Main Environmental Features Leading to Recent Land Abandonment in Murcia Region (Southeast Spain)

Land abandonment is a global phenomenon whose environmental consequences are difficult to assess. The Murcia region is one of the most arid regions in southern Europe and also one of the most prone to land abandonment. This study researches which environmental features are more relevant to explain a...

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Glavni autor: Canovas Garcia, F. (author)
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Izdano: 2016
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Online pristup:http://dspace.utpl.edu.ec/handle/123456789/18955
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author Canovas Garcia, F.
author_facet Canovas Garcia, F.
author_role author
collection Repositorio Universidad Técnica Particular de Loja
dc.creator.none.fl_str_mv Canovas Garcia, F.
dc.date.none.fl_str_mv 01/11/2015
2016-04-01
2017-06-16T22:02:40Z
2017-06-16T22:02:40Z
dc.identifier.none.fl_str_mv 10.1002/ldr.2447
10853278
10.1002/ldr.2447
http://dspace.utpl.edu.ec/handle/123456789/18955
dc.language.none.fl_str_mv Inglés
dc.publisher.none.fl_str_mv Land Degradation and Development
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
dc.source.none.fl_str_mv reponame:Repositorio Universidad Técnica Particular de Loja
instname:Universidad Técnica Particular de Loja
instacron:UTPL
dc.subject.none.fl_str_mv Data analysis
Feature selection
Land abandonment
Logistic regression
Random forest
dc.title.none.fl_str_mv Main Environmental Features Leading to Recent Land Abandonment in Murcia Region (Southeast Spain)
dc.type.none.fl_str_mv info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/article
description Land abandonment is a global phenomenon whose environmental consequences are difficult to assess. The Murcia region is one of the most arid regions in southern Europe and also one of the most prone to land abandonment. This study researches which environmental features are more relevant to explain abandonment at the agricultural plot scale. Geomorphometric features were measured at different scales to investigate which scales could be more relevant. Two different models have been used: logistic regression, a statistical model that allows the interpretation of the involved features, and Random Forest, a machine learning model with a higher predictive power but lower interpretability. The combined use of both such models allows a set of predictors to be selected, which, when used with Random Forest, produce a map that is highly accurate for predicting abandonment and, when used with logistic regression, produce an interpretable model. The main conclusion is that climate is the most relevant factor to explain land abandonment.
eu_rights_str_mv openAccess
format article
id UTPL_c53c2ed05eb55cb15c787de8e0939d9e
identifier_str_mv 10.1002/ldr.2447
10853278
instacron_str UTPL
institution UTPL
instname_str Universidad Técnica Particular de Loja
language_invalid_str_mv Inglés
network_acronym_str UTPL
network_name_str Repositorio Universidad Técnica Particular de Loja
oai_identifier_str oai:dspace.utpl.edu.ec:123456789/18955
publishDate 2016
publisher.none.fl_str_mv Land Degradation and Development
reponame_str Repositorio Universidad Técnica Particular de Loja
repository.mail.fl_str_mv .
repository.name.fl_str_mv Repositorio Universidad Técnica Particular de Loja - Universidad Técnica Particular de Loja
repository_id_str 1227
spelling Main Environmental Features Leading to Recent Land Abandonment in Murcia Region (Southeast Spain)Canovas Garcia, F.Data analysisFeature selectionLand abandonmentLogistic regressionRandom forestLand abandonment is a global phenomenon whose environmental consequences are difficult to assess. The Murcia region is one of the most arid regions in southern Europe and also one of the most prone to land abandonment. This study researches which environmental features are more relevant to explain abandonment at the agricultural plot scale. Geomorphometric features were measured at different scales to investigate which scales could be more relevant. Two different models have been used: logistic regression, a statistical model that allows the interpretation of the involved features, and Random Forest, a machine learning model with a higher predictive power but lower interpretability. The combined use of both such models allows a set of predictors to be selected, which, when used with Random Forest, produce a map that is highly accurate for predicting abandonment and, when used with logistic regression, produce an interpretable model. The main conclusion is that climate is the most relevant factor to explain land abandonment.Land Degradation and Development2017-06-16T22:02:40Z2017-06-16T22:02:40Z2016-04-0101/11/2015info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article10.1002/ldr.24471085327810.1002/ldr.2447http://dspace.utpl.edu.ec/handle/123456789/18955Inglésinfo:eu-repo/semantics/openAccessreponame:Repositorio Universidad Técnica Particular de Lojainstname:Universidad Técnica Particular de Lojainstacron:UTPL2017-06-16T22:02:40Zoai:dspace.utpl.edu.ec:123456789/18955Institucionalhttps://dspace.utpl.edu.ec/Institución privadahttps://www.utpl.edu.ec/https://dspace.utpl.edu.ec/oai.Ecuador...opendoar:12272017-06-16T22:02:40Repositorio Universidad Técnica Particular de Loja - Universidad Técnica Particular de Lojafalse
spellingShingle Main Environmental Features Leading to Recent Land Abandonment in Murcia Region (Southeast Spain)
Canovas Garcia, F.
Data analysis
Feature selection
Land abandonment
Logistic regression
Random forest
status_str publishedVersion
title Main Environmental Features Leading to Recent Land Abandonment in Murcia Region (Southeast Spain)
title_full Main Environmental Features Leading to Recent Land Abandonment in Murcia Region (Southeast Spain)
title_fullStr Main Environmental Features Leading to Recent Land Abandonment in Murcia Region (Southeast Spain)
title_full_unstemmed Main Environmental Features Leading to Recent Land Abandonment in Murcia Region (Southeast Spain)
title_short Main Environmental Features Leading to Recent Land Abandonment in Murcia Region (Southeast Spain)
title_sort Main Environmental Features Leading to Recent Land Abandonment in Murcia Region (Southeast Spain)
topic Data analysis
Feature selection
Land abandonment
Logistic regression
Random forest
url http://dspace.utpl.edu.ec/handle/123456789/18955