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1. Identity statement
Reference TypeJournal Article
Sitemtc-m12.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier6qtX3pFwXQZGivnJRY/Np6d4
Repositorysid.inpe.br/mtc-m12@80/2006/12.08.16.06   (restricted access)
Last Update2006:12.08.16.06.56 (UTC) marciana
Metadata Repositorysid.inpe.br/mtc-m12@80/2006/12.08.16.06.57
Metadata Last Update2018:06.05.00.40.48 (UTC) administrator
Secondary KeyINPE--PRE/
ISSN0099-1112
Citation KeyCarreirasPereShim:2006:LaMaBr
TitleLand-cover mapping in the Brazilian Amazon using SPOT-4 vegetation data and machine learning classification methods
ProjectSensoriamento Remoto Aplicado à Ecossistemas Terrestres
Year2006
MonthAug.
Access Date2024, May 18
Type of Workjournal article
Secondary TypePRE PI
Number of Files1
Size459 KiB
2. Context
Author1 Carreiras, João M. B.
2 Pereira, José M. C.
3 Shimabukuro, Yosio Edemir
Resume Identifier1
2
3 8JMKD3MGP5W/3C9JJCQ
Group1
2
3 DSR-INPE-MCT-BR
Affiliation1 Department of Forestry, Instituto Superior de Agronomic, Tapada da Ajuda, 1349-017 Lisboa, Portugal
2 Department of Forestry, Instituto Superior de Agronomic, Tapada da Ajuda, 1349-017 Lisboa, Portugal
3 Instituto Nacional de Pesquisas Espaciais (INPE)
JournalPhotogrammetric Engineering and Remote Sensing
Volume72
Number8
Pages897-910
History (UTC)2006-12-08 16:06:57 :: yosio -> simone ::
2007-04-20 12:06:07 :: simone -> administrator ::
2014-08-18 22:06:16 :: administrator -> marciana :: 2006
2016-08-17 17:03:06 :: marciana -> administrator :: 2006
2018-06-05 00:40:48 :: administrator -> marciana :: 2006
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
KeywordsVEGETAÇÃO
remotely-sensed data
decision-tree classification
resolution satellite data
spatial-resolution
accuracy assessment
avhrr data
multispectral data
tropical regions
eastern amazon
mixing models
AbstractThe main objective of this study is to evaluate the feasibility of deriving a land-cover map of the state of Mato Grosso, Brazil, for the year 2000, using data from the 1 km SPOT-4 VEGETATION (VGT) sensor. For this purpose we used a VGT temporal series of 12 monthly composite images, which were further transformed to physicalmeaningful fraction images of vegetation, soil, and shade. Classification of fraction images was implemented using several recent machine learning developments, namely, filtering input training data and probability bagging in a classification tree approach. A 10-fold cross validation accuracy assessment indicates that filtering and probability bagging are effective at increasing overall and class-specific accuracy. Overall accuracy and mean probability of class membership were 0.88 and 0.80, respectively. The map of probability of class membership indicates that the larger errors are associated with cerrado savanna and semi-deciduous forest.
AreaSRE
Arrangementurlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDSR > Land-cover mapping in...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Contentthere are no files
4. Conditions of access and use
Languageen
Target FileCarreiras_etal_PERS2006.pdf
User Groupadministrator
simone
yosio
Reader Groupadministrator
marciana
Visibilityshown
Archiving Policydenypublisher denyfinaldraft
Read Permissiondeny from all and allow from 150.163
Update Permissionnot transferred
5. Allied materials
Next Higher Units8JMKD3MGPCW/3ER446E
DisseminationWEBSCI
Host Collectionsid.inpe.br/banon/2001/04.06.10.52
6. Notes
Empty Fieldsalternatejournal archivist callnumber copyholder copyright creatorhistory descriptionlevel doi e-mailaddress electronicmailaddress format isbn label lineage mark mirrorrepository nextedition notes orcid parameterlist parentrepositories previousedition previouslowerunit progress rightsholder schedulinginformation secondarydate secondarymark session shorttitle sponsor subject tertiarymark tertiarytype url
7. Description control
e-Mail (login)marciana
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