Tropical Cyclone in the North of the South China Sea as a Factor Affecting the Structure of the Vietnamese Current

Izvestiya, Atmospheric and Oceanic Physics - Tropical cyclones play a significant role in the formation of the hydrodynamic regime of the South China Sea, including its western part, which is...     

Multi-scale solution for building extraction from LiDAR and image data

This paper presents a multi-scale solution based on mathematical morphology for extracting the building features from remotely sensed elevation and spectral data. Elevation data are used as the primary data to delineate the structural information and are firstly represented on a morphological scale-space. The behaviors of elevation clusters across the scale-space are the cues for feature extraction. As a result, a complex structure can be extracted as a multi-part object in which each part is represented on a scale depending on its size. The building footprint is represented by the boundary of the largest part. Other object attributes include the area, height or number of stories. The spectral data is used as an additional source to remove vegetation and possibly classify the building roof material. Finally, the results can be stored in a multi-scale database introduced in this paper. The proposed solution is demonstrated using the data derived from a Light Detection And Ranging (LiDAR) surveying flight over Tokyo, Japan. The results show a reasonable match with reference data and prove the capability of the proposed approach in accommodation of diverse building shapes. Higher density LiDAR is expected to produce better accuracy in extraction, and more spectral sources are necessary for further classification of building roof material. It is also recommended that parallel processing should be implemented to reduce the computation time.     

Effects of fungal infection on lipid extract composition of higher plant remains: comparison of shoots of a Cenomanian conifer, uninfected and infected by extinct fungi

The lipid fraction extracted from uninfected shoots of a fossil conifer, Frenelopsis alata, was analysed by gas-chromatography–mass-spectrometry, and compared with shoots of the same conifer infected by extinct epiphyllous fungi, so as to study the effects of fungal infection on the chemical composition of extracts from higher plant remains. The extracts from the uninfected shoots appeared to be composed of (i) common constituents of higher plant lipids such as n-alkanes and fatty acids, (ii) elemental sulphur, and (iii) substantial amounts of terpenoids characteristic of conifers, such as cadalene, beyerane, dehydroabietane and related compounds. Comparison of this extract with that of fungal-infected shoots revealed, in addition to the aforementioned compounds, the presence of substantial amounts of hydroxysuccinic acid and functionalised benzoic compounds that were interpreted as degradation products of lignin by fungi. This study afforded preliminary indications of the composition of extracts from higher plant remains infected by fungi.     

飓风Juan(2003)路径附近实测飓风浪的谱特性研究

Hurricane Juan provides an excellent opportunity to probe into the detailed wave spectral patterns and spectral parameters of a hurricane system, with enough wave spectral observations around Juan＇s track in the deep ocean and shallow coastal water. In this study, Hurricane Juan and wave observation stations around Juan＇s track are introduced. Variations of wave composition are discussed and analyzed based on time series of one-dimensional frequency spectra, as well as wave steepness around Juan＇s track： before, during, and after Juan＇s passing. Wave spectral involvement is studied based on the observed one-dimensional spectra and two-dimensional spectra during the hurricane. The standardization method of the observed wave spectra during Hurricane Juan is discussed, and the standardized spectra show relatively conservative behavior, in spite of the huge variation in wave spectral energy, spectral peak, and peak frequency during this hurricane. Spectral widths＇ variation during Hurricane Juan are calculated and analyzed. A two-layer nesting WW3 model simulation is applied to simulate the one-dimensional and two-dimensional wave spectra, in order to examine WW3＇s ability in simulating detailed wave structure during Hurricane Juan.     

Predicting water flow in fully and partially saturated porous media: a new fractal-based permeability model

Hydrogeology Journal - Predicting the permeability of porous media in saturated and partially saturated conditions is of crucial importance in many geo-engineering areas, from water resources to...     

The occurrence of cyanobacteria and microcystins in the Hoan Kiem Lake and the Nui Coc reservoir (North Vietnam)

The occurrence of increasing blooms of toxic cyanobacteria in freshwaters has received much attention due to the ability of many cyanobacteria to produce potent cyanotoxins. In this paper, the occurrence of dominant cyanobacteria and the concentration of microcystins (MCs) analysis were investigated monthly from July 2008 to April 2009 in the Hoan Kiem Lake and from February to April 2009 in the Nui Coc reservoir. Concentrations of intracellular MCs from water, bloom samples, and isolated strains were quantified by using high performance liquid chromatography (HPLC). During the study period, the microscopic examination of the phytoplankton samples showed the dominance of the genus Microcystis in the water environment of the Hoan Kiem Lake and the Nui Coc reservoir. The toxin analysis by HPLC demonstrated the presence of two MC variants: MC-LR and MC-RR in water samples. Total concentrations of the toxins in filtered samples from surface water ranged from non-detected to 0.91 μg L^?1 at Nui Coc reservoir and they ranged from 2.1 to 46.0 μg L^?1 at Hoan Kiem Lake. The results of the HPLC analysis confirmed the production of MCs in bloom samples (ranged from 115.9 to 184.6 μg L^?1 in the Hoan Kiem Lake and from 726.5 to 1116 μg L^?1 in the Nui Coc reservoir) and isolated strains of Anabaena sp. and Microcystis with the concentration of MC ranging from 152 to 396.2 μg g^?1 dry mass, respectively.     

A remarkable example of a Late Jurassic shallow-water ophiuroid assemblage from the Swiss Jura Mountains

Although much effort has been put into attempts to unravel the Mesozoic ophiuroid fossil record, surprisingly little attention has been paid to the geological history of family-level compositions of brittle star assemblages. Here, we describe new ophiuroid material from the Late Oxfordian Bure Member as exposed in a construction site along the federal Highway A16 (Transjurane Highway) near Boncourt–Queue au Loup (Swiss Jura Mountains). The remains are exceptionally well preserved and partially articulated, enabling detailed morphological observations at a level comparable to Recent material. Three species are identified; they are here shown to display unequivocal affinities with extant ophiuroid lineages. The assemblage comprises a new ophiacanthid genus and species, Juracantha hottingeri, which is closely related to extant Ophientrema, an ophiolepidid close to extant Ophiozonella described here as a new genus and species, Eozonella bergeri, and a species previously described as Ophiacantha? francojurassica, which is here reassigned to the extant ophionereidid genus Ophiodoris. The assemblage was found in association with Nanogyra oyster buildups encrusted by serpulids and associated with partially-articulated crinoids, deposited in a shallow subtidal setting. Its family level composition is highly unusual with respect to modern equivalents, and instead is reminiscent of modern bathyal assemblages.     

越南西北部莱州地区新生代煌斑岩地球化学特征及其成因

越南西北部菜州地区出露的新生代煌斑岩岩脉对理解特提斯造山带东段的深部岩石圈特征和演化具有重要的地质意义.本文报道莱州地区煌斑岩的元素地球化学和Sr-Nd-Pb同位素组成特征,探讨其岩石成因.该地区煌斑岩属于钙碱性,钾质-超钾质煌斑岩特征.地球化学特征对比表明,菜州地区煌斑岩与哀牢山断裂带碱性岩具有相似的地球化学特征,但与海南和越南南部火山岩存在明显差异.分析结果表明,煌斑岩具有高的87Sr/86Sr比值、低143Nd/144Nd比值和高放射性成因Pb同位素组成特征.岩石的微量元素组成特征指示,形成煌斑岩的地幔源区可能经历过流体交代作用或沉积物组分的加入.低208Pb*/206Pb*比值暗示地幔源区富集事件是近期发生的,可能与晚古生代-早中生代印支地块向扬子地块俯冲事件有关.     

Flood susceptibility modelling using advanced ensemble machine learning models

Floods are one of nature's most destructive disasters because of the immense damage to land, buildings, and human fatalities.It is difficult to forecast the areas that are vulnerable to flash flooding due to the dynamic and complex nature of the flash floods.Therefore, earlier identification of flash flood susceptible sites can be performed using advanced machine learning models for managing flood disasters.In this study, we applied and assessed two new hybrid ensemble models, namely Dagging and Random Subspace(RS) coupled with Artificial Neural Network(ANN), Random Forest(RF), and Support Vector Machine(SVM) which are the other three state-of-the-art machine learning models for modelling flood susceptibility maps at the Teesta River basin, the northern region of Bangladesh.The application of these models includes twelve flood influencing factors with 413 current and former flooding points, which were transferred in a GIS environment.The information gain ratio, the multicollinearity diagnostics tests were employed to determine the association between the occurrences and flood influential factors.For the validation and the comparison of these models, for the ability to predict the statistical appraisal measures such as Freidman, Wilcoxon signed-rank, and t-paired tests and Receiver Operating Characteristic Curve(ROC) were employed.The value of the Area Under the Curve(AUC) of ROC was above 0.80 for all models.For flood susceptibility modelling, the Dagging model performs superior, followed by RF,the ANN, the SVM, and the RS, then the several benchmark models.The approach and solution-oriented outcomes outlined in this paper will assist state and local authorities as well as policy makers in reducing flood-related threats and will also assist in the implementation of effective mitigation strategies to mitigate future damage.     

Relating Radar Remote Sensing of Biomass to Modelling of Forest Carbon Budgets

This paper addresses the use of radar remote sensing to map forest above-ground biomass, and discusses the use of biomass maps to test a dynamic vegetation model that identifies carbon sources and sinks and predicts their variation over time. For current radar satellite data, only the biomass of young/sparse forests or regrowth after disturbances can be recovered. An example from central Siberia illustrates that biomass can be measured by radar at a continental scale, and that a significant proportion of the Siberian forests have biomass values less than 50 tonnes/ha. Comparison between the radar map and calculations by the Sheffield Dynamic Global Vegetation Model (SDGVM) indicates that the model considerably overestimates biomass; under-representation of managed areas, disturbed areas and areas of low site quality in the model are suggested reasons for this effect. A case study carried out at the Büdingen plantation forest in Germany supports the argument that inadequate representations of site quality and forest management may cause model overestimates of biomass. Comparison of the calculated biomass of stands planted after 1990 with biomass estimates by radar allows identification of forest stands where the growth conditions assumed by the model are not valid. This allows a quality check on model calculations of carbon fluxes: only calculations for stands where there is good agreement between the data and the model predictions should be accepted. Although the paper only uses the SDGVM model, similar effects are likely in other dynamic vegetation models, and the results show that model calculations attempting to quantify the role of forests as carbon sources or sinks could be qualified and potentially improved by exploiting remotely sensed measurements of biomass.