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1. Identity statement
Reference TypeJournal Article
Sitemtc-m21c.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP3W34R/3UMEFSB
Repositorysid.inpe.br/mtc-m21c/2020/01.03.17.03   (restricted access)
Last Update2020:01.03.17.03.24 (UTC) simone
Metadata Repositorysid.inpe.br/mtc-m21c/2020/01.03.17.03.24
Metadata Last Update2022:01.04.01.34.56 (UTC) administrator
DOI10.1016/j.spasta.2019.100393
ISSN2211-6753
Citation KeySilvaFonsKörtEsca:2020:SpBaNe
TitleA spatio-temporal Bayesian Network approach for deforestation prediction in an Amazon rainforest expansion frontier
Year2020
MonthMar.
Access Date2024, May 07
Type of Workjournal article
Secondary TypePRE PI
Number of Files1
Size3158 KiB
2. Context
Author1 Silva, Alexsandro Cândido de Oliveira
2 Fonseca, Leila Maria Garcia
3 Körting, Thales Sehn
4 Escada, Maria Isabel Sobral
Resume Identifier1
2 8JMKD3MGP5W/3C9JHLD
3
4 8JMKD3MGP5W/3C9JHRG
Group1 DIDPI-CGOBT-INPE-MCTIC-GOV-BR
2 DIDPI-CGOBT-INPE-MCTIC-GOV-BR
3 DIDPI-CGOBT-INPE-MCTIC-GOV-BR
4 DIDPI-CGOBT-INPE-MCTIC-GOV-BR
Affiliation1 Instituto Nacional de Pesquisas Espaciais (INPE)
2 Instituto Nacional de Pesquisas Espaciais (INPE)
3 Instituto Nacional de Pesquisas Espaciais (INPE)
4 Instituto Nacional de Pesquisas Espaciais (INPE)
Author e-Mail Address1 alexsandro.silva@inpe.br
2 leila.fonseca@inpe.br
3 thales.korting@inpe.br
4 isabel.escada@inpe.br
JournalSpatial Statistics
Volume35
Pagese100393
History (UTC)2020-01-03 17:04:05 :: simone :: 2019 -> 2020
2020-01-03 17:04:05 :: simone -> administrator :: 2020
2020-06-19 13:54:24 :: administrator -> simone :: 2020
2020-06-23 22:48:53 :: simone -> administrator :: 2020
2022-01-04 01:34:56 :: administrator -> simone :: 2020
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
KeywordsBayesian Networks
Spatio-temporal modeling
Environmental modeling
Deforestation
Brazilian Amazon forest
AbstractIn the last decade, Brazil has successfully managed to reduce deforestation in the Amazon forest. However, continued increases in annual deforestation rates call for environmental modeling to support short-term decision-making. This paper presents the functioning of a stepwise spatio-temporal Bayesian Network approach for spatially explicit analysis of deforestation risk based on observation data. The study area comprises a deforestation expansion frontier located in the southwest of Pará state, Brazil. The proposed approach has been successful in estimating deforestation risk over the years. Among the selected variables to compose the Bayesian Network model, distance from hot spots and distance from degraded areas present the highest contribution, while protected areas variable present a significant mitigation effect on the phenomenon. Accuracy assessment indices corroborate the agreement between deforestation events and predictions.
AreaSRE
Arrangementurlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDPI > A spatio-temporal Bayesian...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
agreement.html 03/01/2020 14:03 1.0 KiB 
4. Conditions of access and use
Languageen
Target Filesilva_spatio.pdf
User Groupsimone
Reader Groupadministrator
simone
Visibilityshown
Read Permissiondeny from all and allow from 150.163
Update Permissionnot transferred
5. Allied materials
Next Higher Units8JMKD3MGPCW/3EQCCU5
Citing Item Listsid.inpe.br/bibdigital/2013/09.09.15.05 2
sid.inpe.br/mtc-m21/2012/07.13.14.55.44 1
DisseminationSCOPUS
Host Collectionurlib.net/www/2017/11.22.19.04
6. Notes
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