Seismic Network,Seismic Activity and Seismological Research of Vietnam

In this paper,a historical review of the seismic network of Vietnam and the ability of the network to monitor earthquakes is presented.The seismic activity of Vietnam as a country of high seismictiy in the last century and since the beginning of the new century is described.The current state of seismological research in Vietnam and cooperation with institutions outside of Vietnam is outlined.     

Schwermetallbestimmung in Klärwerksschlämmen mittels Röntgenfluoreszenzanalyse

The X-ray fluorescence analysis is an important means for the determination of the heavy metal concentrations in sewage sludges and for the evaluation of their usability in agriculture. Analysis devices from the GDR were used for that. For the investigations there was used as the zero sample a sludge which was not loaded with heavy metals, homogeneous and constantly dried at 105 °C. The drying bed sludge samples were treated with different heavy metal solutions with different concentrations. Preparation of samples, measuring conditions and general spectra are described. The measuring results of tests up to 100 g heavy metal in 1 kg dry substance are critically evaluated. The analysis of a sewer sludge sample takes only about ten minutes. There are determined: Cr, Ni, Cu, Zn, Mo, Cd, Hg and Pb.     

Bisexual populations of the brine shrimpArtemia in Sète-Villeroy and Villeneuve Saltworks (Languedoc,France)

So far,Artemia populations in solar saltponds of southern France have been considered as belonging to the Old World parthenogenetic strain. However, two populations sampled monthly in abandoned salinas of Sète-Villeroy and Villeneuve appear to be bisexual throughout their life-cycle. Their sex ratio regularly fluctuates near equilibrium, and mating, present throughout life, is very common in early winter. Morphological characteristics, particularly the male frontal knobs structure, are discussed in relation to the diploid bisexual Mediterranean strain,Artemia tunisiana, which is distributed around the Mediterranean basin. The existence of sexual populations is discussed in regard to variation in environmental conditions in abandoned salinas.     

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.     

WinASEAN for remote sensing data analysis

The image analysis system ASEAN (Advanced System for Environmental ANalysis with Remote Sensing Data) was designed and programmed by a software development group, ImaSOFr, Department of Remote Sensing Technology and GIS, Institute for Geography, National Centre for Natural Science and Technology of Vietnam under technical cooperation with the Remote Sensing Technology Centre of Japan and financial support from the National Space Development Agency of Japan. ASEAN has been in continuous development since 1989, with different versions ranging from the simplest one for MS-DOS with standard VGA 320×200×256 colours, through versions supporting SpeedStar 1.0 and SpeedStar PRO 2.0 true colour graphics cards, up to the latest version named WinASEAN, which is designed for the Windows 3.1 operating system. The most remarkable feature of WinASEAN is the use of algorithms that speed up the image analysis process, even on PC platforms. Today WinASEAN is continuously improved in cooperation with NASDA (National Space Development Agency of Japan), RESTEC (Remote Sensing Technology Center of Japan) and released as public domain software for training, research and education through the Regional Remote Sensing Seminar on Tropical Eco-system Management which is organised by NASDA and ESCAR In this paper, the authors describe the functionality of WinASEAN, some of the relevant analysis algorithms, and discuss its possibilities of computer-assisted teaching and training of remote sensing.     

Canal-side highway in Ho Chi Minh City (HCMC), Vietnam – Issues of urban cultural conservation and tourism development

Since the city’s founding back in 1698 on the old Khmer city of Prei Nokor, rivers and canals have always been Ho Chi Minh City’s (HCMC’s) primary means of transportation. Despite the decline of the utilization of the city’s abundant waterways due to the increasing development of modern aerial services and more convenient roads, the HCMC’s canal-based urban form, with its water-based socio-economic structures that include port-boat-market activities and canal handcraft villages, still processes a rich cultural and religious diversity that dates back to the history of the city itself. Currently, developments in the city’s transportation systems are having a damaging impact on the canals’ cultural background. Rather than being recognized as a significant cultural asset that identifies the city, the canal system has been physically reorganized as the city’s drainage system and valued as only a supplementary landscape element; canal communities have been evicted, and canal built fabric has been removed due to recent canal-side transportation developments. To revitalize the urban cultural identity that is possessed by the canal system and to enrich it as a contemporary valuable layer in the city’s dynamic regional development, it is critical to implement cultural conservation and tourism in the development of canal-side transportation and supplementary projects in the canal vicinity.     

Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran

The identification of landslide-prone areas is an essential step in landslide hazard assessment and mitigation of landslide-related losses.In this study,we applied two novel deep learning algorithms,the recurrent neural network(RNN)and convolutional neural network(CNN),for national-scale landslide susceptibility mapping of Iran.We prepared a dataset comprising 4069 historical landslide locations and 11 conditioning factors(altitude,slope degree,profile curvature,distance to river,aspect,plan curvature,distance to road,distance to fault,rainfall,geology and land-sue)to construct a geospatial database and divided the data into the training and the testing dataset.We then developed RNN and CNN algorithms to generate landslide susceptibility maps of Iran using the training dataset.We calculated the receiver operating characteristic(ROC)curve and used the area under the curve(AUC)for the quantitative evaluation of the landslide susceptibility maps using the testing dataset.Better performance in both the training and testing phases was provided by the RNN algorithm(AUC=0.88)than by the CNN algorithm(AUC=0.85).Finally,we calculated areas of susceptibility for each province and found that 6%and 14%of the land area of Iran is very highly and highly susceptible to future landslide events,respectively,with the highest susceptibility in Chaharmahal and Bakhtiari Province(33.8%).About 31%of cities of Iran are located in areas with high and very high landslide susceptibility.The results of the present study will be useful for the development of landslide hazard mitigation strategies.     

GIS-based landslide susceptibility modeling: A comparison between fuzzy multi-criteria and machine learning algorithms

Hazards and disasters have always negative impacts on the way of life.Landslide is an overwhelming natural as well as man-made disaster that causes loss of natural resources and human properties throughout theworld.The present study aimed to assess and compare the prediction efficiency of different models in landslide susceptibility in the Kysuca river basin,Slovakia.In this regard,the fuzzy decision-making trial and evaluation laboratory combining with the analytic network process(FDEMATEL-ANP),Na?ve Bayes(NB)classifier,and random forest(RF)classifier were considered.Initially,a landslide inventory map was produced with 2000 landslide and nonlandslide points by randomly dividedwith a ratio of 70%:30%for training and testing,respectively.The geospatial database for assessing the landslide susceptibility was generated with the help of 16 landslide conditioning factors by allowing for topographical,hydrological,lithological,and land cover factors.The ReliefF methodwas considered for determining the significance of selected conditioning factors and inclusion in the model building.Consequently,the landslide susceptibility maps(LSMs)were generated using the FDEMATEL-ANP,Na?ve Bayes(NB)classifier,and random forest(RF)classifier models.Finally,the area under curve(AUC)and different arithmetic evaluation were used for validating and comparing the results and models.The results revealed that random forest(RF)classifier is a promising and optimum model for landslide susceptibility in the study area with a very high value of area under curve(AUC=0.954),lower value of mean absolute error(MAE=0.1238)and root mean square error(RMSE=0.2555),and higher value of Kappa index(K=0.8435)and overall accuracy(OAC=92.2%).