle in the boreal biome. They are a significant source of carbon emissions, and current observed changes in wildfire regimes due to changes in climate could affect the balance of the boreal carbon pool. A better understanding of postwildfire vegetation dynamics in boreal forests will help predict the future role of boreal forests as a carbon sink or source. Time series of Normalized Difference Vegetation Index (NDVI) and Normalized Difference Shortwave Infrared Index (NDSWIR) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra satellite were used to investigate whether characteristic temporal patterns exist for stands of different ages in the Siberian boreal forests and whether their postwildfire dynamics are influenced by variables such as prewildfire vegetation cover. Two types of forests, evergreen needle-leaf (ENF) and deciduous needle-leaf (DNF), were studied by analysing a sample of 78 burned forest areas. In order to study a longer time frame, a chronosequence of burned areas of different ages was built by coupling information on location and age provided by a forest burned area database (from 1992 to 2003) to MODIS NDVI and NDSWIR time series acquired from 2001 to 2005. For each of the burned areas, an adjacent unburned control plot representing the same forest type was selected, with the aim of separating the interannual variations caused by climate from changes in NDVI and NDSWIR behaviour due to a wildfire. The results suggest that it takes more than 13 years for the temporal NDVI and NDSWIR signal to recover fully after wildfire. NDSWIR, which is associated to canopy moisture, needs a longer recovery period than NDVI, which is associated to vegetation greenness. The results also suggest that variability observed in postwildfire NDVI and NDSWIR can be explained partially by the dominant forest type: while 13 years after a fire NDVI and NDSWIR are similar for ENF and DNF, the initial impact appears to be greater on the NDVI and NDSWIR of ENF, suggesting a faster recovery by ENF.