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1. Identificação
Tipo de ReferênciaArtigo em Evento (Conference Proceedings)
Sitemtc-m21c.sid.inpe.br
Código do Detentorisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identificador8JMKD3MGP3W34R/3U5ULQE
Repositóriosid.inpe.br/mtc-m21c/2019/09.30.12.38
Repositório de Metadadossid.inpe.br/mtc-m21c/2019/09.30.12.38.43
Última Atualização dos Metadados2020:01.06.11.42.22 (UTC) administrator
Chave SecundáriaINPE--PRE/
Chave de CitaçãoRodriguesEsca:2019:FaAsFo
TítuloFactors associated with forest degradation on an Amazonian logging frontier area in southwestern Pará, Brazil
Ano2019
Data de Acesso10 maio 2024
Tipo SecundárioPRE CI
2. Contextualização
Autor1 Rodrigues, Danilo Avancini
2 Escada, Maria Isabel Sobral
Identificador de Curriculo1
2 8JMKD3MGP5W/3C9JHRG
Grupo1 DIDSR-CGOBT-INPE-MCTIC-GOV-BR
2 DIDPI-CGOBT-INPE-MCTIC-GOV-BR
Afiliação1 Instituto Nacional de Pesquisas Espaciais (INPE)
2 Instituto Nacional de Pesquisas Espaciais (INPE)
Endereço de e-Mail do Autor1 danilo.rodrigues@inpe.br
2 isabel.escada@inpe.br
Nome do EventoCongresso Mundial da IUFRO
Localização do EventoCuritiba, PR
Data29 set. - 05 out.
Histórico (UTC)2019-09-30 12:38:43 :: simone -> administrator ::
2020-01-06 11:42:22 :: administrator -> simone :: 2019
3. Conteúdo e estrutura
É a matriz ou uma cópia?é a matriz
Estágio do Conteúdoconcluido
Transferível1
Tipo do ConteúdoExternal Contribution
ResumoForest degradation is a long-term process that reduces forests biodiversity and impoverishes the ecosystems. Understanding the factors that generates and intensifies forest degradation, and its consequences, allows directing public policies to prevent this process. This study performed a spatial regression analysis between forest degradation intensity (dependent variable), environmental, political and economic variables in Novo Progresso (PA), a logging frontier expansion area in the Amazon Forest, from 2009 to 2011. The independent variables were related to fire occurrence, deforestation, conservation units, indigenous lands, logging poles and roads. The relationship between the dependent and independent variables (R2 and p-value) was individually tested, and the variables with the highest relationship were included in the regression model. Then, the variables with multicollinearity were excluded from the model with the stepwise backward technique. It was used the Moran test to detect spatial dependency on the data. Spatial dependency was detected with statistical significance (I = 0,2434; p-valor = 0 e z-score = 9,91), justifying the use of a spatial regression model. The Lagrange Multiplier test pointed out Spatial Lag Model as the best model adjusted to the data. The variables area of deforestation and total edge area explained 40% (R2 -adj: 0,4092) of the forest degradation intensity, indicating that fragmented forest and the forest areas closest to deforested areas are more likely to suffer higher degradation. For further studies, the R2 of the model can be raised by adding variables related to pasture expansion, forest management, colonization projects and updating the roads data yearly.
ÁreaSRE
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Grupo de Usuáriossimone
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Permissão de Atualizaçãonão transferida
5. Fontes relacionadas
Unidades Imediatamente Superiores8JMKD3MGPCW/3EQCCU5
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Acervo Hospedeirourlib.net/www/2017/11.22.19.04
6. Notas
Campos Vaziosarchivingpolicy archivist booktitle callnumber copyholder copyright creatorhistory descriptionlevel dissemination doi e-mailaddress edition editor format isbn issn keywords label lineage mark mirrorrepository nextedition notes numberoffiles numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project publisher publisheraddress readergroup readpermission rightsholder schedulinginformation secondarydate secondarymark serieseditor session shorttitle size sponsor subject targetfile tertiarymark tertiarytype type url versiontype volume
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