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Wide-area ground motion monitoring is nowadays achievable via advanced Differential Interferometry SAR (A-DInSAR) techniques which benefit from the availability of large sets of Copernicus Sentinel-1 images. However, it is of primary importance to implement automated solutions aimed at performing integrated analysis of large amounts of interferometric data. To effectively detect high-displacement areas and classify ground motion sources, here we explore the feasibility of a machine learning-based approach. This is achieved by applying the random forest (RF) technique to large-scale deformation maps spanning 2015–2018. Focusing on the northern part of Italy, we train the model to identify landslide, subsidence, and mining-related ground motion with which to construct a balanced training dataset. The presence of noisy signals and other sources of deformation is also tackled within the model construction. The proposed approach relies on the use of explanatory variables extracted from the A-DInSAR datasets and from freely accessible informative layers such as Digital Elevation Model (DEM), land cover maps, and geohazard inventories. In general, the model performance is very promising as we achieved an overall accuracy of 0.97, a true positive rate of 0.94 and an F1-Score of 0.93. The obtained outcomes demonstrate that such transferable and automated approach may constitute an asset for stakeholders in the framework of geohazards risk management.

The need for implementing efficient value-adding tools able to optimise Earth Observation data usage, compels the scientific community to find innovative solutions for the downstream of Earth Observation information. In this paper we present an unsupervised and automated approach based on Principal Component Analysis (PCA) and K-means clustering to detect patterns of natural or anthropogenic ground deformation from Interferometric Synthetic Aperture Radar (InSAR) Time Series. For our proof-of-concept, we focus on the Valle d’Aosta region (Northwest Italy) where mass wasting processes frequently occurs, interacting with human activities and infrastructures. The large volumes of Sentinel-1 data produced allows for retrieving horizontal and vertical Time Series from multi-geometry data fusion of Line-of-Sight (LOS) InSAR measurements. The added benefit of combining ascending/descending InSAR data and interpolating displacements in time at different time steps is here explored prior to data dimensionality reduction and feature extraction through PCA. The retrieved principal components serve as a continuous solution for cluster membership indicators in the K-means clustering method, allowing to define spatially and temporally coherent displacement phenomena. The signal of the ground deformation clusters is then deconstructed into the underlying trend and seasonality components to enhance the interpretability of the classified satellite InSAR features. Using InSAR Time series data spanning 2014–2020, the proposed approach detects several slope movements and anthropogenic deformations with both linear and seasonal displacement behaviours. The results demonstrate the potential applicability of our transferable approach to the development of automated ground motion analysis systems.

The recent release of European Ground Motion Service (EGMS) products implemented under the responsibility of the Copernicus Land Monitoring Service (CLMS) guarantees free and accessible Europe-wide ground motion data for ground deformation analysis at the local and regional scales. The need for value-adding services and tools for optimal dissemination of radar data from the Copernicus Sentinel-1 satellite mission urges the scientific community to find efficient solutions. A desktop R-based application with a user-friendly interface capable of automatically downloading and transforming EGMS products delivered as large .csv tiles, equivalent to a radar burst into geospatial databases, is presented here. EGMStream is a self-contained desktop app that enables users to systematically store, customize, and convert ground movement data into geospatial databases, burst per burst or for an area of interest directly selectable on the app interface.

In Italy, three different operational continuous monitoring experiences based on the exploitation of Multi Temporal Synthetic Aperture Radar data (MTInSAR) Sentinel-1 data are here depicted, and the results obtained in one year have been analysed. Tuscany region (Central Italy) has been the first region to implement such service, followed by Valle d’Aosta and Veneto regions (North-West and North-East Italy, respectively). In detail, the services benefit from regularly updated deformation maps (every 12 days) to promptly detect anomalies of deformation, i.e., trend variations in the time series of displacement. In this work, anomalies detected between September 2019 and September 2020 are thus correlated with several types of factors, either related to the environment, intrinsic of the data or derived from ancillary data. A statistical analysis has been performed on the three regions, and are discretized into five macro-areas, namely: i) spatial and temporal statistics, related to the geographic setting and the temporal distribution of the anomalies; ii) parametric, i.e., related to the interferometric processing; iii) triggering factors; iv) environmental and geological factors; v) urban setting. The results derived from the analysis of this work show the obvious differences between the three regions, highlighting distinct distributions of the anomalies according to the different settings of each study area. Furthermore, results were analyzed, to provide a summary of the main findings obtained, giving a first evaluation of the services and hypothesizing future further improvements and applications.