A Bayesian Network for Flood Detection Combining SAR Imagery and Ancillary Data

Accurate flood mapping is important for both planning activities during emergencies and as a support for the successive assessment of damaged areas. A valuable information source for such a procedure can be remote sensing synthetic aperture radar (SAR) imagery. However, flood scenarios are typical examples of complex situations in which different factors have to be considered to provide accurate and robust interpretation of the situation on the ground. For this reason, a data fusion approach of remote sensing data with ancillary information can be particularly useful. In this paper, a Bayesian network is proposed to integrate remotely sensed data, such as multitemporal SAR intensity images and interferometric-SAR coherence data, with geomorphic and other ground information. The methodology is tested on a case study regarding a flood that occurred in the Basilicata region (Italy) on December 2013, monitored using a time series of COSMO-SkyMed data. It is shown that the synergetic use of different information layers can help to detect more precisely the areas affected by the flood, reducing false alarms and missed identifications which may affect algorithms based on data from a single source. The produced flood maps are compared to data obtained independently from the analysis of optical images; the comparison indicates that the proposed methodology is able to reliably follow the temporal evolution of the phenomenon, assigning high probability to areas most likely to be flooded, in spite of their heterogeneous temporal SAR/InSAR signatures, reaching accuracies of up to 89%

Multisource data integration to investigate one century of evolution for the Agnone landslide (Molise, southern Italy)

Landslides are one of the most relevant geohazards worldwide, causing direct and indirect costs and fatalities. Italy is one of the countries most affected by mass movements, and the Molise region, southern Italy, is known to be susceptible to erosional processes and landslides. In January 2003, a landslide in the municipality of Agnone, in the Colle Lapponi-Piano Ovetta (CL-PO) territory, occurred causing substantial damage to both structures and civil infrastructure. To investigate the evolution of the landslide-affected catchment over approximately one century, different data were taken into account: (i) literature information at the beginning of the twentieth century; (ii) historical sets of aerial optical photographs to analyse the geomorphological evolution from 1945 to 2003; (iii) SAR (Synthetic Aperture Radar) data fromthe ERS1/2, ENVISATand COSMO-SkyMed satellites tomonitor the displacement from 1992 to 2015; (iv) traditional measurements carried out through geological and geomorphological surveys, inclinometers and GPS campaigns to characterize the geological setting of the area; and (v) recent optical photographs of the catchment area to determine the enlargement of the landslide. Using the structure from motion technique, a 3D reconstruction of each set of historical aerial photographs was made to investigate the geomorphological evolution and to trace the boundary of the mass movements. As a result, the combination of multitemporal and multitechnique analysis of the evolution of the CL-PO landslide enabled an assessment of the landslide expansion, which resulted in a maximum length of up to approximately 1500 m. A complete investigation of the past and present deformational sequences of the area was performed to potentially plan further mitigation and prevention strategies to avoid possible reactivations

Machine Learning and Pattern Recognition Methods for Remote Sensing Image Registration and Fusion

In the last decade, the remote sensing world has dramatically evolved. New types of sensor, each one collecting data with possibly different modalities, have been designed, developed, and deployed. Moreover, new missions have been planned and launched, aimed not only at collecting data of the Earth's surface, but also at acquiring planetary data in support of the study of the whole Solar system. Indeed, such a variety of technologies highlights the need for automatic methods able to effectively exploit all the available information. In the last years, lot of effort has been put in the design and development of advanced data fusion methods able to extract and make use of all the information available from as many complementary information sources as possible. Indeed, the goal of this thesis is to present novel machine learning and pattern recognition methodologies designed to support the exploitation of diverse sources of information, such as multisensor, multimodal, or multiresolution imagery. In this context, image registration plays a major role as is allows bringing two or more digital images into precise alignment for analysis and comparison. Here, image registration is tackled using both feature-based and area-based strategies. In the former case, the features of interest are extracted using a stochastic geometry model based on marked point processes, while, in the latter case, information theoretic functionals and the domain adaptation capabilities of generative adversarial networks are exploited. In addition, multisensor image registration is also applied in a large scale scenario by introducing a tiling-based strategy aimed at minimizing the computational burden, which is usually heavy in the multisensor case due to the need for information theoretic similarity measures. Moreover, automatic change detection with multiresolution and multimodality imagery is addressed via a novel Markovian framework based on a linear mixture model and on an ad-hoc multimodal energy function minimized using graph cuts or belied propagation methods. The statistics of the data at the various spatial scales is modelled through appropriate generalized Gaussian distributions and by iteratively estimating a set of virtual images, at the finest resolution, representing the data that would have been collected in case all the sensors worked at that resolution. All such methodologies have been experimentally evaluated with respect to different datasets, and with particular focus on the trade-off between the achievable performances and the demands in terms of computational resources. Moreover, such methods are also compared with state-of-the-art solutions, and are analyzed in terms of future developments, giving insights to possible future lines of research in this field

Remote Sensing of Snow Cover Using Spaceborne SAR: A Review

The importance of snow cover extent (SCE) has been proven to strongly link with various natural phenomenon and human activities; consequently, monitoring snow cover is one the most critical topics in studying and understanding the cryosphere. As snow cover can vary significantly within short time spans and often extends over vast areas, spaceborne remote sensing constitutes an efficient observation technique to track it continuously. However, as optical imagery is limited by cloud cover and polar darkness, synthetic aperture radar (SAR) attracted more attention for its ability to sense day-and-night under any cloud and weather condition. In addition to widely applied backscattering-based method, thanks to the advancements of spaceborne SAR sensors and image processing techniques, many new approaches based on interferometric SAR (InSAR) and polarimetric SAR (PolSAR) have been developed since the launch of ERS-1 in 1991 to monitor snow cover under both dry and wet snow conditions. Critical auxiliary data including DEM, land cover information, and local meteorological data have also been explored to aid the snow cover analysis. This review presents an overview of existing studies and discusses the advantages, constraints, and trajectories of the current developments

Multisource data integration to investigate one century of evolution for the Agnone landslide (Molise, southern Italy)

Landslides are one of the most relevant geohazards worldwide, causing direct and indirect costs and fatalities. Italy is one of the countries most affected by mass movements, and the Molise region, southern Italy, is known to be susceptible to erosional processes and landslides. In January 2003, a landslide in the municipality of Agnone, in the Colle Lapponi-Piano Ovetta (CL-PO) territory, occurred causing substantial damage to both structures and civil infrastructure. To investigate the evolution of the landslide-affected catchment over approximately one century, different data were taken into account: (i) literature information at the beginning of the twentieth century; (ii) historical sets of aerial optical photographs to analyse the geomorphological evolution from 1945 to 2003; (iii) SAR (Synthetic Aperture Radar) data from the ERS1/2, ENVISAT and COSMO-SkyMed satellites to monitor the displacement from 1992 to 2015; (iv) traditional measurements carried out through geological and geomorphological surveys, inclinometers and GPS campaigns to characterize the geological setting of the area; and (v) recent optical photographs of the catchment area to determine the enlargement of the landslide. Using the structure from motion technique, a 3D reconstruction of each set of historical aerial photographs was made to investigate the geomorphological evolution and to trace the boundary of the mass movements. As a result, the combination of multitemporal and multitechnique analysis of the evolution of the CL-PO landslide enabled an assessment of the landslide expansion, which resulted in a maximum length of up to approximately 1500 m. A complete investigation of the past and present deformational sequences of the area was performed to potentially plan further mitigation and prevention strategies to avoid possible reactivations.This work was partially funded by the Spanish Ministry of Economy, Industry and Competitiveness (MINECO); the State Agency of Research (AEI); and the European Funds for Regional Development (FEDER) under projects TEC2017-85244-C2-1-P and TIN2014-55413-C2-2-P and the Spanish Ministry of Education, Culture and Sport under project PRX17/00439

A Human-Centered Framework for the Understanding of Synthetic Aperture Radar Images

The limited usage of SAR data in the end-user community and in applicative contexts testified the failure of the recent literature, in which the research privileged the automatic extraction of information at the expense of users' experience with data. The development of new products and processing frameworks providing user-friendly representations and extraction of the physical information is a necessary condition for the full exploitation of SAR sensors. In this Book, the necessity to restore users’ centrality in remote sensing data analysis is analyzed and achieved through the introduction of two new classes of RGB SAR products obtained via multitemporal processing, whose principal characteristics are the ease of interpretation and the possibility to be processed with simple, end-user-oriented technique. These proposed approach aims to definitely fill the gap between the academy and the applications. The rationale is to provide ready-to-use images, in which the technical expertise with electromagnetic models, SAR imaging and image processing has been absorbed in the products formation phase. In such way, the idea that SAR images are too complicated to be interpreted and processed without a high technical expertise in order to extract physical information is overcame

Google earth engine as multi-sensor open-source tool for supporting the preservation of archaeological areas: The case study of flood and fire mapping in metaponto, italy

In recent years, the impact of Climate change, anthropogenic and natural hazards (such as earthquakes, landslides, floods, tsunamis, fires) has dramatically increased and adversely affected modern and past human buildings including outstanding cultural properties and UNESCO heritage sites. Research about protection/monitoring of cultural heritage is crucial to preserve our cultural properties and (with them also) our history and identity. This paper is focused on the use of the open-source Google Earth Engine tool herein used to analyze flood and fire events which affected the area of Metaponto (southern Italy), near the homonymous Greek-Roman archaeological site. The use of the Google Earth Engine has allowed the supervised and unsupervised classification of areas affected by flooding (2013–2020) and fire (2017) in the past years, obtaining remarkable results and useful information for setting up strategies to mitigate damage and support the preservation of areas and landscape rich in cultural and natural heritage

Evidence of instability in previously-mapped landslides as measured using GPS, optical, and SAR data between 2007 and 2017: A case study in the Portuguese Bend Landslide Complex, California

Velocity dictates the destructive potential of a landslide. A combination of synthetic aperture radar (SAR), optical, and GPS data were used to maximize spatial and temporal coverage to monitor continuously-moving portions of the Portuguese Bend landslide complex on the Palos Verdes Peninsula in Southern California. Forty SAR images from the COSMO-SkyMed satellite, acquired between 19 July 2012 and 27 September 2014, were processed using Persistent Scatterer Interferometry (PSI). Eight optical images from the WorldView-2 satellite, acquired between 20 February 2011 and 16 February 2016, were processed using the Co-registration of Optically Sensed Images and Correlation (COSI-Corr) technique. Displacement measurements were taken at GPS monuments between September 2007 and May 2017. Incremental and average deformations across the landslide complex were measured using all three techniques. Velocity measured within the landslide complex ranges from slow (\u3e 1.6 m/year) to extremely slow (\u3c 16 mm/year). COSI-Corr and GPS provide detailed coverage of m/year-scale deformation while PSI can measure extremely slow deformation rates (mm/year-scale), which COSI-Corr and GPS cannot do reliably. This case study demonstrates the applicability of SAR, optical, and GPS data synthesis as a complimentary approach to repeat field monitoring and mapping to changes in landslide activity through time

Innovative Techniques for the Retrieval of Earth’s Surface and Atmosphere Geophysical Parameters: Spaceborne Infrared/Microwave Combined Analyses

With the advent of the first satellites for Earth Observation: Landsat-1 in July 1972 and ERS-1 in May 1991, the discipline of environmental remote sensing has become, over time, increasingly fundamental for the study of phenomena characterizing the planet Earth. The goal of environmental remote sensing is to perform detailed analyses and to monitor the temporal evolution of different physical phenomena, exploiting the mechanisms of interaction between the objects that are present in an observed scene and the electromagnetic radiation detected by sensors, placed at a distance from the scene, operating at different frequencies. The analyzed physical phenomena are those related to climate change, weather forecasts, global ocean circulation, greenhouse gas profiling, earthquakes, volcanic eruptions, soil subsidence, and the effects of rapid urbanization processes. Generally, remote sensing sensors are of two primary types: active and passive. Active sensors use their own source of electromagnetic radiation to illuminate and analyze an area of interest. An active sensor emits radiation in the direction of the area to be investigated and then detects and measures the radiation that is backscattered from the objects contained in that area. Passive sensors, on the other hand, detect natural electromagnetic radiation (e.g., from the Sun in the visible band and the Earth in the infrared and microwave bands) emitted or reflected by the object contained in the observed scene. The scientific community has dedicated many resources to developing techniques to estimate, study and analyze Earth’s geophysical parameters. These techniques differ for active and passive sensors because they depend strictly on the type of the measured physical quantity. In my P.h.D. work, inversion techniques for estimating Earth’s surface and atmosphere geophysical parameters will be addressed, emphasizing methods based on machine learning (ML). In particular, the study of cloud microphysics and the characterization of Earth’s surface changes phenomenon are the critical points of this work