Document Detail


Learning machines applied to potential forest distribution.
MedLine Citation:
PMID:  15984068     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
The clearing of forests to obtain land for pasture and agriculture and the replacement of autochthonous species by other faster-growing varieties of trees for timber have both led to the loss of vast areas of forest worldwide. At present, many developed countries are attempting to reverse these effects, establishing policies for the restoration of older woodland systems. Reforestation is a complex matter, planned and carried out by experts who need objective information regarding the type of forest that can be sustained in each area. This information is obtained by drawing up feasibility models constructed using statistical methods that make use of the information provided by morphological and environmental variables (height, gradient, rainfall, etc.) that partially condition the presence or absence of a specific kind of forestation in an area. The aim of this work is to construct a set of feasibility models for woodland located in the basin of the River Liébana (NW Spain), to serve as a support tool for the experts entrusted with carrying out the reforestation project. The techniques used are multilayer perceptron neural networks and support vector machines. Their results will be compared to the results obtained by traditional techniques (such as discriminant analysis and logistic regression) by measuring the degree of fit between each model and the existing distribution of woodlands. The interpretation and problems of the feasibility models are commented on in the Discussion section.
Authors:
Celestino Ordóñez; Javier Taboada; Fernando Bastante; Jose María Matías; Angel Manuel Felicísimo
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Environmental management     Volume:  35     ISSN:  0364-152X     ISO Abbreviation:  Environ Manage     Publication Date:  2005 Jan 
Date Detail:
Created Date:  2005-06-28     Completed Date:  2005-07-12     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  7703893     Medline TA:  Environ Manage     Country:  United States    
Other Details:
Languages:  eng     Pagination:  109-20     Citation Subset:  IM    
Affiliation:
Department of Natural Resource and Environmental Engineering, University of Vigo, E.T.S.I. Minas, Lagoas-Marcosende 9, 36200, Vigo, Spain. tino@lidia.uvigo.es
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MeSH Terms
Descriptor/Qualifier:
Artificial Intelligence
Conservation of Natural Resources*
Decision Support Techniques*
Ecosystem
Environment*
Forestry*
Regression Analysis
Spain

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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