Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/351680
Full metadata record
DC FieldValueLanguage
dc.contributor.CRUESPUNIVERSIDADE ESTADUAL DE CAMPINASpt_BR
dc.contributor.authorunicampAlmeida Junior, Jurandy Gomes de-
dc.contributor.authorunicampSantos, Jefersson Alex dos-
dc.contributor.authorunicampTorres, Ricardo da Silva-
dc.typeArtigopt_BR
dc.titleApplying machine learning based on multiscale classifiers to detect remote phenology patterns in Cerrado savanna treespt_BR
dc.contributor.authorAlmeida, Jurandy-
dc.contributor.authorSantos, Jefersson A. dos-
dc.contributor.authorAlberton, Bruna-
dc.contributor.authorTorres, Ricardo da S.-
dc.contributor.authorMorellato, Leonor Patricia C.-
dc.subjectAprendizado de máquinapt_BR
dc.subject.otherlanguageMachine learningpt_BR
dc.description.abstractPlant phenology is one of the most reliable indicators of species responses to global climate change, motivating the development of new technologies for phenological monitoring. Digital cameras or near remote systems have been efficiently applied as multi-channel imaging sensors, where leaf color information is extracted from the RGB (Red, Green, and Blue) color channels, and the changes in green levels are used to infer leafing patterns of plant species. In this scenario, texture information is a great ally for image analysis that has been little used in phenology studies. We monitored leaf-changing patterns of Cerrado savanna vegetation by taking daily digital images. We extract RGB channels from the digital images and correlate them with phenological changes. Additionally, we benefit from the inclusion of textural metrics for quantifying spatial heterogeneity. Our first goals are: (1) to test if color change information is able to characterize the phenological pattern of a group of species; (2) to test if the temporal variation in image texture is useful to distinguish plant species; and (3) to test if individuals from the same species may be automatically identified using digital images. In this paper, we present a machine learning approach based on multiscale classifiers to detect phenological patterns in the digital images. Our results indicate that: (1) extreme hours (morning and afternoon) are the best for identifying plant species; (2) different plant species present a different behavior with respect to the color change information; and (3) texture variation along temporal images is promising information for capturing phenological patterns. Based on those results, we suggest that individuals from the same species and functional group might be identified using digital images, and introduce a new tool to help phenology experts in the identification of new individuals from the same species in the image and their location on the groundpt_BR
dc.relation.ispartofEcological informaticspt_BR
dc.publisher.cityAmsterdampt_BR
dc.publisher.countryPaíses Baixospt_BR
dc.publisherElsevierpt_BR
dc.date.issued2014-
dc.date.monthofcirculationSep.pt_BR
dc.language.isoengpt_BR
dc.description.volume23pt_BR
dc.description.firstpage49pt_BR
dc.description.lastpage61pt_BR
dc.rightsFechadopt_BR
dc.sourceSCOPUSpt_BR
dc.identifier.issn1574-9541pt_BR
dc.identifier.eissn1878-0512pt_BR
dc.identifier.doi10.1016/j.ecoinf.2013.06.011pt_BR
dc.identifier.urlhttps://www.sciencedirect.com/science/article/pii/S1574954113000654pt_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.sponsordocumentnumber306243/2010-5; 306587/2009-2pt_BR
dc.description.sponsordocumentnumbersem informaçãopt_BR
dc.description.sponsordocumentnumber2010/52113-5; 2011/11171-5; 2008/58528-2; 2007/52015-0; 2007/59779-6; 2009/18438-7pt_BR
dc.date.available2020-10-28T16:16:41Z-
dc.date.accessioned2020-10-28T16:16:41Z-
dc.description.provenanceSubmitted by Sanches Olivia (olivias@unicamp.br) on 2020-10-28T16:16:40Z No. of bitstreams: 0. Added 1 bitstream(s) on 2021-02-11T21:12:40Z : No. of bitstreams: 1 2-s2.0-84905117659.pdf: 11907274 bytes, checksum: b321f2e1bd60bb7507bf5836fabf7369 (MD5)en
dc.description.provenanceMade available in DSpace on 2020-10-28T16:16:41Z (GMT). No. of bitstreams: 0 Previous issue date: 2014en
dc.identifier.urihttp://repositorio.unicamp.br/jspui/handle/REPOSIP/351680-
dc.contributor.departmentsem informaçãopt_BR
dc.contributor.departmentsem informaçãopt_BR
dc.contributor.departmentDepartamento de Sistemas de Informaçãopt_BR
dc.contributor.unidadeInstituto de Computaçãopt_BR
dc.subject.keywordRemote phenologypt_BR
dc.subject.keywordDigital cameraspt_BR
dc.subject.keywordImage analysispt_BR
dc.subject.keywordTropical forestspt_BR
dc.identifier.source2-s2.0-84905117659pt_BR
dc.creator.orcid0000-0002-4998-6996pt_BR
dc.creator.orcid0000-0002-8889-1586pt_BR
dc.creator.orcid0000-0001-9772-263Xpt_BR
dc.type.formArtigopt_BR
Appears in Collections:IC - Artigos e Outros Documentos

Files in This Item:
File Description SizeFormat 
2-s2.0-84905117659.pdf11.63 MBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.