Alberto Refice

Probabilistic modeling of uncertainties in earthquake-induced landslide hazard assessment

Probabilistic analysis is gaining more attention in the field of landslide hazard assessment, due to the possibility of taking into account estimation uncertainties and spatial variability of geological, geotechnical, geomorphological and seismological parameters. In this paper, an implementation of a simple approach to derive probabilistic earthquake triggered landslide hazard maps is described. The method is based on the simplified Newmark slope stability model, applied on a pixel-by-pixel basis, which fully integrates into current GIS computational environments. Uncertainties and fluctuations in input parameters are considered by treating these quantities as statistical distributions. Various probability density functions can be simulated through Monte Carlo techniques on a pixel-by-pixel basis, and the simulated samples are retained through all the computing steps. This allows the resulting quantities to be cast into probabilistic hazard maps, without restrictions about the symmetry or the mathematical complexity of the underlying distributions. First results on a test landslide site in Southern Italy show good performances for realistic landslide hazard zonation. The simplicity of the adopted framework allows the current approach to be easily expanded and improved the current approach.

SAR and InSAR for Flood Monitoring: Examples With COSMO-SkyMed Data

We apply high-resolution, X-band, stripmap COSMO-SkyMed data to the monitoring of flood events in the Basilicata region (Southern Italy), where multitemporal datasets are available with short spatial and temporal baselines, allowing interferometric (InSAR) processing. We show how the use of the interferometric coherence information can help to detect more precisely the areas affected by the flood, reducing false alarms and missed identifications which affect algorithms based on SAR intensity alone. The effectiveness of using the additional InSAR information layer is illustrated by RGB composites of various combinations of intensity and coherence data. Analysis of multitemporal SAR intensity and coherence trends reveals complex behavior of various field types, which we interpret through a Bayesian inference approach, based on a manual identification of representative scattering and coherence signatures of selected homogeneous fields. The approach allows to integrate external, ancillary information to derive a posteriori probabilistic maps of flood inundation accounting for different scattering responses to the presence of water. First results of this semiautomated methodology, using simple assumptions for the SAR signatures and a priori information based on the distance from river courses, show encouraging results, and open a path to improvement through use of more complex hydrologic and topo-hydrographic information.

Impact of DEM-Assisted Coregistration on High-Resolution SAR Interferometry

Image alignment is a crucial step in synthetic aperture radar (SAR) interferometry. Interferogram formation requires images to be coregistered with an accuracy of better than a few tenths of a resolution cell to avoid significant loss of phase coherence. In conventional interferometric precise coregistration methods for full-resolution SAR data, a 2-D polynomial of low degree is usually chosen as warp function, and the polynomial parameters are estimated through least squares fit from the shifts measured on image windows. In case of rough topography or long baselines, the polynomial approximation may become inaccurate, leading to local misregistrations. These effects increase with spatial resolution of the sensor. An improved elevation-assisted image-coregistration procedure can be adopted to provide better prediction of the offset vectors. This approach computes pixel by pixel the correspondence between master and slave acquisitions by using the orbital data and a reference digital elevation model (DEM). This paper aims to assess the performance of this procedure w.r.t. the “standard” one based on polynomial approximation. Analytical relationships and simulations are used to evaluate the improvement of the DEM-assisted procedure w.r.t. the polynomial approximation as well as the impact of the finite vertical accuracy of the DEM on the final coregistration precision for different resolutions and baselines. The two approaches are then evaluated experimentally by processing high-resolution SAR data provided by the COnstellation of small Satellites for the Mediterranean basin Observation (COSMO/SkyMed) and TerraSAR-X missions, acquired over mountainous areas in Italy and Tanzania, respectively. Residual-range pixel offsets and interferometric coherence are used as quality figure.

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%.

A Wide-Band Approach to the Absolute Phase Retrieval in SAR Interferometry

Because of possible multiple solutions allowed, the unwrapping of interferometric fringe patterns in the spatial domain is an ill-posed problem which needs some a priori knowledge of the ground morphology for the solution of ambiguities. This is especially true for interferometric SAR (Synthetic Aperture Radar) data. In this paper we propose a different approach to InSAR processing for retrieving the height of ground points independently from each other, unlike most conventional phase unwrapping procedures, which operate in the spatial domain. The basic idea is to repeat raw data focusing by using range sub-bands centered at different frequencies, in order to find a point history of the interferometric phase variation vs. frequency. We introduce the general framework of the method together with considerations on the theoretical limits of applicability, then we report results of our simulations related to a wide-band SAR system. We show that, under certain conditions, height values can be retrieved over a network of coherent and strong scatterers, even when enclosed into low-coherence areas.

SIGNUM: A Matlab, TIN-based landscape evolution model

Several numerical landscape evolution models (LEMs) have been developed to date, and many are available as open source codes. Most are written in efficient programming languages such as Fortran or C, but often require additional code efforts to plug in to more user-friendly data analysis and/or visualization tools to ease interpretation and scientific insight. In this paper, we present an effort to port a common core of accepted physical principles governing landscape evolution directly into a high-level language and data analysis environment such as Matlab. SIGNUM (acronym for Simple Integrated Geomorphological Numerical Model) is an independent and self-contained Matlab, TIN-based landscape evolution model, built to simulate topography development at various space and time scales. SIGNUM is presently capable of simulating hillslope processes such as linear and nonlinear diffusion, fluvial incision into bedrock, spatially varying surface uplift which can be used to simulate changes in base level, thrust and faulting, as well as effects of climate changes. Although based on accepted and well-known processes and algorithms in its present version, it is built with a modular structure, which allows to easily modify and upgrade the simulated physical processes to suite virtually any user needs. The code is conceived as an open-source project, and is thus an ideal tool for both research and didactic purposes, thanks to the high-level nature of the Matlab environment and its popularity among the scientific community. In this paper the simulation code is presented together with some simple examples of surface evolution, and guidelines for development of new modules and algorithms are proposed.

DInSAR applications to landslide studies

Operational monitoring of slope instabilities by SAR interferometry poses a number of challenges due to the limited spatial extent of the landsliding areas and the rainy conditions usually associated with mass movement events. In this work, we present applications of DInSAR techniques to the assessment of the stability of landslide-prone areas. A long-term analysis over single, stable scatterers can be attempted, in order to overcome the intrinsic low-coherence conditions associated with landslide sites. The technique, known as the permanent scatterers approach, has been shown to give excellent results over areas with high densities of man-made targets. In this work, some aspects of the PS processing are reviewed and possible improvements are proposed to bring the method to give reliable results over sites with low urbanization such as the rural settings associated with landslide-prone areas in Southern Italy.

Weights determination for minimum cost flow InSAR phase unwrapping

The authors investigate the problem of generating weight masks to be used in conjunction with the minimum cost flow algorithm for InSAR phase unwrapping. Different weight values are automatically assigned to the interferogram pixels through a classification of several features extracted from the dataset. Examples on simulated images show that the approach gives good results, and confirm that less error is committed as more information is taken into account in generating the weights.

Use of InSAR data for landslide monitoring: a case study from southern Italy

Use of InSAR techniques in the study of unstable slopes has been suggested in recent works. However, in the ease of mass movements, which typically occur in high-relief terrain and are of limited areal extent, the detection of ground surface deformation is difficult. Moreover, the presence of vegetation cover and atmospheric effects introduces coherence loss and resolution problems in the analysis of interferometric pairs. Thus, extreme care must be taken in every step of interferometric SAR processing in order to obtain results that can be easily interpreted and be of practical utility in landslide hazard studies. The authors present the results of the application of InSAR and DInSAR techniques to a landslide test area located in the Southern Apennines. A number of SAR images was selected, whose dates coincide with periods of mass movement activity documented by in situ controls. DInSAR processing was conducted in order to assess the potential of satellite radar data for landslide monitoring. Coarse resolution is an important limiting factor for effective information extraction. Advanced processing approaches may help to overcome this limit.

Statistical mechanics approach to the phase unwrapping problem

The use of mean-field theory to unwrap principal phase patterns has been recently proposed. In this paper we generalize the mean-field approach to process phase patterns with arbitrary degree of undersampling. The phase unwrapping problem is formulated as that of finding the ground state of a locally constrained, finite size, spin-L Ising model under a non-uniform magnetic field. The optimization problem is solved by the mean-field annealing technique. Synthetic experiments show the effectiveness of the proposed algorithm.