We present a new method for the automatic classification of Persistent Scatters Interferometry (PSI) time series based on a conditional sequence of statistical tests. Time series are classified into distinctive predefined target trends, such as uncorrelated, linear, quadratic, bilinear and discontinuous, that describe different styles of ground deformation. Our automatic analysis overcomes limits related to the visual classification of PSI time series, which cannot be carried out systematically for large datasets. The method has been tested with reference to landslides using PSI datasets covering the northern Apennines of Italy. The clear distinction between the relative frequency of uncorrelated, linear and non-linear time series with respect to mean velocity distribution suggests that different target trends are related to different physical processes that are likely to control slope movements. The spatial distribution of classified time series is also consistent with respect the known distribution of flat areas, slopes and landslides in the tests area. Classified time series enhances the radar interpretation of slope movements at the site scale, pointing out significant advantages in comparison with the conventional analysis based solely on the mean velocity. The test application also warns against potentially misleading classification outputs in case of datasets affected by systematic errors. Although the method was developed and tested to investigate landslides, it should be also useful for the analysis of other ground deformation processes such as subsidence, swelling/shrinkage of soils, or uplifts due to deep injections in reservoirs

Abstract. We present a new method for the automatic classification of Persistent Scatters Interferometry (PSI) time series based on a conditional sequence of statistical tests. Time series are classified into distinctive predefined target trends (such as uncorrelated, linear, quadratic, bilinear and discontinuous) that describe different styles of ground deformation. Our automatic analysis overcomes limits related to the visual classification of PSI time series, which cannot be carried out systematically for large datasets. The method has been tested with reference to landslides using PSI datasets covering the northern Apennines of Italy. The clear distinction between the relative frequency of uncorrelated, linear and non-linear time series with respect to mean velocity distribution suggests that different target trends are related to different physical processes that are likely to control slope movements. The spatial distribution of classified time series is also consistent with respect the known distribution of flat areas, slopes and landslides in the tests area. Classified time series enhances the radar interpretation of slope movements at the site scale, pointing out significant advantages in comparison with the conventional analysis based solely on the mean velocity. The test application also warns against potentially misleading classification outputs in case of datasets affected by systematic errors. Although the method was developed and tested to investigate landslides, it should be also useful for the analysis of other ground deformation processes such as subsidence, swelling/shrinkage of soils, uplifts due to deep injections in reservoirs.

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The monitoring of surface subsidence is an important aspect in many underground mines. There are various ground-based methods that can be used for deformation monitoring, including optical levelling, GPS, and tiltmeters. This study proposes the use of satellite-based InSAR for the monitoring of surface movement over the Metropolitan Mine, an underground coal mine located in the Southern Coalfields of New South Wales, Australia where ground subsidence has been documented. An advanced multi-image InSAR approach, characterised by a high density of measurement points and millimetre precision, is applied to illustrate how results provide an overview of surface displacement dynamics before, during and after active mining. Two stacks of ENVISAT radar imagery (87 total images) acquired between June 2006 and August 2010 were analysed with the SqueeSAR™ algorithm to reconstruct ground movement patterns during this period. Movements were assessed on a 35-day interval (the revisitation frequency of the ENVISAT satellite), and a time series of deformation was generated for every measurement point. The use of two image stacks acquired from different viewing geometries allowed both the vertical and east-west components of ground movement over this site to be determined. Results illustrate the surface-level impact of underground mining by quantifying the spatial extent and timing of surface movement. The precision of the InSAR data were briefly assessed by comparing results with ground-based GPS survey measurements. While the timing and direction of movements were similar, the comparison was limited by the lack of both spatial and temporal overlap of the data sets. The use of a radar satellite with a higher temporal frequency is recommended for future monitoring of this site. The Southern Coalfield of New South Wales in Australia contains extensive regions of valuable coal deposits. Many of these coal resources are vulnerable to mining-induced subsidence, which may result in operational liability and/or ecological impacts on the ground surface. The ability to track and monitor these surface-level impacts caused by underground mining activities is a key element in the development of sustainable mining practices and concepts. However, conventional monitoring methods (i.e. ground-based) can be labour intensive, time-consuming and costly when requiring detailed spatial coverage over large areas. Furthermore, field surveys can be challenging to implement in variable terrain and in potentially unsafe or restricted access conditions. Ground-based techniques have been used in several cases to gather detailed subsidence data over localised areas, or hot spots. For instance, Strata Control Technology (SCT), a company engaged in providing solutions to mining geotechnical and strata control problems, has measured ground movements via three dimensional surveying at the Baal Bone, Clarence and Springvale Collieries (Mills, 2001). At Illawarra Coal, a GPS network of control points extending beyond the mining region was used to identify possibl...

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