Uncertainties involved in leaf fall phenology detected by digital camera

Shin Nagai, Tomoharu Inoue, Toshiyuki Ohtsuka, Shimpei Yoshitake, Kenlo Nishida Nasahara, Taku M. Saitoh

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

We evaluated the uncertainty in the estimation of year-to-year variability in the timing of leaf fall detected by the analysis of red, green and blue (RGB) values extracted from daily phenological images in a deciduous broad-leaved forest in Japan. We examined (1) the spatial distribution of individual tree species within a 1-ha permanent plot and the spatio-temporal variability of leaf litter of various species for 8 years; and (2) the relationship between the year-to-year variability of leaf fall detected by leaf litter and that detected by phenological images of various species. Uncertainties were caused by (1) the heterogeneous distribution of each species within the whole forest community; (2) the year-to-year variability of the timing of leaf fall among species; and (3) differences in leaf colouring and leaf fall patterns among species. Our results indicate the importance of integrating RGB analysis of each species and of the whole canopy on the basis of spatial locations of individuals and proportions of tree species within a forest to reduce uncertainty.

Original languageEnglish
Pages (from-to)124-132
Number of pages9
JournalEcological Informatics
Volume30
DOIs
Publication statusPublished - 2015 Nov 1
Externally publishedYes

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Keywords

  • Deciduous broad-leaved forest
  • Leaf fall
  • Phenological observations
  • Time-lapse digital camera
  • Uncertainty

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Modelling and Simulation
  • Ecology
  • Ecological Modelling
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Applied Mathematics

Cite this

Nagai, S., Inoue, T., Ohtsuka, T., Yoshitake, S., Nasahara, K. N., & Saitoh, T. M. (2015). Uncertainties involved in leaf fall phenology detected by digital camera. Ecological Informatics, 30, 124-132. https://doi.org/10.1016/j.ecoinf.2015.10.005