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1. Identity statement
Reference TypeJournal Article
Siteplutao.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
IdentifierJ8LNKAN8RW/38JED83
Repositorydpi.inpe.br/plutao/2010/11.11.17.29.25
Last Update2011:02.04.13.21.19 (UTC) administrator
Metadata Repositorydpi.inpe.br/plutao/2010/11.11.17.29.26
Metadata Last Update2018:06.05.00.12.21 (UTC) administrator
Secondary KeyINPE--PRE/
ISSN0560-4613
1808-0936
Labellattes: 4872965504009836 1 MelloRudoVieiAgui:2010:ClAuCo
Citation KeyMelloRudoVieiAgui:2010:ClAuCo
TitleClassificação automática da colheita da cana-de-açúcar utilizando Modelo Linear de Mistura Espectral/Automatic Classification of Sugarcane Harvest Using Spectral Linear Mixing Model
Year2010
Access Date2024, May 19
Secondary TypePRE PN
Number of Files1
Size389 KiB
2. Context
Author1 Mello, Márcio Pupin de
2 Rudorff, Bernardo Friedrich Theodor
3 Vieira, Carlos Antonio Oliveira
4 Aguiar, Daniel Alves de
Resume Identifier1
2 8JMKD3MGP5W/3C9JGKP
Group1
2 DSR-OBT-INPE-MCT-BR
Affiliation1
2 Instituto Nacional de Pesquisas Espaciais (INPE)
3 2Universidade Federal de Viçosa – UFV Departamento de Engenharia Civil - DEC
Author e-Mail Address1 mello@dsr.inpe.br
2 bernardo@ltid.inpe.br
e-Mail Addressmello@dsr.inpe.br
JournalRevista Brasileira de Cartografia
Volume62
Number2
Pages181-188
Secondary MarkB5_CIÊNCIAS_AGRÁRIAS_I B5_CIÊNCIAS_BIOLÓGICAS_I B5_ECOLOGIA_E_MEIO_AMBIENTE B3_ENGENHARIAS_I B3_ENGENHARIAS_II B4_ENGENHARIAS_III B5_ENGENHARIAS_IV B2_GEOCIÊNCIAS B1_GEOGRAFIA B1_INTERDISCIPLINAR
History (UTC)2010-12-06 14:15:20 :: lattes -> ricardo :: 2010
2010-12-07 11:40:33 :: ricardo -> administrator :: 2010
2010-12-08 15:13:05 :: administrator -> marciana :: 2010
2011-02-04 13:21:19 :: marciana -> administrator :: 2010
2018-06-05 00:12:21 :: administrator -> marciana :: 2010
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Keywordssensoriamento remoto
imagens multitemporais
queima
monitoramento
Remote Sensing
Multitemporal Images
Burning
Monitoring
AbstractSugarcane is currently the best option to produce ethanol which can significantly contribute in the mitigation of the greenhouse effect intensification. However, the sugarcane straw burning prior to harvest is still a critical environmental problem that has to be eliminated. The S o Paulo State government together with the private sugarcane production sector established a protocol to gradually stop the sugarcane straw burning by 2014. Remote sensing images have a great potential to monitor the harvest management procedure with and without straw burning prior to harvest. Currently, this monitoring is carried out using visual interpretation which provides high quality results but is a quite tedious work. The present article has the objective to propose an automated classification procedure based on Spectral Linear Mixing Model technique to identify sugarcane fields that were harvested with and without burning. A visual interpreted reference map was used to assess the automated classification map accuracy which showed an overall index of 89.7%. The proposed methodology showed to be a promising alternative to automate the monitoring of sugarcane harvested with and without straw burning. ABSTRACT Sugarcane is currently the best option to produce ethanol which can significantly contribute in the mitigation of the greenhouse effect intensification. However, the sugarcane straw burning prior to harvest is still a critical environmental problem that has to be eliminated. The São Paulo State government together with the private sugarcane production sector established a protocol to gradually stop the sugarcane straw burning by 2014. Remote sensing images have a great potential to monitor the harvest management procedure with and without straw burning prior to harvest. Currently, this monitoring is carried out using visual interpretation which provides high quality results but is a quite tedious work. The present article has the objective to propose an automated classification procedure based on Spectral Linear Mixing Model technique to identify sugarcane fields that were harvested with and without burning. A visual interpreted reference map was used to assess the automated classification map accuracy which showed an overall index of 89.7%. The proposed methodology showed to be a promising alternative to automate the monitoring of sugarcane harvested with and without straw burning.
AreaSRE
Arrangementurlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDSR > Classificação automática da...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Contentthere are no files
4. Conditions of access and use
data URLhttp://urlib.net/ibi/J8LNKAN8RW/38JED83
zipped data URLhttp://urlib.net/zip/J8LNKAN8RW/38JED83
Languagept
Target File62_02_7.pdf
User Groupadministrator
lattes
marciana
Visibilityshown
Archiving Policyallowpublisher allowfinaldraft
Read Permissionallow from all
Update Permissionnot transferred
5. Allied materials
Next Higher Units8JMKD3MGPCW/3ER446E
Citing Item Listsid.inpe.br/mtc-m21/2012/07.13.14.41 1
URL (untrusted data)http://www.rbc.ufrj.br/_2010/_RBC62_2.htm
DisseminationPORTALCAPES
Host Collectiondpi.inpe.br/plutao@80/2008/08.19.15.01
6. Notes
Empty Fieldsalternatejournal archivist callnumber copyholder copyright creatorhistory descriptionlevel doi format isbn lineage mark mirrorrepository month nextedition notes orcid parameterlist parentrepositories previousedition previouslowerunit progress project readergroup rightsholder schedulinginformation secondarydate session shorttitle sponsor subject tertiarymark tertiarytype typeofwork versiontype
7. Description control
e-Mail (login)marciana
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