Relationship between MODIS based Aerosol Optical Depth and PM10 over Croatia

This study analyzes the relationship between Aerosol Optical Depth (AOD) obtained from Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) and ground-based PM10 mass concentration distribution over a period of 5 years (2008–2012), and investigates the applicability of satellite AOD data for ground PM10 mapping for the Croatian territory. Many studies have shown that satellite AOD data are correlated to ground-based PM mass concentration. However, the relationship between AOD and PM is not explicit and there are unknowns that cause uncertainties in this relationship. The relationship between MODIS AOD and ground-based PM10 has been studied on the basis of a large data set where daily averaged PM10 data from the 12 air quality stations across Croatia over the 5 year period are correlated with AODs retrieved from MODIS Terra and Aqua. A database was developed to associate coincident MODIS AOD (independent) and PM10 data (dependent variable). Additional tested independent variables (predictors, estimators) included season, cloud fraction, and meteorological parameters — including temperature, air pressure, relative humidity, wind speed, wind direction, as well as planetary boundary layer height — using meteorological data from WRF (Weather Research and Forecast) model. It has been found that 1) a univariate linear regression model fails at explaining the data variability well which suggests nonlinearity of the AOD-PM10 relationship, and 2) explanation of data variability can be improved with multivariate linear modeling and a neural network approach, using additional independent variables.     

Gas exsolution and flow during supersaturated water injection in porous media: II. Column experiments and continuum modeling

Degassing and in situ development of a mobile gas phase takes place when an aqueous phase equilibrated with a gas at a pressure higher than the subsurface pressure is injected in water-saturated porous media. This process, which has been termed supersaturated water injection (SWI), is a novel and hitherto unexplored means of introducing a gas phase in the subsurface. We give herein a first macroscopic account of the SWI process on the basis of continuum scale simulations and column experiments with CO₂ as the dissolved gas. A published empirical mass transfer correlation [Nambi IM, Powers SE. Mass transfer correlations for nonaqueous phase liquid dissolution from regions with high initial saturations. Water Resour Res 2003;39(2):1030. doi:10.1029/2001WR000667] is found to adequately describe non-equilibrium transfer of CO₂ between the aqueous and gas phases. Remarkably, the dynamics of gas-water two-phase flow, observed in a series of SWI experiments in homogeneous columns packed with silica sand or glass beads, are accurately predicted by traditional two-phase flow theory and the corresponding gas relative permeability is determined. A key consequence of this finding, namely that the displacement of the aqueous phase by gas is compact at the macroscopic scale, is consistent with pore scale simulations of repeated mobilization, fragmentation and coalescence of large gas clusters (i.e., large ganglion dynamics) driven entirely by mass transfer. The significance of this finding for the efficient delivery of a gas phase below the water table is discussed in connection to the alternative process of in situ air sparging, and potential advantages of SWI are highlighted.     

Delineation and geometric modeling of road networks

In this work we present a novel vision-based system for automatic detection and extraction of complex road networks from various sensor resources such as aerial photographs, satellite images, and LiDAR. Uniquely, the proposed system is an integrated solution that merges the power of perceptual grouping theory (Gabor filtering, tensor voting) and optimized segmentation techniques (global optimization using graph-cuts) into a unified framework to address the challenging problems of geospatial feature detection and classification.Firstly, the local precision of the Gabor filters is combined with the global context of the tensor voting to produce accurate classification of the geospatial features. In addition, the tensorial representation used for the encoding of the data eliminates the need for any thresholds, therefore removing any data dependencies.Secondly, a novel orientation-based segmentation is presented which incorporates the classification of the perceptual grouping, and results in segmentations with better defined boundaries and continuous linear segments.Finally, a set of gaussian-based filters are applied to automatically extract centerline information (magnitude, width and orientation). This information is then used for creating road segments and transforming them to their polygonal representations.     

Statistical fusion of two-scale images of porous media

The reconstruction of the architecture of void space in porous media is a challenging task, since porous media contain pore structures at multiple scales. Whereas past methods have been limited to producing samples with matching statistical behavior, the patterns of grey-level values in a measured sample actually say something about the unresolved details, thus we propose a statistical fusion framework for reconstructing high-resolution porous media images from low-resolution measurements. The proposed framework is based on a posterior sampling approach in which information obtained by low-resolution (MRI or X-ray) measurements is combined with prior models inferred from high-resolution microscopic data, typically 2D. In this paper, we focus on two-scale reconstruction tasks in which the measurements resolve only the large scale structures, leaving the small-scale to be inferred. The evaluation of the results generated by the proposed method shows the strong ability of the proposed method in reconstructing fine-scale structures positively correlated with the underlying ground truth. Comparing our method with the recent method of Okabe and Blunt [12], in which the measurements are also used in the reconstruction, we conclude that our method is more robust to the resolution of the measurement, and more closely matches the underlying fine-scale field.     

Scale- and orientation-invariant scene similarity metrics for image queries

In this paper we extend our previous work on shape-based queries to support queries on configurations of image objects. Here we consider spatial reasoning, especially directional and metric object relationships. Existing models for spatial reasoning tend to rely on pre-identified cardinal directions and minimal scale variations, assumptions that cannot be considered as given in our image applications, where orientations and scale may vary substantially, and are often unknown. Accordingly, we have developed the method of varying baselines to identify similarities in direction and distance relations. Our method allows us to evaluate directional similarities without a priori knowledge of cardinal directions, and to compare distance relations even when query scene and database content differ in scale by unknown amounts. We use our method to evaluate similarity between a user-defined query scene and object configurations. Here we present this new method, and discuss its role within a broader image retrieval framework.     

Effect of uncertainty in Digital Surface Models on the extent of inundated areas

The impact of uncertainty in ground elevation on the extent of areas that are inundated due to flooding is investigated. Land surface is represented through a Digital Surface Model (DSM). The effect of uncertainty in DSM is compared to that of the uncertainty due to rainfall. The Monte Carlo method is used to quantify the uncertainty. A typical photogrammetric procedure and conventional maps are used to obtain a reference DSM, later altered to provide DSMs of lower accuracy. Also, data from the Shuttle Radar Topography Mission are used. Floods are simulated in two stages. In the first stage, flood hydrographs for typical return periods are synthesized using generated storm hyetographs, the Soil Conservation Service–Curve Number method for effective rainfall, and the Soil Conservation Service synthetic unit hydrograph. In the second stage, hydrographs are routed via a one‐dimensional hydraulic model. Uncertainty in DSM is considered only in the second stage. Data from two real‐world basins in Greece are used. To characterize the inundated area, we employ the 90% quantile of the inundation extent and inundation topwidth for peak water level at specific river cross‐sections. For topwidths, apart from point estimates, also interval estimates are acquired using the bootstrap method. The effect of DSM uncertainty is compared to that for rainfall. Low uncertainty in DSM is found to widen the inundated area; whereas, the opposite occurred with high uncertainty. SRTM data proved unsuitable for our test basins and modelling context.     

Gas exsolution and flow during supersaturated water injection in porous media: I. Pore network modeling

Degassing and in situ development of a mobile gas saturation take place when an aqueous phase saturated with gas at a pressure higher than the subsurface pressure is injected in water-saturated porous media. In the first part of this work, a pore network model is used to study the key physical aspects of this novel and hitherto unexplored way of introducing a gas phase in the subsurface. Following heterogeneous nucleation, growth of gas phase clusters driven by convective diffusion of solute from the bulk aqueous phase, is shown to result in a ramified pattern of gas-occupied pores, which is controlled by capillary and buoyancy forces. The interplay between mass transfer and immiscible displacement processes, namely gas cluster coalescence, mobilization under the action of buoyancy forces and fragmentation resulting from capillary instabilities, is seen to favour the propagation of a stable gas saturation front. Pore network model predictions of the macroscopic mass transfer rate coefficient are in fair agreement with a recently published empirical correlation.     

Burnt area delineation from a uni-temporal perspective based on Landsat TM imagery classification using Support Vector Machines

Information on burnt area is of critical importance in many applications as for example in assessing the disturbance of natural ecosystems due to a fire or in proving important information to policy makers on the land cover changes for establishing restoration policies of fire-affected regions. Such information is commonly obtained through remote sensing image thematic classification and a wide range of classifiers have been suggested for this purpose. The objective of the present study has been to investigate the use of Support Vector Machines (SVMs) classifier combined with multispectral Landsat TM image for obtaining burnt area mapping. As a case study a typical Mediterranean landscape in Greece was used, in which occurred one of the most devastating fires during the summer of 2007. Accuracy assessment was based on the classification overall statistical accuracy results and also on comparisons of the derived burnt area estimates versus validated estimates from the Risk-EOS Burnt Scar Mapping service. Results from the implementation of the SVM using diverse kernel functions showed an average overall classification accuracy of 95.87% and a mean kappa coefficient of 0.948, with the burnt area class always clearly separable from all the other classes used in the classification scheme. Total burnt area estimate computed from the SVM was also in close agreement with that from Risk-EOS (mean difference of less than 1%). Analysis also indicated that, at least for the studied here fire, the inclusion of the two middle infrared spectral bands TM5 and TM7 of TM sensor as well as the selection of the kernel function in SVM implementation have a negligible effect in both the overall classification performance and in the delineation of total burnt area. Overall, results exemplified the appropriateness of the spatial and spectral resolution of the Landsat TM imagery combined with the SVM in obtaining rapid and cost-effective post-fire analysis. This is of considerable scientific and practical value, given the present open access to the archived and new observations from this satellite radiometer globally.     

Hierarchical Annealing for Synthesis of Binary Images

This paper introduces a hierarchical annealing algorithm which addresses the very large computational costs associated with simulated annealing for the synthesis of binary images. In real-world examples, the large configuration space of such models has led to the disappointing performance of annealing approaches. Our method essentially approaches different natural scales in the image separately and in a hierarchical manner. We demonstrate orders-of-magnitude improvement compared to existing results, and discuss the inherent computational difficulties encountered with these types of approaches.     

Raman spectroscopic carbonaceous material thermometry of low-grade metamorphic rocks: Calibration and application to tectonic exhumation in Crete, Greece

We present new Raman spectra data of carbonaceous material (CM) to extend the range of the Raman spectra of CM thermometer (RSCM) to temperatures as low as 100 °C. Previous work has demonstrated that Raman spectroscopy is an excellent tool to describe the degree of graphitization of CM, a process that is independent of pressure but strongly dependent on metamorphic temperature. A linear relationship between temperature and the Raman parameter R2 (derived from the area of the defect band relative to the ordered graphite band) forms the basis of a previous thermometer. Because R2 shows little variability in low-temperature samples, 330 °C serves as a lower limit on the existing thermometer. Herein, we present Raman spectra from a suite of low-temperature (100 to 300 °C) samples from the Olympics Mountains and describe other aspects of the Raman spectra of CM that vary over this range. In particular, the Raman parameter R1 (the ratio of heights of the disordered peak to ordered peak) varies regularly between 100 and 350 °C. These data, together with published results from higher-temperature rocks, are used to calibrate a modified RSCM thermometer, applicable from 100 to 700 °C. Application to low-grade metasediments in the Otago region in the South Island of New Zealand gives temperatures consistent with previous estimates, demonstrating the reliability of the modified RSCM thermometer.We apply the modified RSCM thermometer to 53 samples from Crete to evaluate the role of the Cretan detachment fault in exhuming Miocene high pressure/low-temperature metamorphic rocks exposed there. The metamorphic rocks below the detachment (the Plattenkalk and Phyllite-Quartzite units) give metamorphic temperatures that range from 250 to 400 °C, consistent with previous petrologic estimates. We also demonstrate that the Tripolitza unit, which lies directly above the detachment, gives an average metamorphic temperature of about 260 °C. The modest break in metamorphic temperature in central Crete indicates that the Cretan detachment accounts for only 5 to 7 km of exhumation of the underlying HP-LT metamorphic rocks, which were initially accreted at ∼ 35 km. We argue that the bulk of the exhumation (∼ 28 km out of 35 km total) occurred by pervasive brittle stretching and erosion of structural units above the detachment.