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.     

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.     

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.     

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.     

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.     

Seismic behaviour of timber-laced stone masonry buildings before and after interventions: shaking table tests on a two-storey masonry model

The present work focuses on the seismic behaviour of timber-laced masonry buildings with timber floors, before and after the application of intervention techniques. A two-storey building with timber ties (scale 1:2) was subjected to biaxial seismic actions. Prior to the execution of shaking table tests, the dynamic characteristics of the model were identified. The base acceleration was increased step-wise until the occurrence of significant but repairable damages. Afterwards, the masonry was strengthened by means of grouting, whereas the diaphragm action of the top floor of the building was enhanced and the model was re-tested. The tests on the timber reinforced model before strengthening show that the presence of timber ties within the masonry elements contributes to improved seismic behaviour. The performance of the model after strengthening shows that the selected intervention techniques led to a significant improvement of the seismic behaviour of the building model.