GENERALIZED LINEAR MODELS FOR TREE SURVIVAL IN FOREST STANDS
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Cerne (Online) |
Texto Completo: | https://cerne.ufla.br/site/index.php/CERNE/article/view/2199 |
Resumo: | Quantify the surviving trees in a forest stand and estimating the probability of an individual tree survives to environment conditions are fundamental in forest management planning. Therefore, the main goal of this paper is to estimate the tree survival probability in Pinus taeda stands based on generalized linear models (GLM). The data set was obtained from forest inventories carried out in the Midwest of Santa Catarina State, Brazil. The data analysis combined four strategies for covariate selection with four link functions in the specification of the Bernoulli GLM. We performed strategies for covariate selection along with the standard stepwise procedure, where we considered the elastic net approach, as well as its special cases the lasso and ridge penalization. Our analyses showed that the stepwise procedure combined with the complement log-log link function provide the best fit. The final model is composed by five covariates and presents 81,5% of accuracy given by ROC curve. Finally, we evaluated the fitted model by means of the half-Normal plots and randomized quantile residuals, whose results show evidence of a suitable fit. We suggest the stepwise procedure for selecting covariates for predicting the tree survival probability with complement log-log link function. |
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Cerne (Online) |
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GENERALIZED LINEAR MODELS FOR TREE SURVIVAL IN FOREST STANDSElastic net, Link function, Logistic regression, Ridge regression, Stepwise method.Quantify the surviving trees in a forest stand and estimating the probability of an individual tree survives to environment conditions are fundamental in forest management planning. Therefore, the main goal of this paper is to estimate the tree survival probability in Pinus taeda stands based on generalized linear models (GLM). The data set was obtained from forest inventories carried out in the Midwest of Santa Catarina State, Brazil. The data analysis combined four strategies for covariate selection with four link functions in the specification of the Bernoulli GLM. We performed strategies for covariate selection along with the standard stepwise procedure, where we considered the elastic net approach, as well as its special cases the lasso and ridge penalization. Our analyses showed that the stepwise procedure combined with the complement log-log link function provide the best fit. The final model is composed by five covariates and presents 81,5% of accuracy given by ROC curve. Finally, we evaluated the fitted model by means of the half-Normal plots and randomized quantile residuals, whose results show evidence of a suitable fit. We suggest the stepwise procedure for selecting covariates for predicting the tree survival probability with complement log-log link function.CERNECERNE2020-02-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://cerne.ufla.br/site/index.php/CERNE/article/view/2199CERNE; Vol. 25 No. 4 (2019); 347-356CERNE; v. 25 n. 4 (2019); 347-3562317-63420104-7760reponame:Cerne (Online)instname:Universidade Federal de Lavras (UFLA)instacron:UFLAenghttps://cerne.ufla.br/site/index.php/CERNE/article/view/2199/1152Copyright (c) 2020 CERNEinfo:eu-repo/semantics/openAccessFiorentin, Luan DemarcoBonat, Wagner HugoPelissari, Allan LibanioMachado, Sebastião do AmaralTéo, Saulo JorgeOrso, Gabriel2020-02-21T11:40:18Zoai:cerne.ufla.br:article/2199Revistahttps://cerne.ufla.br/site/index.php/CERNEPUBhttps://cerne.ufla.br/site/index.php/CERNE/oaicerne@dcf.ufla.br||cerne@dcf.ufla.br2317-63420104-7760opendoar:2024-05-21T19:54:41.942496Cerne (Online) - Universidade Federal de Lavras (UFLA)true |
dc.title.none.fl_str_mv |
GENERALIZED LINEAR MODELS FOR TREE SURVIVAL IN FOREST STANDS |
title |
GENERALIZED LINEAR MODELS FOR TREE SURVIVAL IN FOREST STANDS |
spellingShingle |
GENERALIZED LINEAR MODELS FOR TREE SURVIVAL IN FOREST STANDS Fiorentin, Luan Demarco Elastic net, Link function, Logistic regression, Ridge regression, Stepwise method. |
title_short |
GENERALIZED LINEAR MODELS FOR TREE SURVIVAL IN FOREST STANDS |
title_full |
GENERALIZED LINEAR MODELS FOR TREE SURVIVAL IN FOREST STANDS |
title_fullStr |
GENERALIZED LINEAR MODELS FOR TREE SURVIVAL IN FOREST STANDS |
title_full_unstemmed |
GENERALIZED LINEAR MODELS FOR TREE SURVIVAL IN FOREST STANDS |
title_sort |
GENERALIZED LINEAR MODELS FOR TREE SURVIVAL IN FOREST STANDS |
author |
Fiorentin, Luan Demarco |
author_facet |
Fiorentin, Luan Demarco Bonat, Wagner Hugo Pelissari, Allan Libanio Machado, Sebastião do Amaral Téo, Saulo Jorge Orso, Gabriel |
author_role |
author |
author2 |
Bonat, Wagner Hugo Pelissari, Allan Libanio Machado, Sebastião do Amaral Téo, Saulo Jorge Orso, Gabriel |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
Fiorentin, Luan Demarco Bonat, Wagner Hugo Pelissari, Allan Libanio Machado, Sebastião do Amaral Téo, Saulo Jorge Orso, Gabriel |
dc.subject.por.fl_str_mv |
Elastic net, Link function, Logistic regression, Ridge regression, Stepwise method. |
topic |
Elastic net, Link function, Logistic regression, Ridge regression, Stepwise method. |
description |
Quantify the surviving trees in a forest stand and estimating the probability of an individual tree survives to environment conditions are fundamental in forest management planning. Therefore, the main goal of this paper is to estimate the tree survival probability in Pinus taeda stands based on generalized linear models (GLM). The data set was obtained from forest inventories carried out in the Midwest of Santa Catarina State, Brazil. The data analysis combined four strategies for covariate selection with four link functions in the specification of the Bernoulli GLM. We performed strategies for covariate selection along with the standard stepwise procedure, where we considered the elastic net approach, as well as its special cases the lasso and ridge penalization. Our analyses showed that the stepwise procedure combined with the complement log-log link function provide the best fit. The final model is composed by five covariates and presents 81,5% of accuracy given by ROC curve. Finally, we evaluated the fitted model by means of the half-Normal plots and randomized quantile residuals, whose results show evidence of a suitable fit. We suggest the stepwise procedure for selecting covariates for predicting the tree survival probability with complement log-log link function. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-02-12 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://cerne.ufla.br/site/index.php/CERNE/article/view/2199 |
url |
https://cerne.ufla.br/site/index.php/CERNE/article/view/2199 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://cerne.ufla.br/site/index.php/CERNE/article/view/2199/1152 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2020 CERNE info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2020 CERNE |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
CERNE CERNE |
publisher.none.fl_str_mv |
CERNE CERNE |
dc.source.none.fl_str_mv |
CERNE; Vol. 25 No. 4 (2019); 347-356 CERNE; v. 25 n. 4 (2019); 347-356 2317-6342 0104-7760 reponame:Cerne (Online) instname:Universidade Federal de Lavras (UFLA) instacron:UFLA |
instname_str |
Universidade Federal de Lavras (UFLA) |
instacron_str |
UFLA |
institution |
UFLA |
reponame_str |
Cerne (Online) |
collection |
Cerne (Online) |
repository.name.fl_str_mv |
Cerne (Online) - Universidade Federal de Lavras (UFLA) |
repository.mail.fl_str_mv |
cerne@dcf.ufla.br||cerne@dcf.ufla.br |
_version_ |
1799874943801884672 |