Mesozooplankton spatial distribution and community structure in the South Adriatic Sea during two winters (2015, 2016)

Whereas the data on mesozooplankton in the epipelagic offshore Mediterranean Sea are extensive, less information is available about plankton in the deeper layers. The present study aims to describe the vertical and horizontal structure and distribution of mesozooplankton species and their associations down to 1,200 m in the water of the Southern Adriatic Sea. Zooplankton were sampled using a Nansen net of 200‐μm mesh size during two cruises in the winters of 2015 and 2016, extending from the coast to the open sea. In total, 203 zooplankton taxa were identified. The community was dominated by copepods, representing between 67% and 91% of the total abundance. The highest total densities were recorded in the upper layers where a high proportion (up to 36%) of appendicularians was also observed in the first sampled year. Five groups of samples were determined based on their community structure. In 2015 communities were distinct between the 0 and 50 m layer and the underlying one (50–100 m), whereas in 2016 epipelagic waters were inhabited by a more uniform mesozooplankton community. The mesopelagic and deep‐water fauna, especially copepods, showed a relatively stable composition in both sampling years Overall, our study confirms the oligotrophic character of the Southern Adriatic, with occasional density outbreaks of appendicularians under favourable conditions.     

The response in floodplain respiration of an alpine river to experimental inundation under different temperature regimes

The respiratory potential [i.e. electron transport system activity (ETSA)] of soils and sediments from five floodplain habitats (channel, gravel, islands, riparian forest and grassland) of the Urbach River, Switzerland, and actual respiration rate (R) of the same samples exposed to experimental inundation were measured. Measurements were carried out at three incubation temperatures (4°C, 12°C and 20°C), and ETSA/R ratios (i.e. exploitation of the overall metabolic capacity) were investigated to better understand the effects of temperature and inundation on floodplain functional heterogeneity. Furthermore, ETSA/R ratios obtained during experimental inundation were compared with ETSA/R ratios from field measurements to investigate the exploitation in total metabolic potential at different conditions. Lowest ETSA and R were measured in samples from channel and gravel habitats, followed by those from islands. Substantially higher values were measured in soils from riparian forest and grassland. Both ETSA and R increased with increasing temperature in samples from all habitats, while the ETSA/R ratio decreased because of a rapid response in microbial community respiration to higher temperatures. The metabolic capacity exploitation (i.e. ETSA/R) during experimental inundation was lowest in predominantly terrestrial samples (riparian forest and grassland), indicating the weakest response to wetted conditions. Comparison of experimentally inundated and field conditions revealed that in rarely flooded soils, the metabolic capacity was less exploited during inundation than during non‐flooded conditions. The results suggest high sensitivity in floodplain respiration to changes in temperature and hydrological regime. ETSA/R ratios are considered good indicators of changes in metabolic activity of floodplain soils and sediments, and thus useful to estimate the impact of changes in hydrological regime or to evaluate success of floodplain restoration actions. Copyright © 2015 John Wiley & Sons, Ltd.     

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.     

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.     

Removal of heavy metal ions from drinking water by alginate-immobilised <Emphasis Type="Italic">Chlorella sorokiniana</Emphasis>

This paper investigates the potential of alginate-immobilised Chlorella sorokiniana for removing Cu²⁺, Ni²⁺ and Cd²⁺ ions from drinking water solutions. The effects of initial metal concentrations, contact times and temperatures on the biosorptions and removal efficiencies of the tested metals were investigated at initial pH values of 5, and pH effects were studied within the range of 3–7. When studying the effects of initial metal concentrations, the highest experimental removal yields achieved for Cu²⁺, Ni²⁺ and Cd²⁺ ions were 97.10, 50.94 and 64.61 %, respectively. The maximum biosorption capacities obtained by the Langmuir isotherm model for the biosorptions of Cu²⁺, Ni²⁺ and Cd²⁺ ions by alginate-immobilised C. sorokiniana were found to be 179.90, 86.49 and 164.50 mg/g biosorbent, respectively. The experimental data followed pseudo-second-order kinetics. At an initial metal concentration of 25 mg/L, immobilised algae could be used in at least 5 successive biosorption–desorption cycles. SEM and EDS analyses revealed that the metals bonded to the biosorbent. Bi- and multi-metal systems of Cu²⁺, Ni²⁺ and Cd²⁺ were investigated at initial metal concentrations of 30, 50 and 100 mg/L. The removal of Cd²⁺ as well as Ni²⁺ in such systems was negatively affected by the presence of Cu²⁺. The removal efficiency for Cu²⁺ in multi-metal systems decreased by 5–7 %, whilst in the cases of Cd²⁺ and Ni²⁺ the efficiencies decreased by up to 30 %. Nevertheless, the results obtained show that alginate-immobilised C. sorokiniana can efficiently remove the metals tested from polluted drinking water sources.