A model to choose a management team for a tourism company

In Portugal, many tourism companies deal with a management problem regarding their low dimension and productivity. Therefore, an additional concern must be given to the management model, in order to increase their success. An innovating model is given for a situation in which a hotel aims to choose a management team according to a set of competences and skills to give the company the necessary ability to be competitive and to survive in a very competitive market. This paper aims at presenting, through game theory, a way to have the best choice that allows ordering the potential candidates that will constitute the co-leadership team. This requires a successful team with a renewed co-leadership model of co-leaders.info:eu-repo/semantics/acceptedVersio

Estimation of total biomass in Aleppo pine forest stands applying parametric and nonparametric methods to low-density airborne laser scanning data

The account of total biomass can assist with the evaluation of climate regulation policies from local to global scales. This study estimates total biomass (TB), including tree and shrub biomass fractions, in Pinus halepensis Miller forest stands located in the Aragon Region (Spain) using Airborne Laser Scanning (ALS) data and fieldwork. A comparison of five selection methods and five regression models was performed to relate the TB, estimated in 83 field plots through allometric equations, to several independent variables extracted from ALS point cloud. A height threshold was used to include returns above 0.2 m when calculating ALS variables. The sample was divided into training and test sets composed of 62 and 21 plots, respectively. The model with the lower root mean square error (15.14 tons/ha) after validation was the multiple linear regression model including three ALS variables: the 25th percentile of the return heights, the variance, and the percentage of first returns above the mean. This study confirms the usefulness of low-density ALS data to accurately estimate total biomass, and thus better assess the availability of biomass and carbon content in a Mediterranean Aleppo pine forest

Spin-density-wave magnetism in dilute copper-manganese alloys

Elastic neutron-scattering measurements on two samples of Cu alloyed with 1.3% Mn and 0.55% Mn show that the spin-density-wave (SDW) features found in more concentrated alloys persist in the limit of very dilute alloys. These features consist of temperature-dependent incommensurate peaks in magnetic neutron scattering, with positions and strengths which are fully consistent with those in the concentrated alloys. The implications of these measurements are twofold. First, it is clear from this data that SDW magnetic ordering occurs across the entire range of CuMn alloys which have typically been interpreted as spin glasses. Second, the more fundamental significance of this work is the suggestion via extrapolation that a peak in the magnetic susceptibility x(q) occurs in pure copper, at a value of q given by the Fermi-surface diameter 2k{sub F}

Assessing the potential of the dart model to discrete return lidar simulation—application to fuel type mapping

Fuel type is one of the key factors for analyzing the potential of fire ignition and propaga-tion in agricultural and forest environments. The increase of three-dimensional datasets provided by active sensors, such as LiDAR (Light Detection and Ranging), has improved the classification of fuel types through empirical modelling. Empirical methods are site and sensor specific while Radiative Transfer Models (RTM) approaches provide broader universality. The aim of this work is to analyze the suitability of Discrete Anisotropic Radiative Transfer (DART) model to replicate low density small-footprint Airborne Laser Scanning (ALS) measurements and subsequent fuel type classification. Field data measured in 104 plots are used as ground truth to simulate LiDAR response based on the sensor and flight characteristics of low-density ALS data captured by the Spanish National Plan for Aerial Orthophotography (PNOA) in two different dates (2011 and 2016). The accuracy assessment of the DART simulations is performed using Spearman rank correlation coefficients between the simulated metrics and the ALS-PNOA ones. The results show that 32% of the computed metrics overpassed a correlation value of 0.80 between simulated and ALS-PNOA metrics in 2011 and 28% in 2016. The highest correlations were related to high height percentiles, canopy variability metrics as for example standard deviation and Rumple diversity index, reaching correlation values over 0.94. Two metric selection approaches and Support Vector Machine classification method with variants were compared to classify fuel types. The best-fitted classification model, trained with the DART simulated sample and validated with ALS-PNOA data, was obtained using Support Vector Machine method with radial kernel. The overall accuracy of the classification after validation was 88% and 91% for the 2011 and 2016 years, respectively. The use of DART demonstrates its value for simulating generalizable 3D data for fuel type classification providing relevant information for forest managers in fire prevention and extinction

Forest fire severity estimation based on the LiDAR-PNOA data and the values of the Composite Burn Index

Revista oficial de la Asociación Española de Teledetección[EN] Mediterranean pine forests in Spain experience wildland fire events with different frequencies, intensities, and severities. An estimation of the fire severity as accurate as possible is required by forest managers to decide which strategy is most appropriate to mitigate the effect of fire. The aim of this research is to estimate the post-fire severity, relating a pool of independent variables derived from the LiDAR (Light Detection And Ranging) points clouds delivered by the National Plan for Aerial Orthophotography (PNOA) to field data based on Composite Burn Index collected in four fires located in Aragón (Spain). Logistic regression models were developed and statistically tested and validated to map fire severity with up to 85.5% accuracy. The canopy relief ratio and the percentage of all returns above one meter height were the most significant variables. In addition, the obtained results are compared to different spectral indices derived from Landsat Thematic Mapper.[ES] Los pinares mediterráneos españoles se ven afectados por incendios forestales con diferente frecuencia, intensidad y severidad. Para su valoración, hay que recurrir a estimaciones lo más precisas posibles de la severidad, la cual podrá ayudar a los gestores del bosque a decidir qué estrategia es más adecuada para mitigar el efecto del fuego. El objetivo de esta investigación es la estimación de la severidad post-incendio, relacionando un conjunto de variables independientes derivadas de las nubes de puntos del vuelo LiDAR (Light Detection And Ranging) del Plan Nacional de Ortofotografía Aérea (PNOA), con datos de campo basados en el índice CBI (Composite Burn Index) y recogidos en cuatro incendios localizados en Aragón. Se ha ajustado un modelo de regresión logística, que tras la validación, ha reportado una precisión del 85,5%, siendo las variables “canopy relief ratio” y el porcentaje de puntos por encima de 1 m de altura sobre el terreno, las incluidas en dicho modelo. Por otro lado, se ha realizado una comparativa de los resultados con índices espectrales derivados de imágenes Landsat Thematic Mapper.Estos trabajos han sido financiados por la beca pre-doctoral (FPI BOA 30, 11/02/2011) del Gobierno de Aragón y el proyecto CGL2014-57013-C2-2-R, y han contado con la ayuda de Francisco Palú, Marco Lorenzo y Emilio Pérez-Aguilar del Servicio Provincial de Agricultura, Ganadería y Medio Ambiente del Gobierno de Aragón. Los au-tores agradecen al Centro Nacional de Información Geográfica y al Centro de Información Territorial de Aragón por proporcionar los datos LiDAR-PNOA y al CENAD “San Gregorio” por facilitar el acceso a las parcelas de campo.Montealegre, AL.; Lamelas, MT.; Tanase, MA.; De La Riva, J. (2017). Estimación de la severidad en incendios forestales a partir de datos LiDAR-PNOA y valores de Composite Burn Index. Revista de Teledetección. (49):1-16. https://doi.org/10.4995/raet.2017.7371SWORD1164