The ecological land scape classification (ELC) allows the identification of homogeneous land units (LUs), which are fundamental for investigation and management purposes. ELC is particularly relevant in international cooperation programmes targeting areas characterized by very scarce and scattered thematic information. In this study, three techniques are compared to find the best classification of LUs of Socotra Island (Yemen): a Self-Organizing Map neural network, the Clustering LARge Applications method and a Two-Step clustering algorithm. In this way, three LU maps of Socotra Island were produced and the relationship between LUs and plant communities, as identified by a phytosociologiacl investigation, was analysed. Based on evaluation procedures, TS method emerged as the most suitable being able to better discriminate the plant communities so as to reach a trade-off between classification complexity and class homogeneity. Even though the identification of LUs depends on the selection of the classification method and criteria, the scale adopted and the specific management and planning purposes, this study represents a contribution towards a standardization of ecological classification systems thanks also to the worldwide availability of georeferenced environmental data.

Analysing the relationship between land units and plant communities: The case of Socotra Island (Yemen) / Attorre, Fabio; A., Issa; L., Malatesta; A., Adeeb; DE SANCTIS, Michele; Vitale, Marcello; Farcomeni, Alessio. - In: PLANT BIOSYSTEMS. - ISSN 1126-3504. - STAMPA. - 148:3(2014), pp. 529-539. [10.1080/11263504.2014.900127]

Analysing the relationship between land units and plant communities: The case of Socotra Island (Yemen)

ATTORRE, Fabio;L. Malatesta;DE SANCTIS, Michele;VITALE, MARCELLO;FARCOMENI, Alessio
2014

Abstract

The ecological land scape classification (ELC) allows the identification of homogeneous land units (LUs), which are fundamental for investigation and management purposes. ELC is particularly relevant in international cooperation programmes targeting areas characterized by very scarce and scattered thematic information. In this study, three techniques are compared to find the best classification of LUs of Socotra Island (Yemen): a Self-Organizing Map neural network, the Clustering LARge Applications method and a Two-Step clustering algorithm. In this way, three LU maps of Socotra Island were produced and the relationship between LUs and plant communities, as identified by a phytosociologiacl investigation, was analysed. Based on evaluation procedures, TS method emerged as the most suitable being able to better discriminate the plant communities so as to reach a trade-off between classification complexity and class homogeneity. Even though the identification of LUs depends on the selection of the classification method and criteria, the scale adopted and the specific management and planning purposes, this study represents a contribution towards a standardization of ecological classification systems thanks also to the worldwide availability of georeferenced environmental data.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/548232
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