RadialPheno: A tool for near‐surface phenology analysis through radial layouts
Premise Increasingly, researchers studying plant phenology are exploring novel technologies to remotely observe plant changes over time. The increasing use of phenocams to monitor leaf phenology, based on the analysis of indices extracted from sequences of daily digital vegetation images, has demand...
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Published in | Applications in plant sciences Vol. 7; no. 6; pp. e01253 - n/a |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
United States
John Wiley & Sons, Inc
01.06.2019
John Wiley and Sons Inc Wiley |
Subjects | |
Online Access | Get full text |
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Abstract | Premise
Increasingly, researchers studying plant phenology are exploring novel technologies to remotely observe plant changes over time. The increasing use of phenocams to monitor leaf phenology, based on the analysis of indices extracted from sequences of daily digital vegetation images, has demanded the development of appropriate tools for data visualization and analysis. Here, we describe RadialPheno, a tool that uses radial layouts to represent time series from digital repeat photographs, and applies them to the analysis of leafing patterns and leaf exchange strategies of different vegetations.
Methods and Results
We developed a web tool, RadialPheno, provided with the R and Shiny environments, which uses radial visual structures to represent cyclical multidimensional temporal data associated with digital image time series. We demonstrate the application of our methods and tool for a savanna vegetation phenology in the Brazilian Cerrado. We visually represented the greenness index extracted from sequential imagery using the RadialPheno tool.
Conclusions
RadialPheno was successfully applied for the visualization and interpretation of individual, species, and community long‐term leafing phenology data associated with near‐surface phenological observations of Cerrado vegetation. RadialPheno was also effective for intercomparisons of ground‐based direct visual observations and camera‐derived phenology observations. |
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AbstractList | Increasingly, researchers studying plant phenology are exploring novel technologies to remotely observe plant changes over time. The increasing use of phenocams to monitor leaf phenology, based on the analysis of indices extracted from sequences of daily digital vegetation images, has demanded the development of appropriate tools for data visualization and analysis. Here, we describe RadialPheno, a tool that uses radial layouts to represent time series from digital repeat photographs, and applies them to the analysis of leafing patterns and leaf exchange strategies of different vegetations.
We developed a web tool, RadialPheno, provided with the R and Shiny environments, which uses radial visual structures to represent cyclical multidimensional temporal data associated with digital image time series. We demonstrate the application of our methods and tool for a savanna vegetation phenology in the Brazilian Cerrado. We visually represented the greenness index extracted from sequential imagery using the RadialPheno tool.
RadialPheno was successfully applied for the visualization and interpretation of individual, species, and community long-term leafing phenology data associated with near-surface phenological observations of Cerrado vegetation. RadialPheno was also effective for intercomparisons of ground-based direct visual observations and camera-derived phenology observations. Premise Increasingly, researchers studying plant phenology are exploring novel technologies to remotely observe plant changes over time. The increasing use of phenocams to monitor leaf phenology, based on the analysis of indices extracted from sequences of daily digital vegetation images, has demanded the development of appropriate tools for data visualization and analysis. Here, we describe RadialPheno, a tool that uses radial layouts to represent time series from digital repeat photographs, and applies them to the analysis of leafing patterns and leaf exchange strategies of different vegetations. Methods and Results We developed a web tool, RadialPheno, provided with the R and Shiny environments, which uses radial visual structures to represent cyclical multidimensional temporal data associated with digital image time series. We demonstrate the application of our methods and tool for a savanna vegetation phenology in the Brazilian Cerrado. We visually represented the greenness index extracted from sequential imagery using the RadialPheno tool. Conclusions RadialPheno was successfully applied for the visualization and interpretation of individual, species, and community long‐term leafing phenology data associated with near‐surface phenological observations of Cerrado vegetation. RadialPheno was also effective for intercomparisons of ground‐based direct visual observations and camera‐derived phenology observations. PremiseIncreasingly, researchers studying plant phenology are exploring novel technologies to remotely observe plant changes over time. The increasing use of phenocams to monitor leaf phenology, based on the analysis of indices extracted from sequences of daily digital vegetation images, has demanded the development of appropriate tools for data visualization and analysis. Here, we describe RadialPheno, a tool that uses radial layouts to represent time series from digital repeat photographs, and applies them to the analysis of leafing patterns and leaf exchange strategies of different vegetations.Methods and ResultsWe developed a web tool, RadialPheno, provided with the R and Shiny environments, which uses radial visual structures to represent cyclical multidimensional temporal data associated with digital image time series. We demonstrate the application of our methods and tool for a savanna vegetation phenology in the Brazilian Cerrado. We visually represented the greenness index extracted from sequential imagery using the RadialPheno tool.ConclusionsRadialPheno was successfully applied for the visualization and interpretation of individual, species, and community long‐term leafing phenology data associated with near‐surface phenological observations of Cerrado vegetation. RadialPheno was also effective for intercomparisons of ground‐based direct visual observations and camera‐derived phenology observations. Premise Increasingly, researchers studying plant phenology are exploring novel technologies to remotely observe plant changes over time. The increasing use of phenocams to monitor leaf phenology, based on the analysis of indices extracted from sequences of daily digital vegetation images, has demanded the development of appropriate tools for data visualization and analysis. Here, we describe RadialPheno, a tool that uses radial layouts to represent time series from digital repeat photographs, and applies them to the analysis of leafing patterns and leaf exchange strategies of different vegetations. Methods and Results We developed a web tool, RadialPheno, provided with the R and Shiny environments, which uses radial visual structures to represent cyclical multidimensional temporal data associated with digital image time series. We demonstrate the application of our methods and tool for a savanna vegetation phenology in the Brazilian Cerrado. We visually represented the greenness index extracted from sequential imagery using the RadialPheno tool. Conclusions RadialPheno was successfully applied for the visualization and interpretation of individual, species, and community long‐term leafing phenology data associated with near‐surface phenological observations of Cerrado vegetation. RadialPheno was also effective for intercomparisons of ground‐based direct visual observations and camera‐derived phenology observations. Increasingly, researchers studying plant phenology are exploring novel technologies to remotely observe plant changes over time. The increasing use of phenocams to monitor leaf phenology, based on the analysis of indices extracted from sequences of daily digital vegetation images, has demanded the development of appropriate tools for data visualization and analysis. Here, we describe RadialPheno, a tool that uses radial layouts to represent time series from digital repeat photographs, and applies them to the analysis of leafing patterns and leaf exchange strategies of different vegetations.PREMISEIncreasingly, researchers studying plant phenology are exploring novel technologies to remotely observe plant changes over time. The increasing use of phenocams to monitor leaf phenology, based on the analysis of indices extracted from sequences of daily digital vegetation images, has demanded the development of appropriate tools for data visualization and analysis. Here, we describe RadialPheno, a tool that uses radial layouts to represent time series from digital repeat photographs, and applies them to the analysis of leafing patterns and leaf exchange strategies of different vegetations.We developed a web tool, RadialPheno, provided with the R and Shiny environments, which uses radial visual structures to represent cyclical multidimensional temporal data associated with digital image time series. We demonstrate the application of our methods and tool for a savanna vegetation phenology in the Brazilian Cerrado. We visually represented the greenness index extracted from sequential imagery using the RadialPheno tool.METHODS AND RESULTSWe developed a web tool, RadialPheno, provided with the R and Shiny environments, which uses radial visual structures to represent cyclical multidimensional temporal data associated with digital image time series. We demonstrate the application of our methods and tool for a savanna vegetation phenology in the Brazilian Cerrado. We visually represented the greenness index extracted from sequential imagery using the RadialPheno tool.RadialPheno was successfully applied for the visualization and interpretation of individual, species, and community long-term leafing phenology data associated with near-surface phenological observations of Cerrado vegetation. RadialPheno was also effective for intercomparisons of ground-based direct visual observations and camera-derived phenology observations.CONCLUSIONSRadialPheno was successfully applied for the visualization and interpretation of individual, species, and community long-term leafing phenology data associated with near-surface phenological observations of Cerrado vegetation. RadialPheno was also effective for intercomparisons of ground-based direct visual observations and camera-derived phenology observations. |
Author | Mariano, Greice C. Morellato, Leonor Patrícia C. Alberton, Bruna Torres, Ricardo da S. |
AuthorAffiliation | 2 Institute of Biosciences, Botany Department, Phenology Lab Universidade Estadual Paulista (UNESP) Rio Claro, São Paulo Brazil 1 Institute of Computing University of Campinas Campinas, São Paulo Brazil |
AuthorAffiliation_xml | – name: 2 Institute of Biosciences, Botany Department, Phenology Lab Universidade Estadual Paulista (UNESP) Rio Claro, São Paulo Brazil – name: 1 Institute of Computing University of Campinas Campinas, São Paulo Brazil |
Author_xml | – sequence: 1 givenname: Greice C. orcidid: 0000-0003-0084-1830 surname: Mariano fullname: Mariano, Greice C. email: gmariano@ic.unicamp.br organization: University of Campinas – sequence: 2 givenname: Bruna orcidid: 0000-0003-4835-8389 surname: Alberton fullname: Alberton, Bruna organization: Universidade Estadual Paulista (UNESP) – sequence: 3 givenname: Leonor Patrícia C. orcidid: 0000-0001-5265-8988 surname: Morellato fullname: Morellato, Leonor Patrícia C. organization: Universidade Estadual Paulista (UNESP) – sequence: 4 givenname: Ricardo da S. orcidid: 0000-0001-9772-263X surname: Torres fullname: Torres, Ricardo da S. organization: University of Campinas |
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Cites_doi | 10.1145/989863.989891 10.1016/j.agrformet.2012.09.012 10.1007/978-90-481-3335-2_16 10.1890/14-0005.1 10.1109/eScience.2017.44 10.1016/j.patrec.2015.11.028 10.3354/cr00811 10.1016/j.ins.2016.08.052 10.13031/2013.27838 10.1016/j.ecoleng.2016.03.001 10.1007/978-94-007-0632-3_21 10.1007/978-0-85729-079-3 10.1016/j.pecon.2017.06.004 10.1111/j.1744-7429.2000.tb00620.x 10.1016/j.ecoinf.2013.12.011 10.1016/j.biocon.2015.12.033 10.1007/s00442-006-0657-z 10.1038/sdata.2018.28 10.1016/j.agrformet.2016.01.006 10.1890/070217 10.1016/j.agrformet.2011.09.009 10.1016/j.agrformet.2011.05.012 10.1111/btp.12552 10.1016/j.ecoinf.2018.05.003 10.1007/978-90-481-3335-2_5 10.1890/08-2022.1 |
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Increasingly, researchers studying plant phenology are exploring novel technologies to remotely observe plant changes over time. The increasing use of... Increasingly, researchers studying plant phenology are exploring novel technologies to remotely observe plant changes over time. The increasing use of... PremiseIncreasingly, researchers studying plant phenology are exploring novel technologies to remotely observe plant changes over time. The increasing use of... PREMISE: Increasingly, researchers studying plant phenology are exploring novel technologies to remotely observe plant changes over time. The increasing use of... Premise Increasingly, researchers studying plant phenology are exploring novel technologies to remotely observe plant changes over time. The increasing use of... |
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SubjectTerms | cerrado cyclical temporal data data visualization Digital cameras digital images information visualization leafing Leaves phenocameras Phenology Plant sciences radial layouts savannas Seasons Senescence Software Note Time series time series analysis Vegetation Visualization world wide web |
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Title | RadialPheno: A tool for near‐surface phenology analysis through radial layouts |
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