RESUMENLas clasifi caciones zonales -macroclimáticas-son difícilmente aplicables en ám-bitos regionales, donde existe una particular interacción de factores climáticos de tipo geográfi co y dinámico. Especialmente complejas resultan las heterogé-neas regiones sometidas al cambiante clima mediterráneo, que adolecen de una sistematización escalar adecuada. Atendiendo a esta necesidad se presenta, por primera vez, una propuesta de clasifi cación interescalar basada en estadística multivariante y el criterio experto. El método se aplica a la comunidad autónoma de Andalucía (España), identifi cándose y caracterizándose cinco regiones climáticas y veintiún tipos climáticos. Se concluye que la metodología utilizada permite su extrapolación a otros ámbitos geográfi cos complejos, independientemente de la escala espacial y temporal de trabajo. Palabras clave: Clasifi cación climática, estadística multivariante, Andalucía ABSTRACTZonal classifi cations -macroclimatic-are diffi cult to apply in regional areas where there is a particular interaction of climatic factors of both a geographic and dynamic type. Especially complex are the heterogeneous regions subject to changing Mediterranean climate, which lack proper scalar systematization. In response to this need we present, for the fi rst time, an interscalar classifi cation based on multivariate statistics and expert judgment. The method has been applied to the autonomous community of Andalusia (Spain), where we identifi ed and characterized fi ve climatic regions and twenty one different climatic types. We concluded that this method allows for extrapolation to other geographical areas, regardless of the spatial and temporal scale under investigation.

This paper proposes an empirical climate classification method based on the application of multivariate statistics. In this method, the technique of supervised and unsupervised image classification is used to classify the data and define climatic units. ENVI software is used to perform the image classification, specifically, the Iterative Self-Organizing Data Analysis Technique algorithm. Supervised classification is also applied based on reference variables, fundamental parameters and a classifier. The obtained results display greater objectivity, reliability, operability, accessibility and reproducibility than previous climate classifications devised for the region of Andalusia (Spain), taking into account that these previous classifications were not based on quantitative criteria.

RESUMEN • Los sistemas de información geográfica (sig) suponen una ventana al pasado, presente y futuro del territorio, facilitando el acceso a información relativa a aspectos naturales, sociales y culturales, de tal manera que se están haciendo indispensables a la hora de conocer, explorar y valorar el medio ambiente. Aquí se pretende mostrar la aplicabilidad de los sig Google Earth en educación ambiental mediante la generación de itinerarios didácticos realizada por estudiantes del itinerario bilingüe del

Interpreting the landscape that surrounds us is somewhat personal. However, it is necessary to explore the interpretation made by future teachers so that we can reflect on their observations. This study collected data from five academic years of undergraduates in Early Childhood Education (N = 421), who were asked to fill out a questionnaire with different items while observing the landscape features when walking through a park. This article demonstrates for the very first time that the trend shown in childhood of not considering plants as living beings remains in the landscape perception of the university students. The students must be confronted with their own biases in interpreting the landscape, so that they are aware and do not transfer these biases to their future students.

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