PhenoVis – A tool for visual phenological analysis of digital camera images using chronological percentage maps

Detalhes bibliográficos
Autor(a) principal: Leite, Roger A.
Data de Publicação: 2016
Outros Autores: Schnorr, Lucas Mello, Almeida, Jurandy, Alberton, Bruna [UNESP], Morellato, Leonor Patricia C. [UNESP], Torres, Ricardo da S., Comba, João L.D.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1016/j.ins.2016.08.052
http://hdl.handle.net/11449/178321
Resumo: PhenoVis is framework for the visual phenological analysis of forest ecosystems. It contains the chronological percentage maps (CPM), a novel representation that is capable of discovering additional patterns by encoding percentage distributions of the data. Two types of masks are used in PhenoVis: a community mask , which considers all plant species in the image; and a species mask , associated with a given plant species. Among the several images taken at different times of the day, the image taken at noon is preferred for the analysis because it minimizes shadow effects. Therefore, only one image per day is used. The analysis considers the chromatic co- efficients associated with each pixel in the image. In PhenoVis we associate different colors with each bucket of the percentage histogram. The histogram granularity defines the size of a given bucket of the percentage distribution. The number of buckets is given by the number of colors available, and the range of the distribution is given by the IOI. The percentage map of a single input image consists of a normalized stacked bar chart. The chronological percentage map consists of a sequence of percentage maps stacked in chronological order, from top to bottom (portrait) or left to right (landscape).
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spelling PhenoVis – A tool for visual phenological analysis of digital camera images using chronological percentage mapsPhenologyRemote sensingSimilarity rankingVegetation indexVisual analyticsPhenoVis is framework for the visual phenological analysis of forest ecosystems. It contains the chronological percentage maps (CPM), a novel representation that is capable of discovering additional patterns by encoding percentage distributions of the data. Two types of masks are used in PhenoVis: a community mask , which considers all plant species in the image; and a species mask , associated with a given plant species. Among the several images taken at different times of the day, the image taken at noon is preferred for the analysis because it minimizes shadow effects. Therefore, only one image per day is used. The analysis considers the chromatic co- efficients associated with each pixel in the image. In PhenoVis we associate different colors with each bucket of the percentage histogram. The histogram granularity defines the size of a given bucket of the percentage distribution. The number of buckets is given by the number of colors available, and the range of the distribution is given by the IOI. The percentage map of a single input image consists of a normalized stacked bar chart. The chronological percentage map consists of a sequence of percentage maps stacked in chronological order, from top to bottom (portrait) or left to right (landscape).Institute of Informatics Federal University of Rio Grande do Sul – UFRGSInstitute of Science and Technology Federal University of São Paulo – UNIFESPDept. of Botany São Paulo State University – UNESPInstitute of Computing University of Campinas – UNICAMPDept. of Botany São Paulo State University – UNESPFederal University of Rio Grande do Sul – UFRGSUniversidade de São Paulo (USP)Universidade Estadual Paulista (Unesp)Universidade Estadual de Campinas (UNICAMP)Leite, Roger A.Schnorr, Lucas MelloAlmeida, JurandyAlberton, Bruna [UNESP]Morellato, Leonor Patricia C. [UNESP]Torres, Ricardo da S.Comba, João L.D.2018-12-11T17:29:46Z2018-12-11T17:29:46Z2016-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article181-195application/pdfhttp://dx.doi.org/10.1016/j.ins.2016.08.052Information Sciences, v. 372, p. 181-195.0020-0255http://hdl.handle.net/11449/17832110.1016/j.ins.2016.08.0522-s2.0-849899648882-s2.0-84989964888.pdfScopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengInformation Sciences1,635info:eu-repo/semantics/openAccess2023-11-18T06:14:52Zoai:repositorio.unesp.br:11449/178321Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T18:04:40.302787Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv PhenoVis – A tool for visual phenological analysis of digital camera images using chronological percentage maps
title PhenoVis – A tool for visual phenological analysis of digital camera images using chronological percentage maps
spellingShingle PhenoVis – A tool for visual phenological analysis of digital camera images using chronological percentage maps
Leite, Roger A.
Phenology
Remote sensing
Similarity ranking
Vegetation index
Visual analytics
title_short PhenoVis – A tool for visual phenological analysis of digital camera images using chronological percentage maps
title_full PhenoVis – A tool for visual phenological analysis of digital camera images using chronological percentage maps
title_fullStr PhenoVis – A tool for visual phenological analysis of digital camera images using chronological percentage maps
title_full_unstemmed PhenoVis – A tool for visual phenological analysis of digital camera images using chronological percentage maps
title_sort PhenoVis – A tool for visual phenological analysis of digital camera images using chronological percentage maps
author Leite, Roger A.
author_facet Leite, Roger A.
Schnorr, Lucas Mello
Almeida, Jurandy
Alberton, Bruna [UNESP]
Morellato, Leonor Patricia C. [UNESP]
Torres, Ricardo da S.
Comba, João L.D.
author_role author
author2 Schnorr, Lucas Mello
Almeida, Jurandy
Alberton, Bruna [UNESP]
Morellato, Leonor Patricia C. [UNESP]
Torres, Ricardo da S.
Comba, João L.D.
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv Federal University of Rio Grande do Sul – UFRGS
Universidade de São Paulo (USP)
Universidade Estadual Paulista (Unesp)
Universidade Estadual de Campinas (UNICAMP)
dc.contributor.author.fl_str_mv Leite, Roger A.
Schnorr, Lucas Mello
Almeida, Jurandy
Alberton, Bruna [UNESP]
Morellato, Leonor Patricia C. [UNESP]
Torres, Ricardo da S.
Comba, João L.D.
dc.subject.por.fl_str_mv Phenology
Remote sensing
Similarity ranking
Vegetation index
Visual analytics
topic Phenology
Remote sensing
Similarity ranking
Vegetation index
Visual analytics
description PhenoVis is framework for the visual phenological analysis of forest ecosystems. It contains the chronological percentage maps (CPM), a novel representation that is capable of discovering additional patterns by encoding percentage distributions of the data. Two types of masks are used in PhenoVis: a community mask , which considers all plant species in the image; and a species mask , associated with a given plant species. Among the several images taken at different times of the day, the image taken at noon is preferred for the analysis because it minimizes shadow effects. Therefore, only one image per day is used. The analysis considers the chromatic co- efficients associated with each pixel in the image. In PhenoVis we associate different colors with each bucket of the percentage histogram. The histogram granularity defines the size of a given bucket of the percentage distribution. The number of buckets is given by the number of colors available, and the range of the distribution is given by the IOI. The percentage map of a single input image consists of a normalized stacked bar chart. The chronological percentage map consists of a sequence of percentage maps stacked in chronological order, from top to bottom (portrait) or left to right (landscape).
publishDate 2016
dc.date.none.fl_str_mv 2016-01-01
2018-12-11T17:29:46Z
2018-12-11T17:29:46Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1016/j.ins.2016.08.052
Information Sciences, v. 372, p. 181-195.
0020-0255
http://hdl.handle.net/11449/178321
10.1016/j.ins.2016.08.052
2-s2.0-84989964888
2-s2.0-84989964888.pdf
url http://dx.doi.org/10.1016/j.ins.2016.08.052
http://hdl.handle.net/11449/178321
identifier_str_mv Information Sciences, v. 372, p. 181-195.
0020-0255
10.1016/j.ins.2016.08.052
2-s2.0-84989964888
2-s2.0-84989964888.pdf
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Information Sciences
1,635
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 181-195
application/pdf
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
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