Change detection (CD) based on multi-temporal remote sensing imagery is a crucial step for various earth observation applications. While deep learning (DL) has revolutionized CD, its data-driven nature demands substantial labeled images for supervised model training, which is costly and time-consuming. This paper addresses the challenge of limited training samples by proposing a novel object-based change augmentation (OCA) method. Unlike conventional image-level augmentation methods that can introduce irrelevant contextual dependencies, OCA decomposes the augmentation process into few-shot object classification and foreground-background pasting, thereby generating in-distribution synthetic images with increased change diversity. An object-based training strategy is developed to create a high-confidence binary classifier for pseudo-semantic segmentation, facilitating the copy-paste operation. Experimental results on the very high-resolution remote sensing images demonstrate the superior performance of OCA compared to existing augmentation-based and generation-based methods. A comprehensive analysis of parameter sensitivity, adaptability to varying training data volumes, and compatibility with diverse CD methods validate its robustness. This approach provides a practical and effective solution for few-shot CD scenarios, advancing the applicability of DL-based CD methods in training data-limited environments. Codes and data are available: https://github.com/openrsgis/OCA.

Agricultural drought is one of the most critical effects of climate change. This work proposes a machine learning based approach for agricultural drought monitoring that integrates seven standard remote sensing indices computed from Sentinel-2 multispectral imagery, agricultural drought damage percentage assessed in situ and six meteo-climatic variables, including Standardized Precipitation Index and Standardized Precipitation Evapotranspiration Index. We applied the approach to 117 agricultural fields in Italy, using a multinomial logistic regression model to classify the fields into zero-risk, medium-risk, and high-risk drought damage classes. The overall performances of the proposed classification model, summarized by an F1 score equal to 0.61, are not particularly encouraging as the model struggles to distinguish between medium-risk damage and high-risk damage classes. Nonetheless, the model shows promising results in identifying fields with zero drought damage and could be applied to reduce the time and cost of in situ measurements by excluding fields with no damage from the ground data collection.

Remote sensing has increasingly supported archaeological and cultural heritage applications over the past century, and satellite Synthetic Aperture Radar (SAR) has played a key role in advancing this application field. In this paper, two case studies from the wider Province of Rome (Italy) are exploited to investigate SAR imaging capabilities for archaeological prospection and heritage site protection. Medium to very high spatial resolution SAR data acquired by RADARSAT-2, Sentinel-1, ALOS-1 and COSMO-SkyMed are used to trial the detection of crop marks at (semi-)buried and sub-surface archaeological features in Ostia-Portus. Big data stacks of Sentinel-1 imagery are processed with the parallelized Small BAseline Subset (SBAS) Interferometric SAR (InSAR) method to monitor the stability of cultural heritage assets within the UNESCO World Heritage Site of Rome.

The planetary crisis regarding water resources means that new methods are needed to monitor large areas of water basins that are threatened by chemical and natural pollutants, together with climate change. With the aim to monitor the pollution status of some Sites of National Interest (SIN) which represent very large contaminated Italian areas classified as dangerous, we are applying new or already well established algorithms to optical satellite images. In particular, a recently introduced oil spill detection algorithm [1] was able to, consistently and reliably, confirm the presence of oil in five polluted lake waters analysed. The combination of this algorithm with metagenomic analysis of the spill areas detected by the satellite allowed to identify drivers of the microbial response to oil pollution. Based on ortholog abundances, metabolic pathway reconstruction carried out in Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt2) software highlighted the degradative capacity of these microorganisms. These microorganisms could be suitable candidates for treatment of crude oil, aromatic hydrocarbons and the desulfurization of persistent petroleum substances like dibenzothiophene. Building upon this recent algorithmic development, the SatellOmic project funded by the Italian Space Agency (ASI) focuses on the pre-operational application of such integrated approach combining satellite sensing and metagenomics analyses for real-time monitoring of water bodies threatened by oil spills, as well as for the design of recovery strategies based on the use of valuable hydrocarbonoclastic microorganisms.

The GEORES project is funded by the Italian Space Agency (ASI) and aims to develop a geospatial application meant to improve environmental sustainability and resilience to climate changes in urban areas, based on the synergistic use of the most advanced Earth Observation (EO) technologies, Artificial Intelligence (AI) and eXplainable AI (XAI). GEORES is organized into four main modules to support management of the main risks associated with land degradation: (1) Sediment Connectivity; (2) Land Displacement; (3) Urban Floods; (4) Urban Wildfires. For each module, EO data, calculation models and algorithms are integrated to identify "hot-spots" of urban and peri-urban territory at high risk from the point of view of land degradation caused by phenomena of hydrogeological instability, sediment flow or vegetation fires. The extracted information is expressed with specific indicators ("geo-analytics") calculated dynamically and automatically. The demonstration is undertaken in the Metropolitan City of Bari and Gargano Promontory, Apulia Region, southern Italy, and foresees the engagement of final users (i.e. Regional Civil Protection and Municipality of Bari).

The activities undertaken within the EcoNet project aim at the design and development of an integrated system for the monitoring of changes in surface waters natural status based on different sensoristic techniques. The proposed integration approach combines ground measurements and hyperspectral satellite images. The promising dialogue that occurs between these two multi-sensoristic technologies requires the implementation of appropriate tools for data handling and analysis which in this work are represented by Artificial Intelligence (AI), particularly suitable to retrieve very subtle relationships among the data. This integration can open enormous potential for overcoming the limits of traditional environmental monitoring and diagnostic techniques.

Bayesian estimation of posterior probabilities for the presence of floodwaters, coupled with accurate time series regression methods, show good performance in the monitoring of inundations at high temporal and spatial resolution from long stacks of synthetic aperture radar (SAR) data. We report results on the integration of SAR intensity and cascaded InSAR coherence time series in different polarization channels within a Bayesian framework. The method is being tested over sites in both northern and southern Italy, with X- and C-band SAR data. The results indicate some advantage in using more than one independent channel in the Bayesian inference for some types of land cover, in terms of area under the curve (AUC) when compared to independent flood maps acquired over known events. Stacks of surface water confidence levels computed over both test sites show promising characteristics, both on agricultural and coastal areas.

This paper provides an in-depth exploration of the Crop Module within the "EarTH Observation for the Early forecasT of Irrigation needS (THETIS)" project, specifically addressing challenges in precision agriculture. The study unfolds in the "Fortore" irrigation district (Southern Italy), focusing in particular on the 6/B district. The Crop Module, rooted in AquaCrop crop model architecture, emerges as a pivotal component in simulating and predicting crop growth, development, and water dynamics. It operates across leaf development, crop growth and productivity, and water balance levels, ensuring adaptability to daily temperature variations for real-time simulations. In interaction with the Soil Water Balance Module (SWB) and leveraging insights from satellite imagery, the Crop Module undergoes meticulous calibration and validation. The expected outcomes encompass increased precision in irrigation scheduling, early anticipation of water demand, and improved seasonal forecasting. This comprehensive approach positions stakeholders for informed decision-making, fostering sustainability and efficiency in agricultural practices.

This paper reports on a Spatial Decision Support System (SDSS) for the early, medium, and short-term forecast of irrigation needs in a semi-arid Mediterranean environment. The SDSS is developed in the context of the "EarTH Observation for the Early forecasT of Irrigation needS (THETIS)" project supported by the Italian Space Agency (ASI). THETIS integrates hydrologic and crop growth models with advanced Earth Observation (EO) products, Artificial Intelligence (AI) and a WEBGIS interface to provide basin-scale information for efficient planning of irrigation resources. The study describes initial results concerning the irrigated area of the Apulian Tavoliere (AT) served by the Reclamation Consortium of the Capitanata, Foggia, Italy.

The objective of this paper was to assess the use of multi-frequency SAR data for the mapping and monitoring of the spatial and temporal variability of land surface parameters and agricultural practices. In particular, the focus was on the retrieval of surface soil moisture (SSM) and vegetation water content (VWC) and on the classification and monitoring of irrigation extent and tillage practices at high resolution. The paper illustrates the data basis collected over three European sites, namely Apulian Tavoliere (Southern Italy), Jolanda di Savoia (Northern Italy), and Castilla y Leon (Spain), and the main results.

EcoNet is a joint project between the Italian Space Agency (ASI) and the Institute of Nanostructured Materials of the National Research Council of Italy (CNR-ISMN) with the participation of University of Tor Vergata (UTOV) that aims to develop an integrated sensor-driven system managed by artificial intelligence (AI) for monitoring surface waters near human settlements. The present paper outlines the foundational concepts of the project, the methodology and the ongoing activities, with an outlook towards the innovation for downstream applications in the field of aquatic ecosystem monitoring and management.

It is more than a decade that the Italian Space Agency (ASI) and the Japan Aerospace Exploration Agency (JAXA) are cooperating on the topic of disaster management, by sharing their expertise and satellite assets for the operational use of X-band and L-band SAR data. Over the years, mutual COSMO-SkyMed and ALOS-2 archives were created over Italy and Japan, ad hoc acquisitions were made in response to emergency requests, and data were exchanged to support joint SAR research activities related to Disaster Risk Management. The present paper provides the current status of the cooperation in light of the most recent emergencies, as well as the near future perspectives.

The Italian Space Agency (ASI) and the Indian Space Research Organisation (ISRO) established the joint Earth Observation Working Group (EOWG) that is currently focusing on hyperspectral (HYP) activity. Eleven projects are investigating the use of ASI’s PRISMA data for agriculture, land cover classification, mineral and soil mapping, Martian analogues, urban lakes, biodiversity and calibration. The paper provides an overview and first results after one year and half since the EOWG HYP was launched.

The "Multi-mission and multi-frequency SAR" program (2021-2023) of the Italian Space Agency supported the national community to develop algorithms combining SAR data collected in C, X and L-bands. Main achievements in terms of SAR data exploitation, integration and data fusion, and new products prototyping are presented with regard to agriculture, natural hazards, urban areas, cryosphere, sea and coast. Perspectives are finally outlined with regard to future SAR missions and towards downstream applications.

The unique spectral content provided by PRISMA's hyperspectral sensor gives the possibility to study the Earth's surface and environment from space in unprecedented detail. In this respect, our work presents the results of an Italian Space Agency-funded project aiming to develop eight prototypes for providing Value Added products based on such data. Prototypes focus on vegetation, urban areas, water quality, material detection, and natural hazards, combining multiple state-of-the-art techniques based on Machine Learning, physical models, and index-based algorithms. This is particularly relevant given the increasing demand for accurate information to address sustainable policies and support decision-making processes. Through a series of case studies, we highlight the versatility and utility of PRISMA's hyperspectral data for various scientific and operational applications.

In this paper the role of the COSMO-SkyMed constellation data in the framework of the Italian Space Agency (ASI)’s "Multimission and Multifrequency SAR" program has been analyzed. The key aspects regarding the provenance of the Principal Investigators, the application fields and information about the exploitation of the COSMO-SkyMed data, for both the first- and second-generation satellites, are highlighted in the study.

The Italian Space Agency (ASI) promotes the use of space assets to address not only scientific research questions, but also civilian applications and to the social-economic benefit of citizens. In the current scenario, when efforts are made to achieve the United Nations' 2030 Agenda Sustainable Development Goals (SDGs), the COSMO-SkyMed data exploitation initiatives launched by ASI - “Open Call for Science” and “Open Call for National Industry/Small and Medium Enterprises (SMEs)” - show increasing statistics proving that users are exploring how this state-of-the-art space technology can be used to address several applications feeding into SDGs. A selection of the successful experiences is discussed in relation to infrastructure monitoring, environmental assessment, understanding of and mitigation to natural hazards and climate change. The ASI's initiatives also helped companies to develop new businesses and cooperate with local administrations to address sustainability and land management issues.

This study presents a novel bridge monitoring approach for transport assets, based on the synergistic use of high-resolution (X-band) SAR imagery. A multi-temporal SAR Interferometry analysis is performed to detect potential issues related to the Rochester Bridge, located in Rochester, UK. A displacement map for the structure was produced using space-based SAR measurements acquired by the Italian constellation COSMO-SkyMed over the period 2017–2019, provided by the Italian Space Agency (ASI) in the framework of the Open-call for Science Project “Motib - ID742”. The outcomes of this study demonstrate that multi-temporal InSAR remote sensing techniques can be applied to complement information from non-destructive ground-based methods (e.g., ground-penetrating radars, laser scanners, accelerometers etc.), paving the way for future integrated approaches in the smart monitoring of infrastructure assets.

The study aims to consolidate and validate a suite of Earth Observation algorithms of interest for applications in agriculture. The algorithms are at different levels of maturity. Still, they share the objective of contributing to sustainable water management and food security. They deal with monitoring the soil moisture, the vegetation water content, the extent of irrigated areas and the changes in the surface roughness of agricultural fields. The paper introduces the data sets, the algorithms and discusses some examples of initial results.

When using multi frequency radars, “color” view and penetration are achieved by sensing the backscattered energy; in addition, it is also possible to compare the multi temporal amplitude signals. Thus, mechanical features of the targets like coherence, coherence changes, or travel path changes are made visible. When a multi-wavelength penetration of the medium is possible, the measurements can be extended to the third dimension, with frequency dependent penetration depth, using the tomography techniques. Applications of multi frequency SAR surveys, simultaneous or not, will be recalled referring also to the algorithms used for focusing, tomography, modeling and classification. This tutorial is part of an IGARSS 2022 special session, sponsored by ASI (Italian Space Agency), dedicated to multi frequency research.

The research results described in this paper have been obtained in the framework of the 2019–2022 ALGORITMI project between the Italian Space Agency (ASI) and the Institute of Applied Physics of the National Research Council (CNR-IFAC). The focus of the research was the development of innovative algorithms for the estimation of geophysical parameters of soil, snow, and vegetation with the aim of monitoring soil, snow cover and agricultural crop conditions. The estimation of soil moisture, vegetation biomass, snow water equivalent, and crop classification was improved by using retrieval algorithms based on machine- learning approaches and temporal series of SAR images from COSMO-SkyMed (CSK) and Sentinel-1 (S-1) missions, along with optical images from Sentinel-2. This paper provides an overview of the most recent and valuable results obtained during the project. In particular, the validation of soil moisture provided R=0.89 and RMSE=0.025 m3/m3by integrating data from S-1 and CSK and that one of snow water equivalent gave R=0.85 with RMSE=86.24 mm (CSK HIMAGE) and R=0.86 with RMSE=71.59 mm (CSK PP). Early mapping results showed an almost monotonic progression in overall accuracy over time higher than 90% by increasing the available images.

In continuity with the investments made on SAR technologies in the last two decades by the Italian Space Agency (ASI), the “Multi-mission and multi-frequency SAR” program currently supports ten R&D projects aiming to design, develop and test innovative algorithms for exploitation of multi-mission/multi-frequency SAR data. Perspectives of engineering and pre-operational development are demonstrated with regard to various application domains (i.e. agriculture, urban areas, natural hazards, cryosphere, sea and coast). Access to COSMO-SkyMed and SAOCOM images is facilitated by ASI to allow the research consortia to achieve an effective multi-frequency SAR data integration. The program represents a foundational step in the ASI's roadmap towards “scientific downstream applications”, i.e. applications enabled by the exploitation of mature and validated algorithms that have been originally developed to answer scientific questions and/or retrieve geophysical parameters, and have been brought to the stage that they can generate products that address specific user needs beyond scientific and academic purposes only.

This study aims at estimating the dry snow water equivalent (SWE) by using X-band SAR data from the COSMO-SkyMed (CSK) satellite constellation. Time series of CSK acquisitions have been collected during the dry snow period in the Alto Adige test site, in the Italian Alps, during the winter seasons from 2013 to 2015 and from 2019 to 2021. The SAR data have been analyzed and compared with the in-situ measurements to understand the X-band SAR sensitivity to SWE, which has been further assessed by Dense Media Radiative Transfer (DMRT) model simulations. The sensitivity analysis provided the basis for addressing the SWE retrieval from the CSK data, by exploiting two different machine learning (ML) techniques, namely Artificial Neural Networks (ANN) and Support Vector Regression (SVR). To ensure a statistical independence of training and validation processes, the algorithms are trained and tested using SWE predictions of the fully distributed snow model AMUNDSEN as reference data and are subsequently validated on the experimental dataset. Due to its influence on the CSK estimates, the effect of forest canopy was accounted for in the analysis. Depending on the algorithm, the validation resulted in a correlation coefficient 0.78 ≤ R ≤ 0.91, and a Root Mean Square Error 55.5 mm ≤ RMSE ≤ 87.4 mm between estimated and in-situ SWE. Further analysis and validation are needed; however, the obtained results seem suggesting the Cosmo-SkyMed constellation as effective tool for the retrieval of the dry snow water equivalent in alpine areas.

This study demonstrates the use of a novel satellite remote sensing approach to map coastal erosion vulnerability in the Italian site of Piscinas (Sardinia). We focused on the land/water transitional ecosystem, to identify potential coastal erosion phenomena. For this analysis, a synergistic approach between multi-spectral satellite data (Sentinel-2) and SAR imagery (COSMO-SkyMed and Sentinel-1B) was exploited. Two vulnerability maps were created: one longterm (2016–2018) and one short-term (wind event). The results confirm how the coastal vulnerability of this site seems to be linked to episodic events, consequently, the dune system of Piscinas might be considered safe from coastal erosion processes.

This work provides Synthetic Aperture Radar (SAR) big data investigations based on ESA's Geohazards Exploitation Platform (GEP) and the Parallel Small BAseline Subset (P-SBAS) Interferometric SAR (InSAR) on-demand service. Six Sentinel-1 IW SAR stacks for a total of 981 scenes acquired in 2014–2020 were processed to generate advanced ground deformation products providing key geo-information on natural and anthropogenic processes affecting 4 study areas in the Mediterranean: Tunis (Tunisia), the town of Gela (Italy), Methana volcano (Greece), and Crotone and the Capo Colonna promontory (Italy). The identified geohazards comprise subsidence due to land drainage, reclamation and compaction, soil consolidation and infrastructure settlement following engineering works, groundwater pumping for irrigation and industrial use, hydrocarbon extraction, slow-moving landslides and erosion landforms.

In this paper, an algorithm based on Convolutional Neural Networks (CNNs) was developed to correctly classify an agricultural area in central Italy, by using SAR images. This preliminary step is vital for mastering the different influence of crop types in SAR data before the implementation of algorithms devoted to estimate of vegetation biomass. In situ data collected on the test site were used for validating the CNN algorithm-based classification. After the agricultural species recognition, a sensitivity analysis between C-band Sentinel-1 and X-band COSMO-SkyMed backscatter coefficients and crop biomass was carried out, laying the foundation for the implementation of algorithms able to estimate the biomass of different crop types.

The monitoring of snow conditions in Alpine areas to support water management and avalanche warning applications would require the estimate of snow parameters, such as the snow water equivalent (SWE). In this research, COSMO-SkyMed (CSK) X-band SAR data were exploited to estimate the SWE. In-situ snow measurements (depth, density, snow grain radius, temperature) collected in South Tyrol (Italy), were used to simulate the X-band backscatter with the Dense Medium Radiative Transfer (DMRT) electromagnetic model. Two SWE retrieval algorithms based on machine learning approach were implemented. The algorithms are based on Artificial Neural Networks (ANN) and Support Vector Regression (SVR) and have been trained with both experimental data and DMRT model simulations. These algorithms were applied to a selection of CSK StripMap HIMAGE HH-polarized scenes collected over the test area. The obtained results are promising and they confirm the potential of SAR data at X-band to retrieve snow parameters, although the algorithm validation should be improved in the future, with more consistent measurement dataset.

Vessel detection and their activities in the sea can provide updates on latest trends in maritime trade. Space-borne synthetic aperture radar (SAR) can aid in detecting vessels in (almost) all weather conditions. In this study high resolution SAR data are used to analyze the maritime traffic activities, especially the underscored independency in transport trends in Wuhan, the major port-hub on the central Yangtze river in China, before and during the COVID-19 pandemic. Time-series of COSMO-SkyMed SAR images covering Wuhan from 2018 to 2020 were exploited to detect vessels. We applied multi-mode feature and shape (MMFS) image enhancement for fast and accurate vessel detection. Variations in number of vessels were detected, especially a huge drop was observed during the COVID-19 lockdown. HwkEye360 radio frequency monitoring data were used to validate our results.

An unprecedented time series of 293 COSMO-SkyMed StripMap SAR images acquired in 2011–2020 is exploited to investigate land subsidence and vehicle traffic in Wuhan, China. Persistent Scatterer Interferometry using linear and non-linear deformation models suggests that the spatial and temporal evolution of subsidence relates with the dynamic urban development across the main city districts. Traffic patterns along bridges were captured by detecting vehicles based on their azimuth shift caused by their across-track motion, and identified by type based on their radar cross section and speed. The results of vehicle counting confirm an increasing number of vehicles over the last years, which is currently an urban challenge for Wuhan.

Long stacks of Copernicus Sentinel-1 IW SAR images acquired in 2014-2019 are processed with the Small Baseline Subset (SBAS) and Permanent Scatterers (PS) Interferometric SAR (InSAR) methods to retrieve present-day land deformation rates across major cities in central Mexico. InSAR-derived subsidence velocity reflects intense groundwater pumping from shallow and deep aquifers for public, agricultural and industrial use, and consequent water level drop and aquifer depletion. In the capital Mexico City, as well as in the valleys of Toluca and Tulancingo, which all belong to aquifers recognized by the National Water Commission as in deficit in 2018, vertical deformation rates are as high as 40, 8 and 6 cm/year, respectively. Most pronounced rates occur mainly on highly compressible, Quaternary clay and silt-rich deposits. Rates of 6.5 cm/year are also observed at well-defined subsiding zones in Puebla, in response to groundwater abstraction for public-urban and industrial use.

The 4 year-long Recovery Observatory project was triggered by the Committee on Earth Observation Satellites (CEOS) to define a sustainable vision for increased use of satellite EO in support of recovery after 2016 Hurricane Matthew struck southwestern Haiti. ESA's Geohazards Exploitation Platform (GEP) was exploited to develop a SAR-based workflow to access, process and generate value-added products with Sentinel-1, TerraSAR-X and COSMO-SkyMed imagery that Haitian end-users can use to support their decision-making processes and recovery progress monitoring. Sentinel-1 IW data were processed with SNAP and SNAC tools to generate change detection products (e.g. coherence and amplitude change maps to detect flooded areas). InSAR ground deformation products generated with PS-InSAR FASTVEL and P-SBAS tools allowed the identification of unstable areas in the town of Jérémie and along its western coastline, which highlight potential concern for urban development and reconstruction. Ground truth and evidence of land instability were collected in the field in mid-2019 to validate satellite observations.

Modern agriculture is facing new challenges about food production for a growing population in a sustainable manner. Crop mapping at local and regional scale could provide valuable information in support of agricultural policy. This paper describes a field mapping investigation in a populated area in Tuscany (Italy). Satellite images from Sentinel-1 C-band and COSMO-SkyMed X-band SAR and Sentinel-2 optical sensors are input of classifiers based on deep learning and convolutional neural networks. Results pinpointed that the use of optical images allowed the best overall classification accuracy (99.7%), nevertheless X-band SAR imagery, providing an accuracy of 94.6%, could be a good substitute of optical indices in case of lack of cloud-free multispectral data.

The characterization of snow conditions and the estimation of snow water equivalent (SWE) are the main goals of this paper, achieved through the exploitation of multi-frequency SAR data at both C- and X-bands from Sentinel-1 (S-1) and COSMO-SkyMed (CSK) satellites, respectively. Dry/wet snow conditions have first been assessed using C-band S-1 images. Subsequently, a sensitivity analysis was carried out by using datasets of in-situ snow measurements (i.e. snow depth, density, snow grain radius, temperature and wetness) collected in South Tyrol region, in north-eastern Italy. Simulations based on the Dense Medium Radiative Transfer (DMRT) forward electromagnetic model were considered to interpret and assess the experimental findings. Two retrieval algorithms for SWE estimation from X-band SAR data were implemented. These algorithms are based on machine learning approaches, i.e. Artificial Neural Networks (ANN) and Support Vector Regression (SVR). The training of the algorithms accounts for experimental data and DMRT model simulations and, then is applied to a selection of X-band CSK StripMap HIMAGE scenes collected over the test area. The results are promising, and pave the way for further analysis and validation to exploit the potential of SAR for snow parameter retrieval.

The monitoring of the recovery phase in the aftermath of an emergency scenario is tackled in this paper in terms of a change-detection perspective and through the integration of multisensor, multisource, and contextual information associated with high resolution optical and SAR data. The method makes use of the Markov random field theory to integrate the spatial context and the temporal correlation associated with images acquired at different dates. Moreover, the adoption of a region-based approach allows the characterization of the geometrical structures in the images through the employment of multiple segmentation maps at different scales. The performances of the proposed approach are evaluated on pairs of COSMO-SkyMed/Pléiades images acquired over Haiti in the aftermath of Hurricane Matthew.

Stability of monuments and subsidence of residential quarters in Rome (Italy) are depicted based on geospatial analysis of more than 310,000 Persistent Scatterers (PS) obtained from Stanford Method for Persistent Scatterers (StaMPS) processing of 32 COSMO-SkyMed 3m-resolution HH StripMap ascending mode scenes acquired between 21 March 2011 and 10 June 2013. COSMO-SkyMed PS densities and associated displacement velocities are compared with almost 20 years of historical C-band ERS-1/2, ENVISAT and RADARSAT-1/2 imagery. Accounting for differences in image processing algorithms and satellite acquisition geometries, we assess the feasibility of ground motion monitoring in big cities and metropolises by coupling newly acquired and legacy SAR time series. Limitations and operational benefits of the transition from medium resolution C-band to high resolution X-band PS data are discussed, alongside the potential impact on the management of expanding urban environments.

36 ENVISAT ASAR images acquired in 2002 to 2010 along descending passes with nominal revisiting time of 35 days were processed over the whole region of Homs, western Syria, by implementing the low-pass Small Baseline Subset (SBAS) technique. More than 280,000 coherent pixels with ~100m ground resolution were obtained. We analysed pixel spatial distribution in respect of local geology and land use, to assess to what extent these factors can influence the performance of an interferometric deformation analysis in semi-arid environment. Filtering out the amount of pixels associated with the urban fabric of Homs and surrounding villages, it is apparent that limestone and marl units are less prone to generate coherent pixels if compared with the basalt units in the north-western sector of the processed region. The latter resulted in pixel density of ~50-60 pixels/km2, which is comparable with that found over urban settlements and man-made structures.

A holistic methodology combining conventional diagnostic investigations and kinematic analysis performed on 3D laser scanning survey is here proposed for rockfall hazard assessment and erosion patterns study, to map critical sectors and evaluate potential impacts on the conservation of cultural heritage sites built on unstable rock masses. Experiments carried out on the fortifications of Citadel, Gozo (Malta), led us to classify the susceptibility of the cliff surfaces to instability mechanisms, recognizing the wedge failure as the highest hazardous one. Observations on thin section of the rock textural properties and measurements of the resistance to abrasion completed the laser-based analysis, clarifying the intrinsic weakness of the outcropping limestones. Levels of conservation criticality were assigned to the rock mass sectors located underneath the historical buildings, and on site monitoring system was installed to follow the evolution of the crack patterns.