Predicting Eucalyptus stand attributes in Minas Gerais state, Brazil: an approach using machine learning algorithms with multi-source datasets

Detalhes bibliográficos
Autor(a) principal: ALINY APARECIDA DOS REIS
Data de Publicação: 2018
Tipo de documento: Tese
Título da fonte: Portal de Dados Abertos da CAPES
Texto Completo: https://sucupira.capes.gov.br/sucupira/public/consultas/coleta/trabalhoConclusao/viewTrabalhoConclusao.jsf?popup=true&id_trabalho=6641330
id BRCRIS_ee19a2c38f9e983320870566fce78e75
network_acronym_str CAPES
network_name_str Portal de Dados Abertos da CAPES
dc.title.pt-BR.fl_str_mv Predicting Eucalyptus stand attributes in Minas Gerais state, Brazil: an approach using machine learning algorithms with multi-source datasets
title Predicting Eucalyptus stand attributes in Minas Gerais state, Brazil: an approach using machine learning algorithms with multi-source datasets
spellingShingle Predicting Eucalyptus stand attributes in Minas Gerais state, Brazil: an approach using machine learning algorithms with multi-source datasets
Forest Management. Remote Sensing. Random Forest. Terrain Attributes.
Manejo florestal. Sensoriamento remoto. Random Forest. Atributos de terreno.
ALINY APARECIDA DOS REIS
title_short Predicting Eucalyptus stand attributes in Minas Gerais state, Brazil: an approach using machine learning algorithms with multi-source datasets
title_full Predicting Eucalyptus stand attributes in Minas Gerais state, Brazil: an approach using machine learning algorithms with multi-source datasets
title_fullStr Predicting Eucalyptus stand attributes in Minas Gerais state, Brazil: an approach using machine learning algorithms with multi-source datasets
Predicting Eucalyptus stand attributes in Minas Gerais state, Brazil: an approach using machine learning algorithms with multi-source datasets
title_full_unstemmed Predicting Eucalyptus stand attributes in Minas Gerais state, Brazil: an approach using machine learning algorithms with multi-source datasets
Predicting Eucalyptus stand attributes in Minas Gerais state, Brazil: an approach using machine learning algorithms with multi-source datasets
title_sort Predicting Eucalyptus stand attributes in Minas Gerais state, Brazil: an approach using machine learning algorithms with multi-source datasets
topic Forest Management. Remote Sensing. Random Forest. Terrain Attributes.
Manejo florestal. Sensoriamento remoto. Random Forest. Atributos de terreno.
publishDate 2018
format doctoralThesis
url https://sucupira.capes.gov.br/sucupira/public/consultas/coleta/trabalhoConclusao/viewTrabalhoConclusao.jsf?popup=true&id_trabalho=6641330
author_role author
author ALINY APARECIDA DOS REIS
author_facet ALINY APARECIDA DOS REIS
dc.contributor.authorLattes.fl_str_mv http://lattes.cnpq.br/5364437916631071
dc.identifier.orcid.none.fl_str_mv https://orcid.org/0000-0002-7115-1485
dc.contributor.advisor1.fl_str_mv JOSE MARCIO DE MELLO
dc.contributor.advisor1Lattes.fl_str_mv http://lattes.cnpq.br/9805647108156583
dc.contributor.advisor1orcid.por.fl_str_mv https://orcid.org/0000000205225060
dc.publisher.none.fl_str_mv UNIVERSIDADE FEDERAL DE LAVRAS
publisher.none.fl_str_mv UNIVERSIDADE FEDERAL DE LAVRAS
instname_str UNIVERSIDADE FEDERAL DE LAVRAS
dc.publisher.program.fl_str_mv ENGENHARIA FLORESTAL
dc.description.course.none.fl_txt_mv ENGENHARIA FLORESTAL
reponame_str Portal de Dados Abertos da CAPES
collection Portal de Dados Abertos da CAPES
spelling CAPESPortal de Dados Abertos da CAPESPredicting Eucalyptus stand attributes in Minas Gerais state, Brazil: an approach using machine learning algorithms with multi-source datasetsPredicting Eucalyptus stand attributes in Minas Gerais state, Brazil: an approach using machine learning algorithms with multi-source datasetsPredicting Eucalyptus stand attributes in Minas Gerais state, Brazil: an approach using machine learning algorithms with multi-source datasetsPredicting Eucalyptus stand attributes in Minas Gerais state, Brazil: an approach using machine learning algorithms with multi-source datasetsPredicting Eucalyptus stand attributes in Minas Gerais state, Brazil: an approach using machine learning algorithms with multi-source datasetsPredicting Eucalyptus stand attributes in Minas Gerais state, Brazil: an approach using machine learning algorithms with multi-source datasetsPredicting Eucalyptus stand attributes in Minas Gerais state, Brazil: an approach using machine learning algorithms with multi-source datasetsForest Management. Remote Sensing. Random Forest. Terrain Attributes.2018doctoralThesishttps://sucupira.capes.gov.br/sucupira/public/consultas/coleta/trabalhoConclusao/viewTrabalhoConclusao.jsf?popup=true&id_trabalho=6641330authorALINY APARECIDA DOS REIShttp://lattes.cnpq.br/5364437916631071https://orcid.org/0000-0002-7115-1485JOSE MARCIO DE MELLOhttp://lattes.cnpq.br/9805647108156583https://orcid.org/0000000205225060UNIVERSIDADE FEDERAL DE LAVRASUNIVERSIDADE FEDERAL DE LAVRASUNIVERSIDADE FEDERAL DE LAVRASENGENHARIA FLORESTALENGENHARIA FLORESTALPortal de Dados Abertos da CAPESPortal de Dados Abertos da CAPES
identifier_str_mv REIS, ALINY APARECIDA DOS. Predicting Eucalyptus stand attributes in Minas Gerais state, Brazil: an approach using machine learning algorithms with multi-source datasets. 2018. Tese.
dc.identifier.citation.fl_str_mv REIS, ALINY APARECIDA DOS. Predicting Eucalyptus stand attributes in Minas Gerais state, Brazil: an approach using machine learning algorithms with multi-source datasets. 2018. Tese.
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