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RadialPheno: A tool for near-surface phenology analysis through radial layouts

Mariano, Greice C., Alberton, Bruna, Morellato, Leonor Patrícia C. and Torres, Ricardo da S. (2019) RadialPheno: A tool for near-surface phenology analysis through radial layouts. Applications in Plant Sciences, 7 (6). e01253.

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Official URL: https://bsapubs.onlinelibrary.wiley.com/doi/abs/10...


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.

Item Type: Article
Uncontrolled Keywords: cyclical temporal data, information visualization, leafing, phenocameras, radial layouts
Divisions: Research Labs > Visual Analytics Lab (VAL)
Date Deposited: 08 Oct 2019 19:42
Last Modified: 20 Dec 2021 19:01
URI: http://openresearch.ocadu.ca/id/eprint/2786

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