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Multi-decadal data sets are already available from various types of EO sensors, but their effective exploitation is hindered by the lack of data centres which offer dedicated EO processing chains and high-performance processing (HPC) capabilities. Recognizing this need, TU Wien founded the EODC Earth Observation Data Centre for Water Resources Monitoring which aims at providing an independent science-driven platform that is transparent for its users and offers a high diversity and flexibility in terms of data sets and algorithms used.

One of the major advantages of the Sentinel-1 data is its capability to provide very high spatio-temporal coverage allowing the mapping of large areas as well as creation of dense time-series of the Sentinel-1 acquisitions. The SGRT software developed at TU Wien aims at automated processing of Sentinel-1 data for global and regional products. The first step of the processing consists of the Sentinel-1 data geocoding with the help of S1TBX software and their resampling to a common grid. These resampled images serve as an input for the product derivation. Thus, it is very important to select the most reliable processing settings and assess the geocoding uncertainty for both backscatter and projected local incidence angle images. Within this study, selection of Sentinel-1 acquisitions over 3 test areas in Europe were processed manually in the S1TBX software, testing multiple software versions, processing settings and digital elevation models (DEM) and the accuracy of the resulting geocoded images were assessed. Secondly, all available Sentinel-1 data over the areas were processed using selected settings and detailed quality check was performed. Overall, strong influence of the used DEM on the geocoding quality was confirmed with differences up to 80 meters in areas with higher terrain variations. In flat areas, the geocoding accuracy of backscatter images was overall good, with observed shifts between 0 and 30m. Larger systematic shifts were identified in case of projected local incidence angle images. These results encourage the automated processing of large volumes of Sentinel-1 data.

Glaciers are sensitive indicators of climate change. Particularly, glacier surface velocity constitutes a key parameter for estimating ice volume variations as response to global warming and its incidence in sea level rise. Several methodologies based in remotely sensed data have been employed for estimating ice velocity fields. They are mostly based in cross-correlating pairs of images in order to track features displacement between two dates. High ice flux velocity, which can reach more than 1 km/year, constitute a challenge for the existing methodologies, in practice limiting to a few days the time span between useful data. In this work we present an extension of the known Pixel Offset – Small Baseline Subsets (PO-SBAS) technique, that profit a set of successive Synthetic Aperture Radar (SAR) scenes for computing displacement time series and ice velocity fields. The algorithm is guided by a preliminary ice velocity model estimated from the data itself, which significantly improves the results reliability and reduces the overall computational cost. Furthermore, it implements a processing scheme that considers the displacement estimations (PO) quality in order to decide which pixels are included in the time-series inversion. The proposed technique is applied to 22 COSMO-Skymed SAR images of Viedma Glacier (Southern Patagonian Icefield, Argentina) spanning roughly a year. The results obtained are robust and make profit of the whole available dataset. Resulting mean velocity field and displacement time series show the algorithm suitability for retrieving and characterizing complex ice motion patterns. Fil: Euillades, Pablo Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Cuyo. Facultad de Ingeniería; Argentina Fil: Euillades, Pablo Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Cuyo. Facultad de Ingeniería; Argentina Fil: Riveros, Natalia Cecilia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Cuyo. Facultad de Ingeniería; Argentina Fil: Masiokas, Mariano Hugo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Provincia de Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Universidad Nacional de Cuyo. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales; Argentina Fil: Ruiz, Lucas Ernesto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Provincia de Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Universidad Nacional de Cuyo. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales; Argentina Fil: Pitte, Pedro Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Provincia de Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Universidad Nacional de Cuyo. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales; Argentina Fil: Elefante, Stefano. Istituto per il Rilevamento Elettromagnetico dell'Ambiente; Italia Fil: Casu, Francesco. Istituto per il Rilevamento Elettromagnetico dell'Ambiente; Italia Fil: Balbarani, Sebastian. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Cuyo. Facultad de Ingeniería; Argentina

Purpose: There is ongoing debate about whether testing low-risk genes at multiple loci will be useful in clinical care and public health. We investigated the usefulness of multiple genetic testing using simulated data. Methods: Usefulness was evaluated by the area under the receiver-operating characteristic curve (AUC), which indicates the accuracy of genetic profiling in discriminating between future patients and nonpatients. The AUC was investigated in relation to the number of genes assumed to be involved, the risk allele frequency, the odds ratio of the risk genotypes, and to the proportion of variance explained by genetic factors as an approximation of the heritability of the disease. Results: We demonstrated that a high (AUC > 0.80) to excellent discriminative accuracy (AUC > 0.95) can be obtained by simultaneously testing multiple susceptibility genes. A higher discriminative accuracy is obtained when genetic factors play a larger role in the disease, as indicated by the proportion of explained variance. The maximum discriminative accuracy of future genetic profiling can be estimated at present from the heritability and prevalence of disease. Conclusions: Genetic profiling may have the potential to identify individuals at higher risk of disease depending on the prevalence and heritability of the disease.

We present two applications of a novel approach to analyze surface deformation in areas experiencing large and/or rapid surface displacements. We show how the joint consideration of the phase and amplitude information of a space-borne Synthetic Aperture Radar (SAR) dataset can be used to better describe the near-field and the far-field characteristics of the surface deformation. This methodology has potential applications in the analysis of surface deformation phenomena relevant to natural hazards and/or anthropic activities. The consideration of this approach on SAR data acquired by high resolution and short revisit time satellites opens new scenarios in the use of SAR data to monitor the Earth’s crust.

The effective exploitation of the available huge SAR data archives in reasonable time-frames has motivated the development of P-SBAS, a parallel computing solution for the SBAS (Small BAseline Subset) processing chain. Hence, P-SBAS parallel solution represents a valuable tool for the analysis of the complex phenomena characterizing the surface deformation dynamics of Earth large areas, since it permits to exploit the parallelism offered by the modern computational platforms. In this paper, the performance of the parallel algorithm P-SBAS is investigated. The quantitative evaluation of the computational efficiency of the implemented parallel prototype in terms of achieved speedup is addressed to demonstrate the effectiveness of the proposed approach. An experimental analysis has been carried out on real data by employing a computational platform comprising 32 processors. In particular, the performance analysis has been conducted by exploiting different SAR datasets pertinent to different sensors (Envisat and Cosmo Sky-Med) and the factors limiting the inherent scalability are discussed.

This paper proposes a parallel model for the Differential Interferometry Synthetic Aperture Radar approach referred to as Small BAseline Subset (SBAS) algorithm. This new computational model has been designed to be specifically exploited within the emerging cloud computing environments. An experimental analysis, involving two different case studies, has been carried out to demonstrate the effectiveness of the methodology. The major novelty of the proposed Parallel SBAS (P-SBAS) model consists in the capability of processing large SAR data sets in reasonable time-frames. This key feature may be of great impact not only for hazard monitoring and risk mitigation activities but also for data sharing and knowledge spreading within the scientific community.

We present some of the main advances on long term deformation time series analysis achieved thanks to the 10 years of operation of ENVISAT ASAR sensor. This is done by considering a number of selected case studies, which are mostly based on the use of the Small BAseline Subset (SBAS) technique. In particular, we show how we benefit from the ENVISAT data availability to extend the ERS time series, thus allowing us to obtain almost 20 years of deformation history. Moreover, we demonstrate how the multi-mode capability of ENVISAT permits improving the temporal sampling in SBAS time series. Finally, we introduce a novel technique, tested on ENVISAT data, to generate displacement time series in areas affected by large deformation phenomena.

Distant consanguineous loops are often unknown or ignored during homozygosity mapping analysis. This may potentially lead to an increased rate of false-positive linkage results. We show that failure to take into account the distant loops may seriously underestimate the degree of consanguinity, especially for people from genetically isolated populations; in 6 Alzheimer's disease (AD) patients the distant loops accounted for 57.7 % of inbreeding on average. Theoretical evaluation showed that ignoring distant loops, which account for 18-75% of inbreeding, inflates the frequency of false positive conclusions substantially in 2-point linkage analysis, up to several hundred times. In multipoint linkage analysis of the 6 AD patients a chromosome-wide "empirical" significance of 5% corresponded to a true false positive rate of 11.1%. We show that converting multiple loops to a hypothetical loop capturing all inbreeding may be a convenient solution to avoid false positive results. When extended genealogic data are not available a hypothetical loop may still be constructed based on genomic data.