University of Lorraine

About: University of Lorraine is a education organization based out in Nancy, France. It is known for research contribution in the topics: Population & Context (language use). The organization has 11942 authors who have published 25010 publications receiving 425227 citations. The organization is also known as: Lorraine University.

Nb-Ta fractionation in peraluminous granites: a marker of the magmatic-hydrothermal transition

Abstract: In their last stages of evolution (ie at the magmatic-hydrothermal transition), peraluminous granitic melts exsolve a large amounts of fluids which can modify the chemical composition of whole-rock granitic samples and lead to the deposition of economicaly significant mineralization (Sn, W) Nb and Ta are lithophile elements considered to be “geochemical twins” because they have the same charge and a similar ionic radius However, Nb/Ta ratios are highly variable in granites Some authors have demonstrated that the Nb/Ta ratios decrease in granites during fractional crystallization [1] Other studies have suggested that Nb and Ta could be fractionated in evolved peraluminous granites during the interaction with late magmatic fluids [2] In this study [3], we demonstrate, using a compilation of whole-rock geochemical data available in the litterature that fractional crystallization alone is not sufficient to explain the distribution of Nb-Ta in most peraluminous granites However, we notice that most of the granitic samples displaying evidence of interactions with fluids have Nb/Ta < ∼5 We propose that the decrease of the Nb/Ta ratio in evolved melts is the consequence of both fractional crystallization and sub-solidus hydrothermal alteration We suggest that the Nb/Ta value of ~5 fingerprints the magmatic-hydrothermal transition in peraluminous granites Furthermore, a Nb/Ta ratio of ~5 appears to be a good marker to discriminate mineralized from barren peraluminous granites

Fire detection using infrared images for UAV-based forest fire surveillance

Abstract: Unmanned aerial vehicle (UAV) based computer vision system, as a more and more promising option for forest fires surveillance and detection, is now widely employed. In this paper, an image processing method for the application to UAV is presented for the automatic detection of forest fires in infrared (IR) images. The presented algorithm makes use of brightness and motion clues along with image processing techniques based on histogram-based segmentation and optical flow approach for fire pixels detection. First, the histogram-based segmentation is used to extract the hot objects as fire candidate regions. Then, the optical flow method is adopted to calculate motion vectors of the candidate regions. The motion vectors are also further analyzed to distinguish fires from other fire analogues. Through performing morphological operations and blob counter method, a fire can be finally tracked in each IR image. Experimental results verified that the designed method can effectively extract and track fire pixels in IR video sequences.

A high-resolution dataset of water fluxes and states for Germany accounting for parametric uncertainty

Abstract: . Long-term, high-resolution data about hydrologic fluxes and states are needed for many hydrological applications. Because continuous large-scale observations of such variables are not feasible, hydrologic or land surface models are applied to derive them. This study aims to analyze and provide a consistent high-resolution dataset of land surface variables over Germany, accounting for uncertainties caused by equifinal model parameters. The mesoscale Hydrological Model (mHM) is employed to derive an ensemble (100 members) of evapotranspiration, groundwater recharge, soil moisture, and runoff generated at high spatial and temporal resolutions (4 km and daily, respectively) for the period 1951–2010. The model is cross-evaluated against the observed daily streamflow in 222 basins, which are not used for model calibration. The mean (standard deviation) of the ensemble median Nash–Sutcliffe efficiency estimated for these basins is 0.68 (0.09) for daily streamflow simulations. The modeled evapotranspiration and soil moisture reasonably represent the observations from eddy covariance stations. Our analysis indicates the lowest parametric uncertainty for evapotranspiration, and the largest is observed for groundwater recharge. The uncertainty of the hydrologic variables varies over the course of a year, with the exception of evapotranspiration, which remains almost constant. This study emphasizes the role of accounting for the parametric uncertainty in model-derived hydrological datasets.