Geodaysit 2023

Multi-temporal SAR interferometry (MTInSAR), by providing both mean displacement maps and displacement time series over coherent objects on the Earth’s surface, allows analysing wide areas, identifying ground displacements, and studying the phenomenon evolution on long time scales. This technique has also been proven to be very useful for detecting and monitoring instabilities affecting both terrain slopes and man-made objects. In this contest, an automatic and reliable characterization of MTInSAR displacements trends is of particular relevance as pivotal for the detection of warning signals related to pre-failure of natural and artificial structures. Warning signals are typically characterised by high rates and non-linear kinematics. The Sentinel-1 (S1) C-band mission from the European Space Agency (ESA) as well as the high-resolution X-band COSMO-SkyMed (CSK) constellations from Italian Space Agency, both shorten the revisit times up to a few days, thus being very promising for detecting non-linear displacement trends related to warning signals. However, a detailed analysis of MTInSAR displacement products looking for specific trends, is often hindered by the large number of coherent targets (up to millions) to be inspected by expert users to recognize different signal components and also possible artifacts, such as, for instance, those related to phase unwrapping errors.

This work concerns the development of methods able to fully exploit the content of MTInSAR products, by automatically identifying relevant changes in displacement time series and to classify the targets on the ground according to their kinematic regime. We introduced a new statistical test based on the Fisher distribution with the aim of evaluating the reliability of a parametric displacement model fit with a determined statistical confidence. We also proposed a new set of rules based on the statistical characterization of displacement time series, which allows different polynomial approximations for MTInSAR time series to be ranked. The method was applied to model warning signals. Moreover, in order to measure the degree of regularity of a given time series, an innovative index was introduced based on the fuzzy entropy, which basically evaluates the gain in information by comparing signal segments of different lengths. This fuzzy entropy index, without postulating any a priori model, allows highlighting time series which show interesting trends, including strong non linearities, jumps related to phase unwrapping errors, and the so-called partially coherent scatterers. These procedures were used for analysing MTInSAR products derived by processing both S1 and CSK datasets acquired over Southern Italian Apennine (Basilicata region), in an area where several landslides occurred in the recent past. Both approaches were very effective in supporting the analysis of ground displacements provided by MTInSAR, since they helped focusing on a smaller set of coherent targets identifying areas or structures on the ground which deserved further detailed geotechnical investigations. Moreover, the joint exploitation of MTInSAR datasets acquired at different wavelengths, resolutions, and revisit times provided valuable insights, with CSK more effective over man-made structures, and S1 over outcrops.

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. Our analysis performed on CSK MTInSAR products over Pomarico was able to capture the building deformations preceding the landslide and the collapse. This allows the understanding of the phenomenon evolution, highlighting a change in velocities that occurred two years before the collapse. This variation probably influenced the dynamics of the landslide leading to the collapse of an area considered to be at a medium-risk level by the regional landslide risk map. Results from the analysis performed on S1 MTInSAR products were instead useful to identify post-failure signals within the Montescaglioso landslide body. The selected trends confirm the stability of the landslide area with some local displacements due to restoration works. In this case, the value of the MTInSAR displacement time series analysis emerges in the assessment phase of post-landslide stability, resulting in a useful support tool in the planning of safety measures in landslide areas.

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 ASI under the Project “CRIOSAR: Applicazioni SAR multifrequenza alla criosfera”, grant agreement N. 2021-12-U.0.

Multi-temporal SAR Interferometry (MTInSAR) techniques allow detecting and monitoring millimetric displacements occurring on selected point targets that exhibit coherent radar backscattering properties over time. Successful applications to different geophysical phenomena have been already demonstrated in literature. New application opportunities have emerged in the last years thanks to the greater data availability offered by recent launches of radar satellites, and the improved capabilities of the new space radar sensors in terms of both resolution and revisit time. Currently, different space-borne Synthetic Aperture Radar (SAR) missions are operational, e.g. the Italian COSMO-SkyMed (CSK) constellation and the Copernicus Sentinel-1 (S1) mission.

Each CSK satellite is equipped with an X-band SAR sensor that acquires data with high spatial resolution (3x3 m2), thus leading to a very high spatial density of the measurable targets and allowing the monitoring of very local scale events. Thanks to the nationwide acquisition plan “MapItaly”, CSK constellation covers the Italian territory with a best effort revisit time of 16 days since 2010.

S1 mission is instead operational since 2014 and acquires in C-band at medium resolution (5x20 m2) with a minimum revisit time of 12 days (only 6 days between 2016 and 2021, when the full S1 constellation was operational), thus allowing to monitor ground instabilities back in time almost all over the Earth. Moreover, all data acquired by the S1 mission are provided on an open and free basis by the European Space Agency (ESA) and the European Commission (EC), for promoting full utilization of S1 data, with the aim of increasing the scientific research, growing the EO markets and fostering the development of continuous monitoring services, such as the European Ground Motion Service (EGMS) and the Rheticus® Displacement Geo-information Service.

The EGMS is based on the MTInSAR analysis of S1 radar images at full resolution, updated annually, and provides consistent and reliable information regarding natural and anthropogenic ground motion over the Copernicus Participating States and across national borders.

Rheticus® offers monthly updates of the millimetric displacements of the ground surface, through the MTInSAR processing chain based on the SPINUA© algorithm (“Stable Point Interferometry even in Un-urbanized Areas”). Rheticus® is capable to process SAR images acquired by different SAR missions, including CSK and S1. Thanks to the technological maturity as well as to the wide availability of SAR data, these ground motion services can be used to support systems devoted to environmental monitoring and risk management. This work shows the results obtained in the framework of the SeVaRA project (“Environmental Risk Assessment Service”), coordinated by Omnitech srl. The goal of SeVaRA is to implement an innovative system for calculating an aggregate environmental risk index, derived from several parameters related to hydrogeological instability phenomena and/or Weather-related extreme events. In particular, the present work is focused on the analysis of the “Deformation Sub-System”, that has been designed for the computation of risk indices related to structural and ground instabilities (landslides). The first step consists in the Hazard Map computation, which requires the following input data:

• Susceptibility Map (i.e., the European Landslide Susceptibility Map, provided by the Joint Research Centre European Soil Data Centre)
• National mosaic of landslide hazard zones, provided by ISPRA (River Basin Plans PAI)
• Cumulated precipitations (derived by cumulating ground measurement data collected by weather stations, if available, or by interpolating hourly rainfall data provided by the Global Satellite Mapping of Precipitation service, GSMaP, offered by the JAXA Global Rainfall Watch)
• Land Cover Change (i.e., the CORINE Land Cover inventory)
• Seismic events inventory, provided by INGV, to account for earthquake-induced landslides
• MTInSAR ground displacement time series.

The last input is essential for detecting instable areas, whose MTInSAR displacement trend exhibits a significant velocity in the whole observation period and/or an acceleration in the acquisition dates of the last year. The SeVaRA “Deformation Sub-System” has been primarily designed to be interfaced with the Rheticus® Displacement Service, but it supports also products offered by the EGMS service as well as by other MTInSAR services available on the EO market. The final step consists in the computation of the landslide risk index, obtained by combining the previous hazard index with the vulnerability and the exposure of the area of interest. The results of this study over specific areas of interest will be presented and commented.

Acknowledgments

Study carried out in the framework of the SeVaRA project, funded by Apulia Region (PO FESR 2014/2020).

Slow and very slow landslides are quite common in territory which is involved in orogenetic processes like Italian territory. These movements are not immediately evident, since displacements are often a few millimetres per year, and they could be unknown.

Landslides are a common natural hazard that can cause significant damage to infrastructure, including bridges, tunnels, railways and buildings. In particular, slow landslides may have a long-term impact on bridges as they often occur over extended periods, and the resulting deformation can be difficult to detect. Remote sensing technologies have emerged as an effective tool for detecting slow landslides and monitoring their impact on bridges.

This work provides a comprehensive review of the interaction between slow and very slow landslides and bridges and their analysis using remote sensing techniques. First, the causes and types of landslides are discussed, with a focus on slow landslides and their impact on bridges. The several factors that contribute to slow landslides, including geology and geomorphology, are also presented.
Hence we introduce remote sensing technologies that have been used to detect ground displacement and monitor slow landslides, including satellite imagery and multi-temporal synthetic aperture radar interferometry. The use of remote sensing for analysing the impact of slow landslides on bridges is also examined.

Finally, the challenges and limitations of using remote sensing for analysing the interaction between slow landslides and bridges are discussed, including their spatial and temporal resolution, and the need for (i) ground truth data for calibration and validation and (ii) for interdisciplinary collaboration between engineers, geologists, and remote sensing experts.

The main findings of this study are presented, by highlighting the potential for remote sensing technologies to improve our understanding of the interaction between slow landslides and bridges.

Acknowledgements

This work is part of the project: “Analysis of the impacts on slow landslides based on remote sensing techniques”, granted by Apulian Regional Government, RIPARTI, project number 39786e0f.

This study presents a novel approach to monitor oil spills and ships using Synthetic Aperture Radar (SAR) raw data and deep learning techniques. The proposed methodology involves several steps including pre-processing (focusing, filtering and land sea mask), semantic segmentation, and classification using a deep convolutional neural network (DCNN) model, as well as real-time (FFT-based) processing to ensure a fast response.

To train the DCNN model, the study combined three datasets: CleanSeaNet, TenGeoP-SARwv, and GAP_OilSpill_DB. The first two datasets are publicly available, while the third dataset was specifically built by the authors by integrating known and documented case studies from news articles and cases identified in the sea area in front of the port of Brindisi (Southern Italy), internally validated by expert GAP operators.

Data augmentation techniques were also utilized to improve the model's performance by generating additional training data. The DCNN model uses DeepLab v3+ based on ResNet-18 and is trained on a large dataset of SAR images that includes various types of oil spills, look-alikes, novelty objects, and ships.

The proposed system is optimized to process data on board the satellite to ensure a real-time response. The system transmits images to the ground segment only if there is an event of interest (e.g. a novelty object or an oil spill detected eventually involving the nearest ships).

The study demonstrates that the proposed approach provides a promising solution for real-time monitoring of oil spills, ships and novelty objects using satellite SAR raw data. The use of deep learning and data augmentation techniques can significantly improve the accuracy and speed of detection, which can ultimately lead to better environmental management and oil spill response. .Additionally, the proposed approach can be applied to a variety of SAR datasets and has the potential to be integrated with existing oil spill response systems.

Acknowledgments 

This work was carried out in the framework of the APP4AD project (“Advanced Payload data Processing for Autonomy & Decision”, Bando ASI “Tecnologie Abilitanti Trasversali”, Codice Unico di Progetto F95F21000020005), funded by the Italian Space Agency (ASI). ERS, ENVISAT and Sentinel-1 data are provided by the European Space Agency (ESA).

Giacomo Caporusso(1), Alberto Refice(1), Domenico Capolongo(2), Rosa Colacicco(2), Raffaele Nutricato(3), Davide Oscar Nitti(3), Francesco P. Lovergine(1), Fabio Bovenga(1), Annarita D’Addabbo(1)
1 IREA-CNR – Bari, Italy
2 Earth and Geoenvironmental Sciences Dept., University of Bari, Italy
3 GAP srl, Bari, Italy

As part of the analysis of flood events, ongoing studies aim to identify methods of using optical and SAR data in order to be able to map in an ever more precise way the flooded areas that are defined following a flood. At the same time, institutions responsible for territorial security have concrete needs of both monitoring tools capable of describing the susceptibility to flooding and of forecast tools for events with a fixed return time, consistent with the hazard and risk approaches defined, for example, at European or National regulatory level.
As far as flood hazards are concerned, hydraulic modeling is currently the most widely used reference for responding to forecasting needs, while the concrete value of remote sensing support emerges in the monitoring context, given the possibility of examining historical series of images referring to any portion of the territory.
A statistical approach to the analysis of historical series of satellite images can take into consideration the study of the probability connected to the presence/absence of water in the area, through the analysis of specific indices derived from multi- and hyperspectral optical images (NDVI, NDWI, LSWI) and/or intensity, coherence and radar indices derived from SAR images. In particular, for the study of time series of the variables considered, algorithmic approaches of a probabilistic nature are suitable, such as the Bayesian model and the Theory of Extreme Values.
The objective of this work is the assessment of a methodology to return the historical series of the probability of flooding, as well as the corresponding maps, relating to a test area.
In this context we present some results related to the study of an agricultural area near the city of Vercelli (Northern Italy), characterized by the presence of widespread rice fields and affected by a major flood of the Sesia river in October 2020.
Sentinel-1 SAR images were considered, from which the intensity and interferometric coherence variables can be deduced. The hydrogeomorphological support consist of slope, Height Above the Nearest Drainage (HAND), and Land Cover maps. Through the Copernicus Emergency Management, the flood maps relating to the 2020 event were acquired, to validate the results.
Regarding the methodology, the probabilistic modeling of the InSAR intensity and coherence time stacks is cast in a Bayesian framework. It is assumed that floods are temporally impulsive events lasting a single, or a few consecutive acquisitions. The Bayesian framework also allows to consider ancillary information such as the above-mentioned hydrogeomorphology and satellite acquisition geometry, which allow to characterize the a priori probabilities in a more realistic way, especially for areas with low probability of flooding. According to this approach it is possible to express the posterior probability p(F|v) for the presence of flood waters (F) given the variable v (intensity or coherence) at a certain pixel and at a certain time t as a function of the a priori and conditioned probabilities, through the Bayes equation:
p(F|v) = p(v|F)p(F) / (p(v|F)p(F) + p(v|NF)p(NF)),
with p(F) and p(NF) = 1 − p(F) indicating respectively the a priori probability of flood or no flood, while p(v|F) and p(v|NF) are the likelihoods of v, given the two events.
The flood likelihood can be estimated on permanent water bodies, while, to estimate the likelihood of areas potentially affected by flood events, the residuals of the historical series are considered with respect to a regular temporal modeling of the variable v.
Gaussian processes (GP) are used to fit the time series of the variable v. GPs are valid alternatives to parametric models, in which data trends are modeled by "learning" their stochastic behavior by optimizing some "hyperparameters" of a given autocorrelation function (kernel). The residuals with respect to this model can be used to derive conditional probabilities and then plugged into the Bayes equation.
The availability of the flood maps will allow to tackle the forecasting aspect in the next future, taking the time series of satellite images as a reference.