The present paper presents the application of a finite mixture model (FMM) to analyze spatially explicit data on forest composition and environmental variables to produce a high-resolution map of their current potential distribution. FMM provides a convenient yet formal setting for model-based clustering. Within this framework, forest data are assumed to come from an underlying FMM, where each mixture component corresponds to a cluster and each cluster is characterized by a different composition of tree species. An important extension of this model is based on including a set of covariates to predict class membership. These covariates can be climatic and topographical parameters as well as geographical coordinates and the class membership of neighbouring plots. FMM was applied to a national forest inventory of Italy consisting of 6,714 plots with a measure of abundance for 27 tree species. In this way, a map of potential forest types was produced. The limitations and usefulness of the proposed modelling approach were analyzed and discussed, comparing the results with an independently derived expert map.

Classifying and Mapping Potential Distribution of Forest Types Using a Finite Mixture Model / Attorre, Fabio; Fabio, Francesconi; DE SANCTIS, Michele; Alfo', Marco; Martella, Francesca; Roberto, Valenti; Vitale, Marcello. - In: FOLIA GEOBOTANICA. - ISSN 1211-9520. - STAMPA. - 49:(2014), pp. 313-335. [10.1007/s12224-012-9139-8]

Classifying and Mapping Potential Distribution of Forest Types Using a Finite Mixture Model

ATTORRE, Fabio;DE SANCTIS, Michele;ALFO', Marco;MARTELLA, Francesca;VITALE, MARCELLO
2014

Abstract

The present paper presents the application of a finite mixture model (FMM) to analyze spatially explicit data on forest composition and environmental variables to produce a high-resolution map of their current potential distribution. FMM provides a convenient yet formal setting for model-based clustering. Within this framework, forest data are assumed to come from an underlying FMM, where each mixture component corresponds to a cluster and each cluster is characterized by a different composition of tree species. An important extension of this model is based on including a set of covariates to predict class membership. These covariates can be climatic and topographical parameters as well as geographical coordinates and the class membership of neighbouring plots. FMM was applied to a national forest inventory of Italy consisting of 6,714 plots with a measure of abundance for 27 tree species. In this way, a map of potential forest types was produced. The limitations and usefulness of the proposed modelling approach were analyzed and discussed, comparing the results with an independently derived expert map.
2014
finite mixture model; classification; potential distribution; forest types; italy
01 Pubblicazione su rivista::01a Articolo in rivista
Classifying and Mapping Potential Distribution of Forest Types Using a Finite Mixture Model / Attorre, Fabio; Fabio, Francesconi; DE SANCTIS, Michele; Alfo', Marco; Martella, Francesca; Roberto, Valenti; Vitale, Marcello. - In: FOLIA GEOBOTANICA. - ISSN 1211-9520. - STAMPA. - 49:(2014), pp. 313-335. [10.1007/s12224-012-9139-8]
File allegati a questo prodotto
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/474488
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 18
  • ???jsp.display-item.citation.isi??? 16
social impact