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Phenopix: A R package for image-based vegetation phenology

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Migliavacca,  Mirco
Biosphere-Atmosphere Interactions and Experimentation, Dr. M. Migliavacca, Department Biogeochemical Integration, Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society;

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Forkel,  Matthias
Model-Data Integration, Dr. Nuno Carvalhais, Department Biogeochemical Integration, Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society;
IMPRS International Max Planck Research School for Global Biogeochemical Cycles, Max Planck Institute for Biogeochemistry , Max Planck Society;

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Tomelleri,  Enrico
Research Group Biogeochemical Model-data Integration, Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society;

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Zitation

Filippa, G., Cremonese, E., Migliavacca, M., Galvagno, M., Forkel, M., Wingate, L., et al. (2016). Phenopix: A R package for image-based vegetation phenology. Agricultural and Forest Meteorology, 220, 141-150. doi:10.1016/j.agrformet.2016.01.006.


Zitierlink: https://hdl.handle.net/11858/00-001M-0000-002A-367A-B
Zusammenfassung
In this paper we extensively describe new software available as a R package that allows for the extraction of phenological information from time-lapse digital photography of vegetation cover. The phenopix R package includes all steps in data processing. It enables the user to: draw a region of interest (ROI) on an image; extract red green and blue digital numbers (DN) from a seasonal series of images; depict greenness index trajectories; fit a curve to the seasonal trajectories; extract relevant phenological thresholds (phenophases); extract phenophase uncertainties. The software capabilities are illustrated by analyzing one year of data from a selection of seven sites belonging to the PhenoCam network (http://phenocam.sr.unh.edu/), including an unmanaged subalpine grassland, a tropical grassland, a deciduous needle-leaf forest, three deciduous broad-leaf temperate forests and an evergreen needle-leaf forest. One of the novelties introduced by the package is the spatially explicit, pixel-based analysis, which potentially allows to extract within-ecosystem or within-individual variability of phenology. We examine the relationship between phenophases extracted by the traditional ROI-averaged and the novel pixel-based approaches, and further illustrate potential applications of pixel-based image analysis available in the phenopix R package.