Correct use of multi-temporal Interferometric Synthetic Aperture Radar (InSAR) datasets to complement geodetic surveying for geo-hazard applications requires rigorous assessment of their precision and accuracy. Published inter-comparisons are mostly limited to ground displacement estimates obtained from different algorithms belonging to the same family of InSAR approaches, either Persistent Scatterer Interferometry (PSI) or Small BAseline Subset (SBAS); and accuracy assessments are mainly focused on vertical displacements or based on few Global Navigation Satellite System (GNSS) or geodetic leveling points. To fill this demonstration gap, two years of Sentinel-1 SAR ascending and descending mode data are processed with both PSI and SBAS consolidated algorithms to extract vertical and horizontal displacement velocity datasets, whose accuracy is then assessed against a wealth of contextual geodetic data. These include permanent GNSS records, static GNSS benchmark repositioning, and geodetic leveling monitoring data that the National Institute of Statistics, Geography, and Informatics (INEGI) of Mexico collected in 2014−2016 in the Aguascalientes Valley, where structurally-controlled land subsidence exhibits fast vertical rates (up to −150 mm/year) and a non-negligible east-west component (up to ±30 mm/year). Despite the temporal constraint of the data selected, the PSI-SBAS inter-comparison reveals standard deviation of 6 mm/year and 4 mm/year for the vertical and east-west rate differences, respectively, thus reassuring about the similarity between the two types of InSAR outputs. Accuracy assessment shows that the standard deviations in vertical velocity differences are 9−10 mm/year against GNSS benchmarks, and 8 mm/year against leveling data. Relative errors are below 20% for any locations subsiding faster than −15 mm/year. Differences in east-west velocity estimates against GNSS are on average −0.1 mm/year for PSI and +0.2 mm/year for SBAS, with standard deviations of 8 mm/year. When discrepancies are found between InSAR and geodetic data, these mostly occur at benchmarks located in proximity to the main normal faults, thus falling within the same SBAS ground pixel or closer to the same PSI target, regardless of whether they are in the footwall or hanging wall of the fault. Establishing new benchmarks at higher distances from the fault traces or exploiting higher resolution SAR scenes and/or InSAR datasets may improve the detection of the benchmarks and thus consolidate the statistics of the InSAR accuracy assessments.

Ground-Based Synthetic Aperture Radar Interferometry (GBInSAR) and Terrestrial Laser Scanning (TLS) were purposely integrated to obtain 3D interferometric radar point clouds to facilitate the spatial interpretation of displacements affecting archaeological monuments. The paper describes the procedure to implement this integrated approach in the real-world situations of surveillance of archaeological and built heritage. Targeted tests were carried out on the case study of the Domus Tiberiana sited along the northern side of the Palatino Hill in the central archaeological area of Rome, Italy, and displacements of the monument were monitored over almost one year of acquisition. The GBInSAR – TLS integration provided updated information about the condition of the archaeological structures in relation to their history of instability mechanisms, and did not highlighted a general worsening for the stability of the entire monument. Point-wise and prompt detection of displacement anomalies and/or sudden changes in displacement trends proved the suitability of the method to support early warning procedures, also to evaluate effects on the masonry due to human activities

In archaeological remote sensing, space-borne Synthetic Aperture Radar (SAR) has not been used so far to monitor ‘looting’ (i.e. illegal excavations in heritage sites) mainly because of the spatial resolution of SAR images, typically not comparable to the ground dimensions of looting features. This paper explores the potential of the new TerraSAR-X beam mode Staring Spotlight (ST) to investigate looting within a workflow of radar backscattering change detection. A bespoke time series of five single polarisation, ascending mode, ST scenes with an unprecedented azimuth resolution of 0.24 m was acquired over the archaeological site of Apamea in western Syria, from October 2014 to June 2015 with a regular sampling of one image every two months. Formerly included in the Tentative List of UNESCO, the site has been heavily looted from at least early 2012 to May 2014, as confirmed by Google Earth Very High Resolution (VHR) optical imagery. Building upon the theory of SAR imaging, we develop a novel conceptual model of ‘looting marks’, identify marks due to occurrence of new looting and discriminate them from alteration (e.g. filling) of pre-existing looting holes. ‘Looting marks’ appear as distinctive patterns of shadow and layover which are visible in the ground-range reprojected ST image and generated by the morphology of the holes. The recognition of looting marks within ratio maps of radar backscatter (σ0) between consecutive ST scenes allows quantification of the magnitude, spatial distribution and rates of looting activities. In agreement with the estimates based on Google Earth imagery, the ST acquired in October 2014 shows that ~ 45% of the site was looted. In the following eight months new looting happened locally, with holes mainly dug along the margins of the already looted areas. Texture values of ~ 0.31 clearly distinguish these holes from the unaltered, bare ground nearby. Hot spots of change are identified based on the temporal variability of σ0, and colour composites indicate where repeated looting and alteration of existing holes occurred. Most looting marks are observed north of the two main Roman decumani. Looting intensified almost steadily from December 2014, with over 1500 new marks in February–April 2015. The estimated rates of looting increased from 214 looting marks/month in October–December 2014 to over 780 marks/month in April–June 2015, and numerically express the dynamic nature of the phenomenon to which Apamea is still exposed. The method of identifying looting marks in VHR radar images therefore proves a reliable opportunity for archaeologists and image analysts to measure remotely the scale of looting and monitor its temporal evolution