An organization design redefinition for the tourism sector using design thinking: Sustainable hotels case study

This chapter shows how the Classic Design Theory may be applied to create a new philosophy for organizations. In this study it is applied to a project in tourism sector. An investigation was made first in different companies in several sectors of activity in Portugal and has been recently successfully applied. The purpose of the chapter is to show how the identification and experimentation of concepts and methods used in classical design allow a better understanding of the implications they have in the engineering organization design theory. To renew the engineering organization design, an organizational design framework was conceived in order to use classical design methods and adapt them to organizational design theories. A contribution is made not just to the art of science of a designed-based organization design theory, but also to create and to test any organization design. Several alternative organization designs came out from the involved action research. We will present several examples of organization design interface based on real Eco Hotel. This represents a useful tool for organization design practitioners and non-organization design practitioners. For this purpose, an appliance was made and tested involving real organizations, interviews and focus groups (namely in the wine sector, or in the design sector, or yet on a NGO). The final result of this action research was a design-based organization design framework and its outcomes - which are unique, beautiful, functional, simple and sustainable – a design-based organization design interface that people love, considering always the context and user profile on which it is inserted.info:eu-repo/semantics/submittedVersio

UAV-derived photogrammetric point clouds and multispectral indices for fuel estimation in Mediterranean forests

Sensors attached to unmanned aerial vehicles (UAVs) allow estimating a large number of forest attributes related to forest fuels. This study assesses photogrammetric point clouds and multispectral indices obtained from a fixed-wing UAV for the classification of Prometheus fuel types in 82 forest plots in Aragón (NE Spain). Images captured by an RGB camera and a multispectral sensor allowed generating high density photogrammetric point clouds (RGB: 3000 points/m2; multispectral: 85 points/m2), which were normalized using alternatively a Digital Elevation Model (DEM) of 0.5, 1, and 2 m resolution. A set of structural and textural variables were derived from the normalized point cloud heights, and for the latter, the gray-level co-occurrence matrix (GLCM) approach was used. Multispectral images were also used to create seven spectral vegetation indices. The most relevant structural, textural, and spectral variables to introduce into the fuel types classification models were selected using Dunn's test, which included: the vegetation height at the 50th percentile, the coefficient of variation of the heights, the percentage of returns above 4 m, the mean textural dissimilarity, and the mean of the Green Chlorophyll Index. Three different data samples were introduced in the models: i) the relevant structural and textural variables from the RGB camera (RGB data sample); ii) the relevant structural, textural, and spectral variables from the multispectral sensor (MS data sample); and iii) the relevant structural and textural variables from the RGB camera plus the relevant spectral variable from the multispectral sensor (integrated data sample). After comparing three machine learning classification techniques (Random Forest, and Linear and Radial Support Vector Machine), the best results were obtained with Random Forest with k-fold cross-validation (k-10) and the integrated data sample with normalized point clouds at 0.5 m DEM resolution (overall accuracy = 71%). The variables successfully identified the Prometheus main fire carriers (i.e., shrubs or trees) and confusions were mainly located within the fuel types of the same dominant stratum, especially in fuel types 3 and 6. These results demonstrate the ability of UAV imagery to classify forest fuels in Mediterranean environments when RGB and multispectral data are combined

Estimación de variables dasométricas a partir de datos LiDAR PNOA en masas regulares de Pinus halepensis Mill.

El conocimiento de las masas forestales es fundamental para su correcta gestión y ordenación. En ocasiones no basta con un inventario cualitativo del monte, siendo necesaria una valoración cuantitativa, mediante la estimación de variables dasométricas. La tecnología LiDAR aporta una nueva perspectiva a los inventarios forestales al ofrecer de forma directa información tridimensional de toda la superficie. El IGN inició en 2008-2009 la captura de datos LiDAR para gran parte de España, dentro del Plan Nacional de Ortofotografía Área (PNOA). Este trabajo pretende evaluar la adecuación de estos datos para estimar variables dasométricas en masas regulares de Pinus halepensis Mill. El área de estudio son los montes “Dehesa de los Enebrales” y “Valdá y Carrilanga” (Daroca, Zaragoza). Se han generado modelos de regresión lineal múltiple entre las variables dasométricas, obtenidas en 61 parcelas de campo, y una colección de variables estadísticas extraídas de la nube de puntos LiDAR. Los coeficientes de determinación corregidos obtenidos son 0,867 para la estimación del volumen, 0,854 para el área basimétrica, 0,858 para la densidad y 0,799 para la altura media. Las variables LiDAR introducidas en los modelos en general incluyen al menos un estadístico referente a altura (m) y otro a la distribución horizontal de la nube de puntos

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

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

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