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1. Identity statement
Reference TypeJournal Article
Sitemtc-m21c.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP3W34R/3TJBDKH
Repositorysid.inpe.br/mtc-m21c/2019/07.02.11.31   (restricted access)
Last Update2019:07.02.11.31.09 (UTC) simone
Metadata Repositorysid.inpe.br/mtc-m21c/2019/07.02.11.31.09
Metadata Last Update2020:01.06.11.42.15 (UTC) administrator
DOI10.1080/15481603.2018.1550245
ISSN1548-1603
Citation KeySilveiraEAGWBMSDS:2019:ReEfVe
TitleReducing the effects of vegetation phenology on change detection in tropical seasonal biomes
Year2019
MonthJuly
Access Date2024, May 18
Type of Workjournal article
Secondary TypePRE PI
Number of Files1
Size3049 KiB
2. Context
Author 1 Silveira, Eduarda Martiniano de Oliveira
 2 Espírito Santo, Fernando Del Bon
 3 Acerbi Júnior, Fausto Weimar
 4 Galvão, Lênio Soares
 5 Withey, Kieran Daniel
 6 Blackburn, George Alan
 7 Mello, José Márcio de
 8 Shimabukuro, Yosio Edemir
 9 Domingues, Tomas
10 Scolforo, José Roberto Soares
Resume Identifier 1
 2
 3
 4 8JMKD3MGP5W/3C9JHLF
 5
 6
 7
 8 8JMKD3MGP5W/3C9JJCQ
ORCID 1 0000-0002-1015-4973
 2 0000-0001-7497-3639
 3 0000-0002-9553-0148
 4 0000-0002-8313-0497
 5 0000-0002-9550-4249
 6 0000-0002-3815-4916
 7 0000-0002-0522-5060
 8 0000-0002-1469-8433
 9 0000-0003-2857-9838
10 0000-0002-5888-6751
Group 1
 2
 3
 4 DIDSR-CGOBT-INPE-MCTIC-GOV-BR
 5
 6
 7
 8 DIDSR-CGOBT-INPE-MCTIC-GOV-BR
Affiliation 1 Universidade Federal de Lavras (UFLA)
 2 University of Leicester
 3 Universidade Federal de Lavras (UFLA)
 4 Instituto Nacional de Pesquisas Espaciais (INPE)
 5 Lancaster University
 6 Lancaster University
 7 Universidade Federal de Lavras (UFLA)
 8 Instituto Nacional de Pesquisas Espaciais (INPE)
 9 Universidade de São Paulo (USP)
10 Universidade Federal de Lavras (UFLA)
Author e-Mail Address 1 dudalavras@hotmail.com
 2
 3
 4 lenio.galvao@inpe.br
 5
 6
 7
 8 yosio.shimabukuro@inpe.br
JournalGIScience and Remote Sensing
Volume56
Number5
Pages699-717
Secondary MarkB1_GEOCIÊNCIAS B1_CIÊNCIAS_AGRÁRIAS_I B2_INTERDISCIPLINAR B3_CIÊNCIAS_AMBIENTAIS
History (UTC)2019-07-02 11:31:09 :: simone -> administrator ::
2019-07-02 11:31:09 :: administrator -> simone :: 2019
2019-07-02 11:35:09 :: simone -> administrator :: 2019
2020-01-06 11:42:15 :: administrator -> simone :: 2019
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
Keywordsremote sensing
geostatistics
seasonality
LULCC
AbstractTropical seasonal biomes (TSBs), such as the savannas (Cerrado) and semi-arid woodlands (Caatinga) of Brazil, are vulnerable ecosystems to human-induced disturbances. Remote sensing can detect disturbances such as deforestation and fires, but the analysis of change detection in TSBs is affected by seasonal modifications in vegetation indices due to phenology. To reduce the effects of vegetation phenology on changes caused by deforestation and fires, we developed a novel object-based change detection method. The approach combines both the spatial and spectral domains of the normalized difference vegetation index (NDVI), using a pair of Operational Land Imager (OLI)/Landsat-8 images acquired in 2015 and 2016. We used semivariogram indices (SIs) as spatial features and descriptive statistics as spectral features (SFs). We tested the performance of the method using three machine-learning algorithms: support vector machine (SVM), artificial neural network (ANN) and random forest (RF). The results showed that the combination of spatial and spectral information improved change detection by correctly classifying areas with seasonal changes in NDVI caused by vegetation phenology and areas with NDVI changes caused by human-induced disturbances. The use of semivariogram indices reduced the effects of vegetation phenology on change detection. The performance of the classifiers was generally comparable, but the SVM presented the highest overall classification accuracy (92.27%) when using the hybrid set of NDVI-derived spectral-spatial features. From the vegetated areas, 18.71% of changes were caused by human-induced disturbances between 2015 and 2016. The method is particularly useful for TSBs where vegetation exhibits strong seasonality and regularly spaced time series of satellite images are difficult to obtain due to persistent cloud cover.
AreaSRE
Arrangementurlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDSR > Reducing the effects...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
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4. Conditions of access and use
Languageen
Target FileReducing the effects of vegetation phenology on change detection in tropical seasonal biomes.pdf
User Groupsimone
Reader Groupadministrator
simone
Visibilityshown
Archiving Policydenypublisher denyfinaldraft
Read Permissiondeny from all and allow from 150.163
Update Permissionnot transferred
5. Allied materials
Next Higher Units8JMKD3MGPCW/3ER446E
Citing Item Listsid.inpe.br/bibdigital/2013/09.13.21.11 4
sid.inpe.br/mtc-m21/2012/07.13.14.53.28 1
sid.inpe.br/mtc-m21/2012/07.13.15.02.10 1
DisseminationWEBSCI
Host Collectionurlib.net/www/2017/11.22.19.04
6. Notes
Empty Fieldsalternatejournal archivist callnumber copyholder copyright creatorhistory descriptionlevel e-mailaddress format isbn label lineage mark mirrorrepository nextedition notes parameterlist parentrepositories previousedition previouslowerunit progress project rightsholder schedulinginformation secondarydate secondarykey session shorttitle sponsor subject tertiarymark tertiarytype url
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