Eugenio Martínez-Falero

Diseño de un cuestionario para evaluar conocimientos básicos de estadística de estudiantes del último curso de ingeniería

The design of a questionnaire to assess knowledge of basic statistics for engineering students is presented. The original questionnaire included a large number of items grouped by subjects. This questionnaire was submitted to a panel of experts, consisting of nine teachers of Applied Statistics from different engineering majors belonging to iberoamerican universities. This group of experts evaluated the questions and provided valuable information to prepare the final questionnaire. The final questionnaire proposed includes 20 questions divided in four subjects: descriptive statistics, sampling, hypothesis test and general linear model. These subjects are similar to other studies found in the literature on evaluation of knowledge of basic statistical concepts.

Sustainability assessment in forest management based on individual preferences

This paper presents a methodology to elicit the preferences of any individual in the assessment of sustainable forest management at the stand level. The elicitation procedure was based on the comparison of the sustainability of pairs of forest locations. A sustainability map of the whole territory was obtained according to the individuals preferences. Three forest sustainability indicators were pre-calculated for each point in a study area in a Scots pine forest in the National Park of Sierra de Guadarrama in the Madrid Region in Spain to obtain the best management plan with the sustainability map. We followed a participatory process involving fifty people to assess the sustainability of the forest management and the methodology. The results highlighted the demand for conservative forest management, the usefulness of the methodology for managers, and the importance and necessity of incorporating stakeholders into forestry decision-making processes.

Most similar neighbor imputation of forest attributes using metrics derived from combined airborne LIDAR and multispectral sensors

ABSTRACT In the context of predicting forest attributes using a combination of airborne LIDAR and multispectral (MS) sensors, we suggest the inclusion of normalized difference vegetation index (NDVI) metrics along with the more traditional LIDAR height metrics. Here the data fusion method consists of back-projecting LIDAR returns onto original MS images, avoiding co-registration errors. The prediction method is based on non-parametric imputation (the most similar neighbor). Predictor selection and accuracy assessment include hypothesis tests and over-fitting prevention methods. Results show improvements when using combinations of LIDAR and MS compared to using either of them alone. The MS sensor has little explanatory capacity for forest variables dependent on tree height, already well determined from LIDAR alone. However, there is potential for variables dependent on tree diameters and their density. The combination of LIDAR and MS sensors can be very beneficial for predicting variables describing forests structural heterogeneity, which are best described from synergies between LIDAR heights and NDVI dispersion. Results demonstrate the potential of NDVI metrics to increase prediction accuracy of forest attributes. Their inclusion in the predictor dataset may, however, in a few cases be detrimental to accuracy, and therefore we recommend to carefully assess the possible advantages of data fusion on a case-by-case basis.