Remote phenology: Applying machine learning to detect phenological patterns in a cerrado savanna

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dc.contributor.authorAlmeida, Jurandy
dc.contributor.authorDos Santos, Jefersson A.
dc.contributor.authorAlberton, Bruna [UNESP]
dc.contributor.authorTorres, Ricardo Da S.
dc.contributor.authorMorellato, Leonor Patricia C. [UNESP]
dc.contributor.institutionUniversidade Estadual de Campinas (UNICAMP)
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2014-05-27T11:27:17Z
dc.date.available2014-05-27T11:27:17Z
dc.date.issued2012-12-01
dc.description.abstractPlant phenology has gained importance in the context of global change research, stimulating the development of new technologies for phenological observation. Digital cameras have been successfully used as multi-channel imaging sensors, providing measures of leaf color change information (RGB channels), or leafing phenological changes in plants. We monitored leaf-changing patterns of a cerrado-savanna vegetation by taken daily digital images. We extract RGB channels from digital images and correlated with phenological changes. Our first goals were: (1) to test if the color change information is able to characterize the phenological pattern of a group of species; and (2) to test if individuals from the same functional group may be automatically identified using digital images. In this paper, we present a machine learning approach to detect phenological patterns in the digital images. Our preliminary results indicate that: (1) extreme hours (morning and afternoon) are the best for identifying plant species; and (2) different plant species present a different behavior with respect to the color change information. Based on those results, we suggest that individuals from the same functional group might be identified using digital images, and introduce a new tool to help phenology experts in the species identification and location on-the-ground. ©2012 IEEE.en
dc.description.affiliationRECOD Lab. Institute of Computing University of Campinas - UNICAMP, 13083-852, Campinas, SP
dc.description.affiliationPhenology Lab. Dept. of Botany Sao Paulo State University - UNESP, 13506-900, Rio Claro, SP
dc.description.affiliationUnespPhenology Lab. Dept. of Botany Sao Paulo State University - UNESP, 13506-900, Rio Claro, SP
dc.identifierhttp://dx.doi.org/10.1109/eScience.2012.6404438
dc.identifier.citation2012 IEEE 8th International Conference on E-Science, e-Science 2012.
dc.identifier.doi10.1109/eScience.2012.6404438
dc.identifier.lattes1012217731137451
dc.identifier.scopus2-s2.0-84873694426
dc.identifier.urihttp://hdl.handle.net/11449/73807
dc.language.isoeng
dc.relation.ispartof2012 IEEE 8th International Conference on E-Science, e-Science 2012
dc.rights.accessRightsAcesso aberto
dc.sourceScopus
dc.subjectCerrado
dc.subjectColor changes
dc.subjectDigital image
dc.subjectGlobal change
dc.subjectLeaf color
dc.subjectMachine learning approaches
dc.subjectMultichannel imaging
dc.subjectNew technologies
dc.subjectPhenological changes
dc.subjectPhenological observations
dc.subjectPlant phenology
dc.subjectPlant species
dc.subjectSpecies identification
dc.subjectBiology
dc.subjectColorimetry
dc.subjectForestry
dc.subjectLearning systems
dc.subjectPhenols
dc.titleRemote phenology: Applying machine learning to detect phenological patterns in a cerrado savannaen
dc.typeTrabalho apresentado em evento
dcterms.licensehttp://www.ieee.org/publications_standards/publications/rights/rights_policies.html
unesp.author.lattes1012217731137451
unesp.campusUniversidade Estadual Paulista (Unesp), Instituto de Biociências, Rio Claropt

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