Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/341867
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dc.contributor.CRUESPUNIVERSIDADE ESTADUAL DE CAMPINASpt_BR
dc.contributor.authorunicampSantos, Cecilia Lira Melo de Oliveira-
dc.contributor.authorunicampLamparelli, Rubens Augusto Camargo-
dc.contributor.authorunicampFigueiredo, Gleyce Kelly Dantas Araújo-
dc.contributor.authorunicampLuciano, Ana Claudia dos Santos-
dc.contributor.authorunicampTorres, Ricardo da Silva-
dc.contributor.authorunicampLe Maire, Guerric Beaudouin Cathel Marie-
dc.typeArtigopt_BR
dc.titleClassification of crops, pastures, and tree plantations along the season with multi-sensor image time series in a subtropical agricultural regionpt_BR
dc.contributor.authorMelo de Oliveira Santos, Cecilia Lira-
dc.contributor.authorCamargo Lamparelli, Rubens Augusto-
dc.contributor.authorDantas Araujo Figueiredo, Gleyce Kelly-
dc.contributor.authorDupuy, Stephane-
dc.contributor.authorBoury, Julie-
dc.contributor.authordos Santos Luciano, Ana Claudia-
dc.contributor.authorTorres, Ricardo da Silva-
dc.contributor.authorle Maire, Guerric-
dc.subjectAnálise de séries temporaispt_BR
dc.subjectFloresta aleatóriapt_BR
dc.subject.otherlanguageTime-series analysispt_BR
dc.subject.otherlanguageRandom forestpt_BR
dc.description.abstractTimely and efficient land-cover mapping is of high interest, especially in agricultural landscapes. Classification based on satellite images over the season, while important for cropland monitoring, remains challenging in subtropical agricultural areas due to the high diversity of management systems and seasonal cloud cover variations. This work presents supervised object-based classifications over the year at 2-month time-steps in a heterogeneous region of 12,000 km(2) in the Sao Paulo region of Brazil. Different methods and remote-sensing datasets were tested with the random forest algorithm, including optical and radar data, time series of images, and cloud gap-filling methods. The final selected method demonstrated an overall accuracy of approximately 0.84, which was stable throughout the year, at the more detailed level of classification; confusion mainly occurred among annual crop classes and soil classes. We showed in this study that the use of time series was useful in this context, mainly by including a small number of highly discriminant images. Such important images were eventually distant in time from the prediction date, and they corresponded to a high-quality image with low cloud cover. Consequently, the final classification accuracy was not sensitive to the cloud gap-filling method, and simple median gap-filling or linear interpolations with time were sufficient. Sentinel-1 images did not improve the classification results in this context. For within-season dynamic classes, such as annual crops, which were more difficult to classify, field measurement efforts should be densified and planned during the most discriminant window, which may not occur during the crop vegetation peakpt_BR
dc.relation.ispartofRemote sensingpt_BR
dc.relation.ispartofabbreviationRemote sens.pt_BR
dc.publisher.cityBaselpt_BR
dc.publisher.countrySuíçapt_BR
dc.publisherMDPIpt_BR
dc.date.issued2019-
dc.date.monthofcirculationFeb.pt_BR
dc.language.isoengpt_BR
dc.description.volume11pt_BR
dc.description.issuenumber3pt_BR
dc.rightsAbertopt_BR
dc.sourceWOSpt_BR
dc.identifier.eissn2072-4292pt_BR
dc.identifier.doi10.3390/rs11030334pt_BR
dc.identifier.urlhttps://www.mdpi.com/2072-4292/11/3/334pt_BR
dc.description.sponsorshipCONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQpt_BR
dc.description.sponsorshipCOORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESpt_BR
dc.description.sponsorshipFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESPpt_BR
dc.description.sponsordocumentnumber454292/2014-7; 307560/2016-3pt_BR
dc.description.sponsordocumentnumbersem informaçãopt_BR
dc.description.sponsordocumentnumber2014/50715-9; 2015/24494-8; 2014/12236-1; 2013/50155-0pt_BR
dc.date.available2020-05-21T12:27:37Z-
dc.date.accessioned2020-05-21T12:27:37Z-
dc.description.provenanceSubmitted by Cintia Oliveira de Moura (cintiaom@unicamp.br) on 2020-05-21T12:27:37Z No. of bitstreams: 0. Added 1 bitstream(s) on 2020-08-27T19:15:02Z : No. of bitstreams: 1 000459944400122.pdf: 5962805 bytes, checksum: 9d4d0b9b63fb318df6816784e1b51a47 (MD5)en
dc.description.provenanceMade available in DSpace on 2020-05-21T12:27:37Z (GMT). No. of bitstreams: 0 Previous issue date: 2019en
dc.identifier.urihttp://repositorio.unicamp.br/jspui/handle/REPOSIP/341867-
dc.contributor.departmentsem informaçãopt_BR
dc.contributor.departmentsem informaçãopt_BR
dc.contributor.departmentsem informaçãopt_BR
dc.contributor.departmentsem informaçãopt_BR
dc.contributor.departmentDepartamento de Sistemas de Informaçãopt_BR
dc.contributor.departmentsem informaçãopt_BR
dc.contributor.unidadeFaculdade de Engenharia Agrícolapt_BR
dc.contributor.unidadeNúcleo Interdisciplinar de Planejamento Energéticopt_BR
dc.contributor.unidadeFaculdade de Engenharia Agrícolapt_BR
dc.contributor.unidadeFaculdade de Engenharia Agrícolapt_BR
dc.contributor.unidadeInstituto de Computaçãopt_BR
dc.contributor.unidadeNúcleo Interdisciplinar de Planejamento Energéticopt_BR
dc.subject.keywordLand-coverpt_BR
dc.subject.keywordDecision treept_BR
dc.identifier.source000459944400122pt_BR
dc.creator.orcid0000-0003-3951-5952pt_BR
dc.creator.orcid0000-0003-4344-1263pt_BR
dc.creator.orcid0000-0002-5017-8320pt_BR
dc.creator.orcid0000-0003-4862-9863pt_BR
dc.creator.orcid0000-0001-9772-263Xpt_BR
dc.creator.orcid0000-0002-5227-958Xpt_BR
dc.type.formArtigopt_BR
dc.identifier.articleid334pt_BR
dc.description.sponsorNoteCirad, France; Brazilian Research Council CNPq (Conselho Nacional do Desenvolvimento Cientifico e Tecnologico)National Council for Scientific and Technological Development (CNPq) [454292/2014-7, 307560/2016-3]; Coordenacao de Aperfeicoamento de Pesssoal de Nivel Superior-Brazil (CAPES) [001]; Fundacao de Amparo a Pesquisa do Estado de Sao Paulo, (FAPESP-Microsoft Research) [2014/50715-9]; CES-OSO project (TOSCA program Grant of the French Space Agency, CNES); SIGMA European Collaborative Project (FP7-ENV-2013 SIGMA-Stimulating Innovation for Global Monitoring of Agriculture and its Impact on the Environment in support of GEOGLAM) [603719]; GEOSUD Program (French National Research Agency) [ANR-10-EQPX-20]; Fundacao de Amparo a Pesquisa do Estado de Sao PauloFundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP) [2015/24494-8, 2014/12236-1, 2013/50155-0]pt_BR
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IC - Artigos e Outros Documentos
NIPE - Artigos e Outros Documentos

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