Silver and silver microparticles of the Cretaceous-Paleogene boundary sediment of Fish Clay Højerup (Stevns Klint,Denmark)

We report the occurrence of micrometer-sized silver particles in the red (“impact” or “ejecta”) layer of the Fish Clay. These appear to be embedded into the biogenic/abiogenic calcite matrix. Energy dispersive X-ray analysis indicates that these microparticles are composed of pure silver (>99% of the total weight). Scanning electron microscopy images reveal irregular and predominantly rounded shapes with rugged surfaces. Numerous silver microparticles are spherical, and some exhibit dendritic textures. These microparticles were probably originated during the Chicxulub asteroid impact event, or immediately afterwards, and dispersed globally.     

Examination of physical processes of convective cell evolved from a MCS — Using a different model initialization

The present study is focused on examination of the physical processes of convective cell evolved from a MCS occurred on 4 November 2011 over Genoa, Italy. The Quantitative Precipitation Forecasts (QPF) have been performed using WRF v3.6 model under different configurations and cloud permitting simulations. The results indicate underestimation of the amount of precipitation and spatial displacement of the area with a peak 24-h accumulated rainfall in (mm). Our main objective in the research is to test the cloud model ability and performance in simulation of this particular case. For that purpose a set of sensitivity experiments under different model initializations and initial data have been conducted. The results also indicate that the merging process apparently alters the physical processes through low- and middle-level forcing, increasing cloud depth, and enhancing convection. The examination of the microphysical process simulated by the model indicates that dominant production terms are the accretion of rain by graupel and snow, probabilistic freezing of rain to form graupel and dry and wet growth of graupel. Experiment under WRF v3.6 model initialization has shown some advantage in simulation of the physical processes responsible for production and initiation of heavy rainfall compared to other model runs. Most of the precipitation came from ice-phase particles-via accretion processes and the graupel melting at temperature T₀ ≥ 0°C. The rainfall intensity and accumulated rainfall calculated by the model closely reflect the amount of rainfall recorded. Thus, the main benefit is to better resolve convective showers or storms which, in extreme cases, can give rise to major flooding events. In such a way, this model may become major contributor to improvements in weather analysis and small-scale atmospheric predictions and early warnings of such subscale processes.     

Modeling Urban Land Use Changes Using Support Vector Machines

Support Vector Machines (SVM) is a machine learning (ML) algorithm commonly applied to the classification of remotely sensing data and more recently for modeling land use changes. However, in most geospatial applications the current literature does not elaborate on specifications of the SVM method with respect to data sampling, attribute selection and optimal parameters choices. Therefore the main objective of this study is to present and investigate the SVM technique for modeling urban land use change. The SVM model building procedure is presented together with the detailed evaluation of the output results with respect to the choice of datasets, attributes and the change of SVM parameters. Geospatial datasets containing nine land use classes and spatial attributes for the Municipality of Zemun, Republic of Serbia were used for years 2001, 2003, 2007 and 2011. The Correlation‐based Feature Subset method, kappa coefficient, Area Under Receiver Operating Characteristic Curve (AUC) and kappa simulation were used to perform the model evaluation and compare the model outputs with the real land use datasets. The obtained results indicate that the SVM‐based models perform better when implementing balanced data sampling, reduced data sets to informative subsets of attributes and properly identify the optimal learning parameters.     

Stratigraphic and lithologic characteristics of Pleistocene fluvial deposits in the Danube and Sava riparian area near Belgrade (Serbia)

The Quaternary sediments in the Danube and Sava riparian area near Belgrade have a considerable thickness. Several categories of deposits (fluvial-lacustrine, fluvial and aeolian) of Pliocene and Quaternary age have been identified. Their thickness, granulometric composition and paleontological features change depending on the distance from the recent Danube and Sava riverbeds. The Pleistocene fluvial deposits are underlain by sediments of the Late Miocene (Sarmatian and Pannonian) or the Plio-Pleistocene age, and are overlain by fluvial-palustrine deposits of the Pleistocene age and recent alluvial deposits. Pleistocene fluvial deposits that form a major part of the Quaternary sediments, have a great significance, since they are proved to be excellent collectors of ground water. Although these deposits are at lower altitudes in the area of Srem, they could be correlated with the high Danube and Morava terraces in Serbia and Drava in Croatia on the basis of their lithologic and paleontological features.     

Integration of GIS and video surveillance

This paper discusses the integration of three-dimensional (3D) geographic information systems (GIS) and video surveillance systems using augmented reality (AR) techniques. The motivation for this integration is to overcome problems faced by conventional video surveillance systems. Explicit information concerning which camera currently monitors what area in such systems is missing; therefore, insight into the situation depends heavily on the operator’s training and experience. To ensure the complete coordination and monitoring of a situation in a system with multiple cameras, it is necessary to introduce a single reference system. GIS arises as a natural solution because it not only provides a solid ground truth but also provides semantic information that can be highly important in certain video surveillance applications. To integrate information into a GIS application, that information must be georeferenced. Based on our previous research regarding the addition of georeferencing information to surveillance video, this paper introduces models that can be applied to help integrate video and GIS. With an analogy to Milgram’s continuum between the real world and virtual reality, and analogous to the augmented reality and augmented virtuality in Milgram’s continuum, two models of integration are defined here: GIS-augmented video and video-augmented GIS. Then, we define the architecture of GIS-based video surveillance based on these proposed integration models, and finally, a prototype is implemented. The implemented prototype serves as a basis for analysing possible applications of real-world systems based on the integration of GIS and video.     

Were merely storm-landslides driven by the 2015-2016 Niño in the Mendoza River valley?

Since Holocene time, above-mean precipitations recorded during the El Niño warm ENSO phase have been linked to the occurrence of severe debris flows in the arid Central Andes. The 2015–2016 El Niño, for its unusual strength, began driving huge and dangerous landslides in the Central Andes (32°) in the recent South Hemisphere summer. The resulting damages negatively impacted the regional economy. Despite this, causes of these dangerous events were ambiguously reported. For this reason, a multidisciplinary study was carried out in the Mendoza River valley. Firstly, a geomorphological analysis of affected basins was conducted, estimating morphometric parameters of recorded events such as velocity, stream flow, and volume. Atmospheric conditions during such events were analyzed, considering precipitations, snow cover, temperature range, and the elevation of the zero isotherm. Based on our findings, the role of El Niño on the slope instability in the Central Andes is more complex in the climate change scenario. Even though some events were effectively triggered by intense summer rainstorm following expectations, the most dangerous events were caused by the progressive uplifting of the zero isotherm in smaller basins where headwaters are occupied by debris rock glaciers. Our research findings give light to the dynamic coupled system ENSO–climate change–landslides (ECCL) at least in this particular case study of the Mendoza River valley. Landslide activity in this Andean region is driven by wetter conditions linked to the ENSO warm phase, but also to progressive warming since the twentieth century in the region. This fact emphasizes the future impact of the natural hazards on Andean mountain communities.