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  • Author or Editor: P. Ganis x
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We propose a simple method to validate vegetation types in phytosociological tables obtained with the Braun-Blanquet approach. The method is based on fuzzy set theory and it measures the sharpness of a classification of relevés based on degrees of belonging calculated by similarity functions. The idea animating this work is that the validated vegetation types offer non-random species combinations that can be used to model environmental changes taking biodiversity into account. Phytosociological tables are widely available today and the information they contain can be very useful in studying environmental changes at different scales, from local to global.

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We suggest a classificatory approach for land cover analysis that integrates fuzzy set theory with permutation techniques. It represents a non parametric alternative and/or a complement of traditional multivariate statistics when data are scarce, missing, burdened with high degree of uncertainty and originated from different sources and/or times. According to this approach, the Operational Geographic Units (OGUs) in which landscape is subdivided and sampled are classified with hierarchical clustering methods. The clusters of a classification which are significantly sharp are used to define fuzzy sets. In this way, the original data scores are transformed by degrees of belonging. We introduce the concepts of endogenous and exogenous fuzzy sets and we suggest to apply the Mantel test between the similarity matrices of these fuzzy sets to test the predictivity of internal variables with respect to external variables. The approach is applied to OGUs corresponding to the smallest administrative units (kebeles) of the Ethiopian Rift Valley, a degrading area with high risk of further degradation. We found that: 1) there is a high correlation between geo-physical features of the landscape (geology, rainfall and elevation) and some indicators of the human pressure such as land use/cover, land management for livestock breeding and human, household and livestock densities, 2) there is a high correlation between land degradation, measured with relative loss of Normalized Difference Vegetation Index (NDVI) and the human pressure. However, the correlation is higher when the human pressure is considered in the geo-physical context of the landscape. The approach can be easily applied to produce maps useful for planning purposes thanks to geographical information system (GIS) technology that is becoming available at low cost even to small administrative units of developing countries.

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We propose a method that has general relevance to the digital representation of spatial variation of multivariate landscape data. It is based on the average similarity that operational geographic units (OGU) have with the adjacent ones according to characters relevant understanding landscape patterns and dynamics. The method is flexible and easily executable within the technological framework of geographic information systems (GIS) that today is available even free of charge or at very low cost. An example shows how the method, applied to spatial data of a floristic project for the urban area of Trieste (NE-Italy), can identify floristically homogeneous patches and can quantify the heterogeneity of the transition zones between such patches.

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In this paper, we want to support the idea of using a family of indices of similarity, that we call the Simpson's family indices or nestedness-based similarity functions (NBSF) for comparing operational geographic units (OGUs) (phytosociological relevés, animal traps, watersheds, administrative units, industrial areas, islands etc.). In these cases, similarity-dissimilarity depends, in addition to factors that induce replacement, also on factors that produce reduction or increment in the number of features within the same typology of OGUs (e.g., extent, reduction of fertility, anthropogenic pressure etc.). To keep into consideration this aspect, the indices are defined to be equal to 1 when the OGUs are completely nested. The results of the application to four simulated data sets prove that, when the data set does not show clear nested pattern, the use of NBSF produces results similar to the nestedness-free similarity functions, however since NBSF clearly detect nested situations, we should prefer their use in the circumstances where we think important to put in evidence nestedness. In conclusion, we support the idea of using both types of indices in order to improve the knowledge about the structure of any data set.

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