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Title: Modelling natural forest expansion on a landscape level by multinomial logistic regression
Authors: Corona, Piermaria
Calvani, Paolo
Scarascia Mugnozza, Giuseppe
Pompei, Enrico
Keywords: Landscape changes;Forest re-colonisation;Predictive mapping
Issue Date: 2008
Publisher: Taylor & Francis
Source: Corona, P. et al., 2008. Modelling natural forest expansion on a landscape level by multinomial logistic regression. "Plant Biosystems" 142 ( 3): 509–517.
Natural forest expansion is one of the most relevant landscape changes in many temperate countries. Although large areas are
involved, relatively few studies have been carried out with the objective of unravelling the specific impact of the individual
factors characterising the sites prone to such a process. The aim of this article is to present a research tool for assessing the
factors characterising farmland sites prone to natural conversion from crop growing and pasture to forests and other wooded
land (OWL), and for predicting the probability of such a land-use change. The methodological approach is based on
multinomial logistic regression. As a case study, the approach was applied to land-use classification repeated on the same
sites in a large area of central Italy on two successive occasions, spanning two decades, from the beginning of the 1980s up to
2002. Of all the factors assessed, landscape attributes were identified as a sufficient subset for quantitative prediction of
change from farmland to OWL or to forest. The tested modelling approach is explicitly empirical and planning-oriented.
From a quantitative point of view, the precision of the models may be only indicative for assessing land-use change
probability for single observations, while it is appropriate for predicting mean probabilities at a landscape mapping level,
where it is possible to sample a number of sites. At this level, the approach is a useful tool for simulating future landscape
scenarios related to natural forest expansion.
L'articolo è disponibile sul sito dell'editore
ISSN: 1126-3504
DOI: 10.1080/11263500802410850
Appears in Collections:DiSAFRi - Archivio della produzione scientifica

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