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Metadados

1. Identificação
Tipo de ReferênciaArtigo em Revista Científica (Journal Article)
Sitemtc-m21c.sid.inpe.br
Código do Detentorisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identificador8JMKD3MGP3W34R/3TJBDKH
Repositóriosid.inpe.br/mtc-m21c/2019/07.02.11.31   (acesso restrito)
Última Atualização2019:07.02.11.31.09 (UTC) simone
Repositório de Metadadossid.inpe.br/mtc-m21c/2019/07.02.11.31.09
Última Atualização dos Metadados2020:01.06.11.42.15 (UTC) administrator
DOI10.1080/15481603.2018.1550245
ISSN1548-1603
Chave de CitaçãoSilveiraEAGWBMSDS:2019:ReEfVe
TítuloReducing the effects of vegetation phenology on change detection in tropical seasonal biomes
Ano2019
MêsJuly
Data de Acesso09 maio 2024
Tipo de Trabalhojournal article
Tipo SecundárioPRE PI
Número de Arquivos1
Tamanho3049 KiB
2. Contextualização
Autor 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
Identificador de Curriculo 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
Grupo 1
 2
 3
 4 DIDSR-CGOBT-INPE-MCTIC-GOV-BR
 5
 6
 7
 8 DIDSR-CGOBT-INPE-MCTIC-GOV-BR
Afiliação 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)
Endereço de e-Mail do Autor 1 dudalavras@hotmail.com
 2
 3
 4 lenio.galvao@inpe.br
 5
 6
 7
 8 yosio.shimabukuro@inpe.br
RevistaGIScience and Remote Sensing
Volume56
Número5
Páginas699-717
Nota SecundáriaB1_GEOCIÊNCIAS B1_CIÊNCIAS_AGRÁRIAS_I B2_INTERDISCIPLINAR B3_CIÊNCIAS_AMBIENTAIS
Histórico (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. Conteúdo e estrutura
É a matriz ou uma cópia?é a matriz
Estágio do Conteúdoconcluido
Transferível1
Tipo do ConteúdoExternal Contribution
Tipo de Versãopublisher
Palavras-Chaveremote sensing
geostatistics
seasonality
LULCC
ResumoTropical 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.
ÁreaSRE
ArranjoReducing the effects...
Conteúdo da Pasta docacessar
Conteúdo da Pasta sourcenão têm arquivos
Conteúdo da Pasta agreement
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4. Condições de acesso e uso
Idiomaen
Arquivo AlvoReducing the effects of vegetation phenology on change detection in tropical seasonal biomes.pdf
Grupo de Usuáriossimone
Grupo de Leitoresadministrator
simone
Visibilidadeshown
Política de Arquivamentodenypublisher denyfinaldraft
Permissão de Leituradeny from all and allow from 150.163
Permissão de Atualizaçãonão transferida
5. Fontes relacionadas
Unidades Imediatamente Superiores8JMKD3MGPCW/3ER446E
Lista de Itens Citandosid.inpe.br/bibdigital/2013/09.13.21.11 3
sid.inpe.br/mtc-m21/2012/07.13.14.53.28 1
sid.inpe.br/mtc-m21/2012/07.13.15.02.10 1
DivulgaçãoWEBSCI
Acervo Hospedeirourlib.net/www/2017/11.22.19.04
6. Notas
Campos Vaziosalternatejournal 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
7. Controle da descrição
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