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Climate change and anthropogenic impact are intensifying the frequency and intensity of extreme flood events. This is particularly worrying in the Mediterranean area, which is highly vulnerable and therefore subject to increased flood risk. The monitoring of flooded areas at high-resolution plays an important role in all phases of disaster management, from alert to the emergency and civil protection phase, up to damage assessment, for compensation and risk reduction purposes.This study aims at the multi-temporal analysis of remote sensing data, mainly radar data, through the implementation of a semi-automated system for the high-resolution mapping of river flooding effects. The objective is also to develop a system based on the fusion of different data sources and for different land cover types. The system includes an algorithm for the computation of multi-temporal, probabilistic flood maps, based on the analysis of amplitude series (in dB) of a stack of SAR images, acquired both in areas with permanent water and in areas with potential flooding. Exploiting a Bayesian inference framework, conditioned probabilities are estimated for the presence of water. The procedure relies on the temporal modelling of the SAR amplitudes time series, in order to account for seasonal and other slow temporal trends, and thus highlighting floods as events causing abrupt variations of the backscatter, lasting for a single or a few acquisitions. The methodology is particularly suited to data from sensors characterized by a high temporal frequency, such as the European Sentinel-1 constellation, whose two sensors acquire with the same geometrical configuration every 6 days over Europe. In parallel, a land use classification, at high resolution, is produced for each year within the period of acquisition of the satellite image stack (late 2014 to present) using Google Earth Engine [1]. This cloud-based platform makes it easy to access high-performance computing resources for processing geospatial data, allowing for the independent development of algorithms and subsequently specific applications. This supervised classification, achieved with the 'random forest' machine learning technique, is obtained through the combined use of SAR Sentinel 1 and optical Sentinel 2 images, over each entire year of interest. We show how the combination of these techniques can help gaining insight on the land cover, and on the expected changes of their appearance in the remotely sensed data in flooded conditions. This information can be used to improve the performance of the monitoring algorithm over various land cover scenarios and climatic settings.The procedure is tested over the Metaponto plain, in the Basilicata region (southern Italy). The proposed methodologies can however be used for other contexts affected by similar events, in the Mediterranean area and worldwide.ReferencesGorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., Moore, R. (2017) - Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, Volume 202, 2017, Pages 18-27, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.06.031.

Multi-temporal SAR interferometry (MTInSAR), allows analysing wide areas, identifying critical ground instabilities, and studying the phenomenon evolution in a long time-scale. The identification of MTInSAR displacements trends showing non-linear kinematics is of particular interest since they include warning signals related to pre-failure of natural and artificial structures. Recently, the authors have introduced two innovative indexes for characterising MTInSAR time series: one relies on the fuzzy entropy and measures the disorder in a time series [1], the other performs a statistical test based on the Fisher distribution for selecting the polynomial model that more reliably approximate the displacement trend [2].This work reviews the theoretical formulation of these indexes and evaluate their performances by simulating time series with different characteristics in terms of kinematic (stepwise linear with different breakpoints and velocities), level of noise, signal length and temporal sampling. Finally, the proposed procedures are used for analysing displacement time series derived by processing Sentinel-1 and COSMO-SkyMed datasets acquired over Southern Italian Apennine (Basilicata region), in an area where several landslides occurred in the recent past. The MTInSAR displacement time series have been analysed by using the proposed methods, searching for nonlinear trends that are possibly related to relevant ground instabilities and, in particular, to potential early warning signals for the landslide events. Specifically, the work presents an example of slope pre-failure monitoring on Pomarico landslide, an example of slope post-failure monitoring on Montescaglioso landslide, and few examples of structures (such as buildings and roads) affected by instability related to different causes.References[1] Refice, A.; Pasquariello, G.; Bovenga, F. Model-Free Characterization of SAR MTI Time Series. IEEE Geosci. Remote Sens. Lett. 2020, doi:10.1109/lgrs.2020.3031655.[2] Bovenga, F.; Pasquariello, G.; Refice, A. Statistically‐based trend analysis of mtinsar displacement time series. Remote Sens. 2021, doi:10.3390/rs13122302.Acknowledgments This work was supported in part by the Italian Ministry of Education, University and Research, D.D. 2261 del 6.9.2018, Programma Operativo Nazionale Ricerca e Innovazione (PON R&I) 2014–2020 under Project OT4CLIMA; and in part by Regione Puglia, POR Puglia FESR-FSE 204-2020 - Asse I - Azione 1.6 under Project DECiSION (p.n. BQS5153).

Multi-temporal SAR interferometry (MTInSAR) allows analysing wide areas, identifying critical ground instabilities, and studying the phenomenon evolution in a long time-scale. Nowadays satellite SAR constellations provide datasets covering time periods of several years with short revisit times, which allow investigating ground displacements showing non-linear kinematics. These are particularly interesting since they include warning signals related to pre-failure of natural and artificial structures. Recently, approaches have been proposed for recognising and analysing nonlinear displacements, which use different strategies. The authors have introduced two innovative indexes for characterising MTInSAR time series: one relies on the fuzzy entropy and measures the disorder in a time series, the other performs a statistical test based on the Fisher distribution for selecting the polynomial model that more reliably approximate the displacement trend. This work reviews the theoretical formulation of these indexes and evaluate their performances by simulating time series with different characteristics in terms of kinematic, level of noise, signal length and temporal sampling. Finally, the proposed procedures are used for analysing displacement time series derived by processing real datasets acquired by both Sentinel-1 and COSMO-SkyMed constellations. In particular the hilly villages of Pomarico and Montescaglioso have been investigated, which are located in Southern Italian Apennine (Basilicata region), in an area where several landslides occurred in the recent past, causing damages to houses, commercial buildings, and infrastructures. The MTInSAR displacement time series have been analysed by using the proposed methods, searching for nonlinear trends that are possibly related to relevant ground instabilities and, in particular, to potential early warning signals for the landslide events affecting Mtescaglioso in 2013 and Pomarico in 2019. Acknowledgments - This work was supported in part by the Italian Ministry of Education, University and Research, D.D. 2261 del 6.9.2018, Programma Operativo Nazionale Ricerca e Innovazione (PON R&I) 2014–2020 under Project OT4CLIMA.

The Earth Observation is proven to be extremely useful in the management of the aftermaths of tsunami and earthquakes. ESA-Disaster Risk Reduction (DRR) is a project within the Earth Observation for Sustainable Development (EO4SD) program aiming to demonstrate on large scale in developing countries the usefulness of Earth Observation to reduce disaster risks and to support the management of the post-event rehabilitation and reconstructions. Among the activities of the project, the ESA-DRR team, following the dramatic event of the Sulawesi earthquake (28 September 2018) supported the Indonesian authorities, through the cooperation with the Asian Development Bank (ADB), the reconstruction and rehabilitation of Palu area. The provided EO services are presented.

Multi-temporal Interferometric SAR (InSAR) is a promising modality for spaceborne analysis of ground and structure deformations for predictive alerting. With increasing deployments of highly capable commercial satellites offering high spectral and spatial resolution, global coverage and site revisit rates, predictive structural instability/integrity assessment is becoming accessible at a fraction of cost of aerial and ground based solutions. This paper presents an overview of the opportunities and challenges in affordably monitoring new and longstanding large buildings, monuments and ground infrastructure. This is necessitated by increasingly intense exploitation of environmental resources, hydrogeological instabilities and risk of natural disasters in environments perturbed by human activities that has created new risks. Planetek's Rheticus® Displacement (RD) solution provides actionable, accurate assessment of land subsidence and slow changes. The underlying SPINUA (Stable Point Interferometry Even Over Unurbanized Areas) technique has already been shown to be robust in non- and scarcely urbanized areas. The talk will provide an overview of InSAR usage and multitemporal analysis processing chain to estimate ground displacement and velocity, including results from well-characterized distributed scatterers. New results are presented that combine Quantum Resonance Interferometry (QRI) SNR and SCR enhancement processing within RD computational pipeline for extending the reliability of displacement computations to complex, vegetated ground topography and reducing the number of ground fiducials. New results are presented on dataset acquired using ESA's Sentinel-l C-band InSAR instrument (with revisit time of 6 days) with high swath-width over a site in Southern Italy. QRI-enhanced displacement maps, compared in terms of the density of measured coherent targets and the precision of the estimated velocity, will be provided. The paper will discuss the utility of improved InSAR processing in all-weather, early warning land hazard monitoring satellite system.

Multi-temporal SAR interferometry (MTInSAR) provides mean displacement maps and displacement time series over coherent objects on the Earth surface, allowing analysis of wide areas to identify ground deformations, and studying evolution of displacement phenomena over long time scales. MTInSAR techniques have proven very useful for detecting and monitoring also slope instabilities.Nowadays, several satellite missions are available providing InSAR data at different wavelengths, spatial resolutions, and revisit times. The Italian X-Band COSMO-SkyMed constellation acquires data with spatial resolution reaching metric values, and provides revisit times of up to a few days, leading to an increase in the density of the measurable targets, thus improving the monitoring of local scale events as well as the detection of non-linear displacements. The recent Sentinel-1 C-band mission from the European Space Agency (ESA) provides a spatial resolution comparable to previous ESA SAR missions, but a nominal revisit time reduced to 6 days. By offering regular global-scale coverage, better temporal resolution and freely available imagery, Sentinel-1 improves the performance of MTInSAR for ground displacement investigations. In particular, the short revisit time allows a better time series analysis by improving the temporal sampling and thus the chances to catch pre-failure signals characterised by high rate and non-linear behaviour. Moreover, it allows collecting large data stacks in a short time periods, thus improving MTInSAR performance in emergency (post-event) scenarios. These characteristics are very promising for early warning of slope failure events and monitoring subsequent displacements trends. In this work, we present the results obtained by using both COSMO-SkyMed and Sentinel-1 data for investigating the ground stability of hilly villages located in Southern Italian Apennine (Basilicata region). In the area of interest, several landslides occurred in the recent past (e.g. Montescaglioso in 2013) and more recently (e.g. Pomarico in 2019), causing extensive damage to houses, commercial buildings, and infrastructures.SAR datasets acquired by COSMO-SkyMed and Sentinel-1 from both ascending and descending orbits have been processed by using the SPINUA MTInSAR algorithm, in order to exploit the potentials of these two satellite missions to investigate ground displacements related to slope instabilities. Mean velocity maps and displacement time series have been analysed looking, in particular, for non-linear trends that are possibly related to relevant ground instability episodes and, thanks to the high spatial resolution, useful in terms of early warning, in the case of rigid soil masses. Results are presented and discussed in relation to known events occurred in the area of interest.

Oggetto del documento è l'individuazione delle applicazioni di osservazione della Terra che meglio si prestano all'utilizzo di dati ottici acquisiti da una costellazione di satelliti in orbite molto basse con le caratteristiche della missione in oggetto. In particolare saranno esplorate le possibilità di avere: i) bassi tempi di rivisitazione grazie all'uso di più satelliti, ii) alta risoluzione geometrica e/o radiometrica; e di definire dei requisiti necessari all'implementazione delle applicazioni individuate in termini di: i) specifiche radiometriche dei sensori, ii) geometrie di acquisizione, iii) caratteristiche orbitali dei satelliti della costellazione.

Multi-temporal SAR interferometry (MTInSAR), by providing both mean displacement maps and displacement time series over coherent objects on the Earth, allows analysing wide areas, identifying critical ground instabilities, and studying the phenomenon evolution in a long time-scale. This technique has been proved to be very useful for detecting and monitoring also slope instabilities. Nowadays, several satellite missions are available providing InSAR data at different wavelengths, spatial resolutions, and revisit time. The high-resolution X-Band COSMO-SkyMed constellation, acquires data with spatial resolution reaching metric values, and provides revisit times of up to a few days, leading to an increase in the density of the measurable targets, thus improving the monitoring of local scale events as well as the detection of non-linear displacements. The recent Sentinel-1 C-band mission from the European Space Agency (ESA), provides a spatial resolution comparable to the previous ESA missions, but a nominal revisit time reduced to 6 days. By offering regular global-scale coverage, better temporal resolution and freely available imagery, Sentinel-1 improves the performance of MTInSAR for ground displacement investigations. In particular, the short revisit time allows a better time series analysis by improving the temporal sampling and the chances to catch pre-failure signals characterised by high rate and non-linear behaviour signals. Moreover, it allows collecting large data stacks in a short time period, thus improving the MTInSAR performance in emergency (post-event) scenarios. These characteristics are very promising for early warning of slope failure events and monitoring subsequent displacements trends. In this work, we present the results obtained by using both COSMO-SkyMed and Sentinel-1 data for investigating the ground stability of hilly villages located in Southern Italian Apennine (Basilicata region). In the area of interest, several landslides occurred in the recent past (e.g. on Montescaglioso in 2013) and also very recently (e.g. on Pomarico in 2019), causing damages to houses, commercial buildings, and infrastructures. SAR datasets acquired by COSMO-SkyMed and Sentinel-1 from both ascending and descending orbits have been processed by using SPINUA MTInSAR algorithm, in order to exploit the potentials of these two satellite missions to investigate ground displacements related to the slope instabilities. Mean velocity maps and displacement time series have been analysed looking, in particular, to non-linear trends that are possibly related to relevant ground instabilities and, thanks to the high spatial resolution, useful in terms of early warning, in the case of rigid soil masses. Results are presented and discussed in relation to the well know events occurred in the area of interest.

The traditional approach for coastal monitoring consists in ground investigations that are burdensome both in terms of logistics and costs, on a national or even regional scale. Earth Observation (EO) techniques can represent a cost-effective alternative for a wide scale coastal monitoring. Thanks to the all-weather day/night radar imaging capability and to the nationwide acquisition plan named MapItaly, devised by the Italian Space Agency and active since 2010, COSMO-SkyMed (CSK) constellation is able to provide X-band images covering the Italian territory. However, any remote sensing approach must be accurately calibrated and corrected taking into account the marine conditions. Therefore, in situ data are essential for proper EO data selection, geocoding, tidal corrections and validation of EO products. A combined semi-automatic technique for coastal risk assessment and monitoring, named COSMO-Beach, is presented here, integrating ground truths with EO data, as well as its application on two different test sites in Apulia Region (South Italy). The research has shown that CSK data for coastal monitoring ensure a shoreline detection accuracy lower than image pixel resolution, and also providing several advantages: low-cost data, a short revisit period, operational continuity and a low computational time.

We present an example of integration of persistent scatterer interferometry (PSI) and in situ measurements over a landslide in the Bovino hilltop town, in Southern Italy. First, a wide-area analysis of PSI data, derived from legacy ERS and ENVISAT SAR image time series, highlighted the presence of ongoing surface displacements over the known limits of the Pianello landslide, located at the outskirts of the Bovino municipality, in the periods 1995–1999 and 2003–2008, respectively. This prompted local authorities to install borehole inclinometers on suitable locations. Ground data collected by these sensors during the following years were then compared and integrated with more recent PSI data from a series of Sentinel-1 images, acquired from March 2014 to October 2016. The integration allows sketching a consistent qualitative model of the landslide spatial and subsurface structure, leading to a coherent interpretation of remotely sensed and ground measurements. The results were possible thanks to the synergistic operation of local authorities and remote sensing specialists, and could represent an example for best practices in environmental management and protection at the regional scale.

Remote sensing images find application in several different domains, such as land cover or land usage observation, environmental monitoring, and urbanization. This latter field has recently witnessed an interesting development with the use of remote sensing for infrastructural monitoring. In this work, we present an analysis of Sentinel-1 images, which were used to monitor the Italian provinces of Bologna and Modena located at the Emilia Region Apennines foothill. The goal of this study was the development of a machine learning-based detection system to monitor the deterioration of public aqueduct infrastructures based on Persistent Scatterer Interferometry (PSI). We evaluated the land deformation over a temporal range of five years; these series feed a k-means clustering algorithm to separate the pixels of the region according to different deformation patterns. Furthermore, we defined the critical areas as those areas where different patterns collided or overlapped. The proposed approach provides an informative tool for the structural health monitoring of underground infrastructures.

Multi-temporal Synthetic Aperture Radar Interferometry (MTInSAR) techniques allow detecting and monitoring millimetric displacements occurring on selected targets exhibiting coherent radar backscattering properties. Because of the bandwidth limitation of Synthetic Aperture Radar images, pulse responses are significantly affected by sidelobes that should be identified and masked out before the selection of coherent targets candidates. In fact, sidelobes often exhibit a coherent backscattering behavior and can be hence erroneously identified as persistent scatterers (PS). These artifacts can be also clearly visible in the images, in case of strong scatterers embedded in a surrounding weak clutter environment, such as the echoes generated by ships or buildings. The actual large amount of Geospatial Big Data fosters the implementation of automatic tools for sidelobe detection and cancelation. A number of algorithms have been proposed in the scientific literature for sidelobe reduction in Single-Look-Complex images, ranging from apodization methods to target extraction techniques. In this study we deal in particular with the Spatially Variant Apodization (SVA), a nonlinear filter based on cosine-on-pedestal weighting functions. This approach is attractive because it is able – in theory – to achieve a total sidelobe cancelation, without degrading the original image resolution, and for its computational efficiency. However, as shown in recent works, SVA suffers from several drawbacks: (i) it modifies the statistics of speckle-dominated areas, (ii) a negative bias is introduced in the reflectivity images over homogeneous areas, (iii) the amplitude and phase of the original images could be distorted by the SVA filtering. In particular, it has been demonstrated that SVA filtering compromise the quality of MTInSAR outcomes. Therefore, recent works propose the use of SVA for just identifying and masking out the radar coordinates of the detected side-lobes in the PS candidates position map. In this work we apply SVA for proper coherent target selection in COSMO-SkyMed STRIPMAP co-polarized interferometric data takes, acquired in onshore and offshore environments. In order to highlight both advantages and limitations of this technique, we analyze the performances of SVA approaches in terms of computational efficiency and robustness to focusing artifacts (responsible of non-ideal pulse responses). In particular, we test the SVA filtering both in simulation and in complex real scenarios, where sidelobes of close strong scatterers are mixed and superimposed upon each other. We show the outcomes of MTInSAR techniques before and after the application of SVA filtering. Acknowledgments The research is partly co-funded by the Italian Space Agency in the framework of the FAST4MAP project (“Fast & Advanced SAR Techniques for Monitoring & Alerting Processes” - ASI Contract n. 2015-020-R.0). CSK® Products© of ASI delivered by ASI under a license to use.